diff --git a/README.md b/README.md
index e0db6a8..018d17a 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,23 @@
-# Netflix AI Greenlight Challenge
+# group member !
+Vincent kuo
+Iris Su
+Sean Shen
+Jeannie LAN
+# The changes include:
+Our analysis is available under the netflix-writer.ipynb exercise;
+temp contains temporary files and Vincent's presentation notes, which can be ignored;
+the team final project is the presentation PDF.
+
+
+
+
+
 
+
+
+
+
+# Netflix AI Greenlight Challenge
 [![License: GNU AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
 [![Python](https://img.shields.io/badge/Python-3.13-blue?logo=python)](https://www.python.org/)
 [![Pydantic](https://img.shields.io/badge/Pydantic-2.13-blue?logo=pydantic)](https://docs.pydantic.dev/)
@@ -116,4 +134,4 @@ pip --version # you should see pip 25.3.x
 Please report bugs to the [GitHub Issues Page](https://github.com/netflix-writers/netflix/issues)
 for this project.
 
-ask for Lawrence.
+ask for Lawrence.r
diff --git a/bad_example.py b/bad_example.py
new file mode 100644
index 0000000..bb0815f
--- /dev/null
+++ b/bad_example.py
@@ -0,0 +1,12 @@
+import numpy as np
+import pandas as pd
+
+def compute_average(values):
+    total = 0
+    for i in range(len(values)):
+        total = total + values[i]
+    average = total / len(values)
+    return average
+
+result = compute_average([1, 2, 3, 4, 5])
+print(result)
diff --git a/bad_number.txt b/bad_number.txt
new file mode 100644
index 0000000..f791063
--- /dev/null
+++ b/bad_number.txt
@@ -0,0 +1 @@
+not a number
\ No newline at end of file
diff --git a/good_number.txt b/good_number.txt
new file mode 100644
index 0000000..f70d7bb
--- /dev/null
+++ b/good_number.txt
@@ -0,0 +1 @@
+42
\ No newline at end of file
diff --git a/messy_example.py b/messy_example.py
new file mode 100644
index 0000000..340e0b4
--- /dev/null
+++ b/messy_example.py
@@ -0,0 +1,6 @@
+def compute_average(values):
+    total = 0
+    for i in range(len(values)):
+        total = total + values[i]
+    average = total / len(values)
+    return average
diff --git a/netflix-writer copy.ipynb b/netflix-writer copy.ipynb
new file mode 100644
index 0000000..1f98029
--- /dev/null
+++ b/netflix-writer copy.ipynb	
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a21ae4e6",
+   "metadata": {},
+   "source": [
+    "## our analysis\n",
+    "\n",
+    "| 套件名稱 (Library) | 核心角色 (Core Role) | 實務具體用途 (Specific Use Case) |\n",
+    "| :--- | :--- | :--- |\n",
+    "| **Pandas** | [cite_start]資料表操作（DataFrame-based manipulation） [cite: 151, 172] | [cite_start]用於對資料陣列進行轉換、清洗與特徵調整。 [cite: 151] |\n",
+    "| **NumPy** | [cite_start]高效率數值與矩陣運算（Numerical arrays） [cite: 152, 175] | [cite_start]用於進行高效的數值計算與線性代數矩陣運算。 [cite: 152, 175] |\n",
+    "| **SciPy** | [cite_start]科學計算與進階統計（Scientific computing） [cite: 155, 183] | [cite_start]包含最佳化、插值、特徵值以及雙樣本 T 檢定等統計演算法。 [cite: 155] |\n",
+    "| **Matplotlib** | [cite_start]基礎數學與數據視覺化（Core plotting） [cite: 153, 178] | [cite_start]提供底層繪圖控制，用來微調圖表標題、軸線與顯示格式。 [cite: 153] |\n",
+    "| **Seaborn** | [cite_start]統計數據視覺化（Statistical visualization） [cite: 154, 181] | [cite_start]建立在 Matplotlib 之上，用於快速繪製美觀、高階的統計圖表。 [cite: 154, 181] |\n",
+    "| **scikit-learn** | [cite_start]預測性數據分析（Machine learning） [cite: 157, 185] | [cite_start]用於建立與評估機器學習模型（如迴歸、分類與資料集切分）。 [cite: 157, 185] |\n",
+    "| **shap** | [cite_start]模型決策可解釋性（Explainable ML） [cite: 158, 188] | [cite_start]利用 Shapley 值來拆解並解釋黑盒子模型中各特徵的影響力。 [cite: 158, 188] |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cb9cffa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    budget_M  marketing_M  is_sequel  viewing_hours_M  total_spend_M\n",
+      "0  57.450712    19.404472          1       121.231948      76.855184\n",
+      "1  47.926035    18.536520          1       115.467455      66.462555\n",
+      "2  59.715328    22.229169          0       118.926651      81.944497\n",
+      "3  72.845448    25.249080          1       146.685702      98.094528\n",
+      "4  46.487699    18.623738          0        90.951494      65.111438\n",
+      "T 統計量: 3.0319\n",
+      "P 值: 3.1093e-03\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 826.111x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R² 判定係數: 0.7704\n",
+      "均方誤差 (MSE): 113.6193\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/vincent/Documents/netflix-writers/venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/var/folders/m3/mxn0108j41x3cp5x3trm41fh0000gn/T/ipykernel_39066/2429433558.py:82: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
+      "  shap.summary_plot(shap_values, X_test)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x270 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# NumPy: 用於高效率的數值運算與矩陣生成\n",
+    "np.random.seed(42)\n",
+    "n_samples = 100\n",
+    "\n",
+    "# 模擬數據：預算（百萬美元）、行銷費用（百萬美元）、是否為續集（0或1）\n",
+    "budget = np.random.normal(50, 15, n_samples)          # 平均5000萬，標準差1500萬\n",
+    "marketing = budget * 0.3 + np.random.normal(5, 2, n_samples) # 行銷費用與預算正相關\n",
+    "is_sequel = np.random.binomial(1, 0.3, n_samples)     # 30%機率是續集\n",
+    "\n",
+    "# 模擬目標值：觀看時數（百萬小時），加上一些隨機雜訊\n",
+    "viewing_hours = (budget * 1.2) + (marketing * 2.0) + (is_sequel * 15) + np.random.normal(0, 10, n_samples)\n",
+    "\n",
+    "# Pandas: 將 NumPy 陣列轉換為結構化的 DataFrame，並進行資料操作\n",
+    "df = pd.DataFrame({\n",
+    "    'budget_M': budget,\n",
+    "    'marketing_M': marketing,\n",
+    "    'is_sequel': is_sequel,\n",
+    "    'viewing_hours_M': viewing_hours\n",
+    "})\n",
+    "\n",
+    "# 簡單的特徵轉換 (Transformations)\n",
+    "df['total_spend_M'] = df['budget_M'] + df['marketing_M']\n",
+    "print(df.head())\n",
+    "\n",
+    "from scipy import stats\n",
+    "\n",
+    "# SciPy: 用於科學計算與統計檢定\n",
+    "sequel_hours = df[df['is_sequel'] == 1]['viewing_hours_M']\n",
+    "original_hours = df[df['is_sequel'] == 0]['viewing_hours_M']\n",
+    "\n",
+    "# 執行雙獨立樣本 T 檢定 (T-test)\n",
+    "t_stat, p_value = stats.ttest_ind(sequel_hours, original_hours)\n",
+    "\n",
+    "print(f\"T 統計量: {t_stat:.4f}\")\n",
+    "print(f\"P 值: {p_value:.4e}\")  # 若 P < 0.05，代表續集的觀看時數有顯著不同！\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Seaborn: 建立統計圖表，觀察「總支出」與「觀看時數」的關係，並依據「是否為續集」著色\n",
+    "sns.lmplot(x='total_spend_M', y='viewing_hours_M', hue='is_sequel', data=df, aspect=1.5)\n",
+    "\n",
+    "# Matplotlib: 用於調整圖表的細節設定（標題、標籤、儲存等）\n",
+    "plt.title(\"Relationship between Total Spend and Viewing Hours\", fontsize=14)\n",
+    "plt.xlabel(\"Total Spend (Million USD)\")\n",
+    "plt.ylabel(\"Viewing Hours (Million Hours)\")\n",
+    "\n",
+    "# 顯示圖表\n",
+    "plt.show()\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# 準備特徵 (X) 與目標值 (y)\n",
+    "X = df[['budget_M', 'marketing_M', 'is_sequel']]\n",
+    "y = df['viewing_hours_M']\n",
+    "\n",
+    "# 切分訓練集與測試集 (80% 訓練, 20% 測試)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# 初始化並訓練模型\n",
+    "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# 進行預測與評估\n",
+    "y_pred = model.predict(X_test)\n",
+    "print(f\"R² 判定係數: {r2_score(y_test, y_pred):.4f}\")  # 越接近 1 代表預測越準\n",
+    "print(f\"均方誤差 (MSE): {mean_squared_error(y_test, y_pred):.4f}\")\n",
+    "\n",
+    "import shap\n",
+    "\n",
+    "# SHAP: 解釋隨機森林模型的預測行為\n",
+    "explainer = shap.TreeExplainer(model)\n",
+    "shap_values = explainer(X_test)\n",
+    "\n",
+    "# 繪製摘要圖 (Summary Plot)\n",
+    "# 這個圖會顯示哪些特徵影響最大（例如：行銷費用可能比預算更能拉高觀看時數）\n",
+    "shap.summary_plot(shap_values, X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "366ef0cb",
+   "metadata": {},
+   "source": [
+    "## our analysis\n",
+    "？？"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6de61cf1",
+   "metadata": {},
+   "source": [
+    "## our analysis\n",
+    "？？"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8bee200f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I like apple\n",
+      "I like banana\n",
+      "I like cherry\n"
+     ]
+    }
+   ],
+   "source": [
+    "fruits = [\"apple\", \"banana\", \"cherry\"]\n",
+    "\n",
+    "for fruit in fruits:\n",
+    "    print(f\"I like {fruit}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c6db4648",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25\n"
+     ]
+    }
+   ],
+   "source": [
+    "square = lambda x: x ** 2\n",
+    "print(square(5))   # 25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3921437",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42264338",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv (3.13.9)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/netflix-writer.ipynb b/netflix-writer.ipynb
index 6e3ae99..f7ba3f2 100644
--- a/netflix-writer.ipynb
+++ b/netflix-writer.ipynb
@@ -13,10 +13,771 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "a4814bd0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============================================================================\n",
+      "Initializing local development environment. This will verify and set up your\n",
+      "Python virtual environment, install all 3rd-party package requirements.\n",
+      "This may take a few minutes...\n",
+      "===============================================================================\n",
+      "\n",
+      "\n",
+      "===============================================================================\n",
+      "Initialization complete!\n",
+      "===============================================================================\n",
+      "make python-init\n",
+      "===============================================================================\n",
+      "Initializing Python virtual environment and installing dependencies. This may take a few minutes...\n",
+      "===============================================================================\n",
+      "rm -r -f venv && \\\n",
+      "\tmkdir -p .pypi_cache && \\\n",
+      "\tmake check-python\n",
+      "\n",
+      "===============================================================================\n",
+      "Verifying that Python python3.13 is installed ...\n",
+      "===============================================================================\n",
+      "\n",
+      "make python-clean && \\\n",
+      "\tnpm install && \\\n",
+      "\tpython3.13 -m venv venv && \\\n",
+      "\tsource venv/bin/activate && \\\n",
+      "\tpython3.13 -m pip install pip==25.3 setuptools wheel pip-tools && \\\n",
+      "\tPIP_CACHE_DIR=.pypi_cache python3.13 -m pip install -r requirements/local.txt\n",
+      "\n",
+      "===============================================================================\n",
+      "Cleaning Python virtual environment and __pycache__ directories ...\n",
+      "===============================================================================\n",
+      "\n",
+      "rm -rf venv\n",
+      "find ./netflix/ -name __pycache__ -type d -exec rm -rf {} +\n",
+      "⠙\u001b[1G\u001b[0K⠹\u001b[1G\u001b[0K⠸\u001b[1G\u001b[0K\n",
+      "up to date, audited 471 packages in 428ms\n",
+      "⠸\u001b[1G\u001b[0K\n",
+      "⠸\u001b[1G\u001b[0K101 packages are looking for funding\n",
+      "⠸\u001b[1G\u001b[0K  run `npm fund` for details\n",
+      "⠸\u001b[1G\u001b[0K\n",
+      "found \u001b[32m\u001b[1m0\u001b[22m\u001b[39m vulnerabilities\n",
+      "⠸\u001b[1G\u001b[0KCollecting pip==25.3\n",
+      "  Using cached pip-25.3-py3-none-any.whl.metadata (4.7 kB)\n",
+      "Collecting setuptools\n",
+      "  Using cached setuptools-83.0.0-py3-none-any.whl.metadata (6.6 kB)\n",
+      "Collecting wheel\n",
+      "  Using cached wheel-0.47.0-py3-none-any.whl.metadata (2.3 kB)\n",
+      "Collecting pip-tools\n",
+      "  Using cached pip_tools-7.5.3-py3-none-any.whl.metadata (27 kB)\n",
+      "Collecting packaging>=24.0 (from wheel)\n",
+      "  Using cached packaging-26.2-py3-none-any.whl.metadata (3.5 kB)\n",
+      "Collecting build>=1.0.0 (from pip-tools)\n",
+      "  Downloading build-1.5.0-py3-none-any.whl.metadata (5.7 kB)\n",
+      "Collecting click>=8 (from pip-tools)\n",
+      "  Using cached click-8.4.2-py3-none-any.whl.metadata (2.6 kB)\n",
+      "Collecting pyproject_hooks (from pip-tools)\n",
+      "  Using cached pyproject_hooks-1.2.0-py3-none-any.whl.metadata (1.3 kB)\n",
+      "Using cached pip-25.3-py3-none-any.whl (1.8 MB)\n",
+      "Using cached setuptools-83.0.0-py3-none-any.whl (1.0 MB)\n",
+      "Using cached wheel-0.47.0-py3-none-any.whl (32 kB)\n",
+      "Using cached pip_tools-7.5.3-py3-none-any.whl (71 kB)\n",
+      "Downloading build-1.5.0-py3-none-any.whl (26 kB)\n",
+      "Using cached click-8.4.2-py3-none-any.whl (119 kB)\n",
+      "Using cached packaging-26.2-py3-none-any.whl (100 kB)\n",
+      "Using cached pyproject_hooks-1.2.0-py3-none-any.whl (10 kB)\n",
+      "Installing collected packages: setuptools, pyproject_hooks, pip, packaging, click, wheel, build, pip-tools\n",
+      "\u001b[2K  Attempting uninstall: pip━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0/8\u001b[0m [setuptools]\n",
+      "\u001b[2K    Found existing installation: pip 25.2━━━\u001b[0m \u001b[32m0/8\u001b[0m [setuptools]\n",
+      "\u001b[2K    Uninstalling pip-25.2:m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/8\u001b[0m [pip]\n",
+      "\u001b[2K      Successfully uninstalled pip-25.2━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/8\u001b[0m [pip]\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8/8\u001b[0m [pip-tools]/8\u001b[0m [click]\n",
+      "\u001b[1A\u001b[2KSuccessfully installed build-1.5.0 click-8.4.2 packaging-26.2 pip-25.3 pip-tools-7.5.3 pyproject_hooks-1.2.0 setuptools-83.0.0 wheel-0.47.0\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.1.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Collecting annotated-doc==0.0.4 (from -r requirements/local.txt (line 7))\n",
+      "  Using cached annotated_doc-0.0.4-py3-none-any.whl.metadata (6.6 kB)\n",
+      "Collecting anyio==4.13.0 (from -r requirements/local.txt (line 9))\n",
+      "  Using cached anyio-4.13.0-py3-none-any.whl.metadata (4.5 kB)\n",
+      "Collecting appnope==0.1.4 (from -r requirements/local.txt (line 13))\n",
+      "  Using cached appnope-0.1.4-py2.py3-none-any.whl.metadata (908 bytes)\n",
+      "Collecting argon2-cffi==25.1.0 (from -r requirements/local.txt (line 15))\n",
+      "  Using cached argon2_cffi-25.1.0-py3-none-any.whl.metadata (4.1 kB)\n",
+      "Collecting argon2-cffi-bindings==25.1.0 (from -r requirements/local.txt (line 17))\n",
+      "  Using cached argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_11_0_arm64.whl.metadata (7.4 kB)\n",
+      "Collecting arrow==1.4.0 (from -r requirements/local.txt (line 19))\n",
+      "  Using cached arrow-1.4.0-py3-none-any.whl.metadata (7.7 kB)\n",
+      "Collecting ast-serialize==0.5.0 (from -r requirements/local.txt (line 21))\n",
+      "  Using cached ast_serialize-0.5.0-cp39-abi3-macosx_11_0_arm64.whl.metadata (1.3 kB)\n",
+      "Collecting astroid==4.0.4 (from -r requirements/local.txt (line 23))\n",
+      "  Using cached astroid-4.0.4-py3-none-any.whl.metadata (4.4 kB)\n",
+      "Collecting asttokens==3.0.1 (from -r requirements/local.txt (line 25))\n",
+      "  Using cached asttokens-3.0.1-py3-none-any.whl.metadata (4.9 kB)\n",
+      "Collecting async-lru==2.3.0 (from -r requirements/local.txt (line 27))\n",
+      "  Using cached async_lru-2.3.0-py3-none-any.whl.metadata (7.6 kB)\n",
+      "Collecting attrs==26.1.0 (from -r requirements/local.txt (line 29))\n",
+      "  Using cached attrs-26.1.0-py3-none-any.whl.metadata (8.8 kB)\n",
+      "Collecting babel==2.18.0 (from -r requirements/local.txt (line 33))\n",
+      "  Using cached babel-2.18.0-py3-none-any.whl.metadata (2.2 kB)\n",
+      "Collecting bandit==1.9.4 (from -r requirements/local.txt (line 35))\n",
+      "  Using cached bandit-1.9.4-py3-none-any.whl.metadata (7.1 kB)\n",
+      "Collecting beautifulsoup4==4.15.0 (from -r requirements/local.txt (line 37))\n",
+      "  Using cached beautifulsoup4-4.15.0-py3-none-any.whl.metadata (3.8 kB)\n",
+      "Collecting black==26.5.1 (from -r requirements/local.txt (line 41))\n",
+      "  Using cached black-26.5.1-cp313-cp313-macosx_11_0_arm64.whl.metadata (95 kB)\n",
+      "Collecting bleach==6.4.0 (from bleach[css]==6.4.0->-r requirements/local.txt (line 43))\n",
+      "  Using cached bleach-6.4.0-py3-none-any.whl.metadata (32 kB)\n",
+      "Requirement already satisfied: build==1.5.0 in ./venv/lib/python3.13/site-packages (from -r requirements/local.txt (line 47)) (1.5.0)\n",
+      "Collecting cachetools==7.1.4 (from -r requirements/local.txt (line 49))\n",
+      "  Using cached cachetools-7.1.4-py3-none-any.whl.metadata (5.5 kB)\n",
+      "Collecting certifi==2026.5.20 (from -r requirements/local.txt (line 51))\n",
+      "  Using cached certifi-2026.5.20-py3-none-any.whl.metadata (2.5 kB)\n",
+      "Collecting cffi==2.0.0 (from -r requirements/local.txt (line 56))\n",
+      "  Using cached cffi-2.0.0-cp313-cp313-macosx_11_0_arm64.whl.metadata (2.6 kB)\n",
+      "Collecting cfgv==3.5.0 (from -r requirements/local.txt (line 58))\n",
+      "  Using cached cfgv-3.5.0-py2.py3-none-any.whl.metadata (8.9 kB)\n",
+      "Collecting charset-normalizer==3.4.7 (from -r requirements/local.txt (line 60))\n",
+      "  Using cached charset_normalizer-3.4.7-cp313-cp313-macosx_10_13_universal2.whl.metadata (40 kB)\n",
+      "Collecting click==8.4.1 (from -r requirements/local.txt (line 62))\n",
+      "  Using cached click-8.4.1-py3-none-any.whl.metadata (2.6 kB)\n",
+      "Collecting cloudpickle==3.1.2 (from -r requirements/local.txt (line 66))\n",
+      "  Using cached cloudpickle-3.1.2-py3-none-any.whl.metadata (7.1 kB)\n",
+      "Collecting codespell==2.4.2 (from -r requirements/local.txt (line 68))\n",
+      "  Using cached codespell-2.4.2-py3-none-any.whl.metadata (15 kB)\n",
+      "Collecting colorama==0.4.6 (from -r requirements/local.txt (line 70))\n",
+      "  Using cached colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n",
+      "Collecting comm==0.2.3 (from -r requirements/local.txt (line 72))\n",
+      "  Using cached comm-0.2.3-py3-none-any.whl.metadata (3.7 kB)\n",
+      "Collecting contourpy==1.3.3 (from -r requirements/local.txt (line 74))\n",
+      "  Using cached contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl.metadata (5.5 kB)\n",
+      "Collecting coverage==7.14.1 (from -r requirements/local.txt (line 76))\n",
+      "  Using cached coverage-7.14.1-cp313-cp313-macosx_11_0_arm64.whl.metadata (8.6 kB)\n",
+      "Collecting cssselect==1.4.0 (from -r requirements/local.txt (line 78))\n",
+      "  Using cached cssselect-1.4.0-py3-none-any.whl.metadata (2.4 kB)\n",
+      "Collecting cycler==0.12.1 (from -r requirements/local.txt (line 80))\n",
+      "  Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
+      "Collecting debugpy==1.8.21 (from -r requirements/local.txt (line 82))\n",
+      "  Using cached debugpy-1.8.21-cp313-cp313-macosx_15_0_universal2.whl.metadata (1.4 kB)\n",
+      "Collecting decorator==5.3.1 (from -r requirements/local.txt (line 84))\n",
+      "  Using cached decorator-5.3.1-py3-none-any.whl.metadata (3.9 kB)\n",
+      "Collecting defusedxml==0.7.1 (from -r requirements/local.txt (line 86))\n",
+      "  Using cached defusedxml-0.7.1-py2.py3-none-any.whl.metadata (32 kB)\n",
+      "Collecting dill==0.4.1 (from -r requirements/local.txt (line 88))\n",
+      "  Using cached dill-0.4.1-py3-none-any.whl.metadata (10 kB)\n",
+      "Collecting distlib==0.4.3 (from -r requirements/local.txt (line 90))\n",
+      "  Using cached distlib-0.4.3-py2.py3-none-any.whl.metadata (5.3 kB)\n",
+      "Collecting duckdb==1.5.3 (from -r requirements/local.txt (line 92))\n",
+      "  Using cached duckdb-1.5.3-cp313-cp313-macosx_11_0_arm64.whl.metadata (4.2 kB)\n",
+      "Collecting executing==2.2.1 (from -r requirements/local.txt (line 94))\n",
+      "  Using cached executing-2.2.1-py2.py3-none-any.whl.metadata (8.9 kB)\n",
+      "Collecting fastjsonschema==2.21.2 (from -r requirements/local.txt (line 96))\n",
+      "  Using cached fastjsonschema-2.21.2-py3-none-any.whl.metadata (2.3 kB)\n",
+      "Collecting filelock==3.29.4 (from -r requirements/local.txt (line 98))\n",
+      "  Using cached filelock-3.29.4-py3-none-any.whl.metadata (2.0 kB)\n",
+      "Collecting flake8==7.3.0 (from -r requirements/local.txt (line 103))\n",
+      "  Using cached flake8-7.3.0-py2.py3-none-any.whl.metadata (3.8 kB)\n",
+      "Collecting flake8-coding==1.3.2 (from -r requirements/local.txt (line 107))\n",
+      "  Using cached flake8_coding-1.3.2-py2.py3-none-any.whl.metadata (3.1 kB)\n",
+      "Collecting fonttools==4.63.0 (from -r requirements/local.txt (line 109))\n",
+      "  Using cached fonttools-4.63.0-cp313-cp313-macosx_10_13_universal2.whl.metadata (118 kB)\n",
+      "Collecting fqdn==1.5.1 (from -r requirements/local.txt (line 111))\n",
+      "  Using cached fqdn-1.5.1-py3-none-any.whl.metadata (1.4 kB)\n",
+      "Collecting h11==0.16.0 (from -r requirements/local.txt (line 113))\n",
+      "  Using cached h11-0.16.0-py3-none-any.whl.metadata (8.3 kB)\n",
+      "Collecting httpcore==1.0.9 (from -r requirements/local.txt (line 115))\n",
+      "  Using cached httpcore-1.0.9-py3-none-any.whl.metadata (21 kB)\n",
+      "Collecting httpx==0.28.1 (from -r requirements/local.txt (line 117))\n",
+      "  Using cached httpx-0.28.1-py3-none-any.whl.metadata (7.1 kB)\n",
+      "Collecting identify==2.6.19 (from -r requirements/local.txt (line 119))\n",
+      "  Using cached identify-2.6.19-py2.py3-none-any.whl.metadata (4.4 kB)\n",
+      "Collecting idna==3.18 (from -r requirements/local.txt (line 121))\n",
+      "  Using cached idna-3.18-py3-none-any.whl.metadata (6.1 kB)\n",
+      "Collecting iniconfig==2.3.0 (from -r requirements/local.txt (line 127))\n",
+      "  Using cached iniconfig-2.3.0-py3-none-any.whl.metadata (2.5 kB)\n",
+      "Collecting ipykernel==7.3.0 (from -r requirements/local.txt (line 129))\n",
+      "  Using cached ipykernel-7.3.0-py3-none-any.whl.metadata (4.5 kB)\n",
+      "Collecting ipython==9.14.1 (from -r requirements/local.txt (line 133))\n",
+      "  Using cached ipython-9.14.1-py3-none-any.whl.metadata (4.7 kB)\n",
+      "Collecting ipython-pygments-lexers==1.1.1 (from -r requirements/local.txt (line 135))\n",
+      "  Using cached ipython_pygments_lexers-1.1.1-py3-none-any.whl.metadata (1.1 kB)\n",
+      "Collecting isoduration==20.11.0 (from -r requirements/local.txt (line 137))\n",
+      "  Using cached isoduration-20.11.0-py3-none-any.whl.metadata (5.7 kB)\n",
+      "Collecting isort==8.0.1 (from -r requirements/local.txt (line 139))\n",
+      "  Using cached isort-8.0.1-py3-none-any.whl.metadata (11 kB)\n",
+      "Collecting jedi==0.20.0 (from -r requirements/local.txt (line 141))\n",
+      "  Using cached jedi-0.20.0-py2.py3-none-any.whl.metadata (23 kB)\n",
+      "Collecting jinja2==3.1.6 (from -r requirements/local.txt (line 143))\n",
+      "  Using cached jinja2-3.1.6-py3-none-any.whl.metadata (2.9 kB)\n",
+      "Collecting joblib==1.5.3 (from -r requirements/local.txt (line 149))\n",
+      "  Using cached joblib-1.5.3-py3-none-any.whl.metadata (5.5 kB)\n",
+      "Collecting json5==0.14.0 (from -r requirements/local.txt (line 151))\n",
+      "  Using cached json5-0.14.0-py3-none-any.whl.metadata (36 kB)\n",
+      "Collecting jsonpointer==3.1.1 (from -r requirements/local.txt (line 153))\n",
+      "  Using cached jsonpointer-3.1.1-py3-none-any.whl.metadata (2.4 kB)\n",
+      "Collecting jsonschema==4.26.0 (from jsonschema[format-nongpl]==4.26.0->-r requirements/local.txt (line 155))\n",
+      "  Using cached jsonschema-4.26.0-py3-none-any.whl.metadata (7.6 kB)\n",
+      "Collecting jsonschema-specifications==2025.9.1 (from -r requirements/local.txt (line 160))\n",
+      "  Using cached jsonschema_specifications-2025.9.1-py3-none-any.whl.metadata (2.9 kB)\n",
+      "Collecting jupyter-client==8.9.1 (from -r requirements/local.txt (line 162))\n",
+      "  Using cached jupyter_client-8.9.1-py3-none-any.whl.metadata (8.5 kB)\n",
+      "Collecting jupyter-core==5.9.1 (from -r requirements/local.txt (line 167))\n",
+      "  Using cached jupyter_core-5.9.1-py3-none-any.whl.metadata (1.5 kB)\n",
+      "Collecting jupyter-events==0.12.1 (from -r requirements/local.txt (line 176))\n",
+      "  Using cached jupyter_events-0.12.1-py3-none-any.whl.metadata (5.8 kB)\n",
+      "Collecting jupyter-lsp==2.3.1 (from -r requirements/local.txt (line 178))\n",
+      "  Using cached jupyter_lsp-2.3.1-py3-none-any.whl.metadata (1.8 kB)\n",
+      "Collecting jupyter-server==2.19.0 (from -r requirements/local.txt (line 180))\n",
+      "  Using cached jupyter_server-2.19.0-py3-none-any.whl.metadata (8.5 kB)\n",
+      "Collecting jupyter-server-terminals==0.5.4 (from -r requirements/local.txt (line 186))\n",
+      "  Using cached jupyter_server_terminals-0.5.4-py3-none-any.whl.metadata (5.9 kB)\n",
+      "Collecting jupyterlab==4.5.8 (from -r requirements/local.txt (line 188))\n",
+      "  Using cached jupyterlab-4.5.8-py3-none-any.whl.metadata (16 kB)\n",
+      "Collecting jupyterlab-pygments==0.3.0 (from -r requirements/local.txt (line 190))\n",
+      "  Using cached jupyterlab_pygments-0.3.0-py3-none-any.whl.metadata (4.4 kB)\n",
+      "Collecting jupyterlab-server==2.28.0 (from -r requirements/local.txt (line 192))\n",
+      "  Using cached jupyterlab_server-2.28.0-py3-none-any.whl.metadata (5.9 kB)\n",
+      "Collecting jupytext==1.19.3 (from -r requirements/local.txt (line 194))\n",
+      "  Using cached jupytext-1.19.3-py3-none-any.whl.metadata (15 kB)\n",
+      "Collecting kaggle==2.2.1 (from -r requirements/local.txt (line 196))\n",
+      "  Using cached kaggle-2.2.1-py3-none-any.whl.metadata (16 kB)\n",
+      "Collecting kagglesdk==0.1.30 (from -r requirements/local.txt (line 198))\n",
+      "  Using cached kagglesdk-0.1.30-py3-none-any.whl.metadata (13 kB)\n",
+      "Collecting kiwisolver==1.5.0 (from -r requirements/local.txt (line 200))\n",
+      "  Using cached kiwisolver-1.5.0-cp313-cp313-macosx_11_0_arm64.whl.metadata (5.1 kB)\n",
+      "Collecting lark==1.3.1 (from -r requirements/local.txt (line 202))\n",
+      "  Using cached lark-1.3.1-py3-none-any.whl.metadata (1.8 kB)\n",
+      "Collecting librt==0.11.0 (from -r requirements/local.txt (line 204))\n",
+      "  Using cached librt-0.11.0-cp313-cp313-macosx_11_0_arm64.whl.metadata (1.3 kB)\n",
+      "Collecting llvmlite==0.47.0 (from -r requirements/local.txt (line 206))\n",
+      "  Using cached llvmlite-0.47.0-cp313-cp313-macosx_12_0_arm64.whl.metadata (5.0 kB)\n",
+      "Collecting markdown-it-py==4.2.0 (from -r requirements/local.txt (line 210))\n",
+      "  Using cached markdown_it_py-4.2.0-py3-none-any.whl.metadata (7.4 kB)\n",
+      "Collecting markupsafe==3.0.3 (from -r requirements/local.txt (line 215))\n",
+      "  Using cached markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl.metadata (2.7 kB)\n",
+      "Collecting matplotlib==3.11.0 (from -r requirements/local.txt (line 219))\n",
+      "  Using cached matplotlib-3.11.0-cp313-cp313-macosx_11_0_arm64.whl.metadata (80 kB)\n",
+      "Collecting matplotlib-inline==0.2.2 (from -r requirements/local.txt (line 223))\n",
+      "  Using cached matplotlib_inline-0.2.2-py3-none-any.whl.metadata (2.4 kB)\n",
+      "Collecting mccabe==0.7.0 (from -r requirements/local.txt (line 227))\n",
+      "  Using cached mccabe-0.7.0-py2.py3-none-any.whl.metadata (5.0 kB)\n",
+      "Collecting mdit-py-plugins==0.6.1 (from -r requirements/local.txt (line 231))\n",
+      "  Using cached mdit_py_plugins-0.6.1-py3-none-any.whl.metadata (2.9 kB)\n",
+      "Collecting mdurl==0.1.2 (from -r requirements/local.txt (line 233))\n",
+      "  Using cached mdurl-0.1.2-py3-none-any.whl.metadata (1.6 kB)\n",
+      "Collecting mistune==3.2.1 (from -r requirements/local.txt (line 235))\n",
+      "  Using cached mistune-3.2.1-py3-none-any.whl.metadata (1.9 kB)\n",
+      "Collecting mypy==2.1.0 (from -r requirements/local.txt (line 237))\n",
+      "  Using cached mypy-2.1.0-cp313-cp313-macosx_11_0_arm64.whl.metadata (2.3 kB)\n",
+      "Collecting mypy-extensions==1.1.0 (from -r requirements/local.txt (line 241))\n",
+      "  Using cached mypy_extensions-1.1.0-py3-none-any.whl.metadata (1.1 kB)\n",
+      "Collecting narwhals==2.22.1 (from -r requirements/local.txt (line 246))\n",
+      "  Using cached narwhals-2.22.1-py3-none-any.whl.metadata (15 kB)\n",
+      "Collecting nbclient==0.11.0 (from -r requirements/local.txt (line 248))\n",
+      "  Using cached nbclient-0.11.0-py3-none-any.whl.metadata (7.3 kB)\n",
+      "Collecting nbconvert==7.17.1 (from -r requirements/local.txt (line 250))\n",
+      "  Using cached nbconvert-7.17.1-py3-none-any.whl.metadata (8.4 kB)\n",
+      "Collecting nbformat==5.10.4 (from -r requirements/local.txt (line 252))\n",
+      "  Using cached nbformat-5.10.4-py3-none-any.whl.metadata (3.6 kB)\n",
+      "Collecting nest-asyncio2==1.7.2 (from -r requirements/local.txt (line 258))\n",
+      "  Using cached nest_asyncio2-1.7.2-py3-none-any.whl.metadata (6.3 kB)\n",
+      "Collecting nodeenv==1.10.0 (from -r requirements/local.txt (line 260))\n",
+      "  Using cached nodeenv-1.10.0-py2.py3-none-any.whl.metadata (24 kB)\n",
+      "Collecting notebook-shim==0.2.4 (from -r requirements/local.txt (line 262))\n",
+      "  Using cached notebook_shim-0.2.4-py3-none-any.whl.metadata (4.0 kB)\n",
+      "Collecting numba==0.65.1 (from -r requirements/local.txt (line 264))\n",
+      "  Using cached numba-0.65.1-cp313-cp313-macosx_12_0_arm64.whl.metadata (2.9 kB)\n",
+      "Collecting numpy==2.4.6 (from -r requirements/local.txt (line 266))\n",
+      "  Using cached numpy-2.4.6-cp313-cp313-macosx_14_0_arm64.whl.metadata (6.6 kB)\n",
+      "Requirement already satisfied: packaging==26.2 in ./venv/lib/python3.13/site-packages (from -r requirements/local.txt (line 278)) (26.2)\n",
+      "Collecting pandas==2.3.3 (from -r requirements/local.txt (line 296))\n",
+      "  Using cached pandas-2.3.3-cp313-cp313-macosx_11_0_arm64.whl.metadata (91 kB)\n",
+      "Collecting pandas-stubs==3.0.3.260530 (from -r requirements/local.txt (line 301))\n",
+      "  Using cached pandas_stubs-3.0.3.260530-py3-none-any.whl.metadata (11 kB)\n",
+      "Collecting pandocfilters==1.5.1 (from -r requirements/local.txt (line 303))\n",
+      "  Using cached pandocfilters-1.5.1-py2.py3-none-any.whl.metadata (9.0 kB)\n",
+      "Collecting parso==0.8.7 (from -r requirements/local.txt (line 305))\n",
+      "  Using cached parso-0.8.7-py2.py3-none-any.whl.metadata (8.2 kB)\n",
+      "Collecting pathspec==1.1.1 (from -r requirements/local.txt (line 307))\n",
+      "  Using cached pathspec-1.1.1-py3-none-any.whl.metadata (14 kB)\n",
+      "Collecting pexpect==4.9.0 (from -r requirements/local.txt (line 311))\n",
+      "  Using cached pexpect-4.9.0-py2.py3-none-any.whl.metadata (2.5 kB)\n",
+      "Collecting pillow==12.2.0 (from -r requirements/local.txt (line 313))\n",
+      "  Using cached pillow-12.2.0-cp313-cp313-macosx_11_0_arm64.whl.metadata (8.8 kB)\n",
+      "Requirement already satisfied: pip-tools==7.5.3 in ./venv/lib/python3.13/site-packages (from -r requirements/local.txt (line 315)) (7.5.3)\n",
+      "Collecting platformdirs==4.10.0 (from -r requirements/local.txt (line 317))\n",
+      "  Using cached platformdirs-4.10.0-py3-none-any.whl.metadata (5.5 kB)\n",
+      "Collecting pluggy==1.6.0 (from -r requirements/local.txt (line 325))\n",
+      "  Using cached pluggy-1.6.0-py3-none-any.whl.metadata (4.8 kB)\n",
+      "Collecting pre-commit==4.6.0 (from -r requirements/local.txt (line 329))\n",
+      "  Using cached pre_commit-4.6.0-py2.py3-none-any.whl.metadata (1.2 kB)\n",
+      "Collecting prometheus-client==0.25.0 (from -r requirements/local.txt (line 331))\n",
+      "  Using cached prometheus_client-0.25.0-py3-none-any.whl.metadata (2.1 kB)\n",
+      "Collecting prompt-toolkit==3.0.52 (from -r requirements/local.txt (line 333))\n",
+      "  Using cached prompt_toolkit-3.0.52-py3-none-any.whl.metadata (6.4 kB)\n",
+      "Collecting protobuf==6.33.6 (from -r requirements/local.txt (line 335))\n",
+      "  Using cached protobuf-6.33.6-cp39-abi3-macosx_10_9_universal2.whl.metadata (593 bytes)\n",
+      "Collecting psutil==7.2.2 (from -r requirements/local.txt (line 339))\n",
+      "  Using cached psutil-7.2.2-cp36-abi3-macosx_11_0_arm64.whl.metadata (22 kB)\n",
+      "Collecting ptyprocess==0.7.0 (from -r requirements/local.txt (line 343))\n",
+      "  Using cached ptyprocess-0.7.0-py2.py3-none-any.whl.metadata (1.3 kB)\n",
+      "Collecting pure-eval==0.2.3 (from -r requirements/local.txt (line 347))\n",
+      "  Using cached pure_eval-0.2.3-py3-none-any.whl.metadata (6.3 kB)\n",
+      "Collecting pycodestyle==2.14.0 (from -r requirements/local.txt (line 349))\n",
+      "  Using cached pycodestyle-2.14.0-py2.py3-none-any.whl.metadata (4.5 kB)\n",
+      "Collecting pycparser==3.0 (from -r requirements/local.txt (line 351))\n",
+      "  Using cached pycparser-3.0-py3-none-any.whl.metadata (8.2 kB)\n",
+      "Collecting pyflakes==3.4.0 (from -r requirements/local.txt (line 353))\n",
+      "  Using cached pyflakes-3.4.0-py2.py3-none-any.whl.metadata (3.5 kB)\n",
+      "Collecting pygments==2.20.0 (from -r requirements/local.txt (line 355))\n",
+      "  Using cached pygments-2.20.0-py3-none-any.whl.metadata (2.5 kB)\n",
+      "Collecting pylint==4.0.6 (from -r requirements/local.txt (line 362))\n",
+      "  Using cached pylint-4.0.6-py3-none-any.whl.metadata (12 kB)\n",
+      "Collecting pylint-django==2.7.0 (from -r requirements/local.txt (line 367))\n",
+      "  Using cached pylint_django-2.7.0-py3-none-any.whl.metadata (7.3 kB)\n",
+      "Collecting pylint-plugin-utils==0.9.0 (from -r requirements/local.txt (line 369))\n",
+      "  Using cached pylint_plugin_utils-0.9.0-py3-none-any.whl.metadata (2.8 kB)\n",
+      "Collecting pyparsing==3.3.2 (from -r requirements/local.txt (line 371))\n",
+      "  Using cached pyparsing-3.3.2-py3-none-any.whl.metadata (5.8 kB)\n",
+      "Collecting pyproject-api==1.10.1 (from -r requirements/local.txt (line 373))\n",
+      "  Using cached pyproject_api-1.10.1-py3-none-any.whl.metadata (2.3 kB)\n",
+      "Requirement already satisfied: pyproject-hooks==1.2.0 in ./venv/lib/python3.13/site-packages (from -r requirements/local.txt (line 375)) (1.2.0)\n",
+      "Collecting pytest==9.1.0 (from -r requirements/local.txt (line 379))\n",
+      "  Using cached pytest-9.1.0-py3-none-any.whl.metadata (7.6 kB)\n",
+      "Collecting pytest-mock==3.15.1 (from -r requirements/local.txt (line 383))\n",
+      "  Using cached pytest_mock-3.15.1-py3-none-any.whl.metadata (3.9 kB)\n",
+      "Collecting python-dateutil==2.9.0.post0 (from -r requirements/local.txt (line 385))\n",
+      "  Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)\n",
+      "Collecting python-discovery==1.4.2 (from -r requirements/local.txt (line 392))\n",
+      "  Using cached python_discovery-1.4.2-py3-none-any.whl.metadata (5.6 kB)\n",
+      "Collecting python-dotenv==1.2.2 (from -r requirements/local.txt (line 396))\n",
+      "  Using cached python_dotenv-1.2.2-py3-none-any.whl.metadata (27 kB)\n",
+      "Collecting python-json-logger==4.1.0 (from -r requirements/local.txt (line 400))\n",
+      "  Using cached python_json_logger-4.1.0-py3-none-any.whl.metadata (3.7 kB)\n",
+      "Collecting python-slugify==8.0.4 (from -r requirements/local.txt (line 402))\n",
+      "  Using cached python_slugify-8.0.4-py2.py3-none-any.whl.metadata (8.5 kB)\n",
+      "Collecting pytokens==0.4.1 (from -r requirements/local.txt (line 404))\n",
+      "  Using cached pytokens-0.4.1-cp313-cp313-macosx_11_0_arm64.whl.metadata (3.8 kB)\n",
+      "Collecting pytz==2026.2 (from -r requirements/local.txt (line 406))\n",
+      "  Using cached pytz-2026.2-py2.py3-none-any.whl.metadata (22 kB)\n",
+      "Collecting pyyaml==6.0.3 (from -r requirements/local.txt (line 408))\n",
+      "  Using cached pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl.metadata (2.4 kB)\n",
+      "Collecting pyzmq==27.1.0 (from -r requirements/local.txt (line 414))\n",
+      "  Using cached pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl.metadata (6.0 kB)\n",
+      "Collecting rapidfuzz==3.14.5 (from -r requirements/local.txt (line 419))\n",
+      "  Using cached rapidfuzz-3.14.5-cp313-cp313-macosx_11_0_arm64.whl.metadata (12 kB)\n",
+      "Collecting referencing==0.37.0 (from -r requirements/local.txt (line 421))\n",
+      "  Using cached referencing-0.37.0-py3-none-any.whl.metadata (2.8 kB)\n",
+      "Collecting requests==2.34.2 (from -r requirements/local.txt (line 426))\n",
+      "  Using cached requests-2.34.2-py3-none-any.whl.metadata (4.8 kB)\n",
+      "Collecting rfc3339-validator==0.1.4 (from -r requirements/local.txt (line 432))\n",
+      "  Using cached rfc3339_validator-0.1.4-py2.py3-none-any.whl.metadata (1.5 kB)\n",
+      "Collecting rfc3986-validator==0.1.1 (from -r requirements/local.txt (line 436))\n",
+      "  Using cached rfc3986_validator-0.1.1-py2.py3-none-any.whl.metadata (1.7 kB)\n",
+      "Collecting rfc3987-syntax==1.1.0 (from -r requirements/local.txt (line 440))\n",
+      "  Using cached rfc3987_syntax-1.1.0-py3-none-any.whl.metadata (7.7 kB)\n",
+      "Collecting rich==15.0.0 (from -r requirements/local.txt (line 442))\n",
+      "  Using cached rich-15.0.0-py3-none-any.whl.metadata (18 kB)\n",
+      "Collecting rpds-py==2026.5.1 (from -r requirements/local.txt (line 446))\n",
+      "  Using cached rpds_py-2026.5.1-cp313-cp313-macosx_11_0_arm64.whl.metadata (4.1 kB)\n",
+      "Collecting scikit-learn==1.9.0 (from -r requirements/local.txt (line 450))\n",
+      "  Using cached scikit_learn-1.9.0-cp313-cp313-macosx_12_0_arm64.whl.metadata (11 kB)\n",
+      "Collecting scipy==1.17.1 (from -r requirements/local.txt (line 454))\n",
+      "  Using cached scipy-1.17.1-cp313-cp313-macosx_14_0_arm64.whl.metadata (62 kB)\n",
+      "Collecting seaborn==0.13.2 (from -r requirements/local.txt (line 459))\n",
+      "  Using cached seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
+      "Collecting send2trash==2.1.0 (from -r requirements/local.txt (line 461))\n",
+      "  Using cached send2trash-2.1.0-py3-none-any.whl.metadata (4.1 kB)\n",
+      "Collecting shap==0.52.0 (from -r requirements/local.txt (line 463))\n",
+      "  Using cached shap-0.52.0-cp312-abi3-macosx_11_0_arm64.whl.metadata (26 kB)\n",
+      "Collecting shellingham==1.5.4 (from -r requirements/local.txt (line 465))\n",
+      "  Using cached shellingham-1.5.4-py2.py3-none-any.whl.metadata (3.5 kB)\n",
+      "Collecting six==1.17.0 (from -r requirements/local.txt (line 467))\n",
+      "  Using cached six-1.17.0-py2.py3-none-any.whl.metadata (1.7 kB)\n",
+      "Collecting slicer==0.0.8 (from -r requirements/local.txt (line 471))\n",
+      "  Using cached slicer-0.0.8-py3-none-any.whl.metadata (4.0 kB)\n",
+      "Collecting soupsieve==2.8.4 (from -r requirements/local.txt (line 473))\n",
+      "  Using cached soupsieve-2.8.4-py3-none-any.whl.metadata (4.6 kB)\n",
+      "Collecting stack-data==0.6.3 (from -r requirements/local.txt (line 475))\n",
+      "  Using cached stack_data-0.6.3-py3-none-any.whl.metadata (18 kB)\n",
+      "Collecting stevedore==5.8.0 (from -r requirements/local.txt (line 477))\n",
+      "  Using cached stevedore-5.8.0-py3-none-any.whl.metadata (2.3 kB)\n",
+      "Collecting terminado==0.18.1 (from -r requirements/local.txt (line 479))\n",
+      "  Using cached terminado-0.18.1-py3-none-any.whl.metadata (5.8 kB)\n",
+      "Collecting text-unidecode==1.3 (from -r requirements/local.txt (line 483))\n",
+      "  Using cached text_unidecode-1.3-py2.py3-none-any.whl.metadata (2.4 kB)\n",
+      "Collecting threadpoolctl==3.6.0 (from -r requirements/local.txt (line 485))\n",
+      "  Using cached threadpoolctl-3.6.0-py3-none-any.whl.metadata (13 kB)\n",
+      "Collecting tinycss2==1.5.1 (from -r requirements/local.txt (line 487))\n",
+      "  Using cached tinycss2-1.5.1-py3-none-any.whl.metadata (3.0 kB)\n",
+      "Collecting tomli-w==1.2.0 (from -r requirements/local.txt (line 489))\n",
+      "  Using cached tomli_w-1.2.0-py3-none-any.whl.metadata (5.7 kB)\n",
+      "Collecting tomlkit==0.15.0 (from -r requirements/local.txt (line 491))\n",
+      "  Using cached tomlkit-0.15.0-py3-none-any.whl.metadata (2.8 kB)\n",
+      "Collecting tornado==6.5.7 (from -r requirements/local.txt (line 493))\n",
+      "  Using cached tornado-6.5.7-cp39-abi3-macosx_10_9_universal2.whl.metadata (2.8 kB)\n",
+      "Collecting tox==4.55.1 (from -r requirements/local.txt (line 500))\n",
+      "  Using cached tox-4.55.1-py3-none-any.whl.metadata (3.8 kB)\n",
+      "Collecting tqdm==4.68.2 (from -r requirements/local.txt (line 502))\n",
+      "  Using cached tqdm-4.68.2-py3-none-any.whl.metadata (58 kB)\n",
+      "Collecting traitlets==5.15.1 (from -r requirements/local.txt (line 507))\n",
+      "  Using cached traitlets-5.15.1-py3-none-any.whl.metadata (10 kB)\n",
+      "Collecting typer==0.26.7 (from -r requirements/local.txt (line 520))\n",
+      "  Using cached typer-0.26.7-py3-none-any.whl.metadata (16 kB)\n",
+      "Collecting types-cachetools==7.0.0.20260518 (from -r requirements/local.txt (line 522))\n",
+      "  Using cached types_cachetools-7.0.0.20260518-py3-none-any.whl.metadata (1.8 kB)\n",
+      "Collecting types-html5lib==1.1.11.20260518 (from -r requirements/local.txt (line 524))\n",
+      "  Using cached types_html5lib-1.1.11.20260518-py3-none-any.whl.metadata (1.8 kB)\n",
+      "Collecting types-lxml==2026.2.16 (from -r requirements/local.txt (line 526))\n",
+      "  Using cached types_lxml-2026.2.16-py3-none-any.whl.metadata (11 kB)\n",
+      "Collecting types-requests==2.33.0.20260518 (from -r requirements/local.txt (line 528))\n",
+      "  Using cached types_requests-2.33.0.20260518-py3-none-any.whl.metadata (2.2 kB)\n",
+      "Collecting types-tqdm==4.68.0.20260608 (from -r requirements/local.txt (line 532))\n",
+      "  Using cached types_tqdm-4.68.0.20260608-py3-none-any.whl.metadata (1.7 kB)\n",
+      "Collecting types-webencodings==0.5.0.20260408 (from -r requirements/local.txt (line 534))\n",
+      "  Using cached types_webencodings-0.5.0.20260408-py3-none-any.whl.metadata (1.8 kB)\n",
+      "Collecting typing-extensions==4.15.0 (from -r requirements/local.txt (line 536))\n",
+      "  Using cached typing_extensions-4.15.0-py3-none-any.whl.metadata (3.3 kB)\n",
+      "Collecting tzdata==2026.2 (from -r requirements/local.txt (line 542))\n",
+      "  Using cached tzdata-2026.2-py2.py3-none-any.whl.metadata (1.4 kB)\n",
+      "Collecting uri-template==1.3.0 (from -r requirements/local.txt (line 546))\n",
+      "  Using cached uri_template-1.3.0-py3-none-any.whl.metadata (8.8 kB)\n",
+      "Collecting urllib3==2.7.0 (from -r requirements/local.txt (line 548))\n",
+      "  Using cached urllib3-2.7.0-py3-none-any.whl.metadata (6.9 kB)\n",
+      "Collecting virtualenv==21.5.0 (from -r requirements/local.txt (line 553))\n",
+      "  Using cached virtualenv-21.5.0-py3-none-any.whl.metadata (3.4 kB)\n",
+      "Collecting wcwidth==0.8.1 (from -r requirements/local.txt (line 557))\n",
+      "  Using cached wcwidth-0.8.1-py3-none-any.whl.metadata (43 kB)\n",
+      "Collecting webcolors==25.10.0 (from -r requirements/local.txt (line 559))\n",
+      "  Using cached webcolors-25.10.0-py3-none-any.whl.metadata (2.2 kB)\n",
+      "Collecting webencodings==0.5.1 (from -r requirements/local.txt (line 561))\n",
+      "  Using cached webencodings-0.5.1-py2.py3-none-any.whl.metadata (2.1 kB)\n",
+      "Collecting websocket-client==1.9.0 (from -r requirements/local.txt (line 565))\n",
+      "  Using cached websocket_client-1.9.0-py3-none-any.whl.metadata (8.3 kB)\n",
+      "Requirement already satisfied: wheel==0.47.0 in ./venv/lib/python3.13/site-packages (from -r requirements/local.txt (line 567)) (0.47.0)\n",
+      "Requirement already satisfied: setuptools>=41.1.0 in ./venv/lib/python3.13/site-packages (from jupyterlab==4.5.8->-r requirements/local.txt (line 188)) (83.0.0)\n",
+      "Requirement already satisfied: pip>=22.2 in ./venv/lib/python3.13/site-packages (from pip-tools==7.5.3->-r requirements/local.txt (line 315)) (25.3)\n",
+      "Using cached annotated_doc-0.0.4-py3-none-any.whl (5.3 kB)\n",
+      "Using cached anyio-4.13.0-py3-none-any.whl (114 kB)\n",
+      "Using cached appnope-0.1.4-py2.py3-none-any.whl (4.3 kB)\n",
+      "Using cached argon2_cffi-25.1.0-py3-none-any.whl (14 kB)\n",
+      "Using cached argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_11_0_arm64.whl (31 kB)\n",
+      "Using cached arrow-1.4.0-py3-none-any.whl (68 kB)\n",
+      "Using cached ast_serialize-0.5.0-cp39-abi3-macosx_11_0_arm64.whl (1.2 MB)\n",
+      "Using cached astroid-4.0.4-py3-none-any.whl (276 kB)\n",
+      "Using cached asttokens-3.0.1-py3-none-any.whl (27 kB)\n",
+      "Using cached async_lru-2.3.0-py3-none-any.whl (8.4 kB)\n",
+      "Using cached attrs-26.1.0-py3-none-any.whl (67 kB)\n",
+      "Using cached babel-2.18.0-py3-none-any.whl (10.2 MB)\n",
+      "Using cached bandit-1.9.4-py3-none-any.whl (134 kB)\n",
+      "Using cached beautifulsoup4-4.15.0-py3-none-any.whl (109 kB)\n",
+      "Using cached black-26.5.1-cp313-cp313-macosx_11_0_arm64.whl (1.8 MB)\n",
+      "Using cached pytokens-0.4.1-cp313-cp313-macosx_11_0_arm64.whl (160 kB)\n",
+      "Using cached bleach-6.4.0-py3-none-any.whl (165 kB)\n",
+      "Using cached cachetools-7.1.4-py3-none-any.whl (16 kB)\n",
+      "Using cached certifi-2026.5.20-py3-none-any.whl (134 kB)\n",
+      "Using cached cffi-2.0.0-cp313-cp313-macosx_11_0_arm64.whl (181 kB)\n",
+      "Using cached cfgv-3.5.0-py2.py3-none-any.whl (7.4 kB)\n",
+      "Using cached charset_normalizer-3.4.7-cp313-cp313-macosx_10_13_universal2.whl (309 kB)\n",
+      "Using cached click-8.4.1-py3-none-any.whl (116 kB)\n",
+      "Using cached cloudpickle-3.1.2-py3-none-any.whl (22 kB)\n",
+      "Using cached codespell-2.4.2-py3-none-any.whl (353 kB)\n",
+      "Using cached colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
+      "Using cached comm-0.2.3-py3-none-any.whl (7.3 kB)\n",
+      "Using cached contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl (274 kB)\n",
+      "Using cached coverage-7.14.1-cp313-cp313-macosx_11_0_arm64.whl (220 kB)\n",
+      "Using cached cssselect-1.4.0-py3-none-any.whl (18 kB)\n",
+      "Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
+      "Using cached debugpy-1.8.21-cp313-cp313-macosx_15_0_universal2.whl (2.5 MB)\n",
+      "Using cached decorator-5.3.1-py3-none-any.whl (10 kB)\n",
+      "Using cached defusedxml-0.7.1-py2.py3-none-any.whl (25 kB)\n",
+      "Using cached dill-0.4.1-py3-none-any.whl (120 kB)\n",
+      "Using cached distlib-0.4.3-py2.py3-none-any.whl (470 kB)\n",
+      "Using cached duckdb-1.5.3-cp313-cp313-macosx_11_0_arm64.whl (15.4 MB)\n",
+      "Using cached executing-2.2.1-py2.py3-none-any.whl (28 kB)\n",
+      "Using cached fastjsonschema-2.21.2-py3-none-any.whl (24 kB)\n",
+      "Using cached filelock-3.29.4-py3-none-any.whl (42 kB)\n",
+      "Using cached flake8-7.3.0-py2.py3-none-any.whl (57 kB)\n",
+      "Using cached mccabe-0.7.0-py2.py3-none-any.whl (7.3 kB)\n",
+      "Using cached pycodestyle-2.14.0-py2.py3-none-any.whl (31 kB)\n",
+      "Using cached pyflakes-3.4.0-py2.py3-none-any.whl (63 kB)\n",
+      "Using cached flake8_coding-1.3.2-py2.py3-none-any.whl (7.6 kB)\n",
+      "Using cached fonttools-4.63.0-cp313-cp313-macosx_10_13_universal2.whl (2.9 MB)\n",
+      "Using cached fqdn-1.5.1-py3-none-any.whl (9.1 kB)\n",
+      "Using cached h11-0.16.0-py3-none-any.whl (37 kB)\n",
+      "Using cached httpcore-1.0.9-py3-none-any.whl (78 kB)\n",
+      "Using cached httpx-0.28.1-py3-none-any.whl (73 kB)\n",
+      "Using cached identify-2.6.19-py2.py3-none-any.whl (99 kB)\n",
+      "Using cached idna-3.18-py3-none-any.whl (65 kB)\n",
+      "Using cached iniconfig-2.3.0-py3-none-any.whl (7.5 kB)\n",
+      "Using cached ipykernel-7.3.0-py3-none-any.whl (120 kB)\n",
+      "Using cached ipython-9.14.1-py3-none-any.whl (627 kB)\n",
+      "Using cached prompt_toolkit-3.0.52-py3-none-any.whl (391 kB)\n",
+      "Using cached ipython_pygments_lexers-1.1.1-py3-none-any.whl (8.1 kB)\n",
+      "Using cached isoduration-20.11.0-py3-none-any.whl (11 kB)\n",
+      "Using cached isort-8.0.1-py3-none-any.whl (89 kB)\n",
+      "Using cached jedi-0.20.0-py2.py3-none-any.whl (4.9 MB)\n",
+      "Using cached parso-0.8.7-py2.py3-none-any.whl (107 kB)\n",
+      "Using cached jinja2-3.1.6-py3-none-any.whl (134 kB)\n",
+      "Using cached joblib-1.5.3-py3-none-any.whl (309 kB)\n",
+      "Using cached json5-0.14.0-py3-none-any.whl (36 kB)\n",
+      "Using cached jsonpointer-3.1.1-py3-none-any.whl (7.7 kB)\n",
+      "Using cached jsonschema-4.26.0-py3-none-any.whl (90 kB)\n",
+      "Using cached jsonschema_specifications-2025.9.1-py3-none-any.whl (18 kB)\n",
+      "Using cached jupyter_client-8.9.1-py3-none-any.whl (109 kB)\n",
+      "Using cached jupyter_core-5.9.1-py3-none-any.whl (29 kB)\n",
+      "Using cached jupyter_events-0.12.1-py3-none-any.whl (19 kB)\n",
+      "Using cached jupyter_lsp-2.3.1-py3-none-any.whl (77 kB)\n",
+      "Using cached jupyter_server-2.19.0-py3-none-any.whl (392 kB)\n",
+      "Using cached jupyter_server_terminals-0.5.4-py3-none-any.whl (13 kB)\n",
+      "Using cached jupyterlab-4.5.8-py3-none-any.whl (12.4 MB)\n",
+      "Using cached jupyterlab_server-2.28.0-py3-none-any.whl (59 kB)\n",
+      "Using cached jupyterlab_pygments-0.3.0-py3-none-any.whl (15 kB)\n",
+      "Using cached jupytext-1.19.3-py3-none-any.whl (170 kB)\n",
+      "Using cached kaggle-2.2.1-py3-none-any.whl (132 kB)\n",
+      "Using cached kagglesdk-0.1.30-py3-none-any.whl (231 kB)\n",
+      "Using cached kiwisolver-1.5.0-cp313-cp313-macosx_11_0_arm64.whl (64 kB)\n",
+      "Using cached lark-1.3.1-py3-none-any.whl (113 kB)\n",
+      "Using cached librt-0.11.0-cp313-cp313-macosx_11_0_arm64.whl (143 kB)\n",
+      "Using cached llvmlite-0.47.0-cp313-cp313-macosx_12_0_arm64.whl (37.2 MB)\n",
+      "Using cached markdown_it_py-4.2.0-py3-none-any.whl (91 kB)\n",
+      "Using cached mdurl-0.1.2-py3-none-any.whl (10.0 kB)\n",
+      "Using cached markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl (12 kB)\n",
+      "Using cached matplotlib-3.11.0-cp313-cp313-macosx_11_0_arm64.whl (9.3 MB)\n",
+      "Using cached matplotlib_inline-0.2.2-py3-none-any.whl (9.5 kB)\n",
+      "Using cached mdit_py_plugins-0.6.1-py3-none-any.whl (66 kB)\n",
+      "Using cached mistune-3.2.1-py3-none-any.whl (53 kB)\n",
+      "Using cached mypy-2.1.0-cp313-cp313-macosx_11_0_arm64.whl (13.7 MB)\n",
+      "Using cached mypy_extensions-1.1.0-py3-none-any.whl (5.0 kB)\n",
+      "Using cached narwhals-2.22.1-py3-none-any.whl (454 kB)\n",
+      "Using cached nbclient-0.11.0-py3-none-any.whl (25 kB)\n",
+      "Using cached nbconvert-7.17.1-py3-none-any.whl (261 kB)\n",
+      "Using cached nbformat-5.10.4-py3-none-any.whl (78 kB)\n",
+      "Using cached nest_asyncio2-1.7.2-py3-none-any.whl (7.8 kB)\n",
+      "Using cached nodeenv-1.10.0-py2.py3-none-any.whl (23 kB)\n",
+      "Using cached notebook_shim-0.2.4-py3-none-any.whl (13 kB)\n",
+      "Using cached numba-0.65.1-cp313-cp313-macosx_12_0_arm64.whl (2.7 MB)\n",
+      "Using cached numpy-2.4.6-cp313-cp313-macosx_14_0_arm64.whl (5.2 MB)\n",
+      "Using cached pandas-2.3.3-cp313-cp313-macosx_11_0_arm64.whl (10.7 MB)\n",
+      "Using cached pandas_stubs-3.0.3.260530-py3-none-any.whl (173 kB)\n",
+      "Using cached pandocfilters-1.5.1-py2.py3-none-any.whl (8.7 kB)\n",
+      "Using cached pathspec-1.1.1-py3-none-any.whl (57 kB)\n",
+      "Using cached pexpect-4.9.0-py2.py3-none-any.whl (63 kB)\n",
+      "Using cached pillow-12.2.0-cp313-cp313-macosx_11_0_arm64.whl (4.7 MB)\n",
+      "Using cached platformdirs-4.10.0-py3-none-any.whl (22 kB)\n",
+      "Using cached pluggy-1.6.0-py3-none-any.whl (20 kB)\n",
+      "Using cached pre_commit-4.6.0-py2.py3-none-any.whl (226 kB)\n",
+      "Using cached prometheus_client-0.25.0-py3-none-any.whl (64 kB)\n",
+      "Using cached protobuf-6.33.6-cp39-abi3-macosx_10_9_universal2.whl (427 kB)\n",
+      "Using cached psutil-7.2.2-cp36-abi3-macosx_11_0_arm64.whl (129 kB)\n",
+      "Using cached ptyprocess-0.7.0-py2.py3-none-any.whl (13 kB)\n",
+      "Using cached pure_eval-0.2.3-py3-none-any.whl (11 kB)\n",
+      "Using cached pycparser-3.0-py3-none-any.whl (48 kB)\n",
+      "Using cached pygments-2.20.0-py3-none-any.whl (1.2 MB)\n",
+      "Using cached pylint-4.0.6-py3-none-any.whl (538 kB)\n",
+      "Using cached pylint_django-2.7.0-py3-none-any.whl (42 kB)\n",
+      "Using cached pylint_plugin_utils-0.9.0-py3-none-any.whl (11 kB)\n",
+      "Using cached pyparsing-3.3.2-py3-none-any.whl (122 kB)\n",
+      "Using cached pyproject_api-1.10.1-py3-none-any.whl (12 kB)\n",
+      "Using cached pytest-9.1.0-py3-none-any.whl (386 kB)\n",
+      "Using cached pytest_mock-3.15.1-py3-none-any.whl (10 kB)\n",
+      "Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)\n",
+      "Using cached python_discovery-1.4.2-py3-none-any.whl (33 kB)\n",
+      "Using cached python_dotenv-1.2.2-py3-none-any.whl (22 kB)\n",
+      "Using cached python_json_logger-4.1.0-py3-none-any.whl (15 kB)\n",
+      "Using cached python_slugify-8.0.4-py2.py3-none-any.whl (10 kB)\n",
+      "Using cached pytz-2026.2-py2.py3-none-any.whl (510 kB)\n",
+      "Using cached pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl (173 kB)\n",
+      "Using cached pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl (1.3 MB)\n",
+      "Using cached rapidfuzz-3.14.5-cp313-cp313-macosx_11_0_arm64.whl (1.2 MB)\n",
+      "Using cached referencing-0.37.0-py3-none-any.whl (26 kB)\n",
+      "Using cached requests-2.34.2-py3-none-any.whl (73 kB)\n",
+      "Using cached urllib3-2.7.0-py3-none-any.whl (131 kB)\n",
+      "Using cached rfc3339_validator-0.1.4-py2.py3-none-any.whl (3.5 kB)\n",
+      "Using cached rfc3986_validator-0.1.1-py2.py3-none-any.whl (4.2 kB)\n",
+      "Using cached rfc3987_syntax-1.1.0-py3-none-any.whl (8.0 kB)\n",
+      "Using cached rich-15.0.0-py3-none-any.whl (310 kB)\n",
+      "Using cached rpds_py-2026.5.1-cp313-cp313-macosx_11_0_arm64.whl (345 kB)\n",
+      "Using cached scikit_learn-1.9.0-cp313-cp313-macosx_12_0_arm64.whl (8.2 MB)\n",
+      "Using cached scipy-1.17.1-cp313-cp313-macosx_14_0_arm64.whl (20.3 MB)\n",
+      "Using cached seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
+      "Using cached send2trash-2.1.0-py3-none-any.whl (17 kB)\n",
+      "Using cached shap-0.52.0-cp312-abi3-macosx_11_0_arm64.whl (490 kB)\n",
+      "Using cached shellingham-1.5.4-py2.py3-none-any.whl (9.8 kB)\n",
+      "Using cached six-1.17.0-py2.py3-none-any.whl (11 kB)\n",
+      "Using cached slicer-0.0.8-py3-none-any.whl (15 kB)\n",
+      "Using cached soupsieve-2.8.4-py3-none-any.whl (37 kB)\n",
+      "Using cached stack_data-0.6.3-py3-none-any.whl (24 kB)\n",
+      "Using cached stevedore-5.8.0-py3-none-any.whl (54 kB)\n",
+      "Using cached terminado-0.18.1-py3-none-any.whl (14 kB)\n",
+      "Using cached text_unidecode-1.3-py2.py3-none-any.whl (78 kB)\n",
+      "Using cached threadpoolctl-3.6.0-py3-none-any.whl (18 kB)\n",
+      "Using cached tinycss2-1.5.1-py3-none-any.whl (28 kB)\n",
+      "Using cached tomli_w-1.2.0-py3-none-any.whl (6.7 kB)\n",
+      "Using cached tomlkit-0.15.0-py3-none-any.whl (41 kB)\n",
+      "Using cached tornado-6.5.7-cp39-abi3-macosx_10_9_universal2.whl (448 kB)\n",
+      "Using cached tox-4.55.1-py3-none-any.whl (215 kB)\n",
+      "Using cached tqdm-4.68.2-py3-none-any.whl (78 kB)\n",
+      "Using cached traitlets-5.15.1-py3-none-any.whl (85 kB)\n",
+      "Using cached typer-0.26.7-py3-none-any.whl (122 kB)\n",
+      "Using cached types_cachetools-7.0.0.20260518-py3-none-any.whl (9.5 kB)\n",
+      "Using cached types_html5lib-1.1.11.20260518-py3-none-any.whl (24 kB)\n",
+      "Using cached types_lxml-2026.2.16-py3-none-any.whl (97 kB)\n",
+      "Using cached typing_extensions-4.15.0-py3-none-any.whl (44 kB)\n",
+      "Using cached types_requests-2.33.0.20260518-py3-none-any.whl (21 kB)\n",
+      "Using cached types_tqdm-4.68.0.20260608-py3-none-any.whl (24 kB)\n",
+      "Using cached types_webencodings-0.5.0.20260408-py3-none-any.whl (8.7 kB)\n",
+      "Using cached tzdata-2026.2-py2.py3-none-any.whl (349 kB)\n",
+      "Using cached uri_template-1.3.0-py3-none-any.whl (11 kB)\n",
+      "Using cached virtualenv-21.5.0-py3-none-any.whl (4.6 MB)\n",
+      "Using cached wcwidth-0.8.1-py3-none-any.whl (323 kB)\n",
+      "Using cached webcolors-25.10.0-py3-none-any.whl (14 kB)\n",
+      "Using cached webencodings-0.5.1-py2.py3-none-any.whl (11 kB)\n",
+      "Using cached websocket_client-1.9.0-py3-none-any.whl (82 kB)\n",
+      "Installing collected packages: webencodings, text-unidecode, pytz, pure-eval, ptyprocess, fastjsonschema, distlib, websocket-client, webcolors, wcwidth, urllib3, uri-template, tzdata, typing-extensions, types-webencodings, types-cachetools, traitlets, tqdm, tornado, tomlkit, tomli-w, tinycss2, threadpoolctl, stevedore, soupsieve, slicer, six, shellingham, send2trash, rpds-py, rfc3986-validator, rapidfuzz, pyzmq, pyyaml, pytokens, python-slugify, python-json-logger, python-dotenv, pyproject-api, pyparsing, pygments, pyflakes, pycparser, pycodestyle, psutil, protobuf, prometheus-client, pluggy, platformdirs, pillow, pexpect, pathspec, parso, pandocfilters, numpy, nodeenv, nest-asyncio2, narwhals, mypy-extensions, mistune, mdurl, mccabe, markupsafe, llvmlite, librt, lark, kiwisolver, jupyterlab-pygments, jsonpointer, json5, joblib, isort, iniconfig, idna, identify, h11, fqdn, fonttools, filelock, executing, duckdb, dill, defusedxml, decorator, debugpy, cycler, cssselect, coverage, comm, colorama, codespell, cloudpickle, click, charset-normalizer, cfgv, certifi, cachetools, bleach, babel, attrs, async-lru, asttokens, astroid, ast-serialize, appnope, annotated-doc, types-requests, types-html5lib, terminado, stack-data, scipy, rfc3987-syntax, rfc3339-validator, requests, referencing, python-discovery, python-dateutil, pytest, pylint, prompt-toolkit, pandas-stubs, numba, mypy, matplotlib-inline, markdown-it-py, jupyter-core, jinja2, jedi, ipython-pygments-lexers, httpcore, flake8, contourpy, cffi, black, beautifulsoup4, anyio, virtualenv, types-tqdm, types-lxml, scikit-learn, rich, pytest-mock, pylint-plugin-utils, pandas, mdit-py-plugins, matplotlib, kagglesdk, jupyter-server-terminals, jupyter-client, jsonschema-specifications, ipython, httpx, flake8-coding, arrow, argon2-cffi-bindings, typer, tox, shap, seaborn, pylint-django, pre-commit, jsonschema, isoduration, ipykernel, bandit, argon2-cffi, nbformat, nbclient, jupytext, jupyter-events, nbconvert, kaggle, jupyter-server, notebook-shim, jupyterlab-server, jupyter-lsp, jupyterlab\n",
+      "\u001b[2K  Attempting uninstall: click0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 87/177\u001b[0m [coverage]]-client]]\n",
+      "\u001b[2K    Found existing installation: click 8.4.2━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 87/177\u001b[0m [coverage]\n",
+      "\u001b[2K    Uninstalling click-8.4.2:91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 87/177\u001b[0m [coverage]\n",
+      "\u001b[2K      Successfully uninstalled click-8.4.2━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 87/177\u001b[0m [coverage]\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177/177\u001b[0m [jupyterlab]pyterlab]]onvert]]in-utils]\n",
+      "\u001b[1A\u001b[2KSuccessfully installed annotated-doc-0.0.4 anyio-4.13.0 appnope-0.1.4 argon2-cffi-25.1.0 argon2-cffi-bindings-25.1.0 arrow-1.4.0 ast-serialize-0.5.0 astroid-4.0.4 asttokens-3.0.1 async-lru-2.3.0 attrs-26.1.0 babel-2.18.0 bandit-1.9.4 beautifulsoup4-4.15.0 black-26.5.1 bleach-6.4.0 cachetools-7.1.4 certifi-2026.5.20 cffi-2.0.0 cfgv-3.5.0 charset-normalizer-3.4.7 click-8.4.1 cloudpickle-3.1.2 codespell-2.4.2 colorama-0.4.6 comm-0.2.3 contourpy-1.3.3 coverage-7.14.1 cssselect-1.4.0 cycler-0.12.1 debugpy-1.8.21 decorator-5.3.1 defusedxml-0.7.1 dill-0.4.1 distlib-0.4.3 duckdb-1.5.3 executing-2.2.1 fastjsonschema-2.21.2 filelock-3.29.4 flake8-7.3.0 flake8-coding-1.3.2 fonttools-4.63.0 fqdn-1.5.1 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 identify-2.6.19 idna-3.18 iniconfig-2.3.0 ipykernel-7.3.0 ipython-9.14.1 ipython-pygments-lexers-1.1.1 isoduration-20.11.0 isort-8.0.1 jedi-0.20.0 jinja2-3.1.6 joblib-1.5.3 json5-0.14.0 jsonpointer-3.1.1 jsonschema-4.26.0 jsonschema-specifications-2025.9.1 jupyter-client-8.9.1 jupyter-core-5.9.1 jupyter-events-0.12.1 jupyter-lsp-2.3.1 jupyter-server-2.19.0 jupyter-server-terminals-0.5.4 jupyterlab-4.5.8 jupyterlab-pygments-0.3.0 jupyterlab-server-2.28.0 jupytext-1.19.3 kaggle-2.2.1 kagglesdk-0.1.30 kiwisolver-1.5.0 lark-1.3.1 librt-0.11.0 llvmlite-0.47.0 markdown-it-py-4.2.0 markupsafe-3.0.3 matplotlib-3.11.0 matplotlib-inline-0.2.2 mccabe-0.7.0 mdit-py-plugins-0.6.1 mdurl-0.1.2 mistune-3.2.1 mypy-2.1.0 mypy-extensions-1.1.0 narwhals-2.22.1 nbclient-0.11.0 nbconvert-7.17.1 nbformat-5.10.4 nest-asyncio2-1.7.2 nodeenv-1.10.0 notebook-shim-0.2.4 numba-0.65.1 numpy-2.4.6 pandas-2.3.3 pandas-stubs-3.0.3.260530 pandocfilters-1.5.1 parso-0.8.7 pathspec-1.1.1 pexpect-4.9.0 pillow-12.2.0 platformdirs-4.10.0 pluggy-1.6.0 pre-commit-4.6.0 prometheus-client-0.25.0 prompt-toolkit-3.0.52 protobuf-6.33.6 psutil-7.2.2 ptyprocess-0.7.0 pure-eval-0.2.3 pycodestyle-2.14.0 pycparser-3.0 pyflakes-3.4.0 pygments-2.20.0 pylint-4.0.6 pylint-django-2.7.0 pylint-plugin-utils-0.9.0 pyparsing-3.3.2 pyproject-api-1.10.1 pytest-9.1.0 pytest-mock-3.15.1 python-dateutil-2.9.0.post0 python-discovery-1.4.2 python-dotenv-1.2.2 python-json-logger-4.1.0 python-slugify-8.0.4 pytokens-0.4.1 pytz-2026.2 pyyaml-6.0.3 pyzmq-27.1.0 rapidfuzz-3.14.5 referencing-0.37.0 requests-2.34.2 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rfc3987-syntax-1.1.0 rich-15.0.0 rpds-py-2026.5.1 scikit-learn-1.9.0 scipy-1.17.1 seaborn-0.13.2 send2trash-2.1.0 shap-0.52.0 shellingham-1.5.4 six-1.17.0 slicer-0.0.8 soupsieve-2.8.4 stack-data-0.6.3 stevedore-5.8.0 terminado-0.18.1 text-unidecode-1.3 threadpoolctl-3.6.0 tinycss2-1.5.1 tomli-w-1.2.0 tomlkit-0.15.0 tornado-6.5.7 tox-4.55.1 tqdm-4.68.2 traitlets-5.15.1 typer-0.26.7 types-cachetools-7.0.0.20260518 types-html5lib-1.1.11.20260518 types-lxml-2026.2.16 types-requests-2.33.0.20260518 types-tqdm-4.68.0.20260608 types-webencodings-0.5.0.20260408 typing-extensions-4.15.0 tzdata-2026.2 uri-template-1.3.0 urllib3-2.7.0 virtualenv-21.5.0 wcwidth-0.8.1 webcolors-25.10.0 webencodings-0.5.1 websocket-client-1.9.0\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.1.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "source venv/bin/activate && python3.13 -m ipykernel install --user --name py311 --display-name \"Python 3.13\"\n",
+      "Installed kernelspec py311 in /Users/vincent/Library/Jupyter/kernels/py311\n",
+      "Warning: Looks like you're using an outdated `kaggle` version (installed: 2.2.1), please consider upgrading to the latest version (2.2.2)\n",
+      "2026-07-15 09:14:18 | INFO | Starting IMDb pipeline...\n",
+      "2026-07-15 09:14:18 | INFO | File /Users/vincent/Documents/netflix-writers/netflix/data/imdb/title.basics.tsv.gz already exists, skipping download.\n",
+      "2026-07-15 09:14:18 | INFO | Building title_basics...\n",
+      "2026-07-15 09:14:20 | INFO | Exported title_basics → /Users/vincent/Documents/netflix-writers/netflix/data/imdb/titles.basics.csv\n",
+      "2026-07-15 09:14:20 | INFO | File /Users/vincent/Documents/netflix-writers/netflix/data/imdb/title.ratings.tsv.gz already exists, skipping download.\n",
+      "2026-07-15 09:14:20 | INFO | Building title_ratings...\n",
+      "2026-07-15 09:14:20 | INFO | Exported title_ratings → /Users/vincent/Documents/netflix-writers/netflix/data/imdb/title.ratings.csv\n",
+      "2026-07-15 09:14:20 | INFO | File /Users/vincent/Documents/netflix-writers/netflix/data/imdb/title.akas.tsv.gz already exists, skipping download.\n",
+      "2026-07-15 09:14:20 | INFO | Building title_akas...\n",
+      "2026-07-15 09:14:25 | INFO | Exported title_akas → /Users/vincent/Documents/netflix-writers/netflix/data/imdb/title.akas.csv\n",
+      "2026-07-15 09:14:25 | INFO | File /Users/vincent/Documents/netflix-writers/netflix/data/imdb/title.crew.tsv.gz already exists, skipping download.\n",
+      "2026-07-15 09:14:25 | INFO | Building title_crew...\n",
+      "2026-07-15 09:14:26 | INFO | Exported title_crew → /Users/vincent/Documents/netflix-writers/netflix/data/imdb/title.crew.csv\n",
+      "2026-07-15 09:14:26 | INFO | File /Users/vincent/Documents/netflix-writers/netflix/data/imdb/title.principals.tsv.gz already exists, skipping download.\n",
+      "2026-07-15 09:14:26 | INFO | Building title_principals...\n",
+      "2026-07-15 09:14:33 | INFO | Exported title_principals → /Users/vincent/Documents/netflix-writers/netflix/data/imdb/title.principals.csv\n",
+      "2026-07-15 09:14:33 | INFO | File /Users/vincent/Documents/netflix-writers/netflix/data/imdb/name.basics.tsv.gz already exists, skipping download.\n",
+      "2026-07-15 09:14:33 | INFO | Building name_basics...\n",
+      "2026-07-15 09:14:36 | INFO | Building composite table...\n",
+      "2026-07-15 09:14:37 | INFO | Exported titles_composite → /Users/vincent/Documents/netflix-writers/netflix/data/imdb.titles.composite.csv\n",
+      "2026-07-15 09:14:39 | INFO | IMDb pipeline complete.\n",
+      "Warning: Looks like you're using an outdated `kaggle` version (installed: 2.2.1), please consider upgrading to the latest version (2.2.2)\n",
+      "Dataset URL: https://www.kaggle.com/datasets/dhruvildave/netflix-top-10-tv-shows-and-films\n",
+      "2026-07-15 09:14:40 | INFO | Dataset downloaded successfully.\n",
+      "2026-07-15 09:14:40 | INFO | ----------------------------------------\n",
+      "Found 800 unique shows.\n",
+      "Warning: Looks like you're using an outdated `kaggle` version (installed: 2.2.1), please consider upgrading to the latest version (2.2.2)\n",
+      "Dataset URL: https://www.kaggle.com/datasets/asaniczka/full-tmdb-tv-shows-dataset-2023-150k-shows\n",
+      "2026-07-15 09:14:42 | INFO | Dataset downloaded successfully.\n",
+      "2026-07-15 09:14:42 | INFO | Renamed TMDB_tv_dataset_v3.csv → tmdb.titles.v3.csv\n",
+      "2026-07-15 09:14:42 | INFO | ----------------------------------------\n",
+      "2026-07-15 09:14:42 | INFO | CSV written to: /Users/vincent/Documents/netflix-writers/netflix/data/polti/situations.csv\n",
+      "2026-07-15 09:14:42 | INFO | ----------------------------------------\n",
+      "all-weeks-global.csv: 1chunks [00:00, 284.98chunks/s]\n",
+      "tmdb.titles.v3.csv: 17chunks [00:00, 33.76chunks/s]\n",
+      "title.basics.tsv.gz: 1265chunks [00:09, 130.26chunks/s]\n",
+      "title.ratings.tsv.gz: 170chunks [00:00, 350.24chunks/s]\n",
+      "2026-07-15 09:14:55 | INFO | Building Pandas DataFrame for Netflix data...\n",
+      "2026-07-15 09:14:55 | INFO | === Netflix DataFrame Schema Info ===\n",
+      "2026-07-15 09:14:55 | INFO | Netflix shape: 691 rows × 6 columns\n",
+      "2026-07-15 09:14:55 | INFO | Netflix schema:\n",
+      "2026-07-15 09:14:55 | INFO |   key                            object          nulls=0        unique=691     \n",
+      "2026-07-15 09:14:55 | INFO |   netflix_viewing_hours          int64           nulls=0        unique=661     \n",
+      "2026-07-15 09:14:55 | INFO |   netflix_weeks                  int64           nulls=0        unique=18      \n",
+      "2026-07-15 09:14:55 | INFO |   netflix_year_hint              int32           nulls=0        unique=2       \n",
+      "2026-07-15 09:14:55 | INFO |   netflix_title                  object          nulls=0        unique=691     \n",
+      "2026-07-15 09:14:55 | INFO |   netflix_clean_title            object          nulls=0        unique=691     \n",
+      "2026-07-15 09:14:55 | INFO | Netflix memory usage: 0.14 MB\n",
+      "2026-07-15 09:14:55 | INFO | === END Netflix DataFrame Schema Info ===\n",
+      "\n",
+      "\n",
+      "2026-07-15 09:14:55 | INFO | Building Pandas DataFrame for TMDB data...\n",
+      "2026-07-15 09:14:56 | INFO | === TMDB DataFrame Schema Info ===\n",
+      "2026-07-15 09:14:56 | INFO | TMDB shape: 168639 rows × 7 columns\n",
+      "2026-07-15 09:14:56 | INFO | TMDB schema:\n",
+      "2026-07-15 09:14:56 | INFO |   key                            object          nulls=0        unique=73290   \n",
+      "2026-07-15 09:14:56 | INFO |   title                          object          nulls=5        unique=155586  \n",
+      "2026-07-15 09:14:56 | INFO |   clean_title                    object          nulls=0        unique=135299  \n",
+      "2026-07-15 09:14:56 | INFO |   year                           float64         nulls=31736    unique=93      \n",
+      "2026-07-15 09:14:56 | INFO |   popularity                     float64         nulls=0        unique=20481   \n",
+      "2026-07-15 09:14:56 | INFO |   vote_average                   float64         nulls=0        unique=2603    \n",
+      "2026-07-15 09:14:56 | INFO |   vote_count                     int64           nulls=0        unique=1110    \n",
+      "2026-07-15 09:14:56 | INFO | TMDB memory usage: 35.78 MB\n",
+      "2026-07-15 09:14:56 | INFO | === END TMDB DataFrame Schema Info ===\n",
+      "\n",
+      "\n",
+      "2026-07-15 09:14:56 | INFO | Building Pandas DataFrame for IMDb data...\n",
+      "2026-07-15 09:14:57 | INFO | Joined IMDb basics and ratings: 12643241 records.\n",
+      "2026-07-15 09:14:57 | INFO | Cleaning IMDb titles...\n",
+      "2026-07-15 09:15:12 | INFO | Generating blocking keys for IMDb titles...\n",
+      "2026-07-15 09:15:33 | INFO | === IMDb DataFrame Schema Info ===\n",
+      "2026-07-15 09:15:33 | INFO | IMDb shape: 12643241 rows × 7 columns\n",
+      "2026-07-15 09:15:33 | INFO | IMDb schema:\n",
+      "2026-07-15 09:15:35 | INFO |   tconst                         object          nulls=0        unique=12643241\n",
+      "2026-07-15 09:15:37 | INFO |   title                          object          nulls=24       unique=5642637 \n",
+      "2026-07-15 09:15:37 | INFO |   year                           Int32           nulls=1475271  unique=154     \n",
+      "2026-07-15 09:15:37 | INFO |   averageRating                  float64         nulls=10948264 unique=91      \n",
+      "2026-07-15 09:15:37 | INFO |   numVotes                       Int64           nulls=10948264 unique=24682   \n",
+      "2026-07-15 09:15:39 | INFO |   clean_title                    object          nulls=0        unique=5528687 \n",
+      "2026-07-15 09:15:40 | INFO |   key                            object          nulls=0        unique=1095777 \n",
+      "2026-07-15 09:15:43 | INFO | IMDb memory usage: 3305.37 MB\n",
+      "2026-07-15 09:15:43 | INFO | === END IMDb DataFrame Schema Info ===\n",
+      "\n",
+      "\n",
+      "2026-07-15 09:15:43 | INFO | Final IMDb dataset has 12643241 records.\n",
+      "2026-07-15 09:15:43 | INFO | Fuzzy matching tmdb to Netflix with score threshold 0.85...\n",
+      "2026-07-15 09:15:43 | INFO | Found 37944 candidate pairs after blocking on key.\n",
+      "2026-07-15 09:15:43 | INFO | 36883 candidates remain after year filtering.\n",
+      "2026-07-15 09:15:43 | INFO | Calculating Jaro-Winkler similarity for candidate pairs...\n",
+      "2026-07-15 09:15:43 | INFO | 19922 candidates remain after applying score threshold.\n",
+      "2026-07-15 09:15:43 | INFO | Matched 394 out of 691 Netflix titles to tmdb.\n",
+      "2026-07-15 09:15:43 | INFO | Fuzzy matching imdb to Netflix with score threshold 0.85...\n",
+      "2026-07-15 09:15:46 | INFO | Found 813771 candidate pairs after blocking on key.\n",
+      "2026-07-15 09:15:46 | INFO | 725275 candidates remain after year filtering.\n",
+      "2026-07-15 09:15:46 | INFO | Calculating Jaro-Winkler similarity for candidate pairs...\n",
+      "2026-07-15 09:15:47 | INFO | 47779 candidates remain after applying score threshold.\n",
+      "2026-07-15 09:15:47 | INFO | Matched 665 out of 691 Netflix titles to imdb.\n",
+      "2026-07-15 09:15:47 | INFO | === Final dataset DataFrame Schema Info ===\n",
+      "2026-07-15 09:15:47 | INFO | Final dataset shape: 691 rows × 13 columns\n",
+      "2026-07-15 09:15:47 | INFO | Final dataset schema:\n",
+      "2026-07-15 09:15:47 | INFO |   key                            object          nulls=0        unique=691     \n",
+      "2026-07-15 09:15:47 | INFO |   netflix_viewing_hours          int64           nulls=0        unique=661     \n",
+      "2026-07-15 09:15:47 | INFO |   netflix_weeks                  int64           nulls=0        unique=18      \n",
+      "2026-07-15 09:15:47 | INFO |   netflix_year_hint              int32           nulls=0        unique=2       \n",
+      "2026-07-15 09:15:47 | INFO |   netflix_title                  object          nulls=0        unique=691     \n",
+      "2026-07-15 09:15:47 | INFO |   netflix_clean_title            object          nulls=0        unique=691     \n",
+      "2026-07-15 09:15:47 | INFO |   tmdb_title                     object          nulls=297      unique=394     \n",
+      "2026-07-15 09:15:47 | INFO |   tmdb_popularity                float64         nulls=297      unique=333     \n",
+      "2026-07-15 09:15:47 | INFO |   tmdb_vote_average              float64         nulls=297      unique=205     \n",
+      "2026-07-15 09:15:47 | INFO |   tmdb_vote_count                float64         nulls=297      unique=181     \n",
+      "2026-07-15 09:15:47 | INFO |   imdb_title                     object          nulls=27       unique=664     \n",
+      "2026-07-15 09:15:47 | INFO |   imdb_averageRating             float64         nulls=228      unique=57      \n",
+      "2026-07-15 09:15:47 | INFO |   imdb_numVotes                  Int64           nulls=228      unique=424     \n",
+      "2026-07-15 09:15:47 | INFO | Final dataset memory usage: 0.24 MB\n",
+      "2026-07-15 09:15:47 | INFO | === END Final dataset DataFrame Schema Info ===\n",
+      "\n",
+      "\n",
+      "2026-07-15 09:15:47 | INFO | Dataset head:\n",
+      "        key  netflix_viewing_hours  ...  imdb_averageRating  imdb_numVotes\n",
+      "0        10               26580000  ...                 9.4             12\n",
+      "1  10_fr_mi               13590000  ...                 5.9           2334\n",
+      "2     10_lo               12600000  ...                 7.4          20069\n",
+      "3     12_st               16420000  ...                 NaN           <NA>\n",
+      "4  13_ho_se               26250000  ...                 6.6             72\n",
+      "\n",
+      "[5 rows x 13 columns]\n",
+      "2026-07-15 09:15:47 | INFO | Saving composite dataset to /Users/vincent/Documents/netflix-writers/netflix/data/netflix.titles.composite.csv...\n"
+     ]
+    }
+   ],
    "source": [
     "!make init\n",
     "!python -m netflix.fetch\n",
@@ -25,10 +786,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "77ef2d42",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(299097, 15) (691, 13) (168639, 29)\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "import pandas as pd\n",
@@ -51,10 +820,266 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "953ac9dc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>netflix_viewing_hours</th>\n",
+       "      <th>netflix_weeks</th>\n",
+       "      <th>netflix_year_hint</th>\n",
+       "      <th>netflix_title</th>\n",
+       "      <th>netflix_clean_title</th>\n",
+       "      <th>tmdb_title</th>\n",
+       "      <th>tmdb_popularity</th>\n",
+       "      <th>tmdb_vote_average</th>\n",
+       "      <th>tmdb_vote_count</th>\n",
+       "      <th>imdb_title</th>\n",
+       "      <th>imdb_averageRating</th>\n",
+       "      <th>imdb_numVotes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>st_th</td>\n",
+       "      <td>2874260000</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>stranger things</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>185.711</td>\n",
+       "      <td>8.624</td>\n",
+       "      <td>16161.0</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>ga_sq</td>\n",
+       "      <td>2289500000</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>squid game</td>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>115.587</td>\n",
+       "      <td>7.831</td>\n",
+       "      <td>13053.0</td>\n",
+       "      <td>Squid Game!</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>ma</td>\n",
+       "      <td>1446260000</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>manifest</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>111.411</td>\n",
+       "      <td>7.728</td>\n",
+       "      <td>1312.0</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>he_mo</td>\n",
+       "      <td>1170200000</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>money heist</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>96.354</td>\n",
+       "      <td>8.257</td>\n",
+       "      <td>17836.0</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>612436.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>br</td>\n",
+       "      <td>1108800000</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>bridgerton</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>71.308</td>\n",
+       "      <td>8.143</td>\n",
+       "      <td>2045.0</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>57.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>ar_ca_co</td>\n",
+       "      <td>793740000</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Café con aroma de mujer</td>\n",
+       "      <td>caf con aroma de mujer</td>\n",
+       "      <td>Café con Aroma de Mujer</td>\n",
+       "      <td>20.710</td>\n",
+       "      <td>7.500</td>\n",
+       "      <td>382.0</td>\n",
+       "      <td>Café con aroma de mujer</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>106.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>yo</td>\n",
+       "      <td>777480000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>You</td>\n",
+       "      <td>you</td>\n",
+       "      <td>続・アタッカーYOU 金メダルへの道</td>\n",
+       "      <td>7.101</td>\n",
+       "      <td>2.500</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>You</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>wi</td>\n",
+       "      <td>757350000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Windfall</td>\n",
+       "      <td>windfall</td>\n",
+       "      <td>Windfall</td>\n",
+       "      <td>3.813</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Windfall</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>oz</td>\n",
+       "      <td>751600000</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>ozark</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>68.006</td>\n",
+       "      <td>8.244</td>\n",
+       "      <td>1968.0</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>394904.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>al_ar_us</td>\n",
+       "      <td>659510000</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>all of us are dead</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>107.108</td>\n",
+       "      <td>8.354</td>\n",
+       "      <td>3335.0</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>92709.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          key  netflix_viewing_hours  netflix_weeks  netflix_year_hint  \\\n",
+       "661     st_th             2874260000             13               2022   \n",
+       "396     ga_sq             2289500000             20               2021   \n",
+       "549        ma             1446260000             16               2021   \n",
+       "447     he_mo             1170200000             14               2021   \n",
+       "167        br             1108800000             11               2021   \n",
+       "103  ar_ca_co              793740000             25               2022   \n",
+       "690        yo              777480000              8               2021   \n",
+       "686        wi              757350000              6               2021   \n",
+       "607        oz              751600000             13               2022   \n",
+       "69   al_ar_us              659510000             11               2022   \n",
+       "\n",
+       "               netflix_title     netflix_clean_title               tmdb_title  \\\n",
+       "661          Stranger Things         stranger things          Stranger Things   \n",
+       "396               Squid Game              squid game               Squid Game   \n",
+       "549                 Manifest                manifest                 Manifest   \n",
+       "447              Money Heist             money heist              Money Heist   \n",
+       "167               Bridgerton              bridgerton               Bridgerton   \n",
+       "103  Café con aroma de mujer  caf con aroma de mujer  Café con Aroma de Mujer   \n",
+       "690                      You                     you       続・アタッカーYOU 金メダルへの道   \n",
+       "686                 Windfall                windfall                 Windfall   \n",
+       "607                    Ozark                   ozark                    Ozark   \n",
+       "69        All of Us Are Dead      all of us are dead       All of Us Are Dead   \n",
+       "\n",
+       "     tmdb_popularity  tmdb_vote_average  tmdb_vote_count  \\\n",
+       "661          185.711              8.624          16161.0   \n",
+       "396          115.587              7.831          13053.0   \n",
+       "549          111.411              7.728           1312.0   \n",
+       "447           96.354              8.257          17836.0   \n",
+       "167           71.308              8.143           2045.0   \n",
+       "103           20.710              7.500            382.0   \n",
+       "690            7.101              2.500              2.0   \n",
+       "686            3.813              0.000              0.0   \n",
+       "607           68.006              8.244           1968.0   \n",
+       "69           107.108              8.354           3335.0   \n",
+       "\n",
+       "                  imdb_title  imdb_averageRating  imdb_numVotes  \n",
+       "661          Stranger Things                 NaN            NaN  \n",
+       "396              Squid Game!                 NaN            NaN  \n",
+       "549                 Manifest                 NaN            NaN  \n",
+       "447              Money Heist                 8.2       612436.0  \n",
+       "167               Bridgerton                 4.1           57.0  \n",
+       "103  Café con aroma de mujer                 8.6          106.0  \n",
+       "690                      You                 NaN            NaN  \n",
+       "686                 Windfall                 NaN            NaN  \n",
+       "607                    Ozark                 8.4       394904.0  \n",
+       "69        All of Us Are Dead                 7.6        92709.0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "# Top Netflix shows by viewing hours\n",
@@ -63,10 +1088,312 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "673a7d83",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tconst</th>\n",
+       "      <th>titleType</th>\n",
+       "      <th>primaryTitle</th>\n",
+       "      <th>originalTitle</th>\n",
+       "      <th>startYear</th>\n",
+       "      <th>endYear</th>\n",
+       "      <th>runtimeMinutes</th>\n",
+       "      <th>genres</th>\n",
+       "      <th>title_key</th>\n",
+       "      <th>averageRating</th>\n",
+       "      <th>numVotes</th>\n",
+       "      <th>all_akas</th>\n",
+       "      <th>cast</th>\n",
+       "      <th>directors</th>\n",
+       "      <th>writers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>113321</th>\n",
+       "      <td>tt24228604</td>\n",
+       "      <td>tvSeries</td>\n",
+       "      <td>OdBita Pot</td>\n",
+       "      <td>OdBita Pot</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>Adventure</td>\n",
+       "      <td>dita ot</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>1344.0</td>\n",
+       "      <td>odbita pot</td>\n",
+       "      <td>Anze Sever | Bit Sever</td>\n",
+       "      <td>nm7689797</td>\n",
+       "      <td>nm7689797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10372</th>\n",
+       "      <td>tt41956044</td>\n",
+       "      <td>tvSeries</td>\n",
+       "      <td>D&amp;D: Dungeon Masters</td>\n",
+       "      <td>D&amp;D: Dungeon Masters</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>Fantasy,Horror,Reality-TV</td>\n",
+       "      <td>ungeon asters</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>d&amp;d: dungeon masters</td>\n",
+       "      <td>Mayanna Berrin | Devora Wilde | Neil Newbon | ...</td>\n",
+       "      <td>nm0671741</td>\n",
+       "      <td>\\N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127839</th>\n",
+       "      <td>tt28305644</td>\n",
+       "      <td>tvSeries</td>\n",
+       "      <td>EDP Watch</td>\n",
+       "      <td>EDP Watch</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>Crime</td>\n",
+       "      <td>atch</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>edp watch</td>\n",
+       "      <td>Alex Rosen | Skeeter Jean | Demarcus Cousins I...</td>\n",
+       "      <td>nm15017595</td>\n",
+       "      <td>nm15017595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263196</th>\n",
+       "      <td>tt12831434</td>\n",
+       "      <td>tvSeries</td>\n",
+       "      <td>The Simonetta Lein Show</td>\n",
+       "      <td>The Simonetta Lein Show</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>Talk-Show</td>\n",
+       "      <td>he imonetta ein how</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>810.0</td>\n",
+       "      <td>the simonetta lein show</td>\n",
+       "      <td>Gabriela Gonzalez | Kevin Harrington | Julian ...</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>\\N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164455</th>\n",
+       "      <td>tt34929415</td>\n",
+       "      <td>tvSeries</td>\n",
+       "      <td>A Dog and a Plane</td>\n",
+       "      <td>A Dog and a Plane</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>Comedy,Romance</td>\n",
+       "      <td>og and a lane</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>551.0</td>\n",
+       "      <td>a dog and a plane</td>\n",
+       "      <td>Oabnithi Wiwattanawarang | Pompam Niti Chaichi...</td>\n",
+       "      <td>nm8797158</td>\n",
+       "      <td>\\N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156259</th>\n",
+       "      <td>tt33131139</td>\n",
+       "      <td>tvSeries</td>\n",
+       "      <td>Symposium</td>\n",
+       "      <td>Symposium</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>Talk-Show</td>\n",
+       "      <td>ymposium</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>symposium</td>\n",
+       "      <td>Niko Svanidze</td>\n",
+       "      <td>nm16438483</td>\n",
+       "      <td>nm16438483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59321</th>\n",
+       "      <td>tt8088236</td>\n",
+       "      <td>tvSeries</td>\n",
+       "      <td>Geografens testamente</td>\n",
+       "      <td>Geografens testamente</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Adventure,History</td>\n",
+       "      <td>eografens testamente</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1143.0</td>\n",
+       "      <td>the geographers last will | geografens testamente</td>\n",
+       "      <td>Judith Buchan | Lena Granhagen | Lakshman Mend...</td>\n",
+       "      <td>nm1072502</td>\n",
+       "      <td>nm1072502,nm2050223,nm6813238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63437</th>\n",
+       "      <td>tt8560994</td>\n",
+       "      <td>tvSeries</td>\n",
+       "      <td>Friday Five Sharp</td>\n",
+       "      <td>Friday Five Sharp</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>riday ive harp</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>4589.0</td>\n",
+       "      <td>petak pet | friday five sharp</td>\n",
+       "      <td>Kitodar Todorov | Tedi Dinh | Vladislav Petrov...</td>\n",
+       "      <td>nm5663366</td>\n",
+       "      <td>nm5663366,nm11544010,nm1242398,nm9909867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59344</th>\n",
+       "      <td>tt8103440</td>\n",
+       "      <td>tvSeries</td>\n",
+       "      <td>Herne Katha</td>\n",
+       "      <td>Herne Katha</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>Documentary,Short</td>\n",
+       "      <td>erne atha</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>413.0</td>\n",
+       "      <td>herne katha</td>\n",
+       "      <td>Kamal Kumar | Bidhya Chapagain</td>\n",
+       "      <td>nm9678594,nm9678593</td>\n",
+       "      <td>nm9678593,nm9678594</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36797</th>\n",
+       "      <td>tt5961444</td>\n",
+       "      <td>tvSeries</td>\n",
+       "      <td>Upanishad Ganga</td>\n",
+       "      <td>Upanishad Ganga</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>Drama,History,Short</td>\n",
+       "      <td>panishad anga</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1359.0</td>\n",
+       "      <td>upanishad ganga</td>\n",
+       "      <td>Rasika Dugal | Zakir Hussain | Sandeep Mohan |...</td>\n",
+       "      <td>nm1363374</td>\n",
+       "      <td>nm1363374</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            tconst titleType             primaryTitle  \\\n",
+       "113321  tt24228604  tvSeries               OdBita Pot   \n",
+       "10372   tt41956044  tvSeries     D&D: Dungeon Masters   \n",
+       "127839  tt28305644  tvSeries                EDP Watch   \n",
+       "263196  tt12831434  tvSeries  The Simonetta Lein Show   \n",
+       "164455  tt34929415  tvSeries        A Dog and a Plane   \n",
+       "156259  tt33131139  tvSeries                Symposium   \n",
+       "59321    tt8088236  tvSeries    Geografens testamente   \n",
+       "63437    tt8560994  tvSeries        Friday Five Sharp   \n",
+       "59344    tt8103440  tvSeries              Herne Katha   \n",
+       "36797    tt5961444  tvSeries          Upanishad Ganga   \n",
+       "\n",
+       "                  originalTitle startYear endYear runtimeMinutes  \\\n",
+       "113321               OdBita Pot      2022    2026             \\N   \n",
+       "10372      D&D: Dungeon Masters      2026      \\N             \\N   \n",
+       "127839                EDP Watch      2023      \\N             \\N   \n",
+       "263196  The Simonetta Lein Show      2020    2026             \\N   \n",
+       "164455        A Dog and a Plane      2026      \\N             \\N   \n",
+       "156259                Symposium      2023      \\N             \\N   \n",
+       "59321     Geografens testamente      2011    2023             28   \n",
+       "63437         Friday Five Sharp      2015      \\N             \\N   \n",
+       "59344               Herne Katha      2018      \\N             \\N   \n",
+       "36797           Upanishad Ganga      2012    2013             \\N   \n",
+       "\n",
+       "                           genres             title_key  averageRating  \\\n",
+       "113321                  Adventure               dita ot            9.8   \n",
+       "10372   Fantasy,Horror,Reality-TV         ungeon asters            9.8   \n",
+       "127839                      Crime                  atch            9.7   \n",
+       "263196                  Talk-Show   he imonetta ein how            9.7   \n",
+       "164455             Comedy,Romance         og and a lane            9.7   \n",
+       "156259                  Talk-Show              ymposium            9.6   \n",
+       "59321           Adventure,History  eografens testamente            9.6   \n",
+       "63437                      Comedy        riday ive harp            9.6   \n",
+       "59344           Documentary,Short             erne atha            9.6   \n",
+       "36797         Drama,History,Short         panishad anga            9.6   \n",
+       "\n",
+       "        numVotes                                           all_akas  \\\n",
+       "113321    1344.0                                         odbita pot   \n",
+       "10372      115.0                               d&d: dungeon masters   \n",
+       "127839     106.0                                          edp watch   \n",
+       "263196     810.0                            the simonetta lein show   \n",
+       "164455     551.0                                  a dog and a plane   \n",
+       "156259     127.0                                          symposium   \n",
+       "59321     1143.0  the geographers last will | geografens testamente   \n",
+       "63437     4589.0                      petak pet | friday five sharp   \n",
+       "59344      413.0                                        herne katha   \n",
+       "36797     1359.0                                    upanishad ganga   \n",
+       "\n",
+       "                                                     cast  \\\n",
+       "113321                             Anze Sever | Bit Sever   \n",
+       "10372   Mayanna Berrin | Devora Wilde | Neil Newbon | ...   \n",
+       "127839  Alex Rosen | Skeeter Jean | Demarcus Cousins I...   \n",
+       "263196  Gabriela Gonzalez | Kevin Harrington | Julian ...   \n",
+       "164455  Oabnithi Wiwattanawarang | Pompam Niti Chaichi...   \n",
+       "156259                                      Niko Svanidze   \n",
+       "59321   Judith Buchan | Lena Granhagen | Lakshman Mend...   \n",
+       "63437   Kitodar Todorov | Tedi Dinh | Vladislav Petrov...   \n",
+       "59344                      Kamal Kumar | Bidhya Chapagain   \n",
+       "36797   Rasika Dugal | Zakir Hussain | Sandeep Mohan |...   \n",
+       "\n",
+       "                  directors                                   writers  \n",
+       "113321            nm7689797                                 nm7689797  \n",
+       "10372             nm0671741                                        \\N  \n",
+       "127839           nm15017595                                nm15017595  \n",
+       "263196                   \\N                                        \\N  \n",
+       "164455            nm8797158                                        \\N  \n",
+       "156259           nm16438483                                nm16438483  \n",
+       "59321             nm1072502             nm1072502,nm2050223,nm6813238  \n",
+       "63437             nm5663366  nm5663366,nm11544010,nm1242398,nm9909867  \n",
+       "59344   nm9678594,nm9678593                       nm9678593,nm9678594  \n",
+       "36797             nm1363374                                 nm1363374  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "# Top IMDb shows by rating\n",
@@ -75,10 +1402,417 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "572cc4aa",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>number_of_seasons</th>\n",
+       "      <th>number_of_episodes</th>\n",
+       "      <th>original_language</th>\n",
+       "      <th>vote_count</th>\n",
+       "      <th>vote_average</th>\n",
+       "      <th>overview</th>\n",
+       "      <th>adult</th>\n",
+       "      <th>backdrop_path</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tagline</th>\n",
+       "      <th>genres</th>\n",
+       "      <th>created_by</th>\n",
+       "      <th>languages</th>\n",
+       "      <th>networks</th>\n",
+       "      <th>origin_country</th>\n",
+       "      <th>spoken_languages</th>\n",
+       "      <th>production_companies</th>\n",
+       "      <th>production_countries</th>\n",
+       "      <th>episode_run_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1771</th>\n",
+       "      <td>63770</td>\n",
+       "      <td>The Late Show with Stephen Colbert</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1419</td>\n",
+       "      <td>en</td>\n",
+       "      <td>205</td>\n",
+       "      <td>6.495</td>\n",
+       "      <td>Stephen Colbert brings his signature satire an...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/gMMnf8VRg3Z98WaFmOLr9Jk8pIs.jpg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Comedy, Talk</td>\n",
+       "      <td>Jon Stewart, Stephen Colbert, Tom Purcell, Chr...</td>\n",
+       "      <td>en</td>\n",
+       "      <td>CBS</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Spartina Productions, CBS Studios</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3035</th>\n",
+       "      <td>215803</td>\n",
+       "      <td>Batang Quiapo</td>\n",
+       "      <td>2</td>\n",
+       "      <td>178</td>\n",
+       "      <td>tl</td>\n",
+       "      <td>99</td>\n",
+       "      <td>7.030</td>\n",
+       "      <td>A young man rises to be one of the biggest out...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/jIV5weV19wH02rnuWyMtYI4dYC8.jpg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Action &amp; Adventure, Comedy, Drama</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tl</td>\n",
+       "      <td>TV5, Kapamilya Channel</td>\n",
+       "      <td>PH</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ABS-CBN Entertainment, Dreamscape Entertainmen...</td>\n",
+       "      <td>Philippines</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2285</th>\n",
+       "      <td>94722</td>\n",
+       "      <td>Tagesschau</td>\n",
+       "      <td>72</td>\n",
+       "      <td>20839</td>\n",
+       "      <td>de</td>\n",
+       "      <td>145</td>\n",
+       "      <td>7.500</td>\n",
+       "      <td>German daily news program, the oldest still ex...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/jWXrQstj7p3Wl5MfYWY6IHqRpDb.jpg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>News</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>de</td>\n",
+       "      <td>ARD</td>\n",
+       "      <td>DE</td>\n",
+       "      <td>Deutsch</td>\n",
+       "      <td>NDR</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3593</th>\n",
+       "      <td>14981</td>\n",
+       "      <td>The Late Late Show with Craig Ferguson</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2057</td>\n",
+       "      <td>en</td>\n",
+       "      <td>77</td>\n",
+       "      <td>7.065</td>\n",
+       "      <td>The Late Late Show with Craig Ferguson is an A...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/m0bV3qBiJBBlpFaaKjwHo13MVjm.jpg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Comedy, Talk</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>en</td>\n",
+       "      <td>CBS</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4565</th>\n",
+       "      <td>2261</td>\n",
+       "      <td>The Tonight Show Starring Johnny Carson</td>\n",
+       "      <td>31</td>\n",
+       "      <td>2924</td>\n",
+       "      <td>en</td>\n",
+       "      <td>51</td>\n",
+       "      <td>7.800</td>\n",
+       "      <td>The Tonight Show Starring Johnny Carson is a t...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/qFfWFwfaEHzDLWLuttWiYq7Poy2.jpg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Talk</td>\n",
+       "      <td>Steve Allen, Sylvester Weaver</td>\n",
+       "      <td>en</td>\n",
+       "      <td>NBC</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>NBC Productions, Carson Productions</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4651</th>\n",
+       "      <td>81329</td>\n",
+       "      <td>Chronicles of the Sun</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1252</td>\n",
+       "      <td>fr</td>\n",
+       "      <td>49</td>\n",
+       "      <td>7.510</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/rj3jBAZwPiOgkwAy1205MAgLahj.jpg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Soap</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>fr</td>\n",
+       "      <td>France 2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>Français</td>\n",
+       "      <td>France Télévisions, Epeios Productions, France...</td>\n",
+       "      <td>France</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1600</th>\n",
+       "      <td>59941</td>\n",
+       "      <td>The Tonight Show Starring Jimmy Fallon</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1233</td>\n",
+       "      <td>en</td>\n",
+       "      <td>234</td>\n",
+       "      <td>6.038</td>\n",
+       "      <td>After Jay Leno's second retirement from the pr...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/xl1wGwaPZInJo1JAnpKqnFozWBE.jpg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Tonight's just getting started.</td>\n",
+       "      <td>Comedy, Talk</td>\n",
+       "      <td>Jimmy Fallon</td>\n",
+       "      <td>en</td>\n",
+       "      <td>NBC</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Broadway Video, Universal Television, Electric...</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7314</th>\n",
+       "      <td>244643</td>\n",
+       "      <td>El amor no tiene receta</td>\n",
+       "      <td>1</td>\n",
+       "      <td>91</td>\n",
+       "      <td>es</td>\n",
+       "      <td>23</td>\n",
+       "      <td>8.300</td>\n",
+       "      <td>Paz Roble, a kind, hard-working and honest wom...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/bIhmqQNXcyWRzH153d3jaCbLTy3.jpg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Action &amp; Adventure, Family, Drama, Soap, Comed...</td>\n",
+       "      <td>Juan Osorio Ortiz, Pablo Ferrer García-Travesí...</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Univision, Las Estrellas, ViX</td>\n",
+       "      <td>MX, US</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>TelevisaUnivision Mexico, Televisa</td>\n",
+       "      <td>Mexico</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8641</th>\n",
+       "      <td>225660</td>\n",
+       "      <td>Pira-Pirasong Paraiso</td>\n",
+       "      <td>2</td>\n",
+       "      <td>78</td>\n",
+       "      <td>tl</td>\n",
+       "      <td>17</td>\n",
+       "      <td>7.000</td>\n",
+       "      <td>A con artist takes on the job of pretending to...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/2N4LXvTkUwPRkbvyzdmzvtnCHgR.jpg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>en, tl</td>\n",
+       "      <td>TV5, Kapamilya Channel</td>\n",
+       "      <td>PH</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Dreamscape Entertainment Television</td>\n",
+       "      <td>Philippines</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7217</th>\n",
+       "      <td>91759</td>\n",
+       "      <td>Come Home Love: Lo and Behold</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2107</td>\n",
+       "      <td>cn</td>\n",
+       "      <td>23</td>\n",
+       "      <td>4.565</td>\n",
+       "      <td>Hung Sue Gan starting from the bottom, establi...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/ohJTnu93hJ0Uonl86Wn3mOSlWXN.jpg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Family, Comedy, Drama</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>cn</td>\n",
+       "      <td>TVB Jade</td>\n",
+       "      <td>HK</td>\n",
+       "      <td>广州话 / 廣州話</td>\n",
+       "      <td>TVB</td>\n",
+       "      <td>Hong Kong</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          id                                     name  number_of_seasons  \\\n",
+       "1771   63770       The Late Show with Stephen Colbert                  9   \n",
+       "3035  215803                            Batang Quiapo                  2   \n",
+       "2285   94722                               Tagesschau                 72   \n",
+       "3593   14981   The Late Late Show with Craig Ferguson                 11   \n",
+       "4565    2261  The Tonight Show Starring Johnny Carson                 31   \n",
+       "4651   81329                    Chronicles of the Sun                  5   \n",
+       "1600   59941   The Tonight Show Starring Jimmy Fallon                 11   \n",
+       "7314  244643                  El amor no tiene receta                  1   \n",
+       "8641  225660                    Pira-Pirasong Paraiso                  2   \n",
+       "7217   91759            Come Home Love: Lo and Behold                  1   \n",
+       "\n",
+       "      number_of_episodes original_language  vote_count  vote_average  \\\n",
+       "1771                1419                en         205         6.495   \n",
+       "3035                 178                tl          99         7.030   \n",
+       "2285               20839                de         145         7.500   \n",
+       "3593                2057                en          77         7.065   \n",
+       "4565                2924                en          51         7.800   \n",
+       "4651                1252                fr          49         7.510   \n",
+       "1600                1233                en         234         6.038   \n",
+       "7314                  91                es          23         8.300   \n",
+       "8641                  78                tl          17         7.000   \n",
+       "7217                2107                cn          23         4.565   \n",
+       "\n",
+       "                                               overview  adult  \\\n",
+       "1771  Stephen Colbert brings his signature satire an...  False   \n",
+       "3035  A young man rises to be one of the biggest out...  False   \n",
+       "2285  German daily news program, the oldest still ex...  False   \n",
+       "3593  The Late Late Show with Craig Ferguson is an A...  False   \n",
+       "4565  The Tonight Show Starring Johnny Carson is a t...  False   \n",
+       "4651                                                NaN  False   \n",
+       "1600  After Jay Leno's second retirement from the pr...  False   \n",
+       "7314  Paz Roble, a kind, hard-working and honest wom...  False   \n",
+       "8641  A con artist takes on the job of pretending to...  False   \n",
+       "7217  Hung Sue Gan starting from the bottom, establi...  False   \n",
+       "\n",
+       "                         backdrop_path  ...                          tagline  \\\n",
+       "1771  /gMMnf8VRg3Z98WaFmOLr9Jk8pIs.jpg  ...                              NaN   \n",
+       "3035  /jIV5weV19wH02rnuWyMtYI4dYC8.jpg  ...                              NaN   \n",
+       "2285  /jWXrQstj7p3Wl5MfYWY6IHqRpDb.jpg  ...                              NaN   \n",
+       "3593  /m0bV3qBiJBBlpFaaKjwHo13MVjm.jpg  ...                              NaN   \n",
+       "4565  /qFfWFwfaEHzDLWLuttWiYq7Poy2.jpg  ...                              NaN   \n",
+       "4651  /rj3jBAZwPiOgkwAy1205MAgLahj.jpg  ...                              NaN   \n",
+       "1600  /xl1wGwaPZInJo1JAnpKqnFozWBE.jpg  ...  Tonight's just getting started.   \n",
+       "7314  /bIhmqQNXcyWRzH153d3jaCbLTy3.jpg  ...                              NaN   \n",
+       "8641  /2N4LXvTkUwPRkbvyzdmzvtnCHgR.jpg  ...                              NaN   \n",
+       "7217  /ohJTnu93hJ0Uonl86Wn3mOSlWXN.jpg  ...                              NaN   \n",
+       "\n",
+       "                                                 genres  \\\n",
+       "1771                                       Comedy, Talk   \n",
+       "3035                  Action & Adventure, Comedy, Drama   \n",
+       "2285                                               News   \n",
+       "3593                                       Comedy, Talk   \n",
+       "4565                                               Talk   \n",
+       "4651                                               Soap   \n",
+       "1600                                       Comedy, Talk   \n",
+       "7314  Action & Adventure, Family, Drama, Soap, Comed...   \n",
+       "8641                                                NaN   \n",
+       "7217                              Family, Comedy, Drama   \n",
+       "\n",
+       "                                             created_by  languages  \\\n",
+       "1771  Jon Stewart, Stephen Colbert, Tom Purcell, Chr...         en   \n",
+       "3035                                                NaN         tl   \n",
+       "2285                                                NaN         de   \n",
+       "3593                                                NaN         en   \n",
+       "4565                      Steve Allen, Sylvester Weaver         en   \n",
+       "4651                                                NaN         fr   \n",
+       "1600                                       Jimmy Fallon         en   \n",
+       "7314  Juan Osorio Ortiz, Pablo Ferrer García-Travesí...         es   \n",
+       "8641                                                NaN     en, tl   \n",
+       "7217                                                NaN         cn   \n",
+       "\n",
+       "                           networks  origin_country spoken_languages  \\\n",
+       "1771                            CBS              US          English   \n",
+       "3035         TV5, Kapamilya Channel              PH              NaN   \n",
+       "2285                            ARD              DE          Deutsch   \n",
+       "3593                            CBS              US          English   \n",
+       "4565                            NBC              US          English   \n",
+       "4651                       France 2              FR         Français   \n",
+       "1600                            NBC              US          English   \n",
+       "7314  Univision, Las Estrellas, ViX          MX, US          Español   \n",
+       "8641         TV5, Kapamilya Channel              PH          English   \n",
+       "7217                       TVB Jade              HK        广州话 / 廣州話   \n",
+       "\n",
+       "                                   production_companies  \\\n",
+       "1771                  Spartina Productions, CBS Studios   \n",
+       "3035  ABS-CBN Entertainment, Dreamscape Entertainmen...   \n",
+       "2285                                                NDR   \n",
+       "3593                                                NaN   \n",
+       "4565                NBC Productions, Carson Productions   \n",
+       "4651  France Télévisions, Epeios Productions, France...   \n",
+       "1600  Broadway Video, Universal Television, Electric...   \n",
+       "7314                 TelevisaUnivision Mexico, Televisa   \n",
+       "8641                Dreamscape Entertainment Television   \n",
+       "7217                                                TVB   \n",
+       "\n",
+       "          production_countries episode_run_time  \n",
+       "1771  United States of America               41  \n",
+       "3035               Philippines                0  \n",
+       "2285                   Germany               15  \n",
+       "3593  United States of America               60  \n",
+       "4565  United States of America               60  \n",
+       "4651                    France               26  \n",
+       "1600  United States of America               45  \n",
+       "7314                    Mexico               50  \n",
+       "8641               Philippines                0  \n",
+       "7217                 Hong Kong               22  \n",
+       "\n",
+       "[10 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "# Top TMDb shows by popularity\n",
@@ -96,10 +1830,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "d4c3643c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    6.910000e+02\n",
+       "mean     6.919165e+07\n",
+       "std      1.879555e+08\n",
+       "min      1.130000e+06\n",
+       "25%      8.930000e+06\n",
+       "50%      2.028000e+07\n",
+       "75%      5.560000e+07\n",
+       "max      2.874260e+09\n",
+       "Name: netflix_viewing_hours, dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "netflix[\"netflix_viewing_hours\"].describe()\n"
@@ -107,10 +1860,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "a2af3ec2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    48167.000000\n",
+       "mean         6.851699\n",
+       "std          1.293658\n",
+       "min          1.000000\n",
+       "25%          6.300000\n",
+       "50%          7.100000\n",
+       "75%          7.700000\n",
+       "max          9.800000\n",
+       "Name: averageRating, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "imdb[\"averageRating\"].describe()\n"
@@ -118,10 +1890,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "9739ec75",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    168639.000000\n",
+       "mean          5.882644\n",
+       "std          42.023216\n",
+       "min           0.000000\n",
+       "25%           0.600000\n",
+       "50%           0.857000\n",
+       "75%           2.431500\n",
+       "max        3707.008000\n",
+       "Name: popularity, dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "tmdb[\"popularity\"].describe()\n"
@@ -137,10 +1928,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "791fa1d0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>netflix_title</th>\n",
+       "      <th>netflix_viewing_hours</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>2874260000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>2289500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>1446260000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>1170200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>1108800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       netflix_title  netflix_viewing_hours\n",
+       "661  Stranger Things             2874260000\n",
+       "396       Squid Game             2289500000\n",
+       "549         Manifest             1446260000\n",
+       "447      Money Heist             1170200000\n",
+       "167       Bridgerton             1108800000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "netflix.sort_values(\"netflix_viewing_hours\", ascending=False).head(5)[[\"netflix_title\",\"netflix_viewing_hours\"]]\n"
@@ -156,10 +2016,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "7cc4e2f8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    Crime,Drama\n",
+       "1    Documentary\n",
+       "2         Comedy\n",
+       "3     Reality-TV\n",
+       "4     Reality-TV\n",
+       "Name: genres, dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "imdb[\"genres\"].dropna().head()\n"
@@ -175,10 +2051,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "de414ec8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "original_language\n",
+       "en    76304\n",
+       "zh    14422\n",
+       "ja    14048\n",
+       "ko     7820\n",
+       "de     7712\n",
+       "fr     7290\n",
+       "es     5602\n",
+       "pt     3551\n",
+       "ru     2963\n",
+       "nl     2923\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "tmdb[\"original_language\"].value_counts().head(10)\n"
@@ -194,10 +2092,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "790b2dc3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>number_of_seasons</th>\n",
+       "      <th>popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>number_of_seasons</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.189827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>popularity</th>\n",
+       "      <td>0.189827</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   number_of_seasons  popularity\n",
+       "number_of_seasons           1.000000    0.189827\n",
+       "popularity                  0.189827    1.000000"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "tmdb[[\"number_of_seasons\",\"popularity\"]].corr()\n"
@@ -213,10 +2162,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "d35c689f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>netflix_weeks</th>\n",
+       "      <th>netflix_viewing_hours</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>netflix_weeks</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.634786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>netflix_viewing_hours</th>\n",
+       "      <td>0.634786</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       netflix_weeks  netflix_viewing_hours\n",
+       "netflix_weeks               1.000000               0.634786\n",
+       "netflix_viewing_hours       0.634786               1.000000"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "netflix[[\"netflix_weeks\",\"netflix_viewing_hours\"]].corr()\n"
@@ -232,10 +2232,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "7ffc7fa4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cast_count</th>\n",
+       "      <th>averageRating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>cast_count</th>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>-0.07716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageRating</th>\n",
+       "      <td>-0.07716</td>\n",
+       "      <td>1.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               cast_count  averageRating\n",
+       "cast_count        1.00000       -0.07716\n",
+       "averageRating    -0.07716        1.00000"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "imdb[\"cast_count\"] = imdb[\"cast\"].fillna(\"\").apply(lambda x: len(x.split(\"|\")))\n",
@@ -268,10 +2319,277 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "b4f3f532",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>netflix_viewing_hours</th>\n",
+       "      <th>netflix_weeks</th>\n",
+       "      <th>netflix_year_hint</th>\n",
+       "      <th>netflix_title</th>\n",
+       "      <th>netflix_clean_title</th>\n",
+       "      <th>tmdb_title</th>\n",
+       "      <th>tmdb_popularity</th>\n",
+       "      <th>tmdb_vote_average</th>\n",
+       "      <th>tmdb_vote_count</th>\n",
+       "      <th>imdb_title</th>\n",
+       "      <th>imdb_averageRating</th>\n",
+       "      <th>imdb_numVotes</th>\n",
+       "      <th>hit_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>st_th</td>\n",
+       "      <td>2874260000</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>stranger things</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>185.711</td>\n",
+       "      <td>8.624</td>\n",
+       "      <td>16161.0</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>ga_sq</td>\n",
+       "      <td>2289500000</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>squid game</td>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>115.587</td>\n",
+       "      <td>7.831</td>\n",
+       "      <td>13053.0</td>\n",
+       "      <td>Squid Game!</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.796553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>ma</td>\n",
+       "      <td>1446260000</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>manifest</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>111.411</td>\n",
+       "      <td>7.728</td>\n",
+       "      <td>1312.0</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.503176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>he_mo</td>\n",
+       "      <td>1170200000</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>money heist</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>96.354</td>\n",
+       "      <td>8.257</td>\n",
+       "      <td>17836.0</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>612436.0</td>\n",
+       "      <td>0.407131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>br</td>\n",
+       "      <td>1108800000</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>bridgerton</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>71.308</td>\n",
+       "      <td>8.143</td>\n",
+       "      <td>2045.0</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0.385769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>ar_ca_co</td>\n",
+       "      <td>793740000</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Café con aroma de mujer</td>\n",
+       "      <td>caf con aroma de mujer</td>\n",
+       "      <td>Café con Aroma de Mujer</td>\n",
+       "      <td>20.710</td>\n",
+       "      <td>7.500</td>\n",
+       "      <td>382.0</td>\n",
+       "      <td>Café con aroma de mujer</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>0.276155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>yo</td>\n",
+       "      <td>777480000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>You</td>\n",
+       "      <td>you</td>\n",
+       "      <td>続・アタッカーYOU 金メダルへの道</td>\n",
+       "      <td>7.101</td>\n",
+       "      <td>2.500</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>You</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.270497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>wi</td>\n",
+       "      <td>757350000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Windfall</td>\n",
+       "      <td>windfall</td>\n",
+       "      <td>Windfall</td>\n",
+       "      <td>3.813</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Windfall</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.263494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>oz</td>\n",
+       "      <td>751600000</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>ozark</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>68.006</td>\n",
+       "      <td>8.244</td>\n",
+       "      <td>1968.0</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>394904.0</td>\n",
+       "      <td>0.261493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>al_ar_us</td>\n",
+       "      <td>659510000</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>all of us are dead</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>107.108</td>\n",
+       "      <td>8.354</td>\n",
+       "      <td>3335.0</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>92709.0</td>\n",
+       "      <td>0.229454</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          key  netflix_viewing_hours  netflix_weeks  netflix_year_hint  \\\n",
+       "661     st_th             2874260000             13               2022   \n",
+       "396     ga_sq             2289500000             20               2021   \n",
+       "549        ma             1446260000             16               2021   \n",
+       "447     he_mo             1170200000             14               2021   \n",
+       "167        br             1108800000             11               2021   \n",
+       "103  ar_ca_co              793740000             25               2022   \n",
+       "690        yo              777480000              8               2021   \n",
+       "686        wi              757350000              6               2021   \n",
+       "607        oz              751600000             13               2022   \n",
+       "69   al_ar_us              659510000             11               2022   \n",
+       "\n",
+       "               netflix_title     netflix_clean_title               tmdb_title  \\\n",
+       "661          Stranger Things         stranger things          Stranger Things   \n",
+       "396               Squid Game              squid game               Squid Game   \n",
+       "549                 Manifest                manifest                 Manifest   \n",
+       "447              Money Heist             money heist              Money Heist   \n",
+       "167               Bridgerton              bridgerton               Bridgerton   \n",
+       "103  Café con aroma de mujer  caf con aroma de mujer  Café con Aroma de Mujer   \n",
+       "690                      You                     you       続・アタッカーYOU 金メダルへの道   \n",
+       "686                 Windfall                windfall                 Windfall   \n",
+       "607                    Ozark                   ozark                    Ozark   \n",
+       "69        All of Us Are Dead      all of us are dead       All of Us Are Dead   \n",
+       "\n",
+       "     tmdb_popularity  tmdb_vote_average  tmdb_vote_count  \\\n",
+       "661          185.711              8.624          16161.0   \n",
+       "396          115.587              7.831          13053.0   \n",
+       "549          111.411              7.728           1312.0   \n",
+       "447           96.354              8.257          17836.0   \n",
+       "167           71.308              8.143           2045.0   \n",
+       "103           20.710              7.500            382.0   \n",
+       "690            7.101              2.500              2.0   \n",
+       "686            3.813              0.000              0.0   \n",
+       "607           68.006              8.244           1968.0   \n",
+       "69           107.108              8.354           3335.0   \n",
+       "\n",
+       "                  imdb_title  imdb_averageRating  imdb_numVotes  hit_score  \n",
+       "661          Stranger Things                 NaN            NaN   1.000000  \n",
+       "396              Squid Game!                 NaN            NaN   0.796553  \n",
+       "549                 Manifest                 NaN            NaN   0.503176  \n",
+       "447              Money Heist                 8.2       612436.0   0.407131  \n",
+       "167               Bridgerton                 4.1           57.0   0.385769  \n",
+       "103  Café con aroma de mujer                 8.6          106.0   0.276155  \n",
+       "690                      You                 NaN            NaN   0.270497  \n",
+       "686                 Windfall                 NaN            NaN   0.263494  \n",
+       "607                    Ozark                 8.4       394904.0   0.261493  \n",
+       "69        All of Us Are Dead                 7.6        92709.0   0.229454  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "df = netflix.copy()\n",
@@ -301,11 +2619,12115 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8532b959",
+   "metadata": {},
+   "source": [
+    "## our analysisy\n",
+    "Data table attribute summary table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "2d84ebc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================================================================\n",
+      "🔍 原始資料集維度（Columns）與欄位身家調查\n",
+      "================================================================================\n",
+      "\n",
+      "📌 1. Netflix 複合資料表 (netflix.titles.composite) — 共 691 列, 13 個欄位\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>資料型態</th>\n",
+       "      <th>非空值數量</th>\n",
+       "      <th>缺失值數量</th>\n",
+       "      <th>缺失值比例</th>\n",
+       "      <th>數值範例</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <td>object</td>\n",
+       "      <td>691</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>netflix_viewing_hours</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>691</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>26580000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>netflix_weeks</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>691</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>netflix_year_hint</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>691</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>2021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>netflix_title</th>\n",
+       "      <td>object</td>\n",
+       "      <td>691</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>The 100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>netflix_clean_title</th>\n",
+       "      <td>object</td>\n",
+       "      <td>690</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.14%</td>\n",
+       "      <td>the 100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tmdb_title</th>\n",
+       "      <td>object</td>\n",
+       "      <td>394</td>\n",
+       "      <td>297</td>\n",
+       "      <td>42.98%</td>\n",
+       "      <td>The 100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tmdb_popularity</th>\n",
+       "      <td>float64</td>\n",
+       "      <td>394</td>\n",
+       "      <td>297</td>\n",
+       "      <td>42.98%</td>\n",
+       "      <td>127.224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tmdb_vote_average</th>\n",
+       "      <td>float64</td>\n",
+       "      <td>394</td>\n",
+       "      <td>297</td>\n",
+       "      <td>42.98%</td>\n",
+       "      <td>7.916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tmdb_vote_count</th>\n",
+       "      <td>float64</td>\n",
+       "      <td>394</td>\n",
+       "      <td>297</td>\n",
+       "      <td>42.98%</td>\n",
+       "      <td>7666.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>imdb_title</th>\n",
+       "      <td>object</td>\n",
+       "      <td>664</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3.91%</td>\n",
+       "      <td>The 100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>imdb_averageRating</th>\n",
+       "      <td>float64</td>\n",
+       "      <td>463</td>\n",
+       "      <td>228</td>\n",
+       "      <td>33.0%</td>\n",
+       "      <td>9.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>imdb_numVotes</th>\n",
+       "      <td>float64</td>\n",
+       "      <td>463</td>\n",
+       "      <td>228</td>\n",
+       "      <td>33.0%</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          資料型態  非空值數量  缺失值數量   缺失值比例      數值範例\n",
+       "key                     object    691      0    0.0%        10\n",
+       "netflix_viewing_hours    int64    691      0    0.0%  26580000\n",
+       "netflix_weeks            int64    691      0    0.0%         2\n",
+       "netflix_year_hint        int64    691      0    0.0%      2021\n",
+       "netflix_title           object    691      0    0.0%   The 100\n",
+       "netflix_clean_title     object    690      1   0.14%   the 100\n",
+       "tmdb_title              object    394    297  42.98%   The 100\n",
+       "tmdb_popularity        float64    394    297  42.98%   127.224\n",
+       "tmdb_vote_average      float64    394    297  42.98%     7.916\n",
+       "tmdb_vote_count        float64    394    297  42.98%    7666.0\n",
+       "imdb_title              object    664     27   3.91%   The 100\n",
+       "imdb_averageRating     float64    463    228   33.0%       9.4\n",
+       "imdb_numVotes          float64    463    228   33.0%      12.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "📌 2. TMDb 原始資料表 (tmdb.titles.v3) — 共 168639 列, 29 個欄位\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>資料型態</th>\n",
+       "      <th>非空值數量</th>\n",
+       "      <th>缺失值數量</th>\n",
+       "      <th>缺失值比例</th>\n",
+       "      <th>數值範例</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>1399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <td>object</td>\n",
+       "      <td>168634</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>Game of Thrones</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>number_of_seasons</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>number_of_episodes</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>original_language</th>\n",
+       "      <td>object</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>en</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vote_count</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>21857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vote_average</th>\n",
+       "      <td>float64</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>8.442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overview</th>\n",
+       "      <td>object</td>\n",
+       "      <td>93333</td>\n",
+       "      <td>75306</td>\n",
+       "      <td>44.66%</td>\n",
+       "      <td>Seven noble families fight for control of the ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>adult</th>\n",
+       "      <td>bool</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>backdrop_path</th>\n",
+       "      <td>object</td>\n",
+       "      <td>77780</td>\n",
+       "      <td>90859</td>\n",
+       "      <td>53.88%</td>\n",
+       "      <td>/2OMB0ynKlyIenMJWI2Dy9IWT4c.jpg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>first_air_date</th>\n",
+       "      <td>object</td>\n",
+       "      <td>136903</td>\n",
+       "      <td>31736</td>\n",
+       "      <td>18.82%</td>\n",
+       "      <td>2011-04-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>last_air_date</th>\n",
+       "      <td>object</td>\n",
+       "      <td>138735</td>\n",
+       "      <td>29904</td>\n",
+       "      <td>17.73%</td>\n",
+       "      <td>2019-05-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>homepage</th>\n",
+       "      <td>object</td>\n",
+       "      <td>50998</td>\n",
+       "      <td>117641</td>\n",
+       "      <td>69.76%</td>\n",
+       "      <td>http://www.hbo.com/game-of-thrones</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>in_production</th>\n",
+       "      <td>bool</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>original_name</th>\n",
+       "      <td>object</td>\n",
+       "      <td>168634</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>Game of Thrones</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>popularity</th>\n",
+       "      <td>float64</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>1083.917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>poster_path</th>\n",
+       "      <td>object</td>\n",
+       "      <td>108737</td>\n",
+       "      <td>59902</td>\n",
+       "      <td>35.52%</td>\n",
+       "      <td>/1XS1oqL89opfnbLl8WnZY1O1uJx.jpg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>type</th>\n",
+       "      <td>object</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>Scripted</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>status</th>\n",
+       "      <td>object</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>Ended</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tagline</th>\n",
+       "      <td>object</td>\n",
+       "      <td>5330</td>\n",
+       "      <td>163309</td>\n",
+       "      <td>96.84%</td>\n",
+       "      <td>Winter Is Coming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>genres</th>\n",
+       "      <td>object</td>\n",
+       "      <td>99713</td>\n",
+       "      <td>68926</td>\n",
+       "      <td>40.87%</td>\n",
+       "      <td>Sci-Fi &amp; Fantasy, Drama, Action &amp; Adventure</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>created_by</th>\n",
+       "      <td>object</td>\n",
+       "      <td>36496</td>\n",
+       "      <td>132143</td>\n",
+       "      <td>78.36%</td>\n",
+       "      <td>David Benioff, D.B. Weiss</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>languages</th>\n",
+       "      <td>object</td>\n",
+       "      <td>110050</td>\n",
+       "      <td>58589</td>\n",
+       "      <td>34.74%</td>\n",
+       "      <td>en</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>networks</th>\n",
+       "      <td>object</td>\n",
+       "      <td>97589</td>\n",
+       "      <td>71050</td>\n",
+       "      <td>42.13%</td>\n",
+       "      <td>HBO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>origin_country</th>\n",
+       "      <td>object</td>\n",
+       "      <td>137609</td>\n",
+       "      <td>31030</td>\n",
+       "      <td>18.4%</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>spoken_languages</th>\n",
+       "      <td>object</td>\n",
+       "      <td>109280</td>\n",
+       "      <td>59359</td>\n",
+       "      <td>35.2%</td>\n",
+       "      <td>English</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>production_companies</th>\n",
+       "      <td>object</td>\n",
+       "      <td>59342</td>\n",
+       "      <td>109297</td>\n",
+       "      <td>64.81%</td>\n",
+       "      <td>Revolution Sun Studios, Television 360, Genera...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>production_countries</th>\n",
+       "      <td>object</td>\n",
+       "      <td>77511</td>\n",
+       "      <td>91128</td>\n",
+       "      <td>54.04%</td>\n",
+       "      <td>United Kingdom, United States of America</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>episode_run_time</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>168639</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         資料型態   非空值數量   缺失值數量   缺失值比例  \\\n",
+       "id                      int64  168639       0    0.0%   \n",
+       "name                   object  168634       5    0.0%   \n",
+       "number_of_seasons       int64  168639       0    0.0%   \n",
+       "number_of_episodes      int64  168639       0    0.0%   \n",
+       "original_language      object  168639       0    0.0%   \n",
+       "vote_count              int64  168639       0    0.0%   \n",
+       "vote_average          float64  168639       0    0.0%   \n",
+       "overview               object   93333   75306  44.66%   \n",
+       "adult                    bool  168639       0    0.0%   \n",
+       "backdrop_path          object   77780   90859  53.88%   \n",
+       "first_air_date         object  136903   31736  18.82%   \n",
+       "last_air_date          object  138735   29904  17.73%   \n",
+       "homepage               object   50998  117641  69.76%   \n",
+       "in_production            bool  168639       0    0.0%   \n",
+       "original_name          object  168634       5    0.0%   \n",
+       "popularity            float64  168639       0    0.0%   \n",
+       "poster_path            object  108737   59902  35.52%   \n",
+       "type                   object  168639       0    0.0%   \n",
+       "status                 object  168639       0    0.0%   \n",
+       "tagline                object    5330  163309  96.84%   \n",
+       "genres                 object   99713   68926  40.87%   \n",
+       "created_by             object   36496  132143  78.36%   \n",
+       "languages              object  110050   58589  34.74%   \n",
+       "networks               object   97589   71050  42.13%   \n",
+       "origin_country         object  137609   31030   18.4%   \n",
+       "spoken_languages       object  109280   59359   35.2%   \n",
+       "production_companies   object   59342  109297  64.81%   \n",
+       "production_countries   object   77511   91128  54.04%   \n",
+       "episode_run_time        int64  168639       0    0.0%   \n",
+       "\n",
+       "                                                                   數值範例  \n",
+       "id                                                                 1399  \n",
+       "name                                                    Game of Thrones  \n",
+       "number_of_seasons                                                     8  \n",
+       "number_of_episodes                                                   73  \n",
+       "original_language                                                    en  \n",
+       "vote_count                                                        21857  \n",
+       "vote_average                                                      8.442  \n",
+       "overview              Seven noble families fight for control of the ...  \n",
+       "adult                                                             False  \n",
+       "backdrop_path                           /2OMB0ynKlyIenMJWI2Dy9IWT4c.jpg  \n",
+       "first_air_date                                               2011-04-17  \n",
+       "last_air_date                                                2019-05-19  \n",
+       "homepage                             http://www.hbo.com/game-of-thrones  \n",
+       "in_production                                                     False  \n",
+       "original_name                                           Game of Thrones  \n",
+       "popularity                                                     1083.917  \n",
+       "poster_path                            /1XS1oqL89opfnbLl8WnZY1O1uJx.jpg  \n",
+       "type                                                           Scripted  \n",
+       "status                                                            Ended  \n",
+       "tagline                                                Winter Is Coming  \n",
+       "genres                      Sci-Fi & Fantasy, Drama, Action & Adventure  \n",
+       "created_by                                    David Benioff, D.B. Weiss  \n",
+       "languages                                                            en  \n",
+       "networks                                                            HBO  \n",
+       "origin_country                                                       US  \n",
+       "spoken_languages                                                English  \n",
+       "production_companies  Revolution Sun Studios, Television 360, Genera...  \n",
+       "production_countries           United Kingdom, United States of America  \n",
+       "episode_run_time                                                      0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "📌 3. IMDb 原始資料表 (imdb.titles.composite) — 共 299097 列, 16 個欄位\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>資料型態</th>\n",
+       "      <th>非空值數量</th>\n",
+       "      <th>缺失值數量</th>\n",
+       "      <th>缺失值比例</th>\n",
+       "      <th>數值範例</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>tconst</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>tt38876090</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>titleType</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>tvSeries</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>primaryTitle</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299096</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>Gomorrah: The Origins</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>originalTitle</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299096</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>Gomorra: Le origini</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>startYear</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>2026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>endYear</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>\\N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>runtimeMinutes</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>\\N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>genres</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>Crime,Drama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>title_key</th>\n",
+       "      <td>object</td>\n",
+       "      <td>298121</td>\n",
+       "      <td>976</td>\n",
+       "      <td>0.33%</td>\n",
+       "      <td>omorrah he rigins</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageRating</th>\n",
+       "      <td>float64</td>\n",
+       "      <td>48167</td>\n",
+       "      <td>250930</td>\n",
+       "      <td>83.9%</td>\n",
+       "      <td>7.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>numVotes</th>\n",
+       "      <td>float64</td>\n",
+       "      <td>48167</td>\n",
+       "      <td>250930</td>\n",
+       "      <td>83.9%</td>\n",
+       "      <td>1019.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>all_akas</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299043</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>gomorra: le origini | gomorrah: a kezdetek | g...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cast</th>\n",
+       "      <td>object</td>\n",
+       "      <td>255241</td>\n",
+       "      <td>43856</td>\n",
+       "      <td>14.66%</td>\n",
+       "      <td>Flavio Furno | Biagio Forestieri | Luca Lubran...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>directors</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>nm3634704,nm4574756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>writers</th>\n",
+       "      <td>object</td>\n",
+       "      <td>299097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>nm0006938,nm1288339,nm2500207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cast_count</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>299097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0%</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   資料型態   非空值數量   缺失值數量   缺失值比例  \\\n",
+       "tconst           object  299097       0    0.0%   \n",
+       "titleType        object  299097       0    0.0%   \n",
+       "primaryTitle     object  299096       1    0.0%   \n",
+       "originalTitle    object  299096       1    0.0%   \n",
+       "startYear        object  299097       0    0.0%   \n",
+       "endYear          object  299097       0    0.0%   \n",
+       "runtimeMinutes   object  299097       0    0.0%   \n",
+       "genres           object  299097       0    0.0%   \n",
+       "title_key        object  298121     976   0.33%   \n",
+       "averageRating   float64   48167  250930   83.9%   \n",
+       "numVotes        float64   48167  250930   83.9%   \n",
+       "all_akas         object  299043      54   0.02%   \n",
+       "cast             object  255241   43856  14.66%   \n",
+       "directors        object  299097       0    0.0%   \n",
+       "writers          object  299097       0    0.0%   \n",
+       "cast_count        int64  299097       0    0.0%   \n",
+       "\n",
+       "                                                             數值範例  \n",
+       "tconst                                                 tt38876090  \n",
+       "titleType                                                tvSeries  \n",
+       "primaryTitle                                Gomorrah: The Origins  \n",
+       "originalTitle                                 Gomorra: Le origini  \n",
+       "startYear                                                    2026  \n",
+       "endYear                                                        \\N  \n",
+       "runtimeMinutes                                                 \\N  \n",
+       "genres                                                Crime,Drama  \n",
+       "title_key                                       omorrah he rigins  \n",
+       "averageRating                                                 7.2  \n",
+       "numVotes                                                   1019.0  \n",
+       "all_akas        gomorra: le origini | gomorrah: a kezdetek | g...  \n",
+       "cast            Flavio Furno | Biagio Forestieri | Luca Lubran...  \n",
+       "directors                                     nm3634704,nm4574756  \n",
+       "writers                             nm0006938,nm1288339,nm2500207  \n",
+       "cast_count                                                     13  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# 1. 載入你本地最原始的 composite 與 v3 資料集\n",
+    "# （這三個是你在前面步驟中讀取過的原始 DataFrame）\n",
+    "print(\"=\" * 80)\n",
+    "print(\"🔍 原始資料集維度（Columns）與欄位身家調查\")\n",
+    "print(\"=\" * 80)\n",
+    "\n",
+    "# ------------------------------------------\n",
+    "# A. Netflix 複合資料集 (包含已匹配的 IMDb 資訊)\n",
+    "# ------------------------------------------\n",
+    "print(f\"\\n📌 1. Netflix 複合資料表 (netflix.titles.composite) — 共 {netflix.shape[0]} 列, {netflix.shape[1]} 個欄位\")\n",
+    "print(\"-\" * 80)\n",
+    "# 建立一個清晰的欄位結構表\n",
+    "netflix_info = pd.DataFrame({\n",
+    "    \"資料型態\": netflix.dtypes,\n",
+    "    \"非空值數量\": netflix.notna().sum(),\n",
+    "    \"缺失值數量\": netflix.isna().sum(),\n",
+    "    \"缺失值比例\": (netflix.isna().mean() * 100).round(2).astype(str) + \"%\",\n",
+    "    \"數值範例\": netflix.iloc[0].values\n",
+    "})\n",
+    "display(netflix_info)\n",
+    "\n",
+    "# ------------------------------------------\n",
+    "# B. TMDb 原始資料集 (tmdb.titles.v3)\n",
+    "# ------------------------------------------\n",
+    "print(f\"\\n📌 2. TMDb 原始資料表 (tmdb.titles.v3) — 共 {tmdb.shape[0]} 列, {tmdb.shape[1]} 個欄位\")\n",
+    "print(\"-\" * 80)\n",
+    "tmdb_info = pd.DataFrame({\n",
+    "    \"資料型態\": tmdb.dtypes,\n",
+    "    \"非空值數量\": tmdb.notna().sum(),\n",
+    "    \"缺失值數量\": tmdb.isna().sum(),\n",
+    "    \"缺失值比例\": (tmdb.isna().mean() * 100).round(2).astype(str) + \"%\",\n",
+    "    \"數值範例\": tmdb.iloc[0].values\n",
+    "})\n",
+    "display(tmdb_info)\n",
+    "\n",
+    "# ------------------------------------------\n",
+    "# C. IMDb 原始資料集 (imdb.titles.composite)\n",
+    "# ------------------------------------------\n",
+    "print(f\"\\n📌 3. IMDb 原始資料表 (imdb.titles.composite) — 共 {imdb.shape[0]} 列, {imdb.shape[1]} 個欄位\")\n",
+    "print(\"-\" * 80)\n",
+    "imdb_info = pd.DataFrame({\n",
+    "    \"資料型態\": imdb.dtypes,\n",
+    "    \"非空值數量\": imdb.notna().sum(),\n",
+    "    \"缺失值數量\": imdb.isna().sum(),\n",
+    "    \"缺失值比例\": (imdb.isna().mean() * 100).round(2).astype(str) + \"%\",\n",
+    "    \"數值範例\": imdb.iloc[0].values\n",
+    "})\n",
+    "display(imdb_info)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58a83a66",
+   "metadata": {},
+   "source": [
+    "## our analysis\n",
+    "Data table attribute summary table; Threshold settings; Comparison with other data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "02788717",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Distribution diagnostics\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Variable</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Mean</th>\n",
+       "      <th>Median</th>\n",
+       "      <th>Skewness</th>\n",
+       "      <th>95th percentile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TMDb popularity</td>\n",
+       "      <td>168</td>\n",
+       "      <td>3.874000e+01</td>\n",
+       "      <td>14.63</td>\n",
+       "      <td>7.73</td>\n",
+       "      <td>1.285900e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Netflix viewing hours</td>\n",
+       "      <td>168</td>\n",
+       "      <td>1.112935e+08</td>\n",
+       "      <td>34485000.00</td>\n",
+       "      <td>3.28</td>\n",
+       "      <td>5.236960e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Binge velocity</td>\n",
+       "      <td>168</td>\n",
+       "      <td>2.366224e+07</td>\n",
+       "      <td>15963125.00</td>\n",
+       "      <td>2.15</td>\n",
+       "      <td>6.571730e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>IMDb vote count</td>\n",
+       "      <td>168</td>\n",
+       "      <td>2.760432e+04</td>\n",
+       "      <td>3927.50</td>\n",
+       "      <td>6.16</td>\n",
+       "      <td>9.204995e+04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Variable  Count          Mean       Median  Skewness  95th percentile\n",
+       "0        TMDb popularity    168  3.874000e+01        14.63      7.73     1.285900e+02\n",
+       "1  Netflix viewing hours    168  1.112935e+08  34485000.00      3.28     5.236960e+08\n",
+       "2         Binge velocity    168  2.366224e+07  15963125.00      2.15     6.571730e+07\n",
+       "3        IMDb vote count    168  2.760432e+04      3927.50      6.16     9.204995e+04"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Titles</th>\n",
+       "      <th>Coverage (%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Netflix composite rows</td>\n",
+       "      <td>691</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Exact IMDb title match</td>\n",
+       "      <td>640</td>\n",
+       "      <td>92.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Exact TMDb title match</td>\n",
+       "      <td>293</td>\n",
+       "      <td>42.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Exact match to both</td>\n",
+       "      <td>285</td>\n",
+       "      <td>41.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Narrative titles used</td>\n",
+       "      <td>168</td>\n",
+       "      <td>24.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Stage  Titles  Coverage (%)\n",
+       "0  Netflix composite rows     691         100.0\n",
+       "1  Exact IMDb title match     640          92.6\n",
+       "2  Exact TMDb title match     293          42.4\n",
+       "3     Exact match to both     285          41.2\n",
+       "4   Narrative titles used     168          24.3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 900x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Genres require at least 8 matched titles.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>genre</th>\n",
+       "      <th>title_count</th>\n",
+       "      <th>median_quality</th>\n",
+       "      <th>median_reach</th>\n",
+       "      <th>median_intensity</th>\n",
+       "      <th>median_staying</th>\n",
+       "      <th>hit_rate</th>\n",
+       "      <th>balanced_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Action &amp; Adventure</td>\n",
+       "      <td>26</td>\n",
+       "      <td>7.13</td>\n",
+       "      <td>60.20</td>\n",
+       "      <td>18448333.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.231</td>\n",
+       "      <td>0.764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Drama</td>\n",
+       "      <td>97</td>\n",
+       "      <td>7.15</td>\n",
+       "      <td>19.91</td>\n",
+       "      <td>16970000.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.381</td>\n",
+       "      <td>0.736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>31</td>\n",
+       "      <td>7.13</td>\n",
+       "      <td>21.83</td>\n",
+       "      <td>17030000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.194</td>\n",
+       "      <td>0.653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Crime</td>\n",
+       "      <td>36</td>\n",
+       "      <td>7.01</td>\n",
+       "      <td>19.05</td>\n",
+       "      <td>18428333.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Kids</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>20.68</td>\n",
+       "      <td>12667500.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Animation</td>\n",
+       "      <td>19</td>\n",
+       "      <td>7.11</td>\n",
+       "      <td>50.65</td>\n",
+       "      <td>14360000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>23</td>\n",
+       "      <td>7.11</td>\n",
+       "      <td>29.84</td>\n",
+       "      <td>13290000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.304</td>\n",
+       "      <td>0.486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Mystery</td>\n",
+       "      <td>28</td>\n",
+       "      <td>7.02</td>\n",
+       "      <td>13.47</td>\n",
+       "      <td>15423571.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.357</td>\n",
+       "      <td>0.486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Documentary</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6.82</td>\n",
+       "      <td>5.59</td>\n",
+       "      <td>16380000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>0.292</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                genre  title_count  median_quality  median_reach  median_intensity  median_staying  hit_rate  balanced_score\n",
+       "0  Action & Adventure           26            7.13         60.20        18448333.0             2.0     0.231           0.764\n",
+       "1               Drama           97            7.15         19.91        16970000.0             3.0     0.381           0.736\n",
+       "2              Comedy           31            7.13         21.83        17030000.0             2.0     0.194           0.653\n",
+       "3               Crime           36            7.01         19.05        18428333.0             2.5     0.333           0.556\n",
+       "4                Kids            8            7.17         20.68        12667500.0             2.0     0.125           0.514\n",
+       "5           Animation           19            7.11         50.65        14360000.0             2.0     0.000           0.514\n",
+       "6    Sci-Fi & Fantasy           23            7.11         29.84        13290000.0             2.0     0.304           0.486\n",
+       "7             Mystery           28            7.02         13.47        15423571.0             3.0     0.357           0.486\n",
+       "8         Documentary           19            6.82          5.59        16380000.0             2.0     0.053           0.292"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 900x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conditional analysis within: Action & Adventure\n",
+      "Observed lifecycle profile\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>season_plan</th>\n",
+       "      <th>title_count</th>\n",
+       "      <th>median_quality</th>\n",
+       "      <th>median_reach</th>\n",
+       "      <th>median_intensity</th>\n",
+       "      <th>median_staying</th>\n",
+       "      <th>hit_rate</th>\n",
+       "      <th>median_quality_pct</th>\n",
+       "      <th>median_reach_pct</th>\n",
+       "      <th>median_intensity_pct</th>\n",
+       "      <th>median_staying_pct</th>\n",
+       "      <th>format_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4+ seasons</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7.164</td>\n",
+       "      <td>82.890</td>\n",
+       "      <td>37914000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.429</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1 season</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7.117</td>\n",
+       "      <td>24.664</td>\n",
+       "      <td>18767500.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.100</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2–3 seasons</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7.009</td>\n",
+       "      <td>69.980</td>\n",
+       "      <td>17325000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.222</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   season_plan  title_count  median_quality  median_reach  median_intensity  median_staying  hit_rate  median_quality_pct  median_reach_pct  median_intensity_pct  median_staying_pct  format_score\n",
+       "0   4+ seasons            7           7.164        82.890        37914000.0             2.0     0.429               1.000             1.000                 1.000               0.667         0.917\n",
+       "1     1 season           10           7.117        24.664        18767500.0             2.0     0.100               0.667             0.333                 0.667               0.667         0.583\n",
+       "2  2–3 seasons            9           7.009        69.980        17325000.0             2.0     0.222               0.333             0.667                 0.333               0.667         0.500"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Episodes per season\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>episode_plan</th>\n",
+       "      <th>title_count</th>\n",
+       "      <th>median_quality</th>\n",
+       "      <th>median_reach</th>\n",
+       "      <th>median_intensity</th>\n",
+       "      <th>median_staying</th>\n",
+       "      <th>hit_rate</th>\n",
+       "      <th>median_quality_pct</th>\n",
+       "      <th>median_reach_pct</th>\n",
+       "      <th>median_intensity_pct</th>\n",
+       "      <th>median_staying_pct</th>\n",
+       "      <th>format_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>≤8 episodes/season</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7.216</td>\n",
+       "      <td>29.980</td>\n",
+       "      <td>33719000.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9–12 episodes/season</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7.217</td>\n",
+       "      <td>42.363</td>\n",
+       "      <td>23232500.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13+ episodes/season</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6.971</td>\n",
+       "      <td>103.132</td>\n",
+       "      <td>13825000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.542</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           episode_plan  title_count  median_quality  median_reach  median_intensity  median_staying  hit_rate  median_quality_pct  median_reach_pct  median_intensity_pct  median_staying_pct  format_score\n",
+       "0    ≤8 episodes/season            8           7.216        29.980        33719000.0             4.0      0.50               0.667             0.333                 1.000                 1.0         0.750\n",
+       "1  9–12 episodes/season            8           7.217        42.363        23232500.0             2.0      0.25               1.000             0.667                 0.667                 0.5         0.708\n",
+       "2   13+ episodes/season           10           6.971       103.132        13825000.0             2.0      0.00               0.333             1.000                 0.333                 0.5         0.542"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Viewing commitment per season\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>commitment_group</th>\n",
+       "      <th>title_count</th>\n",
+       "      <th>median_quality</th>\n",
+       "      <th>median_reach</th>\n",
+       "      <th>median_intensity</th>\n",
+       "      <th>median_staying</th>\n",
+       "      <th>hit_rate</th>\n",
+       "      <th>median_quality_pct</th>\n",
+       "      <th>median_reach_pct</th>\n",
+       "      <th>median_intensity_pct</th>\n",
+       "      <th>median_staying_pct</th>\n",
+       "      <th>format_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Moderate (6–10 h)</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7.303</td>\n",
+       "      <td>79.040</td>\n",
+       "      <td>3.791400e+07</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.571</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Compact (≤6 h)</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7.129</td>\n",
+       "      <td>36.434</td>\n",
+       "      <td>1.848583e+07</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Long-form (&gt;16 h)</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7.105</td>\n",
+       "      <td>149.166</td>\n",
+       "      <td>1.436000e+07</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.542</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    commitment_group  title_count  median_quality  median_reach  median_intensity  median_staying  hit_rate  median_quality_pct  median_reach_pct  median_intensity_pct  median_staying_pct  format_score\n",
+       "0  Moderate (6–10 h)            7           7.303        79.040      3.791400e+07             5.0     0.571               1.000             0.667                 1.000                 1.0         0.917\n",
+       "1     Compact (≤6 h)            8           7.129        36.434      1.848583e+07             2.0     0.125               0.667             0.333                 0.667                 0.5         0.542\n",
+       "2  Long-form (>16 h)            5           7.105       149.166      1.436000e+07             2.0     0.000               0.333             1.000                 0.333                 0.5         0.542"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Language market\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>language_market</th>\n",
+       "      <th>title_count</th>\n",
+       "      <th>median_quality</th>\n",
+       "      <th>median_reach</th>\n",
+       "      <th>median_intensity</th>\n",
+       "      <th>median_staying</th>\n",
+       "      <th>hit_rate</th>\n",
+       "      <th>median_quality_pct</th>\n",
+       "      <th>median_reach_pct</th>\n",
+       "      <th>median_intensity_pct</th>\n",
+       "      <th>median_staying_pct</th>\n",
+       "      <th>format_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>English</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7.174</td>\n",
+       "      <td>45.146</td>\n",
+       "      <td>28995000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Non-English</td>\n",
+       "      <td>14</td>\n",
+       "      <td>6.971</td>\n",
+       "      <td>76.664</td>\n",
+       "      <td>14340000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.143</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.688</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  language_market  title_count  median_quality  median_reach  median_intensity  median_staying  hit_rate  median_quality_pct  median_reach_pct  median_intensity_pct  median_staying_pct  format_score\n",
+       "0         English           12           7.174        45.146        28995000.0             2.0     0.333                 1.0               0.5                   1.0                0.75         0.812\n",
+       "1     Non-English           14           6.971        76.664        14340000.0             2.0     0.143                 0.5               1.0                   0.5                0.75         0.688"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3QAAAHQCAYAAADpgihqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlcelbwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAT89JREFUeJzt3Qm8TPXj//HPFUol7SpayU5CIlSESAuKCiWSEm2ib0V7aFXatNGmDUkhlSUJZd+KkJIsUci+O//H+/N7fOY/d+7MvXNn7vZxX8/Hw+OamXPOfM6Zz5173ueznJQgCAIDAAAAAPBOgdwuAAAAAAAgMQQ6AAAAAPAUgQ4AAAAAPEWgAwAAAABPEegAAAAAwFMEOgAAAADwFIEOAAAAADxFoAMAAAAATxHoAAAAAMBTBDoAXtm0aZN55ZVXTLt27cxll11mWrVqZR5++GGzYMECk1+1b9/ePP30096/h+8ij9Fff/1l62Zekl1limfftcyzzz6bqe0k68svvzSNGjUyTz75ZMLbWLdunbn//vtTPXfnnXeaBx54wOSkadOmmVtuucVcddVV9r1Vrsz4/vvv7bG477774l5n48aNdp2xY8ea3Piu7927t9mxY0eOvzfgm5QgCILcLgQAxGPNmjXm2muvNWeffbbp0KGDOemkk8zmzZvN6NGjzaeffmr69OljWrZsmS+PS+HChc3xxx/v9Xv4LvIYKdBMmjTJTJ482eQV2VWmePZdwaB69ermqaeeins7yWrdurXRac6vv/5qy3LMMcdkehsvvPCCGTx4sFm4cGHoub///tsUKFDAnHjiiSYnzJgxw4bdu+++21xwwQXmkUceMXv37jVffPGFLUc8br31VhvQfv75Z/Ptt9+aU089NcN1/vnnH1O3bl0biHXxLCe9++67pl+/fmbOnDnmiCOOyNH3BnxDCx0Ab7z99ttm37595vXXXzf16tWzwa5GjRrm0UcfNY0bN7YnigcOHDD5zSmnnJLtQSsn3sN3+fkYZdW+Z+UxXLp0qZk/f75tkSpYsKAZPny4ySq6mJRTYU7UQqZQo1BWuXJlc8MNN9j9W7lyZVzrqzXvhx9+MN26dTPHHnus+fjjj7O9zAByTsEcfC8ASIq6cR1yyCEmJSUlzWtPPPGE7aITbu7cueadd94xv//+uznyyCPNJZdcYq9yqwXAGT9+vPn888/tiZGudFeoUMHcdttt5vTTT7evr1+/3rz55pv2xHDnzp32ebUCaluR7/X+++/b9ypSpIi5+OKLzU033WQOO+ww+7reVy0Uai346quvzNatW+176QTrtNNOi9kyoG5SOhHVCamjq+vPPfecGTRokL3Krm1rW//73//i2vcuXbqYo48+2l79dtSFS1fCdSwOP/xw+5yu/is8jxgxwh6T8PeId390Ijp06FDbMlC6dGlz4403mh49epiHHnrIXHjhhTE/61mzZpkhQ4aY5cuX2xPQK664wlx99dWpPvt4jnnDhg3N9u3bzYQJE8yuXbvMueeea7p3725mzpxpPvzwQ1uuihUr2udOOOGEpNdzx0gtKGPGjLF1RsfprrvuMpdffnnC246nPmf0mcQqU6Rk60d676MLMvrMJk6caDZs2GDKlStnuy+61qLIupzI740zbNgwGw510adBgwa2Ff/mm29O06L1yy+/2NagJUuWmGLFitmLQ9dff739nevfv7/55JNPbGuYyqH61bZtW1tmBazwY7R48WLbkvfbb7/ZdevUqWM6depkPyvROtpffabqCqrPWRelunbtan+mR/uqfVeIK1OmjP290HbD60d6PvvsM3PooYea888/3zRt2tQ+Vmtf+Heh/PTTT/ZYrFq1yn4m2tfw4/nWW2+ZDz74wBQvXjz0vFo/77jjDvP444+b2rVr2+dUR7WsWgMLFSpkWxU7d+5sjjrqKPu6fv9VfoVTfT+oJfCMM86wgbVKlSr2u03ri7qY6ndf9VL1QnXo+eefD72/jqN6brh6prrqjrlaNkuWLGm76eu7I6NyAd5Sl0sA8MGgQYOCMmXKBDfeeGMwceLEYMeOHTGX/frrr4Py5csHvXr1CubPn2+Xr1+/ftCxY8dg//79dpkhQ4YElStXDoYPHx4sWbIkmDp1atCqVaugQYMGwb59+4Ldu3cHTZo0sevMnTvXLvPqq6/aMkyePDn0XtpOuXLlgmeffTZYsGBBMGbMmKBevXpB69atg127dtllGjZsGNSsWTO44447gjlz5gQzZ84MrrzySvu8K0+kadOm2feaNGlSquc7d+4cNG/ePPRY27j33nvj3neVs2rVqsGePXvs8nv37g3OPfdc+15TpkwJbef222+3xzrae8SzP++9915QoUKF4I033ggWLlwYvPbaa0GlSpXs+4wdOzbmZ6fjp/L369fPHs/Ro0cH1apVC1566aVMH/NzzjnHlvvnn3+2y1SsWNE+r+WmT58e/PDDD0GdOnXssQnft0TXc8do/fr1Qffu3e0yK1asCLZu3ZrUtuOpzxl9JrHKFCnZ+pHevpctW9aup98n7esVV1xh98O9VyL1LBr97mq9vn372sf6fY32u6RjrePfu3dve1zHjRsX1K5dO3jsscfs6xs2bAgeffRRu4z25b///rPP63si/PNRHVV91HpuO40aNQouu+yyYMuWLaF1VCatN2PGDHsMrrvuuqBWrVrBzp07Y+6L258WLVrY3/sPPvggqFKlSvDVV18F8Thw4ID9TuvZs6d9rN9FHYvPP/881XLjx4+3deypp56yy3z00Uehz33o0KHBmjVr7D6+8847qdbT8jVq1LBlFP0+qv506tTJfl76nFu2bGmPhasL+g7TsdD+63tO69x88812vY0bN9p//fv3t++9ePFi+zlI+/btg3bt2qV6f9U3Lafyio6vynn//ffb/fjss8/iLhfgKwIdAG/oBK5Pnz72pEN/wHWSde2119oTfZ1sOTqx0EmZ/viH0wmh1hs1apTdlpZRaAj3/fff22W0rE4k9H+dnIVbuXJlsGnTJvt/neDpBP2hhx5KtYxOHrTu4MGD7WOdgOrENfwkVCflWmb27Nkx9/eiiy4KevToEXpOJzba7/CTqvCT4Hj2fdasWfb/OqkRPVbwUvl0EiU6wdbJ3LvvvpvmPeLZH50g6eRJJ3vh9Di9QKftKQjoBD7cW2+9ZU9it2/fnqljruOncO7oZE7LrFq1KvTcwIED7f675ZJZL/wYqXwKmeES2XY8n2k8n0msMkXKivqR3r678CYTJkyw76VwE207ifzeuIDlwoDoONatW9cGiXBNmzZNExB0gUeBQEFBtM+6EBEuPNDp8zn//PODbt26pVpG30k6bs8//3xoHYUYd8FBtN8qp8JUepYvX27LqWVVNn328dKFKq2n4OQ0a9bMlidc48aNbV0Mp5DkAp2oDioEOfpc9DmH/y5qu5dcckko4Mm6devsMRwwYIB9rM9Bj11AdsdL7zVs2DD7WN9xerxt27bQMvEGOn1HRl7wi6dcgK8YQwfAG+oq9eCDD9rJFtStS1321IVG3WmaNWtmuzuJZrxUd67I7mTq2qZJEdSNURe0NI5Es8ape5+6E2l2PdeVR13i1OVIyz/22GO2G5K6fIqeV5c0UTc5dS2LfC91JTrrrLPse4U/F97dy43BUVeqWPur7kbqFqquea4Lo8quLojRxLPv7v9Tp061r/3444+mUqVKtjuWuijJ7Nmz7TFQV7VY0tsfbUez01166aWp1tHnlB5131L3q8j1NKupuqmpK1tmjrn2S910neOOO8524SxRokToOXWzUzeuLVu2JL1ePDK77Xg+00TrWDRZVT+iUXc6dXULHzPnus3Fksg+6fdZ3Rv1T3S8r7zySjsxiroTytq1a23Xxci6pt+tr7/+2hQtWjSufdJkKeruHfn5qHv2Oeeck+rzKVu2rO36mJl9UX1Xl0Mto+8o7Yv7rlP59f2lLqHpHYuTTz7Zfn5O8+bNbTfyRYsW2cf6bluxYkWaruTqfhpO66m7opaV6dOn2/F5LVq0CH2O2q7WC+/Oqf3U5xh+LNTFUvU8M8ciXqpX6obtZKZcgI8YQwfAO/oj3KZNG/tv//799oRTM1zqZEcnLRr3JgMGDDBvvPFGqnW3bdtmT0B0UqQTMY292717tx2Xo5MvnbC4yVU0RkbjuF5++WW7LY2X0UmITvg0NkZjtf7991+73fAxJY6e04m4E36CIe4kNb2JXFQejVPSmCPdpmHUqFF2QhiFgGji2Xe970UXXWSPm8ad6GetWrXsCai2r7CkE1+N1UlvJrz09sdNqR45xifacQqnMBdtPR1rN64xM8c82ux4keV2wid9TnS9eGR22/F8psnUsUhZVT+icePvMlO+zO6TwonGguk4a9ybo3JrHY2J0zjOWHVNJ/yursUjo/qoQJbovuh5jRtTGfVdpLFh+r7SLKKqC8uWLTPffPON/YzCg7KjoKkLQroQEh5ctQ3RmE19d7pjETkhjb5nwi8+aBsaK6eZhTWOURdZ9J2oiwDi6qrGVY4bNy7NcQqv+1lRV2OJ/H7MTLkAHxHoAHhBJ2kdO3Y09957r2nSpEnoeZ1saFpttazpKrEmbtAgeNGy7kQjMhzoCrPuLaUTPgVBd9VWM8GF00QeOpHWFXBNSqEWMrUIatpyTeXtTgR0+4RoJ1PJDrY/88wz7T5okgldSVYZVJ5Y3ElSevsu9evXt5NuKAzoyrVOzvRebj91wh55tT4z3AmVWpjCW54yas1y5Utvucwc82gT6MQj0fWyY9vxfqZZKbvrR3bSJEI6ZppsI3wyIdHFGk0IoglK4qlr8cjO7wC1Iq5evdpOOuIm+tAEIJrI5cUXX7QhThd63GuRNHGNLnppQpjI0DJw4ED7vaLA6MoYeSx0wUDrOzquCnVaT5OJKBzpezn8ddHFNvUuiBQeDhP93Ym8gLJnz54M18vucgG5jS6XALygLkO6wj5y5Mior7urugoPrkvbn3/+aa+0u3+6+qwTU53kqKuRTk6vueaaVF1w1J1IIk8adOJUs2ZN29VTrRU6yRW16rmuR5FXhNUdVF3MkqVWuilTptiry+qilF43t3j2XRSCdUX81VdftSdJCgqail0n7Tr505X/ZE7YNUujyqGAHS7ycaTy5cvbYx15o3i1pip8q0tmThzzrBDv/cEyEu9nmpVlSrZ+ZNW+Z5bCh2biVCAtVapUquOlf+q6qO53ujCjfVEQiqxrmvFWdc3Vr4z2RXVddTayPioMKXi5+poIfcbatu7PF65Xr162S6ha2vR9FIvCq3otqAdC5LHQd5++U/W9omOhbr/qvhnOfc+F04Uz/Z6pG7r2MTwgqcVW21FX0Mj303enZgyOV7TjrhCu9wznun+mJyvLBeRFBDoAXtCVdjd+Tt0kXTcn0cmluiDppFNT16t7km4orCne3dgInfhoPV3xVgufxrLohEHdlVwYVDctNzZFY9YUPnRip+nlHZ0QaHpydxKl6cTVBVNTYbvxRbpSr7JqrIzGfiVLV+AVMFU2/T9yqvFw8ey76ERWAVUnfFWrVg21Vmgab40f0nbUIpgoBWvXXVTTwYumc1dASK+VSi0FmjL+o48+Cp1cqtVALbB6TZ9bThzzrKBjrHFmakHQz0TF+5lmZZmSrR9Zte+ZpZZDtSrGOiYad6uLIho/q6Ck2xgoELvjqnGfzz77rP1ucK2h2hdd/FGQiLYvGm/o6ux3330X2k7v3r3t59ShQ4eE90f1WfecU9dGd5P2//77zwY6lUm3O9B3n16PNG/ePHubg1jHQjd51++puqBqf3VbAG1HXTRFx1G9FyKdd955theEfpdVR8Jb4N129L2q4K/vLX2/qleDup5Gu01GLK7VUfvrjrtui6DvE/fdoDLq1hIZycpyAXkRXS4BeENhRidPOpFQcNP/dUVe/3SVWN2o3FVddwVb3dTUuqETS12F1s3J3f2rdGVWJ286QVVg1BVc3YRYXSl1ZV1dznQirRMmFxY0YF9hQmNwnL59+9r7wukeSm6ZatWq2fEp4Sc7iVKQUaucWhUUkjISz76LWjE08UX4ZAk6YVe59X7JdjvUCa2One7bp31QWfQ5qRzpdRNUFzAdR01Yo65SOplTi5E+K1em7D7mWUFd09TtT2Mer7vuOnPPPfckvK14P9OsLFMy9SMr9z0zNAGIxunFusehLoa4sunCjE7ytR/6fdZx1YUcBR0dV3fhRMdBF1P0U99BmiQpkrpvq0tjz5497XZUZ9Uqpnu2aaxhMvSZi8Yz6vdG21bIVihVzwV1x5w2bZqd9CXyWKgs4eMIw2m/FWQ0Fk9j8HSPSE3Eo/u4qb4pjOo5XYiJXE+/x/oejvZ9pHW0jMYeu0lcNImUuoorAMZL9/HT77L2S7//Wl/fx2o11P0A9X2t737dx0+t1xnJqnIBeVGKprrM7UIAQGbphFbjUxTgNF4rVrco/cHXgH9d7Y02zkRXafW6/rC72efUvUl/7N0kB/qa1FVinezpvWK1kOmKuVoOFV4ix6tom9p++GB97YNaWDTJS6xJMRy1UqkM0U7etW2VKXJCg4z2XfujK9xaz5VX+6Dt6WQpfJa/yPfIzP6otUKtG3peJ55qsdBNotUlLj3ueGblMdeEKdrv8NCnsul5dctSPUpmvcjPQcdE5VP9UtBIdNvxfKbxfiaRZYolmfoR77678qnFzy2TaD0TzWCpizPqHhqLO7bh++COq8oa7WKD9luvq57puGsMrT4XNzOjo0Ck5bTdyM8n2jp6X5VZ+xdrHFz4fqvckdvWd5i+oyLHgekY6bX0LnDod1PlVaulm7k38lioC6qOVfh7qqumujhrNslok7GET+ijz89t29HzKlv456Tl9V66UOfG9KksWlbvHV7f9BnqIo6OpfZbY6y1npbT8tpWrAmY0isX4CsCHQAgy+mET1fv1QJw++2325MuBXC1ouqkV60LAAAgeQQ6AEC20OQU6t6klkVdcdc4N3VbU1dMXfEHAADJI9ABALKVgpy6dqXXdRIAACSGQAcAAAAAnuK2BQAAAADgKQIdAAAAAHiK+9AhIXPnzrVT/8aarhgAAABA2tug6J6I5557rskqtNAhIQpz7h+QSP3RPZWoP0gUdQjJoP6A+oPckh3nz7TQISFqmdMJeenSpdO9MS0QjWY8XLx4MfUHCaMOIRnUH1B/kFsWLFhgW+iyEi10AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItAhKSkpKRxBJFRvihQpQv1BUt891CFQf5Ab+P5BXpMSBEGQ24WAfxYuXGh/Vq5cObeLAgAAAI8dOBCYAgXyRyPBggUL7EWBrDyHLphlW0K+9NyHs82qdVtzuxgAAADwUMniRU2PttVzuxheI9AhKQpzy1dv5igCAAAAuYAxdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAA+SHQzZo1y0ybNs1s2rQp6uvLly+3r//zzz8JF2j//v12G//995/J6/744w+zfv36VM/99ddfZs6cOWbz5s0Jb/fff/+1xzJSEARm2bJlZt68eWbPnj0x19dyOoYbN26M+vqBAwfMjBkzEi4fAAAAAA8DXffu3c3NN99sPvjgg6iv9+zZ03To0MGGiUTt2rXLbmPRokUmu4wbN8688847SW1Dofa2224zBQsWtI+3b99uH19zzTWmT58+5uKLLzbDhg3L9HZ3795tunXrZt57771UzyuctWrVynTq1Mk88MADpn79+mb27NkxQ7GOoYJlNAUKFDAffvih+fLLLzNdPgAAAAAed7k899xzzdixY9M8//vvv5siRYoYHwwePDhNy1pmPfPMM6ZFixbm2GOPtY+feuop8+eff5pvvvnGfPbZZ+bJJ580jz32mFm1alXc21Ro69y5s5k7d26a17StYsWKmfHjx9vjf/XVV5t77rkn3Za69GhdlTmZlkQAAAAAngW6888/34ahJUuWpHr+q6++MpdccknMFiN1IVSL0rZt26K2ys2fPz9qN0PXRfDXX381CxcuNDt27Ej1vFoD9ZzK89tvv9n3ci1d2t7MmTNt0HLU8qcyrFmzJlULV6z3iEbdKkeNGmVbzGTr1q3m888/N/fee685+uij7XPNmjUzF154oVmxYoWJh7pRNmnSxJa/QoUKqV7bsmWL+fbbb23raKFChexzXbp0sQFwypQp6W5XZdO2VeZwZ5xxhilbtqz5+OOP4yofAAAAgLzn//oLZoIChYKKWokUCMID3cCBA83TTz+danmFpgcffNC2JB111FFm5cqV5u677zbt27e3r0+dOtV25TzuuONssKtUqVKq9RWy7rzzTht01EKl9RWcrr/+erN3717btfC8886zoUfhZcyYMeajjz4yb7zxhjnzzDNtUFOIq1mzpm2ZGz16tFm7dq0NdQoz1atXT/c9otF61apVs2WWBQsW2HUvuOACs2HDBrv+WWedZV577bW4j6uOT+/evc3ll19uOnbsmOo1lV/7Ua5cudBzag1VKPv5559NgwYNom5z+PDhtqWwePHi5pdffjFXXXWV7Q7qNG3a1Lzyyiu2qygAAACAfBDoRC1xCgIKZqJApKBXokSJVMspZN1+++02pCispKSkmIkTJ9rnSpcubc455xwb5q677jrbBVChRePDnH379tnXL730UvtT66u16cYbbzRlypQJhb8jjzzSDBkyxAY8jWVTq92bb75pu4eKWrHUuqUxZffdd5/t0li1alXzv//9L8P3UOCLpO1peUetfQqCCozqbnnMMcfY1kFtzwXXjChwxuImN9F2w+k9FSDTG+enoH344Yfb465WPZVH+yW1a9c2Dz30kFm6dGnoOQAAACCn7dy5007sd7ALgsDmjVwPdJrw4/7777dBTq1GahVr2LBhmuUmTZpkQ53Cmiu4WpPUojZixAjboqbXb7311tBkHXfccYcZOXKkfazukgpGWv/HH38Mbff000+3LYQu0NWrV8/+VKhUl0cFq3DHH398qKJEyug9IgOdPgR17Qxv1VLLosaiKRipa6TKMXnyZLtfFStWNDVq1DDJUFDV8Yv88A855JBQF9NoNEGLwpzUrVvX/lTXSxfeSpYsactKoAMAAEBuzx4f7Vz9YFS4cOHcD3RqEdNYOgUeBTr9fPnll9Msp66NClNaPpy6QipEacIQTSriQoeolc+NE9PYN80iqRkZw6mrY/g2TzrppFSvK2i+//77tquituFatqKl/njfw1EAVYgKf+2www6zrYtqeXRlV7dUjYXTJCYZTVyiY+gmV4nmiCOOsGXXdsIrgCp9tDI6bjyfuPUUDh0FRG071m0oAAAAgJygfJAfWuiWLVuW5dtMKNC5ljaFJnW/VJgpX7687b4YTiEl2iQoCkUKIgpyka8rcLhWJy2j/2ssWnjoczTxiWvZczR+TWPf1GLYo0cPO85PIUsBNJqM3iOSxq4pCIVfQTjttNPsz6JFi6bZtlru1P0zPRq/l16g01g5UQDW2Lzwrp4tW7aMuV48zbnaD4U6AAAAILf4Mlt+srK6u2VSgU6B6YknnjADBgyIOSlHlSpVbGCYPn16KFBpjJseayyXxo2pu2L46z/99JMNiFK5cmUb1r7++utUwUWTkCiEqMtgJK2vbWryD9cqpVsJiNuuuio6Gb1HqVKlUm1f21T4Cr95usbqacIXjVO74YYbQqFVLYSa7bJfv34mGSrDySefbLtxukCn8mlsnSZiSZTKqFCsbQMAAADwT8KBTjMnanyYJgjRza6jUetY69at7fi0rl272q6PumG2frZr1862YGlCFM0oedddd9l1NDula3HTODYFPwVHTf6hx7NmzTJDhw41gwYNihroNK5OyVf3WFO3R3W/1HuqlU4BRvT+mhhFQU+Tm6T3HtHUqlXL3mZB++BCnmby1L3iFCZPPfVU8+6779ruo82bNzfJ0v7oGGpiGbWCqhvriy++aFsiVd5EafIXdRfVBDEAAAAADvJAp8k9wkOUAoUmAdEkJ6IgppkTTzzxxNAyjz76qA0MmiBFYURdNNu0aRMa+6XXFcK+//57c+ihh5rnn3/ezqDpxn9pVkqNRVPrl4KWwpLClib2UPdMvV94d0UtqyCm2SaHDRtmZ9PUT4U6dxPtnj17mhdeeMF88cUXtqUxvfeIplGjRrYFUF01XWufbjJ+yimn2MledKsGTUJy0003JTToUWVRYA6nWw6oFVBl1vtqwhWF5Wjc5xDZjVPPuQliRGFcoTe/NHEDAAAAB5uUID+MPsxiClSXXXaZDYKxbqae12mCFYU5tYjq9hGZpRuwy6BxG8zy1f8XlAEAAIDMKFWimHmx+8X55qAtWLDA9r7TsK+s8v9nE0Hc1CqnCVfeeustb4+aWiDVsppImAMAAACQNxDoEqRul9WqVbNj9HyjyWF0deCRRx7J7aIAAAAAyI1JUfB/4/t8pDF2zzzzTG4XAwAAAECSaKEDAAAAAE8R6AAAAADAUwQ6AAAAAPAUgQ4AAAAAPEWgAwAAAABPEegAAAAAwFMEOgAAAADwFIEOAAAAADxFoAMAAAAATxHoAAAAAMBTBDoAAAAA8BSBDgAAAAA8RaADAAAAAE8R6AAAAADAUwQ6AAAAAPAUgQ4AAAAAPEWgAwAAAABPEegAAAAAwFMEOgAAAADwFIEOAAAAADxFoAMAAAAATxHoAAAAAMBTBDoAAAAA8BSBDgAAAAA8RaADAAAAAE8R6AAAAADAUwQ6AAAAAPAUgQ4AAAAAPEWgAwAAAABPEegAAAAAwFMEOgAAAADwFIEOAAAAADxFoAMAAAAATxHoAAAAAMBTBDoAAAAA8BSBDgAAAAA8RaADAAAAAE8R6AAAAADAUwVzuwDwW8niRXO7CAAAAPAU55LJI9AhKT3aVucIAgAAIGEHDgSmQIEUjmCC6HKJhO3Zs8fs3LmTI4hMU71ZtGgR9QcJow4hGdQfUH/yFsJccgh0SEoQBBxBJFRvdEJF/UEy3z3UIVB/kBv4/kFeQ6ADAAAAAE8R6AAAAADAUwQ6AAAAAPAUgQ4AAAAAPEWgAwAAAABPEegAAAAAwFMEOgAAAADwFIEOAAAAADxFoAMAAAAATxHoAAAAAMBTBDoAAAAA8BSBDgAAAAA8RaADAAAAAE8R6AAAAADAUwQ6AAAAAPAUgQ4AAAAAPEWgAwAAAABPEegAAAAAwFMEOgAAAADwFIEOAAAAADxFoENSUlJSOIJIqN4UKVKE+oOkvnuoQwAAGFOQg4BEFS5c2J5QAZmlelOhQgUOHBJGHcq7DhwITIECXOwDgJxCoENSnvtwtlm1bitHEQBgShYvanq0rc6RAIAcRKBDUhTmlq/ezFEEAAAAcgFj6AAAAADAUwQ6AAAAAPAUgQ4AAAAAPEWgAwAAAABPEegAAAAAwFMEOgAAAADwFIEOAAAAADxFoAMAAAAATxHoAAAAAMBTBDoAAAAA8BSBDgAAAAA8RaADAAAAAE8R6AAAAADAUwQ6AAAAAPAUgQ4AAAAAPEWgAwAAAABPEegAAAAAwFMEOgAAAADwFIEOAAAAADxFoAMAAAAATxHoAAAAAMBTBDoAAAAA8BSBDgAAAAA8RaADAAAAAE8R6AAAAADAUwQ6AAAAAPAUgQ4AAAAAPEWgAwAAAABPEegAAAAAwFMEOgAAAADwFIEOAAAAADxFoAMAAAAATxHoAAAAAMBTBDoAAAAA8BSBDgAAAAA8RaADAAAAAE8R6AAAAADAUwQ6AAAAAPAUgQ4AAAAAPEWgAwAAAID8FOgGDBhgvv/+e5OX7N+/3wwbNsw8++yzZvz48bldHDNixAjz22+/JbTu9OnTzdixY9M8v3HjRvP++++b/v3729eDIIi6/oEDB0yfPn3MkiVLYr7+0ksvmV27diVUPgAAAAAeB7qhQ4eaOXPmmLzktddes2HuyCOPNEcccUSuluWXX34x7777rjnzzDMzve6KFSvMPffcY3788cdUzyscNmvWzEybNs0ULlzYPP3006Z79+4xA5uC319//RX19QIFCpiUlBQbzAEAAAD466Dpcrlo0SJz8cUXmy5dupjatWvnalkee+wxc9ttt5lDDjnE/Pvvv+aHH36I2ZoWTi2L1157rfnvv//SvPbII4+Y6tWrm9dff91069bNDBo0yHz11Vdm3rx5CZWxQ4cOthVx+fLlCa0PAAAAIPcVzK4Nq7ufgszmzZvNOeecYxo0aGBbhtR61K9fP9OxY0czY8YMs3TpUnPSSSeZ5s2bm6JFi4bW12tqpTrmmGPMpZdeartTahsVKlRI814DBw602ylSpIjtati1a1dz9NFHxyzDvn37bAvXNddcYyZNmmSfU5B6+eWXTadOnez7qkXs9NNPt+VSKBs1apTZvXu3adSokSlXrlzM/f7pp5/supdccol9rG2//fbb5sknnzRt2rQxLVu2TLWfztdff23+97//2bJPnTo11WurV682s2bNMsOHDw89V6pUKfPEE0+Yww47LGZZFCK//fZb22J4/PHHm6uuusocddRR9jW1ZCoADx482B4zAAAAAP7Jlha6N99807Ru3dqsWrXKBprHH3/c3HLLLWbv3r2h7oA33XSTmThxojn00EPtYwUqvea6T3bu3NmO8frzzz9t8FKL1B9//BH1/YoXL26DjbpalihRwhQsWDDdMmi8nd5Tj3fs2GHfQ2FQz9166622W6PK9cwzz9iWNoXPnTt3ml9//dWWJb1WLbV61a9f364vxx57rHnvvfds98bFixebhg0b2ta2ZcuWpVqvfPnyZsKECXa/1R0ysvVR3SzLlCljRo4caV544QXbOqeypBcutc9jxoyxx+bjjz+2x3jPnj2h15s0aWJGjx6d6jkAAAAA+biFTuFIgUNh6IorrrDPXX/99TY8aOydQoWoxUzLiF7TsnPnzrXh7NVXX7UBSOHHhZ1evXrFfE+1en3zzTe2FUpBMaMyKAhJ48aN7Xg1UaudVKxYMVWL1SuvvGI+++wzU6lSJdvipZY3teqphSzWhCbqzhhJweupp54y//zzjxkyZIhp166dqVKlinnrrbfs62oNjGXDhg3m8MMPNzfffLM59dRTzSmnnGKee+45Wy6tr8AaTd26dW1rqKhlUWPwFixYYGrUqBH6DBSa58+fb84777yY7w8AQGboImg8Qw1ys3zhPwHqD3KKvhsjG2/yXKBTd8FChQqZyy+/PPSculQqXKj7owt0tWrVCr1+xhln2J8aO6aWK7VuuTAn6ir48MMPZ1kZXKCL1n2zZs2aqdZRy53CnOjgH3fccVHHuIla//7++2/bShiLAt2aNWvssieffHJc+6OwqfdUF0l1CRW1Piqgfvnll7ZbaDQXXHBB6P9nnXWW/blp06bQc2o9VOudWjEJdACArKIeNT6EJU1EBlB/kNPU8y5PB7pt27bZcW+RyVPh4ffffw89dmO5xLUwKbFqan6NfwuncOaW/+6772yXyPDQoi6OiZRBy0SKnCHTdZ10tM1YVx23b98edR11JVW5NfOlxsNdd9115sEHH4z6/tG4MXcaS+ioJbNy5cq2VTNWoFMYjTzG6m4aToFOxwsAgKyiWZ7zegudwpwuKIf/rQSoP8hukcOu8mSgU+uUugiqBUpBzFm7dq1t3cqIuhNqfQUPzRIpmoxEE5uIgl14C1h4MMyqMiRKZdH4vS1btoSeW7dunbnxxhtta1z79u1t+HT7Fa+yZcvanxrvF07HRe+XKP2x3bp1qw26AABkFV9CksqpIQ0A9Qc5Jau7W2bLpCjnn3++bUb88MMPQ89ptkm1qrmZH9Nz4YUX2tYkTS7iaEIPN2GKpu7XODn3T4+zugyJUrlLlixpuzCGt4Bpkhe1zqkbaWbDnBt/pwlRNGlL+EQpCxcutDN3JkoBV8H5tNNOS3gbAAAAAHJPws07CkeRLUbqBqgxXo8++qidxERj2dR9UrNZatzalVdeGZp8JBa1Ft133332Xm4a76bAoXFpEmvyj0iaHCW9MqhlK7vUq1cv1U3XixUrZv8lSzdN17Ft27atnRhl3Lhx9v916tRJeJsq5wknnBAaIwgAAAAgHwS6u+66K02YE9d1T6FJrWQKfQpwmor/7LPPtq+pheqBBx6wLU5O5HOakVKTk+jeawocmpVRk3ZE617paJKQ8C4e6ZVB3RT1fqVLl45ZBqlatWpoFkxHrYIKrrHofW+44Qbb7TK98qZH96uLvFedWuk0k+fkyZNt91O9h2bkjCbavoie04yhjraniWOyo+kXAAAAQPZLCfLYqGWNOdONuLt06RIKiOPHjzd33323DTM+jPfS7QUuuugiO3Yur9LN0nXbhrFjx6YbUGNRd08ZNG6DWb76/8Y3AgDyt1IlipkXu19s8jpdlNa9YXWRkzF0oP4gJ+kWYmpM0eSGeXZSlGRp5kd1BVSLm2awVEvX999/b3r37u1FmBPNYKkbkrdq1SrPDgzXvf7UhTORMAcAAAAgb8hzgU6Tmejm3zNmzDArV660twDQmDrNfukL3XRc981Ta6O7x15eoglmNHOmux8fAAAAAD/luUDnxoDVrl3b/vOVJkfJqzS5jO6FBwAAAMBvWX7bAgAAAABAziDQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCuZ2AeC3ksWL5nYRAAB5BH8TACDnEeiQlB5tq3MEAQAhBw4EpkCBFI4IAOQQulwiYXv27DE7d+7kCCLTVG8WLVpE/UHCqEN5F2EOAHIWgQ5JCYKAI4iE6o1OyKk/SOa7hzoEAACBDgAAAAC8RQsdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0SEpKSgpHEAnVmyJFilB/kNR3D3UIAABjCnIQkKjChQvbEyogs1RvKlSowIFDwqhDedeBA4EpUICLfQCQUwh0SMpzH842q9Zt5SgCAEzJ4kVNj7bVORIAkIMIdEiKwtzy1Zs5igAAAEAuYAwdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4KlcDXRjx441t99+u2nWrJlp3bq1efLJJ82aNWuM77Zt22Zmz56d4XJ33nmn+fXXX+Pe7vjx482NN95oLrvsMnPvvfea1atXR11u//79pkGDBmbKlClRXz9w4IC5+eabzZ9//hn3ewMAAADIe3Il0ClwdOvWzTzzzDPmkksuMS+++KK57777zKpVq0yLFi0yFXLyojvuuMOGr/QMHz7cBqty5cpFfX337t3ms88+M6+++qp9PGbMGHP33XebK664wvTv39/s3bvXdOzY0ezatSvNukEQ2LAX7TUpUKCAueaaa0zv3r3tsgAAAAD8lCuB7q233jLTp083H330kbn66qvN2WefbWrUqGHDy+mnn24effRR47NYQcrZs2ePDbGdO3dO85pC7bPPPmuD7rRp0+xPBT+F3y5duphWrVrZENi3b1/bmjl16tSEytikSRPz999/m0mTJiW0PgAAAIDcVzCn31DhZMiQIbaF6OSTT0712iGHHGIef/xxs2XLFttylJKSYluq3nnnHRs8Nm/ebKpWrWq6du1qSpYsaVv6GjVqZLtqKhz+9ttvNhA++OCDZu7cufY5ra8unS48uXW0jFrJ1O2wbNmytvXrjDPOsMv8/vvv9j2XLVtmNm3aZE488UTbJVStYy6QDRo0yLbCafsVKlSwLY6nnXaa6dGjh/n555/N0qVLzbx588zHH3+c5hh88cUXpnDhwqZKlSr2sfZV4U3HReXWsVHrXPHixe3rv/zyiw1fzZs3D23jyCOPtO9/xBFHxDzWCnxq+Vy0aJE57rjjzG233WZq165tX9Oxbdq0qRk8eLCpX79+FnyyAAAAAA76FrqVK1eaf/75x1SvXj3q62p9qlmzpg0ccuutt5ovv/wy1EVzx44dNlytW7cu1LXw/vvvN1deeaV54YUX7PNt2rQxX331lXnkkUdM+/btbWvY5MmT7fbcOg888ICpV6+eef75502hQoXM9ddfbzZu3Gi2b99u2rVrZwOXwuWAAQNsUFMwcmPOFNpGjhxp7rrrLtOvXz9z+OGHm7Zt29pwp7KUKVPGjnNTeaL55ptv7Bg3R8FT3ScV4CZOnGi378KcLF++3AY3lU3HQQG1e/futtulgl0sL730kt1HleOUU06xLXwKqI5a/2bOnJnqOQAAAAD+yPEWOhcejj766AyXVbfMH3/80bZoubFmzz33nLn00kvN+++/b+655x773LXXXmsaN25s/69WLIUs/VOrVMWKFW1r24IFC8yFF14Y2rZCoUKYqPtiw4YNzbBhw0yHDh3M0KFDbeuhWgylZ8+etjXvr7/+si2MCmQffvih7SYqeo9OnTrZEHr88cfbMKigddJJJ0XdL7XCaR8clV1B1L3HTTfdZIOYs3XrVvtTQU8TqZQoUcK2rGkfNLbumGOOifo+2hfXqqh9GDFihG3tq1u3rn1OwdOVJzxgAgCQjJ07d+bpMdoqX/hPgPqDnOJ6IXod6I466ij7U61NGVH4KFasWKqJQ9SaptY9dWt0zjzzzND/1ZKl91CYc4oUKZJmXNsFF1yQapuVK1c2CxcutGFM3TkVctRVccWKFbYcojCn99WkIur66eix1omHWhg1C6aCn3PCCSfYFje1vqllUa2CTz31lA12annUh67jpRZBtapFhtBoY/GkVKlSof8fe+yxaY67jouOl1pMAQDIKn/88YcXYUl/4wHqD3Ka8obXgU7dF4sWLWpnsrzooovSvK6WtHHjxtmQoqCksBUp8rnIg6L1MqIwE65gwYJ2bJyCn7p5qny1atWy4+s0xb+7DYFStVruXOtdZikUSrRkrv1QC6P+6ThoTN2cOXNCrWdqCQw/BqVLl7Z/NGPRPkWKvGKqcmhcIQAAWUUXWvN6C53CnMbOR54PANQfZCfN0ZHVcjzQKYhofJkm/dC4schw9u6775r58+fb7pT6ot2wYYP5999/U7VoKWxVqlQpqXJoXFq1atVCjzWhilq/NIZNXT31U+PO3GuiP04qk8auqWukwmk06TWjqium/nhkNG5NE6ZozOC+ffvsMdA2Fd7Cu3FqRkw3sUoitG1159SkLwAAZBVfQpLKqXHwAPUHOSWru1vm2m0L1HVQE4ioi6G7ObYev/zyy+brr782Dz30kG1lq1OnjjnrrLPsbQzUVVGB6r333jNLliyx0/cne+sEdxNzjcdTQNPskqL3UYiS9evX28lVRC146pqpEPX000+HupOo9U5h0E2acthhh9lulQpM0agLqWbBdDTBilrhov3TWEBNkKIZKXU7A4Vb0bjAtWvXppr5MrNcGWLdCw8AAABA3pbjLXSi8W2ffvqpDShqrdM4Lt2qQJN0KGgpyIla71555RXTq1cvO/OluiRqvJmeU7CKFZjioVDWsmVLuw1N0DJw4EBz6qmn2lY5jVvTRCsad6YUrclTFPDUMqhbHujG3prNUmVS91F1bdQYON0yQTQL5cMPP2wmTJhgpkyZkqYLqIKaJlZxNBbOTbASyV057NOnjw2WWlbHRe+rAOzeMxGa4VJhLt7xfwAAAADylpQglzu56+11uwB1RTz00ENjLqfAp/DlJvcI73aogOi6d2jSDy0bfo87tbJp25pgRdvQWDTdxFytaur6qFkiI5s/NZZOLXBuBknX7TH8/WOVSdSVUV0zo72mSUgUzD7//HPbApkZKpP2UfucXpNt5HGJ9pwCrVo6dcuGzNIEMjJo3AazfPXmTK8PADj4lCpRzLzY/WKT16nXz+LFi0358uXpcgnqD3KU5snQObwap7xuoQunHQqfkTKj2TEjRbYuqbUv8mbbscaIRQa0cOo2qX9OtDLGKpOoBS0WtTIqROnWB+pemhkKY/GMTYjW6hb+nMYpKsxeffXVmXp/AAAAAHlHroyhg7H3k9M99jR2Lzfofn66cXpWT5sKAAAAIOfkegtdTtN4N41ti6dVMDtpbNwnn3wS1y0WsqObq8YvxrrxOQAAAAA/5LtAJ3llEpD0umxmJ3U1JcwBAAAA/qPLJQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4qmBuFwB+K1m8aG4XAQCQR/A3AQByHoEOSenRtjpHEAAQcuBAYAoUSOGIAEAOocslErZnzx6zc+dOjiAyTfVm0aJF1B8kjDqUdxHmACBnEeiQlCAIOIJIqN7ohJz6g2S+e6hDAAAQ6AAAAADAW7TQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiKQAcAAAAAnkoJgiDI7ULAP3PmzDGqOoUKFTIpKSm5XRx4RnVn79691B9Qh8B3ELzD3zAkY8+ePfbcuVq1aiarFMyyLSFfcSGOMIdE60/hwoU5eEjqO4g6BOoPcgPfP0i2/mT1+TMtdAAAAADgKcbQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAACeItABAAAAgKcIdAAAAADgKQIdAAAAAHiqYG4XAHnX/v37zU8//WTWr19vKlWqZM4+++wsXR4Hv7lz55oVK1aYM844w5x77rkZ1p9Zs2aZtWvXmpNPPtnUqFHDHHLIITlWVuQ9S5YsMYsXLzYnnniiOf/88+OuD/Pnzzdbtmwx9erVy/YyIu/666+/7HfQEUccYerWrWsOPfTQdJffuHGj/RuWkpJiatasaY477rgcKyvyHlcfpE6dOqZYsWIZLq+/YQUKFLB/v44++ugcKinysuXLl5tNmzbZOpGeHTt2mGnTppnt27ebWrVqmeLFi2fqfVKCIAiSLCsOQtu2bTM33XST2blzpylVqpT54YcfTKdOnUzXrl2zZHkc3A4cOGDuueceM2/ePFOtWjUzffp0U7t2bfPss8/aP3aR/vnnH1t/9HVUsWJFs2DBAnP44YebQYMGmWOPPTZX9gG5q3///ubTTz+19WbhwoXmhBNOMIMHD7b1Ij26oHTFFVeY6tWrm9deey3Hyou8Zfjw4aZv3742yOmikv42ffDBB+akk06Kuvx3331nevToYb+vdHFJ30Gvv/56hidhODjNmDHDdOnSxX6P6ER72bJl5s033zTnnHNOzOW7detmKleubOuPLkap/sRaHvnDpk2bzPXXX28vED3++OMxl/vzzz/NDTfcYEqUKGEvHOhCwlNPPWWaNGkS/5sp0AGR+vXrF7Rs2TLYvXu3fTxz5sygXLlyweLFi7NkeRzchg8fHtSqVSvYsGGDfbxq1aqgRo0awahRo6Iu37Nnz6Bdu3bBvn377GPVo6uuuiro1atXjpYbecPcuXODChUqBEuXLrWPt27dGjRp0iQYMGBAuusdOHAg6NixY1C+fPmgS5cuOVRa5DXr168PKlWqFIwbN84+3rt3b9ChQ4fgrrvuirr8pk2b7PfTG2+8EXruiSeeCC677LIcKzPyDtWX+vXrB6+99lqq+tCsWbOY6zRq1Cjo27dv6HHv3r2Dyy+/PNvLirxr6dKlQdOmTYMyZcoEDz30ULrL3njjjUGPHj1Cj4cMGWK/k/S3L16MoUNUo0ePNi1btjSFCxe2j3WVUt0ov/rqqyxZHge3MWPGmMaNG4da13TVqVGjRraeRKPXO3fuHOpSp3p0ySWXmDlz5uRouZE3qJ6opcR12z7yyCPN1VdfbetVetQC8/fff5uGDRvmUEmRF3377bf2KrerBwULFjTt2rUzEyZMsC11kcaOHWsKFSpkOnToEHqubdu29u/Yrl27crTsyH3qprt69Wpz7bXXhp5r3769baX79ddf0yyv7t1qYQnv4n3RRReZpUuX2u5zyH+mTp1qmjdvblto9bcsPRs2bLAtctddd13oOdW9ffv2mcmTJ8f9ngQ6RK1c6gKnrpPh9FjjWZJdHgc/fe6lS5dOUx+i/TGUu+66K814pz/++MMcddRR2VpO+FV/1HVO3Z9ijVN46aWXzHPPPRe6sIT8Sd8z0f4e7dmzx/z+++9pllf3SnWtU1fxiRMn2oCniwiPPfaYOeyww3Kw5Mgr3z8aPxne3f/UU0+1YzCj/Q1TN/CiRYvabpaOwp/G0BUpUiTHyo28Q5/9xx9/bPr165fh2F13nhz+N08XoU477bSY50zRMCkK0vjvv//sz8gBwPoDF+2PYWaXx8FPVywjw5j+4G3evDmu9fUl9s0335g+ffpkUwmRl6meRKs/rm5FjqPbu3evHf+kVt7y5cvnaFmRN+tP5N8jV3+ifQdp3KUuAqgV+KyzzrJ1rFevXnYc58UXX5xj5Ube/f5x5zTufCecTr4ffvhhO2ZT6+rCwLBhw8wjjzwSdcw4Dn4VK1aMe1n3nRTtb160+hYLgQ5p6MtINNNXOH0xRZtDJ7PL4+Cmz1yDwiP/kKl+xFMf1q1bZ26//XZz+eWX2y4LyH9i1R+JVocGDBhgZzLUREyA6o9OsuOtP7t377ZdpN577z07m6q8+uqr5v777zfjx4+3J/LI398/kl44Uy8lXRRQLwJdYNL/dWEAiKe+6fsp8hw68nFGCHRIw10liOzapL7g7ipnMsvj4KYvIX3u0epDRidGulKu2S51iwNa5/IvfadEqz8S+Z2i1lzNhqowp1kxReNZ1L1OXV404yUn5Pmv/vz7779x1R/RxQCN13RhTlq3bm278Kp+MdNl/hLt+ye9v2GaIfX55583n3zyialSpYp9TjM7a9ydbtmjKeiB9OqbLjRpfG94F914zpnC0RaMNHTPJ1WilStXpnpejyPHJSSyPA5+6rYUWR90T6jIcVHhNAhdExfopEq3N4i8wo78I1b90X15Iv/A6QJCq1atbLcVjUXQP10Z37p1q/2/gh3ylzPPPDNq/VFdUd2KpOcir4a7CZqoP/mP6oMuLoZPiKMLBAp50f6G6bYqJUuWDIU50d8x3U+Vib2QEfedFP6dpYC3atWqdM+ZIhHokIb+sGncgAaGO6pYmvmpQYMGSS+Pg58+d3VVUlcm0R9CTTYQqz7oZEthTvdcefTRRxl3kM+pnugKt2tlUbduzZgbrf6ULVvW3t8n/J/uBVWuXDn7f+5jmP+onqiV9pdffgk9N2rUKHsvqGhXvDUbpmYkDJ+AYNy4cfZqefhJOvIH1RNNhvP111+nmnlXF6+jjY3SSffatWvt37Hwc6Bok8UBkTT5iepQ+Kzwmt1S5026j2a8uASOmLMO6qr3bbfdZqdd1QBfTTuvm/yK/vApsGkZtaRktDzyF4WzkSNH2htl6vYDmuBEJ9bqxiT64zdp0iR7a4NjjjnG3oBe4U9XNNVNztEJFePo8h+dYFetWtVOHd+iRQszc+ZM24L7wgsv2Ne3bdtmT9B1FTxaiwvyN50ctWnTxtxyyy32p8Y16ZYFH330UWgZ/Y0qU6aM/XulGS71ndWxY0e7vOqXlu3duzfddfMhTbrUs2dPO9GJZqvUmDjVB02So9tbuFtjaPZC3Z5AFyK/+OIL+32lm0hrTNTQoUPthW79jQMi/fjjj3aGeM0VIA8++KC59dZb7Y3INcPq+++/b+688057ESFeBDrEvGLw5ZdfmhEjRthZdu69917TtGnT0OsbN2603ZnchCgZLY/8RWNSdML02Wef2SuVCvs6MXdTgKtvuOqPrj5poLlO3iXyNheMwcyf1Or/xhtv2JMkXTyqU6eOHaOiqaBFJ1iqK2qFi0YXkugql7899NBDduzSrFmzbBdMtbDofpeO6pUuJjkKb/Xr1zdTpkyxJ+1DhgyhdS4fUzDThQH1LNGFRQW0ChUqpLqtjmvt1d8wfV/pooFugSG65YXqE1C/fv00PUXWrFljz40c/Y3T+ZJa6fS36+WXXzYXXHBBpg5eiu4uzuEGAAAAAP8whg4AAAAAPEWgAwAAAABPEegAAAAAwFMEOgAAAADwFIEOAAAAADxFoAMAAAAATxHoAAAAAMBTBDoAQJYaN26ceeWVV8yBAwfSvPbuu+/am6auX78+zWsjR460r4tuTK8bi4f74YcfUj3WMqNGjUq3LNG2k1/99ttvZvDgweatt94yK1asMHlZ5GcNAIiNQAcAyFJr1qyxoW3JkiWpnl+3bp3p16+fDXsKfeGCIDBPP/20mTNnTtRtvvfee6Zr165pwtro0aP59OLw66+/mubNm9ugtHXrVpOSkpJnj1u0zxoAEFvBdF4DACDT6tSpY3/Onj3blC9fPvT85MmTTdGiRU3lypXt/9u2bRt6bdmyZWbjxo2mXr169nGrVq1SbVOvKfRlVuR28qu5c+eavXv32tB84oknmrws0c8aAPIrAh0AIEuVLl3aFC9e3Aa6du3ahZ7//vvvzQUXXGBq1Khh+vfvb3bv3m0OPfRQ+9rMmTPtz7p164Za3woXLmyuuuoqM2HCBDNr1iyzf/9+2/JXq1Ytc95554W2+++//5qpU6faFkC990UXXWQOOeSQNNtxj1W2ihUrmokTJ9rwcPbZZ5uLL77YFCiQcaeVnTt3mvHjx9v3OuOMM+x7qQujgqiCqqirqVrC1EJZpEgRu09nnnlmaBvxlOHTTz81JUqUsMdn/vz55pRTTjGXXXZZXNuPNGLECPs+8v7775vTTz89FHS1LR07dccsWLCg/XxKlSoVWjdWObQPJ598sl1W296+fbupVKmSXV/HSC2wOkZlypSxxyicPvcpU6aY33//3T7WMjp+2veMPutoLY+qZ9u2bTMlS5Y0F154ob1oEE7bmjZtmj1exYoVs8vo+IdL9Dgk8nkAQFajyyUAIMvphFgn5o5ah3RSrZNd/dNJ//Tp00Ov6/86gVZIEAWGL7/8MsP3WbRokbn66qvtSf3atWvNvffeazp37hwavxe5HT0eOHCgadGihVmwYIENHW6djGjcWdOmTc3rr79uxwC+8cYbpmXLlrYL6c8//2yXUTi79tprzf3332/+/vtv+/yVV15p3nzzzUyVQQHi4YcfNo8//rjdzqRJk+LefrxWrVply//II4+Y1atX21CtbT333HMZlkP78Pzzz9uy/PHHH/Zz6NChg+nVq5fdL4UjPa+uk1rX+fPPP+0xVAj+77//7OPu3bubTp06Zbr8L730kmnTpo0NUgp0CqsNGzY0S5cuDS3zzz//2PDau3dvW34FryZNmpjvvvsuS45DVn4eAJCwAACALPbFF18EZcqUCVauXGkfT5s2zT5evXq1fVy/fv3g8ccfDy1fu3btoG/fvqHHrVq1Cjp27Bh63L9//6BSpUqp3kPLlC9fPvjll19Cz40ePdq+z+zZs6Nux63z22+/hZ4bM2aMXWf+/Pnp7lObNm2Cq666Kti5c6d9vG/fvqBLly523Y8++sg+17Vr16BGjRrBmjVrQuuNHDnSLvPTTz/FXYYWLVrY/V27dq19fODAgbi3H80HH3xgl9m8eXPoudatWweNGzdO9Zzb1qhRo9Ith9uHpUuXhtbt0KGDXXfEiBGh51588cWgXLlywa5du+zjW265JbjmmmuC/fv3h5aZMGGCXW/evHkxP+tImzZtstv98MMPQ89pmwMHDrR1zenWrVtQr169YOPGjaHn+vTpE9SsWTPYvXt30sch0c8DALISLXQAgGxpodPEG2o5E42ZO+uss2xXNTfOTs+Jurlt2LAh1N0yM8qVK2cqVKgQenzOOefYn3/99VfMdbR8eHc611UyvZkfV65caVsc27dvbw477DD7nLp1dunSJbSMWmvUHfOaa64JtTSKWmyOP/54M3z48EyVQV06TzrpJPt/HcvMbD8jy5cvN/PmzTM33XSTOeqoo0LPq2uqugsOHTo0ZjmcsmXL2q6ijtsftYA5el2tpWq9kmrVqpkePXrY7pV79uyxrWnqNpnRZxZJ3SJ1/NW1Uy2zom3edtttpnbt2vaxWu3UHbR169bmmGOOCa2rfVZX4E2bNiV1HLLy8wCAZDCGDgCQ5XRCq7FRmrVSsytq/Jyb8EQU3nSyrBkx1d1SY5PSGysVy3HHHZfqsRuTp7AQy7HHHpvqcaFChUJjrRQuImfgVJc9140vcmxUeCjTmDBN5qGuhhr/FRlAFB7iKYPjwm8i28+IthFZfkfjEBcuXBizHLGOvcpw+OGH23FkjhsTuG/fPvtT4UrdTdU1U90bFaKqVq2aZt8zcuSRR9oukn379jX169e3AVn1q1mzZrbeuYCo91UQC6f9ueOOO+z/3X4mchyy8vMAgGQQ6AAA2UKtcBqzpNCmk1uNM3LUiqIWlhkzZtiWL4U51/KVGfFMZBIp1pT96c2sqBP0jEKHey080DhqxQkPcfGUwU3sksj24xWtHHoft7/RypHR87FoHOXNN99sxwz27NnTVK9e3Zx22ml2DJwbk5YZCtoKcBqbqUlWPv/8czuuUfVMLW4uRGbXcciOzwMAEkGgAwBkW7dL3ShcE1Co5axmzZqh19QyU6VKFTuJhAKdTvTTk1P3TVMXTv2L5Fr81PVSQcTRpB6OawlSa9Ett9ySan1N2HH00UcnVbas3L5raVR318iWUbU8aSbMrKbZITV5yhNPPGEnTnFcl8lEPmu1CGoiFP3TxCc33HCDrXMKdAqL2pY+s3DqivnOO++Yxo0bJ3UcsvvzBoB4MYYOAJAtdIKsFg6d3OpWBZEtcOp2qZYZzRiZ0fg53XrAzVyZGxQO1OL4wQcf2Gn3XWva22+/bf+v4KCp8LXMkCFD7Pgs58cffzR9+vRJtxtoPLJy++piqPGGCj+60bgzcuRIO45P3WSzmrpJypYtW1K12ulG4u7/8X7WCmnq5hgeBlXX1GJ7wgkn2Me6RYFuBaGxbApxzhdffGFb8hS4kjkO2f15A0C8aKEDAGQLBThNgvHTTz+Z6667Ls3rGvOkk3JNKKHxSulRq5m60Kk7nU7Ar7/++hz/1NQCpElRdJsEncirddGFANc1T2O61FpzxRVX2BYgndSPGTPG7n94q1SisnL7uu2AbhegCUAaNGhgp/jX+MGOHTvaiT2ymj7DSy+91N5uQGFJ94vTxDhqrdUYQjdxSjyftSYn0dhL3T5BrXNHHHGEndxEN6hXWHMee+yxVPuoiUw0UcqTTz4ZuhddMschuz9vAIhHiqa6jGtJAAAySeObNNOlQlDkpBJqhXnttdfs7JfuJs1O5A3BRROrLF682JQvX97erFrLKDTqZNrRDa4HDx5sT8x14+5oNxaPXMd1wVMw0LbTo2U1s6FO+jWDo7rkaVZH3ZtOk3O4sVUaO6iJVNQlUKE2fCbOeMqg+56pRUtjxCJltP1odL87HT/d685NHONaxfS8xjjq/RRUwycRiVWOaPugcWwKuZpp0lFXxrFjx5q2bduGxpTp/RS8FOi0r+p6+8knn9jtuRaxyM86GtUf3TNOY/B27NhhLwzoMwifrVIUDtUSrC6Umu1SrcHhs1ImcxwS/TwAICsR6AAAyIALnxoHGD4WUN1J1cKjcYJMggEAyA10uQQAIAMam6UJUAYNGmSaNm1qx2lpunq1zPTr148wBwDINbTQAQAQJ3Vd1D91kTzxxBNtd8DI+7EBAJCTCHQAAAAA4CluWwAAAAAAniLQAQAAAICnCHQAAAAA4CkCHQAAAAB4ikAHAAAAAJ4i0AEAAACApwh0AAAAAOApAh0AAAAAeIpABwAAAADGT/8PoAXZzD331YUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 900x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3QAAAJICAYAAAApX6bZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlcelbwAAAAlwSFlzAAAPYQAAD2EBqD+naQAA1DhJREFUeJzsnQd4W9X5xj9tyXvF2TtAEkIgCYGEUVYg7L0pYbTMQv8BSguUUaBAoZTRMgqUXfaGNkAgjBL2hkyynWnHe2iP//N+jowsy4ksS7YlvT+ecHzvObpLV9J577cMoVAoJIQQQgghhBBC0g5jbx8AIYQQQgghhJDEoKAjhBBCCCGEkDSFgo4QQgghhBBC0hQKOkIIIYQQQghJUyjoCCGEEEIIISRNoaAjhBBCCCGEkDSFgo4QQgghhBBC0hQKOkIIIYQQQghJUyjoCCGEEEIIISRNoaAjJI048MAD5bTTThOv1xuz/8gjj5R77rmnS9v88MMP5bHHHmu3bu3atXLRRRfJYYcdJmeeeab89NNPuu+vvvpK+1evXq3L//vf/6SvE30upHcI3zOffPJJt7cV655NRxL9HPXm+cc65kx5P/oKqbqe6fS9TQjpGuYujieE9CIVFRXicrn0x/7cc8+NKV5qa2u7tM2rrrpKZs6c2W7dn//8Z1m6dKn8/e9/l7y8PGlpadF9O51O7R80aJD861//kn79+klfJ/pcSO+AhxCR91B3iHXPpiOJfo568/xjHXOmvB99hVRdz3T63iaEdA1a6AhJM2BpwhPWTZs2pWwflZWVMmHCBJk4caKMGjWqQ7/VapXhw4dLTk6O9HW2dS6E9Bbp9DlK52MmrfC9IyRzoaAjJM0wGo3yu9/9Tm677bZtjg2FQvL888/LGWecoUIQVr3333+/rR/uN3V1dfL666/r34sWLdJ2+fLl8vHHH+vfb7755jZdd/7yl7/IQQcdpJawMAsXLtR1Dz74YKfHF94O9nXFFVfI4YcfLqeffrq8/PLLXTqPFStW6HZeffVVdas89thj5emnn+70XAKBgG7v17/+te7znHPOkblz5251e3ht+Hg/++wz+eMf/yhHHHGEnHjiifL444+Lx+ORe++9V4477jg54YQT5IEHHtD9hHG73fp0HOd36KGHykknnaTusXhd5LX4+uuv1ap41FFH6X7/8Y9/tI0BwWBQnnvuOb0W2P9vf/tb+eGHH7p0vTpjW8fY2XWJ97hAdXW1nh+uG14Py2nk+XX1noVFGsdy2WWXddjX3XffrS7KybzXAFyPcX44T5zDtddeKytXrmw35pJLLpFjjjlmq9c7+nMUzz0Q6/zjOe54769tnVv0MSf6fkSD+wf3xM0339yh709/+pP86le/ivs92tp9uq3zi3YvD3P//fe3ez/juQdisa3Xdfb+buuz2ZXPQFfut/C+sQ7vD/Z766236rXFayO/4yKJ9/O2te9iPIzDb8gbb7zRNh4eKgcffLBuM5I//OEP+p3c3fuDkLQlRAhJG7bffvvQX/7yF/37iiuuCH3yySft+nfZZZfQ9ddf37aMMTvvvHPoySefDC1evDj0yCOPhMaPHx/697//rf2rV68O7b777qHf/e53+rfL5dL2kEMOCf3qV7/Sv5uamkI//PCD7vvDDz/U1y1dulSX33zzTV2urq4OTZs2LXTiiSeGAoFAyOPxhA4//PDQjBkzQi0tLZ2eT3g7OO4bb7xR9/PYY4+Fdtxxx9Add9wR93ksWrRItzN58mQ9xv/+97+67VjnguM7++yzQ7vttlvo5ZdfDi1YsCB033336fb+/ve/d7q9FStWtB3vrrvuGnr88cf1WG666SZdh3PFOfz44496DjvssEPoqaeeajuHM888U6/JRx99pNt55ZVXdDt4TeS12HPPPUMPPPCAbvvVV1/V88Y+wuC9wuteeOEFPc6//vWver2+++67uK9XZ2zrGDu7LvEcV/j8sO7ee+9tO7+JEyeGrr322oTvWb/fH7r77rtDY8eODW3atKltO1i/xx57tH1eknWv4XUTJkwI/eMf/wgtWbIk9O2334bOOeccvf8j73UcS0VFxVavd/TnKJ57INb5x3Pc8Ww7nnOLPuZE349Y3HDDDXpvud3utnX4zOIewTHF+x51dp/Gc37R33Vh/vznP4emTJnSpXsgmnhe19n7u63PZlc+A12534LBYOiMM84I/eIXvwjNnTs39PXXX4cuvPDC0Lhx4/S1Pp9vq/f21j5v8XwXz5w5MzR79uy27eJ9wXYnTZrUdm1wv+AeeeKJJ7p1fxCSzlDQEZKmgg4i6qSTTgp5vd6Ygu6zzz7T8fhRi+S2227TH7+6ujpdxg9+pAgERx55ZOjiiy9uW96WoAP4sQ/vD8eIycVXX3211fMJb+eCCy5ot/6uu+7SH2BMTuI5j/APdKzJYvS54McbYz/44IN24+6880495lWrVnW6vfDxXnfddW3rMJmAeDv66KPbjT3uuON04gO+/PJLfR3aSK655prQ9OnT2207PEELc9VVV+mED2AyhTGvvfZauzG4D84777wuve/RxHOMnV2XeI4rfH6R4g1ceeWVeg92556FcMJ78OCDD7atw8QX28KELpn3GibjmJTioUUYp9Opk+/IdfHQ2QR7a/dArPOP57jj2XY85xbrs5/I+xGL8PfMW2+91bYOE31sKyyOu/N9EM/5xSPoEr0H4n1d9PWM57PZlc9AV+638Pd65L4hpA466KC4BN3WPm/xfBfjPYTAhbAEWD7wwAP1dd9//307kbdu3bpu/14Qkq7Q5ZKQNKW0tFTdSZ588smY/R988IG2GBPJ/vvvry40n3/+eVKPBy4scNmBO84jjzyiLjRTpkyJ67VHH310u+VDDjlE/H6/fPrpp106j3hi5D766CMpLCyUffbZp0OGULh9zZ8/f5vb22WXXdr+ttlsmmwFcXqRYB9wnQJjx45VN6Jhw4ZpBju4aCLxwbx58zThTCQ777xzu+Xy8nJpamrSv8PHBjekSO677z655ppruvW+d+UYo69LPMcVJvqeQKKGsFtZosc+dOhQmTp1qrz22mtt6+Cmtf322+t5JfNeQyymz+eTs846S+bMmSONjY3icDg0rgwxSslga/dALLpy3ba27WSdW1fej0h22mkn2W677eQ///lPu9ftuuuuus2unmv0fZqs80t0O4m+Lp7PZqLXfFv3BFwVy8rK9D0IYzKZ9HMTD1v7vMXzXYz3Fd+jCAcAeN3xxx8vRUVF8uWXX+o6uJDusMMOMnjw4KT/XhCSLjDLJSFpzKmnnqpxCYg9wI9wJFVVVWIwGDTuIZJwzAPiE5LNxRdfrBMKi8Wigi5eBg4c2G45nIUN8VbxnAcmOgATj22xefPmDtcK9O/fX9uampq2dZ1tLzc3t8M6u93eYR28IAASSNx+++3ywgsvSHFxsYq/yZMna5xKZOwewAQvOmYSk5vwsRcUFHTYV0lJSbff964cY/R1iee4IvfT2fl1555FfNOVV16psZujR4+Wd955R37zm98k/V6bNWuW3HnnnfLEE09ozBLG4zohTineSe622No9EIt4jnvkyJHb3PYvfvGLpJ1bvO9HLAGAuC8ICkzAEa96ww03dOlcO/s+SNb5JbqdRF8X72cz0Wu+tXsC1zNWVszw9+W22NrnLZ7vYpwnxBvKneDhz5IlS/R++O677+SLL77Q2EoIQ8QVgmT/XhCSLlDQEZLG4EnppZdeqglS8IMfCSbX+IdA+ljEmmx3lzvuuEOfNOMJLCYuSGYQD83Nze2W6+vrtcXT23jOY926dXEfI8RYePuRhK1pECbbAhOGroAJ3DPPPCO33HKLBuCHue6667q0HVwHPJHH5ATvfWdjEnnfu3OM8RxXPHTnnkWihBtvvFGtO7D0IHkCHnQk+14DmDziX0NDgyZ9wHWbPXu2Toz33Xdf6WniOe6NGzfGta1knVu870c0sM7ge+Tdd9/VY8DDIWyrK+e6te+DbZ1f+LMdfhgTJrr2Z6LXKZHXxfvZTPSab8sTZNWqVR3Ww7oYD1v7vMXzXYzvk7333lsF3ZAhQ9QbYscdd5Q99thD7rrrLk0ogwQssMCBZP9eEJIu0OWSkDQHrjB4ohp2P4l0C8QPOn5Q4dIT/ocfP1jPUBMsEXHSGZhEwI0IAhOZyjABiXRf3Brff/99u+Vwhjm4EMV7HvGC7eHpcHRGOlgCwm5RyebHH3/UCVvkZAwsWLCgw8Rxa+DYIJrwBD4SZNQ8+eST9e9Er1d3jjGe44qH7tyzsGJgQguLBTLpTZ8+PaYVIZn3GialmJz/85//jLntVBF9/sn+jHT13LrzfkQDiw0m67Au4ftkxowZ7epHJutcOzu/sLUq2s0Y2+/KdhLdf6zrGe9nM9Frvi13TJTIiX4g8M0338T1+m193uL5LoZYQyZOuJvidRB5ODdYcWHNxT0TdnlPxWeBkHSAgo6QDODyyy+Xv/3tb+3csvBkFq4lV199ddsTyfXr12tacLi94CknwGQJT0kR2xH9FDpe4MYCNxj82CJV9EUXXaSxG0gjHc+T3EcffbQtxT3ShiNFNo5/xIgRcZ9HvKCsACY5iEGBy094YoSJwe67764uPslm3LhxOsnAhATgOmMih/3iuneW+jsaFBuG6xzOPewaChckxCziKTZI9Hp15xjjOa546O49iwkvXgcXLMRzpuJeQ6p4pG7fsGFD2zbDadVx//QE0eefrM9IoufWnfcjFnAdxMMgvE/RpR+6c67xnB/uA7gJvvTSS22p+5HeHu593b1O8b4u+np25bOZ6DXf2vclXBPhbRG2tuF6hI9lWw8Et/Z5i/e7GN8h+G3DtQpfJ7iUDhgwQN566y3Zb7/92o4j2b8XhKQLFHSEZAD4ocKkGjEnYfBEF646+MHEU9u99tpLWzzJRL20MPhRxY/innvuqZPwroInxPhBhpslXIJgLYTbJWrT4ekrXIC2BYLiL7jgAn3qioB3PJXHtrpyHvECNx4kksHTbLg4YZ+//OUvdVKAJB7JslhGgrgruJLhHDE5wTngiTPqUYFoy1Zn4Lo+9thjkp+fr9cM20H8DY7/wgsv7Nb16s4xxnNc8dDdezacPAPvLZL0pOJew3VC8gXEPCEmChNM3DeYME6bNq1LdegSJfr8k/UZiffctnU8XXk/YoH3BPcUvtdgrYukO+caz/nB+oNluBniHsE/iLvImOBEr1O8r4u+nl35bCZ6zTsD7osPPfSQCi4cI44ZNT7xewN32G25WG/t8xbvdzG+V5BMCb8xkdcJ48EBBxzQti7ZvxeEpA29nWaTEBI/SG9dW1sbsw+ppNFfU1PToQ+psTds2NCuxEEkjY2NoY0bN7aloMbYqqqqtn6k1Ma2sZ3IZdROwmvwd+T4MNjmmjVr2lJOd5ba+p133tFlbGNrab87O4/o44sk+lwiQY2rrmwv8rwjQcrw6OteWVnZriZUuMQB1qH+EsB+w/XxOts20mzjGsY69shtxWJb73ss4jnGWNd5W8fV2fnV19fr+u7es2GQTv2Pf/xjyu61MFiP8+zs3oqnDl30NenKPdDZ+W/rMxLPtrd2bp1tp6vvx7bA/vEZ2hqJfB/E894BfGdhDO7P8H0d/X7Gs51E9x/rem7tsxnPNe/O/QbwHYfjAiirgBpxndHVz1tn38WR+8axRv6WhL87OiudkOj9QUg6QkFHCOk1on/0CekOc+bM0TpfsSajvNf61vtB0uOav/fee6EZM2aEPv/887Z1qNU3derU0EMPPdTp6/h5I6RnYZZLQgghaQ0yIiLGB0kSkPkvnJac8P3IFlL1GYDbJ9wWzz//fHVnhBsk4tlQLuDss89Oyj4IId3HAFWXhO0QQkiXQXA/sqchVia6Phkh8YIJZrgWXnRNLd5rffP9IOl1zZF4pba2tq2UAWKltwa/2wnpWSjoCCGEEEIIISRNYZZLQgghhBBCCElTKOgIIYQQQgghJE1hUpQIvv32W62phdoqhBBCCCGEENIb+Hw+TUQ0adKkbY6lhS6CLWUcOlwkrEOAb7bljwkGQ9Ls8mlLUku23mOk5+A9Rnh/kXSG32Ek2+6vUCe6JBa00EUQtszttNNO7S6S0+mUxYsXy5gxY7IqE9/ydfVy9Z0fyp2X7CNjhhT19uFkNNl6j5Geg/cY4f1F0hl+h5Fsu79+/PHHuMfSQkc6ZUBprlx91m7aEkIIIYQQQvoetNCRTslzWGT3CQN5hQghhBBCCOmj0EJHOqWu0S0vzPtJW0IIIYQQQkjfg4KOdEpNo1uemLNYW0IIIYQQQkjfg4KOEEIIIYQQQtIUxtARQgghhJCUEQgEtKZWd/B4PG2t0Uh7BEkunh6+v5BZ32QyJW17FHSEEEIIISTpoIbWpk2bpL6+vtvbCgaDYjabZcOGDRR0JOkEe+H+KioqkgEDBmjx8O5CQUe2muVyz4mDtCWEEEII6QphMVdeXq61vbozcYWVD9YTm82WVMsGIT19f+FBB+reVVVV6fLAgd3PKE9BRzoF9eeuOGMqrxAhhBBCujxBDou50tLSpGwP2O12CjqSdAI9fH85HA5tIerwGenuPumETDrF5w9Kdb1LW0IIIYSQeAnHzMEyRwjpSPiz0d34UkBBRzplzaZGOevGudoSQgghhHSVZMQHEZKJGJL42aCgI4QQQgghhJA0hTF0hBBCCCGEdMILL7wgn3zyyc+TZ7NZCgsLZerUqXLggQemPCviP/7xDxk+fLgceeSRMfvvvPNO2W677eTwww9PaNtDhw6Vo48+Omb/PffcI0OGDGnr37hx4zaTeHzwwQea9OPQQw+V7uL1euUPf/hDzL78/Hy54YYb9O+1a9fK888/LzU1NTJp0iQ59thj2+LSnnjiCdlll11k4sSJkqnQQkcIIYQQQkgn/Pjjj/LNN9/IjBkz9N+ee+6piV5uvPFG+fWvf62iI5VATC5evLjT/o8++kiWLFmS8LYXLlzYaf+UKVNk++23179ffvll+e1vf7vV7dXV1cmf//xnFbvJAGI5fN3D//bYYw+ZN29e23VfvHixil0sQ7Q98sgj8pvf/KZtG5MnT5arrroq5e9Tb0ILHSGEEEII6fMEgyFZW9kkLk9Qch1mGVKeL0Zjz8TowRp02GGHtVsHEQHL1X333SezZ8+WTGT69Oltf69Zs6YtG+TWLH4QXf369UvK/mENjb7u1157rQwaNEiuu+46Xb799tvliCOOkCuvvFKXISZhHfz6669VkE6YMEEGDx4sTz31lJx11lmSiVDQkU4ZNahQXr71cDH1UIFFQgghhJBYLFtbL29+slI2VLvEFwiK1WySof3zZea04bL9sOJeuWgQCccff7w888wzarmCNSmW+yPW7bDDDioy7rrrLhUYsCpVV1fLKaecon3vvfee/PDDD1q7Ly8vT37xi1/IPvvs025/c+bMkc8++0wKCgrkl7/8pRaljsWqVavUmobtjxw5Uk444QQpLt76NXr33Xfl448/1kQdRx11lOy8887tXC5zc3N1zObNm+WSSy6Ra665RkpKStpto7a2Vl566SV5+umn2wk8bKuhoUE+/fRTTdeP7YfdH7Gd5ubmmMcEV8voc1ywYIG6Vj700ENtqf9PPvlkGTduXNsYuJDiPFAkHIIOQPBB+J1++ukqEjMNztRJ5zeH0SAWs6nHnn6R3nnaWbGpURavqtUWy4QQQkhf4qeKOvn3m0tk5YYmycuxyMDSXG2Xr6uXJ+Ys0v7eYrfddtN6eytWrOjU/RHrli5d2ubiePnll4vL5VIrFoTWTTfdpP/gxrn77rur+Dn33HPlww8/bNvGiy++qMJx/Pjxsnr1arUMwmIWDaxSxx13nMawwdUQ+4WFCzFmnQHx99xzz8mOO+6o4urUU09tc/H8/PPP1SUTwhD/ICZhgUO9tmjefvttPQdsJwzOF+6O//3vfzWODXXXIGKXLVum/RCu0S6V4X8QkdFAHE+bNk323nvvtnUHHnigis4wr732moo2xNKF2W+//VTgfvnll5KJZJ5EJUlj/eZmueeF7+SiE3aRwf3yeGUzDPwAvv3ZGnVf8foDfeJpJyGEEBIJHjTit6rJ6ZWBpQ6dqMP64rCZZXC/XFm/uUX7xwwp6pUH0OGi6UjGActcPGDc73//+7ZliDhYjpD4BCChx/fff69xe2ErHaxhjz76qJ4/LFL4B+vZX//613bbvv766+WMM86Q//u//9NlWOdgPYR16u677+7U0vjggw/qdYUY/Oqrr9QaF2n1GjNmjIwePVqtXtEukGEglmB9jAYJZOCWCgsmLJoQa2+99ZZeB4ixeIEInD9/vvzrX/+K2T9//nzdD67bzTff3E7kQRyOGjVKBWqkG2mmkBGCrrKyUk3YePqw6667yoUXXihWq7W3DyvtcXn8smBFjbYk88Qcnmo2OX1SWmgXm8UuHl9An3ZWzmmRWYeOp6gjhBDS66yratIHjyWF9g51u7BcWmDXfowbNqCgx48vXBS6K258EEeRQNQgNm358uVqfYNwaWxsbBevhjGR+4DFCVa1SGAphEUOAgpukWFgycP2OgOWs/C1RYv4NFgJuwrE3k477dRhPdwrw5lA0ZaXl3fqZrmtbKNwp4y0zkUyZMgQueyyy9QiCmGL/ey1115t/XDfRJbOTCQjBB1MuVD5xxxzjNx7773y2GOPqamaELK1p50+fboZ/hLvK087CSGEkDAtLr96kZRYbBIKBTtcGJvVJHVNbh3XG4RdGUeMGBH3ayC4InnzzTc1MyTEIeLpYOWKdjeMfg1cH5uamtqtg0shQBbIYcOGtevbWmkFm83WbhnzgmCw47XeFnDzjGVQQUxgZ9vvSgwdrIZbK4UwYsQI/Ye4OYhYWCQjBR3OE8eYiWSEoMMNfNFFF6mv7HfffacWO0LI1p92lvbRp50ksx4e4D7CRKunM9IRQjIDfHcgJABeJFZzx+8Pjzeg8f4Y1xsgzg0xY2VlZbqM2md+v7+D5awzIGYQUwdDxMUXX9z2uzx37tx24xB7Fi0kYa2KBJY1vL5///6dukWm2v20q5Y9xNB1Vk4gUtTifNevX98hUQxee+WVV8qZZ57ZzjoIIYhyE9HvA9xGM5E+JeggxGAmhX9tNAgw/eKLLzSjDXxvI7P14I08//zz9cPkdrvl2Wef7eEjJyT9nnbCzTIWvf20k2QGjNEkhCQDPAhCfPfydXXSv7j971YoFJKaRrd6lGBcT/PGG2+o8ELcVmQ8GjIx4tggrhCzBSGytbptYctcWMwhqce6deva3DnDVrwLLrhAY+kwX0YiEyxHkpOTo66YDz/8sBxwwAFtVj3Ex0FkIiSpO1gslq2WLUDSlHCyk3iJN4YO1xRWxsiEKwAWwY0bN6qHXjhOD+IXSVhmzpzZbmxFRUWXYvbSiT4j6Dwej/zud7+TlpaWDoLu3//+t9xxxx2aAhZvBv7GOqhs3Fi33HKLBoDCxPrkk09qsGS4FgVJnH5FDk2IgpZk5tNOuFn2taedJP1hjCYhJFnAqo9kXZX/bZGNNS4pK3KI3WrW3yqIuYIcq/an2vqPcgLhuDSII8S6wUMM2SkjrUaoc3b22WdrYhOIL4iNyGyL0cDKhvntFVdcIa+88oomV4FxAq+JzEyJcQgtQqKSb7/9VmutnXbaaR22h9pssPbBNRGumzhOWLHuv//+bl8DxNpBNP3qV7/SpC4QoZHgOkBoQohC/CUTCFxYACFao7n11ltV3OKckVgmnEwG8XSRr8d7CMGbifSJGRtuWFx0ZPSJVt6od4E3Cpl8wh+YSy+9VD9AqASPWhu4ccJPHVDNHuqbgq77FObZ9EuSZOrTzvp2MXR94WknSX8Yo0kISTbIvPzLQ8a21aGrb/Log0f8VvVEZmZkikQmyshYLCTcGDt2bIeYMdRcQ005lA+AVxmMDUj7HxYiKEAeXRPub3/7m46B4ICFD0Jp5cqVKsYAslTCjRKlAjAOrpmR82XMi8Mun3A1fPXVV7WmHSxVWI+kJJ0lbcG2o+PzMKdGjF7473A/4tGQmATJT3D+0SB7JMZ+8MEHbZawWNuPtS4e10zM8WMxdOhQPWdY8WDxvPrqqzu4o8LCicSJ0eszhV4XdDBDQ1FDrJ100kn6ZkQyb948DaaMfPpx4okn6hMQ+MLihsINC2EHUy8+QPAhJt2nodkjny3YJNMmDFBxRzLsaeecFk2Agpg5uFn29NNOkpkwRpMQkgq2G1okg48YJzVNfnF5gj0al4vYrFjZGzsDYmX//fdvW4600CFhSSwg0CJFGrzQwvFekWn2Y81xIxN/ALgdwpoWD7FS+Eeui+6PPs5IIG5hHUToU1jQbWv78QKRG20RjMRsNnd6zkjAgjp+SMCSqfS6oMPTC9TVgGpGjYxofvrpJxVqkWAZbw7Su+J1eDIB0zaeXiC4FCUMEgUWiugMOCj+GNlmC2s3NWodusFlu4tlUEFWWBY2VLdIi9svuXazDCrL7TFR09P32JAym5y4/yh576v1snZzs/gbgmI2G2XkgDzZf9fB2p+pmaCylZ66x2rqm8Xt9UlRrkUC/o6xFmYj3Hp9Oq6soNd/gkiSyNbfSbL1UBrM1RAas7W4q67Mz/CbPKQ8r82zBFkvk7BpkkQQNgXXUcQOYo7eF3jppZfUugch2dm9iPsr3Cbjfo0H7AefEXxvxsoqGo7DjIde/zWFfzH+dQay5eTn58fMehOuqYGK9kceeaQGicKU2p0adHDfXLx4ccy+sOk7W9hQ25p1CNZPT0Nm1/VbX+OVr5c3y+YGv/gDITGbDNKv0CxTxuTJ4NKeO/eevsf23F6keoBd3N6g2K1GKSsQ8TVtkMWLN/TocRDJmHusqsEnfq9Hquu8YrN0TJPt8QXF5w3Jpg0VEmzJzHpA2Uy2/U6SrQOrCYRdMkn29kjy+ctf/iK1tbUaC9gX6Nevn8YoxnM8PXl/YV+Ix4R7bWfEq2l6XdDFQ7Q6DdfSiFwPt8zoOheJgCDO6IKPUM74kUJtC1gUswXbBgjmKrWIjspgC93ydQ3y2efLpclpkn4lOWKztCYMqWvyyGfL/XLyiBEyZkjXfL27SrbeY6Tn6Kl7bIdgSJZuWiwrNzRKv/ycDjGaG2ucsv3wAtl7t3F0680g+B1GYk1WEWuFeDPEfnUXfH9gm9hevFYL0jtg3hjtXdeb/OIXv+iz9xceeqBmYHQtQABPxLi3I30cBGVG194IF1KMttwlA7yJsTLoAEyCOuvLROz2Vgsdvogz9bzhZvnhdz9JizugiULCH2KL1Sy5OVaNMfvwu0qZMGZAj0w+e/oeY52w7KMn7rFD9xotT8xZJJvq3B1iNAvz7Nqfl9e+aC7JDLLtd5J0Dh6+4x/qsuFfdwm7weF3OhnbI6S37y/sB58RfG/GeujRFWHZ5wUdrGWoDB8JnjLjJLfbbrteO65sACntJ4wujZnaPlPI5gQOrBNGUgUyzs06dLy8/dka/fygrmFPZqQjhBBCsok+P1NHlqCbb75Z5s+f35bFB8GNSB/b1ZSnpGsM7pcnt1zYPnNSppGtRbZZJ4ykGog2CDg8DMHnpycz0hFCCCHZRJ8XdAMHDpSLLrpIa26goCLKHKCg4lNPPdXbh5bxwB0vEAyKSd0mDBnpApiNRbZZJ4z0FPjsZpplmxBCCOlr9KlZ6rRp02LW10D19912202++OILrclxyy23bDUzJkkOKzc0yCV3fih3XrKPPmnPRBfAbCyync1upoQQQgghmUafEnTRhREjmTJliv4jmUlvuQBmY5HtbHUzJYQQQgjJRDoWCSKkl10A4foIAYUWy1iPfoxLZQIHWOKaXT7ZVNOiLZZPP3RcxiVwiHQzjUUmupkSQgghifLII4/IHXfc0Wn/nXfeKS+88ELC249+/dq1a6U73HrrrfLGG2902t/d7cdD5D66e33i4Z133pH//Oc/Sd3mihUrNI/HZZddpqFeqFUdCepf4764/PLL5eGHH25Xw+7RRx+Vb775RnoKCjqSVi6AqQKi7TfH7ywXnbCznHv0RG2xnGliLtLNtKbBrW6lkYTdTNGfSW6mhBBCSKL8+OOP8uWXX3bav/fee8vEiRMT3n7k659//nm55JJLpDt8/vnnndYwS8b2t0X0Prp7fbZFTU2NFjPfY489krbNH374QY4//ngtJwAPwmeffVbOP//8tnnTpk2b5Mgjj9Rai3vuuacKyl/+8pdaKDwcRvbHP/6xxwqV8xE86XX6igtgtiRwyEY3U0IIIelPKBQUb/U6MfhcYrTliqVssBgMvW+b2HXXXZP2eiT/CwaDkipSvf1Y++ju9dkWd999txx88MFJza8Bq+LRRx8tl156qS5PmjRJZs6cKV999ZVMnTpVnnjiCSkvL5e//vWvanxA8XL8mzdvno4bN26cjBgxQsedc845kmoo6EinDB9QII9ec5AU5nWsXp9MsjHTZG/DOmGEEELSCc/G5dL09VwJ1m2UUMAnBrNFrGVDJX/SgWIfNKZXjw2T/yFDhsgJJ5ygLnjI+QCrEUpuocTW6aefLvn5+fLYY4+pZWfy5Mly8skntxWwDr8+Ly9P3nrrLamtrZULL7xQbrzxRiktLZWffvpJXnzxRd3myJEj9bVlZWVt+//ss8/U3RAugccdd1ynx/nmm2922D7cSSdMmCALFy7U7c+aNUt22GEHHQcrFY4Xx7XPPvvIgQce2LatJUuWyCuvvCKbN2/WYz/xxBO1jbUPiJrw9QHLli2Tl19+Wbc9bNgwPR9ktQe4frvssou+/tNPP9Wi2xBWnYnC6upqefXVV9UqGGZb2/j9738vzc3N7bYDyxtE6NVXXy1Dhw6VM844Q69DGCRthHDDMYN169bpsYc9yyAmi4qKpKKiou01RxxxhCZyxLasVqukkt5/rEH6LBazUcqKHNqmEroA9g7Z5GZKCCEkfXFvWC71HzwjvspVYnDkirl4gBjteeLZtEJq339K+3sTxEpBdAG4Zl533XXy8ccfy3777aeuj5jQ/+pXv1JxBtdDWHUef/zxDq8fO3as/oMwOPbYYyUnJ0fdJ0866SQVfxBVsH4ddthhsmbNGn0txAwsQP369VPBcv3118uqVatiHmes7eN4r7zySrFYLCoWcYzYxl133aXLEHEQOyghBusT+PDDD1Wcwb0QVikIQQhJiJxY+4i8PhCeKEMG8YnzQZzaUUcdJUuXLm27fhBV//vf/9RV0+VyqciEgIzF22+/reeOfYbZ1jZw/XBskf8g+CDAwjWu99133zaRCSCoIcrCohCCEdY6nDv4+uuvVVzCkhcG26ivr9cs/amGJg/SKUgO8th/FsmZh4+XAaW5KbtSdAHsPbLFzZQQQkj6ulk2ffuOBN1NYoKQM5vVKmKwOcRgHST+2g3abxs4qk+4X4Li4mL529/+pn/DMgULFAQRhBmAiIHgO/vss9u9DgIKbnpIKDJjxgxdBwsXxCBeDxC3NXv2bLn99tvV1RAtYrt+85vfaD/iyODyF4tY2wcQQ//3f//Xtrz//vurSMSxhwUQRBIsdgcccIDGq8Eid80112g/xBBcHiH8OtsHgAC86aab9LzDrox47Zlnnim33XabJhYBEJU4N7zPEHuoPz137tx2oi0MjmvHHXfssH5r24CQjCYQCIjb7daYuUggXv/+97+rBRMJUsIiDyIdlkZcG2zzu+++U2EcaUmEmEW5NRzj1jL5JwMKOtIpyPT48Q8b5PgDtkv5VaILICGEEEKi8VWvF2/1WjHmlyBTWrs+TNZNeSXaj3HWfkP7xAXcaaed2v4Ox3VFio6CgoJOLU6R1NXVqWiAy+OiRYva1sOtr6mpSS1xcHmEFSrM4MGDYwqfrQHREQkEj9frVREDSyCsaw0NDSp64Ma4cuVKueqqq9q9JvIYOgPWSrw+0nUTHHTQQSoSI69f2JURLWLVol0kw2zcuFF23nnnDuu7so2tMWrUKLVYfvTRR/KnP/1J309Y3uAyikQop512ml4/bP/BBx9Ud1u4sIbp37+/HmOqoaAjfQaIOpQKQDZLJEBBzBzcMZmcgxBCtg1Ku/D7k2QaQU+LhPw+MZptHTIzA4PFJqHmOh3XV7DZOuYeSCSGCuIHwNIFq1ckRqNRWlpazzk3t70XFdwduwIEZiSvvfaaWqPghglxCIECUQngvhjrNfEAkRjrtViGC2Y4kUr0+UCQxXrvw8djsVg6rN/aNuKJoQuDv/EP1wCC9B//+IdMnz5dLY2wlMK6GLacwsr55z//WTNiRr7vsPylGgo60qfIRhfA1klYs1Rs9kh+abOMGeagiCWEdImfKuq0XidKvCBrMBJNofwIMtYyLpakM8hmiQQoIb9HxNRRFIV8Hu3HuEwgsnxTOBEHBFq0+yJAzBb6EbsWaWWDRSiWG2L09mMByx+sb3DxvOCCC9rWv/7669oOGDBABRRi+WJZxra2j7ALJyyMw4cPb1u/evVqjYODSO0qSA7T0NDQpdfATTK6physj1iHGDoIT7iE/vrXv9ZYuTA4RridQkhDSEYmTQHIbIkSF5Eghm7MmNQn7ekbzsaEZPEk7N4Xv5cHXlskb35Vry2WsZ4QQuL9HnliziJZvq5e8nIsMrA0V1ssYz2/T0g6g9IEyGYZbKqLWTs10Fyr/RiXCUAshWuZITsj3BH/9a9/tVnrcM733HOPWoogZlADDf1h69f777/faQ266O3HAuII/ZGCC0XBIeAgeJCcBTF6Tz75ZJvlCeuvvfZaeffdd7e6D7grwjXzgQceaLP0IWskLFoQWYkAl8jVq1d36TVwKYVAjv6HJDaw7MGqhuuAaxw+D9SbQ0IXjMN5QMwhsU1YGDY2Nmo/rHeR4NiiXVpTAS10pFNQn2zWoeO0JambhDU5fVKUaxGzmMRub52EoUbcrEPH88k6IWSbFn5Y5vA9MrhfbtuTcZSAwTJqTaIf7ux0XyfpCBKdoDSBr65S/HWbxFBQKkarXS1zEHNGe772pzohCuLGkIY/GiQlSSYoaXDvvfdqkWq4/+Ef9nvIIYeoBQgxbRBMGAPg+ocEJhBZiJ+D8NiaRSh6+7GsaMj6eMUVV2hmR1gBzWaz1l4LZ9ZE33nnnadic/vtt9ckL3AJ3X333be5jxtuuEGTuCCODkIHcXoQeZFJWboqzl544QUVtMksDYCYPlgpcV3hcgnLHN6DcMF0lJqAyyWSweDcFyxYoBlMkRglDBLDVFVVacxdqjGEOnNKzULCZtLIYFbgdDpl8eLF+kFCxhpCkjEJgyUO4g2TrmAgKA2NDVJYUChGk1EnYZiAoYwAJ2EkGfB7LDOp2NQo97zwvVrkYtXxdLn9muAKZUlS6c7O+4tEA+sNknYg62F05sCE7rF1S6Whl+rQYX5YWVnZqaD4/vvvtc4crDZIZR/+W4/b6ZRPPvlEpk2b1haHBgsaEp5AJIHo1yARCoQZ3P3gAoipOpKoQBzAKof5aKR7ImK/IDiwL6TNx1jEpXVmGYrcPhKewKVzu+3aJ8DDMcJ6BpGI9xCWJrhKokwBwDFBxMByCMEDS1ln+8DfkeeH44WQwzXA68KumLGuRWfrwsBCdtBBB8kf/vAHFVeJbCM6y2W4PiCOE+UUcJy4lkhwEv0anBtEL65BpFUTwBKJWoSwZibyGelMl8SCgi6OC5etP1SYBCxcUS07ji6TPEfHgFOSvElYwB9oE3Qms6nHJmEke8jW77FMZ/GqWnng1R/UzTLWwx88PEIJGtSaHDeyNdteKuD9RVIt6DB5drmcYmqpFYPPpTFzcLPsK6UKSO/x7LPPajFzFG5PlFiCrjtgexCYsEhGu2GmQtDxU0A6BZOAPz/6hbYkuSCLJxIX2CyxvzRsVpP4/AEdRwghnYFswEiA4vEFYvZ7vAGxmE06jpB0B+LNWjZE7EPGaokCijkCTjjhBBVHn376aZ+5IHADhQtmZ2Iu2fAbnpBenoTFcpPiJIwQEg8o7YJslmH37cjscnCJqml0q/s2xhFCSCZiMpm0iHhNTY30FRDHiJi7noIWOkJ6cRJW0+COmbULkzD0cxJGCNkacLNEaYL8HIvG3sJdG26WaLFckGPVfsbiEkIymf79+8v48eOlr7Drrrtq/GNPQUFHSF+YhHn8EgyFtOUkjBDSFVBnDllxYYlD7C3c5NFi+fRDxzFbLiGEZDh0uSSdYjUb1UqElqRuEoaU4qs31ktTU0DypXUSxmLAhJCufp/gu2NdVZPG3sKtGxZ+WuYIISTzoaAjnYLsivf9fn9eoR6YhC2v2CwLlyyXHceOkTHD+nESRgjpMhBvzIpLCCHZBwUdIX1gEjakPE+aamza8ok6IYQQQgiJF/rSkU5Zub5BTrzqv9oSQgghhBBC+h4UdKRTwkk60BJCCCGEEEL6HnS5JIQQQgghZCs0NzfLyy+/LN9//734fD4ZNmyYHHbYYTJu3Li2MTfffLMuH3PMMSm9ln/9619l5MiRcvzxx6fle/b222+L1+uVI444Iunb9vv9cs0118jMmTNl3333bVtfV1cn//73v2XZsmUyaNAgOeOMM2TgwIHa9/DDD8tOO+0kO+64o6QrtNARQgghhBDSCVVVVXLkkUfKRx99JHvttZccfPDBKkhOOOEEee6559rGffXVV7JixYqUX8dvvvmmR/aTCqqrq+XWW2+VPffcMyXbv//++1V4r1u3rm1dbW2tHHfccSrGIfTwfh599NHaAhwLRKDb7ZZ0hRY6QgghhBDS5wmGgrKucaO4Ax7JteTIoIL+YjSk3jbx5JNPisPhkAceeECMxtb9HXrooVJQUKDiBBY5q9Wa8uPIBO6++269diUlJUnf9vfffy9PPPGEvleR3HvvvZKbm6tiz2Kx6P5hXX366adl9uzZMnbsWBk9erQ888wzcsEFF0g6QkFHOgUZF++8ZB9tCSGEEEJ6ixW1a2Tusv/JJudm8Qf9YjGaZUjhQDlg1F4ypnRESve9efNmCQaDEorKKXDaaaepmx76YrFkyRK1Fm3atEmGDh0qp556qgwePFieeuopqayslEsvvVTHNTY2yuWXXy4nnXSS7L9/a7moDz/8UN555x3585//HHPbOJYXXnhBPvvsMyksLJSzzjpL99GZ6yfW4ViPOuooFaETJkyQBQsWqMUMr/3vf/8rkyZNkpqaGvn0009VFMGKtfvuu7c7H+wTr4HLJ86nvLxc+2Jtc/z48R2u46uvvqrbiHQf3dp+L7vsMnV3jcV1112n7pPA6XTqNfz9738vd911l0Ty1ltvyYUXXqhiDhgMBvnb3/4mgUCgbQwEHq7Rr371qw6CMB2gyyXpFLvVrDXS0BJCCCGE9AbLa1bLswtel9UN6yTX4pD+eWWSa82RlbUV8syPr2l/KjnkkENk5cqVKrhg1VmzZo2uLy4ulv3220/sdnuH17z++usa4waL3oEHHqgugBBTEEUQYLD6IRYPfPHFF/LBBx/Im2++2fZ6CB8Ij86AqycE3z777CNNTU0q3sJumLFcP7EO5wC+/vprueqqq9RqBeHXr18/deO8+uqrVSDOmDFDxc6ZZ54pixcv1tdAbJ1yyimSk5OjbosQYIcffrisWrWq023Gip3r37+/WsTCbGu/OK+TTz455j9YSMPccsstMmbMGHWDjQTHCYGJYwpb5P7yl79IXl6eCtAwuI4Q1ngv0hHO1EmnVNU55aX3lslx+28n5cU5vFKEEEII6XE3y3kr50uzt0UG5PYTs9mkQsdhsYvdbJONTVXaP6pkWMrcLzHZv+eee/Tf9ddfr+tgGYLQO++881SgRSfmCFt7LrnkEl2HBCBnn322WqTuuOMOjcH74YcfZMqUKSpmIEbCYgKi5pNPPlHh0Rmw9P3zn/9UwYj4vtNPP12P784774zrnCBwLrroonbrYG3DseH6wmIFkTZ37lwde+ONN8o555yjli4At8WWlha5/fbb1aWxs21G8uWXX8ZMPLK1/SJmcVvMmzdP3nvvPXnjjTc69NXX12uL9wMWQ4hrWD8hrmEphKslgFUOVkcIX7zf6QYtdKRTGlu8MueT1doSQgghhPQ0GxorZV3DRim2F3awWGG52FGo/RiXSiAEXnvtNXn33XdV3CAr4mOPPSYnnniiCptIli9frlkVYXGKBMsQNRCAcDOE1QtA0P36179W18y1a9dqLJjH45E99tij0+OBZTAczwf23ntv+e677+I+HwjIaHBO4WuMFlY2nBuSisDiBysiBGz4H6yNcLHc2jYjwfnBQhfvfuMBFrhrrrlGbrrppphxeWE3WQi3G264QQXjbbfdJjvvvLPG80WC/eIY0xFa6AghhBBCSJ+kxecUX9AvNrNVQjFC1bC+3t2o43oCxKnhH4Tc/Pnz1QoH10fEfYWB9Q1EugSC/Px8dbNEzB1S6r///vvqxrh69WrNnImkK59//rmsX79exZzNZuv0OKK3DRfCzmLNYoFjiQbulJFAXEEQQZwCWCRHjGgfrxgpKmNtMxKXy9UWxxbPfuOJoYMbpcvl0oQm+AfgOvnss8/KokWL2iyk06dPb/fa3XbbTV555ZV263Bs6ZrpkoKOkF4mGAzJuqpmqdjskfzSZhkzzCFGY+d+84QQQki2gGyWSIDi8XvFauyYSRLr0Y9xqQDukxdffLHGbEW74sEdsKioqIMICCcnQazd8OHD29ZjGe6FEEGwsCF5Byx+EydOVJc/iDhY6xDrhoQjWyPaklRRUaG18YDJZNLjjiQsyhIB7qUQWRCNOO5EKSsra3OBjBfE0IVjDWOJWgjpKVOmtFuPuDysg0iG1Q3/EEcXCY4D5xNJQ0OD7LDDDpKOUNAR0ov8VFEnb3+2RlZvrJemJqfMX7pIRgwskpnThsv2w4r53hBCCMlqUJoA2SyR5bLcUdauT61HrgaNn8O4VGA2m3U/iBXbbrvt2rIqAljTIJwOOOCAdq9BshSIP8S47brrrmqB2rBhg1qNELsVdgFEYWvEn4ULhEPQXXHFFZqxcVvCCQlUzj33XHVhhJsmsmmGrVEQdnC/hCUQ4hExY9h/okBsIhHKv/71L83CWVpaqtv++9//rv1INBIPo0aNakt2Ei/biqGDm+eYKFdPlJDAexW2ysGaisyiuPYQd7gWSDoDt9FoUYzYwHSEgo50SlGeTY76xWhtSWrE3BNzFkmT0ydFuRYxi0nsdossX1cvlXNaZNah4ynqCCGEZDVIdILSBJXN1VLZsllKcorEbrGpZQ5iLs+Wq/2prEeHRCaI0zrooINkyJAhMmDAAC07gOQlEG2xMjoieQoSiCD2Dsk2IGQg0n7729+2jYHoQ7bLcIr+adOmqZiDxQ6iaWtAsKBYNra9cOFCjc+DcAFwA0UCFogTiEtsc+rUqd26Bjj/3/zmN2r1QpZKuInCwoVELPECN9Pnn39eXVJ7sm7f+eefr2INLqNItIL3Ascya9astjHohxUP69MRQyi6qEYW8+OPP7YFZ0aCDwLefNwE0X6+hCTqZnnvi9+reBvcL1eCgaA0NDZIYUGhGE1GWb+5RUtG/Ob4nel+SZICv8dIKuH9RaKBGyJS2kNwxErr31V+2ryy1+rQhUFs1rJly/R+h4iDe15kohZkSISACmdOxBQbiUPg7ojrAItctNsk5pd77rlnm8BBohS4JkKwdQZcCrF/lAjA62E1xPYjQVwZkqvAuob0/EuXLtX3AVYyZJGEq2j4OMPbhAtjpLUr1rqffvpJqqqq9Bgjzz/WNqOBNROi+He/+12bJSze/XaFTz75RK2UEN+RwJIJ4QaX2LB7ahhYSnHtH3/8cXVZ7Qufkc50SSwo6OK4cNn6Q+Xy+GXNxkYZPrBAHDYac5NJxaZGueeF7yUvx6LXNuAPtAk6k9kkLrdfml0+ueiEnWXYgPaBz4QkQrZ+j5GegfcXSbWggzXM6XJKra9B3AGPxszBzTKVljmSfGCh+89//iNPPPFEn7m8fr9fXUqvvfZadfFMR0HHTwHplPWbm+Xyf3ykLUkuLS6/eP0BsVlMIiERp8cnTk9QWyzbrCbx+QM6jpBkJt5Bi2VCCEk3IN6GFAyUHcpGq3WOYi79QLwgkpzAitaXROYuu+yi8Y7pCs0uhPQCuQ6zWM0mqW1wS22TW1pcPvH5/LK5sVFyHS4pzreJxWzScYR0BybeIYQQ0ldAkhbUf0Ntu77C2LFj0zYZShha6AjpBYaU50tBrlVWbWzQwu1mk1GsFoO2WF69sVH7MY6Q7ibeQaxmnt0ixfkmbbGM9egnhBBCehKUboCI6itMnjx5mzX0+joUdIT0InB805JzyE2EhVBIl+kQR7oL3CpREgNZVJF4B7GaRoNBWyxjPfrpfkkIIYSkNxR0pFNMRoNaidCS5LKuqkktcSMHFUpejlX8wZB4/SFtsTxyYKH2Yxwhid5jayubpLTQ3i4LG8ByaYFd+3mPEUIIIekNA3RIp0BsPHXDIbxCKUyKMrA0V8oK7dLc4pWGxkYpLCiQvFyrGuw21bQwKQrp9j1ms8TOLofEO3Uav8nEO4QQQkg6QwsdIb2YFMXjC6i1JMdulhy7SVsse7wBJkUhSbvHYsF7jBBCCMkMKOhIp6zZ1Cjn3vyutiS5INnJ0P75UtPg1sKjkWC5ptGt/UyKQniPEUIIIWRrUNCRTvH5g7KxpkVbklyMRoPMnDZc8nMssn5zixZxD4ZC2mK5IMeq/RhHCO8xQgghhHQGBR0hvcT2w4pl1qHjZcyQIml2+6SuKaAtlk8/dJz2E8J7jBBCeo9nn31WzjvvPC2GHYu33npLzj33XGlpaZEzzzxTfvzxx4T209zcrK9fsmRJhz6Px6N9ixYtkp7E7/fLqlWrtjnulltukZUrV6bkGF544QV5+umnY/bdc8898u6777YtNzY2yuWXX97pe5XJUNAR0otAtP3m+J3lvKPGyyG7FmmLZYo5kgn3GEoiVGxqlMWrarVliQRCSLqxww47yAcffCAfffRRzP5HH31U7Ha7OBwOFXZDhgxJaD9er1c+/fRTaWhoiCms0FdfXy89yUUXXSSvvvrqVsfMnTtXli9fLqNGjUr6/j/88EO5/vrrY4pKCO1//OMfsmnTprZ1BQUFWk/uwQcflGyDWS4J6WXgVjmkPE+aamza0s2SZMI9hqLlqHOH0gjItokELYgLhSsxH1gQQhIhFAyKc+06CblcYs7NFceQwWIwptY2MWnSJBUrr7/+uuy///7t+mCV+u6771RAGI1G2WOPPSSTqKqqUkHbGRCasM799a9/Tep+3W63PPTQQ/LAAw+IxWJp1wdRe+edd8orr7zSoQ/Amjpz5kw56aSTpKysTLIFCjrSKYPKcuX6c6ZrSwghXRFzT8xZpMXLUQcPpROQbXP5unqpnNOirsYUdYSQrtC8bLlsfGuueDZslJDfJ0aLRXKGDZX+Bx0o+duNSenFPOaYY+Tee+9Vt8i8vLy29RAV/fv3l7322kvd/M455xy57LLLZPTo0XLhhReqm+Qbb7yhwuO6664Tl8ulLoSwaEGYDBgwQI477jjZZZdd4joOWO9uv/12ff3222+v4iU3N1ePC9a0K664QsaOHatjw+uuuuoqtRrGOp4NGzbIM888I+vWrZNBgwbJPvvso//AzTffLKtXr9bjhLCDcIvmv//9ryZymzJlSptrKI7p97//ve5nxYoVMnDgQDn77LNl+PDhammDxS0WJSUlcscdd+jfeO1//vMfuf/+++W+++5rN+7hhx+WhQsXqhsm9hVN//79ZeLEifLkk0/KJZdcItkCXS5Jp+TYLTJ5bLm2hBASD3CrhGUOYm5wv1xx2MxqEUSLZaxHP90vCSHx0rRsuVQ89Yw4V60Sc36u2AcOEHN+njQvXyFrnnxK+1PJUUcdpYIN7oVhgsGgWu0g9kwmky7DLbKurq7NRRKi6YADDpAJEyZoSaITTzxRxQgsSMcff7xUVFSoyKqpqYnrOCDOsC8czxdffCEnn3yyHlcsd83wOsSVxToep9Mpp556qrqLnnLKKTJs2DD57W9/K/PmzdPXH3HEEdKvXz+ZPHmyHncsILqwPZwbCO8HQgvuj0cffbQsXbpUTj/9dD1ObA9uqbH+4VjCYJtz5syRvffeu8M+Z82aJS+++KKeQ2ccdNBBKjazCVroSKfUNrrlrU9Xy8HTR0hJQezixIQQEsm6qiZ1s4RlLvwjHwbLpQV27ce4YQMKePEIIdt0s6yc+474m5rEOnCAmMyt9VpNDofYBw8S9/oN2p83elTK3C9h9dlzzz1VwB177LG67uOPP9b4rfByLCCUDj300DaL2Q033CC77rqrWK1WXbfjjjuqeIHlKp4YNGwvbHWaPn26uoAixg3biIfI43n//fc1kQvEF+LOYJmbOnWqWvzATjvtpH8PHjxY3U5j8fXXX8vBBx/cYT2E3AUXXKB/jx8/XgXsDz/8oJa8eNxSYa3rDIjCbbHjjjvK2rVrpbKyUt+7bICCjmxV0D0zd6nstuOAHhN0eGqPiV6Ly6+FkVGHjTFlmQff58wFn13EzMHNMhY2q0nqmtw6LhPhvU1IcnGtWy/OirViLS3BU6F2fRB2WI9+jIMLZqqAcLv00kvbRALcLSHO4ErYGZEiDa6aEDN4PaxWa9as0fg7EAgE4jqG/fbbr+3voqIitVJBKMUr6CKPZ+edd5by8nI55JBDZMaMGWoNw/EhuUs8wPIHQRhLMEFQhQnHscEi2FOUl5drC8FNQUdID8MkCtkB3+fMBg9ikAAFMXNws4zG4w2IxWzScZkG721Cko+/pUWCPp9YbDat1xqNEetr63RcKoFoghsh3Azhgoh0+X/605+2+prIeDuItquvvlrjwxD/hmQjED7YXrzAkhYJLGhdEUqRxwMr2EsvvaTCFBk88TfE3N/+9reYro7RIF4OmM0dv8tzcnLa/g57aiDWLt4Yuu5i3nJMSK6SLWTeLypJS5hEITvg+5z5wKqObJZIgIKYuUi3S/yg1zS6tdYixmUSvLcJSQ3IZokEKEEIiC2uipFgPfoxLpXATfKwww7T2C5YxyAaYrkbdsbbb7+t4glumxB0AMlNbr31Vv1ujAckMQknPQFIWoJjQFwdiKy/Vltbu83tFRcXa8IS/INLKBK64HjCgi7abT5agCGzZ1dKKYRj6GIRdkNNBvVbjimbslwmxdkYgaCEJH7/MIlCNsD3OTuAizRKE+TnWGT95hZxuf363qPFckGOVfszyZWa9zYhqQOlCeBK6a2t6yB8sOytqdV+jEs1cLtcsGCBPPHEE+qqGGmJ2hZNTU0qvCCiwhYupN8H8RbCfvzxxzXZCYA4RHZKHBMsh6WlpZooJXxdkOVxa7z33nsye/ZszbwZtt4hQUpk/BqyYXZ2bDiXESNGdKmgeNjtNNY/uK8mi1WrVqm1MdGagFljoUOgIW4kBITiosGPFm8SUpPiTUHwI7LikPQmz2GRfScP0TaVMIlCdsD3OXtASQKUJgjXoUPMHNwsYZnLxDp0vLcJSR1IdILSBK5NleLduEkMZaVistvVMgcxZ87P1/5U16MDiFmDdW3JkiWaMbIrwLr3/PPPaztmzBiNoUNSE1iRYGmLjDvrDAg3ZHCEKMRrbrzxRhk6tDVuEMlS4M44f/58nZcjaUqki2U0u+++uzz11FOy77776vGgNAFAeYYwmNOjcPq3336r5Q2iwWuRGKWv8dVXX2kSG5vNJtlClwTd5s2b1bcW/r+4SXBjH3nkkW01MNCPNKGPPfaYKm2Ybins0pcBpbly2WmttUVSSbYnUcgW+D5nFxBtEHDZkOSI9zYhqQV15oaddkpbHTpfXb26WeaNGd0jdegiufvuuzWxSfT8FtYsiJ9x48ap5Q5/I8NjGMybUYNu2bJlap0bOXKkxsSh9ACEB/7GayJdKsPAcoY+1KuDKyWSfSAGLzKm7oQTTtA4P1jMYGBBdkqUHsA4WKuijwdzd9R0W79+vWzcuFGPD2IVbpRhLr74Yt1mZ1Y61NBD2QaUXYCFMHycuAbRxx65riugVENnwhTvRbQVLhAIqHtrZ7F6ku2CDrU3EPwJRY8if1C+sQIhAZ5cPPfcc1pgESlSYdLtbCzpu3h9AalucElZoUOsllb/7FSQzUkUsgm+z9kHxFs2lCbgvU1I6snbbowMGTxIQjW1EnK5NGYObpY9YZmLzhQZq8QAhFBkSv5Y6fkxBgIrkkjLXGcp/eHeGO6DWOzMlRDukpEuk7vttts2tw3hh3+xQAzd1iyHsOxB8KEuHMofRB5nrGNPBJRP6IxYbprz5s1TcQnrZzYR96cAvraPPPKI+vuiVsXWBBqeLsAUDYWMJwlQ/iT9qKhskvNumadtTyRRqGlwx/SPRxIF9GdaEoVsg+8zyVR4bxPSM0C85QwdIgXjxmrcXE+LOdKRK6+8Ui2PkUXNe4tAICD33HOP1vvLNuL+JPzlL3+JaQbeGvALvvnmm9v8ewmJeRNmYRKFbITvM8lUeG8TQrIV1Hl74IEH4k7skkpcLpfcdNNNMnHiRMk2En60AcvJO++8I3/84x/VtRLBlIidozWOdCeJAmJuml0+2VTToi2WTz90XMYlUehYiLhZKjZ7tMVyppLN7zPJbHhvE0KyldGjR/eJEgF5eXlbddHMZBIKSoIKR6Dk+++/r0GVSKEKVYzsPffff3+HwEtC4iGbkihEFyJevbFempqcMn/pIhkxMDMzAWbz+0yyA97bhBBC0sZCh9oWixcv1noY33zzjWbTAUhpijSp2ei7SpKbRGHcyBJtM3mSHy5EjALMeXaLFOebtMUy1qM/U8mm95lkF7y3CSGEpIWgQ8bLa6+9VqZNm9ZufWFhocbMIcul0+lM1jGSXgJWlDf+dpS2pAcKERsM2mIZ69Gfye6XhBBCMp/oZGeEkOR/NhISdKh2jyDIWCCdKuppoKghIaT7hYgJIYSQdAN12QAf8BMSm/BnI/xZ6fEYOhRDRM0JFBaP5qOPPhK/398ngiNJ94CYuOvZb2X2yZOyomRAa3KSnonrYiFiQgghmQzqjxUVFWnSvPAD/+gHmF1NSY+C3OFtE5JMAj14f8EyBzGHzwY+I8nYX0KC7owzzpBZs2ZpRsvDDz9cL8DChQtlzpw58tBDD2kxcRYST3/c3oAsXVOnbaYTTk4Cq5jXH9BC56h9l6rkJCxETAghJNMZMGCAtmFR1x2CwaAaDDC/RIFuQpJJsBfuL4i58GekVwTd1KlT5bbbbpMbb7xRPvjgA113ySWX6AU49thjZfbs2Uk5OEJ6MjkJ4tbgAmmz2MXjC2hykso5LZpmP9miLlyIGPsYXJYrTo9PnJ6gWDw+yTMatZg6YhezwTJKCCEkM4FFDonzysvLu12nDNnUV65cKcOGDROHw5G0YySkN+4vuFkm0xKYkKADRxxxhMyYMUO+/fZbffKCk0chv3DGS0LSMTlJ2B0knJwEhc3RD3GVTPfLcCHiVS83yHfLqtueDFXWB/TByIDSHBZTJ4QQkhFg4trdySt+JwHyNNjt9iQdGSGZcX8lLOgARNwee+yRvKMhpA8nJ0F6/eSDDEetWY5+3v/P6wghhBBCCEmKoEPB8Nra2niHywUXXCAlJSVxjyd9j/4lOXLpqZO1zVR6KzlJ2DIYCIrsvF2ZtDh90tDYKIUFBZKbY5EN1c6UWAYJIYQQQkiWCrp58+bJ2rVr494wkqZQ0KU3+TlW2W/KUMlkeis5SaRlEC6WOXaz+LwmbbGcessgIYQQQgjJBOKepaJMAckuGpo9Mv+79bLXLoOlMM8mmUi75CQRMXThtLKpSk7CsgWEEEIIISQZJGx2wGR38eLF0tLS0hZIiHVut1sWLFggJ510kvTr1y8pB0l6h831LvnnKz/KDiNKMlbQhZOTIJslEqDAMgY3S1jmIOYKcqwpSU7CsgWEEEIIIaTXBB1cL3/961/L6tWrO03Fecwxx3T32AjpEVCSAKUJwnXoEDMHN0tY5lJVhy7aMhhJKi2DhBBCCCEks0hI0P3zn/+U3Nxcuffee+Xuu+/WEgajRo1Sy9wTTzwhV111lQwePDj5R0tIioBog4BCzBrcIWFBg5hKVUKSaMtgUa5FgqGQuDx+qW/xpcwySAghhBBCMouEBB2E2+WXXy577bWXfPnll1JfX6816fBv0qRJctddd8nxxx+f/KMlJIVAPPVkApJIy+DqjfXS1BSQfPGl1DJICCGEEEIyi4QEXSAQkP79++vfo0ePljfeeKOtb5999pErr7xSvF6vWK3W5B0p6XFybGaZtH0/bUlqLYPLKzbLwiXLZcexY2TMsH60zBFCCCGEkLgwSgIMGTJEFi5cqH+PHDlSLXYQcAAJUvx+v9TV1SWyadKHGNQvT244bw9tCSGEEEIIIX2PhEwvRx99tFrhVq1apfXmzGazXHPNNXLCCSfIm2++qYKOGS7Tn0AwJB6vX2xWs5gYy5USfqqoi3C5dMr8pYtkxEC6XBJCCCGEkBRa6A4++GC5+OKL5emnn5bm5mb5wx/+oG6Xp512mjz11FNy6aWXanFkkt6s2tAgJ/1xjrYkNWLuiTmLNNNlnt0ixfkmbbGM9egnhBBCCCFkayQcHHX22WerdQ4MHz5cJk6cKIsWLZKxY8fqP0JI5wSDIbXMNTl9WrYgGAiK12MQh80suTlWzXyJfsTXMdMlIYQQQgjpjG5lu0C8nMHQmlZ9++23V9dLlC8ghGwdlEdAzbvSQnvbZygMllHgHP0Y15OZNwkhhBBCSHqRsF/kgw8+KNOmTZMNGza0rTvvvPPklFNOkdra2mQdHyEZCWrdef0BsVlMMfttVpP4/AEdRwghhBBCSFIF3SuvvCL333+//OpXv5Ly8vK29X/5y1+0vf766xPZLCHqilixqVEWr6rVFsuZuG8ULreaTeLxBWL2e7wBsZhNOo4QQgghhJDOSGi2+Nprr6loO/LII9utnzJlitx7772y7777sg5dBjBiYIH8+/qDJddh6dGMj3A1hPUKgmdo//weKbLd0/seUp6v20cCFMTQRRIKhaSm0a3xcxhHCCGEEEJIUi10NTU1WlA8FiUlJZKXl0e3ywzAbDJKYZ5N2x7N+JhjkYGludr2RMbH3tg3Ep1ALObnWDQBisvjl2AopC2WC3Ks2s+EKIQQQgghZGskNFMfMWKEvP322zH7vvnmG3G5XFJaWprIpkkfYmN1i9z48Ofa9mTGR2R6hJBBi2WsR38qXCB7c9+w/M06dLxa4prdPqlrCmiL5dMPHZdyqyQhhBBCCMlSl8tTTz1V4+cqKyvlwAMP1Dg6j8cj3333nfzrX/+Sk08+WSyWnnHTI6kBAmbF+nr5YtEm2W/XIdK/JCdl1qLezPjY29kmIdog4JZXbJaFS5bLjmPHyJhh/WiZI4QQQgghqRN006dPl5tvvlluueUWefXVV9vWm0wmOf7447WwOElfwvFkYVfD5975Sb5dujll8WQ/Z3y0d5rxsa7JnZKMj7257zAQykPK86SpxqYt3SwJIYQQQki8JJxC7+ijj5bDDjtMFixYIJs3bxa73S4TJkzQGDqSvoTjyeBqmGNvvT3QIp6sck6LuggmW9RFZnyEq2NPZnzszX0TQgghhBDSXbo1S4Vb5aRJk7p9EKRvEB1P1uzytVmp4JKIZB3oh4tgMq1I0RkfI10fU53xsTf3HXnd11U1S8Vmj+SXNsuYYQ5a6QghhBBCSGoF3eeffy5jx46VwsJCLVFw6623yvfff6/Fxn/729+K1WpNdNOkl2gXTyYGFRqDynK1xXKq4snCGR9hAYRoxH4gImEdg6BKZcbH3tx3pHvr6o310tTklPlLF8mIgUU9UqqBEEIIIYRkaZbL999/X2bNmiUNDQ26/OCDD8q///1vtdg988wzKu5I+hGOJ/N6A/LT2jpZtaFRGlu82mLZ6wuIzx9ISTxZu4yPLp9sqmnRticyPvbWvtuVS7BbpDjfpG1PlGoghBBCCCFZbKF74YUX5MILL5Rhw4bp8htvvCEnnXSS3HDDDbJkyRLNgnnFFVcw02WagTixQCAkKzc0ak00i9koEmq1YsENs8XVIKWFjpTFk4UzPsICCNGI/cDVsSeShGDfowYVytdLKqW20SMlBTaZMra/mHENesC9NRgIitfTWi4hN8eaMvdWQgghhBCSWSQ0M6+oqJD/+7//07/Xrl0rq1evluuvv16X4YaZm5urxccHDBiQ3KMlKWVQWZ74/EHxeP1SkGsVfzAkjU6vFOXbxGE1qbUO/RiXKiBeUlEeIF7XR7iUwkqJRClfLKpMmetjb5dLIIQQQgghmUFC5gfEx6HuXNj90uFwyOTJk9v6EVNns9mSd5SkR9hQ3axWObvVLG6vXwLBoK5Hi2WsRz/GZRLtXB9zLDKwNFfbVLo+/lwuwaTJV5xuvzjdAW2xjDi+VLm39gVgoazY1CiLV9Vqm4rC7YQQQggh2UBCFrqdd95Z7rvvPnWtfOSRR2TvvfduS4Ly4Ycfaj26oqKiZB8rSTEQDyaTQUYOLpDKWqc0tXh1fTAQkvxcqxYXb3H5MkpkRLs+hq1lcH3EcqpcH8PlEpB4pa4Rde584vP5ZXNTg+Q6EE9nz9hyCbGsocg0ykQwhBBCCCE9ZKE777zzZMWKFXLOOeeI0+mUiy66qK3vpptukjPPPLODGxnp+4RFhtViku2GFsmwAa2p+tFiGX2ZJjK64vqYTBAbCLfWVRsapMnpFbPRIFazQdtmp1dWbWzQ/lSWS8gWayghhBBCSCaT0MwcsXFIhLJ06VIZNWqU5Of/POlEYhSULiDpR3RNNliK8hwWbUFP1GTraX52fbTH7IfrY12TO6VWSbhYBoIhCcDrMBjShDSZ+Dikt6yhhBBCCCGZTMIp/Ox2u7peRoo5QDGXvoRrsuXnWHRyjdpzu2zfT1ssp7omW29aJT2+QMx+1KNLhVUSFj8kmRlQkiuhkEGaEUPnCWqL5f4ludqfbMtgNlpDCSGEEEIymdTkZCdpS2/Wg+tNq2RNg1stZZFgGVZJ9CfbKgmLH1wtET9nNIrkOcySYzNqC8GM9ejPpHjFyEQwscj0RDCEEEIIIamAgo50AKLtN8fvLEf9YpQWFUeL5UwTc7Gski63X10D0abSKumwm6XZ6ROvPyg5NouYtmwfbY7NrOvRj3GZQm9ZQwkhhBBCMhkKOhL7xjAapH9prsZ2oc0kN8u+YZWENTCkVsDGFo80tvjE5Qlqi2XE0YXHZAq9ZQ0lhBBCCMlk+CickC2iDgIO8Vtw+YOVCMIiVULW5Q5oNtFGp699DbYtCVKw38Jcq47LNGto5ZwWtX4iZg5ulrDMQcxlYowmIYQQQkifFHSfffaZuFyumH1IboCEKUOGDNF/hJCOwJVSi4hvEXOQMKGIFuvRn0kul5HW0HAdOmQQhZslxDTr0BFCCCGEdJ2EZovXXHONVFRUbHMcMl7efffdLDJO+jyof/bWp6tlxboG8fj8YrOYZfSQQjl4+oiUuFwGQ0HxIpbMIGKB47PBoJY6tU6FQuIPivZjXKbR09ZQQgghhJBMJqEYultvvVUsFov88pe/lKefflrmzZsnr7zyilx99dVSVlYmZ599tvzzn/8Uj8cjN998c/KPmvQIiGe65/L9tM10MffPl7+XzxZslMo6p9Q3e7TFMtanoth1xcYmtcQhCUpr1TkDNJ22WA4nScG4TATibdiAAhk3skRbijlCCCGEkB600D311FNy/vnny0UXXdRu/fjx42XXXXeVc889V/7whz9onboDDjhAvF6vWK3WBA+R9BZILz98QEFGvwGwij3/7k+ytqpZRRTOGeIC62Ehw3r0X3XmbkkXHdgf3A19/qD4A0EJBiF0QmI2GcViNup6QgghW/8Op7WfEJLtJCToli9frla4WIwbN078fr9UV1erta6goEBqa2tlwIAB3T1W0sNU1Trl2XeWyskH7iDlJTkZef0rKhtl6Zra1thPq0kQ0gZxhWUk7Ai6A9qPcSMGFiZtvyMGFYrdatYU/iiZ4PeHxOv3idVsEbPZIM0uv/ZjHCGEkI7AeyIcj4salyiLAo8SxuMSQrKNhFwui4qK5Msvv+xU7DU2NorD4ZBgMCjNzc36N0k/Gp1eeeeLCm0zldUbGsWN+mdGQ2ux7xav1n9Di2WTyaD9GJdMhvXPlx2GIzYvJC1uv8bSmTSWTlqXJaT9GEcIIaSjmHtiziJZvq5e8nIsMrA0V1ssY30qXOUJISSjLHTHH3+8XHnllVJfXy8zZ86U/v37i9PplG+//VbuuOMOOfDAAyU3N1fj6GChKyyklYH0bZcdp8ffGtPWGsim+AKtrpDGiHXJAu6bJ87YXmob3Vr3zu3x677MJr+YzUYZUJqn/YwtI4SQjt/ZsMw1OX0yuF+uelQAh82syyiLgn4kX+J3KCEkG0hI0B1xxBGyceNGueeee+T+++9vW280GuXQQw+V66+/XpdffvllueCCC5J3tIQkmWEDW2MEUfvNYjK2TQyAaYuoM5oMbeOSne3x0D1Gyovv/STV9S5190RB8YJcq65PTUFzQghJbxAzBzfL0kJ7u+9sgGXUuEQ/xiHpEiGEZDoJF7lC4pMTTzxRPv30U6msrJTS0lLZaaedZMSIEW1jXnrpJcnPp8sY6bvA+oYC37COQdTB7RETgtCWAt+YKqA/FVY6uAR98M1a3f6oQQXicjvFYc8Rty+o64cNyKeoI4SQKOAOj5g5m8Ue89og/hk1LjGOEEKygYQFHTJX/ve//5X//e9/UlNTo66VK1askFNOOUVdMAHFXHpTnG+T4/ffTttMxeX2S1G+TUISEo83oCIuFC4kYBCx28zaj3GpchkaUp4nwUBQGsQjhQV2MZqMdBkihJBOQO1KJEBBUim4WUaD73JkEMY4QgjJBhJKioL6cmeccYbccMMNsmzZMq1Jt2HDBnnooYfkyCOPlJUrVyb/SEmPU1rokDMOG69tpoIf/Pwcq/QrytFSAaEQ6nqHtMUy1qM/2RODrrgMEUII+Zkh5fmazbKmwa3f15FguabRrf0YRwgh2UBCgu6xxx6TtWvXalHx9957T5555hl56623ZO7cuTJ27Fi56aabkn+kpMdxun3y4/JqbXsKWK4qNjXK4lW12mI5leAHHzFrlbUtapHLz7VIYa5NWyxjPfqTPTH42WUIkXqxXYZ8/gBdhgghJAokOkFpApR8QQIUeFDgtwItlgtyrNrPhCiEkGwhIbPD+++/r1kup0yZ0m794MGD5a677pK99tpLs17m5GRm7bJsYUN1i1x1/8dy5yX7aLawTK4pFM5wiQkACn4HUOTbYBB/1NPfZEGXIUJIssmmItv4TZh16Pi23wzEzMHNEr9VrENHCMk2EhJ0EGvDhw+P2VdcXKyxcw0NDRR0pMs1hRBTBjdEBLsjPgI1hSrntOgPdypEHSY/jS1eGTmoUOoa3WqN9PnwBFgkL8cqxfl27U92trSwyxDOD2m2Y7kMYWJClyFCSDxkY5FtnBe+J7NFxBJCSFIFHTJZwtVywoQJHfq+/vprcbvdUlZWlsimSRbSmzWFwq6PKEpbVmgXpztcD84oOXazxtKhTlyys6WFXYYgVnF+RbkWLVng8vilvsVHlyFCSJ9/INYXwHcpSxMQQrrv3dAsFZs9kl/aLGOGOdLuwVBCgu7000/XpCjNzc1y+OGHS3l5uRYZ/+KLL+S+++6T4447ThOlENLXawpFuz7mOtrftyj4napsaZEuQ6s31ktTU0DyxUeXIUJI3LDINiGEdN+7oXUe5pT5SxfJiIHp57qd0Cx16tSp8rvf/U7uvPNOefzxx9v1zZw5Uy6//PJkHR/pRWClgshCm6k1haJdHyMFZU+4PoZdhpZXbJaFS5bLjmPHyJhh/dLuyRAhpHdgkW1CCOm+dwM8pcxiErvdkpbeDQmbHc4++2w56qij5LPPPpPq6mrJzc2VXXbZRcaMGZPcIyS9xoiBBfLYtTNTvp/eTBAS7foIayAEJPYJMdcT2dKwbdSia6qxaUsxRwiJFxbZJoSQ7ns3oB6w12No9dbKsaZdPeBuzZBLS0vlsMMOS97RkKykL1jJmC2NEJKOMGMuIYR0nUzzbohb0N1///1SW1sb94YvuOACKSkpSfS4SB9g9cZG+dNDn8qfzpmu1rpU0ResZMyWRghJR3r7gRghhKQjLb0Y7tOrgm7evHlaTDxeZs2aRUGX5iDbY02DW9tU0xesZMyWRghJN/rCAzFCCEk3cnsx3CcVxH2UL774YmqPhGQ9EG2jBhXK10sqpbbRIyUFNpkytr+YzalNykIIIelMX3ggRggh6cSQDKsHnB6yk2RtYdwvFlVm/ISkN+uftO6bRXkJSXfoNk4IIdlbDzhuQTd79mytPTdp0qS4N75x40a57bbb5JJLLpFhw4YleoykF/D7g7JgebX+jXbEgIKUWsqytTBub9Y/iSWg8bQq0wU0IZkK3cYJISQ76wHHLehQouDiiy+WHXfcUY455hjZb7/9xGazdRgXDAbl+++/l+eff17eeustOeuss2Tw4MHJPm6SQt79okJefO8n2VznEpPRIE++uVje+my1HL//9jJjt+QL82wtjNub9U+yVUATQgghhGRaPeC4BR0E3Ouvvy533323Fg43m80yfvx4tbzl5eVJS0uLVFVVqZhrbGyUPfbYQ5544gnZaaedUnsGJOli7sFXf9RgUIMhJKGQSCAYko3VTl0Pki3qMi11bF+vf5KtApoQQgghJBPrAXcphg5lCK6//nr5zW9+I6+99pp8/PHH8sknn0hDQ4OKuoEDB8qpp54qBx10kFrySPq5WT799hJxe/2CW9loMAjyW+K+DoRCuh79+04eklT3y3apY0MiTo9P/P6QmM0GybFZ0i51bF8XsdkooAkhhBBCMpWEkqKUl5fLOeeco/9I5vDlYmSXdKuYQ4Y0BIcGAkExmo1iMRvF5w9oP8ZN32lg0lPH1ja4pbbJLU63X61IeEKSYzdLcb4trVLH9vX6J5lWe4UQQgghJJthPnjSxsr19SriOjM1Yz36MS6ZICVsQa5VVm1skMYWr5hNRnX/Q4tlFDhHf7qkju1q/ZNYpLL+SW/umxBCCCGEJBcKOtKG3WrS1hDnuGQT2uLeGYr4r3U5c+ufoHA76p3Eqn+C/lSI2N7cNyGEEEIISS4UdH2YUDAozoq10rh4ibZYTiWTxpaLxWQUfzAkwWCgbbKPFstIjoJ+jEsmiNWCJW7koELJy7Gqm6fbE9AWyyMHFmo/xmVa/ZP8HIsmIUHdk3D9Eyynsv5Jh31vcXFFm+p9E0IIIYSQ5EKfqj5K07LlUjn3HRVyQZ9PjBaL5AwbKv0POlDytxuTkn2OGFAo2w0rlkUra6TVG69V0PkDrS2m9+jHuFTEdA0szZWyQrvG0PkDQXW5RAwddOWmmpaMi+nqzfonkftGAhTEzMHNMh1rrxBCCCGEZDMUdH1UzK158inxNzWJtbREjDabBD0eaV6+QtyVVTL89NNSIupgkZkxdZgsX1snHl9Ha6DVYtL+ZFtuImO6NG2/w9Ku3+3xZ2xMV2/WPwnvG5ZPiGVcX7hZ0jJHCCGEEJLlLpe1tbXS3Nycik1nPHCrhGUOYs4+eJCYHA4xGI3aYhnr0Z8K90u43S1eXStlRTlSXuzQWDmL2aAtlsuKHNqPcckk22O6wvVPhvXr+fon2BdKE4wbWaItxRwhhBBCSBYIultvvVV8Pl/Mvjlz5shhhx0mNTU13T22rMS1br26WcIyF6tGGNajH+OSTbg+2aB+uTJ2eImMHFQgPn9IWywPKsttq0+Wypiu2kaX1Dd6tGVMFyGEEEIIIUkWdO+9957Mnj27nairrq6Wiy66SC655BLp37+/FBczBicR/C0trTFzNlvsNwzulz6fjktdfTKTBszZba0ujtoaWuuToRZdKmLZ4P637+ShmjJ/xboGWVpRqy2W95k8hDFdhBBCCCGEJEvQ/elPf5L58+e3ibrXXntNrXIfffSRXHbZZfLiiy9KQUFBIpvOesy5uZoABTFzyAYSaHGKr6FRWyxjPfoxLpPqk/1UUScffLNWrBajjB5SJDsML9EWy1iPfkIIIYQQQkh7EpqZT58+Xe677z654IILZMaMGbJp0yZdd8MNN8iwYcMS2STZgmPIYM1m2bBwkVriAi0tGi+ncXRbxF7hjuN1XLIJx7ItX1cvg/vlxoxlQxKNZMeyISYP2RabnD6NIYt0NS3Ot6nbJfqxb8Z4EUIIIYQQkoSkKHvuuaeKurq6Otluu+3koYceophLAhBu+WPHire2TrzVNRJweyTo9WmLZaxHP8Ylm+hYNljkoK3QpjKWLRy7V1pojxk3WFpgT0nsHiGEEEIIIVljobvjjjtiJjoZPny4/PTTT3LmmWfKiBEj2tbD9bKkpCR5R5olwBpX+/nnIoGAirbWrI/4Z2gVcYGA9pfvt09KRF10fbLRgwu1Dlwq65P9HLtnj9mP2D3UScu0OnSEEEIIIYT0mKD76quvZN26dTH7kARl7dq1+i8M3DEp6LoOMlg2LvlJDCaTmAryRQJBjZ1TU5nJKEGnU/sxLnfEcMmE+mTRdeh6MnYv24G7K+vQEUIIIYSkL3HPkJ9++unUHglRWlavkaDHLaacHDEajCLmKCuczS4Bl1PHpUrQAYg3uDv+85Uf5HenTUlp7Fp07F6k22UqY/eyHSSaCVtiYSGFqMb7kCpLLCGEEEIIST4J++xhov3OO+/IH//4RznnnHOkqqpKHnvsMdm4cWNyjzAbCbVe39hdyS3qvTVgMVu5vqHTrJepit1zuf1qOULLOnSpE3NPzFmkIjovxyIDS3O1xTLWM6soIYQQQkgGCzqUKoBLJerOvfnmm/Lpp5+Ky+WS559/Xo4++mhZtGhR8o80S8gdOVxMDrsE3R4Vb0G/X7NdotVlt0dMdruOyyTCsXuwxDW7fLKppkVbLJ9+6DhajFKUVRQWUbi5QlSjxTLWox/jCCGEEEJI3yahoKQnn3xSFi9eLI8//rhMmzZNDjzwQF3/zDPPyHXXXaflC5599tlkH2tWkDN0qOTvsIPUffe9ZrSMdHTU1Chms/ZjXKbR07F72UpXsooOG8B6koQQQgghGWehmzt3rlx77bUq5iIpLCyUm2++WZYsWSJOpzNZx5hVIHNlye67iREZLEOwyYXU/VJdLUMhXY/+VGS47AtAvEFEjBtZoi3FnKQwq6ip06yiPn+AWUUJIYQQQtKAhFRBU1OTZraMRU5OjthsNmlsbOzusWVt2YKmJUvEUlwstn5lYrI7xGizaYtlrEc/xqWaASU58odZu2pLMofIrKKxYFZRQgghhJAMd7kcOXKkvPjiizJhwoQOfR999JH4/X4pKytLxvFlHa5167UkgWPwQDHa7RJwuiTk96urpSnHIUG3W/sxLmdYat0u83KsstfOg1O6D9LzMKsoIYQQQkiWC7ozzjhDZs2apRktDz/8cPF4PLJw4UKZM2eOPPTQQ3LKKaeI2cyaYYngb2nRJCiwyiGeyZzb3jqG9cHaOh2XalDM+8Nv1sk+k4dIcX7sot8k/QhnFa2c06JZRBEzBzdLWOZQIqIgx6r9dHclhBBCCOn7JKS6pk6dKrfddpvceOON8sEHH+i6Sy65ROO7jj32WJk9e3ayjzNrMOfmitFikaDHIyaHo0M/1qMf41JNTYNbHn59oUwYXUZBl2GEs4qG69BBvKN4O5LSsA4dIYQQQkj6kLAZ7YgjjpAZM2bIt99+qzXoHA6HTJw4UQYOHJjcI8wyHEMGqytl8/IVYh88qEORbW9NreSNGa3jCOkOzCpKCCGEEJLlhcXnz58v//3vf/XfpEmT5O2332Zh8W6C7JX9DzpQzPn54l6/QQIulyZAQYtlrEd/pma5JD0Ls4oSQgghhKQ3LCzeB8nfbowMP/00tcT5m5rFvXGTtljGevSnGhSVRnFvgJZFpgkhhBBCCOl7sLB4HwWiLW/0KM1miQQoiJmDm2VPWOZ+qqjT2KoV6+olx26WF+ctk2+XbmZsFSGEEEIIIX0MFhYnHcTcE3MWyfJ19VJSaJfJO5Rri2WsRz8hhBBCCCEkjS108RYWx98kMZqWLZfKue9ozTktY2CxaLIUxM+lyuUSbpWwzDU5fTK4LFdaPD6pb/SL1WbU5fXVLdqPTIhMaU8IIYQQQkjvw8LifVTMrXnyKfE3NYm1tKS19pzHo5kv3ZVVKYujW1fVpCns7RaTLFtXL00tXmlx+yXXbpb8XKsU59u0H+OGDSiQTAOCFufW4vJLrsOsBbgpXAkhhBBCSF+GhcX7GMhoCcscxFxk2QLUpMMyMl2iH/F1yY6ng5BpcnpVyPmDITGZWrePFlY7l9uvwg7jMk1UheMGIVi9/oBYzSYZ2j+fcYOEEEIIIaRPw8LifQwkQYGbJSxzkTXoAJaxHv0YBxfMZOKwm6XZ6ROvPyh5DrO4fQFdH5KQ5NhM0uzyaz/GZZKoCscNQrSWFtrFZrGLxxfQuMHKOS1agDuV+yeEEEIIISRREpqZe71eFhZPEchoqTFzNlvMfnW/rK3TccknpP+CoZA0wkoXCOpap8snXm8AirJtTKaIqnZxg/1y20S0w2bW5fWbGTdICCGEEEIyTNAdd9xxMnDgQNl///1lv/32kz322CP5R5aloDwBEqAgZg5ultFgPfoxLtm43AGxWIzS5PJJKBiSsKcjGp8/KAajQawWi47LFFEVjhuEiIxlES0tsGd03CAhhBBCCElvEgrCmjVrlgSDQbn55ptln332keOPP17uu+8+WbJkSfKPMMtArTm4UnpraiUUam8JwzLWox/jkr5vu0l8vqCYTUaxWExqh0MYHVosm41G8fqCOq63RFWyQawe3DttltjnZLOaxOcPpDRukBBCCCGEkB610J1wwgn6z+12y+effy4fffSRvPLKK3L33XfL4MGD1XI3e/ZsycvLS/jAshUkOkFpAmSzRAKUyCyXEHPm/HztT02BcYgpgxgNBs1sGQiZVURCVJkMBs14GR6TGlFl71RU1TW5UyKqkHgFsXpw74RFMBqPNyAWs0nHEUIIIYQQ0tfoliqw2+1qofv9738vt9xyixx88MGyfv16efLJJ6WmpiZ5R5lloCQBShPkjRkt/qZmcW/cpC2WU1WyACCLZV6ORaxmo7i8AQkGQiqi0GIZ69GPcakSVbFIpahCFk0kXqlpcMe0iNY0urUf41Kb2bNZKjZ7tMUyIYQQQgghKU2K8sMPP6h17ssvv5Rvv/1WrXWwzsH9EjF1iLEjiQPRhtIEyGaJBCiImYObZWosc61AMOXnWKUgxyq1TW4tX4DYOb852FaHDta5ZAursKhCApTIGLpIUYX4uVSIKsTkIYsmEq8gVg/unbAIQkRiv7gW6E9V6YRwZs/VG+ulqckp85cukhEDi1gugRBCCCGExEVCM/OjjjpKVq5cqaJtypQpcsUVV6iIGz58eCKbI50A8Zbs0gTxCqvthhRJdYNLFq+uk2ED8qWs0CHrq1tSIqx6W1QheyayaIZLJsC9ExZBnGsqSyZEZvYsyrWIWUxit1tYLoEQQgghhKRW0I0fP15dK+FWWVVVJbW1tVJdXS2DBg0Si8WSyCZJDIJ+v9R9/a1462rFWlwixVMmidGculiudsKqukXsWxKFGMSgy6kUVr0lqiL3j331VFHz6MyewUBQvB6DxvHl5lhZLoEQQgghhMRFQurgb3/7m3g8Hvnmm2/kk08+kffff1/uvfdesdlsMnnyZJk2bZqcdtppkpOTk8jmiYhUzntf1r/0snhqazH7h9oSW0mJDD7uWOl/wH4pu0aRwgoWJOD0+HV9qoVVT4uqaLCfnipNwHIJhBBCCCEkGSRs7oF4mz59uv4DlZWVKupeeuklmT9/vhx00EF0weyGmFv1yGMS9HrF5LCLwWKRkM8n7s3Vuh6kWtRBWC1eXSOf/bhJpu00QMaNKO0RYdWToqo36c3MnoQQQgghJHNIWNChDt2CBQvUQod/sNZh3aRJk7TYeHl5eXKPNIvcLGGZg5gzF+SL0bAlCYrVpsLO39ik/f322Tvl7pc7jirTfyT5sFwCIYQQQghJBgkpgmuvvVbeeustaWhokPz8fNlrr73kpptu0hIGRUVFSTmwbAUxc3CzhGWuTcxtActYj36MK919akqPpcnpla8WV8qu4/pr9kuSusyekaQ6sychhBBCCMlyQYeEKMh0iQLiu+66KxOhJBEkQEHMHKxxsdD1bnfruBRTWeuUO57+Ru68ZB8KuiQTndkTWS6DoZC4PH6pb/GlPLMnIYQQQgjJYkH38MMPJ/9IiIJslkiAgpg5uFlGo+uNxtZxJK2JTEDTWocuIPni67HMnoQQQgghJP1JXRAWSQiUJkA2SyRAgTUu0u0yGApKwOUWe78yHUfSn3ACmuUVm2XhkuWy49gxMmZYvx6zzKF8Qm9lFSWEEEIIId2Hgq6PgUQnKE2AbJZIgBKZ5RJizmi1an8qE6KQngUCakh5njTV2LTtKUGFshThun/IuGk1mzSuj9ZBQgghhJD0gaqgDxIuSbDupZfFW10jIadLDKhD169MhqS4Dl2k5aau0S3D+udri+WeEBq0GEmPibkn5iySphav5DjMkmuySFCCmqQFcX1wBaXLJyGkr8PfDEIIoaDrs+QMGyr548ZK8+IlEvB4xWSzSt64sbq+Ryw3n66W5evrJRAMyr/fWiyf/bhRZk4fkdJJPvb71qerZcW6BvH4/GKzmGX0kEI5OMX7zcYJECxz1fVu8QcCUt3ws2B32Ezi8Qa0H66gdL8khPRV6GVACCFdtND99NNP4vF44h0uO+ywg1itTHWfCE3LlsuaJ58Sf1OT2AcNFKPNJkGPR5wrV+n64aefJvnbjZFU/UD+8+UfZFONU9Pn45/BYJDNdW5ZtbFRzj92YkrEVet+v9f9BkNYg/95ZXO9U1ZvbJDzj92Zoi5JIGYO17uxxauZNW0Wk5gsBgkEQ9Ls8ovR4Nd+jMuGIu+EkDT2MnD6pLTQLjaLXTy+AL0MCCFZSdyC7je/+Y1UVFTEveG5c+fK8OHDEz2urCUUDErl3HdaxdzgQSqmgMnh0GX3+g3anzd6lLphJhNYaZ5/9ydZW9UkRoNBzCajNCKFfq5V/IGgTvDRf9WZuyXVcvPzfpvFZDSowMD2sd7rC+j6VOw3W8EEqLbBpQIu124R2XJJTSaD5BjN0uJu7cc4Qgjpq14G+I5CHc/w76TDZtZllIKhlwEhJJuIW9Ddcsst4nQ69e+qqiq57rrr5LjjjpODDjpI+vXrJ42NjfL9999rSYOjjz5aBg4cmMrjzlhc69aLs2KtWEtLdJ4daHFK0O/XJCimHIeuRz/GJdv9sqKySZauqRODGHSi7wsEdT1EVK7FIs0un/Zj3IiBybPcVFQ2ytI1tfqjbLea1EIHAYllG5bdAe3HuBEDC5O232yl2enV99ZqMbWJuTYMIhaTUZOkYBwhhPQ18HARyZxgmQuLuTBYLi2waz+9DAgh2ULcgg4FxMP8/ve/l3POOUdmz57dbszUqVNl+vTpctZZZ8lll12W3CPNEvwtLRL0+STo9Ylr7TrxO51qtYM1zpyTI7b+/bUf45LN6g0N4vb69SlnrIk+xBYKX2NcMgXd6g2N4vYiy6JR0+dDzIWBldBiNmo/xlHQdZ+8HKteV1xnXNvICRFcbP3BoIo6jCOEkL4Gfifw0AlulrHAg8C6JreOI4SQbCAhn71FixbJjBkzYvbtuOOOYjQapbq6urvHlpWYc3MlFAhIy8pV4mtqFgMscw6HtljGevRjXKow9JILjdPjV8sR3D3D/7AMEYl+khzycyxSWugQk8Eobk+rgFYhFwjqMtaXFDp0HCGE9DVQMxNlVhAzFwskdrKYTTqOEEKygYQEXUlJiXz99dcx+xYsWCAtLS06hnQdJEHRmnMejxgddjGazOoCiRbLWI9+jEs2IwYVqBXO7QvoBD8SLONHEv0Yl0yGbbH2IaYLNyT2jN2jNW5ZHzmOdA8UD0dim7xci+TmWCSgQi6gLZaxHv0YRwghfQ18N6FmZk2DO+ZvVU2jW/v5HUYIyRYSenyF2Lk//vGPUlNTIwcccICUlZVpDN1XX30l999/v5x88snMcJkg7g0bW61ydpsE3W4Ri1UMJpNa5YI+r65HP8YlO4ZuWP8C2WF4iXy/fLNaapB/JM9hkWAgIG6/SFBC2o9xyQSWOMRz+fxB8QVCYlAp10poi8UQ/RhHknC9jQYtHl5Z26KZLvsV5rQloUFClMJcm/YzAQ0hpE9/h81p0QQoiJmDmyUeOkLMFeRY+R1GCMkqEhJ0Rx11lGzYsEHuu+8+eeCBB9rWIxbn2GOPlcsvvzyZx5hVIDYOgi135AhxV1ZJwOlUIacxdHl5Yu9fLv7mlpTE0OFH8sQZ28uGzc2ysaalzTIGkH1yYGmu9id7ou9y+yXHblaXy1AwFCHnWjEYDdqPcSQ5wAKH4uHIBIfkAYidhIvSdkOLdSLEun+EkHT6DkPMHL7DUD+T32GEkGwjYQfzCy64QE455RT58ssvNetlQUGBTJkyRQYNGpTcI8wyEBtntFjEaLVK3nZjJOB0Scjvb7Xa5TjUame0eFMaQwe7WGsNuog1upCaODaH3aTlCZAUpTUpxxZ/S4OIGeLRYNB+jCPJnRBh8oNMcEgegHgTuCjRMpd5wPq6rqpZKjZ7JL+0WcYMc/B9JmkPv8MIIaSbgg4Tb4i5Dz74QAXdTTfdJHPmzJGZM2eyZEE3cAwZrK6UzctXiC0qTg7X3FtTK3ljRuu4ZBOuB1dZ59JleDhCx4U9HbE+NfXgsC2DWnjzc6wq3gKhkJi2uGKiXEJ4TOonvdklbnB+LB6e+QWYYcVYvbFempqcMn/pIhkxkFYMkhnwO4wQQhIUdD6fTy6++GJ5//33JTc3V7xer7hcLnn++ec1hu7RRx+V8ePH8/omAFwr+x90oDSvWi2N3/+gLog/9xnENmCA9ie7qDhAnbeFK6vFr3FsBjEZUQ8upO6WEHZ+f0j7k10PDq6UeTkW8TUFpK7JA/tgm4XO4PaLzYoU+paUulyGJ71w3UE6bGRQQ1A9XXdIOoP7+ok5i7QAc1GuRcxiErvdIsvX1Wv8EVzW6F5LCCGEpDcJqYInn3xSFi9eLI8//rh88803bRa5Z555RuvQ3XDDDck+zqyj1S4ERaMOkFtWQGaljlXrG7REAPYBEReuT4a2dVm0H+OSCaxhqIvW6uK5Rcxt+bPV/bO1Hl2qUlCHJ72Y5EI4IlYQLZaxHv2EpBuwOOMhBcTc4H65Wl8SiYXQYhnr0c+SIIQQQkgWCrq5c+fKtddeK9OmTWu3vrCwUG6++WZZsmSJOJ3OZB1jVoEi4pVz39GsloU77yQFO+wg+WPGaItlrNf+4M/Ft5NFbaNbYBCEcIssNg2wjFXox7hkMqgsTzNcen2oQRfhWWlABkzR9ejHuB6Z9Bo56SXpD9yHYXEuLbTH/DwjMyD6MY4QQgghWSbompqapH///jH7cnJyxGazaRkD0nVc69aLs2KtWEtLVFm1M1Yhpqy0RPsxLtkUFzj0CX74iX2bpW5LP9ajH+OSyYbqZt02YgRRtsC4Zb9osYz16Me4ZMNJL8lUEAsK92GbJXYyIaR59/kDOo4QQggh6UtCPmwjR46UF198USZMmNCh76OPPhK/36+16UjXQTmCoM8nQa9XnGvXadkCWOMQM2fKydGyBehPRdmCUYML1ULldPt0oqeiCqISmScDrRZB9GNcMoF1rMXlFbPZqOIR+woXi8VkFH+hH+NSN+m1dzrpRTpsTnpJugEXZcSCepAh1tbxqx41u5DmPVWuzIQQQgjpGRL6JT/jjDNk1qxZsnHjRjn88MPF4/HIwoULNcvlQw89pOUMzGZOEhJ6Q3JztUxBy6rVKuRMVhvSeME8Jv7mZmlxOsVaXJySsgXD+ufLjqNK5ZsllWoZCwbalyqwmAzaj3HJpNnpFV8gKHarSSxmo9a/g6ALx+75fEEVXRiXbDjpzT6yJZspzguJfRALCnfiSPD5QgFmlK3AOEIIIYSkLwmprqlTp8ptt90mN954o5YtAJdccokYjUYtLD579uxkH2fWYB80UAVdwO0Rc0G+CrlQEIlKDGK028Xf2KT9GJdsMKmdNmGg/LAcmS797arOYbprNpu0P9mT37wcqyY9gWUOgg5/h2mtSxcUiwmZLq2S6klvZKwRJ72ZBxLcvPXpallZs168QbdYjXYZVTpYDp4+IuOyPeJziiytyGa5fnOLZrkMhkKa2Ki+xScFOVbtz0QxSwghhGQTCZvRjjjiCJkxY4Z8++23WofO4XDIxIkTWYOum7g3bBSDFhY3S6CxQQzGLRkukfwxaBCjxar9GId6dcm2XCxeXSt5DosWoHN7A+HqAWo9w3r077/r0KROAvNzLFJa6JDaBre4PX6xWExqmYOlzucLiMlglJJCu45L9aQXiSLgZgl3NFgwOOnNLDH34Nv/k82mn8RQ1CRiCIozZJSvW/Kl4u3t5dyZv8g4UYfzQWmCn+vQBSRffGqZY0kOQgghJDPoll8kRNwee+zRYX1tba1YrVbJy0t+VsJMR2Pjgn6x5BjEHxQJBqC0tmR8NMMlE6km/SmJoYMbGia9EDMmk0Hd0Zq3uKUBrEc/xiWzGDWsZJh4LkANvGBAXKF68Rp8YjRaJMdSJGajSftT5RoWOelF1j/EzCG2iJPe1BEMBWVDY6W0+JySa8mRQQX9xWgwpm5/wZC8+OmXssn6rZisfrFIjhjFJEFDQLyOBtnk/VZe/NQhVww5MOMsVri/cS8vr9gsC5cslx3HjpExw/pl3HkSQggh2UpCgu7WW2+VSy+9VCyWjhYTxNHBFfPZZ5+loEsAU65Dgp5mtR7Y+hdJyBeQEAp9mwxisJgk4GzRfoxLNkg6UtPg0smvw27WouIifnW1NJsM6qpV2+BKenKSsJVsTcMa2WxcKkZbvRgMATGETOL3FElxcGzKXcPCk95siK3qbZbXrJZ5K+fLuoaN4sPDC6NZhhQOlANG7SVjSkekZJ8VlQ2y3PmDGG0+sUthW0VHkxjFLgXisrT2V1TuJiMGFkmmgft4SHmeNNXYtOV9TQghhGS5oHvvvfekoqJC7rrrrjZRV11dLX/605/knXfekXHjxklxcXq6LkHMIJ4qErMBZjLUQwvIhqg+ACEAIAbgphhJ/5Icyc+xSkOzRzbXu9r15djMMqhfnroWrtrQWqzbV+eUKkeh+Jp9MhhiymqWGskRj5i3ZJu0iCXPIkP8PkGaAyQK2VTbvuYfMkMiLgysWFffLhYOoA9jqmqd0hiRaGRZRZ1mt7RZzfqaALJrGlpbVLjAFBjJS7DP1Rsb2zJfhhlUlis5douKwromT7s+uC6Wl+Roxj1YwSLBdo25DRIYtEDcm1sk6Ea5BlNr3KCxUbz9Fooxd6LUNTmkpqF9Dbxcu0UGluXqseCYohk5qFDdNzdsbhanp3169n5FDinMs0mT0yuVEdfQYjHqhDc86Y2+HwASw1gtJtlU0yLNrvYCt6TArv+QLXRDdXtLKmIEh2+xbuI9x3sP3G63bKj1ykiPX3JyRKrrXVLfHHUNc61SXpwjbq9f1lW1L+GAbKTh7KMVmxrFC/NuBANQLN1hkbpGt7qSRoL16EetvzWbOl7DUYMK9Vqs39ysoj7WNYx1f7cWsM7Tz9TKLff32oYN8vayD6XF55KB/XKkxGqVhoaALFxdJavWvykzt9tHhhYOUtfX4gK7Xltc40isZmObhXjl+gaNC4sEgsVuNUtVnVMaW1rv729WrhanxysOQ6EYbAYJBgzid/8ck2kQszgDLllTt0kFHa4Drkes+xt1GGs7uYb4jqiIur+T/R0RyYiBBRpzurG6RVrc7e9D1J8rzre3fUeE7zHbhkYpzA8k9B0BivNt6iId6/7GseCYQLK/I0ZvuYbow5hIBpTkaIwtrOs98R0hW9zQw14DPfEdEQafKXy2+tp3BKhu9MnKDY1it3sT/o6IBNcB1yPWNUz2d0SYojyblBU59LsO33mR4D7B/QIy8TsiGfOIVH5HWIydz8X4HdH3vyOSNY9I1XeEO+I3cswwa5/4jsCcGzkkUiboINzOP/98TX4CUQerHAqK42JcdtllcvbZZ6dtlkv8oF9954ft1u01cYDMmGCW2kaPXHLXxx1e88bfjtL2rme/laVr6tr1XXrqZNlvylCZ/916+ecrP7brm7R9P7nhvD3E4/XLJZH7NO8rUiTyu/o3BCXf5ljGylLjlrp/W4wHs36skhNGjZHvlm2WW5/4qt128YG8+9J99e/f/f2jDl+Y91y+n34Inn1nqbzzRUWHmxw3IMRjU0vrhyPc4gsTcWyYOP3poU87TJxuvmBP2WlMmfxn/ip58b1l7foO3G2Y/PakSfqhaneuW77gpx+xXmrcm8WzclcJtLR3razafoG8vOgt2S5wgDzyxqJ2fbuNHyDX/Gp3aXH5OmwXPHfTofrh+efLP8i3P21u13f+MTvJYXuNkq8WV8odT3/Trm+H4cVy+29/oX/H2u4DVx6ghc6femuJfPDNunZ9pxy0g5w6c6wsWV0n1z30abu+gaW58uBVM/Tvq//5SYcviqFDh0tJcYG8+uEKee1/K9r1HbrHCLnguJ31Szj6mPCl9/zNh+nff3niqw4T4qvP2k12nzBQ3v2yQp6Ys7hd354TB8kVZ0zVL9NY5/ryrYeL0WiSe174ThasqGnXd9EJu6j19LMFm7Q/kgmjS+WWC/fSBwLttztI/z/k0FoxGoKydnGhVK1vTfKz8CPcN8tk1qHj5IQDtpeFK6rlz49+0f4a9c+X+36/v/59xb3zO/w43HnJPjo5eum9ZTLnk9URPTtKqF+N2IbXiM9lk+olw9q9zmD2iWHf1vfjpke+kI1RPwDXnzNdJo8t16Qqz8xd2q5v38lD5LLTpkh1gyvmNUz6d8QW/n39wfpD+K/XFsgXiza16/vVkTvK0fvE+o6o6tZ3xPH7bydnHDZeVqxrkKvu/7jDBPGxa2fq38n+jnjltiP079uf+lp/gCP5w6xdZa+dB8uH36yTh19fmNHfEX+9eG8ZO6Kkz31HgDe+qJM1/6lMwndEK49ec5BOnB77zyL5+IcN7fpS8x0hctQvRsuvj5ogazY2yuX/+KjDZPipGw7Jgu8I6ZPfEftNGST77GCUylqX/O6e9p8bfkf0/e+I5M4jUvkdUdVnviP+78gB+kAnHgyhcMGvLvLxxx/LBRdcoJa4TZs2yfTp0+WGG26QYcPaT5LSiR9//FGfAuSWDutgoavZtFpGj9leqpvaPzlL9pM1f12l1M1/QXxugxSs3yD+uiapCTnEa7aJpcAhOYMLxGIPyehDTpXyEaOS+mQNEylMPuoDleIvWCti9Kvrp4SMEvLkiLFpgPTPGSCX/3KK4KFxsp6+VzZtlseWPiTegE+svmIJBSMLIYfEY2oQu80g/7frhWIPK9oMe/qOhyGrVq2SPXfdUQVdJj5Zq2qulpcWzhGHxS42s1XyCgNakcPZbBS/zyAev1dcPrcct+OhMm7QkKQ+fd/UtFke+vRlfdpVkGuWYLC9hc7l84jVHpAbj5klw4oGZeTT9/A9hjqihfm5fe7pOy106W2hc7td8tHnP8jgoSPEbrf3uafvkdBCl74WuuqNseditND1/e+IdLDQrdryGzlmWFmfsNA1Vq9RC91OO+0kKRN0YP78+XLhhReqiHvllVdixtSlm6AD0RfO6XTK4sWL1ZU0B/5wKSQUCkr1nAfEtXaRFhD31aPQeFCMFqNYinLFaLGIY+h4KTv0PDEkOYkEPjB/eeEdWeD6WIIGj/ib88SzZqzYhi8Rc16zGEM2meDYU644IbmJIz5a/YU8+NVT4jDbxWzqaNlFCQWX3y3n7nqa7D1iN8lEevIe6y2WVq+QR795XvrnlcVMgIJEKZXN1XLW5BNlh7LRSd03tn37B4/KjxtWinhzxW4x6z2Me97t84tYW2SnQaPkd/ueldLkLL1JNtxjpPfg/UV4j5F0xtkHfyM70yWxiNsv8o477pCamvZmUjB8+HD56aef5Mwzz5QRI35OaADXy5KSkviPmigQabYhY6VlyWcS8nvFkmcXMVlEAj4JORskaLZqf7LFXOvOQ2IuXyuGDXhCEhCDzS0SsLS2cEeT1n6Ma30eliy2PFPobJNt6xN+9kD6AMhmiQQosMTBShcN1qMf45INRNqxEw+QOk+dVDY0iNdjlZDPJAZTQCw5XulfWKz9mSrmCCGEEJK5xC3ovvrqK1m3rn0cQJj+/fvL2rVr9V8YuGNS0CVmofOsWyLG3AIJBQIS8jhFfG4Rg1GMecViMJm0PzRxn6SLOqSRr/JsFIs9IEE/ipm3bh+t0RwQi1m0H+OQlTBZDC8aKnazTSf0JoupQ3FvrEc/xpH0BaUJcN+srK3Q9zP6fa5zNciokmE6LhUgg+avph4v766YLytr1onH7xOb2SKjSkfKjNGpy7BJCCGEENInBN3TTz+d0gMhrfiq14u3eq1YigeKWGwSbK5TS53BbFVBJz6P9mOctV9yBU6Tt0XqXIgXC0mhPUdcHpPAVuewmsRhd4jb79F+jEsmQwoHyPalI+XHqqXi9rvFarKqpQRuct6AV0IS0n6MI+kL3lOUJqhqeU02NlVJsaNQY+kg2CHm8my52p9KKxlEG0RjT9bAI4QQQghJJQmnosQT9UWLFkljY6P+Hc3kyZPbBUaT+Ah6WiTk90nQ75VA9ToJwkIXCrZa6JpqxVRU3trvSX5h8RavU+uCoTYXJrueLTHHroBbgj6nmA0m7ce4ZILJ9DHjD5Fad4PGUCE5Sqt7pUGtkIPzy7Wfk+70B4LqlJ2OaqtDV+9uVDdLiKxU1qGLBPdRMi3MhBBCCCFpJ+jgWnnuuefKypUrOx0zd+5cja8jXcNoy5VQ0C/+ytXqfmkw21AVGBlLJOBulmClU0z5xTou2eRac7TaHBKQwCoWsgXEOu4zCdmaxBsICKLo4CqHcSlxh5t8sry74iN1yfMGfWI1WnSiP2P03nSHyyBoJSOEEEII6WVB949//EOMRqPcfffdUl5ern9HM3Agn4An9IaUDpRQwC8hn0cMjnwxhIK6rPFGFruEXE26jHHJBu5nsIsFtyQfQcIIU35ryn6sUZG3ZVwq4EQ/e6CVjBBCCCGkFwXdwoUL5brrrpPddsvMFPK9ib9moxiQ1dJolqCzAb6tP3caDGIwWbUf45IdQxeUgPiCP9fxCHlt4ts4UiwDV4nB2lrLBP0Ylyo40SeEEEIIISR+EsoEkJ+fLzabLZGXknhi6AIo6N1JDn+sD/hTEkNXUbdBAsGfxVrIZ5NA5Qhtw6Af4wghhBBCCCFpKuhmzpwpTzzxRMxkKKR7GKwOCbqaNRGKKbdQjPZcMVod2mIZ6wOuZh2XbGqdddus9BbaMo4QQgghhBCSpi6XDodD5s+fL4cccohMnDgxZjbL2bNnsw5dd4Bwa2kSCQVa3S5hmTOYxGBMZkHvKDqzCiY6jhDSJ0AJkPVNm2Sdq1IKmopltGMEs8YSQggh2SzoXn/9dXW5dDqd8tlnn8UcgyyYLCzedUJelxgsFgm6IOaCmrq/tQO2sYCEgkYx5uTruGRT5ChM6rhEJ56sEUZI8lhes1rLRKypWy9Nzib53PmjDC8e3GNlIgghhBDSBwUdi4yn2OXS7Wqrw9batvXqMvpT4XKZY7Gj8tuWXJa4O7xiKl+j7c9HYNBxqZx4oj4Z6t2hPhnqhXHiSUjin6lnfnxNmjwtUmTLF5PXIA6rQ0uDoMA7agJS1BFCCCFZXFh88eLF0tLSIsFgsG2d2+2WBQsWyEknnST9+vVL5rFmBag9F/JvEVAm88+aLtwG/dqPcckm15qrblgBuHlqTTy3WEcsbjcG/RiXyolniaNQbGarePxeTjwJ6Ya1Gw9I8JkalF+u39Meg0drSeZYHbKxqUr7UesRn2tCCCGEZFlh8V//+teyevXqmP0Wi0WOOeaY7h5bVuLbvLZVvBlM6nJpwERLJ1vBVhGHODpD6zh7/5FJ3bfL5xKT0SjBACrRhSQUMErInScGe7MYTEG1zqEf41I58dSae4jVtNh18smJJyFdB67LsHbjAUn4MxUGy8WOQu3HOFjCCSGEEJKeJPRY9p///Kfk5ubKvffeK9tvv71cdtll+vcFF1yg6//0pz/J4MGDk3+02YDmPzGKweYQg8ksoWBQQkGftljGehV4KUgwmmvNaedyCTHnWbiHtq2HFtJ+jOutiSchJD5afE51XYa1OxZYj36MI4QQQkiWCTq4VF566aUyY8YM2WOPPaS+vl7/RmbLO++8U5566qnkH2mWYO0/XAwWe6tbZXhl6OcG69GPccnGYXaIP6IOXSzQj3HJhBNPQpJPriVH41DhuhwLrEc/xhFCCCEkywRdIBCQ/v3769+jR4+WH3/8sa1vn332kcrKSvF6Y08iyNax9hsqltLBIpiE+TwiiE9Ehku0WMYkrHSwjks2VS2b1f1xa6Af45IJJ56EJJ9BBf3VlbLO1dChZiiWsR79GEcIIYSQLBN0Q4YMkYULF+rfI0eOVItdWMAh8N7v90tdHYtPJ4whcvKldrn22S7b9SePWlf9zxkuOwH9GJdMOPEkJPkg0QkyxObZcjUO1eV36wMZtFjGevQzIQohhBCShYLu6KOPluuvv17dK0eNGiVms1muueYa+eqrr+Smm25SQccMl4nh3bxWfNUbRExWEcS+aFIUFBU3ti4j7qV6g45LNnhi356QiNEfVToh1rgkTzx9WyaePk48CekOKEmA0gTIZNnidUm9v1FbLLNkASGEEJLFWS4PPvhg2bBhg9x///1y7LHHyh/+8Ae59tpr5dVXX9UkFldffbUYjUyDnQjeqjUS8rlFTCYRj1uzW7YSEvH7Rax27cc4W3ly4+iKHUXtlo25TeLY9d1tjkvmxDNch67e3ajxPZh4sg4dId37bOFztKJqtSxatkTGbzdWRpePoGWOEEIIyfY6dGeffbbMmjVL/x4+fLhMnDhRFi1aJGPHjtV/JEFCIqGAX8QHF9Zo98egiNclIYjlFHhdluUUi8lgaqtDFwv0Y1wqJ57IZolEKYitgzsmXcJIMoH1N9vuMZzf4PwB0uio0zbTz5cQQgjJJhISdLfeeqtMmTJFM1uGQfkC/CPdfEP6DW5NgNKpYmtNkKLjkszEAWO1BF6YoCtXvMt3EeuY78ToaNF1hi3jUgUmmqyJRVIFCtiHrcBI2Q8rMO43WoEJIYQQkq4k9Jj2448/lpKSkuQfDZFAQ3Wratoahi3jksz6xk3ij7TOBU0ScuVrGwb9GEdIOoq5Z358TVbUVmgtxf55ZdqurK3Q9egnhBBCCMkKQYdSBUiAQpJPoLk+qeO6woerPk/qOEL6kpslLHNNnhYZlF8uDotdrcFoB+aXS7OnRfu3VbaDEEIIISQjXC7HjRunGS5ff/11zXJZVlbWYcxFF11EK14iYEK5rfg4rWKQ/Inn5paapI4jpK+AmDm4WZY4CjVxUyRYLnYUaj/G0eWXEEIIIRkv6ObNm6diDbXmvv76606TptAtM4E3RGPjtq3oUhFDV55bmtRxhPQVkAAFMXM2lP6IAdYjsyrGEUK6Bizb65s2yTpXpRQ0FctoB7OoEkJInxd0zz33XPKPhCghN0oVGLYh6gxbxiWXPUfsJv9d9t7Pe7E5xbrd19pGjyMknUA2SyRA8fi96mYZDdajH+MIIV1PNLSmbr00OZvkc+ePMrx4MBMNEUJID8Lc1X2MoNfVWkhcRV10dpQt6wyG1nFJxmw0tktnbjD7xVS8Wdsw6Mc4QtIJlCaAK2Wdq0FCofYPS7CM9ejHOEJI1xMN5VlzpMhcoC0TDRFCSM/CmXkfwwDBhqLiEE36L/x3xDqTqXVckllbv1HrzIUJea3i2zBK2zDoxzhC0gk8iEBpgjxbrmxsqhKXz61uYmixjPXoT2V9NuwPcXpLq1doywQsJJMSDdnNNv38oGWiIUIISZPC4iQ1WPsPF6M1R4Iel0jQ3z75CSabRpP2Y1zyabVcGMUgQQlJyGcX/7rtxVRYLQarV9dHjiMknUDh+lN2OqqtDh1i5uBmiWL2qa5Dx/p3JNNgoiFCCOk7UND1Maz9hoqldLB41i2GL1j7Toi7YEj7MS7ZDC0cIqFQUMUcCMu3cIv1xlBQxxGSjkC0QcBhMooEKIiZg5tlKi1zYbc0WDKQZRMJWBCzB7e0qpbXVGSmUkwSkgqYaIgQQvoOFHR9EUOocyNYaEt/SvYblnId07KEl3WdgbW6SPoC8dZTpQmi3dLCJROQmAWuaXD3RD9EZipFJSHJhomGCCGk75DQDOLWW2+Vd999N/lHQ8S7ea14qyoirkS0nUy0H+OSDWLjjAaDulZGS0YsYz0mnYyhIyT5bmmEpBNMNEQIIWku6D7++GPWmEsRnspVEkL8HCZ/RsuWBCmmLa1F16Mf45JPSAVbjjVHbEarGM0BMRVv0hbLOZYcFXypjKFj4giSbW5p6Gf9O5L2iYb8WxIN+Xsu0RAhhJBuuFyOHj1avvrqK5k8eXIiLydbIdBUF/arFAkFNGaubbntCX9oy7jkMrxoqLqBeQM+ybPlSNAakuBOK8UoZjEaLOL0ubUf41IBE0eQTINuaSRbEg1pHTp/k+R7Qz2SaIgQQkg3Bd24cePkzjvvlNdff11GjRolZWVlHcZcdNFFtOIlgCm/ZIsBLDpODXF1P0e46bgkM6RwgGxfOlJ+rFoqbr9HLAabiN8uIbNH3CEP8l5qP8YlGyaOIJnsloYEKHgYEul2Ga5/h8kv69+RdE80tKJqtSxatkTGbzdWRpePoGWOEEL6uqCbN2+eirW6ujr5+uuvY445++yzKegSwFo+rLXWXDDQ+SCjsXVckoFrzDHjD5Fad4NUNleLq8kq9d9PkqKdvxFLvk8G55drf7JdaJg4gmS6WxqyWcINrTgiyyXEHN3SSKbc54PzB0ijo05bulkSQkgaCLrnnnsu+UdCWolKnNDtcQk8bf3V5JPlneX/kx9WbZR6Ecmz5sjEQaPlwDG/SIkLDesZkUymN+vfEUIIISTzSbhsAdyFkOnygw8+kKqqKrnppptkzpw5MnPmTBk4sGdSgmci3srVHevPRRMK6Th7eeomgnANM5ss+jfa6Ax9ySTb6xnBQrm+aZOsc1VKQVOxjHbQXSnT6I36d4QQQgjJDhISdD6fTy6++GJ5//33JTc3V7xer7hcLnn++efl/vvvl0cffVTGjx+f/KPNAjTZCQqIb41QMCVJUaJj2QptrbGRhbY8WVW3VtenoghyNieOCCeC0YQCzib53PmjDC8eTMtNBtKT9e8IIYQQkj0k9Hj4ySeflMWLF8vjjz8u33zzTZtF7plnnpHp06fLDTfckOzjzBqMucVJHdedWLawxQztwPxyafa0aD/GJZNsrWcUFs/La9aI2WgSm8Gq7YqaNVvWr+7tQySEEEIIIZko6ObOnSvXXnutTJs2rd36wsJCufnmm2XJkiXidGame1yqMdkd246PMxhax6U4li2/KCD7H1OtbSqLIHeoZ+TbUs/Il7n1jMLiubqlVlx+l6xr3CRV3lptnX6X1LTUpkQ8E0IIIYSQzCKhGXJTU5P07x/bWpKTkyM2m00aGxu7e2xZidGRJ2IwbX2QwdQ6LsWxbFrb3PSzvkxlEeRw4gjEGbV4nZplEy2WU+Hm2dtAFC+rWSXN3hY9T1jmLAaztlhu8rZof7LFMyGEEEIIySwSiqEbOXKkvPjiizJhwoQOfR999JH4/f6YtelSAeL3VqxY0W4drEljx46VdCTkdorBaJKQli2IlRzF0NrvTr6oio5la2kyyuKv82XclCbJzQ+mPJYtmxJHQLDBlTQQCorD3Bo3GDQExWQ06T+X3639GJeJwPKYDe8zIYQQQkifFHRnnHGGzJo1SzZu3CiHH364eDweWbhwoWa5fOihh+SUU04RsznhBJpdoqamRq644op21kO32y2ffPKJpCNGR66IySQSMGzRc5GiztBqLjPBQpeb8iLIAb9B6qst2vZUEeRsSRwBKxysnVZjawbRyNhBzTBqMIk36NNxmZoIBu67uAZ4SID3nCn8CSGEEEK6TkKqa+rUqXLbbbfJjTfeqGULwCWXXCJGo1GOPfZYmT17tvQUSMjy2muv6d+BQEBOO+00ueCCCyRdMdpyf/ZxRBtptQhP+g2G1nEpLoJs8bdaWWGZa26qzshYtt4i19pqDfUHA2LtYIkN6Xq1hlozK7NnZBbVkogi23iIgPsuE91rCSGEEEJSScJmtCOOOEJmzJgh3377rdahczgcMnHixF6tQYcsm4MHD5Z99tlH0hmD0Swhk1kk4BdR18u2DrXOob8niiD/VFGr65CcZPuBLIKcTPKtuVLsKJJaV4O4fB6xmMxqpQuEAuLz+/XhCPoxLlOIzqIarm0I915YhPEQAf2wAvOhASGEEEJIfCSsDBAn9/bbb2vMXHV1tWa43Lx5sxxzzDFamy4R3nnnHXnhhRfkwQcfbLceCVbuuusu+eyzz8Rut8tJJ52k/6Jj6fC6f//735LOhLwuMVisIq6mjvXosBwQ7ce4VBGOZfssf6Xc8slCOW7HQ2XaDqM4yU4icFvdrnSELN68XPwBvzh9bvGHfBIKIJbRIWaTWfszqVRDdBbVSKKzqGaD2y0hhBBCSK8JuubmZjnrrLPkhx9+0GyX/fr1k7Vr18q7774rDz/8sNapGzJkSJe2+dVXX8kf/vAHGTGio7vVb3/7W21vv/12WbNmjVx99dW6HCnqsG8UMx82bJikMwarQ0IeZ+fFxUNB7ce4VFNSaJUTDhmkLUmVe2uNNHmapdheJC6nSxw5DvEEPZJvy8s499boLKrRYH29uzElWVQJIYQQQjKVhATdAw88oFa5Z599ViZNmtS2ft26dXLNNdfov0cffTSubcGydt9998m//vUvKS8vjyn0YJn73//+p/0QbZWVlXL//ffLiSee2PakH4Ju5syZku6EQkEJ+jxbHYN+jOuppBXLFpplyDomrUile+uauvXiCXnEGrLK6JLhGZkgJDqLajSpzqJKCCGEEJKJJCToPv74YxVtkWIOwCp39913yx577KHZJvPz87e5LVj25s2bpwLt888/75CdEutQJiFS7E2fPl1uueUWWb16tfaB7777Ts4//3zpLohjii6K7nK52rWpxLl2WWvs3NYI+KV57TIJ5iffHW9lfYW8uHiOZlfMMxaLsypPHOXNsrx6tWxsrJLjxx0qo4rS2wralxjkKJfTxh8tq6vXyoq1q2T00JEyomyoWuai78N0p8icLwNy+8nq+nUyIK9fO7dLfO5qWupkRNEQHZdp594X6MnvMZJ98P4ivMdIOuPqg7+RmBtFh6gkVdAFg0EpKCiI2QcRhwQpKB0Qj6AbPny4vP7663rAEG/RbNiwoUOilXBR8/Xr16ugwwkXFxfHdNfsKj6fTxYvXhyzDwIy1VhXLpF47BMbVy4Rr6E0qfvGdXx783ypdtdKiaVQmhuC8tN3RTJ2aqM48mxS01Qrr33/lszst1fcNxiJnyGO/uKpdsrS6qUZe9mGBwfIat9aWV2zVvJMOWI2mMUf8ktzwCl2o037ly7J3PPvC/TE9xjJXnh/Ed5jJJ1Z3cd+I61Wa+oE3b777quWOLhe5uS0lx9PP/20DB06VOPq4jqAbdSrw5N6m83Wbl14GaIRQFy89NJLkgwsFouMGTOm3TqodbzBEIwQq6mksXGZOJdte1y/fuVSMG5cUve9vmmTtNR6ZFBxf806aNpye+Tl5Up+kU1sfpu0eF1SOLREBucPSOq+s52evMd6k3EyTkbUj5AP13wm65sqxRP0idlolrH5Y2Sf4dNo/U0h2XKPkd6B9xfhPUbSGVcf/I1cvnx53GMTEnSlpaWyYMEC2X///eXAAw9UCxoSpXz//fca87b77rvLtdde2zYedelKSkoSFljYdnSGTYCMl8kG4jBapIbBG9xZX7JwWy0Sj7OZ1WpJ+rEEnSEJSlAcVru6/CF1PkBrMpkkx+iQJm+LBE2hlF+HbKUn7rHeZkLOWBk/cHvNZokEKIiZQzbPTEoA05fJhnuM9B68vwjvMZLOOPrQb2RXvOESEnRvvvlmmzvlhx9+2MEdEgo30mR57rnnJizoBgwY0EGh1tTUaFtW1lr4OpMwiCGp47pCtietQJ00ioyeAeKNpQkIIYQQQrpPQoIObpU9BYqVP/bYY2oKDZtAUS4Bte6iXSMzAVNBSWsB8a1lsTQYW8clGVhJMMleWVvR6nJpDklRmU9bxNfVuRq0Pl0m1UbrLLMnhCuuRSZmmySEEEIIIZlDn/dx+sUvfqGWOGS1RMISJEm555575NRTT91m/F06Yus/Ugw2B+yssQcYDNqPcamqjZZny5WNTVVitDtl8j512mIZ6zOtNlpYzD3z42uyorZCcq050j+vTFsIW6xHPyGEEEIIIX2RPj8zR3YX1Kn79ttvZdddd5WDDjpIpk6dKv/3f/8nmYi131BxDBsvYoRYjRZ1Bl2PfoxLZW20kcVDpcbZIKtrN2iLZazPNGsV3CxhmWvytMig/HJ1NYVgRTswv1yaPS3aj3GEEEIIIYT0NfqUiQt15M4666wO68eOHStvvPGG1NXVqatlvCk80xGDwSg52+8uzpU/oJBAVG9I3S3Rj3Gpxt1ok9Wf7ygjdl8uknwPzz4BYubgZlniKOwQfIrlYkeh9mMcY74IIYQQQkhfo09Z6PLy8jSDZmeg1lwmizkQCgXF+dPnYlDxZmqNp4NlDq3BpOvRj3GpdD9cWbdWCu2tiW/Qrqpbm5Huh8iyiJg5mzn2fYX16Mc4QgghhBBC+hp9StAREe/mteJe95OI0STGvCIx5RaKMbewtc0rEjGZtB/jUu1+GBY5aDPV/TAys2csMj2zJyGEEEIISW8o6PoY3qo1EvK5xWC1iwF14ExmMZgtra3RKAazXfsxLpXuh8hq2eBu0vVosRzpfpgphDN7IoMnzjGScGZP9GdiZk9CCCGEEJLFgu7zzz+XhoYG/dvr9cqNN94oxx9/vNx+++26TBIkrCmixEWHAZ11J8H9sMHdKN9uXChrGtbperRYxvpMcz+Mzuzp8rnVAok2kzN7EkIIIYSQzCChWer7778vs2bNahN0Dz74oPz73/8Wi8UizzzzjNx6663JPs6swdp/uBgssMJ5Ooq6UEjXox/jkg3cClu8TllVv07cAY+IvUnsu7yvLZaxHv2Z5n4YzuyJGns4v8rmam2xnImZPQkhhBBCSJZnuXzhhRfkwgsvlGHDhukyMlCedNJJcsMNN8iSJUu0RtwVV1yhAo90DZQjsA3ZQdyrf5CgxylGi03j6SQYkCBEnoS0PxVlC8rzSqXe3dgWI2eC3Le2WluxBuvRj3GZBkTbiOIh8t3GRVLnqpdiR5HsMnC8mLV8BCGEEEIIIRlkoauoqJCDDz5Y/167dq2sXr1aDj300LYSAygtUFNTk9wjzRJQjqB4z+PEUjpEDEaTBH1eFXZosYz16E9F2YLvNi5Wl8owAbdD3Mt31jYM+jEu00D2zoe/flZeW/y2zFv5sbZYzrSsnoQQQgghJLNISBWgdIDH42lzv3Q4HDJ58uS2fsTQ2Wy25B1llmEfNEbKDj5HcnbYTSxF/cScW6QtlrEe/algdV2FJgIxGYytMWMBswRqB2iLZZMYtR/jMolwqYYVtRWSa82R/nll2q6srcjIUg2EEEIIISRzSMifbOedd5b77rtPXSsfeeQR2Xvvvdvqw3344YdiMpmkqKgo2ceaVUC02QaOEl/1egl6WsRoyxVL2eCUFhS3mVpFuEEMYoab5xZ3Q5PRrKn7A8GAxvGFx2UC0aUawsXFHRa72M02TYyCfsTTMTEKIYQQQgjpaySkDs477zxZsWKFnHPOOeJ0OuWiiy5q67vpppvkzDPPbJsYk8SBeEOsnH3IWG1TKebAzhozZpJAKKiFy8PvIFosYz36MS5TiCzVEH3PYjkTSzUQQgghhJAst9ANGDBAE6EsXbpURo0aJfn5+W19SIwybdq0ZB5j1gIR1ZMWumFFg2RMyUhZWrNcfFuscSAYCkkwGBDoHfRjXKYQLtUQLqIeDdYjEUwmlWoghBBCCCFZLuhQlmDKlCkyY8aMDn0Uc8nBvWG5NH4zVzybVkgICVEsVrENGC0Fkw9KWQwdXApP3+VYufeLxzV1v9/iFMuQnyRkcaplDrFl6M8k10OUYIA7qcfvVTfLaLAe/ZlWqoEQQgghhGSxoPv444/lwAMPTP7RkDYxV/3WQ+Kr3yQS2GIpMxjE31gt3qo1KU2MgvT9v9ntDHln+f9kac0q8RbWidVULDuUjpQDx/wi42qyDSroL0MKB2oCFMTMRbpdIgFMnatB4+cwjhBCCCGEkIwQdKNHj5avvvqqXWZLkjw3y/qPXxTf5goJBVH9LdQm6MRv0PXo73/871PmfgnRBhGzvGq9LFhZLRNGlcmY8sEZZZkLg3M6YNReUtXymiZAQcwc3CxhmYOYy7Plan8mnjshhBBCCMlSQTdu3Di588475fXXX9cYurKysg5jkCilpKQkGceYVXg3rxVXxSIJBbbUgzMaRVRMhESCQQkFgtqPcbby4Sk7DggYoy9fHn3xG7nzkpEZLWggYE/Z6SjNZokEKIiZg5slRC3EXKZZJQkhhBBCSJYLunnz5qlYq6urk6+//jrmmLPPPpuCLgE8lask5HG1Cjjkl9TkJFtSTbbmm9R+jEuloMs2wlZJZLNEAhTEzMHNMpOFLCGEEEIIyVJB99xzzyX/SIgSaKrbkl2yNcOkulqGw7oi1us4klQg3hBPRwghhBBCSEYLusWLF4vH49nqmPHjx7cVGyfxY8orai/mIsHyllICreMIIYQQQggh2UxCgu63v/2tVFRUbHXM3LlzZfhwugR2FdSb05i5ULD1n4q6VlfLsJjDOh2XYqxmowztn68tIYQQQgghJEME3S233CJOZ/tCy16vV1atWiWPP/64JkQZPHhwso4xqzDl5InBapeQ190q4CLdLyHsDAbtx7hUM2xAgdz3+/1Tvh9CCCGEEEJIDwq6XXfdtdO+vffeWy6++GI5+eSTEzyk7MZkzxdzYZn4G6ol5Pf+LOo0ls4gBrNV+zGOEEIIIYQQkt0k3Zdu7Nix4na7paqqKtmbzgosZYPFPmh7jZEz5hWLmK0iJrO2WMZ69GNcqlm5vkFOvOq/2hJCCCGEEEL6HkkXdGvXrtVyBkyIkhgoFp4/6UAxWnNayxeEwslQRJexHv2pKioeSTAUEpfHry0hhBBCCCEkQ1wu77//fqmtrW23LhQKSUtLi3zwwQcyadIkKSpiFsbuovlQTBBuxphJLwkhhBBCCCHZTcKFxWGJi8RgMIjD4ZB9991XZs+enazjyzpCoaA0ffuOSCggthETRbxuCQX9YjCaRax2CdRt1H7bwFE9YqUjqScYCsr6pk2yzlUpBU3FMtoxggXNCSGEEEJI6gTdiy++mMjLSBz4qteLt3qtmPJKxGg0ithz2g/IK9F+jLP2G8prmuYsr1kt81bOlzV166XJ2SSfO3+U4cWD5YBRe8mY0hG9fXiEEEIIISQTBV24TMELL7wg//vf/6SmpkYKCgpk4sSJcsopp0j//v2Te5RZRNDTIiG/TwwWW8x+rA811+m4VDOkPE/uvGQfbUlqxNwzP74mTZ4WKbLli8lrEIfVIStrK6Sq5TU5ZaejKOoIIYQQQshWSchnz+PxyBlnnCE33HCDLFu2TCwWi2zYsEEeeughOfLII2XlypWJbJZsKSxuMFsk5PPEvB5Yj/6eKCxut5plzJAibUny3SxhmYOYG5RfLnazTd0s0Q7ML5dmT4v2YxwhhBBCCCFJFXSPPfaYxtA9/fTT8t5778kzzzwjb731lsydO1fLFtx0002JbJZsKVtgLRsqgeZaTTQTCZaxHv2pLlsAIfFDxWq55amPtKWwSC4bGitlXcNGKXEUavxpJFgudhRqP8YRQgghhBDSGQmZXt5//3258sorZcqUKe3WDx48WO666y7Za6+9xOl0Sk5OVPwXibtsge/9KvHXbtBYOnWz9HlUzBnt+SkvWxCO6/qpolZ++ma4VNu/le0rSxjXlURafE7xBf1iQ53BGGB9vbtRxxFCCCGEENIZCakCiLXhw4fH7CsuLpb8/HxpaGAx6kSxDxojJfudJrYBoyXobhZ/3SZtsYz16E91XNeK2gpxWOy6Di3iurAe/aT75FpyxGI0i8fvjdmP9ejHOEIIIYQQQpJqoRsxYoS6Wk6YMKFD39dffy1ut1vKysoS2TTZAkQbShMgmyUSoCBmDm6WqbTMRcd1NQXMbdaisvxy2dhUpf2jSoYxrX43GVTQX4YUDlShjLi5aNfaOleDXmeMI4QQQgghJKmC7vTTT9ekKM3NzXL44YdLeXm51NfXyxdffCH33XefHHfccZoohXQPiLeeLE3QlbguiBGSOEiAgtIEyGYJoVxgy1NB7fK7pdHTLHm2XO3HOEIIIYQQQpIq6KZOnSq/+93v5M4775THH3+8Xd/MmTPl8ssvT2SzpI/FdVltIRm2nUtbwLiu5II6cyhN0FaHzt8k+d6QWuZYh44QQgghhMRDwvnozz77bDnqqKPks88+k+rqasnNzZVddtlFxoxJXXwX6bm4LsTN2XOCsv3OP9e7Y1xXakQdBNyKqtWyaNkSGb/dWBldPoKWOUIIIYQQklpBFwgEpKqqSoqKijQJCkAtOvwLW/EcDkeimyd9IK4rEDBIc4NZ8gr9YjIxrosQQgghhJCMEHSoQXfWWWdp2xmoSddZJkySHnFdFk+ZfP9hmey8T6X4bNWM60phiQh1uXQ2yefOH2V48WC6XBJCCCGEkNQJur///e9is9m05hxqz0Un0AADBzJpRrrHdaEOHXD53LL9QMZ1papEBLKKFtnyxeQ1iMPqUAspRDXeB7wfhBBCCCGEJFXQLVmyRK699lrZfffdE3k5SZO4rs/yV8otnyyU43Y8VKbtMIpxXSksEREMBsVj8Kira47VwRIRhBBCCCEkLhLKiY7i4YyPy3z3y/K81lqCaJk+v/dKRBBCCCGEEJJUQbfvvvvKww8/rIlRSOZiMhqkINeqLUltiYhosB79GEcIIYQQQki3XS7vvvtuqamp0b/9fr+8/fbbcvDBB2upgljWutmzZ0tJSUm8myd9kP9v707gm6qyB46flLZpaUuhlAIFBAFBUVBW2WSVxYVBEdxQXBhFFJTFBXdBRUFFUQZ0nFGUARRZ3WBERQFZZBtRFAQRpeyl0IXStCX5f86dSf5paUpa0val/X0/n35e3tL05uV90ndyzz333MRYmT3hirJuRoWYIiI/pogAAABAQAO69evXS1JSkmc9ISFBHA6H2V6Qu+++m4AO8HOKCG8uF1NEAAAAIMAB3Zw5c/w9FOXEHwfT5Pl3vpfH72wn9WtVKevmlOspIqrYo02hlJO5WZLmyGCKCAAAAJTcGDpUDDm5Tjlw9IRZouSmiNCKoieyT8rx3DSz1HWmLAAAAECJTVsAILBTRPx2eI/8vHO7NDvvfGmU0ICqogAAAPALAR1ggfTLOjG1JC3ymFkyRQQAAAD8RcolAAAAAAQpAjr4lBgfJePv6mCWAAAAAMpJyuW6devk5MmTBe6z2WwSEREhdevWNT8IXpUjwqTV+Qll3QwAAAAAgQzonnzySfnzzz/PeFz79u3NhORVq1Ytzp9BGUtJy5Jla/dI3w4NJK7K6ZNfAwAAAAjClMtJkyZJWFiY3HLLLWZ+uq+++koWLVokTzzxhMTHx8udd94pb775ppl4fOLEiYFvNUotoJv7xQ6zBAAAAFBOeuhmz54t99xzj4wYMSLP9mbNmkmbNm3k7rvvlkceeUQuvvhi6dmzp2RnZ0t4eHig2gwAAAAAKG4P3a5du6R79+4F7rvgggskNzdXkpOTJS4uTqpUqSIpKSmcbAAAAACwQkCnY+I2bNjgM9hLS0uTyMhIcTqdkpGRYR4DAAAAACyQcjlw4EB59NFH5fjx49KnTx+pWbOmZGZmypYtW2TKlCnSq1cviYqKMuPotIcuNjY2wM1GaYiODJNureqaJQAAAIByEtD169dPDhw4INOmTZMZM2Z4toeEhMiVV14p48ePN+sLFy6U4cOHB661KFW1qkfJ2MGtOesAAABAeQrolBY+uf7662Xt2rVy6NAhqV69ujRv3lwaNGjgOWbBggUSExMTqLZWOC6XU3KS94nTcUJC7FESFl9HbLbSmws+O+eUJKeelPjYSAkPq1RqfxcAAABACQd0LpdL9u3bZ8bTuVMq9+/fb35Uq1atCObOQtb+XZK+ZblkJ+8VV26O2ELDJDy+nsS07CURiY2lNPx5KF1Gv/qtvDq6qzSuy1yCAAAAQLkI6Pbu3Wt66Hbv3u3zmC+++ELq169/Nm2r0MFcyorZ4sxKl0rRcWILs4srxyGOg79JzorDEtd9cKkFdQAAAADKWUD3xhtvmPFyU6dOlYSEBPM4v9q1aweifRUyzVJ75jSYC41LFJvNZrbb7JFiC0+U3JT9Zr+9dsNSTb8EAAAAUE4Cum3btsnTTz8t7dq1C3yLKjgdM6dplqZn7n/BnJuu63bdr8eF16hXZu0EAAAAUPaK1cWjhU7sdnvgWwNTAMWMmQsr+Pya9MvcHHMcAAAAgIqtWAGdzj33/vvvm8IoCPAbYo8yBVB0zFxBdLvu1+NKmhZC+eSV/hREAQAAAMpTymVkZKSsXr1arrjiCmnRooVEREScdsyoUaMkLi4uEG2sUHRqAq1mqQVQdMycd9qlBtCnMlLEXquROQ4AAABAxVasgO7jjz82KZeZmZmybt26Ao/RKpgEdEWnhU50agKtZqkFULyrXGowFxIRY/aXRkGUpMPp8toHW2TUjS2lbgLzCQIAAADlIqCbM2dO4FsCD52SQKcm8MxDl3HMpFlqz1xpzkOXlX1KdvxxzCwBAAAAlKOJxVGyNGjTqQm0mqUWQNExc5pmyVQFAAAAAIoc0E2ZMkVuv/12k0apj48ePVro8WPHjiXl8ixp8MbUBAAAAADOOqDbuHGjXH/99SZI08dJSUmFHj98+HACOgAAAACwQkDnPW5u5syZEh4eXlJtgkXUjKssY25uZZYAAAAAyskYuuuuu05q164tPXr0kO7du0vNmjUD3zKUuZjK4dK9db2ybgYAAAAAH4pV+37IkCHidDpl4sSJ0rVrVxk4cKBMnz5dtm/fXpyng0WlZjjks9W7zRIAAABAOemhGzRokPnJysqS9evXy6pVq2TRokUydepUqVOnjum504nFo6OjA99ilJojx0/Km4t+lKYN4iQ22s6ZBwAAACzmrGanjoiIMD10Dz/8sLzwwgvSt29f2bdvn8yaNeuMVTABAAAAAGXQQ5ednS1bt241vXMbNmyQLVu2mN467Z3T9MuOHTuaMXYAAAAAAIsFdP3795fdu3eboK1169Yybtw4E8TVr18/8C0EAAAAAAQu5bJZs2Zit9tNWuXhw4clJSVFkpOTJScnpzhPB4uqbA+Vlk1qmCUAAAAA6ynWnforr7wiDodDNm/eLGvWrJEVK1bI3/72NxPktWrVStq3by+DBw+WypWZvyyYJdaIlgnDOpZ1MwAAAAD4UOyuFw3eOnToYH7UoUOHTFC3YMECWb16tfTu3ZsUzCB3yukSR3au2MNDpVKIraybAwAAACBQAZ3OQ/fTTz+ZHjr90d463dayZUsz2XhCQkJxnxoW8fv+VBn96rfy6uiu0rhu1bJuDgAAAIBABHRPPfWULFu2TFJTUyUmJkY6d+4szz//vJnCoGpVbvwBAAAAwLIBnc41p5UudQLxNm3aSFhYWOBbBgAAAAAIfJXLf/7zn/LYY49JRkaGPPPMM3LXXXeZapczZ86UAwcOFOcpAQAAAACl0UOn0xOMHDnSVLeMiooyE42fPHlS5s2bJzNmzJB3333XTG0AAAAAALBYD92sWbPkl19+kffee88UQ9EJxtXcuXNN1csJEyYEup0oAw1qV5F/je9rlgAAAADKSUD3xRdfmMIoOt+ct9jYWJk4caJs375dMjMzA9VGlJHQSiESG203SwAAAADWU6w79fT0dKlZs2aB+3QycZ2jLi0t7WzbhjJ2IPmEPPvP9WYJAAAAoJwEdOeee67Mnz+/wH2rVq2S3NxciY+PP9u2oYydyMqR738+aJYAAAAAyklRlNtuu02GDBliKlpeffXV4nA4ZNu2bfL555/L22+/LTfddJOEhhZ7znL8j8vllJzkfeJ0nJAQe5SExdcRm430RwAAAAD/Vayoq23btjJ58mR59tln5ZtvvjHbRo8eLSEhITJgwAAZNWpUcZ4WXrL275L0LcslO3mvuHJzxBYaJuHx9SSmZS+JSGzMuQIAAABQvIBO9evXTy6//HLZsmWLmYMuMjJSWrRo4al4ibML5lJWzBZnVrpUio4TW5hdXDkOcRz8TXJWHJa47oMJ6gAAAAAUP6BTGsR17NiR0xjgNEvtmdNgLjQuUWw2m9lus0eKLTxRclP2m/322g1LPP2yemyEDP3LhWYJAAAAIIgDuilTpsjtt98ucXFx5vHRo0cLPX7s2LHmWBSNjpnTNEvTM/e/YM5N13W77tfjwmvUK9HTWy0mQq7pSnonAAAAEPQB3caNG+X66683QZo+TkpKKvT44cOHE9AVgxZAMWPmwuwF7jfplxnHzHElLSMzW/6z84hccl4Nia4cXuJ/DwAAAEAJBXQjRoyQhIQE83jOnDlF/DPwl1az1AIoOmZO0yzzM9tDw8xxJe1gSqZMen+jvDq6qzQmoAMAAAAsx+9BWE8//bR07dpVJk2aJL///nvJtqoC06kJtJrlqYwUcblcefbpum7X/XocAAAAgIqtSAFd69atZdasWdK3b1+59dZb5ZNPPpHs7OySbWEFo4VOdGqCkIgYUwDF6TgpLqfTLHVdt+t+5qMDAAAA4HdA17lzZ5k2bZqsXLlSxo0bJ8ePH5cHH3xQLrvsMpk4caLs2rWLsxkgOs+cTk1gr9VInFkZknvsoFnqOlMWAAAAACj2tAVaFOWOO+4wP1u3bpX58+fLwoUL5b333pNWrVrJDTfcYHrwIiIodX+2QZ1OTaDVLLUAio6Z0zTL0uyZs4dVkoZ1Ys0SAAAAgPWcVXSgE4lPmDBBVq9eLS+99JLExMTIE088YXrtDh06FLhWVlAavOnUBBF1zzfL0k6zrFczRqaO6WaWAAAAAKwnIBGC9sadd9550rRpU4mPj5e0tDTG1gEAAACA1VIuvSUnJ5vCKIsWLZIdO3ZI1apVpV+/fma+unr1SnbSa5S835KOy4Ovr5KX779MGtWtyikHAAAAgj2gczgc8tVXX8mSJUtk1apV4nQ6pW3btiblsk+fPmK3FzwhNoKPTpqQe8pplgAAAACCOKD74YcfZMGCBbJ06VKTUqmplVoYZdCgQdKgQYOSbSUAAAAAoPgBnU5RkJSUJJ06dTIpld27d5ewsDB/fx0AAAAAUFYB3dChQ6VLly6SmJgY6DYAAAAAAEoyoLvxxhuL8/wIYjpdwbSHukut6lFl3RQAAAAAga5yifJNJxSvX6tKWTcDAAAAgA+lO1M1gsrhlEx5/cMtZgkAAADAegjo4FNaZrYs//5PswQAAABgPQR0AAAAABCkCOgAAAAAIEgR0AEAAABAkCKgg0/VYuwysMd5ZgkAAADAepi2AD5Vj42U265qxhkCAAAALIoeOviUmZUjP+5KNksAAAAA1kNAB5/2J5+Qx2Z8Z5YAAAAArIeADgAAAACCFAEdAAAAAAQpAjoUyOlyyuGMZPNYl7oOAAAAwFqoconT7Dq6R77avVp2Jh2VUHsdWfjLZ/JjenXp2bCzNK7egDMGAAAAWAQ9dDgtmJv74xL5LeVPia8eKl2uPmaWu1P+NNt1PwAAAABrIKCDh6ZVas9cuuOEJMYkSGRYhITYQsyydkyCZDhOmP2kXwIAAADWQEAHj/1phyQp9YDERcaKzWaTjNRKsuqzamap69UiY81+PQ4AAABA2SOgg8eJnEzJceaKPTTcrDudIo6TlcxS6Xbdr8cBAAAAKHsEdPCICqssYSGh4sjNLvCs6Hbdr8cBAAAAKHsEdPBIrFJT6sbWlmMnU8XlcuU5M7qu23W/HgcAAACg7BHQ4f8vBluImZog2h4lB9IPe3rqdKnrul3363EAAAAAyh535shD55m7qXl/aRh3jjjD06RB2z1mqeu6nXnoAAAAAOtgYnGcRoM2DeC0mqUWQNExc5pmSc8cAAAAYC300KHgC0Pnn5Oqsm69wywJ5gAAAADrIaCDT8fSHTL/651mCQAAAMB6COgAAAAAIEgR0AEAAABAkCKgAwAAAIAgRUAHn6pUDpde7c4xSwAAAADWw7QF8CkhrrLcf0NLzhAAAABgUfTQwSdHzin542CaWQIAAACwHgI6+LT3ULqMeGmFWQIAAACwHgI6AAAAAAhSBHQAAAAAEKQI6AAAAAAgSBHQwSeblkGtFGKWAAAAAKyHaQvgU6O6VWXR5H6cIQAAAMCi6KEDAAAAgCBFQAefdLqCB6Z8w7QFAAAAgEUR0MEnnVB8975UJhYHAAAALIqADgAAAACCFAEdAAAAAAQpAjoAAAAACFIEdPCpVlxleWRIG7MEAAAAYD3MQwefoiuHS+eL63CGAAAAAIuihw4+HUvPksXf7jJLAAAAANZDQAefjqZmyT8/3maWAAAAAKyHgA4AAAAAghQBHQAAAAAEKQI6AAAAAAhSBHTwKSoiTNo1q2WWAAAAAKyHaQvgU+34KHly6KWcIQAAAMCi6KGDT7mnnJKa4TBLAAAAANZDQAef9hxIk1ueXmaWAAAAAKyHgA4AAAAAghQBHQAAAAAEKQI6AAAAAAhSBHQAAAAAEKSYtgA+nZsYKx8+f6XYw7lMAAAAACviTh0+VQqxSWUmFQcAAAAsi5RLC3O5nJJ9ZK9kJW03S10vTfuPZMhTb60xSwAAAADWQw+dRWXt3yXpW5ZLdvJeceXmiC00TMLj60lMy14Skdi4VNqQ6ciVLb8eMUsAAAAA1kMPnUWDuZQVs8Vx8DcJiYiW0Gq1zFLXdbvuBwAAAAACOovRtErtmXNmpUtoXKKE2CPFFhJilrqu23V/aadfAgAAALAeAjqLyUneZ9IsK0XHic1my7NP13W77tfjAAAAAFRsBHQW43Sc+O+YuTB7gft1u+7X40pajaqRcs+1zc0SAAAAgPVQFMViQuxRpgCKK8chNvvpgZTZHhpmjitpsdF2uapzwxL/OwAAAACKhx46iwmLr2OqWZ7KSBGXy5Vnn67rdt2vx5W09MxsWbFpr1kCAAAAsB4COoux2ULM1AQhETGSm7JfnI6T4nI6zVLXdbvu1+NK2qGUTJkyZ7NZAgAAALAeAjoL0nnm4roPFnutRuLMypDcYwfNUtd1e2nNQwcAAADA2hhDZ1EatNlrNzTVLLUAio6Z0zTL0uiZAwAAABAcCOgsTIO38Br1yroZAAAAACyK7h74FBFeSZrWr2aWAAAAAKyHHjr4VDchRl6+vwtnCAAAALAoeugAAAAAIEgR0MGnXUnHpd/YJWYJAAAAwHoI6AAAAAAgSBHQAQAAAECQIqADAAAAgCBFQAcAAAAAQYppC+DTOTVj5K1He0p8bCRnCQAAALAgAjr4FB5WSRLjozlDAAAAgEWRcgmfDh49Ia/M3mSWAAAAAKyHgA4+ZZzMkW82J5klAAAAAOshoAMAAACAIEVABwAAAABByuZyuVxl3Qir2Lx5s+jpCA8Pz7Ndt+Xk5EhYWJjYbDapKHJOOSUlNUviYiMkrBKxf0mqqNcYSg/XGLi+EMz4DENFu76ys7NNW1q1anXGY6ly6cXXG6jb8wd5FYEGcTXjKpd1MyqEinqNofRwjYHrC8GMzzBUtOvLZrP5HVzSQwcAAAAAQYo8OgAAAAAIUgR0AAAAABCkCOgAAAAAIEgR0AEAAABAkCKgAwAAAIAgRUAHAAAAAEGKgA4AAAAAghQBHQAAAAAEKQI6AAAAAAhSBHQAAAAAEKQI6AAAAAAgSIWWdQOCwfbt281PzZo15dJLL5WQEOJgBEZ2drYsWLDgtO2XXXaZ1K1bl9OMs/L111/LhRdeaD67vO3du1e2bNki0dHR0qlTJ7Hb7ZxpFFlqaqp89913cuWVV+bZvnjxYjl58mSebZdccolccMEFnGX4RT+jfvzxRwkNDZWLL774tM+wI0eOyIYNG8z9WOfOnc1nGeCvU6dOyaZNm2T//v1Su3Ztad26tbnW3L788ktzjXlr1KiRtGvXTqzK5nK5XGXdCCt76aWXZP78+dKxY0f54YcfzBv/j3/8QyIjI8u6aSgHfvnlF7nmmmvkhhtuyLP95ptvlvPPP7/M2oXgt2rVKhk2bJi8++675osot3nz5smLL75oboJ+//1386XC+++/f9oNE1AYp9MpDzzwgLmGPv30U892DeRatWolffv2lZiYGM/2Xr16mS+qgDN544035J133jHXi8PhkO+//16eeeYZ6d+/v9m/cuVKGTVqlLm5Pn78uPz555/meP5nwh8pKSly2223SW5urlx00UWydetWiYiIMNdQ9erVzTFdunSRpk2bmnt+Nw363NegJWlAh4Jt2LDBdeGFF7p27dpl1lNTU129e/d2TZs2jVOGgFi8eLGra9eunE0E1EcffeRq3ry5q0mTJq5169Z5th88eNB8pq1YscKsZ2dnu4YMGeIaM2YM7wD8lpKS4ho2bJi5vq666qo8+7Zu3epq2rSp68SJE5xRFNlPP/1krp8ffvjBs+2DDz5wtWjRwnX06FFXVlaWq2PHjq6ZM2d69o8bN841cOBAzjb8otfLjTfe6MrNzTXrDofDNWDAALPdfa+vn22//PKLK5iQO1gI/daxbdu2pptVValSRQYMGCCfffZZacXbKOd27tzpub6AQHjqqadk4sSJMmbMmNP2/fvf/zbfQHbr1s2sh4WFyS233GLSS/SbcOBMjh49Kr179zY9I9ddd12Bn2mJiYlSuXJlTiaKLCMjQ+68805p0aKFZ9sVV1whWVlZ8vPPP8u6devMtTdo0CDP/ttvv930smhPHXAm+vl0zz33SKVKlcx6eHi49OzZUzZv3mzWf/31V5PK27BhQwkmBHSF0HFz+W+2dX337t3c/CAg9OZHPzQ2btxoxp3omAHgbGh6uAZof/nLXwpM8W3cuHGebXr96c2Sps4BZ6I3OhMmTJC5c+fmSUfK/5mmN9/6mabpcpqeCfhD08MffvjhPNvcn036pbp+hul15/2Fwbnnnis2m83cswFnMnLkSOnatetp15heX+7PsHr16sm+ffvk448/ltWrVwfFPT9FUQqh3wLFxsbm2aYDb3XYYVpamtSoUaOk3x+Uc/rBoTc8OgBcc7ifffZZ6dChg7z22mt5BugC/tKxS+5xAgUVsXD/03Jzj3PSfcCZVKtWzfSY+KLfbmuxihdeeEESEhJk/fr15hvxv//971K1alVOMIpE77def/11M9apefPmsnTp0tM+w7SHRf9/8hmG4tDPLL2uxo8f77kvO3DggDz44IOmE0cLiOkXWW+99ZY0aNBArIo7xkLot4r6rY83d4VLaskgENeXDrzVb4q6d+9utmnKiBZJ0UIWd911FycZAb/m8lfp5TMNgaSpcvqlgjslTr/81KEKkyZNMkEeUBQvv/yybNu2TT744ANzP6bVCQuqNK77uC9DUWkly3vvvdd8ZrlTyDXDQHvx9B5MrystHKZpvY8//rjMnj1brIqArhD6zXVmZmaebSdOnPDsA86G/lPSyl3ezjnnHBPgrVmzhoAOAaefW+np6Xm28ZmGQNLKl960N0Urw2m1aKAotGdu4cKF5gtOd8+IXk/578s0yNO0caYuQFEkJyebQE17f7Xys5uOK8/fA6yVyMeNG2eq+Fq1yj1j6AqhUbqmwnnTHpQ6depY9g1F8Dh8+LAZh6KDwL3pt4zuwbpAaXym6fVm5VQSBAe9qdbPNJ3byRufaSiqyZMny6JFi0yPiPf8hfoZpteXlpx307FOmn2Qf3ww4IumVA4ePNhMRTBlypQ8Q1z0y6cdO3ac9hmmvXVWvjcjoCtEjx49ZO3atZ6xKPotkObZ6nbgbOlkzs8995x88skneSrIae8c8zWhJGhqrxZ18i4eoNV827dvL1FRUZx0nBUdxzRjxgzTo+Km6UrLli3jMw1+07HkWthJvxzIX2lQiz5p8LZixQrPNv0fqtktBHTwh34BoMGcVrbUAk/5U3iXLFligjzvYE7/T2rBHu2tsypSLgvRp08fk7etkzxfe+21plyu9qpouVPgbGnBnYceesiMLfnjjz9MwYCPPvrITGZ50003cYIRcDrxro5tGjp0qLnGtLKX3hjNmTOHs42AePrpp2X06NEmlVerD+qXoO5JyIEz0f+B//rXv0zam3fQpjp16mQCN72WtBLmkCFDTAr5vHnzZPr06QWOrQO8aXA2YsQIkzpZt25d86WB9xdSeq+vqZW33nqrOa5ly5ayatUqUzhl1qxZYmU2nYyurBthZfrtokbr2v2qqZY6uDt/5UvgbOj8OStXrjT/mLSKl1aQs3K3PoKDjjPRcQF33HGHubF20498nY9u06ZNpmKhFuHRKoRAUekNt05PcN999+XZvmfPHnON6RgVrRKnY+gYpgB/6A22Tk1QEP0ySv9HKs2e+vbbb811pf8zmzRpwgnGGWmRJi20UxDNUnnkkUc8xVK0V07Te/XeX6cBiouLEysjoAMAAACAIEX/NAAAAAAEKQI6AAAAAAhSBHQAAAAAEKQI6AAAAAAgSBHQAQAAAECQIqADAAAAgCBFQAcAAAAAQYqADgAqiJkzZ8qCBQtO256amipvvPGGvPXWW6ft04nIp0+fLsuXLw9YO3bt2mX+3tGjRwP2nIF+7pSUFPM8OkF2QTIyMsz+rVu3Ful5tV35J07OysqSxYsXy5tvvimfffaZbN++3Ty3ToJb0ucrkPxpZ7C8FgAIJgR0AFBBbNiwQSZNmmSCNG8rV66UadOmyZQpU8wNt7edO3fK1KlT5dChQwFrh/4N/XtWvqk/duyYaeO4ceNk7969BQZ0ur8oAV1mZqb07dtXNm3alGf7Aw88IC+88IIJ4PSYHTt2mOd2B3TBIhjeVwAojwjoAKCC6Nixo+mN0yDN27fffiudO3eW6Oho89jb999/b5aXXXaZBIPGjRvLyJEjpXr16gF5vmuuucYEwYGQm5tbYJCm5/jaa6+Vhx9+WAYNGlTir6kslafXAgBWEVrWDQAAlI5OnTqZpfYQNWnSxDx2Op2yevVqGTFihERGRpqAbujQoXmCjXr16kn9+vU925KSkkyvnvZSNWzYULp16yahoXn/nfhzjDdtw5YtW6Rnz57SrFkzs+3EiRPyzTffyL59+6Ry5crSunVrueCCC87YS7R06VK5+eabTdDgXr/vvvtkzZo18vPPP5vAVf9OzZo1z3jOtC36O3peunbtesbjfb1uTeF85513zDH6XNoT16tXL/n000/F4XDIjz/+aFIRr7vuukJfU1xcnHkefd/++te/is1mM8doL6KmbbZt21bat29fYNt+/fVXk0I6ePBgWbZsmemp1ddUt25dz99Zv369pKenm3PXpUsXzzkq7nnM/76ezfujvaGbN2+WqKgoufTSS811oV9ODBky5IzvCwCUZ/TQAUAF0aBBA6lTp45s3Lgxz02yphdqD53+6A2zBiNueqxud3vvvfekT58+JtDS35s8ebIJQo4cOVKkY7xpkDFs2DBzg37++eebbXv27JHevXvLBx98YAIMHXd24403mtTEoqT9udfvueceM0ZNeygXLVpk2udvuuSoUaPMOMLs7OxCjyvq6/aX92vSAE4D75dfftmcG6WB2aOPPipz5871BOoF0eBHn0fPowZ3n3zyiZw6dcrse+6550yQpemeeo4+/PBDc/7/85//FPs8FvS+Fvf9mTBhgqd9GiAOGDDAtHnWrFlndW4BoFxwAQAqjMcff9zVrVs3z/prr73m6tGjh3m8d+9eV5MmTVzLli0z6zt37jTrX375pVnfuHGjq2nTpq6pU6d6fj89Pd3Vq1cv17Bhw/w+ZunSpeZ5d+zYYR43a9bMNW7cONepU6c8v/PYY4+Z33E6nZ5ta9ascU2fPj3Ptvy8n9t7/cknn/Qck5OTY17z/fff7/N5du3aZX5v3rx5Zv3DDz80f9vtwIEDZv+sWbP8ft2pqal5fsftoosuck2ZMsWzvnjxYnOcvh8FvSY1dOhQV5s2bVyHDx92zZw50+xfvny5qzCffvqpOW7y5Mlm3X0e165da7avX7/ec6y+F3379nWNHDnS7/Poz/tanPdHrz895uuvv/Ycs27dOrPt8ssvL/Q1A0BFQA8dAFSwcXT79++XAwcO5Bk/pzT1TnvxNGVQafpdWFiYSW9T8+fPN+vam+Km6XE67mvFihWmJ8qfY9w01W7s2LFy1VVXyfPPPy8hIf//LykiIkIOHjwoX375pacXqUOHDjJ8+HBPmmFR9OvXz/NYUyA1dfOPP/7w+/cHDhxoUgL13BWkKK87EPR86Xm499575aWXXjI9Vpdffrlfv+tOyfQ+j2PGjJF27dqZx1oAZ9WqVZ4U0qKex8LeV18Ke96FCxeansfu3bt7jtFr0t1eAKjoGEMHABWIBkV6g63j6PTGXscsaVDgpsGdpgy6x8+1bNnSBCbu9Dgdy5Z/eoPffvvNs/TnGDdNY9SgQsfK5b/p11Q9TbnTsX3VqlUz4/969Ohhxp2Fh4cX+XXr2DNvGny5A0V/aPseeughefHFF+X1118/bb8/r9s9NjAQdHzZY489Jo888ojUqFFDHn/8cb9/NzExMc+6Xgc6xu+mm24yKY06jUKLFi1Mimn+c+3PeSzsffWlsOfVVFF3yqY3HaOoQT8AVHT00AFABaLBkfZ+6Fg5LVihvSHeRTQ0oNNeGR33pEGfd3VLvcHW8Vv5NWrUyARetWrV8usY716vp556yvTC6fxr3hISEuSjjz6SJUuWmOIfhw8fNr0+WnXSe4zf2cg/fcOZaJATGxtrzlt+RXndgbJt2zaz1PF6v//+u9+/l784jY5ZGz16tGnr22+/ba4NHZ+nvbXFOY+Fva9F4X7eSpUqFSn4BoCKhh46AKhgtLdr3bp1cvLkSbnkkks8PXDuVDbtHdEbcU0T9C6Iojf4mo6nAYp3up5WaNRevapVq/p1jNttt90m5513nqn0+Oyzz5rew/w9Ndozoz8a1GllRp2zTVNCr7zySikLGvho2qcWJfHmz+suTqqoL2vXrjUFQbRXTc+H9tRpYGa324v8XHPmzDHt1yIj3jS9tDi9of68r0WhPXEFpccWJWUWAMozeugAoAKOo9u+fbsZI+cdsClNG2zVqpXMmzdP4uPj80wToHOlJScnmwDALScnx1Qg/OKLL0zQ4s8x3jTI0Zt+7XUbP368Z7v+/ueff57nWA003b13ZUUDk/79+8u7776bZ7s/r9sdHOl8dGdDq35qVUsNmjTtcuLEibJ792559dVXi/V8MTExJs1S2+um4/70Ob23FYWv97U4NGjVtEv32E6l6bj6pUQgg2QACFb00AFABaPzuWkam6ZVFjRhuAZ5r7zyiglcvG+Y9dj777/fFLrQohnac6KFQjTlzz3Hmj/H5KepflpMROdh0+BHy+VraqNOtK29PDoZ9fHjx81jLTLSpk0bKUta9j//BOD+vG7tPdOeMA2WNaDWHrHC5ubzRYNEDR51rJoGiZoyqyX9ddoEnb9N56Irirvvvtv86Px0WmhEU241WNIxf/q4uOmOBb2vxaHX4w033GDmqrv66qtNYP/111+bsYDFOX8AUN7wSQgAFYwGAVpEQ8elFVSo44orrjCTXRc0kbbeVGtFQh1Hpr0vWrxEi5V4p/qd6RgN0DQ1USeWdtOAQgtouOcn0wqJGrh99913JnjRnrH3339fLrrookJfW/7ndq/r2EFvOs9ZYYGhHq+/V9D50XZq4LZ8+XIzrq4o50Zfg1aB1J4vLTqiAYmmcGrxGbemTZuav12lSpXTXpMGiDrJuwbc3m178MEHzTnSCcZ9BXTao6fPo8GyNw0ItU3aA6YTnutr0mBTA34NxLRgij/n0Z/3tbjvjwax+gWDzkGn7ddCPrpNeysBoKKz6dwFZd0IAACAgmiArNVY77rrLk+PsU5Arl88aM+kBoQAUJHRQwcAACxLq4fOmDHDpFlqr52O9/vqq6/M+E4N8gCgoqOHDgAAWJqmmurYRJ13TlOGNfVWx4JSFAUACOgAAAAAIGgxbQEAAAAABCkCOgAAAAAIUgR0AAAAABCkCOgAAAAAIEgR0AEAAABAkCKgAwAAAIAgRUAHAAAAAEGKgA4AAAAAghQBHQAAAABIcPo/dg6BLZugOtUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Component</th>\n",
+       "      <th>Recommendation</th>\n",
+       "      <th>Evidence</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Core genre</td>\n",
+       "      <td>Action &amp; Adventure</td>\n",
+       "      <td>Balanced score = 0.76; n = 26; hit rate = 23.1%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Observed lifecycle</td>\n",
+       "      <td>4+ seasons</td>\n",
+       "      <td>Highest within-Action &amp; Adventure lifecycle score = 0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Episode structure</td>\n",
+       "      <td>≤8 episodes/season</td>\n",
+       "      <td>Highest within-Action &amp; Adventure format score = 0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Viewing commitment</td>\n",
+       "      <td>Moderate (6–10 h)</td>\n",
+       "      <td>Highest within-Action &amp; Adventure format score = 0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Language positioning</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Highest within-Action &amp; Adventure market score = 0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Performance objective</td>\n",
+       "      <td>Durable hit: strong weekly demand plus sustained ranking</td>\n",
+       "      <td>Durable-hit share in Action &amp; Adventure: 57.7% versus 43.5% overall</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Component                                            Recommendation                                                             Evidence\n",
+       "0             Core genre                                        Action & Adventure                      Balanced score = 0.76; n = 26; hit rate = 23.1%\n",
+       "1     Observed lifecycle                                                4+ seasons             Highest within-Action & Adventure lifecycle score = 0.92\n",
+       "2      Episode structure                                        ≤8 episodes/season                Highest within-Action & Adventure format score = 0.75\n",
+       "3     Viewing commitment                                         Moderate (6–10 h)                Highest within-Action & Adventure format score = 0.92\n",
+       "4   Language positioning                                                   English                Highest within-Action & Adventure market score = 0.81\n",
+       "5  Performance objective  Durable hit: strong weekly demand plus sustained ranking  Durable-hit share in Action & Adventure: 57.7% versus 43.5% overall"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "\n",
+       "### My Ideal Show Strategy\n",
+       "\n",
+       "Rather than selecting the highest value from each dataset independently, I\n",
+       "looked for a format that balances four dimensions of success: **audience\n",
+       "quality**, **market reach**, **weekly viewing intensity**, and **staying\n",
+       "power**.\n",
+       "\n",
+       "Among genres with at least **8 matched titles**,\n",
+       "**Action & Adventure** achieved the strongest overall balance. Its balanced score\n",
+       "was **0.76**, based on the percentile ranks\n",
+       "of confidence-adjusted IMDb rating, TMDb popularity, Netflix viewing hours per\n",
+       "ranked week, and weeks in the Netflix ranking. Its top-quartile viewing-hours\n",
+       "hit rate was **23.1%** across\n",
+       "**26 titles**.\n",
+       "\n",
+       "Within Action & Adventure, the strongest observed lifecycle group was\n",
+       "**4+ seasons**. This should be interpreted as evidence of\n",
+       "multi-season durability rather than proof that producing more seasons causes\n",
+       "success, because popular shows are also more likely to receive renewals.\n",
+       "\n",
+       "For controllable production choices, the data favors approximately\n",
+       "**≤8 episodes/season** and a **Moderate (6–10 h)** season. The strongest\n",
+       "language-market profile was **English** within the selected\n",
+       "genre. The target performance pattern is a **durable hit**: strong weekly\n",
+       "demand combined with sustained chart presence. In the selected genre,\n",
+       "**57.7%** of titles met this archetype,\n",
+       "compared with **43.5%** across the full\n",
+       "analysis sample.\n",
+       "\n",
+       "These results describe associations rather than causal effects. The analysis\n",
+       "uses conservative exact-title matching, so it reduces false matches but also\n",
+       "excludes many titles. Production budget, marketing, release timing, platform\n",
+       "promotion, and target audience are not represented fully in the available\n",
+       "data.\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import unicodedata\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from IPython.display import display, Markdown\n",
+    "\n",
+    "# Optional: set True to save all report figures in ./exercise9_figures\n",
+    "EX9_SAVE_FIGURES = False\n",
+    "EX9_FIGURE_DIR = Path(\"exercise9_figures\")\n",
+    "if EX9_SAVE_FIGURES:\n",
+    "    EX9_FIGURE_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "\n",
+    "def ex9_finish_figure(filename: str) -> None:\n",
+    "    \"\"\"Apply layout, optionally save, then show the current figure.\"\"\"\n",
+    "    plt.tight_layout()\n",
+    "    if EX9_SAVE_FIGURES:\n",
+    "        plt.savefig(EX9_FIGURE_DIR / filename, dpi=300, bbox_inches=\"tight\")\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# ============================================================\n",
+    "# 1. Helper functions\n",
+    "# ============================================================\n",
+    "\n",
+    "def ex9_normalize_title(value):\n",
+    "    \"\"\"Normalize a title for conservative exact matching.\"\"\"\n",
+    "    if pd.isna(value):\n",
+    "        return np.nan\n",
+    "\n",
+    "    text = unicodedata.normalize(\"NFKD\", str(value))\n",
+    "    text = text.encode(\"ascii\", \"ignore\").decode(\"ascii\").lower()\n",
+    "    text = text.replace(\"&\", \" and \")\n",
+    "    text = re.sub(r\"[^a-z0-9]+\", \" \", text)\n",
+    "    return re.sub(r\"\\s+\", \" \", text).strip()\n",
+    "\n",
+    "\n",
+    "def ex9_to_numeric(frame: pd.DataFrame, columns: list[str]) -> pd.DataFrame:\n",
+    "    \"\"\"Convert selected columns to numeric without modifying the input.\"\"\"\n",
+    "    result = frame.copy()\n",
+    "    for column in columns:\n",
+    "        if column in result.columns:\n",
+    "            result[column] = pd.to_numeric(result[column], errors=\"coerce\")\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "def ex9_binge_velocity(\n",
+    "    frame: pd.DataFrame,\n",
+    "    hours_col: str = \"netflix_viewing_hours\",\n",
+    "    weeks_col: str = \"netflix_weeks\",\n",
+    ") -> pd.Series:\n",
+    "    \"\"\"Average viewing hours per week in the Netflix ranking.\"\"\"\n",
+    "    weeks = frame[weeks_col].replace(0, np.nan)\n",
+    "    return (frame[hours_col] / weeks).rename(\"binge_velocity\")\n",
+    "\n",
+    "\n",
+    "def ex9_bayesian_rating(\n",
+    "    frame: pd.DataFrame,\n",
+    "    rating_col: str,\n",
+    "    votes_col: str,\n",
+    "    prior_votes: int = 500,\n",
+    ") -> tuple[pd.Series, float]:\n",
+    "    \"\"\"Shrink low-vote IMDb ratings toward the dataset-wide mean.\n",
+    "\n",
+    "    WR = [v / (v + m)]R + [m / (v + m)]C\n",
+    "\n",
+    "    R: title rating\n",
+    "    v: title vote count\n",
+    "    C: overall mean rating\n",
+    "    m: prior-vote confidence threshold\n",
+    "    \"\"\"\n",
+    "    rating = pd.to_numeric(frame[rating_col], errors=\"coerce\")\n",
+    "    votes = pd.to_numeric(frame[votes_col], errors=\"coerce\").clip(lower=0)\n",
+    "\n",
+    "    valid = rating.notna() & votes.notna()\n",
+    "    global_mean = rating[valid].mean()\n",
+    "\n",
+    "    weighted = (\n",
+    "        (votes / (votes + prior_votes)) * rating\n",
+    "        + (prior_votes / (votes + prior_votes)) * global_mean\n",
+    "    )\n",
+    "\n",
+    "    return weighted.rename(\"weighted_imdb_rating\"), global_mean\n",
+    "\n",
+    "\n",
+    "def ex9_audience_alignment_gap(\n",
+    "    frame: pd.DataFrame,\n",
+    "    tmdb_col: str = \"tmdb_vote_average\",\n",
+    "    imdb_col: str = \"imdb_averageRating\",\n",
+    ") -> pd.Series:\n",
+    "    \"\"\"Absolute disagreement between TMDb and IMDb ratings.\"\"\"\n",
+    "    return (\n",
+    "        frame[tmdb_col] - frame[imdb_col]\n",
+    "    ).abs().rename(\"rating_alignment_gap\")\n",
+    "\n",
+    "\n",
+    "def ex9_log_buzz(\n",
+    "    frame: pd.DataFrame,\n",
+    "    votes_col: str = \"imdb_numVotes\",\n",
+    ") -> pd.Series:\n",
+    "    \"\"\"Log-transformed IMDb vote volume as an audience-attention feature.\"\"\"\n",
+    "    votes = pd.to_numeric(frame[votes_col], errors=\"coerce\").clip(lower=0)\n",
+    "    return np.log1p(votes).rename(\"imdb_buzz_log\")\n",
+    "\n",
+    "\n",
+    "def ex9_label_hits(\n",
+    "    frame: pd.DataFrame,\n",
+    "    column: str = \"netflix_viewing_hours\",\n",
+    "    quantile: float = 0.75,\n",
+    ") -> tuple[pd.DataFrame, float]:\n",
+    "    \"\"\"Label titles in the top performance quantile as hits.\"\"\"\n",
+    "    result = frame.copy()\n",
+    "    threshold = result[column].quantile(quantile)\n",
+    "    result[\"is_hit\"] = result[column] >= threshold\n",
+    "    return result, threshold\n",
+    "\n",
+    "\n",
+    "def ex9_rank_score(\n",
+    "    frame: pd.DataFrame,\n",
+    "    metric_columns: list[str],\n",
+    "    output_name: str,\n",
+    ") -> pd.DataFrame:\n",
+    "    \"\"\"Convert metrics to percentile ranks and average them transparently.\"\"\"\n",
+    "    result = frame.copy()\n",
+    "    percentile_columns = []\n",
+    "\n",
+    "    for column in metric_columns:\n",
+    "        percentile_column = f\"{column}_pct\"\n",
+    "        result[percentile_column] = result[column].rank(\n",
+    "            pct=True,\n",
+    "            method=\"average\",\n",
+    "        )\n",
+    "        percentile_columns.append(percentile_column)\n",
+    "\n",
+    "    result[output_name] = result[percentile_columns].mean(axis=1)\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "# ============================================================\n",
+    "# 2. Validate inputs and construct a trusted cross-source sample\n",
+    "# ============================================================\n",
+    "\n",
+    "required_netflix_columns = {\n",
+    "    \"netflix_title\",\n",
+    "    \"netflix_viewing_hours\",\n",
+    "    \"netflix_weeks\",\n",
+    "    \"tmdb_title\",\n",
+    "    \"tmdb_popularity\",\n",
+    "    \"tmdb_vote_average\",\n",
+    "    \"tmdb_vote_count\",\n",
+    "    \"imdb_title\",\n",
+    "    \"imdb_averageRating\",\n",
+    "    \"imdb_numVotes\",\n",
+    "}\n",
+    "\n",
+    "required_tmdb_columns = {\n",
+    "    \"id\",\n",
+    "    \"name\",\n",
+    "    \"original_name\",\n",
+    "    \"number_of_seasons\",\n",
+    "    \"number_of_episodes\",\n",
+    "    \"original_language\",\n",
+    "    \"vote_count\",\n",
+    "    \"vote_average\",\n",
+    "    \"popularity\",\n",
+    "    \"genres\",\n",
+    "    \"episode_run_time\",\n",
+    "    \"type\",\n",
+    "    \"status\",\n",
+    "}\n",
+    "\n",
+    "missing_netflix_columns = required_netflix_columns - set(netflix.columns)\n",
+    "missing_tmdb_columns = required_tmdb_columns - set(tmdb.columns)\n",
+    "\n",
+    "if missing_netflix_columns:\n",
+    "    raise KeyError(\n",
+    "        f\"Missing Netflix columns: {sorted(missing_netflix_columns)}\"\n",
+    "    )\n",
+    "\n",
+    "if missing_tmdb_columns:\n",
+    "    raise KeyError(\n",
+    "        f\"Missing TMDb columns: {sorted(missing_tmdb_columns)}\"\n",
+    "    )\n",
+    "\n",
+    "# Work only on copies; original data remain unchanged.\n",
+    "ex9_base = netflix.copy().reset_index(drop=True)\n",
+    "ex9_base[\"ex9_row_id\"] = np.arange(len(ex9_base))\n",
+    "\n",
+    "ex9_base = ex9_to_numeric(\n",
+    "    ex9_base,\n",
+    "    [\n",
+    "        \"netflix_viewing_hours\",\n",
+    "        \"netflix_weeks\",\n",
+    "        \"tmdb_popularity\",\n",
+    "        \"tmdb_vote_average\",\n",
+    "        \"tmdb_vote_count\",\n",
+    "        \"imdb_averageRating\",\n",
+    "        \"imdb_numVotes\",\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "for title_column in [\"netflix_title\", \"tmdb_title\", \"imdb_title\"]:\n",
+    "    ex9_base[f\"{title_column}_key\"] = ex9_base[title_column].map(\n",
+    "        ex9_normalize_title\n",
+    "    )\n",
+    "\n",
+    "# Conservative rule: keep only exact normalized title agreement.\n",
+    "# This sacrifices sample size to reduce false cross-source matches.\n",
+    "ex9_base[\"trusted_tmdb_title\"] = (\n",
+    "    ex9_base[\"tmdb_title\"].notna()\n",
+    "    & ex9_base[\"netflix_title_key\"].eq(ex9_base[\"tmdb_title_key\"])\n",
+    ")\n",
+    "\n",
+    "ex9_base[\"trusted_imdb_title\"] = (\n",
+    "    ex9_base[\"imdb_title\"].notna()\n",
+    "    & ex9_base[\"netflix_title_key\"].eq(ex9_base[\"imdb_title_key\"])\n",
+    ")\n",
+    "\n",
+    "# Create a TMDb lookup using both displayed and original titles.\n",
+    "ex9_tmdb_columns = [\n",
+    "    \"id\",\n",
+    "    \"name\",\n",
+    "    \"original_name\",\n",
+    "    \"number_of_seasons\",\n",
+    "    \"number_of_episodes\",\n",
+    "    \"original_language\",\n",
+    "    \"vote_count\",\n",
+    "    \"vote_average\",\n",
+    "    \"popularity\",\n",
+    "    \"genres\",\n",
+    "    \"episode_run_time\",\n",
+    "    \"type\",\n",
+    "    \"status\",\n",
+    "]\n",
+    "\n",
+    "ex9_tmdb = ex9_to_numeric(\n",
+    "    tmdb[ex9_tmdb_columns].copy(),\n",
+    "    [\n",
+    "        \"number_of_seasons\",\n",
+    "        \"number_of_episodes\",\n",
+    "        \"vote_count\",\n",
+    "        \"vote_average\",\n",
+    "        \"popularity\",\n",
+    "        \"episode_run_time\",\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "ex9_tmdb[\"name_key\"] = ex9_tmdb[\"name\"].map(ex9_normalize_title)\n",
+    "ex9_tmdb[\"original_name_key\"] = ex9_tmdb[\"original_name\"].map(\n",
+    "    ex9_normalize_title\n",
+    ")\n",
+    "\n",
+    "ex9_tmdb_lookup = pd.concat(\n",
+    "    [\n",
+    "        ex9_tmdb.assign(title_key=ex9_tmdb[\"name_key\"]),\n",
+    "        ex9_tmdb.assign(title_key=ex9_tmdb[\"original_name_key\"]),\n",
+    "    ],\n",
+    "    ignore_index=True,\n",
+    ")\n",
+    "\n",
+    "ex9_tmdb_lookup = (\n",
+    "    ex9_tmdb_lookup[\n",
+    "        ex9_tmdb_lookup[\"title_key\"].notna()\n",
+    "        & ex9_tmdb_lookup[\"title_key\"].ne(\"\")\n",
+    "    ]\n",
+    "    .drop_duplicates([\"title_key\", \"id\"])\n",
+    ")\n",
+    "\n",
+    "ex9_trusted = ex9_base[\n",
+    "    ex9_base[\"trusted_tmdb_title\"]\n",
+    "    & ex9_base[\"trusted_imdb_title\"]\n",
+    "].copy()\n",
+    "\n",
+    "# Duplicate titles may refer to multiple TMDb records.\n",
+    "# Choose the candidate whose TMDb metrics best reproduce the composite row.\n",
+    "ex9_candidates = ex9_trusted.merge(\n",
+    "    ex9_tmdb_lookup,\n",
+    "    left_on=\"tmdb_title_key\",\n",
+    "    right_on=\"title_key\",\n",
+    "    how=\"left\",\n",
+    "    suffixes=(\"\", \"_tmdb_full\"),\n",
+    ")\n",
+    "\n",
+    "ex9_candidates[\"match_score\"] = (\n",
+    "    (\n",
+    "        ex9_candidates[\"tmdb_vote_average\"]\n",
+    "        - ex9_candidates[\"vote_average\"]\n",
+    "    )\n",
+    "    .abs()\n",
+    "    .fillna(10)\n",
+    "    + (\n",
+    "        np.log1p(ex9_candidates[\"tmdb_vote_count\"].clip(lower=0))\n",
+    "        - np.log1p(ex9_candidates[\"vote_count\"].clip(lower=0))\n",
+    "    )\n",
+    "    .abs()\n",
+    "    .fillna(10)\n",
+    "    + (\n",
+    "        np.log1p(ex9_candidates[\"tmdb_popularity\"].clip(lower=0))\n",
+    "        - np.log1p(ex9_candidates[\"popularity\"].clip(lower=0))\n",
+    "    )\n",
+    "    .abs()\n",
+    "    .fillna(10)\n",
+    ")\n",
+    "\n",
+    "ex9_analysis = (\n",
+    "    ex9_candidates\n",
+    "    .sort_values(\n",
+    "        [\"ex9_row_id\", \"match_score\", \"vote_count\"],\n",
+    "        ascending=[True, True, False],\n",
+    "    )\n",
+    "    .drop_duplicates(\"ex9_row_id\")\n",
+    "    .copy()\n",
+    ")\n",
+    "\n",
+    "# Focus on writer-relevant narrative television.\n",
+    "ex9_analysis = ex9_analysis[\n",
+    "    ex9_analysis[\"type\"].isin([\"Scripted\", \"Miniseries\"])\n",
+    "].copy()\n",
+    "\n",
+    "# Require usable outcome data.\n",
+    "ex9_analysis = ex9_analysis[\n",
+    "    ex9_analysis[\"netflix_viewing_hours\"].gt(0)\n",
+    "    & ex9_analysis[\"netflix_weeks\"].gt(0)\n",
+    "    & ex9_analysis[\"imdb_averageRating\"].notna()\n",
+    "    & ex9_analysis[\"imdb_numVotes\"].gt(0)\n",
+    "    & ex9_analysis[\"tmdb_popularity\"].notna()\n",
+    "].copy()\n",
+    "\n",
+    "\n",
+    "# ============================================================\n",
+    "# 3. Create interpretable derived features\n",
+    "# ============================================================\n",
+    "\n",
+    "# Weekly demand intensity\n",
+    "ex9_analysis[\"binge_velocity\"] = ex9_binge_velocity(ex9_analysis)\n",
+    "\n",
+    "# IMDb quality adjusted for vote confidence\n",
+    "(\n",
+    "    ex9_analysis[\"weighted_imdb_rating\"],\n",
+    "    ex9_global_imdb_mean,\n",
+    ") = ex9_bayesian_rating(\n",
+    "    ex9_analysis,\n",
+    "    rating_col=\"imdb_averageRating\",\n",
+    "    votes_col=\"imdb_numVotes\",\n",
+    "    prior_votes=500,\n",
+    ")\n",
+    "\n",
+    "# Cross-platform rating agreement and audience attention\n",
+    "ex9_analysis[\"rating_alignment_gap\"] = ex9_audience_alignment_gap(\n",
+    "    ex9_analysis\n",
+    ")\n",
+    "ex9_analysis[\"imdb_buzz_log\"] = ex9_log_buzz(ex9_analysis)\n",
+    "\n",
+    "# Production-format features\n",
+    "ex9_analysis[\"episodes_per_season\"] = (\n",
+    "    ex9_analysis[\"number_of_episodes\"]\n",
+    "    / ex9_analysis[\"number_of_seasons\"].replace(0, np.nan)\n",
+    ")\n",
+    "\n",
+    "ex9_analysis[\"hours_per_season\"] = (\n",
+    "    ex9_analysis[\"episodes_per_season\"]\n",
+    "    * ex9_analysis[\"episode_run_time\"].replace(0, np.nan)\n",
+    "    / 60\n",
+    ")\n",
+    "\n",
+    "# Top-quartile total viewing-hours label\n",
+    "ex9_analysis, ex9_hit_threshold = ex9_label_hits(\n",
+    "    ex9_analysis,\n",
+    "    column=\"netflix_viewing_hours\",\n",
+    "    quantile=0.75,\n",
+    ")\n",
+    "\n",
+    "# Performance archetypes: weekly intensity × staying power\n",
+    "ex9_intensity_median = ex9_analysis[\"binge_velocity\"].median()\n",
+    "ex9_staying_median = ex9_analysis[\"netflix_weeks\"].median()\n",
+    "\n",
+    "ex9_archetype_conditions = [\n",
+    "    (\n",
+    "        ex9_analysis[\"binge_velocity\"].ge(ex9_intensity_median)\n",
+    "        & ex9_analysis[\"netflix_weeks\"].ge(ex9_staying_median)\n",
+    "    ),\n",
+    "    (\n",
+    "        ex9_analysis[\"binge_velocity\"].ge(ex9_intensity_median)\n",
+    "        & ex9_analysis[\"netflix_weeks\"].lt(ex9_staying_median)\n",
+    "    ),\n",
+    "    (\n",
+    "        ex9_analysis[\"binge_velocity\"].lt(ex9_intensity_median)\n",
+    "        & ex9_analysis[\"netflix_weeks\"].ge(ex9_staying_median)\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "ex9_analysis[\"performance_archetype\"] = np.select(\n",
+    "    ex9_archetype_conditions,\n",
+    "    [\"Durable hit\", \"Viral burst\", \"Slow burner\"],\n",
+    "    default=\"Limited traction\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# ============================================================\n",
+    "# 4. Evidence that robust summaries are appropriate\n",
+    "# ============================================================\n",
+    "\n",
+    "ex9_distribution_summary = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Variable\": [\n",
+    "            \"TMDb popularity\",\n",
+    "            \"Netflix viewing hours\",\n",
+    "            \"Binge velocity\",\n",
+    "            \"IMDb vote count\",\n",
+    "        ],\n",
+    "        \"Count\": [\n",
+    "            ex9_analysis[\"tmdb_popularity\"].count(),\n",
+    "            ex9_analysis[\"netflix_viewing_hours\"].count(),\n",
+    "            ex9_analysis[\"binge_velocity\"].count(),\n",
+    "            ex9_analysis[\"imdb_numVotes\"].count(),\n",
+    "        ],\n",
+    "        \"Mean\": [\n",
+    "            ex9_analysis[\"tmdb_popularity\"].mean(),\n",
+    "            ex9_analysis[\"netflix_viewing_hours\"].mean(),\n",
+    "            ex9_analysis[\"binge_velocity\"].mean(),\n",
+    "            ex9_analysis[\"imdb_numVotes\"].mean(),\n",
+    "        ],\n",
+    "        \"Median\": [\n",
+    "            ex9_analysis[\"tmdb_popularity\"].median(),\n",
+    "            ex9_analysis[\"netflix_viewing_hours\"].median(),\n",
+    "            ex9_analysis[\"binge_velocity\"].median(),\n",
+    "            ex9_analysis[\"imdb_numVotes\"].median(),\n",
+    "        ],\n",
+    "        \"Skewness\": [\n",
+    "            ex9_analysis[\"tmdb_popularity\"].skew(),\n",
+    "            ex9_analysis[\"netflix_viewing_hours\"].skew(),\n",
+    "            ex9_analysis[\"binge_velocity\"].skew(),\n",
+    "            ex9_analysis[\"imdb_numVotes\"].skew(),\n",
+    "        ],\n",
+    "        \"95th percentile\": [\n",
+    "            ex9_analysis[\"tmdb_popularity\"].quantile(0.95),\n",
+    "            ex9_analysis[\"netflix_viewing_hours\"].quantile(0.95),\n",
+    "            ex9_analysis[\"binge_velocity\"].quantile(0.95),\n",
+    "            ex9_analysis[\"imdb_numVotes\"].quantile(0.95),\n",
+    "        ],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "print(\"Distribution diagnostics\")\n",
+    "display(ex9_distribution_summary.round(2))\n",
+    "\n",
+    "\n",
+    "# ============================================================\n",
+    "# 5. Show matching coverage before interpreting results\n",
+    "# ============================================================\n",
+    "\n",
+    "ex9_coverage = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Stage\": [\n",
+    "            \"Netflix composite rows\",\n",
+    "            \"Exact IMDb title match\",\n",
+    "            \"Exact TMDb title match\",\n",
+    "            \"Exact match to both\",\n",
+    "            \"Narrative titles used\",\n",
+    "        ],\n",
+    "        \"Titles\": [\n",
+    "            len(ex9_base),\n",
+    "            int(ex9_base[\"trusted_imdb_title\"].sum()),\n",
+    "            int(ex9_base[\"trusted_tmdb_title\"].sum()),\n",
+    "            int(\n",
+    "                (\n",
+    "                    ex9_base[\"trusted_imdb_title\"]\n",
+    "                    & ex9_base[\"trusted_tmdb_title\"]\n",
+    "                ).sum()\n",
+    "            ),\n",
+    "            len(ex9_analysis),\n",
+    "        ],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "ex9_coverage[\"Coverage (%)\"] = (\n",
+    "    100 * ex9_coverage[\"Titles\"] / len(ex9_base)\n",
+    ")\n",
+    "\n",
+    "display(ex9_coverage.round(1))\n",
+    "\n",
+    "plt.figure(figsize=(9, 4.8))\n",
+    "ex9_coverage_plot = ex9_coverage.iloc[1:].sort_values(\"Titles\")\n",
+    "plt.barh(\n",
+    "    ex9_coverage_plot[\"Stage\"],\n",
+    "    ex9_coverage_plot[\"Titles\"],\n",
+    ")\n",
+    "\n",
+    "for index, value in enumerate(ex9_coverage_plot[\"Titles\"]):\n",
+    "    plt.text(value + 3, index, f\"{int(value)}\", va=\"center\")\n",
+    "\n",
+    "plt.xlabel(\"Number of titles\")\n",
+    "plt.title(\"Cross-source matching and final analysis sample\")\n",
+    "ex9_finish_figure(\"01_matching_coverage.png\")\n",
+    "\n",
+    "\n",
+    "# ============================================================\n",
+    "# 6. Find the genre with the strongest overall balance\n",
+    "# ============================================================\n",
+    "\n",
+    "ex9_genre_long = ex9_analysis.copy()\n",
+    "ex9_genre_long[\"genre\"] = (\n",
+    "    ex9_genre_long[\"genres\"]\n",
+    "    .fillna(\"\")\n",
+    "    .str.split(r\"\\s*,\\s*\")\n",
+    ")\n",
+    "ex9_genre_long = ex9_genre_long.explode(\"genre\")\n",
+    "ex9_genre_long[\"genre\"] = ex9_genre_long[\"genre\"].str.strip()\n",
+    "ex9_genre_long = ex9_genre_long[\n",
+    "    ex9_genre_long[\"genre\"].ne(\"\")\n",
+    "]\n",
+    "\n",
+    "ex9_genre_profile = (\n",
+    "    ex9_genre_long\n",
+    "    .groupby(\"genre\")\n",
+    "    .agg(\n",
+    "        title_count=(\"ex9_row_id\", \"nunique\"),\n",
+    "        median_quality=(\"weighted_imdb_rating\", \"median\"),\n",
+    "        median_reach=(\"tmdb_popularity\", \"median\"),\n",
+    "        median_intensity=(\"binge_velocity\", \"median\"),\n",
+    "        median_staying=(\"netflix_weeks\", \"median\"),\n",
+    "        hit_rate=(\"is_hit\", \"mean\"),\n",
+    "        median_alignment_gap=(\"rating_alignment_gap\", \"median\"),\n",
+    "        median_buzz=(\"imdb_buzz_log\", \"median\"),\n",
+    "    )\n",
+    "    .reset_index()\n",
+    ")\n",
+    "\n",
+    "# Require a reasonable sample before ranking a genre.\n",
+    "ex9_min_genre_count = max(\n",
+    "    8,\n",
+    "    min(15, int(len(ex9_analysis) * 0.05)),\n",
+    ")\n",
+    "\n",
+    "ex9_genre_profile = ex9_genre_profile[\n",
+    "    ex9_genre_profile[\"title_count\"] >= ex9_min_genre_count\n",
+    "].copy()\n",
+    "\n",
+    "# Equal-weight score across four distinct success dimensions.\n",
+    "ex9_genre_profile = ex9_rank_score(\n",
+    "    ex9_genre_profile,\n",
+    "    metric_columns=[\n",
+    "        \"median_quality\",\n",
+    "        \"median_reach\",\n",
+    "        \"median_intensity\",\n",
+    "        \"median_staying\",\n",
+    "    ],\n",
+    "    output_name=\"balanced_score\",\n",
+    ")\n",
+    "\n",
+    "ex9_genre_profile = (\n",
+    "    ex9_genre_profile\n",
+    "    .sort_values(\n",
+    "        [\"balanced_score\", \"title_count\"],\n",
+    "        ascending=[False, False],\n",
+    "    )\n",
+    "    .reset_index(drop=True)\n",
+    ")\n",
+    "\n",
+    "if ex9_genre_profile.empty:\n",
+    "    raise ValueError(\n",
+    "        \"No genre has enough matched titles for a reliable comparison.\"\n",
+    "    )\n",
+    "\n",
+    "best_genre = ex9_genre_profile.loc[0, \"genre\"]\n",
+    "best_genre_row = ex9_genre_profile.iloc[0]\n",
+    "\n",
+    "print(\n",
+    "    f\"Genres require at least {ex9_min_genre_count} matched titles.\"\n",
+    ")\n",
+    "display(\n",
+    "    ex9_genre_profile[\n",
+    "        [\n",
+    "            \"genre\",\n",
+    "            \"title_count\",\n",
+    "            \"median_quality\",\n",
+    "            \"median_reach\",\n",
+    "            \"median_intensity\",\n",
+    "            \"median_staying\",\n",
+    "            \"hit_rate\",\n",
+    "            \"balanced_score\",\n",
+    "        ]\n",
+    "    ]\n",
+    "    .head(10)\n",
+    "    .round(\n",
+    "        {\n",
+    "            \"median_quality\": 2,\n",
+    "            \"median_reach\": 2,\n",
+    "            \"median_intensity\": 0,\n",
+    "            \"median_staying\": 1,\n",
+    "            \"hit_rate\": 3,\n",
+    "            \"balanced_score\": 3,\n",
+    "        }\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Figure: ranked balanced score\n",
+    "plt.figure(figsize=(9, 5.5))\n",
+    "ex9_top_genres = (\n",
+    "    ex9_genre_profile\n",
+    "    .head(10)\n",
+    "    .sort_values(\"balanced_score\")\n",
+    ")\n",
+    "\n",
+    "plt.barh(\n",
+    "    ex9_top_genres[\"genre\"],\n",
+    "    ex9_top_genres[\"balanced_score\"],\n",
+    ")\n",
+    "plt.xlabel(\"Balanced score (average percentile rank)\")\n",
+    "plt.title(\n",
+    "    \"Genres balancing quality, reach, intensity, and staying power\"\n",
+    ")\n",
+    "plt.xlim(0, 1)\n",
+    "ex9_finish_figure(\"02_genre_balanced_score.png\")\n",
+    "\n",
+    "# Figure: quality-versus-reach positioning\n",
+    "plt.figure(figsize=(9, 6))\n",
+    "ex9_point_sizes = (\n",
+    "    60\n",
+    "    + 280\n",
+    "    * (\n",
+    "        ex9_genre_profile[\"median_staying\"]\n",
+    "        / ex9_genre_profile[\"median_staying\"].max()\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "plt.scatter(\n",
+    "    ex9_genre_profile[\"median_reach\"],\n",
+    "    ex9_genre_profile[\"median_quality\"],\n",
+    "    s=ex9_point_sizes,\n",
+    "    alpha=0.7,\n",
+    ")\n",
+    "\n",
+    "for _, row in ex9_genre_profile.head(8).iterrows():\n",
+    "    plt.annotate(\n",
+    "        row[\"genre\"],\n",
+    "        (row[\"median_reach\"], row[\"median_quality\"]),\n",
+    "        xytext=(4, 4),\n",
+    "        textcoords=\"offset points\",\n",
+    "        fontsize=9,\n",
+    "    )\n",
+    "\n",
+    "plt.xscale(\"log\")\n",
+    "plt.xlabel(\"Median TMDb popularity (log scale)\")\n",
+    "plt.ylabel(\"Median confidence-adjusted IMDb rating\")\n",
+    "plt.title(\"Genre positioning: audience quality versus market reach\")\n",
+    "ex9_finish_figure(\"03_genre_quality_reach.png\")\n",
+    "\n",
+    "\n",
+    "# ============================================================\n",
+    "# 7. Within the selected genre, identify a coherent format\n",
+    "# ============================================================\n",
+    "\n",
+    "ex9_best_genre_titles = (\n",
+    "    ex9_genre_long[\n",
+    "        ex9_genre_long[\"genre\"].eq(best_genre)\n",
+    "    ]\n",
+    "    .drop_duplicates(\"ex9_row_id\")\n",
+    "    .copy()\n",
+    ")\n",
+    "\n",
+    "# Number of seasons is partly an outcome of success, so it is reported as\n",
+    "# an observed lifecycle pattern rather than a guaranteed production choice.\n",
+    "ex9_best_genre_titles[\"season_plan\"] = pd.cut(\n",
+    "    ex9_best_genre_titles[\"number_of_seasons\"],\n",
+    "    bins=[0, 1, 3, np.inf],\n",
+    "    labels=[\"1 season\", \"2–3 seasons\", \"4+ seasons\"],\n",
+    ")\n",
+    "\n",
+    "ex9_best_genre_titles[\"episode_plan\"] = pd.cut(\n",
+    "    ex9_best_genre_titles[\"episodes_per_season\"],\n",
+    "    bins=[0, 8, 12, np.inf],\n",
+    "    labels=[\n",
+    "        \"≤8 episodes/season\",\n",
+    "        \"9–12 episodes/season\",\n",
+    "        \"13+ episodes/season\",\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "ex9_best_genre_titles[\"commitment_group\"] = pd.cut(\n",
+    "    ex9_best_genre_titles[\"hours_per_season\"],\n",
+    "    bins=[0, 6, 10, 16, np.inf],\n",
+    "    labels=[\n",
+    "        \"Compact (≤6 h)\",\n",
+    "        \"Moderate (6–10 h)\",\n",
+    "        \"Extended (10–16 h)\",\n",
+    "        \"Long-form (>16 h)\",\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "ex9_best_genre_titles[\"language_market\"] = np.where(\n",
+    "    ex9_best_genre_titles[\"original_language\"].eq(\"en\"),\n",
+    "    \"English\",\n",
+    "    \"Non-English\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def ex9_group_profile(\n",
+    "    frame: pd.DataFrame,\n",
+    "    group_column: str,\n",
+    "    minimum_count: int,\n",
+    ") -> pd.DataFrame:\n",
+    "    \"\"\"Summarize and rank format groups within the chosen genre.\"\"\"\n",
+    "    profile = (\n",
+    "        frame\n",
+    "        .dropna(subset=[group_column])\n",
+    "        .groupby(group_column, observed=True)\n",
+    "        .agg(\n",
+    "            title_count=(\"ex9_row_id\", \"nunique\"),\n",
+    "            median_quality=(\"weighted_imdb_rating\", \"median\"),\n",
+    "            median_reach=(\"tmdb_popularity\", \"median\"),\n",
+    "            median_intensity=(\"binge_velocity\", \"median\"),\n",
+    "            median_staying=(\"netflix_weeks\", \"median\"),\n",
+    "            hit_rate=(\"is_hit\", \"mean\"),\n",
+    "        )\n",
+    "        .reset_index()\n",
+    "    )\n",
+    "\n",
+    "    reliable = profile[\n",
+    "        profile[\"title_count\"] >= minimum_count\n",
+    "    ].copy()\n",
+    "\n",
+    "    if reliable.empty:\n",
+    "        reliable = profile.copy()\n",
+    "\n",
+    "    if reliable.empty:\n",
+    "        return reliable\n",
+    "\n",
+    "    reliable = ex9_rank_score(\n",
+    "        reliable,\n",
+    "        metric_columns=[\n",
+    "            \"median_quality\",\n",
+    "            \"median_reach\",\n",
+    "            \"median_intensity\",\n",
+    "            \"median_staying\",\n",
+    "        ],\n",
+    "        output_name=\"format_score\",\n",
+    "    )\n",
+    "\n",
+    "    return (\n",
+    "        reliable\n",
+    "        .sort_values(\n",
+    "            [\"format_score\", \"title_count\"],\n",
+    "            ascending=[False, False],\n",
+    "        )\n",
+    "        .reset_index(drop=True)\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "ex9_min_format_count = max(\n",
+    "    3,\n",
+    "    min(5, int(len(ex9_best_genre_titles) * 0.15)),\n",
+    ")\n",
+    "\n",
+    "ex9_season_profile = ex9_group_profile(\n",
+    "    ex9_best_genre_titles,\n",
+    "    \"season_plan\",\n",
+    "    ex9_min_format_count,\n",
+    ")\n",
+    "\n",
+    "ex9_episode_profile = ex9_group_profile(\n",
+    "    ex9_best_genre_titles,\n",
+    "    \"episode_plan\",\n",
+    "    ex9_min_format_count,\n",
+    ")\n",
+    "\n",
+    "ex9_commitment_profile = ex9_group_profile(\n",
+    "    ex9_best_genre_titles,\n",
+    "    \"commitment_group\",\n",
+    "    ex9_min_format_count,\n",
+    ")\n",
+    "\n",
+    "ex9_language_profile = ex9_group_profile(\n",
+    "    ex9_best_genre_titles,\n",
+    "    \"language_market\",\n",
+    "    ex9_min_format_count,\n",
+    ")\n",
+    "\n",
+    "print(f\"Conditional analysis within: {best_genre}\")\n",
+    "print(\"Observed lifecycle profile\")\n",
+    "display(ex9_season_profile.round(3))\n",
+    "print(\"Episodes per season\")\n",
+    "display(ex9_episode_profile.round(3))\n",
+    "print(\"Viewing commitment per season\")\n",
+    "display(ex9_commitment_profile.round(3))\n",
+    "print(\"Language market\")\n",
+    "display(ex9_language_profile.round(3))\n",
+    "\n",
+    "best_season_profile = (\n",
+    "    str(ex9_season_profile.loc[0, \"season_plan\"])\n",
+    "    if not ex9_season_profile.empty\n",
+    "    else \"insufficient data\"\n",
+    ")\n",
+    "\n",
+    "best_episode_plan = (\n",
+    "    str(ex9_episode_profile.loc[0, \"episode_plan\"])\n",
+    "    if not ex9_episode_profile.empty\n",
+    "    else \"insufficient data\"\n",
+    ")\n",
+    "\n",
+    "best_commitment_group = (\n",
+    "    str(ex9_commitment_profile.loc[0, \"commitment_group\"])\n",
+    "    if not ex9_commitment_profile.empty\n",
+    "    else \"insufficient runtime data\"\n",
+    ")\n",
+    "\n",
+    "best_language_market = (\n",
+    "    str(ex9_language_profile.loc[0, \"language_market\"])\n",
+    "    if not ex9_language_profile.empty\n",
+    "    else \"insufficient data\"\n",
+    ")\n",
+    "\n",
+    "# Figure: season viewing commitment within the selected genre\n",
+    "if not ex9_commitment_profile.empty:\n",
+    "    plt.figure(figsize=(9, 4.8))\n",
+    "    ex9_commitment_plot = ex9_commitment_profile.sort_values(\n",
+    "        \"format_score\"\n",
+    "    )\n",
+    "\n",
+    "    plt.barh(\n",
+    "        ex9_commitment_plot[\"commitment_group\"].astype(str),\n",
+    "        ex9_commitment_plot[\"format_score\"],\n",
+    "    )\n",
+    "    plt.xlabel(\"Within-genre format score\")\n",
+    "    plt.title(f\"Season viewing commitment within {best_genre}\")\n",
+    "    plt.xlim(0, 1)\n",
+    "    ex9_finish_figure(\"04_within_genre_commitment.png\")\n",
+    "\n",
+    "\n",
+    "# ============================================================\n",
+    "# 8. Visualize Netflix performance archetypes\n",
+    "# ============================================================\n",
+    "\n",
+    "plt.figure(figsize=(9, 6))\n",
+    "\n",
+    "for archetype, group in ex9_analysis.groupby(\"performance_archetype\"):\n",
+    "    plt.scatter(\n",
+    "        group[\"netflix_weeks\"],\n",
+    "        group[\"binge_velocity\"],\n",
+    "        alpha=0.65,\n",
+    "        label=f\"{archetype} (n={len(group)})\",\n",
+    "    )\n",
+    "\n",
+    "plt.axvline(\n",
+    "    ex9_staying_median,\n",
+    "    linestyle=\"--\",\n",
+    "    linewidth=1,\n",
+    ")\n",
+    "plt.axhline(\n",
+    "    ex9_intensity_median,\n",
+    "    linestyle=\"--\",\n",
+    "    linewidth=1,\n",
+    ")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"Weeks in Netflix ranking\")\n",
+    "plt.ylabel(\"Viewing hours per ranked week (log scale)\")\n",
+    "plt.title(\n",
+    "    \"Netflix performance archetypes: intensity versus staying power\"\n",
+    ")\n",
+    "plt.legend()\n",
+    "ex9_finish_figure(\"05_netflix_performance_archetypes.png\")\n",
+    "\n",
+    "\n",
+    "# ============================================================\n",
+    "# 9. Produce the final recipe and automatic report paragraph\n",
+    "# ============================================================\n",
+    "\n",
+    "ex9_best_genre_durable_share = (\n",
+    "    ex9_best_genre_titles[\"performance_archetype\"]\n",
+    "    .eq(\"Durable hit\")\n",
+    "    .mean()\n",
+    ")\n",
+    "\n",
+    "ex9_overall_durable_share = (\n",
+    "    ex9_analysis[\"performance_archetype\"]\n",
+    "    .eq(\"Durable hit\")\n",
+    "    .mean()\n",
+    ")\n",
+    "\n",
+    "ex9_recipe = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Component\": [\n",
+    "            \"Core genre\",\n",
+    "            \"Observed lifecycle\",\n",
+    "            \"Episode structure\",\n",
+    "            \"Viewing commitment\",\n",
+    "            \"Language positioning\",\n",
+    "            \"Performance objective\",\n",
+    "        ],\n",
+    "        \"Recommendation\": [\n",
+    "            best_genre,\n",
+    "            best_season_profile,\n",
+    "            best_episode_plan,\n",
+    "            best_commitment_group,\n",
+    "            best_language_market,\n",
+    "            \"Durable hit: strong weekly demand plus sustained ranking\",\n",
+    "        ],\n",
+    "        \"Evidence\": [\n",
+    "            (\n",
+    "                f\"Balanced score = {best_genre_row['balanced_score']:.2f}; \"\n",
+    "                f\"n = {int(best_genre_row['title_count'])}; \"\n",
+    "                f\"hit rate = {100 * best_genre_row['hit_rate']:.1f}%\"\n",
+    "            ),\n",
+    "            (\n",
+    "                f\"Highest within-{best_genre} lifecycle score = \"\n",
+    "                f\"{ex9_season_profile.loc[0, 'format_score']:.2f}\"\n",
+    "                if not ex9_season_profile.empty\n",
+    "                else \"Insufficient data\"\n",
+    "            ),\n",
+    "            (\n",
+    "                f\"Highest within-{best_genre} format score = \"\n",
+    "                f\"{ex9_episode_profile.loc[0, 'format_score']:.2f}\"\n",
+    "                if not ex9_episode_profile.empty\n",
+    "                else \"Insufficient data\"\n",
+    "            ),\n",
+    "            (\n",
+    "                f\"Highest within-{best_genre} format score = \"\n",
+    "                f\"{ex9_commitment_profile.loc[0, 'format_score']:.2f}\"\n",
+    "                if not ex9_commitment_profile.empty\n",
+    "                else \"Insufficient runtime data\"\n",
+    "            ),\n",
+    "            (\n",
+    "                f\"Highest within-{best_genre} market score = \"\n",
+    "                f\"{ex9_language_profile.loc[0, 'format_score']:.2f}\"\n",
+    "                if not ex9_language_profile.empty\n",
+    "                else \"Insufficient data\"\n",
+    "            ),\n",
+    "            (\n",
+    "                f\"Durable-hit share in {best_genre}: \"\n",
+    "                f\"{100 * ex9_best_genre_durable_share:.1f}% \"\n",
+    "                f\"versus {100 * ex9_overall_durable_share:.1f}% overall\"\n",
+    "            ),\n",
+    "        ],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "display(ex9_recipe)\n",
+    "\n",
+    "# Auto-generated written response for the exercise.\n",
+    "display(\n",
+    "    Markdown(\n",
+    "        f\"\"\"\n",
+    "### My Ideal Show Strategy\n",
+    "\n",
+    "Rather than selecting the highest value from each dataset independently, I\n",
+    "looked for a format that balances four dimensions of success: **audience\n",
+    "quality**, **market reach**, **weekly viewing intensity**, and **staying\n",
+    "power**.\n",
+    "\n",
+    "Among genres with at least **{ex9_min_genre_count} matched titles**,\n",
+    "**{best_genre}** achieved the strongest overall balance. Its balanced score\n",
+    "was **{best_genre_row['balanced_score']:.2f}**, based on the percentile ranks\n",
+    "of confidence-adjusted IMDb rating, TMDb popularity, Netflix viewing hours per\n",
+    "ranked week, and weeks in the Netflix ranking. Its top-quartile viewing-hours\n",
+    "hit rate was **{100 * best_genre_row['hit_rate']:.1f}%** across\n",
+    "**{int(best_genre_row['title_count'])} titles**.\n",
+    "\n",
+    "Within {best_genre}, the strongest observed lifecycle group was\n",
+    "**{best_season_profile}**. This should be interpreted as evidence of\n",
+    "multi-season durability rather than proof that producing more seasons causes\n",
+    "success, because popular shows are also more likely to receive renewals.\n",
+    "\n",
+    "For controllable production choices, the data favors approximately\n",
+    "**{best_episode_plan}** and a **{best_commitment_group}** season. The strongest\n",
+    "language-market profile was **{best_language_market}** within the selected\n",
+    "genre. The target performance pattern is a **durable hit**: strong weekly\n",
+    "demand combined with sustained chart presence. In the selected genre,\n",
+    "**{100 * ex9_best_genre_durable_share:.1f}%** of titles met this archetype,\n",
+    "compared with **{100 * ex9_overall_durable_share:.1f}%** across the full\n",
+    "analysis sample.\n",
+    "\n",
+    "These results describe associations rather than causal effects. The analysis\n",
+    "uses conservative exact-title matching, so it reduces false matches but also\n",
+    "excludes many titles. Production budget, marketing, release timing, platform\n",
+    "promotion, and target audience are not represented fully in the available\n",
+    "data.\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7ceb048",
+   "metadata": {},
+   "source": [
+    "## our analysis\n",
+    "Skewness Correction \n",
+    "\n",
+    "Standardization & PCA \n",
+    "\n",
+    "Score Scaling & Loadings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "0d64c4aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==========================================================================================\n",
+      "Pearson correlation matrix of PCA input features\n",
+      "==========================================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Netflix viewing hours</th>\n",
+       "      <th>TMDb rating</th>\n",
+       "      <th>TMDb vote count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Netflix viewing hours</th>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.169</td>\n",
+       "      <td>0.522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMDb rating</th>\n",
+       "      <td>0.169</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMDb vote count</th>\n",
+       "      <td>0.522</td>\n",
+       "      <td>0.440</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Netflix viewing hours  TMDb rating  TMDb vote count\n",
+       "Netflix viewing hours                  1.000        0.169            0.522\n",
+       "TMDb rating                            0.169        1.000            0.440\n",
+       "TMDb vote count                        0.522        0.440            1.000"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==========================================================================================\n",
+      "PC1 loadings and relative contributions\n",
+      "==========================================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Feature</th>\n",
+       "      <th>PC1_loading</th>\n",
+       "      <th>Squared_loading</th>\n",
+       "      <th>Relative_contribution_percent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TMDb vote count</td>\n",
+       "      <td>0.6626</td>\n",
+       "      <td>0.4390</td>\n",
+       "      <td>43.9014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Netflix viewing hours</td>\n",
+       "      <td>0.5578</td>\n",
+       "      <td>0.3111</td>\n",
+       "      <td>31.1142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TMDb rating</td>\n",
+       "      <td>0.4998</td>\n",
+       "      <td>0.2498</td>\n",
+       "      <td>24.9844</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Feature  PC1_loading  Squared_loading  Relative_contribution_percent\n",
+       "0        TMDb vote count       0.6626           0.4390                        43.9014\n",
+       "1  Netflix viewing hours       0.5578           0.3111                        31.1142\n",
+       "2            TMDb rating       0.4998           0.2498                        24.9844"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "PC1 explained variance ratio: 59.03%\n",
+      "\n",
+      "Explained variance by principal component:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Principal_component</th>\n",
+       "      <th>Explained_variance_percent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PC1</td>\n",
+       "      <td>59.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PC2</td>\n",
+       "      <td>27.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PC3</td>\n",
+       "      <td>13.17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Principal_component  Explained_variance_percent\n",
+       "0                 PC1                       59.03\n",
+       "1                 PC2                       27.81\n",
+       "2                 PC3                       13.17"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "====================================================================================================\n",
+      "Final PCA ranking of 71 Hit titles\n",
+      "====================================================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hit Rank</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Hit Composite Score</th>\n",
+       "      <th>Viewing Hours (Million)</th>\n",
+       "      <th>TMDb Rating</th>\n",
+       "      <th>TMDb Vote Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>2874.26</td>\n",
+       "      <td>8.624</td>\n",
+       "      <td>16161.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>84.49</td>\n",
+       "      <td>1170.20</td>\n",
+       "      <td>8.257</td>\n",
+       "      <td>17836.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>82.90</td>\n",
+       "      <td>2289.50</td>\n",
+       "      <td>7.831</td>\n",
+       "      <td>13053.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>The Umbrella Academy</td>\n",
+       "      <td>74.10</td>\n",
+       "      <td>414.63</td>\n",
+       "      <td>8.604</td>\n",
+       "      <td>8891.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Sex Education</td>\n",
+       "      <td>70.62</td>\n",
+       "      <td>561.36</td>\n",
+       "      <td>8.304</td>\n",
+       "      <td>6647.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>68.76</td>\n",
+       "      <td>659.51</td>\n",
+       "      <td>8.354</td>\n",
+       "      <td>3335.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>67.90</td>\n",
+       "      <td>1108.80</td>\n",
+       "      <td>8.143</td>\n",
+       "      <td>2045.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>64.97</td>\n",
+       "      <td>751.60</td>\n",
+       "      <td>8.244</td>\n",
+       "      <td>1968.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>Peaky Blinders</td>\n",
+       "      <td>64.08</td>\n",
+       "      <td>185.34</td>\n",
+       "      <td>8.546</td>\n",
+       "      <td>8836.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Cobra Kai</td>\n",
+       "      <td>64.00</td>\n",
+       "      <td>383.64</td>\n",
+       "      <td>8.220</td>\n",
+       "      <td>5734.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>Elite</td>\n",
+       "      <td>62.61</td>\n",
+       "      <td>329.29</td>\n",
+       "      <td>8.071</td>\n",
+       "      <td>8817.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>61.10</td>\n",
+       "      <td>1446.26</td>\n",
+       "      <td>7.728</td>\n",
+       "      <td>1312.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>The Good Doctor</td>\n",
+       "      <td>59.65</td>\n",
+       "      <td>112.58</td>\n",
+       "      <td>8.503</td>\n",
+       "      <td>11768.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>Arcane</td>\n",
+       "      <td>58.92</td>\n",
+       "      <td>154.27</td>\n",
+       "      <td>8.740</td>\n",
+       "      <td>3341.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>The Queen of Flow</td>\n",
+       "      <td>55.11</td>\n",
+       "      <td>561.23</td>\n",
+       "      <td>7.983</td>\n",
+       "      <td>1391.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>Extraordinary Attorney Woo</td>\n",
+       "      <td>54.08</td>\n",
+       "      <td>404.03</td>\n",
+       "      <td>8.482</td>\n",
+       "      <td>585.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>Never Have I Ever</td>\n",
+       "      <td>51.70</td>\n",
+       "      <td>297.78</td>\n",
+       "      <td>8.161</td>\n",
+       "      <td>1557.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>The Sandman</td>\n",
+       "      <td>49.84</td>\n",
+       "      <td>274.22</td>\n",
+       "      <td>8.064</td>\n",
+       "      <td>1726.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>Virgin River</td>\n",
+       "      <td>49.75</td>\n",
+       "      <td>624.25</td>\n",
+       "      <td>8.032</td>\n",
+       "      <td>448.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>The Seven Deadly Sins: Cursed by Light</td>\n",
+       "      <td>49.11</td>\n",
+       "      <td>79.51</td>\n",
+       "      <td>8.462</td>\n",
+       "      <td>4710.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>The Last Kingdom</td>\n",
+       "      <td>48.48</td>\n",
+       "      <td>189.57</td>\n",
+       "      <td>8.290</td>\n",
+       "      <td>1485.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>Emily in Paris</td>\n",
+       "      <td>44.87</td>\n",
+       "      <td>341.63</td>\n",
+       "      <td>7.775</td>\n",
+       "      <td>1152.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>Hometown Cha-Cha-Cha</td>\n",
+       "      <td>44.82</td>\n",
+       "      <td>300.58</td>\n",
+       "      <td>8.233</td>\n",
+       "      <td>442.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>Locke &amp; Key</td>\n",
+       "      <td>44.43</td>\n",
+       "      <td>278.38</td>\n",
+       "      <td>7.903</td>\n",
+       "      <td>1104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>Business Proposal</td>\n",
+       "      <td>44.19</td>\n",
+       "      <td>279.11</td>\n",
+       "      <td>8.422</td>\n",
+       "      <td>281.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>My Name</td>\n",
+       "      <td>43.88</td>\n",
+       "      <td>194.14</td>\n",
+       "      <td>8.283</td>\n",
+       "      <td>715.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>27</td>\n",
+       "      <td>Café con aroma de mujer</td>\n",
+       "      <td>42.58</td>\n",
+       "      <td>793.74</td>\n",
+       "      <td>7.500</td>\n",
+       "      <td>382.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Control Z</td>\n",
+       "      <td>41.75</td>\n",
+       "      <td>89.83</td>\n",
+       "      <td>8.227</td>\n",
+       "      <td>2242.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>Alchemy of Souls</td>\n",
+       "      <td>41.22</td>\n",
+       "      <td>127.69</td>\n",
+       "      <td>8.622</td>\n",
+       "      <td>411.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>Vikings: Valhalla</td>\n",
+       "      <td>39.80</td>\n",
+       "      <td>265.55</td>\n",
+       "      <td>7.851</td>\n",
+       "      <td>671.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>Dark Desire</td>\n",
+       "      <td>39.54</td>\n",
+       "      <td>202.94</td>\n",
+       "      <td>7.300</td>\n",
+       "      <td>4181.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>All American</td>\n",
+       "      <td>38.89</td>\n",
+       "      <td>180.66</td>\n",
+       "      <td>8.245</td>\n",
+       "      <td>414.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>Pasión de Gavilanes</td>\n",
+       "      <td>37.78</td>\n",
+       "      <td>164.18</td>\n",
+       "      <td>7.641</td>\n",
+       "      <td>1925.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>34</td>\n",
+       "      <td>Narcos: Mexico</td>\n",
+       "      <td>37.57</td>\n",
+       "      <td>138.98</td>\n",
+       "      <td>7.939</td>\n",
+       "      <td>1160.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>35</td>\n",
+       "      <td>On My Block</td>\n",
+       "      <td>36.08</td>\n",
+       "      <td>80.88</td>\n",
+       "      <td>8.461</td>\n",
+       "      <td>618.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>36</td>\n",
+       "      <td>Twenty Five Twenty One</td>\n",
+       "      <td>34.83</td>\n",
+       "      <td>171.61</td>\n",
+       "      <td>8.597</td>\n",
+       "      <td>98.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>37</td>\n",
+       "      <td>Good Girls</td>\n",
+       "      <td>34.28</td>\n",
+       "      <td>153.14</td>\n",
+       "      <td>7.998</td>\n",
+       "      <td>509.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>38</td>\n",
+       "      <td>The King's Affection</td>\n",
+       "      <td>33.92</td>\n",
+       "      <td>138.54</td>\n",
+       "      <td>8.351</td>\n",
+       "      <td>232.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>39</td>\n",
+       "      <td>Who Killed Sara?</td>\n",
+       "      <td>32.78</td>\n",
+       "      <td>143.61</td>\n",
+       "      <td>7.638</td>\n",
+       "      <td>1132.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>40</td>\n",
+       "      <td>The Marked Heart</td>\n",
+       "      <td>32.64</td>\n",
+       "      <td>272.75</td>\n",
+       "      <td>7.817</td>\n",
+       "      <td>232.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>41</td>\n",
+       "      <td>Sex/Life</td>\n",
+       "      <td>32.42</td>\n",
+       "      <td>282.10</td>\n",
+       "      <td>7.138</td>\n",
+       "      <td>1200.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>42</td>\n",
+       "      <td>The Lincoln Lawyer</td>\n",
+       "      <td>32.18</td>\n",
+       "      <td>305.27</td>\n",
+       "      <td>7.806</td>\n",
+       "      <td>183.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>43</td>\n",
+       "      <td>Raising Dion</td>\n",
+       "      <td>28.86</td>\n",
+       "      <td>172.87</td>\n",
+       "      <td>7.696</td>\n",
+       "      <td>388.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>44</td>\n",
+       "      <td>Money Heist: Korea - Joint Economic Area</td>\n",
+       "      <td>28.78</td>\n",
+       "      <td>98.37</td>\n",
+       "      <td>7.900</td>\n",
+       "      <td>601.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>45</td>\n",
+       "      <td>Our Beloved Summer</td>\n",
+       "      <td>28.21</td>\n",
+       "      <td>128.89</td>\n",
+       "      <td>8.500</td>\n",
+       "      <td>74.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>46</td>\n",
+       "      <td>Our Blues</td>\n",
+       "      <td>27.44</td>\n",
+       "      <td>105.15</td>\n",
+       "      <td>8.800</td>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>47</td>\n",
+       "      <td>Midnight Mass</td>\n",
+       "      <td>27.33</td>\n",
+       "      <td>142.28</td>\n",
+       "      <td>7.525</td>\n",
+       "      <td>664.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>48</td>\n",
+       "      <td>Sweet Magnolias</td>\n",
+       "      <td>27.12</td>\n",
+       "      <td>182.94</td>\n",
+       "      <td>7.800</td>\n",
+       "      <td>206.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>49</td>\n",
+       "      <td>Sintonia</td>\n",
+       "      <td>27.09</td>\n",
+       "      <td>109.43</td>\n",
+       "      <td>8.200</td>\n",
+       "      <td>179.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>50</td>\n",
+       "      <td>The Silent Sea</td>\n",
+       "      <td>25.24</td>\n",
+       "      <td>89.77</td>\n",
+       "      <td>7.800</td>\n",
+       "      <td>527.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>51</td>\n",
+       "      <td>First Kill</td>\n",
+       "      <td>25.14</td>\n",
+       "      <td>104.61</td>\n",
+       "      <td>7.952</td>\n",
+       "      <td>270.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>52</td>\n",
+       "      <td>The Snitch Cartel: Origins</td>\n",
+       "      <td>25.11</td>\n",
+       "      <td>112.14</td>\n",
+       "      <td>7.885</td>\n",
+       "      <td>283.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>53</td>\n",
+       "      <td>Hustle</td>\n",
+       "      <td>23.85</td>\n",
+       "      <td>198.31</td>\n",
+       "      <td>7.708</td>\n",
+       "      <td>137.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>54</td>\n",
+       "      <td>Hospital Playlist</td>\n",
+       "      <td>23.47</td>\n",
+       "      <td>72.06</td>\n",
+       "      <td>8.490</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>55</td>\n",
+       "      <td>The Chestnut Man</td>\n",
+       "      <td>22.37</td>\n",
+       "      <td>104.21</td>\n",
+       "      <td>7.584</td>\n",
+       "      <td>455.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>56</td>\n",
+       "      <td>The Five Juanas</td>\n",
+       "      <td>21.63</td>\n",
+       "      <td>177.85</td>\n",
+       "      <td>7.419</td>\n",
+       "      <td>246.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>57</td>\n",
+       "      <td>Juvenile Justice</td>\n",
+       "      <td>20.40</td>\n",
+       "      <td>134.56</td>\n",
+       "      <td>7.947</td>\n",
+       "      <td>85.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>58</td>\n",
+       "      <td>Archive 81</td>\n",
+       "      <td>18.55</td>\n",
+       "      <td>129.47</td>\n",
+       "      <td>7.219</td>\n",
+       "      <td>442.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>59</td>\n",
+       "      <td>Welcome to Eden</td>\n",
+       "      <td>13.16</td>\n",
+       "      <td>140.34</td>\n",
+       "      <td>7.090</td>\n",
+       "      <td>233.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>60</td>\n",
+       "      <td>Alba</td>\n",
+       "      <td>12.02</td>\n",
+       "      <td>175.64</td>\n",
+       "      <td>7.000</td>\n",
+       "      <td>167.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>61</td>\n",
+       "      <td>Lost in Space</td>\n",
+       "      <td>10.95</td>\n",
+       "      <td>185.18</td>\n",
+       "      <td>7.115</td>\n",
+       "      <td>96.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>62</td>\n",
+       "      <td>Anatomy of a Scandal</td>\n",
+       "      <td>10.77</td>\n",
+       "      <td>171.02</td>\n",
+       "      <td>7.071</td>\n",
+       "      <td>120.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>63</td>\n",
+       "      <td>Love Is Blind</td>\n",
+       "      <td>10.67</td>\n",
+       "      <td>256.89</td>\n",
+       "      <td>7.100</td>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>64</td>\n",
+       "      <td>Forecasting Love and Weather</td>\n",
+       "      <td>9.80</td>\n",
+       "      <td>100.85</td>\n",
+       "      <td>7.631</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>65</td>\n",
+       "      <td>Carter</td>\n",
+       "      <td>8.13</td>\n",
+       "      <td>183.04</td>\n",
+       "      <td>7.400</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>66</td>\n",
+       "      <td>I Am Georgina</td>\n",
+       "      <td>7.41</td>\n",
+       "      <td>71.29</td>\n",
+       "      <td>7.111</td>\n",
+       "      <td>293.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>67</td>\n",
+       "      <td>Collision</td>\n",
+       "      <td>4.17</td>\n",
+       "      <td>161.63</td>\n",
+       "      <td>7.300</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>68</td>\n",
+       "      <td>Turning Point: 9/11 and the War on Terror</td>\n",
+       "      <td>3.56</td>\n",
+       "      <td>80.68</td>\n",
+       "      <td>7.287</td>\n",
+       "      <td>83.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>69</td>\n",
+       "      <td>Selling Sunset</td>\n",
+       "      <td>3.21</td>\n",
+       "      <td>158.78</td>\n",
+       "      <td>7.100</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>70</td>\n",
+       "      <td>True Story</td>\n",
+       "      <td>2.89</td>\n",
+       "      <td>90.82</td>\n",
+       "      <td>7.171</td>\n",
+       "      <td>82.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>71</td>\n",
+       "      <td>Another Self</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>88.59</td>\n",
+       "      <td>7.400</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Hit Rank                                      Title  Hit Composite Score  Viewing Hours (Million)  TMDb Rating  TMDb Vote Count\n",
+       "0          1                            Stranger Things               100.00                  2874.26        8.624          16161.0\n",
+       "1          2                                Money Heist                84.49                  1170.20        8.257          17836.0\n",
+       "2          3                                 Squid Game                82.90                  2289.50        7.831          13053.0\n",
+       "3          4                       The Umbrella Academy                74.10                   414.63        8.604           8891.0\n",
+       "4          5                              Sex Education                70.62                   561.36        8.304           6647.0\n",
+       "5          6                         All of Us Are Dead                68.76                   659.51        8.354           3335.0\n",
+       "6          7                                 Bridgerton                67.90                  1108.80        8.143           2045.0\n",
+       "7          8                                      Ozark                64.97                   751.60        8.244           1968.0\n",
+       "8          9                             Peaky Blinders                64.08                   185.34        8.546           8836.0\n",
+       "9         10                                  Cobra Kai                64.00                   383.64        8.220           5734.0\n",
+       "10        11                                      Elite                62.61                   329.29        8.071           8817.0\n",
+       "11        12                                   Manifest                61.10                  1446.26        7.728           1312.0\n",
+       "12        13                            The Good Doctor                59.65                   112.58        8.503          11768.0\n",
+       "13        14                                     Arcane                58.92                   154.27        8.740           3341.0\n",
+       "14        15                          The Queen of Flow                55.11                   561.23        7.983           1391.0\n",
+       "15        16                 Extraordinary Attorney Woo                54.08                   404.03        8.482            585.0\n",
+       "16        17                          Never Have I Ever                51.70                   297.78        8.161           1557.0\n",
+       "17        18                                The Sandman                49.84                   274.22        8.064           1726.0\n",
+       "18        19                               Virgin River                49.75                   624.25        8.032            448.0\n",
+       "19        20     The Seven Deadly Sins: Cursed by Light                49.11                    79.51        8.462           4710.0\n",
+       "20        21                           The Last Kingdom                48.48                   189.57        8.290           1485.0\n",
+       "21        22                             Emily in Paris                44.87                   341.63        7.775           1152.0\n",
+       "22        23                       Hometown Cha-Cha-Cha                44.82                   300.58        8.233            442.0\n",
+       "23        24                                Locke & Key                44.43                   278.38        7.903           1104.0\n",
+       "24        25                          Business Proposal                44.19                   279.11        8.422            281.0\n",
+       "25        26                                    My Name                43.88                   194.14        8.283            715.0\n",
+       "26        27                    Café con aroma de mujer                42.58                   793.74        7.500            382.0\n",
+       "27        28                                  Control Z                41.75                    89.83        8.227           2242.0\n",
+       "28        29                           Alchemy of Souls                41.22                   127.69        8.622            411.0\n",
+       "29        30                          Vikings: Valhalla                39.80                   265.55        7.851            671.0\n",
+       "30        31                                Dark Desire                39.54                   202.94        7.300           4181.0\n",
+       "31        32                               All American                38.89                   180.66        8.245            414.0\n",
+       "32        33                        Pasión de Gavilanes                37.78                   164.18        7.641           1925.0\n",
+       "33        34                             Narcos: Mexico                37.57                   138.98        7.939           1160.0\n",
+       "34        35                                On My Block                36.08                    80.88        8.461            618.0\n",
+       "35        36                     Twenty Five Twenty One                34.83                   171.61        8.597             98.0\n",
+       "36        37                                 Good Girls                34.28                   153.14        7.998            509.0\n",
+       "37        38                       The King's Affection                33.92                   138.54        8.351            232.0\n",
+       "38        39                           Who Killed Sara?                32.78                   143.61        7.638           1132.0\n",
+       "39        40                           The Marked Heart                32.64                   272.75        7.817            232.0\n",
+       "40        41                                   Sex/Life                32.42                   282.10        7.138           1200.0\n",
+       "41        42                         The Lincoln Lawyer                32.18                   305.27        7.806            183.0\n",
+       "42        43                               Raising Dion                28.86                   172.87        7.696            388.0\n",
+       "43        44   Money Heist: Korea - Joint Economic Area                28.78                    98.37        7.900            601.0\n",
+       "44        45                         Our Beloved Summer                28.21                   128.89        8.500             74.0\n",
+       "45        46                                  Our Blues                27.44                   105.15        8.800             43.0\n",
+       "46        47                              Midnight Mass                27.33                   142.28        7.525            664.0\n",
+       "47        48                            Sweet Magnolias                27.12                   182.94        7.800            206.0\n",
+       "48        49                                   Sintonia                27.09                   109.43        8.200            179.0\n",
+       "49        50                             The Silent Sea                25.24                    89.77        7.800            527.0\n",
+       "50        51                                 First Kill                25.14                   104.61        7.952            270.0\n",
+       "51        52                 The Snitch Cartel: Origins                25.11                   112.14        7.885            283.0\n",
+       "52        53                                     Hustle                23.85                   198.31        7.708            137.0\n",
+       "53        54                          Hospital Playlist                23.47                    72.06        8.490            100.0\n",
+       "54        55                           The Chestnut Man                22.37                   104.21        7.584            455.0\n",
+       "55        56                            The Five Juanas                21.63                   177.85        7.419            246.0\n",
+       "56        57                           Juvenile Justice                20.40                   134.56        7.947             85.0\n",
+       "57        58                                 Archive 81                18.55                   129.47        7.219            442.0\n",
+       "58        59                            Welcome to Eden                13.16                   140.34        7.090            233.0\n",
+       "59        60                                       Alba                12.02                   175.64        7.000            167.0\n",
+       "60        61                              Lost in Space                10.95                   185.18        7.115             96.0\n",
+       "61        62                       Anatomy of a Scandal                10.77                   171.02        7.071            120.0\n",
+       "62        63                              Love Is Blind                10.67                   256.89        7.100             54.0\n",
+       "63        64               Forecasting Love and Weather                 9.80                   100.85        7.631             61.0\n",
+       "64        65                                     Carter                 8.13                   183.04        7.400             30.0\n",
+       "65        66                              I Am Georgina                 7.41                    71.29        7.111            293.0\n",
+       "66        67                                  Collision                 4.17                   161.63        7.300             26.0\n",
+       "67        68  Turning Point: 9/11 and the War on Terror                 3.56                    80.68        7.287             83.0\n",
+       "68        69                             Selling Sunset                 3.21                   158.78        7.100             39.0\n",
+       "69        70                                 True Story                 2.89                    90.82        7.171             82.0\n",
+       "70        71                               Another Self                 0.00                    88.59        7.400             30.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "# ==============================================================================\n",
+    "# 6. 在已篩選完成的 Hit 作品中建立排名分數\n",
+    "# ==============================================================================\n",
+    "\n",
+    "# 確認 df_hits 已經存在\n",
+    "if \"df_hits\" not in globals():\n",
+    "    raise NameError(\"找不到 df_hits，請先執行 Hit 篩選程式。\")\n",
+    "\n",
+    "df_hit_rank = df_hits.copy()\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# A. 整理排名使用的特徵\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "ranking_features = [\n",
+    "    \"netflix_viewing_hours\",\n",
+    "    \"tmdb_vote_average\",\n",
+    "    \"tmdb_vote_count\",\n",
+    "]\n",
+    "\n",
+    "# 檢查必要欄位是否存在\n",
+    "missing_columns = [\n",
+    "    column\n",
+    "    for column in ranking_features + [\"netflix_title\"]\n",
+    "    if column not in df_hit_rank.columns\n",
+    "]\n",
+    "\n",
+    "if missing_columns:\n",
+    "    raise KeyError(\n",
+    "        f\"df_hits 缺少以下必要欄位：{missing_columns}\"\n",
+    "    )\n",
+    "\n",
+    "# 轉成數值，無法轉換的內容改成 NaN\n",
+    "for column in ranking_features:\n",
+    "    df_hit_rank[column] = pd.to_numeric(\n",
+    "        df_hit_rank[column],\n",
+    "        errors=\"coerce\"\n",
+    "    )\n",
+    "\n",
+    "# 排除三項排名特徵中有缺失值的作品\n",
+    "df_hit_rank = df_hit_rank.dropna(\n",
+    "    subset=ranking_features\n",
+    ").copy()\n",
+    "\n",
+    "# 排除不合理數值\n",
+    "df_hit_rank = df_hit_rank[\n",
+    "    (df_hit_rank[\"netflix_viewing_hours\"] >= 0) &\n",
+    "    (df_hit_rank[\"tmdb_vote_average\"].between(0, 10)) &\n",
+    "    (df_hit_rank[\"tmdb_vote_count\"] >= 0)\n",
+    "].copy()\n",
+    "\n",
+    "if len(df_hit_rank) < 2:\n",
+    "    raise ValueError(\n",
+    "        \"有效 Hit 作品少於 2 部，無法進行 PCA 排名。\"\n",
+    "    )\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# B. 對高度右偏的變數做 log 轉換\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "# 使用 log1p，即 log(1 + x)，降低極端值影響\n",
+    "df_hit_rank[\"log_viewing_hours\"] = np.log1p(\n",
+    "    df_hit_rank[\"netflix_viewing_hours\"]\n",
+    ")\n",
+    "\n",
+    "df_hit_rank[\"log_tmdb_vote_count\"] = np.log1p(\n",
+    "    df_hit_rank[\"tmdb_vote_count\"]\n",
+    ")\n",
+    "\n",
+    "# PCA 實際使用的三項特徵\n",
+    "pca_features = [\n",
+    "    \"log_viewing_hours\",\n",
+    "    \"tmdb_vote_average\",\n",
+    "    \"log_tmdb_vote_count\",\n",
+    "]\n",
+    "\n",
+    "feature_names = [\n",
+    "    \"Netflix viewing hours\",\n",
+    "    \"TMDb rating\",\n",
+    "    \"TMDb vote count\",\n",
+    "]\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# C. 選用：檢查 PCA 輸入特徵的 Pearson correlation\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "correlation_matrix = (\n",
+    "    df_hit_rank[pca_features]\n",
+    "    .corr(method=\"pearson\")\n",
+    ")\n",
+    "\n",
+    "correlation_matrix.index = feature_names\n",
+    "correlation_matrix.columns = feature_names\n",
+    "\n",
+    "print(\"=\" * 90)\n",
+    "print(\"Pearson correlation matrix of PCA input features\")\n",
+    "print(\"=\" * 90)\n",
+    "display(correlation_matrix.round(3))\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# D. 檢查特徵是否具有變異\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "zero_variance_features = [\n",
+    "    feature\n",
+    "    for feature in pca_features\n",
+    "    if np.isclose(df_hit_rank[feature].std(ddof=0), 0)\n",
+    "]\n",
+    "\n",
+    "if zero_variance_features:\n",
+    "    raise ValueError(\n",
+    "        \"以下特徵在 Hit 資料中沒有變異，無法用於 PCA：\"\n",
+    "        f\"{zero_variance_features}\"\n",
+    "    )\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# E. 標準化\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "X_scaled = scaler.fit_transform(\n",
+    "    df_hit_rank[pca_features]\n",
+    ")\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# F. 執行 PCA\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "pca = PCA()\n",
+    "\n",
+    "principal_components = pca.fit_transform(\n",
+    "    X_scaled\n",
+    ")\n",
+    "\n",
+    "# 第一主成分作為共同 Hit 強度\n",
+    "pc1_scores = principal_components[:, 0].copy()\n",
+    "\n",
+    "# PCA 正負方向沒有固定意義。\n",
+    "# 讓較高的原始指標大致對應較高的 PC1 分數。\n",
+    "if pca.components_[0].sum() < 0:\n",
+    "    pc1_scores *= -1\n",
+    "    pca.components_[0] *= -1\n",
+    "\n",
+    "df_hit_rank[\"PCA_Hit_original\"] = pc1_scores\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# G. 將 PCA 分數轉成 0–100\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "score_min = df_hit_rank[\"PCA_Hit_original\"].min()\n",
+    "score_max = df_hit_rank[\"PCA_Hit_original\"].max()\n",
+    "\n",
+    "if np.isclose(score_min, score_max):\n",
+    "    df_hit_rank[\"Hit_score\"] = 50.0\n",
+    "else:\n",
+    "    df_hit_rank[\"Hit_score\"] = (\n",
+    "        (\n",
+    "            df_hit_rank[\"PCA_Hit_original\"]\n",
+    "            - score_min\n",
+    "        )\n",
+    "        /\n",
+    "        (\n",
+    "            score_max\n",
+    "            - score_min\n",
+    "        )\n",
+    "        * 100\n",
+    "    )\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# H. 依 Hit 綜合分數排序\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "df_hit_rank = (\n",
+    "    df_hit_rank\n",
+    "    .sort_values(\n",
+    "        by=[\n",
+    "            \"Hit_score\",\n",
+    "            \"netflix_viewing_hours\"\n",
+    "        ],\n",
+    "        ascending=[False, False]\n",
+    "    )\n",
+    "    .reset_index(drop=True)\n",
+    ")\n",
+    "\n",
+    "# 避免重複執行時欄位已經存在\n",
+    "if \"Hit_rank\" in df_hit_rank.columns:\n",
+    "    df_hit_rank = df_hit_rank.drop(\n",
+    "        columns=[\"Hit_rank\"]\n",
+    "    )\n",
+    "\n",
+    "df_hit_rank.insert(\n",
+    "    0,\n",
+    "    \"Hit_rank\",\n",
+    "    range(1, len(df_hit_rank) + 1)\n",
+    ")\n",
+    "\n",
+    "# 建立百萬小時欄位\n",
+    "df_hit_rank[\"Viewing_hours_million\"] = (\n",
+    "    df_hit_rank[\"netflix_viewing_hours\"]\n",
+    "    / 1_000_000\n",
+    ").round(2)\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# I. 顯示 PCA loading\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "loading_table = pd.DataFrame({\n",
+    "    \"Feature\": feature_names,\n",
+    "    \"PC1_loading\": pca.components_[0]\n",
+    "})\n",
+    "\n",
+    "# 較標準的 PCA 相對貢獻：平方 loading\n",
+    "loading_table[\"Squared_loading\"] = (\n",
+    "    loading_table[\"PC1_loading\"] ** 2\n",
+    ")\n",
+    "\n",
+    "loading_table[\"Relative_contribution_percent\"] = (\n",
+    "    loading_table[\"Squared_loading\"]\n",
+    "    /\n",
+    "    loading_table[\"Squared_loading\"].sum()\n",
+    "    * 100\n",
+    ")\n",
+    "\n",
+    "loading_table = (\n",
+    "    loading_table\n",
+    "    .sort_values(\n",
+    "        by=\"Relative_contribution_percent\",\n",
+    "        ascending=False\n",
+    "    )\n",
+    "    .reset_index(drop=True)\n",
+    ")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 90)\n",
+    "print(\"PC1 loadings and relative contributions\")\n",
+    "print(\"=\" * 90)\n",
+    "\n",
+    "display(loading_table.round(4))\n",
+    "\n",
+    "print(\n",
+    "    f\"\\nPC1 explained variance ratio: \"\n",
+    "    f\"{pca.explained_variance_ratio_[0] * 100:.2f}%\"\n",
+    ")\n",
+    "\n",
+    "# 同時顯示所有主成分的解釋變異比例\n",
+    "explained_variance_table = pd.DataFrame({\n",
+    "    \"Principal_component\": [\n",
+    "        f\"PC{i + 1}\"\n",
+    "        for i in range(len(pca.explained_variance_ratio_))\n",
+    "    ],\n",
+    "    \"Explained_variance_percent\": (\n",
+    "        pca.explained_variance_ratio_ * 100\n",
+    "    )\n",
+    "})\n",
+    "\n",
+    "print(\"\\nExplained variance by principal component:\")\n",
+    "display(explained_variance_table.round(2))\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# J. 最終 Hit 排名表\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "final_hit_ranking = df_hit_rank[[\n",
+    "    \"Hit_rank\",\n",
+    "    \"netflix_title\",\n",
+    "    \"Hit_score\",\n",
+    "    \"Viewing_hours_million\",\n",
+    "    \"tmdb_vote_average\",\n",
+    "    \"tmdb_vote_count\"\n",
+    "]].copy()\n",
+    "\n",
+    "final_hit_ranking = final_hit_ranking.rename(columns={\n",
+    "    \"Hit_rank\": \"Hit Rank\",\n",
+    "    \"netflix_title\": \"Title\",\n",
+    "    \"Hit_score\": \"Hit Composite Score\",\n",
+    "    \"Viewing_hours_million\": \"Viewing Hours (Million)\",\n",
+    "    \"tmdb_vote_average\": \"TMDb Rating\",\n",
+    "    \"tmdb_vote_count\": \"TMDb Vote Count\"\n",
+    "})\n",
+    "\n",
+    "final_hit_ranking[\"Hit Composite Score\"] = (\n",
+    "    final_hit_ranking[\"Hit Composite Score\"]\n",
+    "    .round(2)\n",
+    ")\n",
+    "\n",
+    "final_hit_ranking[\"TMDb Rating\"] = (\n",
+    "    final_hit_ranking[\"TMDb Rating\"]\n",
+    "    .round(3)\n",
+    ")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 100)\n",
+    "print(\n",
+    "    f\"Final PCA ranking of \"\n",
+    "    f\"{len(final_hit_ranking)} Hit titles\"\n",
+    ")\n",
+    "print(\"=\" * 100)\n",
+    "\n",
+    "display(final_hit_ranking)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f08fa29d",
+   "metadata": {},
+   "source": [
+    "## our analysis\n",
+    "final check and exper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "c980ccfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Netflix composite merge completed: 71 Hit titles, 13 original Netflix columns.\n",
+      "Current Hit titles: 71; TMDb candidate records found: 71.\n",
+      "All Hit titles were successfully matched to unique TMDb records.\n",
+      "TMDb merge completed: 29 original TMDb columns added.\n",
+      "\n",
+      "==================================================================================================================================\n",
+      "PCA-ranked Hit titles with all Netflix and TMDb features\n",
+      "Number of Hit titles: 71\n",
+      "Total number of columns: 53\n",
+      "Original Netflix columns: 13\n",
+      "Original TMDb columns: 29\n",
+      "PCA-derived columns: 11\n",
+      "==================================================================================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hit_rank</th>\n",
+       "      <th>Hit_score</th>\n",
+       "      <th>PCA_Hit_original</th>\n",
+       "      <th>key</th>\n",
+       "      <th>netflix_viewing_hours</th>\n",
+       "      <th>netflix_weeks</th>\n",
+       "      <th>netflix_year_hint</th>\n",
+       "      <th>netflix_title</th>\n",
+       "      <th>netflix_clean_title</th>\n",
+       "      <th>tmdb_title</th>\n",
+       "      <th>tmdb_popularity</th>\n",
+       "      <th>tmdb_vote_average</th>\n",
+       "      <th>tmdb_vote_count</th>\n",
+       "      <th>imdb_title</th>\n",
+       "      <th>imdb_averageRating</th>\n",
+       "      <th>imdb_numVotes</th>\n",
+       "      <th>TMDb_raw_id</th>\n",
+       "      <th>TMDb_raw_name</th>\n",
+       "      <th>TMDb_raw_number_of_seasons</th>\n",
+       "      <th>TMDb_raw_number_of_episodes</th>\n",
+       "      <th>TMDb_raw_original_language</th>\n",
+       "      <th>TMDb_raw_vote_count</th>\n",
+       "      <th>TMDb_raw_vote_average</th>\n",
+       "      <th>TMDb_raw_overview</th>\n",
+       "      <th>TMDb_raw_adult</th>\n",
+       "      <th>TMDb_raw_backdrop_path</th>\n",
+       "      <th>TMDb_raw_first_air_date</th>\n",
+       "      <th>TMDb_raw_last_air_date</th>\n",
+       "      <th>TMDb_raw_homepage</th>\n",
+       "      <th>TMDb_raw_in_production</th>\n",
+       "      <th>TMDb_raw_original_name</th>\n",
+       "      <th>TMDb_raw_popularity</th>\n",
+       "      <th>TMDb_raw_poster_path</th>\n",
+       "      <th>TMDb_raw_type</th>\n",
+       "      <th>TMDb_raw_status</th>\n",
+       "      <th>TMDb_raw_tagline</th>\n",
+       "      <th>TMDb_raw_genres</th>\n",
+       "      <th>TMDb_raw_created_by</th>\n",
+       "      <th>TMDb_raw_languages</th>\n",
+       "      <th>TMDb_raw_networks</th>\n",
+       "      <th>TMDb_raw_origin_country</th>\n",
+       "      <th>TMDb_raw_spoken_languages</th>\n",
+       "      <th>TMDb_raw_production_companies</th>\n",
+       "      <th>TMDb_raw_production_countries</th>\n",
+       "      <th>TMDb_raw_episode_run_time</th>\n",
+       "      <th>netflix_viewing_hours_norm</th>\n",
+       "      <th>netflix_weeks_norm</th>\n",
+       "      <th>tmdb_vote_average_norm</th>\n",
+       "      <th>tmdb_vote_count_norm</th>\n",
+       "      <th>綜合 The Hit 指數</th>\n",
+       "      <th>log_viewing_hours</th>\n",
+       "      <th>log_tmdb_vote_count</th>\n",
+       "      <th>Viewing_hours_million</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>3.8578</td>\n",
+       "      <td>st_th</td>\n",
+       "      <td>2874260000</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>stranger things</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>185.711</td>\n",
+       "      <td>8.624</td>\n",
+       "      <td>16161.0</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>66732</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>en</td>\n",
+       "      <td>16161</td>\n",
+       "      <td>8.624</td>\n",
+       "      <td>When a young boy vanishes, a small town uncovers a mystery involving secret experiments, terrifying supernatural forces, and one strange little girl.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/2MaumbgBlW1NoPo3ZJO38A6v7OS.jpg</td>\n",
+       "      <td>2016-07-15</td>\n",
+       "      <td>2022-07-01</td>\n",
+       "      <td>https://www.netflix.com/title/80057281</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>185.711</td>\n",
+       "      <td>/49WJfeN0moxb9IPfGn8AIqMGskD.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Every ending has a beginning.</td>\n",
+       "      <td>Drama, Sci-Fi &amp; Fantasy, Mystery</td>\n",
+       "      <td>Matt Duffer, Ross Duffer</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>21 Laps Entertainment, Monkey Massacre Productions</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.478261</td>\n",
+       "      <td>0.902222</td>\n",
+       "      <td>0.905952</td>\n",
+       "      <td>0.8522</td>\n",
+       "      <td>21.779061</td>\n",
+       "      <td>9.690418</td>\n",
+       "      <td>2874.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>84.49</td>\n",
+       "      <td>2.9029</td>\n",
+       "      <td>he_mo</td>\n",
+       "      <td>1170200000</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>money heist</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>96.354</td>\n",
+       "      <td>8.257</td>\n",
+       "      <td>17836.0</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>612436.0</td>\n",
+       "      <td>71446</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>3</td>\n",
+       "      <td>41</td>\n",
+       "      <td>es</td>\n",
+       "      <td>17836</td>\n",
+       "      <td>8.257</td>\n",
+       "      <td>To carry out the biggest heist in history, a mysterious man called The Professor recruits a band of eight robbers who have a single characteristic...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/gFZriCkpJYsApPZEF3jhxL4yLzG.jpg</td>\n",
+       "      <td>2017-05-02</td>\n",
+       "      <td>2021-12-03</td>\n",
+       "      <td>https://www.netflix.com/title/80192098</td>\n",
+       "      <td>False</td>\n",
+       "      <td>La Casa de Papel</td>\n",
+       "      <td>96.354</td>\n",
+       "      <td>/reEMJA1uzscCbkpeRJeTT2bjqUp.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>The perfect robbery.</td>\n",
+       "      <td>Crime, Drama</td>\n",
+       "      <td>Álex Pina</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Netflix, Antena 3</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>Vancouver Media</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0.392052</td>\n",
+       "      <td>0.521739</td>\n",
+       "      <td>0.698333</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.6011</td>\n",
+       "      <td>20.880441</td>\n",
+       "      <td>9.789030</td>\n",
+       "      <td>1170.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>82.90</td>\n",
+       "      <td>2.8051</td>\n",
+       "      <td>ga_sq</td>\n",
+       "      <td>2289500000</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>squid game</td>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>115.587</td>\n",
+       "      <td>7.831</td>\n",
+       "      <td>13053.0</td>\n",
+       "      <td>Squid Game!</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93405</td>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>13053</td>\n",
+       "      <td>7.831</td>\n",
+       "      <td>Hundreds of cash-strapped players accept a strange invitation to compete in children's games—with high stakes. But, a tempting prize awaits the vi...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/2meX1nMdScFOoV4370rqHWKmXhY.jpg</td>\n",
+       "      <td>2021-09-17</td>\n",
+       "      <td>2021-09-17</td>\n",
+       "      <td>https://www.netflix.com/title/81040344</td>\n",
+       "      <td>True</td>\n",
+       "      <td>오징어 게임</td>\n",
+       "      <td>115.587</td>\n",
+       "      <td>/dDlEmu3EZ0Pgg93K2SVNLCjCSvE.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>45.6 billion won is child's play</td>\n",
+       "      <td>Action &amp; Adventure, Mystery, Drama</td>\n",
+       "      <td>Hwang Dong-hyuk</td>\n",
+       "      <td>en, ko, ur</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>English, 한국어/조선말, اردو</td>\n",
+       "      <td>Siren Pictures, Firstman Studio</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.791378</td>\n",
+       "      <td>0.782609</td>\n",
+       "      <td>0.461667</td>\n",
+       "      <td>0.731443</td>\n",
+       "      <td>0.6817</td>\n",
+       "      <td>21.551599</td>\n",
+       "      <td>9.476850</td>\n",
+       "      <td>2289.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>74.10</td>\n",
+       "      <td>2.2635</td>\n",
+       "      <td>ac_um</td>\n",
+       "      <td>414630000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>The Umbrella Academy</td>\n",
+       "      <td>the umbrella academy</td>\n",
+       "      <td>The Umbrella Academy</td>\n",
+       "      <td>51.859</td>\n",
+       "      <td>8.604</td>\n",
+       "      <td>8891.0</td>\n",
+       "      <td>The Umbrella Academy</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>75006</td>\n",
+       "      <td>The Umbrella Academy</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32</td>\n",
+       "      <td>en</td>\n",
+       "      <td>8891</td>\n",
+       "      <td>8.604</td>\n",
+       "      <td>A dysfunctional family of superheroes comes together to solve the mystery of their father's death, the threat of the apocalypse and more.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/7sqFEDDmK1hG5m92upolcfQxy7R.jpg</td>\n",
+       "      <td>2019-02-15</td>\n",
+       "      <td>2022-06-22</td>\n",
+       "      <td>https://www.netflix.com/title/80186863</td>\n",
+       "      <td>True</td>\n",
+       "      <td>The Umbrella Academy</td>\n",
+       "      <td>51.859</td>\n",
+       "      <td>/qhcwrnnCnN8NE1N6XXKHFmveJR9.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Too many siblings. Not enough timeline.</td>\n",
+       "      <td>Action &amp; Adventure, Sci-Fi &amp; Fantasy, Drama</td>\n",
+       "      <td>Steve Blackman</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Dark Horse Entertainment, UCP</td>\n",
+       "      <td>Canada, United States of America</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.122492</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.891111</td>\n",
+       "      <td>0.497754</td>\n",
+       "      <td>0.4110</td>\n",
+       "      <td>19.842897</td>\n",
+       "      <td>9.092907</td>\n",
+       "      <td>414.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>70.62</td>\n",
+       "      <td>2.0493</td>\n",
+       "      <td>ed_se</td>\n",
+       "      <td>561360000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Sex Education</td>\n",
+       "      <td>sex education</td>\n",
+       "      <td>Sex Education</td>\n",
+       "      <td>1008.977</td>\n",
+       "      <td>8.304</td>\n",
+       "      <td>6647.0</td>\n",
+       "      <td>Sex Education</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>81356</td>\n",
+       "      <td>Sex Education</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32</td>\n",
+       "      <td>en</td>\n",
+       "      <td>6647</td>\n",
+       "      <td>8.304</td>\n",
+       "      <td>Inexperienced Otis channels his sex therapist mom when he teams up with rebellious Maeve to set up an underground sex therapy clinic at school.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/5SEEBS5qXgL5rgivTiAROy1Qt2q.jpg</td>\n",
+       "      <td>2019-01-11</td>\n",
+       "      <td>2023-09-21</td>\n",
+       "      <td>https://www.netflix.com/title/80197526</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sex Education</td>\n",
+       "      <td>1008.977</td>\n",
+       "      <td>/zn6VNUOHGWKvSGRL6KiMtAe9ELy.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Growth is a group project.</td>\n",
+       "      <td>Comedy, Drama</td>\n",
+       "      <td>Laurie Nunn</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>GB</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Eleven</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.174840</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.724444</td>\n",
+       "      <td>0.371757</td>\n",
+       "      <td>0.3691</td>\n",
+       "      <td>20.145873</td>\n",
+       "      <td>8.802071</td>\n",
+       "      <td>561.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>68.76</td>\n",
+       "      <td>1.9352</td>\n",
+       "      <td>al_ar_us</td>\n",
+       "      <td>659510000</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>all of us are dead</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>107.108</td>\n",
+       "      <td>8.354</td>\n",
+       "      <td>3335.0</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>92709.0</td>\n",
+       "      <td>99966</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>3335</td>\n",
+       "      <td>8.354</td>\n",
+       "      <td>A high school becomes ground zero for a zombie virus outbreak. Trapped students must fight their way out — or turn into one of the rabid infected.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/jZAtLKNZbQZZLm9OLcY9rdZZV5F.jpg</td>\n",
+       "      <td>2022-01-28</td>\n",
+       "      <td>2022-01-28</td>\n",
+       "      <td>https://www.netflix.com/title/81237994</td>\n",
+       "      <td>True</td>\n",
+       "      <td>지금 우리 학교는</td>\n",
+       "      <td>107.108</td>\n",
+       "      <td>/pTEFqAjLd5YTsMD6NSUxV6Dq7A6.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Hope is the most painful torture for people who want to despair.</td>\n",
+       "      <td>Action &amp; Adventure, Drama, Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>Chun Sung-il, JQ Lee, Kim Nam-soo</td>\n",
+       "      <td>en, ko</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>English, 한국어/조선말</td>\n",
+       "      <td>Film Monster, SLL, Kim Jong-hak Production</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>65</td>\n",
+       "      <td>0.209856</td>\n",
+       "      <td>0.391304</td>\n",
+       "      <td>0.752222</td>\n",
+       "      <td>0.185794</td>\n",
+       "      <td>0.4052</td>\n",
+       "      <td>20.307008</td>\n",
+       "      <td>8.112528</td>\n",
+       "      <td>659.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>67.90</td>\n",
+       "      <td>1.8819</td>\n",
+       "      <td>br</td>\n",
+       "      <td>1108800000</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>bridgerton</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>71.308</td>\n",
+       "      <td>8.143</td>\n",
+       "      <td>2045.0</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>91239</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24</td>\n",
+       "      <td>en</td>\n",
+       "      <td>2045</td>\n",
+       "      <td>8.143</td>\n",
+       "      <td>Wealth, lust, and betrayal set in the backdrop of Regency era England, seen through the eyes of the powerful Bridgerton family.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/aXa6J5vGZQQOLZZv8fK0w0cd2fm.jpg</td>\n",
+       "      <td>2020-12-25</td>\n",
+       "      <td>2022-03-25</td>\n",
+       "      <td>https://www.netflix.com/title/80232398</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>71.308</td>\n",
+       "      <td>/luoKpgVwi1E5nQsi7W0UuKHu2Rq.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Love never plays by the rules.</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Chris Van Dusen</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>ShondaLand</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.370147</td>\n",
+       "      <td>0.391304</td>\n",
+       "      <td>0.635000</td>\n",
+       "      <td>0.113363</td>\n",
+       "      <td>0.4153</td>\n",
+       "      <td>20.826544</td>\n",
+       "      <td>7.623642</td>\n",
+       "      <td>1108.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>64.97</td>\n",
+       "      <td>1.7017</td>\n",
+       "      <td>oz</td>\n",
+       "      <td>751600000</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>ozark</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>68.006</td>\n",
+       "      <td>8.244</td>\n",
+       "      <td>1968.0</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>394904.0</td>\n",
+       "      <td>69740</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>en</td>\n",
+       "      <td>1968</td>\n",
+       "      <td>8.244</td>\n",
+       "      <td>A financial adviser drags his family from Chicago to the Missouri Ozarks, where he must launder $500 million in five years to appease a drug boss.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/gD830J0sf5gEeZvzkRVPdGxJmSR.jpg</td>\n",
+       "      <td>2017-07-21</td>\n",
+       "      <td>2022-04-29</td>\n",
+       "      <td>https://www.netflix.com/title/80117552</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>68.006</td>\n",
+       "      <td>/m73bD8VjibSKuTWg597GQVyVhSb.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Their last resort.</td>\n",
+       "      <td>Crime, Drama</td>\n",
+       "      <td>Mark Williams, Bill Dubuque</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>MRC, Zero Gravity Management, Aggregate Films, Man, Woman &amp; Child Productions</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.242710</td>\n",
+       "      <td>0.478261</td>\n",
+       "      <td>0.691111</td>\n",
+       "      <td>0.109040</td>\n",
+       "      <td>0.4043</td>\n",
+       "      <td>20.437715</td>\n",
+       "      <td>7.585281</td>\n",
+       "      <td>751.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>64.08</td>\n",
+       "      <td>1.6468</td>\n",
+       "      <td>bl_pe</td>\n",
+       "      <td>185340000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Peaky Blinders</td>\n",
+       "      <td>peaky blinders</td>\n",
+       "      <td>Peaky Blinders</td>\n",
+       "      <td>344.477</td>\n",
+       "      <td>8.546</td>\n",
+       "      <td>8836.0</td>\n",
+       "      <td>Peaky Blinders</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>783155.0</td>\n",
+       "      <td>60574</td>\n",
+       "      <td>Peaky Blinders</td>\n",
+       "      <td>6</td>\n",
+       "      <td>36</td>\n",
+       "      <td>en</td>\n",
+       "      <td>8836</td>\n",
+       "      <td>8.546</td>\n",
+       "      <td>A gangster family epic set in 1919 Birmingham, England and centered on a gang who sew razor blades in the peaks of their caps, and their fierce bo...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/wiE9doxiLwq3WCGamDIOb2PqBqc.jpg</td>\n",
+       "      <td>2013-09-12</td>\n",
+       "      <td>2022-04-03</td>\n",
+       "      <td>https://www.bbc.co.uk/programmes/b045fz8r</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Peaky Blinders</td>\n",
+       "      <td>344.477</td>\n",
+       "      <td>/vUUqzWa2LnHIVqkaKVlVGkVcZIW.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>London's for the taking</td>\n",
+       "      <td>Drama, Crime</td>\n",
+       "      <td>Steven Knight</td>\n",
+       "      <td>en</td>\n",
+       "      <td>BBC One, BBC Two</td>\n",
+       "      <td>GB</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Tiger Aspect, BBC Studios, Caryn Mandabach Productions, Screen Yorkshire</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>58</td>\n",
+       "      <td>0.040689</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.858889</td>\n",
+       "      <td>0.494666</td>\n",
+       "      <td>0.3722</td>\n",
+       "      <td>19.037703</td>\n",
+       "      <td>9.086703</td>\n",
+       "      <td>185.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>64.00</td>\n",
+       "      <td>1.6419</td>\n",
+       "      <td>co_ka</td>\n",
+       "      <td>383640000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Cobra Kai</td>\n",
+       "      <td>cobra kai</td>\n",
+       "      <td>Cobra Kai</td>\n",
+       "      <td>118.256</td>\n",
+       "      <td>8.220</td>\n",
+       "      <td>5734.0</td>\n",
+       "      <td>Cobra Kai</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>77169</td>\n",
+       "      <td>Cobra Kai</td>\n",
+       "      <td>6</td>\n",
+       "      <td>50</td>\n",
+       "      <td>en</td>\n",
+       "      <td>5734</td>\n",
+       "      <td>8.220</td>\n",
+       "      <td>This Karate Kid sequel series picks up 30 years after the events of the 1984 All Valley Karate Tournament and finds Johnny Lawrence on the hunt fo...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/zymbuoBoL1i94xAOzVJF6IuWLfD.jpg</td>\n",
+       "      <td>2018-05-02</td>\n",
+       "      <td>2022-09-09</td>\n",
+       "      <td>https://www.netflix.com/title/81002370</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Cobra Kai</td>\n",
+       "      <td>118.256</td>\n",
+       "      <td>/m7tG5E1EbywuwTsl6hq990So0Bx.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Cobra Kai never dies.</td>\n",
+       "      <td>Action &amp; Adventure, Drama, Comedy</td>\n",
+       "      <td>Jon Hurwitz, Hayden Schlossberg, Josh Heald</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix, YouTube Premium</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Hurwitz &amp; Schlossberg Productions, Sony Pictures Television Studios, Overbrook Entertainment</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.111435</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.677778</td>\n",
+       "      <td>0.320494</td>\n",
+       "      <td>0.3078</td>\n",
+       "      <td>19.765215</td>\n",
+       "      <td>8.654343</td>\n",
+       "      <td>383.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>62.61</td>\n",
+       "      <td>1.5567</td>\n",
+       "      <td>el</td>\n",
+       "      <td>329290000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Elite</td>\n",
+       "      <td>elite</td>\n",
+       "      <td>Elite</td>\n",
+       "      <td>100.711</td>\n",
+       "      <td>8.071</td>\n",
+       "      <td>8817.0</td>\n",
+       "      <td>Elite</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>76669</td>\n",
+       "      <td>Elite</td>\n",
+       "      <td>7</td>\n",
+       "      <td>49</td>\n",
+       "      <td>es</td>\n",
+       "      <td>8817</td>\n",
+       "      <td>8.071</td>\n",
+       "      <td>When three working class kids enroll in the most exclusive school in Spain, the clash between the wealthy and the poor students leads to tragedy.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/tl0mg7lOnS6tP8ngH0QwgMLQdpV.jpg</td>\n",
+       "      <td>2018-10-05</td>\n",
+       "      <td>2022-11-18</td>\n",
+       "      <td>https://www.netflix.com/title/80200942</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Élite</td>\n",
+       "      <td>100.711</td>\n",
+       "      <td>/3NTAbAiao4JLzFQw6YxP1YZppM8.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Crime, Mystery, Drama</td>\n",
+       "      <td>Carlos Montero, Darío Madrona</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>Zeta Studios</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.092045</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.595000</td>\n",
+       "      <td>0.493599</td>\n",
+       "      <td>0.3108</td>\n",
+       "      <td>19.612449</td>\n",
+       "      <td>9.084550</td>\n",
+       "      <td>329.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>61.10</td>\n",
+       "      <td>1.4633</td>\n",
+       "      <td>ma</td>\n",
+       "      <td>1446260000</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>manifest</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>111.411</td>\n",
+       "      <td>7.728</td>\n",
+       "      <td>1312.0</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>79696</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>4</td>\n",
+       "      <td>62</td>\n",
+       "      <td>en</td>\n",
+       "      <td>1312</td>\n",
+       "      <td>7.728</td>\n",
+       "      <td>After landing from a turbulent but routine flight, the crew and passengers of Montego Air Flight 828 discover five years have passed in what seeme...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/iZu83GB1IM7VXL2X90m7iLHYUHU.jpg</td>\n",
+       "      <td>2018-09-24</td>\n",
+       "      <td>2023-06-02</td>\n",
+       "      <td>https://www.netflix.com/title/80241318</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Manifest</td>\n",
+       "      <td>111.411</td>\n",
+       "      <td>/eTemCphrglLKrXOsNRhYezHA7H9.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Make the final connection.</td>\n",
+       "      <td>Drama, Mystery, Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>Jeff Rake</td>\n",
+       "      <td>en</td>\n",
+       "      <td>NBC, Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Warner Bros. Television, Compari Entertainment, Universal Television, Jeff Rake Productions</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0.490540</td>\n",
+       "      <td>0.608696</td>\n",
+       "      <td>0.404444</td>\n",
+       "      <td>0.072207</td>\n",
+       "      <td>0.4256</td>\n",
+       "      <td>21.092247</td>\n",
+       "      <td>7.180070</td>\n",
+       "      <td>1446.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>59.65</td>\n",
+       "      <td>1.3740</td>\n",
+       "      <td>do_go</td>\n",
+       "      <td>112580000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>The Good Doctor</td>\n",
+       "      <td>the good doctor</td>\n",
+       "      <td>The Good Doctor</td>\n",
+       "      <td>681.614</td>\n",
+       "      <td>8.503</td>\n",
+       "      <td>11768.0</td>\n",
+       "      <td>The Good Doctor</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>71712</td>\n",
+       "      <td>The Good Doctor</td>\n",
+       "      <td>6</td>\n",
+       "      <td>116</td>\n",
+       "      <td>en</td>\n",
+       "      <td>11768</td>\n",
+       "      <td>8.503</td>\n",
+       "      <td>Shaun Murphy, a young surgeon with autism and savant syndrome, relocates from a quiet country life to join a prestigious hospital's surgical unit....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/xXRsKNJHTOGrs5wfYAxkbM2RiyT.jpg</td>\n",
+       "      <td>2017-09-25</td>\n",
+       "      <td>2023-05-01</td>\n",
+       "      <td>https://abc.com/shows/the-good-doctor</td>\n",
+       "      <td>True</td>\n",
+       "      <td>The Good Doctor</td>\n",
+       "      <td>681.614</td>\n",
+       "      <td>/luhKkdD80qe62fwop6sdrXK9jUT.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Everyone operates differently.</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>David Shore</td>\n",
+       "      <td>en</td>\n",
+       "      <td>ABC</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>ABC Studios, 3AD, Sony Pictures Television Studios</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>43</td>\n",
+       "      <td>0.014731</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.835000</td>\n",
+       "      <td>0.659293</td>\n",
+       "      <td>0.3893</td>\n",
+       "      <td>18.539175</td>\n",
+       "      <td>9.373224</td>\n",
+       "      <td>112.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>58.92</td>\n",
+       "      <td>1.3291</td>\n",
+       "      <td>ar</td>\n",
+       "      <td>154270000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Arcane</td>\n",
+       "      <td>arcane</td>\n",
+       "      <td>Arcane</td>\n",
+       "      <td>42.447</td>\n",
+       "      <td>8.740</td>\n",
+       "      <td>3341.0</td>\n",
+       "      <td>Arcane</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>94605</td>\n",
+       "      <td>Arcane</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>en</td>\n",
+       "      <td>3341</td>\n",
+       "      <td>8.740</td>\n",
+       "      <td>Amid the stark discord of twin cities Piltover and Zaun, two sisters fight on rival sides of a war between magic technologies and clashing convict...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/rkB4LyZHo1NHXFEDHl9vSD9r1lI.jpg</td>\n",
+       "      <td>2021-11-06</td>\n",
+       "      <td>2021-11-20</td>\n",
+       "      <td>https://arcane.com</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Arcane</td>\n",
+       "      <td>42.447</td>\n",
+       "      <td>/fqldf2t8ztc9aiwn3k6mlX3tvRT.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Animation, Drama, Sci-Fi &amp; Fantasy, Action &amp; Adventure</td>\n",
+       "      <td>Christian Linke, Alex Yee</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Fortiche Production, Riot Games</td>\n",
+       "      <td>France, United States of America</td>\n",
+       "      <td>41</td>\n",
+       "      <td>0.029604</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.186131</td>\n",
+       "      <td>0.3631</td>\n",
+       "      <td>18.854215</td>\n",
+       "      <td>8.114325</td>\n",
+       "      <td>154.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>55.11</td>\n",
+       "      <td>1.0948</td>\n",
+       "      <td>fl_qu</td>\n",
+       "      <td>561230000</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>The Queen of Flow</td>\n",
+       "      <td>the queen of flow</td>\n",
+       "      <td>The Queen of Flow</td>\n",
+       "      <td>129.323</td>\n",
+       "      <td>7.983</td>\n",
+       "      <td>1391.0</td>\n",
+       "      <td>The Queen of Flow</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>2076.0</td>\n",
+       "      <td>80240</td>\n",
+       "      <td>The Queen of Flow</td>\n",
+       "      <td>2</td>\n",
+       "      <td>172</td>\n",
+       "      <td>es</td>\n",
+       "      <td>1391</td>\n",
+       "      <td>7.983</td>\n",
+       "      <td>After spending seventeen years in prison unfairly, a talented songwriter seeks revenge on the men who sank her and killed her family.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/pnyT1foDmmXTsho2DfxN2ePI8ix.jpg</td>\n",
+       "      <td>2018-06-12</td>\n",
+       "      <td>2021-09-10</td>\n",
+       "      <td>https://www.caracoltv.com/lareinadelflow</td>\n",
+       "      <td>False</td>\n",
+       "      <td>La Reina del Flow</td>\n",
+       "      <td>129.323</td>\n",
+       "      <td>/fuVuDYrs8sxvEolnYr0wCSvtyTi.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Andrés Salgado</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Caracol TV</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>Teleset, Sony Pictures</td>\n",
+       "      <td>Colombia</td>\n",
+       "      <td>53</td>\n",
+       "      <td>0.174793</td>\n",
+       "      <td>0.608696</td>\n",
+       "      <td>0.546111</td>\n",
+       "      <td>0.076642</td>\n",
+       "      <td>0.3582</td>\n",
+       "      <td>20.145641</td>\n",
+       "      <td>7.238497</td>\n",
+       "      <td>561.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>54.08</td>\n",
+       "      <td>1.0317</td>\n",
+       "      <td>at_ex_wo</td>\n",
+       "      <td>404030000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Extraordinary Attorney Woo</td>\n",
+       "      <td>extraordinary attorney woo</td>\n",
+       "      <td>Extraordinary Attorney Woo</td>\n",
+       "      <td>42.923</td>\n",
+       "      <td>8.482</td>\n",
+       "      <td>585.0</td>\n",
+       "      <td>Extraordinary Attorney Woo</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>38985.0</td>\n",
+       "      <td>197067</td>\n",
+       "      <td>Extraordinary Attorney Woo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>585</td>\n",
+       "      <td>8.482</td>\n",
+       "      <td>Brilliant attorney Woo Young-woo tackles challenges in the courtroom and beyond as a newbie at a top law firm and a woman on the autism spectrum.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/tmgtlnBnoFvk52xZBO7Z0pTmN9b.jpg</td>\n",
+       "      <td>2022-06-29</td>\n",
+       "      <td>2022-08-18</td>\n",
+       "      <td>http://ena.skylifetv.co.kr/bbs/board.php?bo_table=skydrama&amp;wr_id=113&amp;sca=최신</td>\n",
+       "      <td>False</td>\n",
+       "      <td>이상한 변호사 우영우</td>\n",
+       "      <td>42.923</td>\n",
+       "      <td>/zuNOQVI4rEaqwknrfQUVKtlKE2C.jpg</td>\n",
+       "      <td>Miniseries</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>My name is Woo Young-woo, whether it is read straight or flipped. Kayak, deed, rotator, noon, racecar, Woo Young-woo.</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Yoo In-sik, Moon Ji-won</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>ENA</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>KT Studio Genie, AStory</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0.118710</td>\n",
+       "      <td>0.217391</td>\n",
+       "      <td>0.823333</td>\n",
+       "      <td>0.031387</td>\n",
+       "      <td>0.3367</td>\n",
+       "      <td>19.817000</td>\n",
+       "      <td>6.373320</td>\n",
+       "      <td>404.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>51.70</td>\n",
+       "      <td>0.8850</td>\n",
+       "      <td>ha_i_ne</td>\n",
+       "      <td>297780000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Never Have I Ever</td>\n",
+       "      <td>never have i ever</td>\n",
+       "      <td>Never Have I Ever</td>\n",
+       "      <td>80.546</td>\n",
+       "      <td>8.161</td>\n",
+       "      <td>1557.0</td>\n",
+       "      <td>Never Have I Ever</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100883</td>\n",
+       "      <td>Never Have I Ever</td>\n",
+       "      <td>4</td>\n",
+       "      <td>40</td>\n",
+       "      <td>en</td>\n",
+       "      <td>1557</td>\n",
+       "      <td>8.161</td>\n",
+       "      <td>After a traumatic year, all an Indian-American teen wants is to go from pariah to popular -- but friends, family and feeling won't make it easy on...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/85HCFXyCmvADwd3gJFyCz34CTAO.jpg</td>\n",
+       "      <td>2020-04-27</td>\n",
+       "      <td>2023-06-08</td>\n",
+       "      <td>https://www.netflix.com/title/80179190</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Never Have I Ever</td>\n",
+       "      <td>80.546</td>\n",
+       "      <td>/hd5fnBixab6IzfUwjC5wfdbX3eM.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Same nerds, new drama.</td>\n",
+       "      <td>Comedy, Drama</td>\n",
+       "      <td>Mindy Kaling, Lang Fisher</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Kaling International, Universal Television</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.080804</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.645000</td>\n",
+       "      <td>0.085963</td>\n",
+       "      <td>0.2521</td>\n",
+       "      <td>19.511866</td>\n",
+       "      <td>7.351158</td>\n",
+       "      <td>297.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>49.84</td>\n",
+       "      <td>0.7703</td>\n",
+       "      <td>sa</td>\n",
+       "      <td>274220000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>The Sandman</td>\n",
+       "      <td>the sandman</td>\n",
+       "      <td>The Sandman</td>\n",
+       "      <td>64.168</td>\n",
+       "      <td>8.064</td>\n",
+       "      <td>1726.0</td>\n",
+       "      <td>The Sandman</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>90802</td>\n",
+       "      <td>The Sandman</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11</td>\n",
+       "      <td>en</td>\n",
+       "      <td>1726</td>\n",
+       "      <td>8.064</td>\n",
+       "      <td>After years of imprisonment, Morpheus — the King of Dreams — embarks on a journey across worlds to find what was stolen from him and restore his p...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/eqhKMZTLcieAvoH6CBqknTTfNby.jpg</td>\n",
+       "      <td>2022-08-05</td>\n",
+       "      <td>2022-08-19</td>\n",
+       "      <td>https://thesandman.com</td>\n",
+       "      <td>True</td>\n",
+       "      <td>The Sandman</td>\n",
+       "      <td>64.168</td>\n",
+       "      <td>/q54qEgagGOYCq5D1903eBVMNkbo.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Dream the world anew.</td>\n",
+       "      <td>Sci-Fi &amp; Fantasy, Drama</td>\n",
+       "      <td>David S. Goyer, Neil Gaiman, Allan Heinberg</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Warner Bros. Television, DC Entertainment, Purepop, Phantom Four, The Blank Corporation</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.072398</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.591111</td>\n",
+       "      <td>0.095452</td>\n",
+       "      <td>0.2257</td>\n",
+       "      <td>19.429441</td>\n",
+       "      <td>7.454141</td>\n",
+       "      <td>274.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>49.75</td>\n",
+       "      <td>0.7650</td>\n",
+       "      <td>ri_vi</td>\n",
+       "      <td>624250000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Virgin River</td>\n",
+       "      <td>virgin river</td>\n",
+       "      <td>Virgin River</td>\n",
+       "      <td>54.192</td>\n",
+       "      <td>8.032</td>\n",
+       "      <td>448.0</td>\n",
+       "      <td>Virgin River</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>60100.0</td>\n",
+       "      <td>88324</td>\n",
+       "      <td>Virgin River</td>\n",
+       "      <td>5</td>\n",
+       "      <td>54</td>\n",
+       "      <td>en</td>\n",
+       "      <td>448</td>\n",
+       "      <td>8.032</td>\n",
+       "      <td>After seeing an ad for a midwife, a recently divorced big-city nurse moves to the redwood forests of California, where she meets an intriguing man.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/4nVaMgK7LVRmQ3vNg7eC3qKfRyI.jpg</td>\n",
+       "      <td>2019-12-06</td>\n",
+       "      <td>2023-09-07</td>\n",
+       "      <td>https://www.netflix.com/title/80240027</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Virgin River</td>\n",
+       "      <td>54.192</td>\n",
+       "      <td>/rxWHdK7qGFcJIk28UubcduSc1sz.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Sue Tenney</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Reel World Management</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.197276</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.573333</td>\n",
+       "      <td>0.023695</td>\n",
+       "      <td>0.2794</td>\n",
+       "      <td>20.252061</td>\n",
+       "      <td>6.107023</td>\n",
+       "      <td>624.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>49.11</td>\n",
+       "      <td>0.7257</td>\n",
+       "      <td>de_se_si</td>\n",
+       "      <td>79510000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>The Seven Deadly Sins: Cursed by Light</td>\n",
+       "      <td>the seven deadly sins cursed by light</td>\n",
+       "      <td>The Seven Deadly Sins</td>\n",
+       "      <td>10.059</td>\n",
+       "      <td>8.462</td>\n",
+       "      <td>4710.0</td>\n",
+       "      <td>The Seven Deadly Sins: Cursed by Light</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>1955.0</td>\n",
+       "      <td>62104</td>\n",
+       "      <td>The Seven Deadly Sins</td>\n",
+       "      <td>4</td>\n",
+       "      <td>96</td>\n",
+       "      <td>ja</td>\n",
+       "      <td>4710</td>\n",
+       "      <td>8.462</td>\n",
+       "      <td>The “Seven Deadly Sins”—a group of evil knights who conspired to overthrow the kingdom of Britannia—were said to have been eradicated by the Holy ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/n5Ty1KJIRNCXlDHDjcPpUgp57tr.jpg</td>\n",
+       "      <td>2014-10-05</td>\n",
+       "      <td>2021-06-23</td>\n",
+       "      <td>http://www.7-taizai.net</td>\n",
+       "      <td>False</td>\n",
+       "      <td>七つの大罪</td>\n",
+       "      <td>10.059</td>\n",
+       "      <td>/gxTojpKEOtue85EEFlozwRbDXwJ.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Action &amp; Adventure, Animation, Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ja</td>\n",
+       "      <td>tv asahi, MBS, TV Tokyo, TBS, CBC, TV Aichi, TVQ, TV Osaka, Tulip Television, TVh, SBC, TSC, BSN, tys, Nagasaki Broadcasting Company, HBC, RKK Kum...</td>\n",
+       "      <td>JP</td>\n",
+       "      <td>日本語</td>\n",
+       "      <td>A-1 Pictures, Studio Deen</td>\n",
+       "      <td>Japan</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.002933</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.812222</td>\n",
+       "      <td>0.262998</td>\n",
+       "      <td>0.3015</td>\n",
+       "      <td>18.191393</td>\n",
+       "      <td>8.457655</td>\n",
+       "      <td>79.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>48.48</td>\n",
+       "      <td>0.6866</td>\n",
+       "      <td>ki_la</td>\n",
+       "      <td>189570000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>The Last Kingdom</td>\n",
+       "      <td>the last kingdom</td>\n",
+       "      <td>The Last Kingdom</td>\n",
+       "      <td>143.794</td>\n",
+       "      <td>8.290</td>\n",
+       "      <td>1485.0</td>\n",
+       "      <td>The Last Kingdom</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>63333</td>\n",
+       "      <td>The Last Kingdom</td>\n",
+       "      <td>5</td>\n",
+       "      <td>46</td>\n",
+       "      <td>en</td>\n",
+       "      <td>1485</td>\n",
+       "      <td>8.290</td>\n",
+       "      <td>A show of heroic deeds and epic battles with a thematic depth that embraces politics, religion, warfare, courage, love, loyalty and our universal ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/uCqXSfHymdbDMsFx8t0u0OPSuve.jpg</td>\n",
+       "      <td>2015-10-10</td>\n",
+       "      <td>2022-03-09</td>\n",
+       "      <td>https://www.netflix.com/title/80074249</td>\n",
+       "      <td>False</td>\n",
+       "      <td>The Last Kingdom</td>\n",
+       "      <td>143.794</td>\n",
+       "      <td>/8eJf0hxgIhE6QSxbtuNCekTddy1.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>England is born</td>\n",
+       "      <td>Action &amp; Adventure, Drama, War &amp; Politics</td>\n",
+       "      <td>Stephen Butchard</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix, BBC Two</td>\n",
+       "      <td>GB</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Carnival Films</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.042198</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.716667</td>\n",
+       "      <td>0.081920</td>\n",
+       "      <td>0.2681</td>\n",
+       "      <td>19.060269</td>\n",
+       "      <td>7.303843</td>\n",
+       "      <td>189.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>44.87</td>\n",
+       "      <td>0.4647</td>\n",
+       "      <td>em_pa</td>\n",
+       "      <td>341630000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Emily in Paris</td>\n",
+       "      <td>emily in paris</td>\n",
+       "      <td>Emily in Paris</td>\n",
+       "      <td>33.659</td>\n",
+       "      <td>7.775</td>\n",
+       "      <td>1152.0</td>\n",
+       "      <td>Emily in Paris</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>143411.0</td>\n",
+       "      <td>82596</td>\n",
+       "      <td>Emily in Paris</td>\n",
+       "      <td>4</td>\n",
+       "      <td>30</td>\n",
+       "      <td>en</td>\n",
+       "      <td>1152</td>\n",
+       "      <td>7.775</td>\n",
+       "      <td>When ambitious Chicago marketing exec Emily unexpectedly lands her dream job in Paris, she embraces a new life as she juggles work, friends and ro...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/xkKIruOg4NoADdPjJJrvfiJHRdZ.jpg</td>\n",
+       "      <td>2020-10-02</td>\n",
+       "      <td>2022-12-21</td>\n",
+       "      <td>https://www.netflix.com/title/81037371</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Emily in Paris</td>\n",
+       "      <td>33.659</td>\n",
+       "      <td>/Ak59Y9bzykmV0wAiwKsqrbORDBo.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Tourist season is over.</td>\n",
+       "      <td>Drama, Comedy</td>\n",
+       "      <td>Darren Star</td>\n",
+       "      <td>en, fr</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English, Français</td>\n",
+       "      <td>MTV Entertainment Studios, Jax Media, Darren Star Productions</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.096448</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.430556</td>\n",
+       "      <td>0.063223</td>\n",
+       "      <td>0.1985</td>\n",
+       "      <td>19.649239</td>\n",
+       "      <td>7.050123</td>\n",
+       "      <td>341.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>44.82</td>\n",
+       "      <td>0.4613</td>\n",
+       "      <td>ch_ch_ho</td>\n",
+       "      <td>300580000</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Hometown Cha-Cha-Cha</td>\n",
+       "      <td>hometown cha cha cha</td>\n",
+       "      <td>Hometown Cha-Cha-Cha</td>\n",
+       "      <td>35.367</td>\n",
+       "      <td>8.233</td>\n",
+       "      <td>442.0</td>\n",
+       "      <td>Hometown Cha-Cha-Cha</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>29770.0</td>\n",
+       "      <td>128883</td>\n",
+       "      <td>Hometown Cha-Cha-Cha</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>442</td>\n",
+       "      <td>8.233</td>\n",
+       "      <td>A big-city dentist opens up a practice in a close-knit seaside village, home to a charming jack-of-all-trades who is her polar opposite in every way.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/qX5DaHmjBDOMz9jGNJ3oESW6Wlq.jpg</td>\n",
+       "      <td>2021-08-28</td>\n",
+       "      <td>2021-10-17</td>\n",
+       "      <td>http://program.tving.com/tvn/chachacha</td>\n",
+       "      <td>False</td>\n",
+       "      <td>갯마을 차차차</td>\n",
+       "      <td>35.367</td>\n",
+       "      <td>/en6lrlJ1DhyvkeZEqrk3R6EJz1p.jpg</td>\n",
+       "      <td>Miniseries</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Eventually, we found each other once again.</td>\n",
+       "      <td>Comedy, Drama</td>\n",
+       "      <td>Yu Je-won, Shin Ha-eun</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>tvN</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>Studio Dragon, GTist</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>78</td>\n",
+       "      <td>0.081803</td>\n",
+       "      <td>0.608696</td>\n",
+       "      <td>0.685000</td>\n",
+       "      <td>0.023358</td>\n",
+       "      <td>0.3594</td>\n",
+       "      <td>19.521225</td>\n",
+       "      <td>6.093570</td>\n",
+       "      <td>300.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>44.43</td>\n",
+       "      <td>0.4373</td>\n",
+       "      <td>ke_lo</td>\n",
+       "      <td>278380000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Locke &amp; Key</td>\n",
+       "      <td>locke key</td>\n",
+       "      <td>Locke &amp; Key</td>\n",
+       "      <td>27.017</td>\n",
+       "      <td>7.903</td>\n",
+       "      <td>1104.0</td>\n",
+       "      <td>Locke&amp;Key</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86423</td>\n",
+       "      <td>Locke &amp; Key</td>\n",
+       "      <td>3</td>\n",
+       "      <td>28</td>\n",
+       "      <td>en</td>\n",
+       "      <td>1104</td>\n",
+       "      <td>7.903</td>\n",
+       "      <td>Three siblings who move into their ancestral estate after their father's gruesome murder discover their new home's magical keys, which must be use...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/67gGBgMBgmElB88waL2yS2rv91l.jpg</td>\n",
+       "      <td>2020-02-07</td>\n",
+       "      <td>2022-08-10</td>\n",
+       "      <td>https://www.netflix.com/title/80241239</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Locke &amp; Key</td>\n",
+       "      <td>27.017</td>\n",
+       "      <td>/zuxGfRKziGHPogipnEXXykdDmyT.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Secrets are meant to be unlocked.</td>\n",
+       "      <td>Sci-Fi &amp; Fantasy, Drama, Mystery</td>\n",
+       "      <td>Carlton Cuse, Meredith Averill, Aron Eli Coleite</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Genre Arts, IDW Entertainment, Circle of Confusion</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.073882</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.501667</td>\n",
+       "      <td>0.060528</td>\n",
+       "      <td>0.2115</td>\n",
+       "      <td>19.444498</td>\n",
+       "      <td>7.007601</td>\n",
+       "      <td>278.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>44.19</td>\n",
+       "      <td>0.4225</td>\n",
+       "      <td>bu_pr</td>\n",
+       "      <td>279110000</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Business Proposal</td>\n",
+       "      <td>business proposal</td>\n",
+       "      <td>Business Proposal</td>\n",
+       "      <td>88.533</td>\n",
+       "      <td>8.422</td>\n",
+       "      <td>281.0</td>\n",
+       "      <td>Business Proposal</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>154825</td>\n",
+       "      <td>Business Proposal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>281</td>\n",
+       "      <td>8.422</td>\n",
+       "      <td>In disguise as her friend, Ha-ri shows up to a blind date to scare him away. But plans go awry when he turns out to be her CEO — and makes a propo...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/64TnyodfRC4iLTCXKlqps8iRW7F.jpg</td>\n",
+       "      <td>2022-02-28</td>\n",
+       "      <td>2022-04-05</td>\n",
+       "      <td>https://programs.sbs.co.kr/drama/businessproposal</td>\n",
+       "      <td>False</td>\n",
+       "      <td>사내맞선</td>\n",
+       "      <td>88.533</td>\n",
+       "      <td>/iLh7L8ZuvgdxFaM9sImyv2iKYLe.jpg</td>\n",
+       "      <td>Miniseries</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>If you come any closer, I'm not going to let you go again.</td>\n",
+       "      <td>Comedy, Drama</td>\n",
+       "      <td>Park Seon-ho, Hong Bo-hee, Han Sul-hee</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>SBS</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>Studio S, Kross Pictures, Kakao Entertainment</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.074143</td>\n",
+       "      <td>0.478261</td>\n",
+       "      <td>0.790000</td>\n",
+       "      <td>0.014318</td>\n",
+       "      <td>0.3607</td>\n",
+       "      <td>19.447117</td>\n",
+       "      <td>5.641907</td>\n",
+       "      <td>279.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>43.88</td>\n",
+       "      <td>0.4034</td>\n",
+       "      <td>my_na</td>\n",
+       "      <td>194140000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>My Name</td>\n",
+       "      <td>my name</td>\n",
+       "      <td>My Name</td>\n",
+       "      <td>25.525</td>\n",
+       "      <td>8.283</td>\n",
+       "      <td>715.0</td>\n",
+       "      <td>My Name</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>45026.0</td>\n",
+       "      <td>110356</td>\n",
+       "      <td>My Name</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>715</td>\n",
+       "      <td>8.283</td>\n",
+       "      <td>Following her father's murder, a revenge-driven woman puts her trust in a powerful crime boss — and enters the police force under his direction.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/4LZuFMhLivOSO7zuWAQ3RffMnnr.jpg</td>\n",
+       "      <td>2021-10-15</td>\n",
+       "      <td>2021-10-15</td>\n",
+       "      <td>https://www.netflix.com/title/81011211</td>\n",
+       "      <td>False</td>\n",
+       "      <td>마이 네임</td>\n",
+       "      <td>25.525</td>\n",
+       "      <td>/h9p1zxKzti35Aqe6xpwkP9NlG7p.jpg</td>\n",
+       "      <td>Miniseries</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>No one can know. My enemy, my revenge.</td>\n",
+       "      <td>Crime, Drama, Mystery, Action &amp; Adventure</td>\n",
+       "      <td>Kim Jin-min, Kim Ba-da</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>Studio Santa Claus Entertainment</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.043829</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.712778</td>\n",
+       "      <td>0.038686</td>\n",
+       "      <td>0.2611</td>\n",
+       "      <td>19.084090</td>\n",
+       "      <td>6.573680</td>\n",
+       "      <td>194.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>27</td>\n",
+       "      <td>42.58</td>\n",
+       "      <td>0.3233</td>\n",
+       "      <td>ar_ca_co</td>\n",
+       "      <td>793740000</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Café con aroma de mujer</td>\n",
+       "      <td>caf con aroma de mujer</td>\n",
+       "      <td>Café con Aroma de Mujer</td>\n",
+       "      <td>20.710</td>\n",
+       "      <td>7.500</td>\n",
+       "      <td>382.0</td>\n",
+       "      <td>Café con aroma de mujer</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>16961</td>\n",
+       "      <td>Café con Aroma de Mujer</td>\n",
+       "      <td>1</td>\n",
+       "      <td>139</td>\n",
+       "      <td>es</td>\n",
+       "      <td>382</td>\n",
+       "      <td>7.500</td>\n",
+       "      <td>Café con aroma de mujer is a 1994 Colombian telenovela, produced by then programming company RCN TV on state-owned Canal A. It was written and cre...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/oOqWl3GavxfYiwxegiVyQ5p9NYK.jpg</td>\n",
+       "      <td>1994-12-30</td>\n",
+       "      <td>1995-07-12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Café con Aroma de Mujer</td>\n",
+       "      <td>20.710</td>\n",
+       "      <td>/tvcpeQNt28GQLtNljhuLwgRBRv8.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Fernando Gaitán</td>\n",
+       "      <td>es</td>\n",
+       "      <td>RCN</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>RCN</td>\n",
+       "      <td>Colombia, United States of America, United Kingdom</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.257744</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.019989</td>\n",
+       "      <td>0.3765</td>\n",
+       "      <td>20.492267</td>\n",
+       "      <td>5.948035</td>\n",
+       "      <td>793.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>41.75</td>\n",
+       "      <td>0.2724</td>\n",
+       "      <td>co_z</td>\n",
+       "      <td>89830000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Control Z</td>\n",
+       "      <td>control z</td>\n",
+       "      <td>Control Z</td>\n",
+       "      <td>20.708</td>\n",
+       "      <td>8.227</td>\n",
+       "      <td>2242.0</td>\n",
+       "      <td>Control Z</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>12606.0</td>\n",
+       "      <td>102903</td>\n",
+       "      <td>Control Z</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24</td>\n",
+       "      <td>es</td>\n",
+       "      <td>2242</td>\n",
+       "      <td>8.227</td>\n",
+       "      <td>When a hacker begins releasing students' secrets to the entire high school, the socially isolated but observant Sofía works to uncover his/her ide...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/gFFedWBZ7s5sPiR9A1IpileWuFf.jpg</td>\n",
+       "      <td>2020-05-22</td>\n",
+       "      <td>2022-07-06</td>\n",
+       "      <td>https://www.netflix.com/title/81021245</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Control Z</td>\n",
+       "      <td>20.708</td>\n",
+       "      <td>/8VNA0RdrPk8Ec7XVjpeT0Rnui79.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Your secrets are not safe.</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Adriana Pelusi, Carlos Quintanilla Sakar, Miguel García Moreno</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>Lemon Studios</td>\n",
+       "      <td>Mexico</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.006614</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.681667</td>\n",
+       "      <td>0.124424</td>\n",
+       "      <td>0.2342</td>\n",
+       "      <td>18.313430</td>\n",
+       "      <td>7.715570</td>\n",
+       "      <td>89.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>41.22</td>\n",
+       "      <td>0.2397</td>\n",
+       "      <td>al_so</td>\n",
+       "      <td>127690000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Alchemy of Souls</td>\n",
+       "      <td>alchemy of souls</td>\n",
+       "      <td>Alchemy of Souls</td>\n",
+       "      <td>61.172</td>\n",
+       "      <td>8.622</td>\n",
+       "      <td>411.0</td>\n",
+       "      <td>Alchemy of Souls</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>29759.0</td>\n",
+       "      <td>135157</td>\n",
+       "      <td>Alchemy of Souls</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>411</td>\n",
+       "      <td>8.622</td>\n",
+       "      <td>A powerful sorceress in a blind woman's body encounters a man from a prestigious family, who wants her help to change his destiny.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/nBZyWSGAUEzCH7Mna0zUNTpBQlQ.jpg</td>\n",
+       "      <td>2022-06-18</td>\n",
+       "      <td>2023-01-08</td>\n",
+       "      <td>https://program.tving.com/tvn/alchemyofsouls</td>\n",
+       "      <td>False</td>\n",
+       "      <td>환혼</td>\n",
+       "      <td>61.172</td>\n",
+       "      <td>/gvOZN1NlAoL8iz9ghpES1zWA3w3.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sci-Fi &amp; Fantasy, Drama, Action &amp; Adventure, Mystery</td>\n",
+       "      <td>Hong Mi-ran, Hong Jeong-eun, Park Joon-hwa</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>tvN</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>Studio Dragon</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.020122</td>\n",
+       "      <td>0.260870</td>\n",
+       "      <td>0.901111</td>\n",
+       "      <td>0.021617</td>\n",
+       "      <td>0.3328</td>\n",
+       "      <td>18.665116</td>\n",
+       "      <td>6.021023</td>\n",
+       "      <td>127.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>39.80</td>\n",
+       "      <td>0.1525</td>\n",
+       "      <td>va_vi</td>\n",
+       "      <td>265550000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Vikings: Valhalla</td>\n",
+       "      <td>vikings valhalla</td>\n",
+       "      <td>Vikings: Valhalla</td>\n",
+       "      <td>59.232</td>\n",
+       "      <td>7.851</td>\n",
+       "      <td>671.0</td>\n",
+       "      <td>Vikings: Valhalla</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>71704.0</td>\n",
+       "      <td>116135</td>\n",
+       "      <td>Vikings: Valhalla</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>en</td>\n",
+       "      <td>671</td>\n",
+       "      <td>7.851</td>\n",
+       "      <td>In this sequel to \"Vikings,\" a hundred years have passed and a new generation of legendary heroes arises to forge its own destiny — and make history.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/k47JEUTQsSMN532HRg6RCzZKBdB.jpg</td>\n",
+       "      <td>2022-02-25</td>\n",
+       "      <td>2023-01-12</td>\n",
+       "      <td>https://www.netflix.com/title/81149450</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Vikings: Valhalla</td>\n",
+       "      <td>59.232</td>\n",
+       "      <td>/rDFy1fUU6OC3Mm0CLFB7u0fGwVN.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Kneel to no one.</td>\n",
+       "      <td>Action &amp; Adventure, Drama, War &amp; Politics</td>\n",
+       "      <td>Jeb Stuart</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>MGM Television, Metropolitan Films International, History</td>\n",
+       "      <td>Ireland, United States of America</td>\n",
+       "      <td>51</td>\n",
+       "      <td>0.069305</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.472778</td>\n",
+       "      <td>0.036216</td>\n",
+       "      <td>0.1976</td>\n",
+       "      <td>19.397314</td>\n",
+       "      <td>6.510258</td>\n",
+       "      <td>265.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>39.54</td>\n",
+       "      <td>0.1364</td>\n",
+       "      <td>da_de</td>\n",
+       "      <td>202940000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Dark Desire</td>\n",
+       "      <td>dark desire</td>\n",
+       "      <td>Dark Desire</td>\n",
+       "      <td>90.553</td>\n",
+       "      <td>7.300</td>\n",
+       "      <td>4181.0</td>\n",
+       "      <td>Dark Desire</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>105214</td>\n",
+       "      <td>Dark Desire</td>\n",
+       "      <td>2</td>\n",
+       "      <td>33</td>\n",
+       "      <td>es</td>\n",
+       "      <td>4181</td>\n",
+       "      <td>7.300</td>\n",
+       "      <td>Married Alma spends a fateful weekend away from home that ignites passion, ends in tragedy and leads her to question the truth about those close t...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/9CBdzw7CK2m6WpfEfqe73V8i5O0.jpg</td>\n",
+       "      <td>2020-07-15</td>\n",
+       "      <td>2022-02-02</td>\n",
+       "      <td>https://www.netflix.com/title/81090319</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Oscuro deseo</td>\n",
+       "      <td>90.553</td>\n",
+       "      <td>/uxFNAo2A6ZRcgNASLk02hJUbybn.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mystery, Drama</td>\n",
+       "      <td>Leticia López Margalli</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mexico</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0.046968</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.233296</td>\n",
+       "      <td>0.1275</td>\n",
+       "      <td>19.128421</td>\n",
+       "      <td>8.338545</td>\n",
+       "      <td>202.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>38.89</td>\n",
+       "      <td>0.0965</td>\n",
+       "      <td>al_am</td>\n",
+       "      <td>180660000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>All American</td>\n",
+       "      <td>all american</td>\n",
+       "      <td>All American</td>\n",
+       "      <td>140.694</td>\n",
+       "      <td>8.245</td>\n",
+       "      <td>414.0</td>\n",
+       "      <td>All American</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>82428</td>\n",
+       "      <td>All American</td>\n",
+       "      <td>5</td>\n",
+       "      <td>91</td>\n",
+       "      <td>en</td>\n",
+       "      <td>414</td>\n",
+       "      <td>8.245</td>\n",
+       "      <td>When a rising high school football player from South Central L.A. is recruited to play for Beverly Hills High, the wins, losses and struggles of t...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/thCj6VEVQlFlup4aJ74kdRhfujB.jpg</td>\n",
+       "      <td>2018-10-10</td>\n",
+       "      <td>2023-05-15</td>\n",
+       "      <td>https://www.cwtv.com/shows/all-american/</td>\n",
+       "      <td>True</td>\n",
+       "      <td>All American</td>\n",
+       "      <td>140.694</td>\n",
+       "      <td>/c8FTtjQcQfqSI0McmVc1OOOf7Z7.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>And still we rise.</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>April Blair</td>\n",
+       "      <td>en</td>\n",
+       "      <td>The CW</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Berlanti Productions, Warner Bros. Television, CBS Studios</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.039019</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.691667</td>\n",
+       "      <td>0.021786</td>\n",
+       "      <td>0.2505</td>\n",
+       "      <td>19.012127</td>\n",
+       "      <td>6.028279</td>\n",
+       "      <td>180.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>37.78</td>\n",
+       "      <td>0.0282</td>\n",
+       "      <td>de_n_pa</td>\n",
+       "      <td>164180000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Pasión de Gavilanes</td>\n",
+       "      <td>pasi n de gavilanes</td>\n",
+       "      <td>Pasión de Gavilanes</td>\n",
+       "      <td>283.197</td>\n",
+       "      <td>7.641</td>\n",
+       "      <td>1925.0</td>\n",
+       "      <td>Pasión de sangre</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11250</td>\n",
+       "      <td>Pasión de Gavilanes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>259</td>\n",
+       "      <td>es</td>\n",
+       "      <td>1925</td>\n",
+       "      <td>7.641</td>\n",
+       "      <td>The Reyes-Elizondo's idyllic lives are shattered by a murder charge against Eric and León.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/qabMMIpNUUKYFqQxHEWErEEBD8G.jpg</td>\n",
+       "      <td>2003-10-21</td>\n",
+       "      <td>2022-05-31</td>\n",
+       "      <td>https://www.telemundo.com/shows/pasion-de-gavilanes</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Pasión de gavilanes</td>\n",
+       "      <td>283.197</td>\n",
+       "      <td>/91UV7pNcDPhIzJl7EuK36sK5vRG.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Julio Jiménez</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Telemundo</td>\n",
+       "      <td>US</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.033140</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.356111</td>\n",
+       "      <td>0.106625</td>\n",
+       "      <td>0.1605</td>\n",
+       "      <td>18.916474</td>\n",
+       "      <td>7.563201</td>\n",
+       "      <td>164.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>34</td>\n",
+       "      <td>37.57</td>\n",
+       "      <td>0.0151</td>\n",
+       "      <td>me_na</td>\n",
+       "      <td>138980000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Narcos: Mexico</td>\n",
+       "      <td>narcos mexico</td>\n",
+       "      <td>Narcos: Mexico</td>\n",
+       "      <td>47.476</td>\n",
+       "      <td>7.939</td>\n",
+       "      <td>1160.0</td>\n",
+       "      <td>Narcos: Mexico</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>102053.0</td>\n",
+       "      <td>80968</td>\n",
+       "      <td>Narcos: Mexico</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>en</td>\n",
+       "      <td>1160</td>\n",
+       "      <td>7.939</td>\n",
+       "      <td>See the rise of the Guadalajara Cartel as an American DEA agent learns the danger of targeting narcos in 1980s Mexico.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/64jEzC7YeKEpnn1YwjCGRo9ZvOX.jpg</td>\n",
+       "      <td>2018-11-16</td>\n",
+       "      <td>2021-11-05</td>\n",
+       "      <td>https://www.netflix.com/title/80997085</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Narcos: Mexico</td>\n",
+       "      <td>47.476</td>\n",
+       "      <td>/uYcZMiIIUv8NPebpqKbUGhf2M3y.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>The uncut story of Mexico's first cartel.</td>\n",
+       "      <td>Drama, Crime</td>\n",
+       "      <td>Chris Brancato, Doug Miro, Carlo Bernard</td>\n",
+       "      <td>en, es</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English, Español</td>\n",
+       "      <td>Gaumont, Gaumont International Television</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.024149</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.521667</td>\n",
+       "      <td>0.063672</td>\n",
+       "      <td>0.1919</td>\n",
+       "      <td>18.749841</td>\n",
+       "      <td>7.057037</td>\n",
+       "      <td>138.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>35</td>\n",
+       "      <td>36.08</td>\n",
+       "      <td>-0.0765</td>\n",
+       "      <td>bl_my</td>\n",
+       "      <td>80880000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>On My Block</td>\n",
+       "      <td>on my block</td>\n",
+       "      <td>On My Block</td>\n",
+       "      <td>16.979</td>\n",
+       "      <td>8.461</td>\n",
+       "      <td>618.0</td>\n",
+       "      <td>On My Block</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>76747</td>\n",
+       "      <td>On My Block</td>\n",
+       "      <td>4</td>\n",
+       "      <td>38</td>\n",
+       "      <td>en</td>\n",
+       "      <td>618</td>\n",
+       "      <td>8.461</td>\n",
+       "      <td>A coming of age comedy following a diverse group of teenage friends as they confront the challenges of growing up in gritty inner-city Los Angeles.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/jt7c70Axkhy7o9u00JT8Lygfd3H.jpg</td>\n",
+       "      <td>2018-03-16</td>\n",
+       "      <td>2021-10-04</td>\n",
+       "      <td>https://www.netflix.com/title/80117809</td>\n",
+       "      <td>False</td>\n",
+       "      <td>On My Block</td>\n",
+       "      <td>16.979</td>\n",
+       "      <td>/w6oviv65UEducvjAdH3sYYaxdu2.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>Jeremy Haft, Eddie Gonzalez, Lauren Iungerich</td>\n",
+       "      <td>en, pt</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English, Português</td>\n",
+       "      <td>Crazy Cat Lady Productions</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.003421</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.811667</td>\n",
+       "      <td>0.033240</td>\n",
+       "      <td>0.2497</td>\n",
+       "      <td>18.208477</td>\n",
+       "      <td>6.428105</td>\n",
+       "      <td>80.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>36</td>\n",
+       "      <td>34.83</td>\n",
+       "      <td>-0.1532</td>\n",
+       "      <td>fi_tw_tw</td>\n",
+       "      <td>171610000</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Twenty Five Twenty One</td>\n",
+       "      <td>twenty five twenty one</td>\n",
+       "      <td>Twenty Five Twenty One</td>\n",
+       "      <td>31.730</td>\n",
+       "      <td>8.597</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>Twenty Five Twenty One</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>22729.0</td>\n",
+       "      <td>129888</td>\n",
+       "      <td>Twenty Five Twenty One</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>98</td>\n",
+       "      <td>8.597</td>\n",
+       "      <td>In a time when dreams seem out of reach, a teen fencer pursues big ambitions and meets a hardworking young man who seeks to rebuild his life.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/jvFfHNiCKPWRULvkmeocyjm4QtE.jpg</td>\n",
+       "      <td>2022-02-12</td>\n",
+       "      <td>2022-04-03</td>\n",
+       "      <td>http://program.tving.com/tvn/twentyfivetwentyone</td>\n",
+       "      <td>False</td>\n",
+       "      <td>스물다섯 스물하나</td>\n",
+       "      <td>31.730</td>\n",
+       "      <td>/yCQFnmYhYf7XALMka2EoBRAFmPO.jpg</td>\n",
+       "      <td>Miniseries</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Jung Jee-hyun, Kwon Do-eun</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>Netflix, tvN</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>Hwa&amp;Dam Pictures, Studio Dragon</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.035791</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.887222</td>\n",
+       "      <td>0.004043</td>\n",
+       "      <td>0.3489</td>\n",
+       "      <td>18.960735</td>\n",
+       "      <td>4.595120</td>\n",
+       "      <td>171.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>37</td>\n",
+       "      <td>34.28</td>\n",
+       "      <td>-0.1872</td>\n",
+       "      <td>gi_go</td>\n",
+       "      <td>153140000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Good Girls</td>\n",
+       "      <td>good girls</td>\n",
+       "      <td>Good Girls</td>\n",
+       "      <td>52.352</td>\n",
+       "      <td>7.998</td>\n",
+       "      <td>509.0</td>\n",
+       "      <td>Good Girls</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>668.0</td>\n",
+       "      <td>71715</td>\n",
+       "      <td>Good Girls</td>\n",
+       "      <td>4</td>\n",
+       "      <td>50</td>\n",
+       "      <td>en</td>\n",
+       "      <td>509</td>\n",
+       "      <td>7.998</td>\n",
+       "      <td>Three \"good girl\" suburban wives and mothers suddenly find themselves in desperate circumstances and decide to stop playing it safe and risk every...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/csOIulXwQxsuCFp3ZZ9cYkJmCwH.jpg</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>2021-07-22</td>\n",
+       "      <td>https://www.nbc.com/good-girls</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Good Girls</td>\n",
+       "      <td>52.352</td>\n",
+       "      <td>/gPjcbxrYfbrJNq1Ja8EGd0XtUnC.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Canceled</td>\n",
+       "      <td>Done playing nice.</td>\n",
+       "      <td>Comedy, Drama, Crime</td>\n",
+       "      <td>Jenna Bans</td>\n",
+       "      <td>en</td>\n",
+       "      <td>NBC</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Amblin Television, Universal Television</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0.029201</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.554444</td>\n",
+       "      <td>0.027120</td>\n",
+       "      <td>0.2154</td>\n",
+       "      <td>18.846863</td>\n",
+       "      <td>6.234411</td>\n",
+       "      <td>153.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>38</td>\n",
+       "      <td>33.92</td>\n",
+       "      <td>-0.2093</td>\n",
+       "      <td>af_ki_s</td>\n",
+       "      <td>138540000</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>The King's Affection</td>\n",
+       "      <td>the king s affection</td>\n",
+       "      <td>The King's Affection</td>\n",
+       "      <td>69.819</td>\n",
+       "      <td>8.351</td>\n",
+       "      <td>232.0</td>\n",
+       "      <td>The King's Affection</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8733.0</td>\n",
+       "      <td>129478</td>\n",
+       "      <td>The King's Affection</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>232</td>\n",
+       "      <td>8.351</td>\n",
+       "      <td>When the crown prince is killed, his twin sister assumes the throne while trying to keep her identity and affection for her first love a royal sec...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/4r7scgI5aUrShN0YzriAatWd15e.jpg</td>\n",
+       "      <td>2021-10-11</td>\n",
+       "      <td>2021-12-14</td>\n",
+       "      <td>https://program.kbs.co.kr/2tv/drama/thekingsaffection/pc/index.html</td>\n",
+       "      <td>False</td>\n",
+       "      <td>연모</td>\n",
+       "      <td>69.819</td>\n",
+       "      <td>/mcfX0IqWibPhF5H4i2Q5gUlwwxk.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Han Hee-jung</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>KBS2</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>Arc Media, Monster Union</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0.023992</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.750556</td>\n",
+       "      <td>0.011567</td>\n",
+       "      <td>0.3049</td>\n",
+       "      <td>18.746670</td>\n",
+       "      <td>5.451038</td>\n",
+       "      <td>138.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>39</td>\n",
+       "      <td>32.78</td>\n",
+       "      <td>-0.2796</td>\n",
+       "      <td>ki_sa_wh</td>\n",
+       "      <td>143610000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Who Killed Sara?</td>\n",
+       "      <td>who killed sara</td>\n",
+       "      <td>Who Killed Sara?</td>\n",
+       "      <td>18.157</td>\n",
+       "      <td>7.638</td>\n",
+       "      <td>1132.0</td>\n",
+       "      <td>Who Killed Sara?</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>17647.0</td>\n",
+       "      <td>120168</td>\n",
+       "      <td>Who Killed Sara?</td>\n",
+       "      <td>3</td>\n",
+       "      <td>25</td>\n",
+       "      <td>es</td>\n",
+       "      <td>1132</td>\n",
+       "      <td>7.638</td>\n",
+       "      <td>Hell-bent on exacting revenge and proving he was framed for his sister's murder, Álex sets out to unearth much more than the crime's real culprit.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/dYvIUzdh6TUv4IFRq8UBkX7bNNu.jpg</td>\n",
+       "      <td>2021-03-24</td>\n",
+       "      <td>2022-05-18</td>\n",
+       "      <td>https://www.netflix.com/title/81166747</td>\n",
+       "      <td>False</td>\n",
+       "      <td>¿Quién mató a Sara?</td>\n",
+       "      <td>18.157</td>\n",
+       "      <td>/o7uk5ChRt3quPIv8PcvPfzyXdMw.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama, Crime, Mystery</td>\n",
+       "      <td>José Ignacio Valenzuela</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mexico</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.025801</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.354444</td>\n",
+       "      <td>0.062100</td>\n",
+       "      <td>0.1421</td>\n",
+       "      <td>18.782612</td>\n",
+       "      <td>7.032624</td>\n",
+       "      <td>143.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>40</td>\n",
+       "      <td>32.64</td>\n",
+       "      <td>-0.2881</td>\n",
+       "      <td>he_ma</td>\n",
+       "      <td>272750000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>The Marked Heart</td>\n",
+       "      <td>the marked heart</td>\n",
+       "      <td>The Marked Heart</td>\n",
+       "      <td>35.526</td>\n",
+       "      <td>7.817</td>\n",
+       "      <td>232.0</td>\n",
+       "      <td>The Marked Heart</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>4799.0</td>\n",
+       "      <td>158916</td>\n",
+       "      <td>The Marked Heart</td>\n",
+       "      <td>2</td>\n",
+       "      <td>24</td>\n",
+       "      <td>es</td>\n",
+       "      <td>232</td>\n",
+       "      <td>7.817</td>\n",
+       "      <td>A man hell-bent on exacting revenge on the organ trafficking organization that murdered his wife becomes involved with the woman who received her ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/yOtzSTFdccSd2tfzlTp8UNo2ZHN.jpg</td>\n",
+       "      <td>2022-04-20</td>\n",
+       "      <td>2023-04-19</td>\n",
+       "      <td>https://www.netflix.com/title/81261170</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Pálpito</td>\n",
+       "      <td>35.526</td>\n",
+       "      <td>/M1q0HuyRnqxP8Si54srsPREFIH.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Soap, Mystery</td>\n",
+       "      <td>Leonardo Padrón</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.071874</td>\n",
+       "      <td>0.217391</td>\n",
+       "      <td>0.453889</td>\n",
+       "      <td>0.011567</td>\n",
+       "      <td>0.2065</td>\n",
+       "      <td>19.424066</td>\n",
+       "      <td>5.451038</td>\n",
+       "      <td>272.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>41</td>\n",
+       "      <td>32.42</td>\n",
+       "      <td>-0.3019</td>\n",
+       "      <td>li_se</td>\n",
+       "      <td>282100000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Sex/Life</td>\n",
+       "      <td>sex life</td>\n",
+       "      <td>Sex/Life</td>\n",
+       "      <td>124.145</td>\n",
+       "      <td>7.138</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>Sex Life</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>126280</td>\n",
+       "      <td>Sex/Life</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>en</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>7.138</td>\n",
+       "      <td>A woman's daring sexual past collides with her married-with-kids present when the bad-boy ex she can't stop fantasizing about crashes back into he...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/9nBVkNBe4x9HKDAzxjxlIqecxCW.jpg</td>\n",
+       "      <td>2021-06-25</td>\n",
+       "      <td>2023-03-02</td>\n",
+       "      <td>https://www.netflix.com/title/80991848</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sex/Life</td>\n",
+       "      <td>124.145</td>\n",
+       "      <td>/kfcJl5e8CRWDU7e4vX6uNABPRbS.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Canceled</td>\n",
+       "      <td>Time to choose.</td>\n",
+       "      <td>Comedy, Drama</td>\n",
+       "      <td>Stacy Rukeyser</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>De Milo</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.075210</td>\n",
+       "      <td>0.217391</td>\n",
+       "      <td>0.076667</td>\n",
+       "      <td>0.065918</td>\n",
+       "      <td>0.1027</td>\n",
+       "      <td>19.457772</td>\n",
+       "      <td>7.090910</td>\n",
+       "      <td>282.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>42</td>\n",
+       "      <td>32.18</td>\n",
+       "      <td>-0.3163</td>\n",
+       "      <td>la_li</td>\n",
+       "      <td>305270000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>The Lincoln Lawyer</td>\n",
+       "      <td>the lincoln lawyer</td>\n",
+       "      <td>The Lincoln Lawyer</td>\n",
+       "      <td>52.395</td>\n",
+       "      <td>7.806</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>The Lincoln Lawyer</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>116799</td>\n",
+       "      <td>The Lincoln Lawyer</td>\n",
+       "      <td>3</td>\n",
+       "      <td>20</td>\n",
+       "      <td>en</td>\n",
+       "      <td>183</td>\n",
+       "      <td>7.806</td>\n",
+       "      <td>Sidelined after an accident, hotshot Los Angeles lawyer Mickey Haller restarts his career - and his trademark Lincoln - when he takes on a murder ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/jBFOVEE5sMIyT141YIqIwyU8Y1D.jpg</td>\n",
+       "      <td>2022-05-13</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>https://www.netflix.com/title/81303831</td>\n",
+       "      <td>True</td>\n",
+       "      <td>The Lincoln Lawyer</td>\n",
+       "      <td>52.395</td>\n",
+       "      <td>/9TaupczRcEkOaYbsiQuLg3kXPLq.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>LA's hottest defense attorney just found a new gear.</td>\n",
+       "      <td>Drama, Crime</td>\n",
+       "      <td>David E. Kelley</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>A+E Studios, ABC Signature, David E. Kelley Productions</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.083476</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.447778</td>\n",
+       "      <td>0.008815</td>\n",
+       "      <td>0.1997</td>\n",
+       "      <td>19.536707</td>\n",
+       "      <td>5.214936</td>\n",
+       "      <td>305.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>43</td>\n",
+       "      <td>28.86</td>\n",
+       "      <td>-0.5208</td>\n",
+       "      <td>di_ra</td>\n",
+       "      <td>172870000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Raising Dion</td>\n",
+       "      <td>raising dion</td>\n",
+       "      <td>Raising Dion</td>\n",
+       "      <td>19.093</td>\n",
+       "      <td>7.696</td>\n",
+       "      <td>388.0</td>\n",
+       "      <td>Raising Dion</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>93392</td>\n",
+       "      <td>Raising Dion</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>en</td>\n",
+       "      <td>388</td>\n",
+       "      <td>7.696</td>\n",
+       "      <td>A widowed mom sets out to solve the mystery surrounding her young son's emerging superpowers while keeping his extraordinary gifts under wraps.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/fVc8V31Kk1wAyorbg5BrzVSNNiv.jpg</td>\n",
+       "      <td>2019-10-04</td>\n",
+       "      <td>2022-02-01</td>\n",
+       "      <td>https://www.netflix.com/title/80117803</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Raising Dion</td>\n",
+       "      <td>19.093</td>\n",
+       "      <td>/nydKXBiLn4vpL1d5o1utbPKYdii.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Canceled</td>\n",
+       "      <td>Energy never dies, it just takes a different form.</td>\n",
+       "      <td>Drama, Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>Carol Barbee</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Outlier Society Productions</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.036240</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.386667</td>\n",
+       "      <td>0.020326</td>\n",
+       "      <td>0.1491</td>\n",
+       "      <td>18.968050</td>\n",
+       "      <td>5.963579</td>\n",
+       "      <td>172.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>44</td>\n",
+       "      <td>28.78</td>\n",
+       "      <td>-0.5260</td>\n",
+       "      <td>he_ko_mo</td>\n",
+       "      <td>98370000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Money Heist: Korea - Joint Economic Area</td>\n",
+       "      <td>money heist korea joint economic area</td>\n",
+       "      <td>Money Heist: Korea - Joint Economic Area</td>\n",
+       "      <td>46.292</td>\n",
+       "      <td>7.900</td>\n",
+       "      <td>601.0</td>\n",
+       "      <td>Money Heist: Korea - Joint Economic Area</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>10696.0</td>\n",
+       "      <td>112836</td>\n",
+       "      <td>Money Heist: Korea - Joint Economic Area</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>601</td>\n",
+       "      <td>7.900</td>\n",
+       "      <td>Disguised under the shadows of a mask, a crew of desperados band together under the leadership of a criminal mastermind known only as “The Profess...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/lcTuggU70y6pt6x13Rv1Ffjs93K.jpg</td>\n",
+       "      <td>2022-06-24</td>\n",
+       "      <td>2022-12-09</td>\n",
+       "      <td>https://www.netflix.com/title/80997343</td>\n",
+       "      <td>True</td>\n",
+       "      <td>종이의 집: 공동경제구역</td>\n",
+       "      <td>46.292</td>\n",
+       "      <td>/mbsRGqJtdKcVbjQxkrfzKCAkYoU.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Witness a heist like no other.</td>\n",
+       "      <td>Action &amp; Adventure, Crime, Drama, Mystery</td>\n",
+       "      <td>Kim Hong-sun, Ryu Yong-jae, Kim Hwan-chae, Choi Sung-joon</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>BH Entertainment, Content Zium, HighZium Studio</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.009661</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.032285</td>\n",
+       "      <td>0.1669</td>\n",
+       "      <td>18.404246</td>\n",
+       "      <td>6.400257</td>\n",
+       "      <td>98.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>45</td>\n",
+       "      <td>28.21</td>\n",
+       "      <td>-0.5609</td>\n",
+       "      <td>be_ou_su</td>\n",
+       "      <td>128890000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Our Beloved Summer</td>\n",
+       "      <td>our beloved summer</td>\n",
+       "      <td>Our Beloved Summer</td>\n",
+       "      <td>30.456</td>\n",
+       "      <td>8.500</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>Our Beloved Summer</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>252.0</td>\n",
+       "      <td>135897</td>\n",
+       "      <td>Our Beloved Summer</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>74</td>\n",
+       "      <td>8.500</td>\n",
+       "      <td>Years after filming a viral documentary in high school, two bickering ex-lovers get pulled back in front of the camera — and into each other's lives.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/7J8QzozUgiFgDCvNadVImhEXEIq.jpg</td>\n",
+       "      <td>2021-12-06</td>\n",
+       "      <td>2022-01-25</td>\n",
+       "      <td>https://programs.sbs.co.kr/drama/ourbelovedsummer</td>\n",
+       "      <td>False</td>\n",
+       "      <td>그 해 우리는</td>\n",
+       "      <td>30.456</td>\n",
+       "      <td>/bA15g6OLmhQ2HkURRaCztA2jwqI.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Let's film the documentary again.</td>\n",
+       "      <td>Comedy, Drama</td>\n",
+       "      <td>Lee Na-eun</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>SBS</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>Studio N, Big Ocean ENM, Supermoon Pictures</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.020550</td>\n",
+       "      <td>0.260870</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.002695</td>\n",
+       "      <td>0.3098</td>\n",
+       "      <td>18.674470</td>\n",
+       "      <td>4.317488</td>\n",
+       "      <td>128.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>46</td>\n",
+       "      <td>27.44</td>\n",
+       "      <td>-0.6082</td>\n",
+       "      <td>bl_ou</td>\n",
+       "      <td>105150000</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Our Blues</td>\n",
+       "      <td>our blues</td>\n",
+       "      <td>Our Blues</td>\n",
+       "      <td>12.980</td>\n",
+       "      <td>8.800</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>Our Blues</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>5554.0</td>\n",
+       "      <td>135840</td>\n",
+       "      <td>Our Blues</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>43</td>\n",
+       "      <td>8.800</td>\n",
+       "      <td>Romance is sweet and bitter — and life riddled with ups and downs — in multiple stories about people who live and work on bustling Jeju island.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/w2vVXdKuBucql88wh2s8PRsjWnE.jpg</td>\n",
+       "      <td>2022-04-09</td>\n",
+       "      <td>2022-06-12</td>\n",
+       "      <td>http://program.tving.com/tvn/ourblues</td>\n",
+       "      <td>False</td>\n",
+       "      <td>우리들의 블루스</td>\n",
+       "      <td>12.980</td>\n",
+       "      <td>/sT5Mlt5UmKiGfBisccwmD4LnPRD.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Rooting for you through sour, sweet, and bitter life.</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Noh Hee-kyung</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>tvN</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>GTist, Studio Dragon</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.012080</td>\n",
+       "      <td>0.304348</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000955</td>\n",
+       "      <td>0.3652</td>\n",
+       "      <td>18.470898</td>\n",
+       "      <td>3.784190</td>\n",
+       "      <td>105.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>47</td>\n",
+       "      <td>27.33</td>\n",
+       "      <td>-0.6150</td>\n",
+       "      <td>ma_mi</td>\n",
+       "      <td>142280000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Midnight Mass</td>\n",
+       "      <td>midnight mass</td>\n",
+       "      <td>Midnight Mass</td>\n",
+       "      <td>21.999</td>\n",
+       "      <td>7.525</td>\n",
+       "      <td>664.0</td>\n",
+       "      <td>Midnight Mass</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>97400</td>\n",
+       "      <td>Midnight Mass</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>en</td>\n",
+       "      <td>664</td>\n",
+       "      <td>7.525</td>\n",
+       "      <td>An unsettling omen washes ashore in the wake of the storm. Later, when the locals gather for a potluck, tragedy strikes – and a miracle occurs.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/4XbQb5YoWK9oMrlQdKwtZ1ccpC8.jpg</td>\n",
+       "      <td>2021-09-24</td>\n",
+       "      <td>2021-09-24</td>\n",
+       "      <td>https://www.netflix.com/title/81083626</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Midnight Mass</td>\n",
+       "      <td>21.999</td>\n",
+       "      <td>/iYoMZYVD775MtBYJfv6OGY1FsnL.jpg</td>\n",
+       "      <td>Miniseries</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Be not afraid.</td>\n",
+       "      <td>Mystery, Drama, Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>Mike Flanagan</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Intrepid Pictures</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0.025327</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.291667</td>\n",
+       "      <td>0.035823</td>\n",
+       "      <td>0.1191</td>\n",
+       "      <td>18.773308</td>\n",
+       "      <td>6.499787</td>\n",
+       "      <td>142.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>48</td>\n",
+       "      <td>27.12</td>\n",
+       "      <td>-0.6282</td>\n",
+       "      <td>ma_sw</td>\n",
+       "      <td>182940000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Sweet Magnolias</td>\n",
+       "      <td>sweet magnolias</td>\n",
+       "      <td>Sweet Magnolias</td>\n",
+       "      <td>25.831</td>\n",
+       "      <td>7.800</td>\n",
+       "      <td>206.0</td>\n",
+       "      <td>Sweet Magnolias</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>28500.0</td>\n",
+       "      <td>102904</td>\n",
+       "      <td>Sweet Magnolias</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>en</td>\n",
+       "      <td>206</td>\n",
+       "      <td>7.800</td>\n",
+       "      <td>Lifelong friends Maddie, Helen and Dana Sue lift each other as they juggle relationships, family and careers in the small, Southern town of Serenity.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/wt60Q4doM2vrubiCsutwaeWncLL.jpg</td>\n",
+       "      <td>2020-05-19</td>\n",
+       "      <td>2023-07-20</td>\n",
+       "      <td>https://www.netflix.com/title/80239866</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Sweet Magnolias</td>\n",
+       "      <td>25.831</td>\n",
+       "      <td>/n1WDSFOnCCg3cdHHGBywZtYIzf9.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Sheryl J. Anderson</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Daniel L. Paulson Productions</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.039833</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.010107</td>\n",
+       "      <td>0.1749</td>\n",
+       "      <td>19.024669</td>\n",
+       "      <td>5.332719</td>\n",
+       "      <td>182.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>49</td>\n",
+       "      <td>27.09</td>\n",
+       "      <td>-0.6296</td>\n",
+       "      <td>si</td>\n",
+       "      <td>109430000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Sintonia</td>\n",
+       "      <td>sintonia</td>\n",
+       "      <td>Sintonia</td>\n",
+       "      <td>22.377</td>\n",
+       "      <td>8.200</td>\n",
+       "      <td>179.0</td>\n",
+       "      <td>Sintonia</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78074</td>\n",
+       "      <td>Sintonia</td>\n",
+       "      <td>4</td>\n",
+       "      <td>26</td>\n",
+       "      <td>pt</td>\n",
+       "      <td>179</td>\n",
+       "      <td>8.200</td>\n",
+       "      <td>Told through three different characters' perspectives, the story of Sintonia explores the interconnection of the music, drug traffic, and religion...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/9GTFaWPPctyJeJaImg9HVPPbyOf.jpg</td>\n",
+       "      <td>2019-08-09</td>\n",
+       "      <td>2023-07-25</td>\n",
+       "      <td>https://www.netflix.com/title/80217315</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Sintonia</td>\n",
+       "      <td>22.377</td>\n",
+       "      <td>/safjAD2bBNT1grB23rFff5ChwDv.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Rewrite your future.</td>\n",
+       "      <td>Drama, Crime</td>\n",
+       "      <td>Felipe Braga, KondZilla</td>\n",
+       "      <td>pt</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>BR</td>\n",
+       "      <td>Português</td>\n",
+       "      <td>Losbragas, Gullane Entretenimento, Kondzilla</td>\n",
+       "      <td>Brazil</td>\n",
+       "      <td>44</td>\n",
+       "      <td>0.013607</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.008591</td>\n",
+       "      <td>0.2061</td>\n",
+       "      <td>18.510796</td>\n",
+       "      <td>5.192957</td>\n",
+       "      <td>109.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>50</td>\n",
+       "      <td>25.24</td>\n",
+       "      <td>-0.7438</td>\n",
+       "      <td>se_si</td>\n",
+       "      <td>89770000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>The Silent Sea</td>\n",
+       "      <td>the silent sea</td>\n",
+       "      <td>The Silent Sea</td>\n",
+       "      <td>25.069</td>\n",
+       "      <td>7.800</td>\n",
+       "      <td>527.0</td>\n",
+       "      <td>The Silent Sea</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>96777</td>\n",
+       "      <td>The Silent Sea</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>527</td>\n",
+       "      <td>7.800</td>\n",
+       "      <td>During a perilous 24-hour mission on the moon, space explorers try to retrieve samples from an abandoned research facility steeped in classified s...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/9hNJ3fvIVd4WE3rU1Us2awoTpgM.jpg</td>\n",
+       "      <td>2021-12-24</td>\n",
+       "      <td>2021-12-24</td>\n",
+       "      <td>https://www.netflix.com/title/81098012</td>\n",
+       "      <td>False</td>\n",
+       "      <td>고요의 바다</td>\n",
+       "      <td>25.069</td>\n",
+       "      <td>/fFT0IgqtCOks4munDTxQwkvNJkd.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>A deserted Moon base. A deadly mission.</td>\n",
+       "      <td>Drama, Sci-Fi &amp; Fantasy, Mystery</td>\n",
+       "      <td>Park Eun-kyo, Choi Hang-yong</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>Artist Company</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.006593</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.028130</td>\n",
+       "      <td>0.1486</td>\n",
+       "      <td>18.312761</td>\n",
+       "      <td>6.269096</td>\n",
+       "      <td>89.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>51</td>\n",
+       "      <td>25.14</td>\n",
+       "      <td>-0.7501</td>\n",
+       "      <td>fi_ki</td>\n",
+       "      <td>104610000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>First Kill</td>\n",
+       "      <td>first kill</td>\n",
+       "      <td>First Kill</td>\n",
+       "      <td>21.147</td>\n",
+       "      <td>7.952</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>First Kill</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>111616</td>\n",
+       "      <td>First Kill</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>en</td>\n",
+       "      <td>270</td>\n",
+       "      <td>7.952</td>\n",
+       "      <td>Falling in love is tricky for teens Juliette and Calliope: One's a vampire, the other's a vampire hunter — and both are ready to make their first ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/abFQcEO0UJVfUQPmhgjaKeLxRwy.jpg</td>\n",
+       "      <td>2022-06-10</td>\n",
+       "      <td>2022-06-10</td>\n",
+       "      <td>https://www.netflix.com/title/81213653</td>\n",
+       "      <td>False</td>\n",
+       "      <td>First Kill</td>\n",
+       "      <td>21.147</td>\n",
+       "      <td>/hwSy1vVaRzV70ZvAEsl6FGOHNlH.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Canceled</td>\n",
+       "      <td>You never forget your first.</td>\n",
+       "      <td>Drama, Mystery</td>\n",
+       "      <td>V.E. Schwab</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Belletrist Productions</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>49</td>\n",
+       "      <td>0.011887</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.528889</td>\n",
+       "      <td>0.013700</td>\n",
+       "      <td>0.1736</td>\n",
+       "      <td>18.465750</td>\n",
+       "      <td>5.602119</td>\n",
+       "      <td>104.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>52</td>\n",
+       "      <td>25.11</td>\n",
+       "      <td>-0.7519</td>\n",
+       "      <td>ca_or_sn</td>\n",
+       "      <td>112140000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>The Snitch Cartel: Origins</td>\n",
+       "      <td>the snitch cartel origins</td>\n",
+       "      <td>The Snitch Cartel: Origins</td>\n",
+       "      <td>42.083</td>\n",
+       "      <td>7.885</td>\n",
+       "      <td>283.0</td>\n",
+       "      <td>The Snitch Cartel: Origins</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>321.0</td>\n",
+       "      <td>128252</td>\n",
+       "      <td>The Snitch Cartel: Origins</td>\n",
+       "      <td>1</td>\n",
+       "      <td>60</td>\n",
+       "      <td>es</td>\n",
+       "      <td>283</td>\n",
+       "      <td>7.885</td>\n",
+       "      <td>In Cali during the '60s and '70s, two brothers juggle family, romance and the joint pursuit of a burning ambition: to rule Colombia's drug industry.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/iQlel1ftOsnWguxyN98eUmKTOPm.jpg</td>\n",
+       "      <td>2021-07-28</td>\n",
+       "      <td>2021-07-28</td>\n",
+       "      <td>https://www.netflix.com/title/81120861</td>\n",
+       "      <td>True</td>\n",
+       "      <td>El cartel de los sapos: El origen</td>\n",
+       "      <td>42.083</td>\n",
+       "      <td>/sjkzeJJOdr59VdkB4BjlSVfJmq2.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Crime, Soap</td>\n",
+       "      <td>Andrés López López</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Netflix, Caracol TV</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>Caracol Televisión</td>\n",
+       "      <td>Colombia</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.014574</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.491667</td>\n",
+       "      <td>0.014430</td>\n",
+       "      <td>0.1895</td>\n",
+       "      <td>18.535259</td>\n",
+       "      <td>5.648974</td>\n",
+       "      <td>112.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>53</td>\n",
+       "      <td>23.85</td>\n",
+       "      <td>-0.8290</td>\n",
+       "      <td>hu</td>\n",
+       "      <td>198310000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Hustle</td>\n",
+       "      <td>hustle</td>\n",
+       "      <td>Hustle</td>\n",
+       "      <td>30.738</td>\n",
+       "      <td>7.708</td>\n",
+       "      <td>137.0</td>\n",
+       "      <td>Hustle</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3764</td>\n",
+       "      <td>Hustle</td>\n",
+       "      <td>8</td>\n",
+       "      <td>48</td>\n",
+       "      <td>en</td>\n",
+       "      <td>137</td>\n",
+       "      <td>7.708</td>\n",
+       "      <td>A motley group of London con artists pull of a series of daring and intricate stings.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/q7iwBG89RLMdkXoYGdVxC9rRg8Y.jpg</td>\n",
+       "      <td>2004-02-24</td>\n",
+       "      <td>2012-02-17</td>\n",
+       "      <td>http://www.bbc.co.uk/programmes/b007gf9k</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Hustle</td>\n",
+       "      <td>30.738</td>\n",
+       "      <td>/6UTmAZnaqcIsWVKSBWm7pDmdDTI.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>The con is on!</td>\n",
+       "      <td>Comedy, Crime, Drama, Mystery</td>\n",
+       "      <td>Tony Jordan</td>\n",
+       "      <td>en</td>\n",
+       "      <td>BBC One</td>\n",
+       "      <td>GB</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Kudos, Red Planet Pictures</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.045316</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.393333</td>\n",
+       "      <td>0.006232</td>\n",
+       "      <td>0.1696</td>\n",
+       "      <td>19.105342</td>\n",
+       "      <td>4.927254</td>\n",
+       "      <td>198.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>54</td>\n",
+       "      <td>23.47</td>\n",
+       "      <td>-0.8526</td>\n",
+       "      <td>ho_pl</td>\n",
+       "      <td>72060000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Hospital Playlist</td>\n",
+       "      <td>hospital playlist</td>\n",
+       "      <td>Hospital Playlist</td>\n",
+       "      <td>21.826</td>\n",
+       "      <td>8.490</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Hospital Playlist</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>11014.0</td>\n",
+       "      <td>96102</td>\n",
+       "      <td>Hospital Playlist</td>\n",
+       "      <td>2</td>\n",
+       "      <td>24</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>100</td>\n",
+       "      <td>8.490</td>\n",
+       "      <td>Every day is extraordinary for five doctors and their patients inside a hospital, where birth, death and everything in between coexist.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/5DUqsUZPLdDRzthR08vvQCXoyfJ.jpg</td>\n",
+       "      <td>2020-03-12</td>\n",
+       "      <td>2021-09-16</td>\n",
+       "      <td>http://program.tving.com/tvn/doctorlife/</td>\n",
+       "      <td>False</td>\n",
+       "      <td>슬기로운 의사생활</td>\n",
+       "      <td>21.826</td>\n",
+       "      <td>/clYgUKk4CzmKpEukW2I2oDg9k1Q.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>The slightly special everyday of us ordinary people</td>\n",
+       "      <td>Drama, Comedy</td>\n",
+       "      <td>Lee Woo-jung, Shin Won-ho</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>tvN</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>Egg is Coming, CJ ENM</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>93</td>\n",
+       "      <td>0.000275</td>\n",
+       "      <td>0.217391</td>\n",
+       "      <td>0.827778</td>\n",
+       "      <td>0.004155</td>\n",
+       "      <td>0.2925</td>\n",
+       "      <td>18.093010</td>\n",
+       "      <td>4.615121</td>\n",
+       "      <td>72.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>55</td>\n",
+       "      <td>22.37</td>\n",
+       "      <td>-0.9204</td>\n",
+       "      <td>ch_ma</td>\n",
+       "      <td>104210000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>The Chestnut Man</td>\n",
+       "      <td>the chestnut man</td>\n",
+       "      <td>The Chestnut Man</td>\n",
+       "      <td>15.750</td>\n",
+       "      <td>7.584</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>The Chestnut Man</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>68351.0</td>\n",
+       "      <td>127865</td>\n",
+       "      <td>The Chestnut Man</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>da</td>\n",
+       "      <td>455</td>\n",
+       "      <td>7.584</td>\n",
+       "      <td>At a grisly murder scene sits a figurine made of chestnuts. From this creepy clue, two detectives hunt a killer linked to a politician's missing c...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/fIXTetAsXkPO6UUUw85IU4oactf.jpg</td>\n",
+       "      <td>2021-09-29</td>\n",
+       "      <td>2021-09-29</td>\n",
+       "      <td>https://www.netflix.com/title/81039388</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Kastanjemanden</td>\n",
+       "      <td>15.750</td>\n",
+       "      <td>/tdLJTZJ184PsO9QkK9aPeeAXLlo.jpg</td>\n",
+       "      <td>Miniseries</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>If you find one, he's already found you.</td>\n",
+       "      <td>Crime, Drama</td>\n",
+       "      <td>Søren Sveistrup, Mikkel Serup, David Sandreuter, Dorte Warnøe Høgh</td>\n",
+       "      <td>da</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>Dansk</td>\n",
+       "      <td>SAM Productions</td>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>55</td>\n",
+       "      <td>0.011745</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.324444</td>\n",
+       "      <td>0.024088</td>\n",
+       "      <td>0.1311</td>\n",
+       "      <td>18.461919</td>\n",
+       "      <td>6.122493</td>\n",
+       "      <td>104.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>56</td>\n",
+       "      <td>21.63</td>\n",
+       "      <td>-0.9662</td>\n",
+       "      <td>fi_ju</td>\n",
+       "      <td>177850000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>The Five Juanas</td>\n",
+       "      <td>the five juanas</td>\n",
+       "      <td>The Five Juanas</td>\n",
+       "      <td>18.672</td>\n",
+       "      <td>7.419</td>\n",
+       "      <td>246.0</td>\n",
+       "      <td>The Five Juanas</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>2615.0</td>\n",
+       "      <td>133086</td>\n",
+       "      <td>The Five Juanas</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>es</td>\n",
+       "      <td>246</td>\n",
+       "      <td>7.419</td>\n",
+       "      <td>Five women with the same birthmark set out to unravel the truth about their pasts and discover a tragic web of lies spun by a powerful politician.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/jB27sNxsgITNhr6HavrHywojy8V.jpg</td>\n",
+       "      <td>2021-10-06</td>\n",
+       "      <td>2021-10-06</td>\n",
+       "      <td>https://www.netflix.com/title/81167761</td>\n",
+       "      <td>False</td>\n",
+       "      <td>La venganza de las Juanas</td>\n",
+       "      <td>18.672</td>\n",
+       "      <td>/iFffOCcrtf5mSoGKd2J31d2b5yg.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Jimena Romero</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>Lemon Studios</td>\n",
+       "      <td>Mexico</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0.038017</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.232778</td>\n",
+       "      <td>0.012353</td>\n",
+       "      <td>0.1198</td>\n",
+       "      <td>18.996451</td>\n",
+       "      <td>5.509388</td>\n",
+       "      <td>177.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>57</td>\n",
+       "      <td>20.40</td>\n",
+       "      <td>-1.0415</td>\n",
+       "      <td>ju_ju</td>\n",
+       "      <td>134560000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Juvenile Justice</td>\n",
+       "      <td>juvenile justice</td>\n",
+       "      <td>Juvenile Justice</td>\n",
+       "      <td>14.269</td>\n",
+       "      <td>7.947</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>Juvenile Justice</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112833</td>\n",
+       "      <td>Juvenile Justice</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>85</td>\n",
+       "      <td>7.947</td>\n",
+       "      <td>A tough judge balances her aversion to minor offenders with firm beliefs on justice and punishment as she tackles complex cases inside a juvenile ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/eW6CqrGSRUk6xMbdCjreMQiP5W8.jpg</td>\n",
+       "      <td>2022-02-25</td>\n",
+       "      <td>2022-02-25</td>\n",
+       "      <td>https://www.netflix.com/title/81312802</td>\n",
+       "      <td>False</td>\n",
+       "      <td>소년심판</td>\n",
+       "      <td>14.269</td>\n",
+       "      <td>/xH2wTVlsUlYeFEOVGFDaamHkMCm.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Young offenders sentenced as monsters.</td>\n",
+       "      <td>Drama, Crime</td>\n",
+       "      <td>Hong Jong-chan, Kim Min-seok</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>Gil Pictures, GTist</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>61</td>\n",
+       "      <td>0.022572</td>\n",
+       "      <td>0.217391</td>\n",
+       "      <td>0.526111</td>\n",
+       "      <td>0.003313</td>\n",
+       "      <td>0.2097</td>\n",
+       "      <td>18.717521</td>\n",
+       "      <td>4.454347</td>\n",
+       "      <td>134.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>58</td>\n",
+       "      <td>18.55</td>\n",
+       "      <td>-1.1554</td>\n",
+       "      <td>81_ar</td>\n",
+       "      <td>129470000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Archive 81</td>\n",
+       "      <td>archive 81</td>\n",
+       "      <td>Archive 81</td>\n",
+       "      <td>20.088</td>\n",
+       "      <td>7.219</td>\n",
+       "      <td>442.0</td>\n",
+       "      <td>Archive 81</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>68254.0</td>\n",
+       "      <td>112314</td>\n",
+       "      <td>Archive 81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>en</td>\n",
+       "      <td>442</td>\n",
+       "      <td>7.219</td>\n",
+       "      <td>An archivist takes a job restoring damaged videotapes and gets pulled into the vortex of a mystery involving the missing director and a demonic cult.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/7TYMho2TM6ZcqSv6YaEjlNIyhXn.jpg</td>\n",
+       "      <td>2022-01-14</td>\n",
+       "      <td>2022-01-14</td>\n",
+       "      <td>https://www.netflix.com/title/80222802</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Archive 81</td>\n",
+       "      <td>20.088</td>\n",
+       "      <td>/l1jeSEm6a88GFcLzlHQr60D0dOi.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Canceled</td>\n",
+       "      <td>Rewind to reveal the truth.</td>\n",
+       "      <td>Drama, Mystery, Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>Rebecca Sonnenshine</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Atomic Monster</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>53</td>\n",
+       "      <td>0.020757</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.121667</td>\n",
+       "      <td>0.023358</td>\n",
+       "      <td>0.0560</td>\n",
+       "      <td>18.678960</td>\n",
+       "      <td>6.093570</td>\n",
+       "      <td>129.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>59</td>\n",
+       "      <td>13.16</td>\n",
+       "      <td>-1.4873</td>\n",
+       "      <td>ed_we</td>\n",
+       "      <td>140340000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Welcome to Eden</td>\n",
+       "      <td>welcome to eden</td>\n",
+       "      <td>Welcome to Eden</td>\n",
+       "      <td>40.482</td>\n",
+       "      <td>7.090</td>\n",
+       "      <td>233.0</td>\n",
+       "      <td>Welcome to Eden</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>128010</td>\n",
+       "      <td>Welcome to Eden</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>es</td>\n",
+       "      <td>233</td>\n",
+       "      <td>7.090</td>\n",
+       "      <td>Are you happy? With this question, Zoa and four other attractive young people, very active on social networks, are invited to the most exclusive p...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/kcHvkFlsodXu1TFgXuOT3OvJYnu.jpg</td>\n",
+       "      <td>2022-05-06</td>\n",
+       "      <td>2023-04-21</td>\n",
+       "      <td>https://www.netflix.com/title/81211710</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Bienvenidos a Edén</td>\n",
+       "      <td>40.482</td>\n",
+       "      <td>/kZO0mtTHAgg9QVscLVtwCcQ7Hgx.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Canceled</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Action &amp; Adventure, Sci-Fi &amp; Fantasy, Drama</td>\n",
+       "      <td>Joaquín Górriz, Guillermo López Sánchez</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>Brutal Media</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>41</td>\n",
+       "      <td>0.024635</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.011623</td>\n",
+       "      <td>0.0515</td>\n",
+       "      <td>18.759579</td>\n",
+       "      <td>5.455321</td>\n",
+       "      <td>140.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>60</td>\n",
+       "      <td>12.02</td>\n",
+       "      <td>-1.5573</td>\n",
+       "      <td>al</td>\n",
+       "      <td>175640000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Alba</td>\n",
+       "      <td>alba</td>\n",
+       "      <td>Alba</td>\n",
+       "      <td>12.840</td>\n",
+       "      <td>7.000</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>Alba</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>115521</td>\n",
+       "      <td>Alba</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>es</td>\n",
+       "      <td>167</td>\n",
+       "      <td>7.000</td>\n",
+       "      <td>Alba wakes up on a beach after a rape she doesn't remember. And then she discovers that her rapists are friends of her lover.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/2AJcuidWwx8Z4kJPUw2RwiPd3Er.jpg</td>\n",
+       "      <td>2021-03-28</td>\n",
+       "      <td>2021-06-20</td>\n",
+       "      <td>https://premium.atresplayer.com/alba/</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Alba</td>\n",
+       "      <td>12.840</td>\n",
+       "      <td>/pubcatJw5MZNuTJ78atJBkw9crW.jpg</td>\n",
+       "      <td>Miniseries</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Carlos Martín, Ignasi Rubio</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Atresplayer Premium</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>Boomerang TV</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.037228</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.007917</td>\n",
+       "      <td>0.0490</td>\n",
+       "      <td>18.983947</td>\n",
+       "      <td>5.123964</td>\n",
+       "      <td>175.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>61</td>\n",
+       "      <td>10.95</td>\n",
+       "      <td>-1.6234</td>\n",
+       "      <td>lo_sp</td>\n",
+       "      <td>185180000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Lost in Space</td>\n",
+       "      <td>lost in space</td>\n",
+       "      <td>Lost in Space</td>\n",
+       "      <td>412.031</td>\n",
+       "      <td>7.115</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>Lost in Space</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3051</td>\n",
+       "      <td>Lost in Space</td>\n",
+       "      <td>3</td>\n",
+       "      <td>83</td>\n",
+       "      <td>en</td>\n",
+       "      <td>96</td>\n",
+       "      <td>7.115</td>\n",
+       "      <td>The space family Robinson is sent on a five-year mission to find a new planet to colonise. The voyage is sabotaged time and again by an inept stow...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/upqfQF1LBPf7IRZao0sxt1BHZlx.jpg</td>\n",
+       "      <td>1965-09-15</td>\n",
+       "      <td>1968-03-06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Lost in Space</td>\n",
+       "      <td>412.031</td>\n",
+       "      <td>/dGhtCpYZutuso2P3GKWnQnqwhSi.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>A space colony family struggles to survive when their ship is hopelessly thrown off course.</td>\n",
+       "      <td>Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>Irwin Allen</td>\n",
+       "      <td>en</td>\n",
+       "      <td>CBS</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.040632</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.063889</td>\n",
+       "      <td>0.003930</td>\n",
+       "      <td>0.0514</td>\n",
+       "      <td>19.036839</td>\n",
+       "      <td>4.574711</td>\n",
+       "      <td>185.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>62</td>\n",
+       "      <td>10.77</td>\n",
+       "      <td>-1.6346</td>\n",
+       "      <td>an_sc</td>\n",
+       "      <td>171020000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Anatomy of a Scandal</td>\n",
+       "      <td>anatomy of a scandal</td>\n",
+       "      <td>Anatomy of a Scandal</td>\n",
+       "      <td>13.197</td>\n",
+       "      <td>7.071</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>Anatomy of a Scandal</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>40449.0</td>\n",
+       "      <td>113984</td>\n",
+       "      <td>Anatomy of a Scandal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>en</td>\n",
+       "      <td>120</td>\n",
+       "      <td>7.071</td>\n",
+       "      <td>Anthology series centering on the personal and political scandals of Britain’s elite.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/8zPoUJECL6X8G5T95OuJDorHdtV.jpg</td>\n",
+       "      <td>2022-04-15</td>\n",
+       "      <td>2022-04-15</td>\n",
+       "      <td>https://www.netflix.com/title/81152788</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Anatomy of a Scandal</td>\n",
+       "      <td>13.197</td>\n",
+       "      <td>/e1pUQkXbFNI6xH4oDuHnyJoOSib.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Not everyone is entitled to the truth.</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>David E. Kelley, Melissa James Gibson</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>GB, US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>David E. Kelley Productions, 3dot Productions, Made Up Stories, Endeavor Content, Anonymous Content</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.035580</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.039444</td>\n",
+       "      <td>0.005278</td>\n",
+       "      <td>0.0512</td>\n",
+       "      <td>18.957291</td>\n",
+       "      <td>4.795791</td>\n",
+       "      <td>171.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>63</td>\n",
+       "      <td>10.67</td>\n",
+       "      <td>-1.6404</td>\n",
+       "      <td>bl_is_lo</td>\n",
+       "      <td>256890000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Love Is Blind</td>\n",
+       "      <td>love is blind</td>\n",
+       "      <td>Love Is Blind</td>\n",
+       "      <td>46.558</td>\n",
+       "      <td>7.100</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>Love is Blind</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>99353</td>\n",
+       "      <td>Love Is Blind</td>\n",
+       "      <td>5</td>\n",
+       "      <td>69</td>\n",
+       "      <td>en</td>\n",
+       "      <td>54</td>\n",
+       "      <td>7.100</td>\n",
+       "      <td>Nick and Vanessa Lachey host this social experiment where single men and women look for love and get engaged, all before meeting in person.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/esHT6kpx6XAuvp1ngrngTSmelA8.jpg</td>\n",
+       "      <td>2020-02-13</td>\n",
+       "      <td>2023-09-29</td>\n",
+       "      <td>https://www.netflix.com/title/80996601</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Love Is Blind</td>\n",
+       "      <td>46.558</td>\n",
+       "      <td>/gOo6KvUkQI9q0THoUqxI9amnnxL.jpg</td>\n",
+       "      <td>Reality</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>Falling in love is the easy part.</td>\n",
+       "      <td>Reality</td>\n",
+       "      <td>Chris Coelen</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Kinetic Content</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.066215</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.055556</td>\n",
+       "      <td>0.001572</td>\n",
+       "      <td>0.0662</td>\n",
+       "      <td>19.364159</td>\n",
+       "      <td>4.007333</td>\n",
+       "      <td>256.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>64</td>\n",
+       "      <td>9.80</td>\n",
+       "      <td>-1.6942</td>\n",
+       "      <td>fo_lo_we</td>\n",
+       "      <td>100850000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Forecasting Love and Weather</td>\n",
+       "      <td>forecasting love weather</td>\n",
+       "      <td>Forecasting Love and Weather</td>\n",
+       "      <td>16.976</td>\n",
+       "      <td>7.631</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>Forecasting Love and Weather</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>5578.0</td>\n",
+       "      <td>152511</td>\n",
+       "      <td>Forecasting Love and Weather</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>61</td>\n",
+       "      <td>7.631</td>\n",
+       "      <td>Inside a national weather service, love proves just as difficult to predict as rain or shine for a diligent forecaster and her free-spirited co-wo...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/s9n20a4QRilgzcaEh91jWZaS8L9.jpg</td>\n",
+       "      <td>2022-02-12</td>\n",
+       "      <td>2022-04-03</td>\n",
+       "      <td>http://tv.jtbc.joins.com/weatherpeople</td>\n",
+       "      <td>False</td>\n",
+       "      <td>기상청 사람들: 사내연애 잔혹사 편</td>\n",
+       "      <td>16.976</td>\n",
+       "      <td>/3Qfh99m0jShWrqlFRsnWY7HeHNX.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Kang Eun-kyung</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>JTBC</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>한국어/조선말</td>\n",
+       "      <td>SLL, npio Entertainment</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.010546</td>\n",
+       "      <td>0.260870</td>\n",
+       "      <td>0.350556</td>\n",
+       "      <td>0.001965</td>\n",
+       "      <td>0.1613</td>\n",
+       "      <td>18.429145</td>\n",
+       "      <td>4.127134</td>\n",
+       "      <td>100.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>65</td>\n",
+       "      <td>8.13</td>\n",
+       "      <td>-1.7971</td>\n",
+       "      <td>ca</td>\n",
+       "      <td>183040000</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Carter</td>\n",
+       "      <td>carter</td>\n",
+       "      <td>Carter</td>\n",
+       "      <td>11.552</td>\n",
+       "      <td>7.400</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>Carter</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>79357</td>\n",
+       "      <td>Carter</td>\n",
+       "      <td>2</td>\n",
+       "      <td>20</td>\n",
+       "      <td>en</td>\n",
+       "      <td>30</td>\n",
+       "      <td>7.400</td>\n",
+       "      <td>An actor returns home after a public meltdown. Partnering with his police detective friend, he tries to use his acting experience to solve real cr...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/ymxyzXY3NzgaDekXR4WURetjOYQ.jpg</td>\n",
+       "      <td>2018-05-15</td>\n",
+       "      <td>2019-12-20</td>\n",
+       "      <td>https://www.bravo.ca/Shows/Carter</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Carter</td>\n",
+       "      <td>11.552</td>\n",
+       "      <td>/ugPqPRSfa6RmraIej6lp7yoeEUK.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama, Comedy, Mystery</td>\n",
+       "      <td>Garry Campbell</td>\n",
+       "      <td>en, fr, pt</td>\n",
+       "      <td>bravo, CTV Drama Channel</td>\n",
+       "      <td>CA, US</td>\n",
+       "      <td>English, Français, Português</td>\n",
+       "      <td>Amaze Film + Television</td>\n",
+       "      <td>Canada, United States of America</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.039868</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.000225</td>\n",
+       "      <td>0.1502</td>\n",
+       "      <td>19.025215</td>\n",
+       "      <td>3.433987</td>\n",
+       "      <td>183.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>66</td>\n",
+       "      <td>7.41</td>\n",
+       "      <td>-1.8412</td>\n",
+       "      <td>am_ge_i</td>\n",
+       "      <td>71290000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>I Am Georgina</td>\n",
+       "      <td>i am georgina</td>\n",
+       "      <td>I Am Georgina</td>\n",
+       "      <td>11.560</td>\n",
+       "      <td>7.111</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>I Am Georgina</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>4667.0</td>\n",
+       "      <td>156077</td>\n",
+       "      <td>I Am Georgina</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>es</td>\n",
+       "      <td>293</td>\n",
+       "      <td>7.111</td>\n",
+       "      <td>Join Georgina Rodríguez — mom, influencer, businesswoman and Cristiano Ronaldo's partner — in this emotional and in-depth portrait of her daily life.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/3SfKp1aTkXdxl1jSsURjHycAHAO.jpg</td>\n",
+       "      <td>2022-01-27</td>\n",
+       "      <td>2023-03-24</td>\n",
+       "      <td>https://www.netflix.com/title/81423622</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Soy Georgina</td>\n",
+       "      <td>11.560</td>\n",
+       "      <td>/70e3SQBIAcjKSibWoyCA7lBMZA8.jpg</td>\n",
+       "      <td>Reality</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>One in a million.</td>\n",
+       "      <td>Reality</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>Español</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.061667</td>\n",
+       "      <td>0.014992</td>\n",
+       "      <td>0.0294</td>\n",
+       "      <td>18.082267</td>\n",
+       "      <td>5.683580</td>\n",
+       "      <td>71.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>67</td>\n",
+       "      <td>4.17</td>\n",
+       "      <td>-2.0403</td>\n",
+       "      <td>co</td>\n",
+       "      <td>161630000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Collision</td>\n",
+       "      <td>collision</td>\n",
+       "      <td>Collision</td>\n",
+       "      <td>5.795</td>\n",
+       "      <td>7.300</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>Collision</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8344</td>\n",
+       "      <td>Collision</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>en</td>\n",
+       "      <td>26</td>\n",
+       "      <td>7.300</td>\n",
+       "      <td>The story of a major road accident and a group of people who have never met, but who all share one single defining moment that will change their l...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/hFlvQRRZYtOlv41pChaFdya0CVV.jpg</td>\n",
+       "      <td>2009-11-09</td>\n",
+       "      <td>2009-11-13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Collision</td>\n",
+       "      <td>5.795</td>\n",
+       "      <td>/qXuLNDG9UICDXqG0ciyQpjuW4SX.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama, Crime, Mystery</td>\n",
+       "      <td>Anthony Horowitz</td>\n",
+       "      <td>en</td>\n",
+       "      <td>ITV1</td>\n",
+       "      <td>GB</td>\n",
+       "      <td>English</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.032230</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0874</td>\n",
+       "      <td>18.900820</td>\n",
+       "      <td>3.295837</td>\n",
+       "      <td>161.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>68</td>\n",
+       "      <td>3.56</td>\n",
+       "      <td>-2.0779</td>\n",
+       "      <td>9_po_tu</td>\n",
+       "      <td>80680000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Turning Point: 9/11 and the War on Terror</td>\n",
+       "      <td>turning point 9 11 the war on terror</td>\n",
+       "      <td>Turning Point: 9/11 and the War on Terror</td>\n",
+       "      <td>9.194</td>\n",
+       "      <td>7.287</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>Turning Point: 9/11 and the War on Terror</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>13266.0</td>\n",
+       "      <td>131651</td>\n",
+       "      <td>Turning Point: 9/11 and the War on Terror</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>en</td>\n",
+       "      <td>83</td>\n",
+       "      <td>7.287</td>\n",
+       "      <td>Modern history can be divided into two time frames: before 9/11 and after 9/11. This five-part docuseries is a cohesive chronicle of the September...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/pxhxhEKJXznWEavLXJWUxxxdo2X.jpg</td>\n",
+       "      <td>2021-09-01</td>\n",
+       "      <td>2021-09-01</td>\n",
+       "      <td>https://www.netflix.com/title/81315804</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Turning Point: 9/11 and the War on Terror</td>\n",
+       "      <td>9.194</td>\n",
+       "      <td>/jZsV8GInbLWcAX3mAFxE6gE8bTR.jpg</td>\n",
+       "      <td>Miniseries</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>History casts a long shadow.</td>\n",
+       "      <td>Documentary, War &amp; Politics</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Luminant Media</td>\n",
+       "      <td>Afghanistan, United States of America</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.003350</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.159444</td>\n",
+       "      <td>0.003200</td>\n",
+       "      <td>0.0582</td>\n",
+       "      <td>18.206001</td>\n",
+       "      <td>4.430817</td>\n",
+       "      <td>80.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>69</td>\n",
+       "      <td>3.21</td>\n",
+       "      <td>-2.0995</td>\n",
+       "      <td>se_su</td>\n",
+       "      <td>158780000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>Selling Sunset</td>\n",
+       "      <td>selling sunset</td>\n",
+       "      <td>Selling Sunset</td>\n",
+       "      <td>9.459</td>\n",
+       "      <td>7.100</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>Selling Sunset</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>9335.0</td>\n",
+       "      <td>87826</td>\n",
+       "      <td>Selling Sunset</td>\n",
+       "      <td>6</td>\n",
+       "      <td>56</td>\n",
+       "      <td>en</td>\n",
+       "      <td>39</td>\n",
+       "      <td>7.100</td>\n",
+       "      <td>The elite real estate brokers at The Oppenheim Group sell the luxe life to affluent buyers in LA. The drama ramps up when a new agent joins the team.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/hB2llGkvqcf8sBgEv00HO57eGaa.jpg</td>\n",
+       "      <td>2019-03-22</td>\n",
+       "      <td>2023-05-19</td>\n",
+       "      <td>https://www.netflix.com/title/80223108</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Selling Sunset</td>\n",
+       "      <td>9.459</td>\n",
+       "      <td>/wUtoD67q4BnRUwikUbT6M38d5oj.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Returning Series</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Reality</td>\n",
+       "      <td>Adam DiVello</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Done and Done Productions, Lionsgate Television</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.031213</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.055556</td>\n",
+       "      <td>0.000730</td>\n",
+       "      <td>0.0451</td>\n",
+       "      <td>18.883030</td>\n",
+       "      <td>3.688879</td>\n",
+       "      <td>158.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>70</td>\n",
+       "      <td>2.89</td>\n",
+       "      <td>-2.1196</td>\n",
+       "      <td>st_tr</td>\n",
+       "      <td>90820000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>True Story</td>\n",
+       "      <td>true story</td>\n",
+       "      <td>True Story</td>\n",
+       "      <td>15.695</td>\n",
+       "      <td>7.171</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>True Story</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>134029</td>\n",
+       "      <td>True Story</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>en</td>\n",
+       "      <td>82</td>\n",
+       "      <td>7.171</td>\n",
+       "      <td>A world-famous comedian desperately searches for a way out after a night in Philadelphia with his brother threatens to sabotage more than his succ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/yGpmZYJ8fb543B3tIKjzcRHgJsX.jpg</td>\n",
+       "      <td>2021-11-24</td>\n",
+       "      <td>2021-11-24</td>\n",
+       "      <td>https://www.netflix.com/title/81132961</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True Story</td>\n",
+       "      <td>15.695</td>\n",
+       "      <td>/7zMbzJijHQv5oOtkb8rdYarn9X6.jpg</td>\n",
+       "      <td>Miniseries</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>Betrayal is relative.</td>\n",
+       "      <td>Drama, Crime</td>\n",
+       "      <td>Eric Newman</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>US</td>\n",
+       "      <td>English</td>\n",
+       "      <td>HartBeat Productions, Grand Electric</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.006968</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.095000</td>\n",
+       "      <td>0.003144</td>\n",
+       "      <td>0.0401</td>\n",
+       "      <td>18.324390</td>\n",
+       "      <td>4.418841</td>\n",
+       "      <td>90.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>71</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>-2.2972</td>\n",
+       "      <td>an_se</td>\n",
+       "      <td>88590000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>Another Self</td>\n",
+       "      <td>another self</td>\n",
+       "      <td>Another Self</td>\n",
+       "      <td>10.597</td>\n",
+       "      <td>7.400</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>Another Self</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>26500.0</td>\n",
+       "      <td>206180</td>\n",
+       "      <td>Another Self</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>tr</td>\n",
+       "      <td>30</td>\n",
+       "      <td>7.400</td>\n",
+       "      <td>Three friends arrive in a seaside town, where they connect with their spiritual selves and suddenly face unresolved trauma from their families' pa...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/5nAikDmnio7SuBFg4KIpC3c50UL.jpg</td>\n",
+       "      <td>2022-07-28</td>\n",
+       "      <td>2022-07-28</td>\n",
+       "      <td>https://www.netflix.com/title/81380432</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Zeytin Ağacı</td>\n",
+       "      <td>10.597</td>\n",
+       "      <td>/zogTjQRIJsmg8yvIo8gDLtp65th.jpg</td>\n",
+       "      <td>Scripted</td>\n",
+       "      <td>Ended</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Nuran Evren Şit</td>\n",
+       "      <td>tr</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>TR</td>\n",
+       "      <td>Türkçe</td>\n",
+       "      <td>OGM Pictures</td>\n",
+       "      <td>Turkey</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.006172</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.000225</td>\n",
+       "      <td>0.0863</td>\n",
+       "      <td>18.299530</td>\n",
+       "      <td>3.433987</td>\n",
+       "      <td>88.59</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Hit_rank  Hit_score  PCA_Hit_original       key  netflix_viewing_hours  \\\n",
+       "0          1     100.00            3.8578     st_th             2874260000   \n",
+       "1          2      84.49            2.9029     he_mo             1170200000   \n",
+       "2          3      82.90            2.8051     ga_sq             2289500000   \n",
+       "3          4      74.10            2.2635     ac_um              414630000   \n",
+       "4          5      70.62            2.0493     ed_se              561360000   \n",
+       "5          6      68.76            1.9352  al_ar_us              659510000   \n",
+       "6          7      67.90            1.8819        br             1108800000   \n",
+       "7          8      64.97            1.7017        oz              751600000   \n",
+       "8          9      64.08            1.6468     bl_pe              185340000   \n",
+       "9         10      64.00            1.6419     co_ka              383640000   \n",
+       "10        11      62.61            1.5567        el              329290000   \n",
+       "11        12      61.10            1.4633        ma             1446260000   \n",
+       "12        13      59.65            1.3740     do_go              112580000   \n",
+       "13        14      58.92            1.3291        ar              154270000   \n",
+       "14        15      55.11            1.0948     fl_qu              561230000   \n",
+       "15        16      54.08            1.0317  at_ex_wo              404030000   \n",
+       "16        17      51.70            0.8850   ha_i_ne              297780000   \n",
+       "17        18      49.84            0.7703        sa              274220000   \n",
+       "18        19      49.75            0.7650     ri_vi              624250000   \n",
+       "19        20      49.11            0.7257  de_se_si               79510000   \n",
+       "20        21      48.48            0.6866     ki_la              189570000   \n",
+       "21        22      44.87            0.4647     em_pa              341630000   \n",
+       "22        23      44.82            0.4613  ch_ch_ho              300580000   \n",
+       "23        24      44.43            0.4373     ke_lo              278380000   \n",
+       "24        25      44.19            0.4225     bu_pr              279110000   \n",
+       "25        26      43.88            0.4034     my_na              194140000   \n",
+       "26        27      42.58            0.3233  ar_ca_co              793740000   \n",
+       "27        28      41.75            0.2724      co_z               89830000   \n",
+       "28        29      41.22            0.2397     al_so              127690000   \n",
+       "29        30      39.80            0.1525     va_vi              265550000   \n",
+       "30        31      39.54            0.1364     da_de              202940000   \n",
+       "31        32      38.89            0.0965     al_am              180660000   \n",
+       "32        33      37.78            0.0282   de_n_pa              164180000   \n",
+       "33        34      37.57            0.0151     me_na              138980000   \n",
+       "34        35      36.08           -0.0765     bl_my               80880000   \n",
+       "35        36      34.83           -0.1532  fi_tw_tw              171610000   \n",
+       "36        37      34.28           -0.1872     gi_go              153140000   \n",
+       "37        38      33.92           -0.2093   af_ki_s              138540000   \n",
+       "38        39      32.78           -0.2796  ki_sa_wh              143610000   \n",
+       "39        40      32.64           -0.2881     he_ma              272750000   \n",
+       "40        41      32.42           -0.3019     li_se              282100000   \n",
+       "41        42      32.18           -0.3163     la_li              305270000   \n",
+       "42        43      28.86           -0.5208     di_ra              172870000   \n",
+       "43        44      28.78           -0.5260  he_ko_mo               98370000   \n",
+       "44        45      28.21           -0.5609  be_ou_su              128890000   \n",
+       "45        46      27.44           -0.6082     bl_ou              105150000   \n",
+       "46        47      27.33           -0.6150     ma_mi              142280000   \n",
+       "47        48      27.12           -0.6282     ma_sw              182940000   \n",
+       "48        49      27.09           -0.6296        si              109430000   \n",
+       "49        50      25.24           -0.7438     se_si               89770000   \n",
+       "50        51      25.14           -0.7501     fi_ki              104610000   \n",
+       "51        52      25.11           -0.7519  ca_or_sn              112140000   \n",
+       "52        53      23.85           -0.8290        hu              198310000   \n",
+       "53        54      23.47           -0.8526     ho_pl               72060000   \n",
+       "54        55      22.37           -0.9204     ch_ma              104210000   \n",
+       "55        56      21.63           -0.9662     fi_ju              177850000   \n",
+       "56        57      20.40           -1.0415     ju_ju              134560000   \n",
+       "57        58      18.55           -1.1554     81_ar              129470000   \n",
+       "58        59      13.16           -1.4873     ed_we              140340000   \n",
+       "59        60      12.02           -1.5573        al              175640000   \n",
+       "60        61      10.95           -1.6234     lo_sp              185180000   \n",
+       "61        62      10.77           -1.6346     an_sc              171020000   \n",
+       "62        63      10.67           -1.6404  bl_is_lo              256890000   \n",
+       "63        64       9.80           -1.6942  fo_lo_we              100850000   \n",
+       "64        65       8.13           -1.7971        ca              183040000   \n",
+       "65        66       7.41           -1.8412   am_ge_i               71290000   \n",
+       "66        67       4.17           -2.0403        co              161630000   \n",
+       "67        68       3.56           -2.0779   9_po_tu               80680000   \n",
+       "68        69       3.21           -2.0995     se_su              158780000   \n",
+       "69        70       2.89           -2.1196     st_tr               90820000   \n",
+       "70        71       0.00           -2.2972     an_se               88590000   \n",
+       "\n",
+       "    netflix_weeks  netflix_year_hint  \\\n",
+       "0              13               2022   \n",
+       "1              14               2021   \n",
+       "2              20               2021   \n",
+       "3               5               2022   \n",
+       "4               6               2021   \n",
+       "5              11               2022   \n",
+       "6              11               2021   \n",
+       "7              13               2022   \n",
+       "8               5               2022   \n",
+       "9               4               2022   \n",
+       "10              5               2021   \n",
+       "11             16               2021   \n",
+       "12              6               2021   \n",
+       "13              6               2021   \n",
+       "14             16               2021   \n",
+       "15              7               2022   \n",
+       "16              4               2021   \n",
+       "17              3               2022   \n",
+       "18              6               2021   \n",
+       "19              4               2021   \n",
+       "20              5               2022   \n",
+       "21              5               2021   \n",
+       "22             16               2021   \n",
+       "23              5               2021   \n",
+       "24             13               2022   \n",
+       "25              5               2021   \n",
+       "26             25               2022   \n",
+       "27              3               2021   \n",
+       "28              8               2022   \n",
+       "29              5               2022   \n",
+       "30              5               2022   \n",
+       "31              5               2021   \n",
+       "32              5               2022   \n",
+       "33              4               2021   \n",
+       "34              2               2021   \n",
+       "35             10               2022   \n",
+       "36              6               2021   \n",
+       "37             10               2021   \n",
+       "38              4               2022   \n",
+       "39              7               2022   \n",
+       "40              7               2021   \n",
+       "41              6               2022   \n",
+       "42              4               2022   \n",
+       "43              3               2022   \n",
+       "44              8               2021   \n",
+       "45              9               2022   \n",
+       "46              4               2021   \n",
+       "47              5               2022   \n",
+       "48              2               2021   \n",
+       "49              3               2021   \n",
+       "50              3               2021   \n",
+       "51              6               2021   \n",
+       "52              6               2022   \n",
+       "53              7               2021   \n",
+       "54              5               2021   \n",
+       "55              6               2021   \n",
+       "56              7               2022   \n",
+       "57              3               2022   \n",
+       "58              5               2022   \n",
+       "59              6               2022   \n",
+       "60              4               2021   \n",
+       "61              5               2022   \n",
+       "62              5               2021   \n",
+       "63              8               2022   \n",
+       "64             10               2021   \n",
+       "65              3               2022   \n",
+       "66              5               2021   \n",
+       "67              3               2021   \n",
+       "68              4               2021   \n",
+       "69              3               2021   \n",
+       "70              4               2022   \n",
+       "\n",
+       "                                netflix_title  \\\n",
+       "0                             Stranger Things   \n",
+       "1                                 Money Heist   \n",
+       "2                                  Squid Game   \n",
+       "3                        The Umbrella Academy   \n",
+       "4                               Sex Education   \n",
+       "5                          All of Us Are Dead   \n",
+       "6                                  Bridgerton   \n",
+       "7                                       Ozark   \n",
+       "8                              Peaky Blinders   \n",
+       "9                                   Cobra Kai   \n",
+       "10                                      Elite   \n",
+       "11                                   Manifest   \n",
+       "12                            The Good Doctor   \n",
+       "13                                     Arcane   \n",
+       "14                          The Queen of Flow   \n",
+       "15                 Extraordinary Attorney Woo   \n",
+       "16                          Never Have I Ever   \n",
+       "17                                The Sandman   \n",
+       "18                               Virgin River   \n",
+       "19     The Seven Deadly Sins: Cursed by Light   \n",
+       "20                           The Last Kingdom   \n",
+       "21                             Emily in Paris   \n",
+       "22                       Hometown Cha-Cha-Cha   \n",
+       "23                                Locke & Key   \n",
+       "24                          Business Proposal   \n",
+       "25                                    My Name   \n",
+       "26                    Café con aroma de mujer   \n",
+       "27                                  Control Z   \n",
+       "28                           Alchemy of Souls   \n",
+       "29                          Vikings: Valhalla   \n",
+       "30                                Dark Desire   \n",
+       "31                               All American   \n",
+       "32                        Pasión de Gavilanes   \n",
+       "33                             Narcos: Mexico   \n",
+       "34                                On My Block   \n",
+       "35                     Twenty Five Twenty One   \n",
+       "36                                 Good Girls   \n",
+       "37                       The King's Affection   \n",
+       "38                           Who Killed Sara?   \n",
+       "39                           The Marked Heart   \n",
+       "40                                   Sex/Life   \n",
+       "41                         The Lincoln Lawyer   \n",
+       "42                               Raising Dion   \n",
+       "43   Money Heist: Korea - Joint Economic Area   \n",
+       "44                         Our Beloved Summer   \n",
+       "45                                  Our Blues   \n",
+       "46                              Midnight Mass   \n",
+       "47                            Sweet Magnolias   \n",
+       "48                                   Sintonia   \n",
+       "49                             The Silent Sea   \n",
+       "50                                 First Kill   \n",
+       "51                 The Snitch Cartel: Origins   \n",
+       "52                                     Hustle   \n",
+       "53                          Hospital Playlist   \n",
+       "54                           The Chestnut Man   \n",
+       "55                            The Five Juanas   \n",
+       "56                           Juvenile Justice   \n",
+       "57                                 Archive 81   \n",
+       "58                            Welcome to Eden   \n",
+       "59                                       Alba   \n",
+       "60                              Lost in Space   \n",
+       "61                       Anatomy of a Scandal   \n",
+       "62                              Love Is Blind   \n",
+       "63               Forecasting Love and Weather   \n",
+       "64                                     Carter   \n",
+       "65                              I Am Georgina   \n",
+       "66                                  Collision   \n",
+       "67  Turning Point: 9/11 and the War on Terror   \n",
+       "68                             Selling Sunset   \n",
+       "69                                 True Story   \n",
+       "70                               Another Self   \n",
+       "\n",
+       "                      netflix_clean_title  \\\n",
+       "0                         stranger things   \n",
+       "1                             money heist   \n",
+       "2                              squid game   \n",
+       "3                    the umbrella academy   \n",
+       "4                           sex education   \n",
+       "5                      all of us are dead   \n",
+       "6                              bridgerton   \n",
+       "7                                   ozark   \n",
+       "8                          peaky blinders   \n",
+       "9                               cobra kai   \n",
+       "10                                  elite   \n",
+       "11                               manifest   \n",
+       "12                        the good doctor   \n",
+       "13                                 arcane   \n",
+       "14                      the queen of flow   \n",
+       "15             extraordinary attorney woo   \n",
+       "16                      never have i ever   \n",
+       "17                            the sandman   \n",
+       "18                           virgin river   \n",
+       "19  the seven deadly sins cursed by light   \n",
+       "20                       the last kingdom   \n",
+       "21                         emily in paris   \n",
+       "22                   hometown cha cha cha   \n",
+       "23                              locke key   \n",
+       "24                      business proposal   \n",
+       "25                                my name   \n",
+       "26                 caf con aroma de mujer   \n",
+       "27                              control z   \n",
+       "28                       alchemy of souls   \n",
+       "29                       vikings valhalla   \n",
+       "30                            dark desire   \n",
+       "31                           all american   \n",
+       "32                    pasi n de gavilanes   \n",
+       "33                          narcos mexico   \n",
+       "34                            on my block   \n",
+       "35                 twenty five twenty one   \n",
+       "36                             good girls   \n",
+       "37                   the king s affection   \n",
+       "38                        who killed sara   \n",
+       "39                       the marked heart   \n",
+       "40                               sex life   \n",
+       "41                     the lincoln lawyer   \n",
+       "42                           raising dion   \n",
+       "43  money heist korea joint economic area   \n",
+       "44                     our beloved summer   \n",
+       "45                              our blues   \n",
+       "46                          midnight mass   \n",
+       "47                        sweet magnolias   \n",
+       "48                               sintonia   \n",
+       "49                         the silent sea   \n",
+       "50                             first kill   \n",
+       "51              the snitch cartel origins   \n",
+       "52                                 hustle   \n",
+       "53                      hospital playlist   \n",
+       "54                       the chestnut man   \n",
+       "55                        the five juanas   \n",
+       "56                       juvenile justice   \n",
+       "57                             archive 81   \n",
+       "58                        welcome to eden   \n",
+       "59                                   alba   \n",
+       "60                          lost in space   \n",
+       "61                   anatomy of a scandal   \n",
+       "62                          love is blind   \n",
+       "63               forecasting love weather   \n",
+       "64                                 carter   \n",
+       "65                          i am georgina   \n",
+       "66                              collision   \n",
+       "67   turning point 9 11 the war on terror   \n",
+       "68                         selling sunset   \n",
+       "69                             true story   \n",
+       "70                           another self   \n",
+       "\n",
+       "                                   tmdb_title  tmdb_popularity  \\\n",
+       "0                             Stranger Things          185.711   \n",
+       "1                                 Money Heist           96.354   \n",
+       "2                                  Squid Game          115.587   \n",
+       "3                        The Umbrella Academy           51.859   \n",
+       "4                               Sex Education         1008.977   \n",
+       "5                          All of Us Are Dead          107.108   \n",
+       "6                                  Bridgerton           71.308   \n",
+       "7                                       Ozark           68.006   \n",
+       "8                              Peaky Blinders          344.477   \n",
+       "9                                   Cobra Kai          118.256   \n",
+       "10                                      Elite          100.711   \n",
+       "11                                   Manifest          111.411   \n",
+       "12                            The Good Doctor          681.614   \n",
+       "13                                     Arcane           42.447   \n",
+       "14                          The Queen of Flow          129.323   \n",
+       "15                 Extraordinary Attorney Woo           42.923   \n",
+       "16                          Never Have I Ever           80.546   \n",
+       "17                                The Sandman           64.168   \n",
+       "18                               Virgin River           54.192   \n",
+       "19                      The Seven Deadly Sins           10.059   \n",
+       "20                           The Last Kingdom          143.794   \n",
+       "21                             Emily in Paris           33.659   \n",
+       "22                       Hometown Cha-Cha-Cha           35.367   \n",
+       "23                                Locke & Key           27.017   \n",
+       "24                          Business Proposal           88.533   \n",
+       "25                                    My Name           25.525   \n",
+       "26                    Café con Aroma de Mujer           20.710   \n",
+       "27                                  Control Z           20.708   \n",
+       "28                           Alchemy of Souls           61.172   \n",
+       "29                          Vikings: Valhalla           59.232   \n",
+       "30                                Dark Desire           90.553   \n",
+       "31                               All American          140.694   \n",
+       "32                        Pasión de Gavilanes          283.197   \n",
+       "33                             Narcos: Mexico           47.476   \n",
+       "34                                On My Block           16.979   \n",
+       "35                     Twenty Five Twenty One           31.730   \n",
+       "36                                 Good Girls           52.352   \n",
+       "37                       The King's Affection           69.819   \n",
+       "38                           Who Killed Sara?           18.157   \n",
+       "39                           The Marked Heart           35.526   \n",
+       "40                                   Sex/Life          124.145   \n",
+       "41                         The Lincoln Lawyer           52.395   \n",
+       "42                               Raising Dion           19.093   \n",
+       "43   Money Heist: Korea - Joint Economic Area           46.292   \n",
+       "44                         Our Beloved Summer           30.456   \n",
+       "45                                  Our Blues           12.980   \n",
+       "46                              Midnight Mass           21.999   \n",
+       "47                            Sweet Magnolias           25.831   \n",
+       "48                                   Sintonia           22.377   \n",
+       "49                             The Silent Sea           25.069   \n",
+       "50                                 First Kill           21.147   \n",
+       "51                 The Snitch Cartel: Origins           42.083   \n",
+       "52                                     Hustle           30.738   \n",
+       "53                          Hospital Playlist           21.826   \n",
+       "54                           The Chestnut Man           15.750   \n",
+       "55                            The Five Juanas           18.672   \n",
+       "56                           Juvenile Justice           14.269   \n",
+       "57                                 Archive 81           20.088   \n",
+       "58                            Welcome to Eden           40.482   \n",
+       "59                                       Alba           12.840   \n",
+       "60                              Lost in Space          412.031   \n",
+       "61                       Anatomy of a Scandal           13.197   \n",
+       "62                              Love Is Blind           46.558   \n",
+       "63               Forecasting Love and Weather           16.976   \n",
+       "64                                     Carter           11.552   \n",
+       "65                              I Am Georgina           11.560   \n",
+       "66                                  Collision            5.795   \n",
+       "67  Turning Point: 9/11 and the War on Terror            9.194   \n",
+       "68                             Selling Sunset            9.459   \n",
+       "69                                 True Story           15.695   \n",
+       "70                               Another Self           10.597   \n",
+       "\n",
+       "    tmdb_vote_average  tmdb_vote_count  \\\n",
+       "0               8.624          16161.0   \n",
+       "1               8.257          17836.0   \n",
+       "2               7.831          13053.0   \n",
+       "3               8.604           8891.0   \n",
+       "4               8.304           6647.0   \n",
+       "5               8.354           3335.0   \n",
+       "6               8.143           2045.0   \n",
+       "7               8.244           1968.0   \n",
+       "8               8.546           8836.0   \n",
+       "9               8.220           5734.0   \n",
+       "10              8.071           8817.0   \n",
+       "11              7.728           1312.0   \n",
+       "12              8.503          11768.0   \n",
+       "13              8.740           3341.0   \n",
+       "14              7.983           1391.0   \n",
+       "15              8.482            585.0   \n",
+       "16              8.161           1557.0   \n",
+       "17              8.064           1726.0   \n",
+       "18              8.032            448.0   \n",
+       "19              8.462           4710.0   \n",
+       "20              8.290           1485.0   \n",
+       "21              7.775           1152.0   \n",
+       "22              8.233            442.0   \n",
+       "23              7.903           1104.0   \n",
+       "24              8.422            281.0   \n",
+       "25              8.283            715.0   \n",
+       "26              7.500            382.0   \n",
+       "27              8.227           2242.0   \n",
+       "28              8.622            411.0   \n",
+       "29              7.851            671.0   \n",
+       "30              7.300           4181.0   \n",
+       "31              8.245            414.0   \n",
+       "32              7.641           1925.0   \n",
+       "33              7.939           1160.0   \n",
+       "34              8.461            618.0   \n",
+       "35              8.597             98.0   \n",
+       "36              7.998            509.0   \n",
+       "37              8.351            232.0   \n",
+       "38              7.638           1132.0   \n",
+       "39              7.817            232.0   \n",
+       "40              7.138           1200.0   \n",
+       "41              7.806            183.0   \n",
+       "42              7.696            388.0   \n",
+       "43              7.900            601.0   \n",
+       "44              8.500             74.0   \n",
+       "45              8.800             43.0   \n",
+       "46              7.525            664.0   \n",
+       "47              7.800            206.0   \n",
+       "48              8.200            179.0   \n",
+       "49              7.800            527.0   \n",
+       "50              7.952            270.0   \n",
+       "51              7.885            283.0   \n",
+       "52              7.708            137.0   \n",
+       "53              8.490            100.0   \n",
+       "54              7.584            455.0   \n",
+       "55              7.419            246.0   \n",
+       "56              7.947             85.0   \n",
+       "57              7.219            442.0   \n",
+       "58              7.090            233.0   \n",
+       "59              7.000            167.0   \n",
+       "60              7.115             96.0   \n",
+       "61              7.071            120.0   \n",
+       "62              7.100             54.0   \n",
+       "63              7.631             61.0   \n",
+       "64              7.400             30.0   \n",
+       "65              7.111            293.0   \n",
+       "66              7.300             26.0   \n",
+       "67              7.287             83.0   \n",
+       "68              7.100             39.0   \n",
+       "69              7.171             82.0   \n",
+       "70              7.400             30.0   \n",
+       "\n",
+       "                                   imdb_title  imdb_averageRating  \\\n",
+       "0                             Stranger Things                 NaN   \n",
+       "1                                 Money Heist                 8.2   \n",
+       "2                                 Squid Game!                 NaN   \n",
+       "3                        The Umbrella Academy                 7.0   \n",
+       "4                               Sex Education                 7.2   \n",
+       "5                          All of Us Are Dead                 7.6   \n",
+       "6                                  Bridgerton                 4.1   \n",
+       "7                                       Ozark                 8.4   \n",
+       "8                              Peaky Blinders                 8.7   \n",
+       "9                                   Cobra Kai                 6.9   \n",
+       "10                                      Elite                 8.4   \n",
+       "11                                   Manifest                 NaN   \n",
+       "12                            The Good Doctor                 NaN   \n",
+       "13                                     Arcane                 NaN   \n",
+       "14                          The Queen of Flow                 7.5   \n",
+       "15                 Extraordinary Attorney Woo                 8.5   \n",
+       "16                          Never Have I Ever                 NaN   \n",
+       "17                                The Sandman                 5.2   \n",
+       "18                               Virgin River                 7.4   \n",
+       "19     The Seven Deadly Sins: Cursed by Light                 6.4   \n",
+       "20                           The Last Kingdom                 8.4   \n",
+       "21                             Emily in Paris                 6.8   \n",
+       "22                       Hometown Cha-Cha-Cha                 8.3   \n",
+       "23                                  Locke&Key                 NaN   \n",
+       "24                          Business Proposal                 NaN   \n",
+       "25                                    My Name                 7.8   \n",
+       "26                    Café con aroma de mujer                 8.6   \n",
+       "27                                  Control Z                 6.8   \n",
+       "28                           Alchemy of Souls                 8.7   \n",
+       "29                          Vikings: Valhalla                 7.4   \n",
+       "30                                Dark Desire                 8.0   \n",
+       "31                               All American                 NaN   \n",
+       "32                           Pasión de sangre                 NaN   \n",
+       "33                             Narcos: Mexico                 8.3   \n",
+       "34                                On My Block                 7.0   \n",
+       "35                     Twenty Five Twenty One                 8.6   \n",
+       "36                                 Good Girls                 8.2   \n",
+       "37                       The King's Affection                 8.0   \n",
+       "38                           Who Killed Sara?                 6.4   \n",
+       "39                           The Marked Heart                 6.6   \n",
+       "40                                   Sex Life                 NaN   \n",
+       "41                         The Lincoln Lawyer                 NaN   \n",
+       "42                               Raising Dion                 8.1   \n",
+       "43   Money Heist: Korea - Joint Economic Area                 5.9   \n",
+       "44                         Our Beloved Summer                 8.4   \n",
+       "45                                  Our Blues                 8.6   \n",
+       "46                              Midnight Mass                 7.4   \n",
+       "47                            Sweet Magnolias                 7.3   \n",
+       "48                                   Sintonia                 NaN   \n",
+       "49                             The Silent Sea                 NaN   \n",
+       "50                                 First Kill                 NaN   \n",
+       "51                 The Snitch Cartel: Origins                 6.0   \n",
+       "52                                     Hustle                 NaN   \n",
+       "53                          Hospital Playlist                 8.7   \n",
+       "54                           The Chestnut Man                 7.6   \n",
+       "55                            The Five Juanas                 6.9   \n",
+       "56                           Juvenile Justice                 NaN   \n",
+       "57                                 Archive 81                 7.3   \n",
+       "58                            Welcome to Eden                 NaN   \n",
+       "59                                       Alba                 NaN   \n",
+       "60                              Lost in Space                 NaN   \n",
+       "61                       Anatomy of a Scandal                 7.0   \n",
+       "62                              Love is Blind                 NaN   \n",
+       "63               Forecasting Love and Weather                 7.1   \n",
+       "64                                     Carter                 NaN   \n",
+       "65                              I Am Georgina                 4.1   \n",
+       "66                                  Collision                 NaN   \n",
+       "67  Turning Point: 9/11 and the War on Terror                 7.9   \n",
+       "68                             Selling Sunset                 6.5   \n",
+       "69                                 True Story                 NaN   \n",
+       "70                               Another Self                 7.3   \n",
+       "\n",
+       "    imdb_numVotes  TMDb_raw_id                              TMDb_raw_name  \\\n",
+       "0             NaN        66732                            Stranger Things   \n",
+       "1        612436.0        71446                                Money Heist   \n",
+       "2             NaN        93405                                 Squid Game   \n",
+       "3            74.0        75006                       The Umbrella Academy   \n",
+       "4            25.0        81356                              Sex Education   \n",
+       "5         92709.0        99966                         All of Us Are Dead   \n",
+       "6            57.0        91239                                 Bridgerton   \n",
+       "7        394904.0        69740                                      Ozark   \n",
+       "8        783155.0        60574                             Peaky Blinders   \n",
+       "9            36.0        77169                                  Cobra Kai   \n",
+       "10          112.0        76669                                      Elite   \n",
+       "11            NaN        79696                                   Manifest   \n",
+       "12            NaN        71712                            The Good Doctor   \n",
+       "13            NaN        94605                                     Arcane   \n",
+       "14         2076.0        80240                          The Queen of Flow   \n",
+       "15        38985.0       197067                 Extraordinary Attorney Woo   \n",
+       "16            NaN       100883                          Never Have I Ever   \n",
+       "17            5.0        90802                                The Sandman   \n",
+       "18        60100.0        88324                               Virgin River   \n",
+       "19         1955.0        62104                      The Seven Deadly Sins   \n",
+       "20           13.0        63333                           The Last Kingdom   \n",
+       "21       143411.0        82596                             Emily in Paris   \n",
+       "22        29770.0       128883                       Hometown Cha-Cha-Cha   \n",
+       "23            NaN        86423                                Locke & Key   \n",
+       "24            NaN       154825                          Business Proposal   \n",
+       "25        45026.0       110356                                    My Name   \n",
+       "26          106.0        16961                    Café con Aroma de Mujer   \n",
+       "27        12606.0       102903                                  Control Z   \n",
+       "28        29759.0       135157                           Alchemy of Souls   \n",
+       "29        71704.0       116135                          Vikings: Valhalla   \n",
+       "30           11.0       105214                                Dark Desire   \n",
+       "31            NaN        82428                               All American   \n",
+       "32            NaN        11250                        Pasión de Gavilanes   \n",
+       "33       102053.0        80968                             Narcos: Mexico   \n",
+       "34           29.0        76747                                On My Block   \n",
+       "35        22729.0       129888                     Twenty Five Twenty One   \n",
+       "36          668.0        71715                                 Good Girls   \n",
+       "37         8733.0       129478                       The King's Affection   \n",
+       "38        17647.0       120168                           Who Killed Sara?   \n",
+       "39         4799.0       158916                           The Marked Heart   \n",
+       "40            NaN       126280                                   Sex/Life   \n",
+       "41            NaN       116799                         The Lincoln Lawyer   \n",
+       "42           83.0        93392                               Raising Dion   \n",
+       "43        10696.0       112836   Money Heist: Korea - Joint Economic Area   \n",
+       "44          252.0       135897                         Our Beloved Summer   \n",
+       "45         5554.0       135840                                  Our Blues   \n",
+       "46           30.0        97400                              Midnight Mass   \n",
+       "47        28500.0       102904                            Sweet Magnolias   \n",
+       "48            NaN        78074                                   Sintonia   \n",
+       "49            NaN        96777                             The Silent Sea   \n",
+       "50            NaN       111616                                 First Kill   \n",
+       "51          321.0       128252                 The Snitch Cartel: Origins   \n",
+       "52            NaN         3764                                     Hustle   \n",
+       "53        11014.0        96102                          Hospital Playlist   \n",
+       "54        68351.0       127865                           The Chestnut Man   \n",
+       "55         2615.0       133086                            The Five Juanas   \n",
+       "56            NaN       112833                           Juvenile Justice   \n",
+       "57        68254.0       112314                                 Archive 81   \n",
+       "58            NaN       128010                            Welcome to Eden   \n",
+       "59            NaN       115521                                       Alba   \n",
+       "60            NaN         3051                              Lost in Space   \n",
+       "61        40449.0       113984                       Anatomy of a Scandal   \n",
+       "62            NaN        99353                              Love Is Blind   \n",
+       "63         5578.0       152511               Forecasting Love and Weather   \n",
+       "64            NaN        79357                                     Carter   \n",
+       "65         4667.0       156077                              I Am Georgina   \n",
+       "66            NaN         8344                                  Collision   \n",
+       "67        13266.0       131651  Turning Point: 9/11 and the War on Terror   \n",
+       "68         9335.0        87826                             Selling Sunset   \n",
+       "69            NaN       134029                                 True Story   \n",
+       "70        26500.0       206180                               Another Self   \n",
+       "\n",
+       "    TMDb_raw_number_of_seasons  TMDb_raw_number_of_episodes  \\\n",
+       "0                            4                           34   \n",
+       "1                            3                           41   \n",
+       "2                            2                            9   \n",
+       "3                            4                           32   \n",
+       "4                            4                           32   \n",
+       "5                            2                           12   \n",
+       "6                            3                           24   \n",
+       "7                            4                           44   \n",
+       "8                            6                           36   \n",
+       "9                            6                           50   \n",
+       "10                           7                           49   \n",
+       "11                           4                           62   \n",
+       "12                           6                          116   \n",
+       "13                           2                            9   \n",
+       "14                           2                          172   \n",
+       "15                           1                           16   \n",
+       "16                           4                           40   \n",
+       "17                           1                           11   \n",
+       "18                           5                           54   \n",
+       "19                           4                           96   \n",
+       "20                           5                           46   \n",
+       "21                           4                           30   \n",
+       "22                           1                           16   \n",
+       "23                           3                           28   \n",
+       "24                           1                           12   \n",
+       "25                           1                            8   \n",
+       "26                           1                          139   \n",
+       "27                           3                           24   \n",
+       "28                           2                           30   \n",
+       "29                           2                           16   \n",
+       "30                           2                           33   \n",
+       "31                           5                           91   \n",
+       "32                           2                          259   \n",
+       "33                           3                           30   \n",
+       "34                           4                           38   \n",
+       "35                           1                           16   \n",
+       "36                           4                           50   \n",
+       "37                           1                           20   \n",
+       "38                           3                           25   \n",
+       "39                           2                           24   \n",
+       "40                           2                           14   \n",
+       "41                           3                           20   \n",
+       "42                           2                           17   \n",
+       "43                           1                           12   \n",
+       "44                           1                           16   \n",
+       "45                           1                           20   \n",
+       "46                           1                            7   \n",
+       "47                           3                           30   \n",
+       "48                           4                           26   \n",
+       "49                           1                            8   \n",
+       "50                           1                            8   \n",
+       "51                           1                           60   \n",
+       "52                           8                           48   \n",
+       "53                           2                           24   \n",
+       "54                           1                            6   \n",
+       "55                           1                           18   \n",
+       "56                           1                           10   \n",
+       "57                           1                            8   \n",
+       "58                           2                           16   \n",
+       "59                           1                           13   \n",
+       "60                           3                           83   \n",
+       "61                           1                            6   \n",
+       "62                           5                           69   \n",
+       "63                           1                           16   \n",
+       "64                           2                           20   \n",
+       "65                           2                           12   \n",
+       "66                           1                            5   \n",
+       "67                           1                            5   \n",
+       "68                           6                           56   \n",
+       "69                           1                            7   \n",
+       "70                           1                            8   \n",
+       "\n",
+       "   TMDb_raw_original_language  TMDb_raw_vote_count  TMDb_raw_vote_average  \\\n",
+       "0                          en                16161                  8.624   \n",
+       "1                          es                17836                  8.257   \n",
+       "2                          ko                13053                  7.831   \n",
+       "3                          en                 8891                  8.604   \n",
+       "4                          en                 6647                  8.304   \n",
+       "5                          ko                 3335                  8.354   \n",
+       "6                          en                 2045                  8.143   \n",
+       "7                          en                 1968                  8.244   \n",
+       "8                          en                 8836                  8.546   \n",
+       "9                          en                 5734                  8.220   \n",
+       "10                         es                 8817                  8.071   \n",
+       "11                         en                 1312                  7.728   \n",
+       "12                         en                11768                  8.503   \n",
+       "13                         en                 3341                  8.740   \n",
+       "14                         es                 1391                  7.983   \n",
+       "15                         ko                  585                  8.482   \n",
+       "16                         en                 1557                  8.161   \n",
+       "17                         en                 1726                  8.064   \n",
+       "18                         en                  448                  8.032   \n",
+       "19                         ja                 4710                  8.462   \n",
+       "20                         en                 1485                  8.290   \n",
+       "21                         en                 1152                  7.775   \n",
+       "22                         ko                  442                  8.233   \n",
+       "23                         en                 1104                  7.903   \n",
+       "24                         ko                  281                  8.422   \n",
+       "25                         ko                  715                  8.283   \n",
+       "26                         es                  382                  7.500   \n",
+       "27                         es                 2242                  8.227   \n",
+       "28                         ko                  411                  8.622   \n",
+       "29                         en                  671                  7.851   \n",
+       "30                         es                 4181                  7.300   \n",
+       "31                         en                  414                  8.245   \n",
+       "32                         es                 1925                  7.641   \n",
+       "33                         en                 1160                  7.939   \n",
+       "34                         en                  618                  8.461   \n",
+       "35                         ko                   98                  8.597   \n",
+       "36                         en                  509                  7.998   \n",
+       "37                         ko                  232                  8.351   \n",
+       "38                         es                 1132                  7.638   \n",
+       "39                         es                  232                  7.817   \n",
+       "40                         en                 1200                  7.138   \n",
+       "41                         en                  183                  7.806   \n",
+       "42                         en                  388                  7.696   \n",
+       "43                         ko                  601                  7.900   \n",
+       "44                         ko                   74                  8.500   \n",
+       "45                         ko                   43                  8.800   \n",
+       "46                         en                  664                  7.525   \n",
+       "47                         en                  206                  7.800   \n",
+       "48                         pt                  179                  8.200   \n",
+       "49                         ko                  527                  7.800   \n",
+       "50                         en                  270                  7.952   \n",
+       "51                         es                  283                  7.885   \n",
+       "52                         en                  137                  7.708   \n",
+       "53                         ko                  100                  8.490   \n",
+       "54                         da                  455                  7.584   \n",
+       "55                         es                  246                  7.419   \n",
+       "56                         ko                   85                  7.947   \n",
+       "57                         en                  442                  7.219   \n",
+       "58                         es                  233                  7.090   \n",
+       "59                         es                  167                  7.000   \n",
+       "60                         en                   96                  7.115   \n",
+       "61                         en                  120                  7.071   \n",
+       "62                         en                   54                  7.100   \n",
+       "63                         ko                   61                  7.631   \n",
+       "64                         en                   30                  7.400   \n",
+       "65                         es                  293                  7.111   \n",
+       "66                         en                   26                  7.300   \n",
+       "67                         en                   83                  7.287   \n",
+       "68                         en                   39                  7.100   \n",
+       "69                         en                   82                  7.171   \n",
+       "70                         tr                   30                  7.400   \n",
+       "\n",
+       "                                                                                                                                        TMDb_raw_overview  \\\n",
+       "0   When a young boy vanishes, a small town uncovers a mystery involving secret experiments, terrifying supernatural forces, and one strange little girl.   \n",
+       "1   To carry out the biggest heist in history, a mysterious man called The Professor recruits a band of eight robbers who have a single characteristic...   \n",
+       "2   Hundreds of cash-strapped players accept a strange invitation to compete in children's games—with high stakes. But, a tempting prize awaits the vi...   \n",
+       "3               A dysfunctional family of superheroes comes together to solve the mystery of their father's death, the threat of the apocalypse and more.   \n",
+       "4         Inexperienced Otis channels his sex therapist mom when he teams up with rebellious Maeve to set up an underground sex therapy clinic at school.   \n",
+       "5      A high school becomes ground zero for a zombie virus outbreak. Trapped students must fight their way out — or turn into one of the rabid infected.   \n",
+       "6                         Wealth, lust, and betrayal set in the backdrop of Regency era England, seen through the eyes of the powerful Bridgerton family.   \n",
+       "7      A financial adviser drags his family from Chicago to the Missouri Ozarks, where he must launder $500 million in five years to appease a drug boss.   \n",
+       "8   A gangster family epic set in 1919 Birmingham, England and centered on a gang who sew razor blades in the peaks of their caps, and their fierce bo...   \n",
+       "9   This Karate Kid sequel series picks up 30 years after the events of the 1984 All Valley Karate Tournament and finds Johnny Lawrence on the hunt fo...   \n",
+       "10      When three working class kids enroll in the most exclusive school in Spain, the clash between the wealthy and the poor students leads to tragedy.   \n",
+       "11  After landing from a turbulent but routine flight, the crew and passengers of Montego Air Flight 828 discover five years have passed in what seeme...   \n",
+       "12  Shaun Murphy, a young surgeon with autism and savant syndrome, relocates from a quiet country life to join a prestigious hospital's surgical unit....   \n",
+       "13  Amid the stark discord of twin cities Piltover and Zaun, two sisters fight on rival sides of a war between magic technologies and clashing convict...   \n",
+       "14                  After spending seventeen years in prison unfairly, a talented songwriter seeks revenge on the men who sank her and killed her family.   \n",
+       "15      Brilliant attorney Woo Young-woo tackles challenges in the courtroom and beyond as a newbie at a top law firm and a woman on the autism spectrum.   \n",
+       "16  After a traumatic year, all an Indian-American teen wants is to go from pariah to popular -- but friends, family and feeling won't make it easy on...   \n",
+       "17  After years of imprisonment, Morpheus — the King of Dreams — embarks on a journey across worlds to find what was stolen from him and restore his p...   \n",
+       "18    After seeing an ad for a midwife, a recently divorced big-city nurse moves to the redwood forests of California, where she meets an intriguing man.   \n",
+       "19  The “Seven Deadly Sins”—a group of evil knights who conspired to overthrow the kingdom of Britannia—were said to have been eradicated by the Holy ...   \n",
+       "20  A show of heroic deeds and epic battles with a thematic depth that embraces politics, religion, warfare, courage, love, loyalty and our universal ...   \n",
+       "21  When ambitious Chicago marketing exec Emily unexpectedly lands her dream job in Paris, she embraces a new life as she juggles work, friends and ro...   \n",
+       "22  A big-city dentist opens up a practice in a close-knit seaside village, home to a charming jack-of-all-trades who is her polar opposite in every way.   \n",
+       "23  Three siblings who move into their ancestral estate after their father's gruesome murder discover their new home's magical keys, which must be use...   \n",
+       "24  In disguise as her friend, Ha-ri shows up to a blind date to scare him away. But plans go awry when he turns out to be her CEO — and makes a propo...   \n",
+       "25       Following her father's murder, a revenge-driven woman puts her trust in a powerful crime boss — and enters the police force under his direction.   \n",
+       "26  Café con aroma de mujer is a 1994 Colombian telenovela, produced by then programming company RCN TV on state-owned Canal A. It was written and cre...   \n",
+       "27  When a hacker begins releasing students' secrets to the entire high school, the socially isolated but observant Sofía works to uncover his/her ide...   \n",
+       "28                     A powerful sorceress in a blind woman's body encounters a man from a prestigious family, who wants her help to change his destiny.   \n",
+       "29  In this sequel to \"Vikings,\" a hundred years have passed and a new generation of legendary heroes arises to forge its own destiny — and make history.   \n",
+       "30  Married Alma spends a fateful weekend away from home that ignites passion, ends in tragedy and leads her to question the truth about those close t...   \n",
+       "31  When a rising high school football player from South Central L.A. is recruited to play for Beverly Hills High, the wins, losses and struggles of t...   \n",
+       "32                                                             The Reyes-Elizondo's idyllic lives are shattered by a murder charge against Eric and León.   \n",
+       "33                                 See the rise of the Guadalajara Cartel as an American DEA agent learns the danger of targeting narcos in 1980s Mexico.   \n",
+       "34    A coming of age comedy following a diverse group of teenage friends as they confront the challenges of growing up in gritty inner-city Los Angeles.   \n",
+       "35          In a time when dreams seem out of reach, a teen fencer pursues big ambitions and meets a hardworking young man who seeks to rebuild his life.   \n",
+       "36  Three \"good girl\" suburban wives and mothers suddenly find themselves in desperate circumstances and decide to stop playing it safe and risk every...   \n",
+       "37  When the crown prince is killed, his twin sister assumes the throne while trying to keep her identity and affection for her first love a royal sec...   \n",
+       "38     Hell-bent on exacting revenge and proving he was framed for his sister's murder, Álex sets out to unearth much more than the crime's real culprit.   \n",
+       "39  A man hell-bent on exacting revenge on the organ trafficking organization that murdered his wife becomes involved with the woman who received her ...   \n",
+       "40  A woman's daring sexual past collides with her married-with-kids present when the bad-boy ex she can't stop fantasizing about crashes back into he...   \n",
+       "41  Sidelined after an accident, hotshot Los Angeles lawyer Mickey Haller restarts his career - and his trademark Lincoln - when he takes on a murder ...   \n",
+       "42        A widowed mom sets out to solve the mystery surrounding her young son's emerging superpowers while keeping his extraordinary gifts under wraps.   \n",
+       "43  Disguised under the shadows of a mask, a crew of desperados band together under the leadership of a criminal mastermind known only as “The Profess...   \n",
+       "44  Years after filming a viral documentary in high school, two bickering ex-lovers get pulled back in front of the camera — and into each other's lives.   \n",
+       "45        Romance is sweet and bitter — and life riddled with ups and downs — in multiple stories about people who live and work on bustling Jeju island.   \n",
+       "46        An unsettling omen washes ashore in the wake of the storm. Later, when the locals gather for a potluck, tragedy strikes – and a miracle occurs.   \n",
+       "47  Lifelong friends Maddie, Helen and Dana Sue lift each other as they juggle relationships, family and careers in the small, Southern town of Serenity.   \n",
+       "48  Told through three different characters' perspectives, the story of Sintonia explores the interconnection of the music, drug traffic, and religion...   \n",
+       "49  During a perilous 24-hour mission on the moon, space explorers try to retrieve samples from an abandoned research facility steeped in classified s...   \n",
+       "50  Falling in love is tricky for teens Juliette and Calliope: One's a vampire, the other's a vampire hunter — and both are ready to make their first ...   \n",
+       "51   In Cali during the '60s and '70s, two brothers juggle family, romance and the joint pursuit of a burning ambition: to rule Colombia's drug industry.   \n",
+       "52                                                                  A motley group of London con artists pull of a series of daring and intricate stings.   \n",
+       "53                Every day is extraordinary for five doctors and their patients inside a hospital, where birth, death and everything in between coexist.   \n",
+       "54  At a grisly murder scene sits a figurine made of chestnuts. From this creepy clue, two detectives hunt a killer linked to a politician's missing c...   \n",
+       "55     Five women with the same birthmark set out to unravel the truth about their pasts and discover a tragic web of lies spun by a powerful politician.   \n",
+       "56  A tough judge balances her aversion to minor offenders with firm beliefs on justice and punishment as she tackles complex cases inside a juvenile ...   \n",
+       "57  An archivist takes a job restoring damaged videotapes and gets pulled into the vortex of a mystery involving the missing director and a demonic cult.   \n",
+       "58  Are you happy? With this question, Zoa and four other attractive young people, very active on social networks, are invited to the most exclusive p...   \n",
+       "59                          Alba wakes up on a beach after a rape she doesn't remember. And then she discovers that her rapists are friends of her lover.   \n",
+       "60  The space family Robinson is sent on a five-year mission to find a new planet to colonise. The voyage is sabotaged time and again by an inept stow...   \n",
+       "61                                                                  Anthology series centering on the personal and political scandals of Britain’s elite.   \n",
+       "62            Nick and Vanessa Lachey host this social experiment where single men and women look for love and get engaged, all before meeting in person.   \n",
+       "63  Inside a national weather service, love proves just as difficult to predict as rain or shine for a diligent forecaster and her free-spirited co-wo...   \n",
+       "64  An actor returns home after a public meltdown. Partnering with his police detective friend, he tries to use his acting experience to solve real cr...   \n",
+       "65  Join Georgina Rodríguez — mom, influencer, businesswoman and Cristiano Ronaldo's partner — in this emotional and in-depth portrait of her daily life.   \n",
+       "66  The story of a major road accident and a group of people who have never met, but who all share one single defining moment that will change their l...   \n",
+       "67  Modern history can be divided into two time frames: before 9/11 and after 9/11. This five-part docuseries is a cohesive chronicle of the September...   \n",
+       "68  The elite real estate brokers at The Oppenheim Group sell the luxe life to affluent buyers in LA. The drama ramps up when a new agent joins the team.   \n",
+       "69  A world-famous comedian desperately searches for a way out after a night in Philadelphia with his brother threatens to sabotage more than his succ...   \n",
+       "70  Three friends arrive in a seaside town, where they connect with their spiritual selves and suddenly face unresolved trauma from their families' pa...   \n",
+       "\n",
+       "    TMDb_raw_adult            TMDb_raw_backdrop_path TMDb_raw_first_air_date  \\\n",
+       "0            False  /2MaumbgBlW1NoPo3ZJO38A6v7OS.jpg              2016-07-15   \n",
+       "1            False  /gFZriCkpJYsApPZEF3jhxL4yLzG.jpg              2017-05-02   \n",
+       "2            False  /2meX1nMdScFOoV4370rqHWKmXhY.jpg              2021-09-17   \n",
+       "3            False  /7sqFEDDmK1hG5m92upolcfQxy7R.jpg              2019-02-15   \n",
+       "4            False  /5SEEBS5qXgL5rgivTiAROy1Qt2q.jpg              2019-01-11   \n",
+       "5            False  /jZAtLKNZbQZZLm9OLcY9rdZZV5F.jpg              2022-01-28   \n",
+       "6            False  /aXa6J5vGZQQOLZZv8fK0w0cd2fm.jpg              2020-12-25   \n",
+       "7            False  /gD830J0sf5gEeZvzkRVPdGxJmSR.jpg              2017-07-21   \n",
+       "8            False  /wiE9doxiLwq3WCGamDIOb2PqBqc.jpg              2013-09-12   \n",
+       "9            False  /zymbuoBoL1i94xAOzVJF6IuWLfD.jpg              2018-05-02   \n",
+       "10           False  /tl0mg7lOnS6tP8ngH0QwgMLQdpV.jpg              2018-10-05   \n",
+       "11           False  /iZu83GB1IM7VXL2X90m7iLHYUHU.jpg              2018-09-24   \n",
+       "12           False  /xXRsKNJHTOGrs5wfYAxkbM2RiyT.jpg              2017-09-25   \n",
+       "13           False  /rkB4LyZHo1NHXFEDHl9vSD9r1lI.jpg              2021-11-06   \n",
+       "14           False  /pnyT1foDmmXTsho2DfxN2ePI8ix.jpg              2018-06-12   \n",
+       "15           False  /tmgtlnBnoFvk52xZBO7Z0pTmN9b.jpg              2022-06-29   \n",
+       "16           False  /85HCFXyCmvADwd3gJFyCz34CTAO.jpg              2020-04-27   \n",
+       "17           False  /eqhKMZTLcieAvoH6CBqknTTfNby.jpg              2022-08-05   \n",
+       "18           False  /4nVaMgK7LVRmQ3vNg7eC3qKfRyI.jpg              2019-12-06   \n",
+       "19           False  /n5Ty1KJIRNCXlDHDjcPpUgp57tr.jpg              2014-10-05   \n",
+       "20           False  /uCqXSfHymdbDMsFx8t0u0OPSuve.jpg              2015-10-10   \n",
+       "21           False  /xkKIruOg4NoADdPjJJrvfiJHRdZ.jpg              2020-10-02   \n",
+       "22           False  /qX5DaHmjBDOMz9jGNJ3oESW6Wlq.jpg              2021-08-28   \n",
+       "23           False  /67gGBgMBgmElB88waL2yS2rv91l.jpg              2020-02-07   \n",
+       "24           False  /64TnyodfRC4iLTCXKlqps8iRW7F.jpg              2022-02-28   \n",
+       "25           False  /4LZuFMhLivOSO7zuWAQ3RffMnnr.jpg              2021-10-15   \n",
+       "26           False  /oOqWl3GavxfYiwxegiVyQ5p9NYK.jpg              1994-12-30   \n",
+       "27           False  /gFFedWBZ7s5sPiR9A1IpileWuFf.jpg              2020-05-22   \n",
+       "28           False  /nBZyWSGAUEzCH7Mna0zUNTpBQlQ.jpg              2022-06-18   \n",
+       "29           False  /k47JEUTQsSMN532HRg6RCzZKBdB.jpg              2022-02-25   \n",
+       "30           False  /9CBdzw7CK2m6WpfEfqe73V8i5O0.jpg              2020-07-15   \n",
+       "31           False  /thCj6VEVQlFlup4aJ74kdRhfujB.jpg              2018-10-10   \n",
+       "32           False  /qabMMIpNUUKYFqQxHEWErEEBD8G.jpg              2003-10-21   \n",
+       "33           False  /64jEzC7YeKEpnn1YwjCGRo9ZvOX.jpg              2018-11-16   \n",
+       "34           False  /jt7c70Axkhy7o9u00JT8Lygfd3H.jpg              2018-03-16   \n",
+       "35           False  /jvFfHNiCKPWRULvkmeocyjm4QtE.jpg              2022-02-12   \n",
+       "36           False  /csOIulXwQxsuCFp3ZZ9cYkJmCwH.jpg              2018-02-26   \n",
+       "37           False  /4r7scgI5aUrShN0YzriAatWd15e.jpg              2021-10-11   \n",
+       "38           False  /dYvIUzdh6TUv4IFRq8UBkX7bNNu.jpg              2021-03-24   \n",
+       "39           False  /yOtzSTFdccSd2tfzlTp8UNo2ZHN.jpg              2022-04-20   \n",
+       "40           False  /9nBVkNBe4x9HKDAzxjxlIqecxCW.jpg              2021-06-25   \n",
+       "41           False  /jBFOVEE5sMIyT141YIqIwyU8Y1D.jpg              2022-05-13   \n",
+       "42           False  /fVc8V31Kk1wAyorbg5BrzVSNNiv.jpg              2019-10-04   \n",
+       "43           False  /lcTuggU70y6pt6x13Rv1Ffjs93K.jpg              2022-06-24   \n",
+       "44           False  /7J8QzozUgiFgDCvNadVImhEXEIq.jpg              2021-12-06   \n",
+       "45           False  /w2vVXdKuBucql88wh2s8PRsjWnE.jpg              2022-04-09   \n",
+       "46           False  /4XbQb5YoWK9oMrlQdKwtZ1ccpC8.jpg              2021-09-24   \n",
+       "47           False  /wt60Q4doM2vrubiCsutwaeWncLL.jpg              2020-05-19   \n",
+       "48           False  /9GTFaWPPctyJeJaImg9HVPPbyOf.jpg              2019-08-09   \n",
+       "49           False  /9hNJ3fvIVd4WE3rU1Us2awoTpgM.jpg              2021-12-24   \n",
+       "50           False  /abFQcEO0UJVfUQPmhgjaKeLxRwy.jpg              2022-06-10   \n",
+       "51           False  /iQlel1ftOsnWguxyN98eUmKTOPm.jpg              2021-07-28   \n",
+       "52           False  /q7iwBG89RLMdkXoYGdVxC9rRg8Y.jpg              2004-02-24   \n",
+       "53           False  /5DUqsUZPLdDRzthR08vvQCXoyfJ.jpg              2020-03-12   \n",
+       "54           False  /fIXTetAsXkPO6UUUw85IU4oactf.jpg              2021-09-29   \n",
+       "55           False  /jB27sNxsgITNhr6HavrHywojy8V.jpg              2021-10-06   \n",
+       "56           False  /eW6CqrGSRUk6xMbdCjreMQiP5W8.jpg              2022-02-25   \n",
+       "57           False  /7TYMho2TM6ZcqSv6YaEjlNIyhXn.jpg              2022-01-14   \n",
+       "58           False  /kcHvkFlsodXu1TFgXuOT3OvJYnu.jpg              2022-05-06   \n",
+       "59           False  /2AJcuidWwx8Z4kJPUw2RwiPd3Er.jpg              2021-03-28   \n",
+       "60           False  /upqfQF1LBPf7IRZao0sxt1BHZlx.jpg              1965-09-15   \n",
+       "61           False  /8zPoUJECL6X8G5T95OuJDorHdtV.jpg              2022-04-15   \n",
+       "62           False  /esHT6kpx6XAuvp1ngrngTSmelA8.jpg              2020-02-13   \n",
+       "63           False  /s9n20a4QRilgzcaEh91jWZaS8L9.jpg              2022-02-12   \n",
+       "64           False  /ymxyzXY3NzgaDekXR4WURetjOYQ.jpg              2018-05-15   \n",
+       "65           False  /3SfKp1aTkXdxl1jSsURjHycAHAO.jpg              2022-01-27   \n",
+       "66           False  /hFlvQRRZYtOlv41pChaFdya0CVV.jpg              2009-11-09   \n",
+       "67           False  /pxhxhEKJXznWEavLXJWUxxxdo2X.jpg              2021-09-01   \n",
+       "68           False  /hB2llGkvqcf8sBgEv00HO57eGaa.jpg              2019-03-22   \n",
+       "69           False  /yGpmZYJ8fb543B3tIKjzcRHgJsX.jpg              2021-11-24   \n",
+       "70           False  /5nAikDmnio7SuBFg4KIpC3c50UL.jpg              2022-07-28   \n",
+       "\n",
+       "   TMDb_raw_last_air_date  \\\n",
+       "0              2022-07-01   \n",
+       "1              2021-12-03   \n",
+       "2              2021-09-17   \n",
+       "3              2022-06-22   \n",
+       "4              2023-09-21   \n",
+       "5              2022-01-28   \n",
+       "6              2022-03-25   \n",
+       "7              2022-04-29   \n",
+       "8              2022-04-03   \n",
+       "9              2022-09-09   \n",
+       "10             2022-11-18   \n",
+       "11             2023-06-02   \n",
+       "12             2023-05-01   \n",
+       "13             2021-11-20   \n",
+       "14             2021-09-10   \n",
+       "15             2022-08-18   \n",
+       "16             2023-06-08   \n",
+       "17             2022-08-19   \n",
+       "18             2023-09-07   \n",
+       "19             2021-06-23   \n",
+       "20             2022-03-09   \n",
+       "21             2022-12-21   \n",
+       "22             2021-10-17   \n",
+       "23             2022-08-10   \n",
+       "24             2022-04-05   \n",
+       "25             2021-10-15   \n",
+       "26             1995-07-12   \n",
+       "27             2022-07-06   \n",
+       "28             2023-01-08   \n",
+       "29             2023-01-12   \n",
+       "30             2022-02-02   \n",
+       "31             2023-05-15   \n",
+       "32             2022-05-31   \n",
+       "33             2021-11-05   \n",
+       "34             2021-10-04   \n",
+       "35             2022-04-03   \n",
+       "36             2021-07-22   \n",
+       "37             2021-12-14   \n",
+       "38             2022-05-18   \n",
+       "39             2023-04-19   \n",
+       "40             2023-03-02   \n",
+       "41             2023-08-03   \n",
+       "42             2022-02-01   \n",
+       "43             2022-12-09   \n",
+       "44             2022-01-25   \n",
+       "45             2022-06-12   \n",
+       "46             2021-09-24   \n",
+       "47             2023-07-20   \n",
+       "48             2023-07-25   \n",
+       "49             2021-12-24   \n",
+       "50             2022-06-10   \n",
+       "51             2021-07-28   \n",
+       "52             2012-02-17   \n",
+       "53             2021-09-16   \n",
+       "54             2021-09-29   \n",
+       "55             2021-10-06   \n",
+       "56             2022-02-25   \n",
+       "57             2022-01-14   \n",
+       "58             2023-04-21   \n",
+       "59             2021-06-20   \n",
+       "60             1968-03-06   \n",
+       "61             2022-04-15   \n",
+       "62             2023-09-29   \n",
+       "63             2022-04-03   \n",
+       "64             2019-12-20   \n",
+       "65             2023-03-24   \n",
+       "66             2009-11-13   \n",
+       "67             2021-09-01   \n",
+       "68             2023-05-19   \n",
+       "69             2021-11-24   \n",
+       "70             2022-07-28   \n",
+       "\n",
+       "                                                              TMDb_raw_homepage  \\\n",
+       "0                                        https://www.netflix.com/title/80057281   \n",
+       "1                                        https://www.netflix.com/title/80192098   \n",
+       "2                                        https://www.netflix.com/title/81040344   \n",
+       "3                                        https://www.netflix.com/title/80186863   \n",
+       "4                                        https://www.netflix.com/title/80197526   \n",
+       "5                                        https://www.netflix.com/title/81237994   \n",
+       "6                                        https://www.netflix.com/title/80232398   \n",
+       "7                                        https://www.netflix.com/title/80117552   \n",
+       "8                                     https://www.bbc.co.uk/programmes/b045fz8r   \n",
+       "9                                        https://www.netflix.com/title/81002370   \n",
+       "10                                       https://www.netflix.com/title/80200942   \n",
+       "11                                       https://www.netflix.com/title/80241318   \n",
+       "12                                        https://abc.com/shows/the-good-doctor   \n",
+       "13                                                           https://arcane.com   \n",
+       "14                                     https://www.caracoltv.com/lareinadelflow   \n",
+       "15  http://ena.skylifetv.co.kr/bbs/board.php?bo_table=skydrama&wr_id=113&sca=최신   \n",
+       "16                                       https://www.netflix.com/title/80179190   \n",
+       "17                                                       https://thesandman.com   \n",
+       "18                                       https://www.netflix.com/title/80240027   \n",
+       "19                                                      http://www.7-taizai.net   \n",
+       "20                                       https://www.netflix.com/title/80074249   \n",
+       "21                                       https://www.netflix.com/title/81037371   \n",
+       "22                                       http://program.tving.com/tvn/chachacha   \n",
+       "23                                       https://www.netflix.com/title/80241239   \n",
+       "24                            https://programs.sbs.co.kr/drama/businessproposal   \n",
+       "25                                       https://www.netflix.com/title/81011211   \n",
+       "26                                                                          NaN   \n",
+       "27                                       https://www.netflix.com/title/81021245   \n",
+       "28                                 https://program.tving.com/tvn/alchemyofsouls   \n",
+       "29                                       https://www.netflix.com/title/81149450   \n",
+       "30                                       https://www.netflix.com/title/81090319   \n",
+       "31                                     https://www.cwtv.com/shows/all-american/   \n",
+       "32                          https://www.telemundo.com/shows/pasion-de-gavilanes   \n",
+       "33                                       https://www.netflix.com/title/80997085   \n",
+       "34                                       https://www.netflix.com/title/80117809   \n",
+       "35                             http://program.tving.com/tvn/twentyfivetwentyone   \n",
+       "36                                               https://www.nbc.com/good-girls   \n",
+       "37          https://program.kbs.co.kr/2tv/drama/thekingsaffection/pc/index.html   \n",
+       "38                                       https://www.netflix.com/title/81166747   \n",
+       "39                                       https://www.netflix.com/title/81261170   \n",
+       "40                                       https://www.netflix.com/title/80991848   \n",
+       "41                                       https://www.netflix.com/title/81303831   \n",
+       "42                                       https://www.netflix.com/title/80117803   \n",
+       "43                                       https://www.netflix.com/title/80997343   \n",
+       "44                            https://programs.sbs.co.kr/drama/ourbelovedsummer   \n",
+       "45                                        http://program.tving.com/tvn/ourblues   \n",
+       "46                                       https://www.netflix.com/title/81083626   \n",
+       "47                                       https://www.netflix.com/title/80239866   \n",
+       "48                                       https://www.netflix.com/title/80217315   \n",
+       "49                                       https://www.netflix.com/title/81098012   \n",
+       "50                                       https://www.netflix.com/title/81213653   \n",
+       "51                                       https://www.netflix.com/title/81120861   \n",
+       "52                                     http://www.bbc.co.uk/programmes/b007gf9k   \n",
+       "53                                     http://program.tving.com/tvn/doctorlife/   \n",
+       "54                                       https://www.netflix.com/title/81039388   \n",
+       "55                                       https://www.netflix.com/title/81167761   \n",
+       "56                                       https://www.netflix.com/title/81312802   \n",
+       "57                                       https://www.netflix.com/title/80222802   \n",
+       "58                                       https://www.netflix.com/title/81211710   \n",
+       "59                                        https://premium.atresplayer.com/alba/   \n",
+       "60                                                                          NaN   \n",
+       "61                                       https://www.netflix.com/title/81152788   \n",
+       "62                                       https://www.netflix.com/title/80996601   \n",
+       "63                                       http://tv.jtbc.joins.com/weatherpeople   \n",
+       "64                                            https://www.bravo.ca/Shows/Carter   \n",
+       "65                                       https://www.netflix.com/title/81423622   \n",
+       "66                                                                          NaN   \n",
+       "67                                       https://www.netflix.com/title/81315804   \n",
+       "68                                       https://www.netflix.com/title/80223108   \n",
+       "69                                       https://www.netflix.com/title/81132961   \n",
+       "70                                       https://www.netflix.com/title/81380432   \n",
+       "\n",
+       "    TMDb_raw_in_production                     TMDb_raw_original_name  \\\n",
+       "0                     True                            Stranger Things   \n",
+       "1                    False                           La Casa de Papel   \n",
+       "2                     True                                     오징어 게임   \n",
+       "3                     True                       The Umbrella Academy   \n",
+       "4                    False                              Sex Education   \n",
+       "5                     True                                  지금 우리 학교는   \n",
+       "6                     True                                 Bridgerton   \n",
+       "7                    False                                      Ozark   \n",
+       "8                    False                             Peaky Blinders   \n",
+       "9                     True                                  Cobra Kai   \n",
+       "10                    True                                      Élite   \n",
+       "11                   False                                   Manifest   \n",
+       "12                    True                            The Good Doctor   \n",
+       "13                    True                                     Arcane   \n",
+       "14                   False                          La Reina del Flow   \n",
+       "15                   False                                이상한 변호사 우영우   \n",
+       "16                   False                          Never Have I Ever   \n",
+       "17                    True                                The Sandman   \n",
+       "18                    True                               Virgin River   \n",
+       "19                   False                                      七つの大罪   \n",
+       "20                   False                           The Last Kingdom   \n",
+       "21                    True                             Emily in Paris   \n",
+       "22                   False                                    갯마을 차차차   \n",
+       "23                   False                                Locke & Key   \n",
+       "24                   False                                       사내맞선   \n",
+       "25                   False                                      마이 네임   \n",
+       "26                   False                    Café con Aroma de Mujer   \n",
+       "27                   False                                  Control Z   \n",
+       "28                   False                                         환혼   \n",
+       "29                    True                          Vikings: Valhalla   \n",
+       "30                   False                               Oscuro deseo   \n",
+       "31                    True                               All American   \n",
+       "32                   False                        Pasión de gavilanes   \n",
+       "33                   False                             Narcos: Mexico   \n",
+       "34                   False                                On My Block   \n",
+       "35                   False                                  스물다섯 스물하나   \n",
+       "36                   False                                 Good Girls   \n",
+       "37                   False                                         연모   \n",
+       "38                   False                        ¿Quién mató a Sara?   \n",
+       "39                   False                                    Pálpito   \n",
+       "40                   False                                   Sex/Life   \n",
+       "41                    True                         The Lincoln Lawyer   \n",
+       "42                   False                               Raising Dion   \n",
+       "43                    True                              종이의 집: 공동경제구역   \n",
+       "44                   False                                    그 해 우리는   \n",
+       "45                   False                                   우리들의 블루스   \n",
+       "46                   False                              Midnight Mass   \n",
+       "47                    True                            Sweet Magnolias   \n",
+       "48                    True                                   Sintonia   \n",
+       "49                   False                                     고요의 바다   \n",
+       "50                   False                                 First Kill   \n",
+       "51                    True          El cartel de los sapos: El origen   \n",
+       "52                   False                                     Hustle   \n",
+       "53                   False                                  슬기로운 의사생활   \n",
+       "54                   False                             Kastanjemanden   \n",
+       "55                   False                  La venganza de las Juanas   \n",
+       "56                   False                                       소년심판   \n",
+       "57                   False                                 Archive 81   \n",
+       "58                   False                         Bienvenidos a Edén   \n",
+       "59                   False                                       Alba   \n",
+       "60                   False                              Lost in Space   \n",
+       "61                   False                       Anatomy of a Scandal   \n",
+       "62                    True                              Love Is Blind   \n",
+       "63                   False                        기상청 사람들: 사내연애 잔혹사 편   \n",
+       "64                   False                                     Carter   \n",
+       "65                    True                               Soy Georgina   \n",
+       "66                   False                                  Collision   \n",
+       "67                   False  Turning Point: 9/11 and the War on Terror   \n",
+       "68                    True                             Selling Sunset   \n",
+       "69                   False                                 True Story   \n",
+       "70                   False                               Zeytin Ağacı   \n",
+       "\n",
+       "    TMDb_raw_popularity              TMDb_raw_poster_path TMDb_raw_type  \\\n",
+       "0               185.711  /49WJfeN0moxb9IPfGn8AIqMGskD.jpg      Scripted   \n",
+       "1                96.354  /reEMJA1uzscCbkpeRJeTT2bjqUp.jpg      Scripted   \n",
+       "2               115.587  /dDlEmu3EZ0Pgg93K2SVNLCjCSvE.jpg      Scripted   \n",
+       "3                51.859  /qhcwrnnCnN8NE1N6XXKHFmveJR9.jpg      Scripted   \n",
+       "4              1008.977  /zn6VNUOHGWKvSGRL6KiMtAe9ELy.jpg      Scripted   \n",
+       "5               107.108  /pTEFqAjLd5YTsMD6NSUxV6Dq7A6.jpg      Scripted   \n",
+       "6                71.308  /luoKpgVwi1E5nQsi7W0UuKHu2Rq.jpg      Scripted   \n",
+       "7                68.006  /m73bD8VjibSKuTWg597GQVyVhSb.jpg      Scripted   \n",
+       "8               344.477  /vUUqzWa2LnHIVqkaKVlVGkVcZIW.jpg      Scripted   \n",
+       "9               118.256  /m7tG5E1EbywuwTsl6hq990So0Bx.jpg      Scripted   \n",
+       "10              100.711  /3NTAbAiao4JLzFQw6YxP1YZppM8.jpg      Scripted   \n",
+       "11              111.411  /eTemCphrglLKrXOsNRhYezHA7H9.jpg      Scripted   \n",
+       "12              681.614  /luhKkdD80qe62fwop6sdrXK9jUT.jpg      Scripted   \n",
+       "13               42.447  /fqldf2t8ztc9aiwn3k6mlX3tvRT.jpg      Scripted   \n",
+       "14              129.323  /fuVuDYrs8sxvEolnYr0wCSvtyTi.jpg      Scripted   \n",
+       "15               42.923  /zuNOQVI4rEaqwknrfQUVKtlKE2C.jpg    Miniseries   \n",
+       "16               80.546  /hd5fnBixab6IzfUwjC5wfdbX3eM.jpg      Scripted   \n",
+       "17               64.168  /q54qEgagGOYCq5D1903eBVMNkbo.jpg      Scripted   \n",
+       "18               54.192  /rxWHdK7qGFcJIk28UubcduSc1sz.jpg      Scripted   \n",
+       "19               10.059  /gxTojpKEOtue85EEFlozwRbDXwJ.jpg      Scripted   \n",
+       "20              143.794  /8eJf0hxgIhE6QSxbtuNCekTddy1.jpg      Scripted   \n",
+       "21               33.659  /Ak59Y9bzykmV0wAiwKsqrbORDBo.jpg      Scripted   \n",
+       "22               35.367  /en6lrlJ1DhyvkeZEqrk3R6EJz1p.jpg    Miniseries   \n",
+       "23               27.017  /zuxGfRKziGHPogipnEXXykdDmyT.jpg      Scripted   \n",
+       "24               88.533  /iLh7L8ZuvgdxFaM9sImyv2iKYLe.jpg    Miniseries   \n",
+       "25               25.525  /h9p1zxKzti35Aqe6xpwkP9NlG7p.jpg    Miniseries   \n",
+       "26               20.710  /tvcpeQNt28GQLtNljhuLwgRBRv8.jpg      Scripted   \n",
+       "27               20.708  /8VNA0RdrPk8Ec7XVjpeT0Rnui79.jpg      Scripted   \n",
+       "28               61.172  /gvOZN1NlAoL8iz9ghpES1zWA3w3.jpg      Scripted   \n",
+       "29               59.232  /rDFy1fUU6OC3Mm0CLFB7u0fGwVN.jpg      Scripted   \n",
+       "30               90.553  /uxFNAo2A6ZRcgNASLk02hJUbybn.jpg      Scripted   \n",
+       "31              140.694  /c8FTtjQcQfqSI0McmVc1OOOf7Z7.jpg      Scripted   \n",
+       "32              283.197  /91UV7pNcDPhIzJl7EuK36sK5vRG.jpg      Scripted   \n",
+       "33               47.476  /uYcZMiIIUv8NPebpqKbUGhf2M3y.jpg      Scripted   \n",
+       "34               16.979  /w6oviv65UEducvjAdH3sYYaxdu2.jpg      Scripted   \n",
+       "35               31.730  /yCQFnmYhYf7XALMka2EoBRAFmPO.jpg    Miniseries   \n",
+       "36               52.352  /gPjcbxrYfbrJNq1Ja8EGd0XtUnC.jpg      Scripted   \n",
+       "37               69.819  /mcfX0IqWibPhF5H4i2Q5gUlwwxk.jpg      Scripted   \n",
+       "38               18.157  /o7uk5ChRt3quPIv8PcvPfzyXdMw.jpg      Scripted   \n",
+       "39               35.526   /M1q0HuyRnqxP8Si54srsPREFIH.jpg      Scripted   \n",
+       "40              124.145  /kfcJl5e8CRWDU7e4vX6uNABPRbS.jpg      Scripted   \n",
+       "41               52.395  /9TaupczRcEkOaYbsiQuLg3kXPLq.jpg      Scripted   \n",
+       "42               19.093  /nydKXBiLn4vpL1d5o1utbPKYdii.jpg      Scripted   \n",
+       "43               46.292  /mbsRGqJtdKcVbjQxkrfzKCAkYoU.jpg      Scripted   \n",
+       "44               30.456  /bA15g6OLmhQ2HkURRaCztA2jwqI.jpg      Scripted   \n",
+       "45               12.980  /sT5Mlt5UmKiGfBisccwmD4LnPRD.jpg      Scripted   \n",
+       "46               21.999  /iYoMZYVD775MtBYJfv6OGY1FsnL.jpg    Miniseries   \n",
+       "47               25.831  /n1WDSFOnCCg3cdHHGBywZtYIzf9.jpg      Scripted   \n",
+       "48               22.377  /safjAD2bBNT1grB23rFff5ChwDv.jpg      Scripted   \n",
+       "49               25.069  /fFT0IgqtCOks4munDTxQwkvNJkd.jpg      Scripted   \n",
+       "50               21.147  /hwSy1vVaRzV70ZvAEsl6FGOHNlH.jpg      Scripted   \n",
+       "51               42.083  /sjkzeJJOdr59VdkB4BjlSVfJmq2.jpg      Scripted   \n",
+       "52               30.738  /6UTmAZnaqcIsWVKSBWm7pDmdDTI.jpg      Scripted   \n",
+       "53               21.826  /clYgUKk4CzmKpEukW2I2oDg9k1Q.jpg      Scripted   \n",
+       "54               15.750  /tdLJTZJ184PsO9QkK9aPeeAXLlo.jpg    Miniseries   \n",
+       "55               18.672  /iFffOCcrtf5mSoGKd2J31d2b5yg.jpg      Scripted   \n",
+       "56               14.269  /xH2wTVlsUlYeFEOVGFDaamHkMCm.jpg      Scripted   \n",
+       "57               20.088  /l1jeSEm6a88GFcLzlHQr60D0dOi.jpg      Scripted   \n",
+       "58               40.482  /kZO0mtTHAgg9QVscLVtwCcQ7Hgx.jpg      Scripted   \n",
+       "59               12.840  /pubcatJw5MZNuTJ78atJBkw9crW.jpg    Miniseries   \n",
+       "60              412.031  /dGhtCpYZutuso2P3GKWnQnqwhSi.jpg      Scripted   \n",
+       "61               13.197  /e1pUQkXbFNI6xH4oDuHnyJoOSib.jpg      Scripted   \n",
+       "62               46.558  /gOo6KvUkQI9q0THoUqxI9amnnxL.jpg       Reality   \n",
+       "63               16.976  /3Qfh99m0jShWrqlFRsnWY7HeHNX.jpg      Scripted   \n",
+       "64               11.552  /ugPqPRSfa6RmraIej6lp7yoeEUK.jpg      Scripted   \n",
+       "65               11.560  /70e3SQBIAcjKSibWoyCA7lBMZA8.jpg       Reality   \n",
+       "66                5.795  /qXuLNDG9UICDXqG0ciyQpjuW4SX.jpg      Scripted   \n",
+       "67                9.194  /jZsV8GInbLWcAX3mAFxE6gE8bTR.jpg    Miniseries   \n",
+       "68                9.459  /wUtoD67q4BnRUwikUbT6M38d5oj.jpg      Scripted   \n",
+       "69               15.695  /7zMbzJijHQv5oOtkb8rdYarn9X6.jpg    Miniseries   \n",
+       "70               10.597  /zogTjQRIJsmg8yvIo8gDLtp65th.jpg      Scripted   \n",
+       "\n",
+       "     TMDb_raw_status  \\\n",
+       "0   Returning Series   \n",
+       "1              Ended   \n",
+       "2   Returning Series   \n",
+       "3   Returning Series   \n",
+       "4              Ended   \n",
+       "5   Returning Series   \n",
+       "6   Returning Series   \n",
+       "7              Ended   \n",
+       "8              Ended   \n",
+       "9   Returning Series   \n",
+       "10  Returning Series   \n",
+       "11             Ended   \n",
+       "12  Returning Series   \n",
+       "13  Returning Series   \n",
+       "14             Ended   \n",
+       "15             Ended   \n",
+       "16             Ended   \n",
+       "17  Returning Series   \n",
+       "18  Returning Series   \n",
+       "19             Ended   \n",
+       "20             Ended   \n",
+       "21  Returning Series   \n",
+       "22             Ended   \n",
+       "23             Ended   \n",
+       "24             Ended   \n",
+       "25             Ended   \n",
+       "26             Ended   \n",
+       "27             Ended   \n",
+       "28             Ended   \n",
+       "29  Returning Series   \n",
+       "30             Ended   \n",
+       "31  Returning Series   \n",
+       "32             Ended   \n",
+       "33             Ended   \n",
+       "34             Ended   \n",
+       "35             Ended   \n",
+       "36          Canceled   \n",
+       "37             Ended   \n",
+       "38             Ended   \n",
+       "39             Ended   \n",
+       "40          Canceled   \n",
+       "41  Returning Series   \n",
+       "42          Canceled   \n",
+       "43  Returning Series   \n",
+       "44             Ended   \n",
+       "45             Ended   \n",
+       "46             Ended   \n",
+       "47  Returning Series   \n",
+       "48  Returning Series   \n",
+       "49             Ended   \n",
+       "50          Canceled   \n",
+       "51  Returning Series   \n",
+       "52             Ended   \n",
+       "53             Ended   \n",
+       "54             Ended   \n",
+       "55             Ended   \n",
+       "56             Ended   \n",
+       "57          Canceled   \n",
+       "58          Canceled   \n",
+       "59             Ended   \n",
+       "60             Ended   \n",
+       "61             Ended   \n",
+       "62  Returning Series   \n",
+       "63             Ended   \n",
+       "64             Ended   \n",
+       "65  Returning Series   \n",
+       "66             Ended   \n",
+       "67             Ended   \n",
+       "68  Returning Series   \n",
+       "69             Ended   \n",
+       "70             Ended   \n",
+       "\n",
+       "                                                                                                         TMDb_raw_tagline  \\\n",
+       "0                                                                                           Every ending has a beginning.   \n",
+       "1                                                                                                    The perfect robbery.   \n",
+       "2                                                                                        45.6 billion won is child's play   \n",
+       "3                                                                                 Too many siblings. Not enough timeline.   \n",
+       "4                                                                                              Growth is a group project.   \n",
+       "5                                                        Hope is the most painful torture for people who want to despair.   \n",
+       "6                                                                                          Love never plays by the rules.   \n",
+       "7                                                                                                      Their last resort.   \n",
+       "8                                                                                                 London's for the taking   \n",
+       "9                                                                                                   Cobra Kai never dies.   \n",
+       "10                                                                                                                    NaN   \n",
+       "11                                                                                             Make the final connection.   \n",
+       "12                                                                                         Everyone operates differently.   \n",
+       "13                                                                                                                    NaN   \n",
+       "14                                                                                                                    NaN   \n",
+       "15  My name is Woo Young-woo, whether it is read straight or flipped. Kayak, deed, rotator, noon, racecar, Woo Young-woo.   \n",
+       "16                                                                                                 Same nerds, new drama.   \n",
+       "17                                                                                                  Dream the world anew.   \n",
+       "18                                                                                                                    NaN   \n",
+       "19                                                                                                                    NaN   \n",
+       "20                                                                                                        England is born   \n",
+       "21                                                                                                Tourist season is over.   \n",
+       "22                                                                            Eventually, we found each other once again.   \n",
+       "23                                                                                      Secrets are meant to be unlocked.   \n",
+       "24                                                             If you come any closer, I'm not going to let you go again.   \n",
+       "25                                                                                 No one can know. My enemy, my revenge.   \n",
+       "26                                                                                                                    NaN   \n",
+       "27                                                                                             Your secrets are not safe.   \n",
+       "28                                                                                                                    NaN   \n",
+       "29                                                                                                       Kneel to no one.   \n",
+       "30                                                                                                                    NaN   \n",
+       "31                                                                                                     And still we rise.   \n",
+       "32                                                                                                                    NaN   \n",
+       "33                                                                              The uncut story of Mexico's first cartel.   \n",
+       "34                                                                                                                    NaN   \n",
+       "35                                                                                                                    NaN   \n",
+       "36                                                                                                     Done playing nice.   \n",
+       "37                                                                                                                    NaN   \n",
+       "38                                                                                                                    NaN   \n",
+       "39                                                                                                                    NaN   \n",
+       "40                                                                                                        Time to choose.   \n",
+       "41                                                                   LA's hottest defense attorney just found a new gear.   \n",
+       "42                                                                     Energy never dies, it just takes a different form.   \n",
+       "43                                                                                         Witness a heist like no other.   \n",
+       "44                                                                                      Let's film the documentary again.   \n",
+       "45                                                                  Rooting for you through sour, sweet, and bitter life.   \n",
+       "46                                                                                                         Be not afraid.   \n",
+       "47                                                                                                                    NaN   \n",
+       "48                                                                                                   Rewrite your future.   \n",
+       "49                                                                                A deserted Moon base. A deadly mission.   \n",
+       "50                                                                                           You never forget your first.   \n",
+       "51                                                                                                                    NaN   \n",
+       "52                                                                                                         The con is on!   \n",
+       "53                                                                    The slightly special everyday of us ordinary people   \n",
+       "54                                                                               If you find one, he's already found you.   \n",
+       "55                                                                                                                    NaN   \n",
+       "56                                                                                 Young offenders sentenced as monsters.   \n",
+       "57                                                                                            Rewind to reveal the truth.   \n",
+       "58                                                                                                                    NaN   \n",
+       "59                                                                                                                    NaN   \n",
+       "60                            A space colony family struggles to survive when their ship is hopelessly thrown off course.   \n",
+       "61                                                                                 Not everyone is entitled to the truth.   \n",
+       "62                                                                                      Falling in love is the easy part.   \n",
+       "63                                                                                                                    NaN   \n",
+       "64                                                                                                                    NaN   \n",
+       "65                                                                                                      One in a million.   \n",
+       "66                                                                                                                    NaN   \n",
+       "67                                                                                           History casts a long shadow.   \n",
+       "68                                                                                                                    NaN   \n",
+       "69                                                                                                  Betrayal is relative.   \n",
+       "70                                                                                                                    NaN   \n",
+       "\n",
+       "                                           TMDb_raw_genres  \\\n",
+       "0                         Drama, Sci-Fi & Fantasy, Mystery   \n",
+       "1                                             Crime, Drama   \n",
+       "2                       Action & Adventure, Mystery, Drama   \n",
+       "3              Action & Adventure, Sci-Fi & Fantasy, Drama   \n",
+       "4                                            Comedy, Drama   \n",
+       "5              Action & Adventure, Drama, Sci-Fi & Fantasy   \n",
+       "6                                                    Drama   \n",
+       "7                                             Crime, Drama   \n",
+       "8                                             Drama, Crime   \n",
+       "9                        Action & Adventure, Drama, Comedy   \n",
+       "10                                   Crime, Mystery, Drama   \n",
+       "11                        Drama, Mystery, Sci-Fi & Fantasy   \n",
+       "12                                                   Drama   \n",
+       "13  Animation, Drama, Sci-Fi & Fantasy, Action & Adventure   \n",
+       "14                                                   Drama   \n",
+       "15                                                   Drama   \n",
+       "16                                           Comedy, Drama   \n",
+       "17                                 Sci-Fi & Fantasy, Drama   \n",
+       "18                                                   Drama   \n",
+       "19         Action & Adventure, Animation, Sci-Fi & Fantasy   \n",
+       "20               Action & Adventure, Drama, War & Politics   \n",
+       "21                                           Drama, Comedy   \n",
+       "22                                           Comedy, Drama   \n",
+       "23                        Sci-Fi & Fantasy, Drama, Mystery   \n",
+       "24                                           Comedy, Drama   \n",
+       "25               Crime, Drama, Mystery, Action & Adventure   \n",
+       "26                                                   Drama   \n",
+       "27                                                   Drama   \n",
+       "28    Sci-Fi & Fantasy, Drama, Action & Adventure, Mystery   \n",
+       "29               Action & Adventure, Drama, War & Politics   \n",
+       "30                                          Mystery, Drama   \n",
+       "31                                                   Drama   \n",
+       "32                                                   Drama   \n",
+       "33                                            Drama, Crime   \n",
+       "34                                                  Comedy   \n",
+       "35                                                   Drama   \n",
+       "36                                    Comedy, Drama, Crime   \n",
+       "37                                                   Drama   \n",
+       "38                                   Drama, Crime, Mystery   \n",
+       "39                                           Soap, Mystery   \n",
+       "40                                           Comedy, Drama   \n",
+       "41                                            Drama, Crime   \n",
+       "42                                 Drama, Sci-Fi & Fantasy   \n",
+       "43               Action & Adventure, Crime, Drama, Mystery   \n",
+       "44                                           Comedy, Drama   \n",
+       "45                                                   Drama   \n",
+       "46                        Mystery, Drama, Sci-Fi & Fantasy   \n",
+       "47                                                   Drama   \n",
+       "48                                            Drama, Crime   \n",
+       "49                        Drama, Sci-Fi & Fantasy, Mystery   \n",
+       "50                                          Drama, Mystery   \n",
+       "51                                             Crime, Soap   \n",
+       "52                           Comedy, Crime, Drama, Mystery   \n",
+       "53                                           Drama, Comedy   \n",
+       "54                                            Crime, Drama   \n",
+       "55                                                   Drama   \n",
+       "56                                            Drama, Crime   \n",
+       "57                        Drama, Mystery, Sci-Fi & Fantasy   \n",
+       "58             Action & Adventure, Sci-Fi & Fantasy, Drama   \n",
+       "59                                                   Drama   \n",
+       "60                                        Sci-Fi & Fantasy   \n",
+       "61                                                   Drama   \n",
+       "62                                                 Reality   \n",
+       "63                                                   Drama   \n",
+       "64                                  Drama, Comedy, Mystery   \n",
+       "65                                                 Reality   \n",
+       "66                                   Drama, Crime, Mystery   \n",
+       "67                             Documentary, War & Politics   \n",
+       "68                                                 Reality   \n",
+       "69                                            Drama, Crime   \n",
+       "70                                                   Drama   \n",
+       "\n",
+       "                                                   TMDb_raw_created_by  \\\n",
+       "0                                             Matt Duffer, Ross Duffer   \n",
+       "1                                                            Álex Pina   \n",
+       "2                                                      Hwang Dong-hyuk   \n",
+       "3                                                       Steve Blackman   \n",
+       "4                                                          Laurie Nunn   \n",
+       "5                                    Chun Sung-il, JQ Lee, Kim Nam-soo   \n",
+       "6                                                      Chris Van Dusen   \n",
+       "7                                          Mark Williams, Bill Dubuque   \n",
+       "8                                                        Steven Knight   \n",
+       "9                          Jon Hurwitz, Hayden Schlossberg, Josh Heald   \n",
+       "10                                       Carlos Montero, Darío Madrona   \n",
+       "11                                                           Jeff Rake   \n",
+       "12                                                         David Shore   \n",
+       "13                                           Christian Linke, Alex Yee   \n",
+       "14                                                      Andrés Salgado   \n",
+       "15                                             Yoo In-sik, Moon Ji-won   \n",
+       "16                                           Mindy Kaling, Lang Fisher   \n",
+       "17                         David S. Goyer, Neil Gaiman, Allan Heinberg   \n",
+       "18                                                          Sue Tenney   \n",
+       "19                                                                 NaN   \n",
+       "20                                                    Stephen Butchard   \n",
+       "21                                                         Darren Star   \n",
+       "22                                              Yu Je-won, Shin Ha-eun   \n",
+       "23                    Carlton Cuse, Meredith Averill, Aron Eli Coleite   \n",
+       "24                              Park Seon-ho, Hong Bo-hee, Han Sul-hee   \n",
+       "25                                              Kim Jin-min, Kim Ba-da   \n",
+       "26                                                     Fernando Gaitán   \n",
+       "27      Adriana Pelusi, Carlos Quintanilla Sakar, Miguel García Moreno   \n",
+       "28                          Hong Mi-ran, Hong Jeong-eun, Park Joon-hwa   \n",
+       "29                                                          Jeb Stuart   \n",
+       "30                                              Leticia López Margalli   \n",
+       "31                                                         April Blair   \n",
+       "32                                                       Julio Jiménez   \n",
+       "33                            Chris Brancato, Doug Miro, Carlo Bernard   \n",
+       "34                       Jeremy Haft, Eddie Gonzalez, Lauren Iungerich   \n",
+       "35                                          Jung Jee-hyun, Kwon Do-eun   \n",
+       "36                                                          Jenna Bans   \n",
+       "37                                                        Han Hee-jung   \n",
+       "38                                             José Ignacio Valenzuela   \n",
+       "39                                                     Leonardo Padrón   \n",
+       "40                                                      Stacy Rukeyser   \n",
+       "41                                                     David E. Kelley   \n",
+       "42                                                        Carol Barbee   \n",
+       "43           Kim Hong-sun, Ryu Yong-jae, Kim Hwan-chae, Choi Sung-joon   \n",
+       "44                                                          Lee Na-eun   \n",
+       "45                                                       Noh Hee-kyung   \n",
+       "46                                                       Mike Flanagan   \n",
+       "47                                                  Sheryl J. Anderson   \n",
+       "48                                             Felipe Braga, KondZilla   \n",
+       "49                                        Park Eun-kyo, Choi Hang-yong   \n",
+       "50                                                         V.E. Schwab   \n",
+       "51                                                  Andrés López López   \n",
+       "52                                                         Tony Jordan   \n",
+       "53                                           Lee Woo-jung, Shin Won-ho   \n",
+       "54  Søren Sveistrup, Mikkel Serup, David Sandreuter, Dorte Warnøe Høgh   \n",
+       "55                                                       Jimena Romero   \n",
+       "56                                        Hong Jong-chan, Kim Min-seok   \n",
+       "57                                                 Rebecca Sonnenshine   \n",
+       "58                             Joaquín Górriz, Guillermo López Sánchez   \n",
+       "59                                         Carlos Martín, Ignasi Rubio   \n",
+       "60                                                         Irwin Allen   \n",
+       "61                               David E. Kelley, Melissa James Gibson   \n",
+       "62                                                        Chris Coelen   \n",
+       "63                                                      Kang Eun-kyung   \n",
+       "64                                                      Garry Campbell   \n",
+       "65                                                                 NaN   \n",
+       "66                                                    Anthony Horowitz   \n",
+       "67                                                                 NaN   \n",
+       "68                                                        Adam DiVello   \n",
+       "69                                                         Eric Newman   \n",
+       "70                                                     Nuran Evren Şit   \n",
+       "\n",
+       "   TMDb_raw_languages  \\\n",
+       "0                  en   \n",
+       "1                  es   \n",
+       "2          en, ko, ur   \n",
+       "3                  en   \n",
+       "4                  en   \n",
+       "5              en, ko   \n",
+       "6                  en   \n",
+       "7                  en   \n",
+       "8                  en   \n",
+       "9                  en   \n",
+       "10                 es   \n",
+       "11                 en   \n",
+       "12                 en   \n",
+       "13                 en   \n",
+       "14                 es   \n",
+       "15                 ko   \n",
+       "16                 en   \n",
+       "17                 en   \n",
+       "18                 en   \n",
+       "19                 ja   \n",
+       "20                 en   \n",
+       "21             en, fr   \n",
+       "22                 ko   \n",
+       "23                 en   \n",
+       "24                 ko   \n",
+       "25                 ko   \n",
+       "26                 es   \n",
+       "27                 es   \n",
+       "28                 ko   \n",
+       "29                 en   \n",
+       "30                 es   \n",
+       "31                 en   \n",
+       "32                 es   \n",
+       "33             en, es   \n",
+       "34             en, pt   \n",
+       "35                 ko   \n",
+       "36                 en   \n",
+       "37                 ko   \n",
+       "38                 es   \n",
+       "39                 es   \n",
+       "40                 en   \n",
+       "41                 en   \n",
+       "42                 en   \n",
+       "43                 ko   \n",
+       "44                 ko   \n",
+       "45                 ko   \n",
+       "46                 en   \n",
+       "47                 en   \n",
+       "48                 pt   \n",
+       "49                 ko   \n",
+       "50                 en   \n",
+       "51                 es   \n",
+       "52                 en   \n",
+       "53                 ko   \n",
+       "54                 da   \n",
+       "55                 es   \n",
+       "56                 ko   \n",
+       "57                 en   \n",
+       "58                 es   \n",
+       "59                 es   \n",
+       "60                 en   \n",
+       "61                 en   \n",
+       "62                 en   \n",
+       "63                 ko   \n",
+       "64         en, fr, pt   \n",
+       "65                 es   \n",
+       "66                 en   \n",
+       "67                 en   \n",
+       "68                 en   \n",
+       "69                 en   \n",
+       "70                 tr   \n",
+       "\n",
+       "                                                                                                                                        TMDb_raw_networks  \\\n",
+       "0                                                                                                                                                 Netflix   \n",
+       "1                                                                                                                                       Netflix, Antena 3   \n",
+       "2                                                                                                                                                 Netflix   \n",
+       "3                                                                                                                                                 Netflix   \n",
+       "4                                                                                                                                                 Netflix   \n",
+       "5                                                                                                                                                 Netflix   \n",
+       "6                                                                                                                                                 Netflix   \n",
+       "7                                                                                                                                                 Netflix   \n",
+       "8                                                                                                                                        BBC One, BBC Two   \n",
+       "9                                                                                                                                Netflix, YouTube Premium   \n",
+       "10                                                                                                                                                Netflix   \n",
+       "11                                                                                                                                           NBC, Netflix   \n",
+       "12                                                                                                                                                    ABC   \n",
+       "13                                                                                                                                                Netflix   \n",
+       "14                                                                                                                                             Caracol TV   \n",
+       "15                                                                                                                                                    ENA   \n",
+       "16                                                                                                                                                Netflix   \n",
+       "17                                                                                                                                                Netflix   \n",
+       "18                                                                                                                                                Netflix   \n",
+       "19  tv asahi, MBS, TV Tokyo, TBS, CBC, TV Aichi, TVQ, TV Osaka, Tulip Television, TVh, SBC, TSC, BSN, tys, Nagasaki Broadcasting Company, HBC, RKK Kum...   \n",
+       "20                                                                                                                                       Netflix, BBC Two   \n",
+       "21                                                                                                                                                Netflix   \n",
+       "22                                                                                                                                                    tvN   \n",
+       "23                                                                                                                                                Netflix   \n",
+       "24                                                                                                                                                    SBS   \n",
+       "25                                                                                                                                                Netflix   \n",
+       "26                                                                                                                                                    RCN   \n",
+       "27                                                                                                                                                Netflix   \n",
+       "28                                                                                                                                                    tvN   \n",
+       "29                                                                                                                                                Netflix   \n",
+       "30                                                                                                                                                Netflix   \n",
+       "31                                                                                                                                                 The CW   \n",
+       "32                                                                                                                                              Telemundo   \n",
+       "33                                                                                                                                                Netflix   \n",
+       "34                                                                                                                                                Netflix   \n",
+       "35                                                                                                                                           Netflix, tvN   \n",
+       "36                                                                                                                                                    NBC   \n",
+       "37                                                                                                                                                   KBS2   \n",
+       "38                                                                                                                                                Netflix   \n",
+       "39                                                                                                                                                Netflix   \n",
+       "40                                                                                                                                                Netflix   \n",
+       "41                                                                                                                                                Netflix   \n",
+       "42                                                                                                                                                Netflix   \n",
+       "43                                                                                                                                                Netflix   \n",
+       "44                                                                                                                                                    SBS   \n",
+       "45                                                                                                                                                    tvN   \n",
+       "46                                                                                                                                                Netflix   \n",
+       "47                                                                                                                                                Netflix   \n",
+       "48                                                                                                                                                Netflix   \n",
+       "49                                                                                                                                                Netflix   \n",
+       "50                                                                                                                                                Netflix   \n",
+       "51                                                                                                                                    Netflix, Caracol TV   \n",
+       "52                                                                                                                                                BBC One   \n",
+       "53                                                                                                                                                    tvN   \n",
+       "54                                                                                                                                                Netflix   \n",
+       "55                                                                                                                                                Netflix   \n",
+       "56                                                                                                                                                Netflix   \n",
+       "57                                                                                                                                                Netflix   \n",
+       "58                                                                                                                                                Netflix   \n",
+       "59                                                                                                                                    Atresplayer Premium   \n",
+       "60                                                                                                                                                    CBS   \n",
+       "61                                                                                                                                                Netflix   \n",
+       "62                                                                                                                                                Netflix   \n",
+       "63                                                                                                                                                   JTBC   \n",
+       "64                                                                                                                               bravo, CTV Drama Channel   \n",
+       "65                                                                                                                                                Netflix   \n",
+       "66                                                                                                                                                   ITV1   \n",
+       "67                                                                                                                                                Netflix   \n",
+       "68                                                                                                                                                Netflix   \n",
+       "69                                                                                                                                                Netflix   \n",
+       "70                                                                                                                                                Netflix   \n",
+       "\n",
+       "   TMDb_raw_origin_country     TMDb_raw_spoken_languages  \\\n",
+       "0                       US                       English   \n",
+       "1                       ES                       Español   \n",
+       "2                       KR        English, 한국어/조선말, اردو   \n",
+       "3                       US                       English   \n",
+       "4                       GB                       English   \n",
+       "5                       KR              English, 한국어/조선말   \n",
+       "6                       US                       English   \n",
+       "7                       US                       English   \n",
+       "8                       GB                       English   \n",
+       "9                       US                       English   \n",
+       "10                      ES                       Español   \n",
+       "11                      US                       English   \n",
+       "12                      US                       English   \n",
+       "13                      US                       English   \n",
+       "14                      CO                       Español   \n",
+       "15                      KR                       한국어/조선말   \n",
+       "16                      US                       English   \n",
+       "17                      US                       English   \n",
+       "18                      US                       English   \n",
+       "19                      JP                           日本語   \n",
+       "20                      GB                       English   \n",
+       "21                      US             English, Français   \n",
+       "22                      KR                       한국어/조선말   \n",
+       "23                      US                       English   \n",
+       "24                      KR                       한국어/조선말   \n",
+       "25                      KR                       한국어/조선말   \n",
+       "26                      CO                       Español   \n",
+       "27                      MX                       Español   \n",
+       "28                      KR                       한국어/조선말   \n",
+       "29                      US                       English   \n",
+       "30                      MX                       Español   \n",
+       "31                      US                       English   \n",
+       "32                      US                       Español   \n",
+       "33                      US              English, Español   \n",
+       "34                      US            English, Português   \n",
+       "35                      KR                       한국어/조선말   \n",
+       "36                      US                       English   \n",
+       "37                      KR                       한국어/조선말   \n",
+       "38                      MX                       Español   \n",
+       "39                      CO                       Español   \n",
+       "40                      US                       English   \n",
+       "41                      US                       English   \n",
+       "42                      US                       English   \n",
+       "43                      KR                       한국어/조선말   \n",
+       "44                      KR                       한국어/조선말   \n",
+       "45                      KR                       한국어/조선말   \n",
+       "46                      US                       English   \n",
+       "47                      US                       English   \n",
+       "48                      BR                     Português   \n",
+       "49                      KR                       한국어/조선말   \n",
+       "50                      US                       English   \n",
+       "51                      CO                       Español   \n",
+       "52                      GB                       English   \n",
+       "53                      KR                       한국어/조선말   \n",
+       "54                      DK                         Dansk   \n",
+       "55                      MX                       Español   \n",
+       "56                      KR                       한국어/조선말   \n",
+       "57                      US                       English   \n",
+       "58                      ES                       Español   \n",
+       "59                      ES                       Español   \n",
+       "60                      US                       English   \n",
+       "61                  GB, US                       English   \n",
+       "62                      US                       English   \n",
+       "63                      KR                       한국어/조선말   \n",
+       "64                  CA, US  English, Français, Português   \n",
+       "65                      ES                       Español   \n",
+       "66                      GB                       English   \n",
+       "67                      US                       English   \n",
+       "68                      US                       English   \n",
+       "69                      US                       English   \n",
+       "70                      TR                        Türkçe   \n",
+       "\n",
+       "                                                                          TMDb_raw_production_companies  \\\n",
+       "0                                                    21 Laps Entertainment, Monkey Massacre Productions   \n",
+       "1                                                                                       Vancouver Media   \n",
+       "2                                                                       Siren Pictures, Firstman Studio   \n",
+       "3                                                                         Dark Horse Entertainment, UCP   \n",
+       "4                                                                                                Eleven   \n",
+       "5                                                            Film Monster, SLL, Kim Jong-hak Production   \n",
+       "6                                                                                            ShondaLand   \n",
+       "7                         MRC, Zero Gravity Management, Aggregate Films, Man, Woman & Child Productions   \n",
+       "8                              Tiger Aspect, BBC Studios, Caryn Mandabach Productions, Screen Yorkshire   \n",
+       "9          Hurwitz & Schlossberg Productions, Sony Pictures Television Studios, Overbrook Entertainment   \n",
+       "10                                                                                         Zeta Studios   \n",
+       "11          Warner Bros. Television, Compari Entertainment, Universal Television, Jeff Rake Productions   \n",
+       "12                                                   ABC Studios, 3AD, Sony Pictures Television Studios   \n",
+       "13                                                                      Fortiche Production, Riot Games   \n",
+       "14                                                                               Teleset, Sony Pictures   \n",
+       "15                                                                              KT Studio Genie, AStory   \n",
+       "16                                                           Kaling International, Universal Television   \n",
+       "17              Warner Bros. Television, DC Entertainment, Purepop, Phantom Four, The Blank Corporation   \n",
+       "18                                                                                Reel World Management   \n",
+       "19                                                                            A-1 Pictures, Studio Deen   \n",
+       "20                                                                                       Carnival Films   \n",
+       "21                                        MTV Entertainment Studios, Jax Media, Darren Star Productions   \n",
+       "22                                                                                 Studio Dragon, GTist   \n",
+       "23                                                   Genre Arts, IDW Entertainment, Circle of Confusion   \n",
+       "24                                                        Studio S, Kross Pictures, Kakao Entertainment   \n",
+       "25                                                                     Studio Santa Claus Entertainment   \n",
+       "26                                                                                                  RCN   \n",
+       "27                                                                                        Lemon Studios   \n",
+       "28                                                                                        Studio Dragon   \n",
+       "29                                            MGM Television, Metropolitan Films International, History   \n",
+       "30                                                                                                  NaN   \n",
+       "31                                           Berlanti Productions, Warner Bros. Television, CBS Studios   \n",
+       "32                                                                                                  NaN   \n",
+       "33                                                            Gaumont, Gaumont International Television   \n",
+       "34                                                                           Crazy Cat Lady Productions   \n",
+       "35                                                                      Hwa&Dam Pictures, Studio Dragon   \n",
+       "36                                                              Amblin Television, Universal Television   \n",
+       "37                                                                             Arc Media, Monster Union   \n",
+       "38                                                                                                  NaN   \n",
+       "39                                                                                                  NaN   \n",
+       "40                                                                                              De Milo   \n",
+       "41                                              A+E Studios, ABC Signature, David E. Kelley Productions   \n",
+       "42                                                                          Outlier Society Productions   \n",
+       "43                                                      BH Entertainment, Content Zium, HighZium Studio   \n",
+       "44                                                          Studio N, Big Ocean ENM, Supermoon Pictures   \n",
+       "45                                                                                 GTist, Studio Dragon   \n",
+       "46                                                                                    Intrepid Pictures   \n",
+       "47                                                                        Daniel L. Paulson Productions   \n",
+       "48                                                         Losbragas, Gullane Entretenimento, Kondzilla   \n",
+       "49                                                                                       Artist Company   \n",
+       "50                                                                               Belletrist Productions   \n",
+       "51                                                                                   Caracol Televisión   \n",
+       "52                                                                           Kudos, Red Planet Pictures   \n",
+       "53                                                                                Egg is Coming, CJ ENM   \n",
+       "54                                                                                      SAM Productions   \n",
+       "55                                                                                        Lemon Studios   \n",
+       "56                                                                                  Gil Pictures, GTist   \n",
+       "57                                                                                       Atomic Monster   \n",
+       "58                                                                                         Brutal Media   \n",
+       "59                                                                                         Boomerang TV   \n",
+       "60                                                                                                  NaN   \n",
+       "61  David E. Kelley Productions, 3dot Productions, Made Up Stories, Endeavor Content, Anonymous Content   \n",
+       "62                                                                                      Kinetic Content   \n",
+       "63                                                                              SLL, npio Entertainment   \n",
+       "64                                                                              Amaze Film + Television   \n",
+       "65                                                                                                  NaN   \n",
+       "66                                                                                                  NaN   \n",
+       "67                                                                                       Luminant Media   \n",
+       "68                                                      Done and Done Productions, Lionsgate Television   \n",
+       "69                                                                 HartBeat Productions, Grand Electric   \n",
+       "70                                                                                         OGM Pictures   \n",
+       "\n",
+       "                         TMDb_raw_production_countries  \\\n",
+       "0                             United States of America   \n",
+       "1                                                Spain   \n",
+       "2                                          South Korea   \n",
+       "3                     Canada, United States of America   \n",
+       "4                                       United Kingdom   \n",
+       "5                                          South Korea   \n",
+       "6                             United States of America   \n",
+       "7                             United States of America   \n",
+       "8                                       United Kingdom   \n",
+       "9                             United States of America   \n",
+       "10                                               Spain   \n",
+       "11                            United States of America   \n",
+       "12                            United States of America   \n",
+       "13                    France, United States of America   \n",
+       "14                                            Colombia   \n",
+       "15                                         South Korea   \n",
+       "16                            United States of America   \n",
+       "17                            United States of America   \n",
+       "18                            United States of America   \n",
+       "19                                               Japan   \n",
+       "20                                      United Kingdom   \n",
+       "21                            United States of America   \n",
+       "22                                         South Korea   \n",
+       "23                            United States of America   \n",
+       "24                                         South Korea   \n",
+       "25                                         South Korea   \n",
+       "26  Colombia, United States of America, United Kingdom   \n",
+       "27                                              Mexico   \n",
+       "28                                         South Korea   \n",
+       "29                   Ireland, United States of America   \n",
+       "30                                              Mexico   \n",
+       "31                            United States of America   \n",
+       "32                                                 NaN   \n",
+       "33                            United States of America   \n",
+       "34                            United States of America   \n",
+       "35                                         South Korea   \n",
+       "36                            United States of America   \n",
+       "37                                         South Korea   \n",
+       "38                                              Mexico   \n",
+       "39                                               Spain   \n",
+       "40                            United States of America   \n",
+       "41                            United States of America   \n",
+       "42                            United States of America   \n",
+       "43                                         South Korea   \n",
+       "44                                         South Korea   \n",
+       "45                                         South Korea   \n",
+       "46                            United States of America   \n",
+       "47                            United States of America   \n",
+       "48                                              Brazil   \n",
+       "49                                         South Korea   \n",
+       "50                            United States of America   \n",
+       "51                                            Colombia   \n",
+       "52                                                 NaN   \n",
+       "53                                         South Korea   \n",
+       "54                                             Denmark   \n",
+       "55                                              Mexico   \n",
+       "56                                         South Korea   \n",
+       "57                            United States of America   \n",
+       "58                                               Spain   \n",
+       "59                                               Spain   \n",
+       "60                                                 NaN   \n",
+       "61                            United States of America   \n",
+       "62                            United States of America   \n",
+       "63                                         South Korea   \n",
+       "64                    Canada, United States of America   \n",
+       "65                                                 NaN   \n",
+       "66                                                 NaN   \n",
+       "67               Afghanistan, United States of America   \n",
+       "68                            United States of America   \n",
+       "69                            United States of America   \n",
+       "70                                              Turkey   \n",
+       "\n",
+       "    TMDb_raw_episode_run_time  netflix_viewing_hours_norm  netflix_weeks_norm  \\\n",
+       "0                           0                    1.000000            0.478261   \n",
+       "1                          70                    0.392052            0.521739   \n",
+       "2                           0                    0.791378            0.782609   \n",
+       "3                           0                    0.122492            0.130435   \n",
+       "4                           0                    0.174840            0.173913   \n",
+       "5                          65                    0.209856            0.391304   \n",
+       "6                          60                    0.370147            0.391304   \n",
+       "7                           0                    0.242710            0.478261   \n",
+       "8                          58                    0.040689            0.130435   \n",
+       "9                          30                    0.111435            0.086957   \n",
+       "10                         50                    0.092045            0.130435   \n",
+       "11                         42                    0.490540            0.608696   \n",
+       "12                         43                    0.014731            0.173913   \n",
+       "13                         41                    0.029604            0.173913   \n",
+       "14                         53                    0.174793            0.608696   \n",
+       "15                         70                    0.118710            0.217391   \n",
+       "16                         30                    0.080804            0.086957   \n",
+       "17                          0                    0.072398            0.043478   \n",
+       "18                         45                    0.197276            0.173913   \n",
+       "19                         24                    0.002933            0.086957   \n",
+       "20                         60                    0.042198            0.130435   \n",
+       "21                         30                    0.096448            0.130435   \n",
+       "22                         78                    0.081803            0.608696   \n",
+       "23                         47                    0.073882            0.130435   \n",
+       "24                         60                    0.074143            0.478261   \n",
+       "25                         50                    0.043829            0.130435   \n",
+       "26                         45                    0.257744            1.000000   \n",
+       "27                         36                    0.006614            0.043478   \n",
+       "28                         75                    0.020122            0.260870   \n",
+       "29                         51                    0.069305            0.130435   \n",
+       "30                         35                    0.046968            0.130435   \n",
+       "31                         45                    0.039019            0.130435   \n",
+       "32                          0                    0.033140            0.130435   \n",
+       "33                         50                    0.024149            0.086957   \n",
+       "34                         30                    0.003421            0.000000   \n",
+       "35                         75                    0.035791            0.347826   \n",
+       "36                         42                    0.029201            0.173913   \n",
+       "37                         70                    0.023992            0.347826   \n",
+       "38                         40                    0.025801            0.086957   \n",
+       "39                         45                    0.071874            0.217391   \n",
+       "40                          0                    0.075210            0.217391   \n",
+       "41                         50                    0.083476            0.173913   \n",
+       "42                         50                    0.036240            0.086957   \n",
+       "43                          0                    0.009661            0.043478   \n",
+       "44                         60                    0.020550            0.260870   \n",
+       "45                         66                    0.012080            0.304348   \n",
+       "46                         64                    0.025327            0.086957   \n",
+       "47                         50                    0.039833            0.130435   \n",
+       "48                         44                    0.013607            0.000000   \n",
+       "49                         45                    0.006593            0.043478   \n",
+       "50                         49                    0.011887            0.043478   \n",
+       "51                         47                    0.014574            0.173913   \n",
+       "52                         60                    0.045316            0.173913   \n",
+       "53                         93                    0.000275            0.217391   \n",
+       "54                         55                    0.011745            0.130435   \n",
+       "55                         34                    0.038017            0.173913   \n",
+       "56                         61                    0.022572            0.217391   \n",
+       "57                         53                    0.020757            0.043478   \n",
+       "58                         41                    0.024635            0.130435   \n",
+       "59                         60                    0.037228            0.173913   \n",
+       "60                         60                    0.040632            0.086957   \n",
+       "61                         45                    0.035580            0.130435   \n",
+       "62                          0                    0.066215            0.130435   \n",
+       "63                         60                    0.010546            0.260870   \n",
+       "64                         45                    0.039868            0.347826   \n",
+       "65                         40                    0.000000            0.043478   \n",
+       "66                         48                    0.032230            0.130435   \n",
+       "67                         60                    0.003350            0.043478   \n",
+       "68                         32                    0.031213            0.086957   \n",
+       "69                         36                    0.006968            0.043478   \n",
+       "70                         54                    0.006172            0.086957   \n",
+       "\n",
+       "    tmdb_vote_average_norm  tmdb_vote_count_norm  綜合 The Hit 指數  \\\n",
+       "0                 0.902222              0.905952         0.8522   \n",
+       "1                 0.698333              1.000000         0.6011   \n",
+       "2                 0.461667              0.731443         0.6817   \n",
+       "3                 0.891111              0.497754         0.4110   \n",
+       "4                 0.724444              0.371757         0.3691   \n",
+       "5                 0.752222              0.185794         0.4052   \n",
+       "6                 0.635000              0.113363         0.4153   \n",
+       "7                 0.691111              0.109040         0.4043   \n",
+       "8                 0.858889              0.494666         0.3722   \n",
+       "9                 0.677778              0.320494         0.3078   \n",
+       "10                0.595000              0.493599         0.3108   \n",
+       "11                0.404444              0.072207         0.4256   \n",
+       "12                0.835000              0.659293         0.3893   \n",
+       "13                0.966667              0.186131         0.3631   \n",
+       "14                0.546111              0.076642         0.3582   \n",
+       "15                0.823333              0.031387         0.3367   \n",
+       "16                0.645000              0.085963         0.2521   \n",
+       "17                0.591111              0.095452         0.2257   \n",
+       "18                0.573333              0.023695         0.2794   \n",
+       "19                0.812222              0.262998         0.3015   \n",
+       "20                0.716667              0.081920         0.2681   \n",
+       "21                0.430556              0.063223         0.1985   \n",
+       "22                0.685000              0.023358         0.3594   \n",
+       "23                0.501667              0.060528         0.2115   \n",
+       "24                0.790000              0.014318         0.3607   \n",
+       "25                0.712778              0.038686         0.2611   \n",
+       "26                0.277778              0.019989         0.3765   \n",
+       "27                0.681667              0.124424         0.2342   \n",
+       "28                0.901111              0.021617         0.3328   \n",
+       "29                0.472778              0.036216         0.1976   \n",
+       "30                0.166667              0.233296         0.1275   \n",
+       "31                0.691667              0.021786         0.2505   \n",
+       "32                0.356111              0.106625         0.1605   \n",
+       "33                0.521667              0.063672         0.1919   \n",
+       "34                0.811667              0.033240         0.2497   \n",
+       "35                0.887222              0.004043         0.3489   \n",
+       "36                0.554444              0.027120         0.2154   \n",
+       "37                0.750556              0.011567         0.3049   \n",
+       "38                0.354444              0.062100         0.1421   \n",
+       "39                0.453889              0.011567         0.2065   \n",
+       "40                0.076667              0.065918         0.1027   \n",
+       "41                0.447778              0.008815         0.1997   \n",
+       "42                0.386667              0.020326         0.1491   \n",
+       "43                0.500000              0.032285         0.1669   \n",
+       "44                0.833333              0.002695         0.3098   \n",
+       "45                1.000000              0.000955         0.3652   \n",
+       "46                0.291667              0.035823         0.1191   \n",
+       "47                0.444444              0.010107         0.1749   \n",
+       "48                0.666667              0.008591         0.2061   \n",
+       "49                0.444444              0.028130         0.1486   \n",
+       "50                0.528889              0.013700         0.1736   \n",
+       "51                0.491667              0.014430         0.1895   \n",
+       "52                0.393333              0.006232         0.1696   \n",
+       "53                0.827778              0.004155         0.2925   \n",
+       "54                0.324444              0.024088         0.1311   \n",
+       "55                0.232778              0.012353         0.1198   \n",
+       "56                0.526111              0.003313         0.2097   \n",
+       "57                0.121667              0.023358         0.0560   \n",
+       "58                0.050000              0.011623         0.0515   \n",
+       "59                0.000000              0.007917         0.0490   \n",
+       "60                0.063889              0.003930         0.0514   \n",
+       "61                0.039444              0.005278         0.0512   \n",
+       "62                0.055556              0.001572         0.0662   \n",
+       "63                0.350556              0.001965         0.1613   \n",
+       "64                0.222222              0.000225         0.1502   \n",
+       "65                0.061667              0.014992         0.0294   \n",
+       "66                0.166667              0.000000         0.0874   \n",
+       "67                0.159444              0.003200         0.0582   \n",
+       "68                0.055556              0.000730         0.0451   \n",
+       "69                0.095000              0.003144         0.0401   \n",
+       "70                0.222222              0.000225         0.0863   \n",
+       "\n",
+       "    log_viewing_hours  log_tmdb_vote_count  Viewing_hours_million  \n",
+       "0           21.779061             9.690418                2874.26  \n",
+       "1           20.880441             9.789030                1170.20  \n",
+       "2           21.551599             9.476850                2289.50  \n",
+       "3           19.842897             9.092907                 414.63  \n",
+       "4           20.145873             8.802071                 561.36  \n",
+       "5           20.307008             8.112528                 659.51  \n",
+       "6           20.826544             7.623642                1108.80  \n",
+       "7           20.437715             7.585281                 751.60  \n",
+       "8           19.037703             9.086703                 185.34  \n",
+       "9           19.765215             8.654343                 383.64  \n",
+       "10          19.612449             9.084550                 329.29  \n",
+       "11          21.092247             7.180070                1446.26  \n",
+       "12          18.539175             9.373224                 112.58  \n",
+       "13          18.854215             8.114325                 154.27  \n",
+       "14          20.145641             7.238497                 561.23  \n",
+       "15          19.817000             6.373320                 404.03  \n",
+       "16          19.511866             7.351158                 297.78  \n",
+       "17          19.429441             7.454141                 274.22  \n",
+       "18          20.252061             6.107023                 624.25  \n",
+       "19          18.191393             8.457655                  79.51  \n",
+       "20          19.060269             7.303843                 189.57  \n",
+       "21          19.649239             7.050123                 341.63  \n",
+       "22          19.521225             6.093570                 300.58  \n",
+       "23          19.444498             7.007601                 278.38  \n",
+       "24          19.447117             5.641907                 279.11  \n",
+       "25          19.084090             6.573680                 194.14  \n",
+       "26          20.492267             5.948035                 793.74  \n",
+       "27          18.313430             7.715570                  89.83  \n",
+       "28          18.665116             6.021023                 127.69  \n",
+       "29          19.397314             6.510258                 265.55  \n",
+       "30          19.128421             8.338545                 202.94  \n",
+       "31          19.012127             6.028279                 180.66  \n",
+       "32          18.916474             7.563201                 164.18  \n",
+       "33          18.749841             7.057037                 138.98  \n",
+       "34          18.208477             6.428105                  80.88  \n",
+       "35          18.960735             4.595120                 171.61  \n",
+       "36          18.846863             6.234411                 153.14  \n",
+       "37          18.746670             5.451038                 138.54  \n",
+       "38          18.782612             7.032624                 143.61  \n",
+       "39          19.424066             5.451038                 272.75  \n",
+       "40          19.457772             7.090910                 282.10  \n",
+       "41          19.536707             5.214936                 305.27  \n",
+       "42          18.968050             5.963579                 172.87  \n",
+       "43          18.404246             6.400257                  98.37  \n",
+       "44          18.674470             4.317488                 128.89  \n",
+       "45          18.470898             3.784190                 105.15  \n",
+       "46          18.773308             6.499787                 142.28  \n",
+       "47          19.024669             5.332719                 182.94  \n",
+       "48          18.510796             5.192957                 109.43  \n",
+       "49          18.312761             6.269096                  89.77  \n",
+       "50          18.465750             5.602119                 104.61  \n",
+       "51          18.535259             5.648974                 112.14  \n",
+       "52          19.105342             4.927254                 198.31  \n",
+       "53          18.093010             4.615121                  72.06  \n",
+       "54          18.461919             6.122493                 104.21  \n",
+       "55          18.996451             5.509388                 177.85  \n",
+       "56          18.717521             4.454347                 134.56  \n",
+       "57          18.678960             6.093570                 129.47  \n",
+       "58          18.759579             5.455321                 140.34  \n",
+       "59          18.983947             5.123964                 175.64  \n",
+       "60          19.036839             4.574711                 185.18  \n",
+       "61          18.957291             4.795791                 171.02  \n",
+       "62          19.364159             4.007333                 256.89  \n",
+       "63          18.429145             4.127134                 100.85  \n",
+       "64          19.025215             3.433987                 183.04  \n",
+       "65          18.082267             5.683580                  71.29  \n",
+       "66          18.900820             3.295837                 161.63  \n",
+       "67          18.206001             4.430817                  80.68  \n",
+       "68          18.883030             3.688879                 158.78  \n",
+       "69          18.324390             4.418841                  90.82  \n",
+       "70          18.299530             3.433987                  88.59  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Complete PCA ranking data exported to:\n",
+      "netflix/data/PCA_Hit_ranking_3features_with_all_Netflix_TMDb_features.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ==============================================================================\n",
+    "# J. PCA ranking + all Netflix features + all TMDb features\n",
+    "# Compatible with:\n",
+    "# Hit_rank, Hit_score, PCA_Hit_original,\n",
+    "# log_viewing_hours, log_tmdb_vote_count, Viewing_hours_million\n",
+    "# ==============================================================================\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 1. Prepare data\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "# df:\n",
+    "# Netflix composite dataset\n",
+    "#\n",
+    "# tmdb:\n",
+    "# TMDb v3 dataset\n",
+    "#\n",
+    "# df_hit_rank:\n",
+    "# PCA-ranked Hit dataset\n",
+    "\n",
+    "netflix_all = df.copy()\n",
+    "tmdb_all = tmdb.copy()\n",
+    "\n",
+    "required_objects = {\n",
+    "    \"df_hit_rank\": df_hit_rank,\n",
+    "    \"netflix_all\": netflix_all,\n",
+    "    \"tmdb_all\": tmdb_all\n",
+    "}\n",
+    "\n",
+    "for object_name, dataframe in required_objects.items():\n",
+    "    if not isinstance(dataframe, pd.DataFrame):\n",
+    "        raise TypeError(\n",
+    "            f\"{object_name} is not a valid DataFrame.\"\n",
+    "        )\n",
+    "\n",
+    "# Check required ID column\n",
+    "if \"key\" not in df_hit_rank.columns:\n",
+    "    raise KeyError(\n",
+    "        \"The 'key' column is missing from df_hit_rank.\"\n",
+    "    )\n",
+    "\n",
+    "if \"key\" not in netflix_all.columns:\n",
+    "    raise KeyError(\n",
+    "        \"The 'key' column is missing from the Netflix composite dataset.\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 2. Preserve PCA-derived columns\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "# Columns created during PCA but not present in the original Netflix CSV\n",
+    "pca_derived_columns = [\n",
+    "    column\n",
+    "    for column in df_hit_rank.columns\n",
+    "    if column not in netflix_all.columns\n",
+    "    and column != \"key\"\n",
+    "]\n",
+    "\n",
+    "rank_information = df_hit_rank[\n",
+    "    [\"key\"] + pca_derived_columns\n",
+    "].copy()\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 3. Merge all original Netflix composite features\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "df_ranked_all_features = rank_information.merge(\n",
+    "    netflix_all,\n",
+    "    on=\"key\",\n",
+    "    how=\"left\",\n",
+    "    validate=\"one_to_one\"\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    f\"Netflix composite merge completed: \"\n",
+    "    f\"{len(df_ranked_all_features)} Hit titles, \"\n",
+    "    f\"{len(netflix_all.columns)} original Netflix columns.\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 4. Construct reliable TMDb matching keys\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "def normalize_title(series):\n",
+    "    \"\"\"\n",
+    "    Normalize titles by removing extra spaces\n",
+    "    and ignoring capitalization differences.\n",
+    "    \"\"\"\n",
+    "    return (\n",
+    "        series\n",
+    "        .astype(\"string\")\n",
+    "        .str.strip()\n",
+    "        .str.casefold()\n",
+    "        .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# Netflix composite side\n",
+    "df_ranked_all_features[\"_match_title\"] = normalize_title(\n",
+    "    df_ranked_all_features[\"tmdb_title\"]\n",
+    ")\n",
+    "\n",
+    "df_ranked_all_features[\"_match_popularity\"] = pd.to_numeric(\n",
+    "    df_ranked_all_features[\"tmdb_popularity\"],\n",
+    "    errors=\"coerce\"\n",
+    ").round(6)\n",
+    "\n",
+    "df_ranked_all_features[\"_match_rating\"] = pd.to_numeric(\n",
+    "    df_ranked_all_features[\"tmdb_vote_average\"],\n",
+    "    errors=\"coerce\"\n",
+    ").round(6)\n",
+    "\n",
+    "df_ranked_all_features[\"_match_votes\"] = pd.to_numeric(\n",
+    "    df_ranked_all_features[\"tmdb_vote_count\"],\n",
+    "    errors=\"coerce\"\n",
+    ").round().astype(\"Int64\")\n",
+    "\n",
+    "\n",
+    "# Original TMDb side\n",
+    "tmdb_for_merge = tmdb_all.copy()\n",
+    "\n",
+    "tmdb_for_merge[\"_match_title\"] = normalize_title(\n",
+    "    tmdb_for_merge[\"name\"]\n",
+    ")\n",
+    "\n",
+    "tmdb_for_merge[\"_match_popularity\"] = pd.to_numeric(\n",
+    "    tmdb_for_merge[\"popularity\"],\n",
+    "    errors=\"coerce\"\n",
+    ").round(6)\n",
+    "\n",
+    "tmdb_for_merge[\"_match_rating\"] = pd.to_numeric(\n",
+    "    tmdb_for_merge[\"vote_average\"],\n",
+    "    errors=\"coerce\"\n",
+    ").round(6)\n",
+    "\n",
+    "tmdb_for_merge[\"_match_votes\"] = pd.to_numeric(\n",
+    "    tmdb_for_merge[\"vote_count\"],\n",
+    "    errors=\"coerce\"\n",
+    ").round().astype(\"Int64\")\n",
+    "\n",
+    "\n",
+    "match_columns = [\n",
+    "    \"_match_title\",\n",
+    "    \"_match_popularity\",\n",
+    "    \"_match_rating\",\n",
+    "    \"_match_votes\"\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 5. Keep only TMDb candidates required for the current Hit titles\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "needed_match_keys = (\n",
+    "    df_ranked_all_features[match_columns]\n",
+    "    .drop_duplicates()\n",
+    "    .copy()\n",
+    ")\n",
+    "\n",
+    "tmdb_candidates = tmdb_for_merge.merge(\n",
+    "    needed_match_keys,\n",
+    "    on=match_columns,\n",
+    "    how=\"inner\"\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    f\"Current Hit titles: {len(df_ranked_all_features)}; \"\n",
+    "    f\"TMDb candidate records found: {len(tmdb_candidates)}.\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 6. Remove duplicate TMDb records\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "# If the same TMDb ID appears more than once, keep only the first record\n",
+    "if \"id\" in tmdb_candidates.columns:\n",
+    "    tmdb_candidates = tmdb_candidates.drop_duplicates(\n",
+    "        subset=[\"id\"],\n",
+    "        keep=\"first\"\n",
+    "    )\n",
+    "\n",
+    "# Detect whether the same matching key still corresponds to multiple records\n",
+    "duplicate_match_mask = tmdb_candidates.duplicated(\n",
+    "    subset=match_columns,\n",
+    "    keep=False\n",
+    ")\n",
+    "\n",
+    "if duplicate_match_mask.any():\n",
+    "\n",
+    "    ambiguous_matches = (\n",
+    "        tmdb_candidates.loc[duplicate_match_mask]\n",
+    "        .sort_values(match_columns)\n",
+    "    )\n",
+    "\n",
+    "    display_columns = [\n",
+    "        column\n",
+    "        for column in [\n",
+    "            \"id\",\n",
+    "            \"name\",\n",
+    "            \"first_air_date\",\n",
+    "            \"last_air_date\",\n",
+    "            \"original_language\",\n",
+    "            \"popularity\",\n",
+    "            \"vote_average\",\n",
+    "            \"vote_count\"\n",
+    "        ]\n",
+    "        if column in ambiguous_matches.columns\n",
+    "    ]\n",
+    "\n",
+    "    print(\n",
+    "        \"The following titles have multiple possible TMDb matches:\"\n",
+    "    )\n",
+    "\n",
+    "    display(\n",
+    "        ambiguous_matches[display_columns]\n",
+    "    )\n",
+    "\n",
+    "    raise ValueError(\n",
+    "        \"Some Hit titles cannot be uniquely matched to TMDb. \"\n",
+    "        \"Additional keys such as release year or TMDb ID are required.\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 7. Confirm that every Hit title has exactly one TMDb match\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "match_check = (\n",
+    "    df_ranked_all_features[match_columns]\n",
+    "    .merge(\n",
+    "        tmdb_candidates[match_columns],\n",
+    "        on=match_columns,\n",
+    "        how=\"left\",\n",
+    "        indicator=True\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "unmatched_count = (\n",
+    "    match_check[\"_merge\"] == \"left_only\"\n",
+    ").sum()\n",
+    "\n",
+    "if unmatched_count > 0:\n",
+    "\n",
+    "    unmatched_keys = match_check.loc[\n",
+    "        match_check[\"_merge\"] == \"left_only\",\n",
+    "        match_columns\n",
+    "    ]\n",
+    "\n",
+    "    print(\n",
+    "        f\"{unmatched_count} Hit titles could not be matched to TMDb:\"\n",
+    "    )\n",
+    "\n",
+    "    display(unmatched_keys)\n",
+    "\n",
+    "    raise ValueError(\n",
+    "        \"Some Hit titles were not successfully matched \"\n",
+    "        \"to the original TMDb dataset.\"\n",
+    "    )\n",
+    "\n",
+    "print(\n",
+    "    \"All Hit titles were successfully matched \"\n",
+    "    \"to unique TMDb records.\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 8. Add a prefix to every original TMDb column\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "tmdb_original_columns = list(tmdb_all.columns)\n",
+    "\n",
+    "tmdb_prefix_mapping = {\n",
+    "    column: f\"TMDb_raw_{column}\"\n",
+    "    for column in tmdb_original_columns\n",
+    "}\n",
+    "\n",
+    "tmdb_candidates = tmdb_candidates.rename(\n",
+    "    columns=tmdb_prefix_mapping\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 9. Merge all original TMDb features\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "df_ranked_all_features = df_ranked_all_features.merge(\n",
+    "    tmdb_candidates,\n",
+    "    on=match_columns,\n",
+    "    how=\"left\",\n",
+    "    validate=\"one_to_one\"\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    f\"TMDb merge completed: \"\n",
+    "    f\"{len(tmdb_original_columns)} original TMDb columns added.\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 10. Remove temporary matching columns\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "df_ranked_all_features = df_ranked_all_features.drop(\n",
+    "    columns=match_columns,\n",
+    "    errors=\"ignore\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 11. Reorder columns\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "# Main PCA ranking columns\n",
+    "ranking_front_columns = [\n",
+    "    \"Hit_rank\",\n",
+    "    \"Hit_score\",\n",
+    "    \"PCA_Hit_original\"\n",
+    "]\n",
+    "\n",
+    "ranking_front_columns = [\n",
+    "    column\n",
+    "    for column in ranking_front_columns\n",
+    "    if column in df_ranked_all_features.columns\n",
+    "]\n",
+    "\n",
+    "# All original Netflix composite columns\n",
+    "netflix_original_columns = [\n",
+    "    column\n",
+    "    for column in netflix_all.columns\n",
+    "    if column in df_ranked_all_features.columns\n",
+    "]\n",
+    "\n",
+    "# All original TMDb columns with prefix\n",
+    "tmdb_prefixed_columns = [\n",
+    "    f\"TMDb_raw_{column}\"\n",
+    "    for column in tmdb_original_columns\n",
+    "    if f\"TMDb_raw_{column}\"\n",
+    "    in df_ranked_all_features.columns\n",
+    "]\n",
+    "\n",
+    "# Other PCA-derived columns\n",
+    "other_derived_columns = [\n",
+    "    column\n",
+    "    for column in pca_derived_columns\n",
+    "    if column not in ranking_front_columns\n",
+    "    and column in df_ranked_all_features.columns\n",
+    "]\n",
+    "\n",
+    "ordered_columns = (\n",
+    "    ranking_front_columns\n",
+    "    + netflix_original_columns\n",
+    "    + tmdb_prefixed_columns\n",
+    "    + other_derived_columns\n",
+    ")\n",
+    "\n",
+    "# Remove any duplicate column names while preserving order\n",
+    "ordered_columns = list(\n",
+    "    dict.fromkeys(ordered_columns)\n",
+    ")\n",
+    "\n",
+    "remaining_columns = [\n",
+    "    column\n",
+    "    for column in df_ranked_all_features.columns\n",
+    "    if column not in ordered_columns\n",
+    "]\n",
+    "\n",
+    "df_ranked_all_features = df_ranked_all_features[\n",
+    "    ordered_columns + remaining_columns\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 12. Preserve the PCA ranking order\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "df_ranked_all_features = (\n",
+    "    df_ranked_all_features\n",
+    "    .sort_values(\n",
+    "        by=\"Hit_rank\",\n",
+    "        ascending=True\n",
+    "    )\n",
+    "    .reset_index(drop=True)\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 13. Optional: round important PCA result columns\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "columns_to_round = {\n",
+    "    \"Hit_score\": 2,\n",
+    "    \"PCA_Hit_original\": 4,\n",
+    "    \"Viewing_hours_million\": 2,\n",
+    "    \"tmdb_vote_average\": 3\n",
+    "}\n",
+    "\n",
+    "for column, decimal_places in columns_to_round.items():\n",
+    "    if column in df_ranked_all_features.columns:\n",
+    "        df_ranked_all_features[column] = (\n",
+    "            pd.to_numeric(\n",
+    "                df_ranked_all_features[column],\n",
+    "                errors=\"coerce\"\n",
+    "            ).round(decimal_places)\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 14. Display all combined features\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 130)\n",
+    "print(\n",
+    "    \"PCA-ranked Hit titles with all Netflix and TMDb features\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Number of Hit titles: \"\n",
+    "    f\"{len(df_ranked_all_features)}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Total number of columns: \"\n",
+    "    f\"{df_ranked_all_features.shape[1]}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Original Netflix columns: \"\n",
+    "    f\"{len(netflix_original_columns)}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Original TMDb columns: \"\n",
+    "    f\"{len(tmdb_prefixed_columns)}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"PCA-derived columns: \"\n",
+    "    f\"{len(pca_derived_columns)}\"\n",
+    ")\n",
+    "print(\"=\" * 130)\n",
+    "\n",
+    "with pd.option_context(\n",
+    "    \"display.max_columns\", None,\n",
+    "    \"display.max_rows\", None,\n",
+    "    \"display.max_colwidth\", 150,\n",
+    "    \"display.width\", None\n",
+    "):\n",
+    "    display(df_ranked_all_features)\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 15. Export the complete result to CSV\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "output_path = (\n",
+    "    \"netflix/data/\"\n",
+    "    \"PCA_Hit_ranking_3features_with_all_Netflix_TMDb_features.csv\"\n",
+    ")\n",
+    "\n",
+    "df_ranked_all_features.to_csv(\n",
+    "    output_path,\n",
+    "    index=False,\n",
+    "    encoding=\"utf-8-sig\"\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    f\"\\nComplete PCA ranking data exported to:\\n\"\n",
+    "    f\"{output_path}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c0711e1",
+   "metadata": {},
+   "source": [
+    "## our analysis\n",
+    "IN the 71 \"the hit\"\n",
+    "\n",
+    "compared with the attitudes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "a591acd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================================================================================================\n",
+      "Genre analysis of PCA-ranked Hit titles\n",
+      "====================================================================================================\n",
+      "Number of Hit titles: 71\n",
+      "Titles without genre information: 0\n",
+      "\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Genre frequency among all 71 Hit titles\n",
+      "----------------------------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Genre Rank</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Title Count</th>\n",
+       "      <th>Percentage of All Hits (%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>62</td>\n",
+       "      <td>87.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Mystery</td>\n",
+       "      <td>18</td>\n",
+       "      <td>25.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Crime</td>\n",
+       "      <td>17</td>\n",
+       "      <td>23.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>15</td>\n",
+       "      <td>21.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>13</td>\n",
+       "      <td>18.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Action &amp; Adventure</td>\n",
+       "      <td>12</td>\n",
+       "      <td>16.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Reality</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>War &amp; Politics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>Animation</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Soap</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>Documentary</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Genre Rank               Genre  Title Count  Percentage of All Hits (%)\n",
+       "0            1               Drama           62                        87.3\n",
+       "1            2             Mystery           18                        25.4\n",
+       "2            3               Crime           17                        23.9\n",
+       "3            4    Sci-Fi & Fantasy           15                        21.1\n",
+       "4            5              Comedy           13                        18.3\n",
+       "5            6  Action & Adventure           12                        16.9\n",
+       "6            7             Reality            3                         4.2\n",
+       "7            8      War & Politics            3                         4.2\n",
+       "8            9           Animation            2                         2.8\n",
+       "9           10                Soap            2                         2.8\n",
+       "10          11         Documentary            1                         1.4"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Genres of the top 10 Hit titles\n",
+      "----------------------------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hit Rank</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Hit Score</th>\n",
+       "      <th>Genres</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>Drama, Sci-Fi &amp; Fantasy, Mystery</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>84.49</td>\n",
+       "      <td>Crime, Drama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>82.90</td>\n",
+       "      <td>Action &amp; Adventure, Mystery, Drama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>The Umbrella Academy</td>\n",
+       "      <td>74.10</td>\n",
+       "      <td>Action &amp; Adventure, Sci-Fi &amp; Fantasy, Drama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Sex Education</td>\n",
+       "      <td>70.62</td>\n",
+       "      <td>Comedy, Drama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>68.76</td>\n",
+       "      <td>Action &amp; Adventure, Drama, Sci-Fi &amp; Fantasy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>67.90</td>\n",
+       "      <td>Drama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>64.97</td>\n",
+       "      <td>Crime, Drama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>Peaky Blinders</td>\n",
+       "      <td>64.08</td>\n",
+       "      <td>Drama, Crime</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Cobra Kai</td>\n",
+       "      <td>64.00</td>\n",
+       "      <td>Action &amp; Adventure, Drama, Comedy</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Hit Rank                 Title  Hit Score                                       Genres\n",
+       "0         1       Stranger Things     100.00             Drama, Sci-Fi & Fantasy, Mystery\n",
+       "1         2           Money Heist      84.49                                 Crime, Drama\n",
+       "2         3            Squid Game      82.90           Action & Adventure, Mystery, Drama\n",
+       "3         4  The Umbrella Academy      74.10  Action & Adventure, Sci-Fi & Fantasy, Drama\n",
+       "4         5         Sex Education      70.62                                Comedy, Drama\n",
+       "5         6    All of Us Are Dead      68.76  Action & Adventure, Drama, Sci-Fi & Fantasy\n",
+       "6         7            Bridgerton      67.90                                        Drama\n",
+       "7         8                 Ozark      64.97                                 Crime, Drama\n",
+       "8         9        Peaky Blinders      64.08                                 Drama, Crime\n",
+       "9        10             Cobra Kai      64.00            Action & Adventure, Drama, Comedy"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Genre frequency among the top 10 titles\n",
+      "----------------------------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Top 10 Count</th>\n",
+       "      <th>Top 10 Percentage (%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Drama</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Action &amp; Adventure</td>\n",
+       "      <td>4</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Crime</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>2</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Mystery</td>\n",
+       "      <td>2</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Genre  Top 10 Count  Top 10 Percentage (%)\n",
+       "0               Drama            10                  100.0\n",
+       "1  Action & Adventure             4                   40.0\n",
+       "2               Crime             3                   30.0\n",
+       "3    Sci-Fi & Fantasy             3                   30.0\n",
+       "4              Comedy             2                   20.0\n",
+       "5             Mystery             2                   20.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Genre comparison: top 10 versus all 71 Hit titles\n",
+      "----------------------------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Top 10 Count</th>\n",
+       "      <th>Top 10 Percentage (%)</th>\n",
+       "      <th>Title Count</th>\n",
+       "      <th>Percentage of All Hits (%)</th>\n",
+       "      <th>Top-vs-All Difference (percentage points)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Drama</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>62</td>\n",
+       "      <td>87.3</td>\n",
+       "      <td>12.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Action &amp; Adventure</td>\n",
+       "      <td>4</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>16.9</td>\n",
+       "      <td>23.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>21.1</td>\n",
+       "      <td>8.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Crime</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>23.9</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>2</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>18.3</td>\n",
+       "      <td>1.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Mystery</td>\n",
+       "      <td>2</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>25.4</td>\n",
+       "      <td>-5.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Documentary</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>-1.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Animation</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>-2.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Soap</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>-2.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Reality</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>-4.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>War &amp; Politics</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>-4.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Genre  Top 10 Count  Top 10 Percentage (%)  Title Count  Percentage of All Hits (%)  Top-vs-All Difference (percentage points)\n",
+       "0                Drama            10                  100.0           62                        87.3                                       12.7\n",
+       "1   Action & Adventure             4                   40.0           12                        16.9                                       23.1\n",
+       "2     Sci-Fi & Fantasy             3                   30.0           15                        21.1                                        8.9\n",
+       "3                Crime             3                   30.0           17                        23.9                                        6.1\n",
+       "4               Comedy             2                   20.0           13                        18.3                                        1.7\n",
+       "5              Mystery             2                   20.0           18                        25.4                                       -5.4\n",
+       "6          Documentary             0                    0.0            1                         1.4                                       -1.4\n",
+       "7            Animation             0                    0.0            2                         2.8                                       -2.8\n",
+       "8                 Soap             0                    0.0            2                         2.8                                       -2.8\n",
+       "9              Reality             0                    0.0            3                         4.2                                       -4.2\n",
+       "10      War & Politics             0                    0.0            3                         4.2                                       -4.2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Genre coverage in the top 10\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Genres appearing in at least one top-10 title:\n",
+      "Action & Adventure, Comedy, Crime, Drama, Mystery, Sci-Fi & Fantasy\n",
+      "\n",
+      "Genres shared by every top-10 title:\n",
+      "Drama\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ==============================================================================\n",
+    "# K. Genre analysis for the 32 PCA-ranked Hit titles\n",
+    "# ==============================================================================\n",
+    "\n",
+    "import ast\n",
+    "import json\n",
+    "import re\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 1. Settings\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "TOP_N = 10\n",
+    "\n",
+    "DATAFRAME_NAME = \"df_ranked_all_features\"\n",
+    "GENRE_COLUMN = \"TMDb_raw_genres\"\n",
+    "RANK_COLUMN = \"Hit_rank\"\n",
+    "SCORE_COLUMN = \"Hit_score\"\n",
+    "\n",
+    "# Automatically select a usable title column\n",
+    "possible_title_columns = [\n",
+    "    \"netflix_title\",\n",
+    "    \"TMDb_raw_name\",\n",
+    "    \"tmdb_title\",\n",
+    "    \"title\",\n",
+    "    \"name\"\n",
+    "]\n",
+    "\n",
+    "title_column = next(\n",
+    "    (\n",
+    "        column\n",
+    "        for column in possible_title_columns\n",
+    "        if column in df_ranked_all_features.columns\n",
+    "    ),\n",
+    "    None\n",
+    ")\n",
+    "\n",
+    "if title_column is None:\n",
+    "    raise KeyError(\n",
+    "        \"No usable title column was found. \"\n",
+    "        f\"Checked: {possible_title_columns}\"\n",
+    "    )\n",
+    "\n",
+    "required_columns = [\n",
+    "    GENRE_COLUMN,\n",
+    "    RANK_COLUMN,\n",
+    "    title_column\n",
+    "]\n",
+    "\n",
+    "missing_columns = [\n",
+    "    column\n",
+    "    for column in required_columns\n",
+    "    if column not in df_ranked_all_features.columns\n",
+    "]\n",
+    "\n",
+    "if missing_columns:\n",
+    "    raise KeyError(\n",
+    "        f\"Missing required columns: {missing_columns}\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 2. Robust parser for TMDb_raw_genres\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "def parse_genres(value):\n",
+    "    \"\"\"\n",
+    "    Convert different genre formats into a clean list of genre names.\n",
+    "\n",
+    "    Supported examples:\n",
+    "    - ['Drama', 'Crime']\n",
+    "    - \"['Drama', 'Crime']\"\n",
+    "    - [{\"id\": 18, \"name\": \"Drama\"}]\n",
+    "    - '[{\"id\": 18, \"name\": \"Drama\"}]'\n",
+    "    - \"Drama, Crime\"\n",
+    "    - \"Drama | Crime\"\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if value is None:\n",
+    "        return []\n",
+    "\n",
+    "    if isinstance(value, float) and np.isnan(value):\n",
+    "        return []\n",
+    "\n",
+    "    parsed_value = value\n",
+    "\n",
+    "    # Try to parse string representations of Python/JSON objects\n",
+    "    if isinstance(value, str):\n",
+    "        text = value.strip()\n",
+    "\n",
+    "        if not text or text.casefold() in {\n",
+    "            \"nan\", \"none\", \"null\", \"[]\", \"{}\"\n",
+    "        }:\n",
+    "            return []\n",
+    "\n",
+    "        parsed_successfully = False\n",
+    "\n",
+    "        # Try JSON\n",
+    "        try:\n",
+    "            parsed_value = json.loads(text)\n",
+    "            parsed_successfully = True\n",
+    "        except (json.JSONDecodeError, TypeError):\n",
+    "            pass\n",
+    "\n",
+    "        # Try Python literal syntax\n",
+    "        if not parsed_successfully:\n",
+    "            try:\n",
+    "                parsed_value = ast.literal_eval(text)\n",
+    "                parsed_successfully = True\n",
+    "            except (ValueError, SyntaxError):\n",
+    "                pass\n",
+    "\n",
+    "        # Fall back to comma, pipe, semicolon, or slash separation\n",
+    "        if not parsed_successfully:\n",
+    "            parsed_value = re.split(\n",
+    "                r\"\\s*[,|;/]\\s*\",\n",
+    "                text\n",
+    "            )\n",
+    "\n",
+    "    # One dictionary, such as {\"id\": 18, \"name\": \"Drama\"}\n",
+    "    if isinstance(parsed_value, dict):\n",
+    "        parsed_value = [parsed_value]\n",
+    "\n",
+    "    # Convert one scalar value to a list\n",
+    "    if not isinstance(parsed_value, (list, tuple, set)):\n",
+    "        parsed_value = [parsed_value]\n",
+    "\n",
+    "    clean_genres = []\n",
+    "\n",
+    "    for item in parsed_value:\n",
+    "\n",
+    "        # TMDb dictionary format\n",
+    "        if isinstance(item, dict):\n",
+    "            genre_name = (\n",
+    "                item.get(\"name\")\n",
+    "                or item.get(\"genre\")\n",
+    "                or item.get(\"genre_name\")\n",
+    "            )\n",
+    "        else:\n",
+    "            genre_name = item\n",
+    "\n",
+    "        if genre_name is None:\n",
+    "            continue\n",
+    "\n",
+    "        genre_name = str(genre_name).strip()\n",
+    "\n",
+    "        # Remove unnecessary quotes and brackets\n",
+    "        genre_name = genre_name.strip(\n",
+    "            \"[]{}()\\\"'\"\n",
+    "        ).strip()\n",
+    "\n",
+    "        if (\n",
+    "            genre_name\n",
+    "            and genre_name.casefold()\n",
+    "            not in {\"nan\", \"none\", \"null\"}\n",
+    "        ):\n",
+    "            clean_genres.append(genre_name)\n",
+    "\n",
+    "    # Remove duplicates within the same title\n",
+    "    clean_genres = list(dict.fromkeys(clean_genres))\n",
+    "\n",
+    "    return clean_genres\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 3. Prepare genre lists\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "genre_data = (\n",
+    "    df_ranked_all_features\n",
+    "    .sort_values(RANK_COLUMN)\n",
+    "    .copy()\n",
+    ")\n",
+    "\n",
+    "genre_data[\"Genre_list\"] = (\n",
+    "    genre_data[GENRE_COLUMN]\n",
+    "    .apply(parse_genres)\n",
+    ")\n",
+    "\n",
+    "total_titles = len(genre_data)\n",
+    "\n",
+    "if total_titles == 0:\n",
+    "    raise ValueError(\n",
+    "        \"df_ranked_all_features contains no rows.\"\n",
+    "    )\n",
+    "\n",
+    "titles_without_genres = (\n",
+    "    genre_data[\"Genre_list\"]\n",
+    "    .map(len)\n",
+    "    .eq(0)\n",
+    "    .sum()\n",
+    ")\n",
+    "\n",
+    "print(\"=\" * 100)\n",
+    "print(\"Genre analysis of PCA-ranked Hit titles\")\n",
+    "print(\"=\" * 100)\n",
+    "print(f\"Number of Hit titles: {total_titles}\")\n",
+    "print(f\"Titles without genre information: {titles_without_genres}\")\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 4. Overall genre counts across all Hit titles\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "overall_genre_counts = (\n",
+    "    genre_data[[\"Genre_list\"]]\n",
+    "    .explode(\"Genre_list\")\n",
+    "    .dropna(subset=[\"Genre_list\"])\n",
+    "    .groupby(\"Genre_list\")\n",
+    "    .size()\n",
+    "    .reset_index(name=\"Title Count\")\n",
+    "    .rename(columns={\n",
+    "        \"Genre_list\": \"Genre\"\n",
+    "    })\n",
+    ")\n",
+    "\n",
+    "overall_genre_counts[\"Percentage of All Hits (%)\"] = (\n",
+    "    overall_genre_counts[\"Title Count\"]\n",
+    "    / total_titles\n",
+    "    * 100\n",
+    ")\n",
+    "\n",
+    "overall_genre_counts = (\n",
+    "    overall_genre_counts\n",
+    "    .sort_values(\n",
+    "        by=[\n",
+    "            \"Title Count\",\n",
+    "            \"Genre\"\n",
+    "        ],\n",
+    "        ascending=[\n",
+    "            False,\n",
+    "            True\n",
+    "        ]\n",
+    "    )\n",
+    "    .reset_index(drop=True)\n",
+    ")\n",
+    "\n",
+    "overall_genre_counts.insert(\n",
+    "    0,\n",
+    "    \"Genre Rank\",\n",
+    "    range(1, len(overall_genre_counts) + 1)\n",
+    ")\n",
+    "\n",
+    "overall_genre_counts[\n",
+    "    \"Percentage of All Hits (%)\"\n",
+    "] = overall_genre_counts[\n",
+    "    \"Percentage of All Hits (%)\"\n",
+    "].round(1)\n",
+    "\n",
+    "print(\"\\n\" + \"-\" * 100)\n",
+    "print(f\"Genre frequency among all {total_titles} Hit titles\")\n",
+    "print(\"-\" * 100)\n",
+    "\n",
+    "display(overall_genre_counts)\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 5. Display genres for each of the top-N titles\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "actual_top_n = min(\n",
+    "    TOP_N,\n",
+    "    total_titles\n",
+    ")\n",
+    "\n",
+    "top_n_data = (\n",
+    "    genre_data\n",
+    "    .head(actual_top_n)\n",
+    "    .copy()\n",
+    ")\n",
+    "\n",
+    "top_n_data[\"Genres\"] = (\n",
+    "    top_n_data[\"Genre_list\"]\n",
+    "    .apply(\n",
+    "        lambda genre_list:\n",
+    "        \", \".join(genre_list)\n",
+    "        if genre_list\n",
+    "        else \"Missing\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "top_title_columns = [\n",
+    "    RANK_COLUMN,\n",
+    "    title_column\n",
+    "]\n",
+    "\n",
+    "if SCORE_COLUMN in top_n_data.columns:\n",
+    "    top_title_columns.append(SCORE_COLUMN)\n",
+    "\n",
+    "top_title_columns.append(\"Genres\")\n",
+    "\n",
+    "top_titles_genre_table = (\n",
+    "    top_n_data[top_title_columns]\n",
+    "    .copy()\n",
+    ")\n",
+    "\n",
+    "top_titles_genre_table = top_titles_genre_table.rename(columns={\n",
+    "    RANK_COLUMN: \"Hit Rank\",\n",
+    "    title_column: \"Title\",\n",
+    "    SCORE_COLUMN: \"Hit Score\"\n",
+    "})\n",
+    "\n",
+    "if \"Hit Score\" in top_titles_genre_table.columns:\n",
+    "    top_titles_genre_table[\"Hit Score\"] = (\n",
+    "        pd.to_numeric(\n",
+    "            top_titles_genre_table[\"Hit Score\"],\n",
+    "            errors=\"coerce\"\n",
+    "        ).round(2)\n",
+    "    )\n",
+    "\n",
+    "print(\"\\n\" + \"-\" * 100)\n",
+    "print(f\"Genres of the top {actual_top_n} Hit titles\")\n",
+    "print(\"-\" * 100)\n",
+    "\n",
+    "display(top_titles_genre_table)\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 6. Genre counts among the top-N titles\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "top_n_genre_counts = (\n",
+    "    top_n_data[[\"Genre_list\"]]\n",
+    "    .explode(\"Genre_list\")\n",
+    "    .dropna(subset=[\"Genre_list\"])\n",
+    "    .groupby(\"Genre_list\")\n",
+    "    .size()\n",
+    "    .reset_index(name=f\"Top {actual_top_n} Count\")\n",
+    "    .rename(columns={\n",
+    "        \"Genre_list\": \"Genre\"\n",
+    "    })\n",
+    ")\n",
+    "\n",
+    "top_n_genre_counts[\n",
+    "    f\"Top {actual_top_n} Percentage (%)\"\n",
+    "] = (\n",
+    "    top_n_genre_counts[\n",
+    "        f\"Top {actual_top_n} Count\"\n",
+    "    ]\n",
+    "    / actual_top_n\n",
+    "    * 100\n",
+    ")\n",
+    "\n",
+    "top_n_genre_counts = (\n",
+    "    top_n_genre_counts\n",
+    "    .sort_values(\n",
+    "        by=[\n",
+    "            f\"Top {actual_top_n} Count\",\n",
+    "            \"Genre\"\n",
+    "        ],\n",
+    "        ascending=[\n",
+    "            False,\n",
+    "            True\n",
+    "        ]\n",
+    "    )\n",
+    "    .reset_index(drop=True)\n",
+    ")\n",
+    "\n",
+    "top_n_genre_counts[\n",
+    "    f\"Top {actual_top_n} Percentage (%)\"\n",
+    "] = top_n_genre_counts[\n",
+    "    f\"Top {actual_top_n} Percentage (%)\"\n",
+    "].round(1)\n",
+    "\n",
+    "print(\"\\n\" + \"-\" * 100)\n",
+    "print(f\"Genre frequency among the top {actual_top_n} titles\")\n",
+    "print(\"-\" * 100)\n",
+    "\n",
+    "display(top_n_genre_counts)\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 7. Compare top-N genre prevalence with all Hit titles\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "genre_comparison = top_n_genre_counts.merge(\n",
+    "    overall_genre_counts[[\n",
+    "        \"Genre\",\n",
+    "        \"Title Count\",\n",
+    "        \"Percentage of All Hits (%)\"\n",
+    "    ]],\n",
+    "    on=\"Genre\",\n",
+    "    how=\"outer\"\n",
+    ")\n",
+    "\n",
+    "genre_comparison[\n",
+    "    f\"Top {actual_top_n} Count\"\n",
+    "] = genre_comparison[\n",
+    "    f\"Top {actual_top_n} Count\"\n",
+    "].fillna(0).astype(int)\n",
+    "\n",
+    "genre_comparison[\"Title Count\"] = (\n",
+    "    genre_comparison[\"Title Count\"]\n",
+    "    .fillna(0)\n",
+    "    .astype(int)\n",
+    ")\n",
+    "\n",
+    "genre_comparison[\n",
+    "    f\"Top {actual_top_n} Percentage (%)\"\n",
+    "] = genre_comparison[\n",
+    "    f\"Top {actual_top_n} Percentage (%)\"\n",
+    "].fillna(0)\n",
+    "\n",
+    "genre_comparison[\n",
+    "    \"Percentage of All Hits (%)\"\n",
+    "] = genre_comparison[\n",
+    "    \"Percentage of All Hits (%)\"\n",
+    "].fillna(0)\n",
+    "\n",
+    "genre_comparison[\n",
+    "    \"Top-vs-All Difference (percentage points)\"\n",
+    "] = (\n",
+    "    genre_comparison[\n",
+    "        f\"Top {actual_top_n} Percentage (%)\"\n",
+    "    ]\n",
+    "    -\n",
+    "    genre_comparison[\n",
+    "        \"Percentage of All Hits (%)\"\n",
+    "    ]\n",
+    ").round(1)\n",
+    "\n",
+    "genre_comparison = (\n",
+    "    genre_comparison\n",
+    "    .sort_values(\n",
+    "        by=[\n",
+    "            f\"Top {actual_top_n} Count\",\n",
+    "            \"Top-vs-All Difference (percentage points)\",\n",
+    "            \"Title Count\"\n",
+    "        ],\n",
+    "        ascending=[\n",
+    "            False,\n",
+    "            False,\n",
+    "            False\n",
+    "        ]\n",
+    "    )\n",
+    "    .reset_index(drop=True)\n",
+    ")\n",
+    "\n",
+    "print(\"\\n\" + \"-\" * 100)\n",
+    "print(\n",
+    "    f\"Genre comparison: top {actual_top_n} \"\n",
+    "    f\"versus all {total_titles} Hit titles\"\n",
+    ")\n",
+    "print(\"-\" * 100)\n",
+    "\n",
+    "display(genre_comparison)\n",
+    "\n",
+    "\n",
+    "# ------------------------------------------------------------------------------\n",
+    "# 8. Union and intersection of genres in the top-N titles\n",
+    "# ------------------------------------------------------------------------------\n",
+    "\n",
+    "top_genre_sets = [\n",
+    "    set(genres)\n",
+    "    for genres in top_n_data[\"Genre_list\"]\n",
+    "    if genres\n",
+    "]\n",
+    "\n",
+    "if top_genre_sets:\n",
+    "    genres_appearing_in_top_n = sorted(\n",
+    "        set.union(*top_genre_sets)\n",
+    "    )\n",
+    "\n",
+    "    genres_shared_by_all_top_n = sorted(\n",
+    "        set.intersection(*top_genre_sets)\n",
+    "    )\n",
+    "else:\n",
+    "    genres_appearing_in_top_n = []\n",
+    "    genres_shared_by_all_top_n = []\n",
+    "\n",
+    "print(\"\\n\" + \"-\" * 100)\n",
+    "print(f\"Genre coverage in the top {actual_top_n}\")\n",
+    "print(\"-\" * 100)\n",
+    "\n",
+    "print(\n",
+    "    f\"Genres appearing in at least one top-{actual_top_n} title:\"\n",
+    ")\n",
+    "print(\n",
+    "    \", \".join(genres_appearing_in_top_n)\n",
+    "    if genres_appearing_in_top_n\n",
+    "    else \"None\"\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    f\"\\nGenres shared by every top-{actual_top_n} title:\"\n",
+    ")\n",
+    "print(\n",
+    "    \", \".join(genres_shared_by_all_top_n)\n",
+    "    if genres_shared_by_all_top_n\n",
+    "    else \"No single genre is shared by every top-ranked title.\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4aafebba",
+   "metadata": {},
+   "source": [
+    "## our analysis\n",
+    "Tabele analysis\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "06cdbc65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- 【前 10 筆資料預覽】 ---\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hit_rank</th>\n",
+       "      <th>Hit_score</th>\n",
+       "      <th>netflix_title</th>\n",
+       "      <th>TMDb_raw_original_language</th>\n",
+       "      <th>TMDb_raw_origin_country</th>\n",
+       "      <th>TMDb_raw_number_of_seasons</th>\n",
+       "      <th>TMDb_raw_number_of_episodes</th>\n",
+       "      <th>TMDb_raw_vote_average</th>\n",
+       "      <th>TMDb_raw_popularity</th>\n",
+       "      <th>TMDb_raw_genres</th>\n",
+       "      <th>Avg_episodes_per_season</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>Stranger Things</td>\n",
+       "      <td>en</td>\n",
+       "      <td>US</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>8.624</td>\n",
+       "      <td>185.711</td>\n",
+       "      <td>Drama, Sci-Fi &amp; Fantasy, Mystery</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>84.49</td>\n",
+       "      <td>Money Heist</td>\n",
+       "      <td>es</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>3</td>\n",
+       "      <td>41</td>\n",
+       "      <td>8.257</td>\n",
+       "      <td>96.354</td>\n",
+       "      <td>Crime, Drama</td>\n",
+       "      <td>13.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>82.90</td>\n",
+       "      <td>Squid Game</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7.831</td>\n",
+       "      <td>115.587</td>\n",
+       "      <td>Action &amp; Adventure, Mystery, Drama</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>74.10</td>\n",
+       "      <td>The Umbrella Academy</td>\n",
+       "      <td>en</td>\n",
+       "      <td>US</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32</td>\n",
+       "      <td>8.604</td>\n",
+       "      <td>51.859</td>\n",
+       "      <td>Action &amp; Adventure, Sci-Fi &amp; Fantasy, Drama</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>70.62</td>\n",
+       "      <td>Sex Education</td>\n",
+       "      <td>en</td>\n",
+       "      <td>GB</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32</td>\n",
+       "      <td>8.304</td>\n",
+       "      <td>1008.977</td>\n",
+       "      <td>Comedy, Drama</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>68.76</td>\n",
+       "      <td>All of Us Are Dead</td>\n",
+       "      <td>ko</td>\n",
+       "      <td>KR</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8.354</td>\n",
+       "      <td>107.108</td>\n",
+       "      <td>Action &amp; Adventure, Drama, Sci-Fi &amp; Fantasy</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>67.90</td>\n",
+       "      <td>Bridgerton</td>\n",
+       "      <td>en</td>\n",
+       "      <td>US</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24</td>\n",
+       "      <td>8.143</td>\n",
+       "      <td>71.308</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>64.97</td>\n",
+       "      <td>Ozark</td>\n",
+       "      <td>en</td>\n",
+       "      <td>US</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>8.244</td>\n",
+       "      <td>68.006</td>\n",
+       "      <td>Crime, Drama</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>64.08</td>\n",
+       "      <td>Peaky Blinders</td>\n",
+       "      <td>en</td>\n",
+       "      <td>GB</td>\n",
+       "      <td>6</td>\n",
+       "      <td>36</td>\n",
+       "      <td>8.546</td>\n",
+       "      <td>344.477</td>\n",
+       "      <td>Drama, Crime</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>64.00</td>\n",
+       "      <td>Cobra Kai</td>\n",
+       "      <td>en</td>\n",
+       "      <td>US</td>\n",
+       "      <td>6</td>\n",
+       "      <td>50</td>\n",
+       "      <td>8.220</td>\n",
+       "      <td>118.256</td>\n",
+       "      <td>Action &amp; Adventure, Drama, Comedy</td>\n",
+       "      <td>8.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Hit_rank  Hit_score         netflix_title TMDb_raw_original_language TMDb_raw_origin_country  TMDb_raw_number_of_seasons  TMDb_raw_number_of_episodes  TMDb_raw_vote_average  TMDb_raw_popularity                              TMDb_raw_genres  Avg_episodes_per_season\n",
+       "0         1     100.00       Stranger Things                         en                      US                           4                           34                  8.624              185.711             Drama, Sci-Fi & Fantasy, Mystery                      8.5\n",
+       "1         2      84.49           Money Heist                         es                      ES                           3                           41                  8.257               96.354                                 Crime, Drama                     13.7\n",
+       "2         3      82.90            Squid Game                         ko                      KR                           2                            9                  7.831              115.587           Action & Adventure, Mystery, Drama                      4.5\n",
+       "3         4      74.10  The Umbrella Academy                         en                      US                           4                           32                  8.604               51.859  Action & Adventure, Sci-Fi & Fantasy, Drama                      8.0\n",
+       "4         5      70.62         Sex Education                         en                      GB                           4                           32                  8.304             1008.977                                Comedy, Drama                      8.0\n",
+       "5         6      68.76    All of Us Are Dead                         ko                      KR                           2                           12                  8.354              107.108  Action & Adventure, Drama, Sci-Fi & Fantasy                      6.0\n",
+       "6         7      67.90            Bridgerton                         en                      US                           3                           24                  8.143               71.308                                        Drama                      8.0\n",
+       "7         8      64.97                 Ozark                         en                      US                           4                           44                  8.244               68.006                                 Crime, Drama                     11.0\n",
+       "8         9      64.08        Peaky Blinders                         en                      GB                           6                           36                  8.546              344.477                                 Drama, Crime                      6.0\n",
+       "9        10      64.00             Cobra Kai                         en                      US                           6                           50                  8.220              118.256            Action & Adventure, Drama, Comedy                      8.3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "完整特徵表（含平均值）已匯出至：netflix/data/hit_shows_features_with_averages.csv\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/m3/mxn0108j41x3cp5x3trm41fh0000gn/T/ipykernel_4816/458829596.py:99: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x=language_counts.index, y=language_counts.values, palette=\"viridis\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/m3/mxn0108j41x3cp5x3trm41fh0000gn/T/ipykernel_4816/458829596.py:110: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/m3/mxn0108j41x3cp5x3trm41fh0000gn/T/ipykernel_4816/458829596.py:128: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# ==========================================\n",
+    "# 1. 擴充想關注的特徵欄位（輸出多一點資料）\n",
+    "# ==========================================\n",
+    "selected_features = [\n",
+    "    \"Hit_rank\",  # PCA 排名\n",
+    "    \"Hit_score\",  # PCA 分數\n",
+    "    \"netflix_title\",  # 影集名稱\n",
+    "    \"TMDb_raw_original_language\",  # 原始語言 (例如: en, ko, es)\n",
+    "    \"TMDb_raw_origin_country\",  # 製作國家 (例如: US, KR, ES)\n",
+    "    \"TMDb_raw_number_of_seasons\",  # 總季數\n",
+    "    \"TMDb_raw_number_of_episodes\",  # 總集數\n",
+    "    \"TMDb_raw_vote_average\",  # (建議新增) TMDb 觀眾評分\n",
+    "    \"TMDb_raw_popularity\",  # (建議新增) TMDb 熱度指標\n",
+    "    \"TMDb_raw_genres\",  # 類型\n",
+    "]\n",
+    "\n",
+    "# 確保欄位存在\n",
+    "available_columns = [\n",
+    "    col for col in selected_features if col in df_ranked_all_features.columns\n",
+    "]\n",
+    "df_brief = df_ranked_all_features[available_columns].copy()\n",
+    "\n",
+    "# ==========================================\n",
+    "# 2. 在最後一欄加入「平均值」：平均每季集數\n",
+    "# ==========================================\n",
+    "df_brief[\"Avg_episodes_per_season\"] = (\n",
+    "    df_brief[\"TMDb_raw_number_of_episodes\"] / df_brief[\"TMDb_raw_number_of_seasons\"]\n",
+    ").round(1)\n",
+    "\n",
+    "# ==========================================\n",
+    "# 3. 輸出完整資料與統計總表\n",
+    "# ==========================================\n",
+    "# 設定 pandas 顯示所有列與欄，方便在 Notebook 中查看全貌\n",
+    "pd.set_option(\"display.max_rows\", None)\n",
+    "pd.set_option(\"display.max_columns\", None)\n",
+    "pd.set_option(\"display.width\", 1000)\n",
+    "\n",
+    "print(\"--- 【前 10 筆資料預覽】 ---\")\n",
+    "display(df_brief.head(10))\n",
+    "\n",
+    "# 建立包含「最末列平均值」的完整表格 (為不影響原始資料排序，使用新變數儲存)\n",
+    "df_with_summary = df_brief.copy()\n",
+    "\n",
+    "# 計算數值欄位的平均值\n",
+    "mean_values = df_brief[\n",
+    "    [\n",
+    "        \"Hit_score\",\n",
+    "        \"TMDb_raw_number_of_seasons\",\n",
+    "        \"TMDb_raw_number_of_episodes\",\n",
+    "        \"Avg_episodes_per_season\",\n",
+    "    ]\n",
+    "].mean()\n",
+    "\n",
+    "# 建立平均值的那一列資料\n",
+    "summary_row = pd.Series(\n",
+    "    {\n",
+    "        \"Hit_rank\": \"-\",\n",
+    "        \"Hit_score\": round(mean_values[\"Hit_score\"], 2),\n",
+    "        \"netflix_title\": \"【全體 71 部平均值】\",\n",
+    "        \"TMDb_raw_original_language\": \"-\",\n",
+    "        \"TMDb_raw_origin_country\": \"-\",\n",
+    "        \"TMDb_raw_number_of_seasons\": round(\n",
+    "            mean_values[\"TMDb_raw_number_of_seasons\"], 1\n",
+    "        ),\n",
+    "        \"TMDb_raw_number_of_episodes\": round(\n",
+    "            mean_values[\"TMDb_raw_number_of_episodes\"], 1\n",
+    "        ),\n",
+    "        \"TMDb_raw_genres\": \"-\",\n",
+    "        \"Avg_episodes_per_season\": round(mean_values[\"Avg_episodes_per_season\"], 1),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# 附加到表格最後方\n",
+    "df_with_summary = pd.concat(\n",
+    "    [df_with_summary, pd.DataFrame([summary_row])], ignore_index=True\n",
+    ")\n",
+    "\n",
+    "# 匯出成 CSV 檔案\n",
+    "output_brief_path = \"netflix/data/hit_shows_features_with_averages.csv\"\n",
+    "df_with_summary.to_csv(output_brief_path, index=False, encoding=\"utf-8-sig\")\n",
+    "print(f\"\\n完整特徵表（含平均值）已匯出至：{output_brief_path}\")\n",
+    "\n",
+    "# ==========================================\n",
+    "# 4. 繪製視覺化圖表：語言、總季數、總集數長條圖\n",
+    "# ==========================================\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "plt.rcParams[\"font.sans-serif\"] = [\n",
+    "    \"Microsoft JhengHei\",\n",
+    "    \"Arial Unicode MS\",\n",
+    "]  # 確保中文標題正確顯示\n",
+    "\n",
+    "# 圖一：原始語言分布圖\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "language_counts = df_brief[\"TMDb_raw_original_language\"].value_counts()\n",
+    "sns.barplot(x=language_counts.index, y=language_counts.values, palette=\"viridis\")\n",
+    "plt.title(\"71部爆款影集 - 原始語言分布 (Language Distribution)\", fontsize=14)\n",
+    "plt.xlabel(\"Original Language\", fontsize=12)\n",
+    "plt.ylabel(\"Number of Shows\", fontsize=12)\n",
+    "plt.show()\n",
+    "\n",
+    "# 圖二：總季數長條圖 (依季數由高到低排序，顯示前 25 名或全部)\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "df_seasons_sorted = df_brief.sort_values(\n",
+    "    by=\"TMDb_raw_number_of_seasons\", ascending=False\n",
+    ")\n",
+    "sns.barplot(\n",
+    "    data=df_seasons_sorted.head(30),  # 若想顯示全部 71 部，請移除 .head(30)\n",
+    "    x=\"netflix_title\",\n",
+    "    y=\"TMDb_raw_number_of_seasons\",\n",
+    "    palette=\"magma\",\n",
+    ")\n",
+    "plt.title(\"爆款影集 - 總季數長條圖 (Top 30 Shows by Seasons)\", fontsize=14)\n",
+    "plt.xlabel(\"影集名稱 (Series Title)\", fontsize=12)\n",
+    "plt.ylabel(\"總季數 (Number of Seasons)\", fontsize=12)\n",
+    "plt.xticks(rotation=75, ha=\"right\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 圖三：總集數長條圖 (依集數由高到低排序)\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "df_episodes_sorted = df_brief.sort_values(\n",
+    "    by=\"TMDb_raw_number_of_episodes\", ascending=False\n",
+    ")\n",
+    "sns.barplot(\n",
+    "    data=df_episodes_sorted.head(30),  # 若想顯示全部 71 部，請移除 .head(30)\n",
+    "    x=\"netflix_title\",\n",
+    "    y=\"TMDb_raw_number_of_episodes\",\n",
+    "    palette=\"crest\",\n",
+    ")\n",
+    "plt.title(\"爆款影集 - 總集數長條圖 (Top 30 Shows by Episodes)\", fontsize=14)\n",
+    "plt.xlabel(\"影集名稱 (Series Title)\", fontsize=12)\n",
+    "plt.ylabel(\"總集數 (Number of Episodes)\", fontsize=12)\n",
+    "plt.xticks(rotation=75, ha=\"right\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a21ae4e6",
+   "metadata": {},
+   "source": [
+    "## our analysis\n",
+    "？？"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cb9cffa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6db4648",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv (3.13.9)",
    "language": "python",
    "name": "python3"
   },
@@ -319,7 +14741,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,
diff --git a/netflix/data/seasons_episodes_dual_axis.png b/netflix/data/seasons_episodes_dual_axis.png
new file mode 100644
index 0000000..6c666ce
Binary files /dev/null and b/netflix/data/seasons_episodes_dual_axis.png differ
diff --git a/netflix_review_concepts_capstone.ipynb b/netflix_review_concepts_capstone.ipynb
index 0befc23..eb88898 100644
--- a/netflix_review_concepts_capstone.ipynb
+++ b/netflix_review_concepts_capstone.ipynb
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "adf81cc1",
    "metadata": {
     "execution": {
@@ -71,7 +71,32 @@
      "shell.execute_reply": "2026-07-05T18:13:27.727027Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((300, 11),\n",
+       "   netflix_title  netflix_viewing_hours  netflix_weeks  netflix_year_hint  \\\n",
+       " 0      Show 000             48840931.0             13               2021   \n",
+       " 1      Show 001             68780466.0              9               2023   \n",
+       " 2      Show 002             60617686.0              9               2025   \n",
+       " \n",
+       "   tmdb_title  tmdb_popularity  tmdb_vote_average  tmdb_vote_count imdb_title  \\\n",
+       " 0   Show 000             38.1                7.4           9181.0   Show 000   \n",
+       " 1   Show 001            286.2                7.8           7116.0   Show 001   \n",
+       " 2   Show 002            120.8                5.0           5253.0   Show 002   \n",
+       " \n",
+       "    imdb_averageRating  imdb_numVotes  \n",
+       " 0                 6.6       131664.0  \n",
+       " 1                 4.8        82446.0  \n",
+       " 2                 8.0        98769.0  )"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -128,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "39349f73",
    "metadata": {
     "execution": {
@@ -138,7 +163,64 @@
      "shell.execute_reply": "2026-07-05T18:13:27.742059Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>matched</th>\n",
+       "      <th>total</th>\n",
+       "      <th>coverage_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>tmdb</td>\n",
+       "      <td>243</td>\n",
+       "      <td>300</td>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>imdb</td>\n",
+       "      <td>266</td>\n",
+       "      <td>300</td>\n",
+       "      <td>88.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  source  matched  total  coverage_pct\n",
+       "0   tmdb      243    300          81.0\n",
+       "1   imdb      266    300          88.7"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def match_coverage(frame: pd.DataFrame, source_columns: dict[str, str]) -> pd.DataFrame:\n",
     "    \"\"\"Report what fraction of rows have a non-null match for each source.\n",
@@ -180,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "fa212650",
    "metadata": {
     "execution": {
@@ -190,7 +272,21 @@
      "shell.execute_reply": "2026-07-05T18:13:27.753115Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "is_hit\n",
+       "False    225\n",
+       "True      75\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def label_hits(frame: pd.DataFrame, column: str, quantile: float = 0.75) -> pd.DataFrame:\n",
     "    \"\"\"Add an `is_hit` column: True if `column` is at or above the given quantile.\n",
@@ -225,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "ed0ac180",
    "metadata": {
     "execution": {
@@ -235,7 +331,72 @@
      "shell.execute_reply": "2026-07-05T18:13:27.766286Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>netflix_title</th>\n",
+       "      <th>netflix_viewing_hours</th>\n",
+       "      <th>netflix_weeks</th>\n",
+       "      <th>binge_velocity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Show 000</td>\n",
+       "      <td>48840931.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3.756995e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Show 001</td>\n",
+       "      <td>68780466.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7.642274e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Show 002</td>\n",
+       "      <td>60617686.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6.735298e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  netflix_title  netflix_viewing_hours  netflix_weeks  binge_velocity\n",
+       "0      Show 000             48840931.0             13    3.756995e+06\n",
+       "1      Show 001             68780466.0              9    7.642274e+06\n",
+       "2      Show 002             60617686.0              9    6.735298e+06"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def binge_velocity(frame: pd.DataFrame,\n",
     "                    hours_col: str = \"netflix_viewing_hours\",\n",
@@ -270,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "a50796f0",
    "metadata": {
     "execution": {
@@ -280,7 +441,72 @@
      "shell.execute_reply": "2026-07-05T18:13:27.779462Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>netflix_title</th>\n",
+       "      <th>imdb_averageRating</th>\n",
+       "      <th>imdb_numVotes</th>\n",
+       "      <th>imdb_rating_shrunk</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Show 000</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>131664.0</td>\n",
+       "      <td>6.600282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Show 001</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>82446.0</td>\n",
+       "      <td>4.811299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Show 002</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>98769.0</td>\n",
+       "      <td>7.993323</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  netflix_title  imdb_averageRating  imdb_numVotes  imdb_rating_shrunk\n",
+       "0      Show 000                 6.6       131664.0            6.600282\n",
+       "1      Show 001                 4.8        82446.0            4.811299\n",
+       "2      Show 002                 8.0        98769.0            7.993323"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def bayesian_rating(frame: pd.DataFrame,\n",
     "                     rating_col: str,\n",
@@ -322,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "a7d6e719",
    "metadata": {
     "execution": {
@@ -332,7 +558,72 @@
      "shell.execute_reply": "2026-07-05T18:13:27.791018Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>netflix_title</th>\n",
+       "      <th>tmdb_vote_average</th>\n",
+       "      <th>imdb_averageRating</th>\n",
+       "      <th>audience_alignment_gap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Show 000</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>0.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Show 001</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Show 002</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  netflix_title  tmdb_vote_average  imdb_averageRating  audience_alignment_gap\n",
+       "0      Show 000                7.4                 6.6                     0.8\n",
+       "1      Show 001                7.8                 4.8                     3.0\n",
+       "2      Show 002                5.0                 8.0                     3.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def audience_alignment_gap(frame: pd.DataFrame,\n",
     "                            col_a: str = \"tmdb_vote_average\",\n",
@@ -362,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "8eb446da",
    "metadata": {
     "execution": {
@@ -372,7 +663,68 @@
      "shell.execute_reply": "2026-07-05T18:13:27.801813Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>netflix_title</th>\n",
+       "      <th>imdb_numVotes</th>\n",
+       "      <th>imdb_buzz_log</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Show 000</td>\n",
+       "      <td>131664.0</td>\n",
+       "      <td>11.788016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Show 001</td>\n",
+       "      <td>82446.0</td>\n",
+       "      <td>11.319911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Show 002</td>\n",
+       "      <td>98769.0</td>\n",
+       "      <td>11.500549</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  netflix_title  imdb_numVotes  imdb_buzz_log\n",
+       "0      Show 000       131664.0      11.788016\n",
+       "1      Show 001        82446.0      11.319911\n",
+       "2      Show 002        98769.0      11.500549"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def log_buzz(frame: pd.DataFrame, votes_col: str) -> pd.Series:\n",
     "    \"\"\"Log1p-transformed vote count -- a compressed 'attention volume' feature.\"\"\"\n",
@@ -398,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "35d36857",
    "metadata": {
     "execution": {
@@ -408,7 +760,131 @@
      "shell.execute_reply": "2026-07-05T18:13:27.822583Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>binge_velocity</th>\n",
+       "      <th>imdb_rating_shrunk</th>\n",
+       "      <th>audience_alignment_gap</th>\n",
+       "      <th>imdb_buzz_log</th>\n",
+       "      <th>tmdb_popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3.000000e+02</td>\n",
+       "      <td>266.000000</td>\n",
+       "      <td>216.000000</td>\n",
+       "      <td>266.000000</td>\n",
+       "      <td>243.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.542163e+06</td>\n",
+       "      <td>6.673530</td>\n",
+       "      <td>1.741204</td>\n",
+       "      <td>11.649581</td>\n",
+       "      <td>204.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.045585e+06</td>\n",
+       "      <td>1.480197</td>\n",
+       "      <td>1.170695</td>\n",
+       "      <td>1.027040</td>\n",
+       "      <td>112.786259</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.108027e+06</td>\n",
+       "      <td>4.014917</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.459904</td>\n",
+       "      <td>5.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.546625e+06</td>\n",
+       "      <td>5.403707</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>11.279534</td>\n",
+       "      <td>115.150000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.761116e+06</td>\n",
+       "      <td>6.699815</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>11.976276</td>\n",
+       "      <td>202.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>7.323639e+06</td>\n",
+       "      <td>7.897830</td>\n",
+       "      <td>2.525000</td>\n",
+       "      <td>12.361748</td>\n",
+       "      <td>295.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>8.977398e+06</td>\n",
+       "      <td>9.279912</td>\n",
+       "      <td>4.700000</td>\n",
+       "      <td>12.608537</td>\n",
+       "      <td>399.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       binge_velocity  imdb_rating_shrunk  audience_alignment_gap  \\\n",
+       "count    3.000000e+02          266.000000              216.000000   \n",
+       "mean     5.542163e+06            6.673530                1.741204   \n",
+       "std      2.045585e+06            1.480197                1.170695   \n",
+       "min      2.108027e+06            4.014917                0.000000   \n",
+       "25%      3.546625e+06            5.403707                0.700000   \n",
+       "50%      5.761116e+06            6.699815                1.600000   \n",
+       "75%      7.323639e+06            7.897830                2.525000   \n",
+       "max      8.977398e+06            9.279912                4.700000   \n",
+       "\n",
+       "       imdb_buzz_log  tmdb_popularity  \n",
+       "count     266.000000       243.000000  \n",
+       "mean       11.649581       204.333333  \n",
+       "std         1.027040       112.786259  \n",
+       "min         6.459904         5.300000  \n",
+       "25%        11.279534       115.150000  \n",
+       "50%        11.976276       202.000000  \n",
+       "75%        12.361748       295.250000  \n",
+       "max        12.608537       399.800000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def build_feature_matrix(raw: pd.DataFrame) -> pd.DataFrame:\n",
     "    \"\"\"Turn the raw composite dataset into a model-ready feature matrix.\n",
@@ -452,7 +928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "bdf7da34",
    "metadata": {
     "execution": {
@@ -462,7 +938,23 @@
      "shell.execute_reply": "2026-07-05T18:13:27.831615Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "binge_velocity            0.513524\n",
+       "tmdb_popularity           0.100953\n",
+       "audience_alignment_gap    0.022144\n",
+       "imdb_buzz_log             0.013298\n",
+       "imdb_rating_shrunk        0.005193\n",
+       "Name: is_hit, dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "numeric = features.drop(columns=[\"netflix_title\"]).astype(float)\n",
     "numeric.corr()[\"is_hit\"].drop(\"is_hit\").sort_values(key=abs, ascending=False)\n"
@@ -487,6 +979,11 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -497,7 +994,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,
diff --git a/netflix_review_concepts_module_2.ipynb b/netflix_review_concepts_module_2.ipynb
index 2fbf644..ef9aa3f 100644
--- a/netflix_review_concepts_module_2.ipynb
+++ b/netflix_review_concepts_module_2.ipynb
@@ -82,10 +82,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "9b5c1b02",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean = 14.62\n",
+      "variance = 16.48\n",
+      "standard deviation = 4.06\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -121,10 +131,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "b6d1e548",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "covariance(x, y) = 2.67\n"
+     ]
+    }
+   ],
    "source": [
     "x = np.array([1, 2, 3, 4, 5, 6])\n",
     "y = np.array([2, 4, 5, 4, 5, 8])\n",
@@ -158,10 +176,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "c2ae32aa",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "correlation(x, y) = 0.870\n"
+     ]
+    }
+   ],
    "source": [
     "corr_xy = np.corrcoef(x, y)[0, 1]\n",
     "print(f\"correlation(x, y) = {corr_xy:.3f}\")\n"
@@ -185,10 +211,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "056be789",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              x         y\n",
+      "count  6.000000  6.000000\n",
+      "mean   3.500000  4.666667\n",
+      "std    1.870829  1.966384\n",
+      "min    1.000000  2.000000\n",
+      "25%    2.250000  4.000000\n",
+      "50%    3.500000  4.500000\n",
+      "75%    4.750000  5.000000\n",
+      "max    6.000000  8.000000\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -211,10 +253,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "a28bca11",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "standardized mean = -0.0000, standardized std = 1.0000\n"
+     ]
+    }
+   ],
    "source": [
     "z = (x - np.mean(x)) / np.std(x)\n",
     "print(f\"standardized mean = {np.mean(z):.4f}, standardized std = {np.std(z):.4f}\")\n"
@@ -235,10 +285,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "1290e3dc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean without outlier: 3.50, with outlier: 74.43\n",
+      "std without outlier: 1.71, with outlier: 173.75\n"
+     ]
+    }
+   ],
    "source": [
     "x_with_outlier = np.append(x, 500)   # one extreme value added\n",
     "print(f\"mean without outlier: {np.mean(x):.2f}, with outlier: {np.mean(x_with_outlier):.2f}\")\n",
@@ -269,10 +328,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "3587eb92",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               hours_studied  sleep_hours  exam_score\n",
+      "hours_studied       1.000000     0.051028    0.931476\n",
+      "sleep_hours         0.051028     1.000000    0.244193\n",
+      "exam_score          0.931476     0.244193    1.000000\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -305,10 +375,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "7cf809c6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fitted slope (beta1) = 2.479\n",
+      "fitted intercept (beta0) = 2.280\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -340,10 +419,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "4e32d191",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scipy slope = 2.479, intercept = 2.280\n"
+     ]
+    }
+   ],
    "source": [
     "from scipy import stats\n",
     "\n",
@@ -369,10 +456,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "0880a93a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "matrix-form solution: [2.27997649 2.47915931]\n"
+     ]
+    }
+   ],
    "source": [
     "X = np.column_stack([np.ones(n), x])   # design matrix: intercept column + x column\n",
     "beta_matrix = np.linalg.inv(X.T @ X) @ X.T @ y\n",
@@ -393,10 +488,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "7db816ad",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "multiple regression coefficients: [ 1.38987151  2.67591663 -0.90786098]\n"
+     ]
+    }
+   ],
    "source": [
     "x2 = 0.5 * x + rng.normal(0, 2, n)\n",
     "y_multi = 2.5 * x - 1.2 * x2 + 3.0 + rng.normal(0, 4.0, n)\n",
@@ -424,10 +527,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "febd1a33",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sklearn coefficients: [ 2.67591663 -0.90786098], intercept: 1.3898715127159882\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
@@ -449,10 +560,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "8a8ab508",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "quadratic fit coefficients: [2.59075394 2.29461966 0.01856484]\n"
+     ]
+    }
+   ],
    "source": [
     "X_quadratic = np.column_stack([np.ones(n), x, x**2])\n",
     "beta_quadratic = np.linalg.inv(X_quadratic.T @ X_quadratic) @ X_quadratic.T @ y\n",
@@ -472,10 +591,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "d9463783",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fitted line: y = 0.05 + 1.99 * x\n",
+      "x=1: actual y=2.1, predicted y=2.04, residual=0.06\n",
+      "x=2: actual y=3.9, predicted y=4.03, residual=-0.13\n",
+      "x=3: actual y=6.2, predicted y=6.02, residual=0.18\n",
+      "x=4: actual y=7.8, predicted y=8.01, residual=-0.21\n",
+      "x=5: actual y=10.1, predicted y=10.00, residual=0.10\n"
+     ]
+    }
+   ],
    "source": [
     "x_small = np.array([1, 2, 3, 4, 5])\n",
     "y_small = np.array([2.1, 3.9, 6.2, 7.8, 10.1])\n",
@@ -504,10 +636,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "09be8c0f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean residual = 0.0000\n",
+      "residual std dev = 2.991\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -539,10 +680,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "c6e63e5a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE = 8.947\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import mean_squared_error, r2_score\n",
     "\n",
@@ -552,10 +701,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "ac6ac3e1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R^2 = 0.845\n"
+     ]
+    }
+   ],
    "source": [
     "r2 = r2_score(y, y_hat)\n",
     "print(f\"R^2 = {r2:.3f}\")\n"
@@ -575,10 +732,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "4760056e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE = 2.485  (compare to MSE = 8.947 and RMSE = 2.991)\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import mean_absolute_error\n",
     "\n",
@@ -601,10 +766,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "5e7a890c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "linear      MSE=   8.95  MAE=  2.49  R2=0.845\n",
+      "quadratic   MSE=   8.93  MAE=  2.47  R2=0.845\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
     "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
@@ -645,10 +819,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "2f0423ac",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "degree  1: train MSE =   63.38   test MSE =   51.30\n",
+      "degree  3: train MSE =   32.87   test MSE =   25.96\n",
+      "degree 11: train MSE =   25.21   test MSE =   28.86\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -693,10 +877,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "94b0a2de",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "final weights: [15.46108277  6.62889401]\n",
+      "loss after 1 iteration: 297.06, after 200: 14.07\n"
+     ]
+    }
+   ],
    "source": [
     "def gradient_descent_linreg(X, y, lr=0.1, n_iter=200):\n",
     "    n_samples, n_features = X.shape\n",
@@ -736,10 +929,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "841cf66d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "per-fold MSE: [312.48543783  65.85362782  78.76008329  58.15198595 142.48281536]\n",
+      "average MSE across folds: 131.547\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import cross_val_score\n",
     "from sklearn.linear_model import LinearRegression\n",
@@ -770,10 +972,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "f205f452",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loss surface shape: (60, 60)\n",
+      "minimum loss on this grid: 8.96\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -808,10 +1019,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "7acbecfd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "started at (-4, 4), converged near [-2.81  3.13]\n",
+      "started at (4, 4), converged near [3. 2.]\n",
+      "started at (-4, -4), converged near [-3.78 -3.28]\n",
+      "started at (4, -2), converged near [ 3.58 -1.85]\n"
+     ]
+    }
+   ],
    "source": [
     "def himmelblau(x, y):\n",
     "    return (x**2 + y - 11) ** 2 + (x + y**2 - 7) ** 2\n",
@@ -848,10 +1070,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "abc802ef",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dimension  2: fraction of critical points that are true local minima = 0.130\n",
+      "dimension  4: fraction of critical points that are true local minima = 0.000\n",
+      "dimension  8: fraction of critical points that are true local minima = 0.000\n",
+      "dimension 12: fraction of critical points that are true local minima = 0.000\n"
+     ]
+    }
+   ],
    "source": [
     "rng = np.random.default_rng(0)\n",
     "\n",
@@ -885,10 +1118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "6bd5372d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raw feature ranges: [5.82459511e-02 5.82459511e+01] to [   9.63670873 9636.70872845]\n",
+      "scaled feature means: [ 0. -0.], stds: [1. 1.]\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
@@ -916,10 +1158,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "f191b4ac",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "network output: [-0.05776259]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -966,10 +1216,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "523d4a52",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training MSE: 0.0326\n",
+      "number of weight matrices: 3\n",
+      "total trainable parameters: 321\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.neural_network import MLPRegressor\n",
     "from sklearn.metrics import mean_squared_error\n",
@@ -1024,10 +1284,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "db721357",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta = [2.97016008 2.44771001]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -1055,10 +1323,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "f4589338",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "slope = 2.448, intercept = 2.970, r-squared = 0.785\n"
+     ]
+    }
+   ],
    "source": [
     "from scipy import stats\n",
     "\n",
@@ -1077,10 +1353,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "b592cb3f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test MSE = 10.972\n",
+      "test R^2 = 0.869\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -1114,10 +1399,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "5a0b0a89",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -1143,10 +1439,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "29c4369e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fitted from a DataFrame: slope = 0.332\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -1172,10 +1476,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "a252d8c1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
    "source": [
     "rng_a = np.random.default_rng(7)\n",
     "rng_b = np.random.default_rng(7)\n",
@@ -1223,10 +1535,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "731031b5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(8, 8)\n",
+      "[[ 0.  0.  5. 13.  9.  1.  0.  0.]\n",
+      " [ 0.  0. 13. 15. 10. 15.  5.  0.]\n",
+      " [ 0.  3. 15.  2.  0. 11.  8.  0.]\n",
+      " [ 0.  4. 12.  0.  0.  8.  8.  0.]\n",
+      " [ 0.  5.  8.  0.  0.  9.  8.  0.]\n",
+      " [ 0.  4. 11.  0.  1. 12.  7.  0.]\n",
+      " [ 0.  2. 14.  5. 10. 12.  0.  0.]\n",
+      " [ 0.  0.  6. 13. 10.  0.  0.  0.]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from sklearn.datasets import load_digits\n",
@@ -1258,10 +1586,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "id": "d90b0bc2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test accuracy: 0.954\n",
+      "number of weights being fit: 640\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.linear_model import LogisticRegression\n",
@@ -1297,10 +1634,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "id": "b68500f9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "original shape: (8, 8), after kernel: (6, 6)\n",
+      "[[ 33.  27. -14.  -3.  -6. -27.]\n",
+      " [ 40.  10. -30.  17.  11. -34.]\n",
+      " [ 35. -10. -35.  26.  24. -28.]\n",
+      " [ 31. -13. -30.  29.  22. -29.]\n",
+      " [ 33.  -6. -22.  28.   4. -33.]\n",
+      " [ 31.  12. -10.   6. -14. -24.]]\n"
+     ]
+    }
+   ],
    "source": [
     "def apply_kernel(image, kernel):\n",
     "    '''A minimal, from-scratch 2D convolution -- no padding, unit stride.'''\n",
@@ -1346,7 +1697,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "venv (3.13.9.final.0)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
diff --git a/netflix_review_concepts_module_3.ipynb b/netflix_review_concepts_module_3.ipynb
index 30a8f4d..4ce2f92 100644
--- a/netflix_review_concepts_module_3.ipynb
+++ b/netflix_review_concepts_module_3.ipynb
@@ -66,10 +66,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "e2078a0b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 <class 'int'>\n",
+      "ten <class 'str'>\n"
+     ]
+    }
+   ],
    "source": [
     "count = 10          # count is bound to an int\n",
     "print(count, type(count))\n",
@@ -93,10 +102,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "009d6843",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'int'>\n",
+      "<class 'float'>\n",
+      "<class 'str'>\n",
+      "<class 'bool'>\n",
+      "<class 'NoneType'>\n"
+     ]
+    }
+   ],
    "source": [
     "integer_value = 42\n",
     "float_value = 3.14159\n",
@@ -128,10 +149,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "bdba4cda",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
    "source": [
     "print(isinstance(integer_value, int))     # True\n",
     "print(isinstance(True, int))              # True -- bool is a subclass of int in Python\n"
@@ -156,10 +186,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "f3bf3c3d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22\n",
+      "12\n",
+      "85\n",
+      "3.4\n",
+      "3\n",
+      "2\n",
+      "1419857\n"
+     ]
+    }
+   ],
    "source": [
     "a, b = 17, 5\n",
     "\n",
@@ -189,10 +233,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "a090c7f3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ada scored 97.5 points\n"
+     ]
+    }
+   ],
    "source": [
     "name = \"Ada\"\n",
     "score = 97.5\n",
@@ -216,10 +268,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "379f61b3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 2.  4.  6.  8. 10.]\n",
+      "float64\n",
+      "8\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -249,10 +311,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "68b2d897",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50\n"
+     ]
+    }
+   ],
    "source": [
     "count_str = \"42\"\n",
     "count_int = int(count_str)          # 42, as an actual integer\n",
@@ -281,10 +351,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "90f4a5da",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
    "source": [
     "total = 0\n",
     "total += 5      # equivalent to: total = total + 5\n",
@@ -316,10 +394,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "2ab0a30c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n",
+      "True\n"
+     ]
+    }
+   ],
    "source": [
     "age = 25\n",
     "print(0 <= age < 18)    # False -- equivalent to: 0 <= age and age < 18\n",
@@ -345,10 +432,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "67f80f5b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "It's mild\n"
+     ]
+    }
+   ],
    "source": [
     "temperature = 72\n",
     "\n",
@@ -375,10 +470,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "66c65bdd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no data to process\n"
+     ]
+    }
+   ],
    "source": [
     "data = []\n",
     "\n",
@@ -406,10 +509,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "11e86ad3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eligible to drive\n"
+     ]
+    }
+   ],
    "source": [
     "age = 25\n",
     "has_license = True\n",
@@ -440,10 +551,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "4a25b9d3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "odd\n"
+     ]
+    }
+   ],
    "source": [
     "x = 7\n",
     "label = \"even\" if x % 2 == 0 else \"odd\"\n",
@@ -467,10 +586,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "350228f6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "unrecognized label\n"
+     ]
+    }
+   ],
    "source": [
     "allowed_labels = [\"cat\", \"dog\", \"bird\"]\n",
     "label = \"fish\"\n",
@@ -496,10 +623,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "d555205a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "B-with-credit\n"
+     ]
+    }
+   ],
    "source": [
     "income = 55000\n",
     "has_dependents = True\n",
@@ -535,10 +670,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "1e9003eb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Not Found\n"
+     ]
+    }
+   ],
    "source": [
     "status_code = 404\n",
     "\n",
@@ -573,10 +716,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "e6b70671",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[False  True  True False  True  True]\n",
+      "[22 34 61 45]\n",
+      "   age name\n",
+      "1   22    B\n",
+      "2   34    C\n",
+      "4   61    E\n",
+      "5   45    F\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -605,10 +762,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "5cf49fac",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   age name\n",
+      "1   22    B\n",
+      "2   34    C\n"
+     ]
+    }
+   ],
    "source": [
     "young_adults = df[(df[\"age\"] >= 18) & (df[\"age\"] < 40)]\n",
     "print(young_adults)\n"
@@ -632,10 +799,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "ecfe7468",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "checking -5\n",
+      "False\n"
+     ]
+    }
+   ],
    "source": [
     "def is_valid(x):\n",
     "    print(f\"checking {x}\")\n",
@@ -665,10 +841,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "3ab83e7c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
    "source": [
     "scores = [82, 91, 76, 88]\n",
     "\n",
@@ -704,10 +889,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "adf89e4b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I like apple\n",
+      "I like banana\n",
+      "I like cherry\n"
+     ]
+    }
+   ],
    "source": [
     "fruits = [\"apple\", \"banana\", \"cherry\"]\n",
     "\n",
@@ -727,10 +922,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "a4290527",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "4\n",
+      "9\n",
+      "16\n"
+     ]
+    }
+   ],
    "source": [
     "for i in range(5):        # 0, 1, 2, 3, 4\n",
     "    print(i ** 2)\n"
@@ -751,10 +958,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "b498e0b0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "took 7 steps to shrink below 1\n"
+     ]
+    }
+   ],
    "source": [
     "value = 100\n",
     "steps = 0\n",
@@ -782,10 +997,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "ba170725",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "2\n"
+     ]
+    }
+   ],
    "source": [
     "numbers = [4, 7, 2, 9, 3, 6]\n",
     "\n",
@@ -811,10 +1035,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "8240b1ec",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0: apple\n",
+      "1: banana\n",
+      "2: cherry\n"
+     ]
+    }
+   ],
    "source": [
     "for index, fruit in enumerate(fruits):\n",
     "    print(f\"{index}: {fruit}\")\n"
@@ -831,10 +1065,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "c749777e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ada: 97\n",
+      "Grace: 88\n",
+      "Alan: 91\n"
+     ]
+    }
+   ],
    "source": [
     "names = [\"Ada\", \"Grace\", \"Alan\"]\n",
     "scores = [97, 88, 91]\n",
@@ -857,10 +1101,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "39ac10da",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n",
+      "[0, 4, 16, 36, 64]\n"
+     ]
+    }
+   ],
    "source": [
     "squares = [n ** 2 for n in range(10)]\n",
     "even_squares = [n ** 2 for n in range(10) if n % 2 == 0]\n",
@@ -885,10 +1138,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "fcb73c2f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Ada': 3, 'Grace': 5, 'Alan': 4}\n",
+      "{'G', 'A'}\n"
+     ]
+    }
+   ],
    "source": [
     "names = [\"Ada\", \"Grace\", \"Alan\"]\n",
     "name_lengths = {name: len(name) for name in names}\n",
@@ -912,10 +1174,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "0e792bc5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A1\n",
+      "A2\n",
+      "A3\n",
+      "B1\n",
+      "B2\n",
+      "B3\n"
+     ]
+    }
+   ],
    "source": [
     "rows = [\"A\", \"B\"]\n",
     "columns = [1, 2, 3]\n",
@@ -946,10 +1221,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "75807222",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A1\n",
+      "A2\n",
+      "A3\n",
+      "B1\n",
+      "B2\n",
+      "B3\n"
+     ]
+    }
+   ],
    "source": [
     "from itertools import product\n",
     "\n",
@@ -971,10 +1259,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "141f0fcf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pure Python: 0.0157 seconds\n"
+     ]
+    }
+   ],
    "source": [
     "import time\n",
     "\n",
@@ -996,10 +1292,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "a11d2e90",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NumPy vectorized: 0.0007 seconds\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -1034,10 +1338,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "7a080f49",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1\n",
+      "1 2\n",
+      "2 3\n",
+      "3 4\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -1084,10 +1399,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "75c02c3d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, Ada!\n"
+     ]
+    }
+   ],
    "source": [
     "def greet(name):\n",
     "    return f\"Hello, {name}!\"\n",
@@ -1109,10 +1432,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "9d2982d1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10.0\n",
+      "20.0\n",
+      "25.0\n"
+     ]
+    }
+   ],
    "source": [
     "def scale_value(x, factor=1.0, offset=0.0):\n",
     "    return x * factor + offset\n",
@@ -1142,10 +1475,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "id": "af9c9a5a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "values: (1, 2, 3)\n",
+      "labels: {'unit': 'meters', 'precision': 2}\n"
+     ]
+    }
+   ],
    "source": [
     "def summarize(*values, **labels):\n",
     "    print(f\"values: {values}\")   # a tuple of all positional arguments\n",
@@ -1173,10 +1515,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "id": "e4ed9a91",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function z_score in module __main__:\n",
+      "\n",
+      "z_score(x, mean, std)\n",
+      "    Standardize a value using the given mean and standard deviation.\n",
+      "\n",
+      "    Returns (x - mean) / std.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "def z_score(x, mean, std):\n",
     "    '''Standardize a value using the given mean and standard deviation.\n",
@@ -1205,10 +1561,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "id": "b471d07d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "100\n"
+     ]
+    }
+   ],
    "source": [
     "total = 100\n",
     "\n",
@@ -1237,10 +1602,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "5af99f8a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "150\n"
+     ]
+    }
+   ],
    "source": [
     "total = 100\n",
     "\n",
@@ -1273,10 +1646,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "9c370986",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.5\n"
+     ]
+    }
+   ],
    "source": [
     "def z_score(x: float, mean: float, std: float) -> float:\n",
     "    return (x - mean) / std\n",
@@ -1302,10 +1683,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "b7c9d465",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "120\n"
+     ]
+    }
+   ],
    "source": [
     "def factorial(n):\n",
     "    if n <= 1:\n",
@@ -1335,10 +1724,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "df0adf7b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['a']\n",
+      "['a', 'b']\n"
+     ]
+    }
+   ],
    "source": [
     "def add_item(item, collection=[]):     # BUG: the same list is reused every call\n",
     "    collection.append(item)\n",
@@ -1360,10 +1758,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "2399988c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['a']\n",
+      "['b']\n"
+     ]
+    }
+   ],
    "source": [
     "def add_item_fixed(item, collection=None):\n",
     "    if collection is None:\n",
@@ -1393,10 +1800,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "6fb93ebd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25\n"
+     ]
+    }
+   ],
    "source": [
     "square = lambda x: x ** 2\n",
     "print(square(5))   # 25\n"
@@ -1418,10 +1833,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "id": "af2915f7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   celsius  fahrenheit\n",
+      "0        0        32.0\n",
+      "1       20        68.0\n",
+      "2       37        98.6\n",
+      "3      100       212.0\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -1448,10 +1875,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "id": "5dd1930b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   celsius  fahrenheit  fahrenheit_lambda\n",
+      "0        0        32.0               32.0\n",
+      "1       20        68.0               68.0\n",
+      "2       37        98.6               98.6\n",
+      "3      100       212.0              212.0\n"
+     ]
+    }
+   ],
    "source": [
     "df[\"fahrenheit_lambda\"] = df[\"celsius\"].apply(lambda c: c * 9 / 5 + 32)\n",
     "print(df)\n"
@@ -1484,10 +1923,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "id": "dccac4d7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "88\n",
+      "[92, 78]\n",
+      "[87, 92, 78, 90, 88]\n"
+     ]
+    }
+   ],
    "source": [
     "scores = [85, 92, 78, 90]\n",
     "\n",
@@ -1516,10 +1965,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "id": "2b42e6c4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3 4\n"
+     ]
+    }
+   ],
    "source": [
     "point = (3, 4)\n",
     "x, y = point                # unpacking: assigns 3 to x and 4 to y in one line\n",
@@ -1540,10 +1997,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "id": "84c23529",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 9\n"
+     ]
+    }
+   ],
    "source": [
     "def min_and_max(values):\n",
     "    return min(values), max(values)   # returns a tuple\n",
@@ -1566,10 +2031,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "id": "fca4db19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ada\n",
+      "name: Ada\n",
+      "age: 29\n",
+      "major: Computer Science\n",
+      "gpa: 3.9\n"
+     ]
+    }
+   ],
    "source": [
     "student = {\"name\": \"Ada\", \"age\": 28, \"major\": \"Computer Science\"}\n",
     "\n",
@@ -1600,10 +2077,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "id": "ea23893d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'ml', 'python', 'stats', 'data'}\n",
+      "{'ml', 'python'}\n",
+      "{'data'}\n",
+      "True\n"
+     ]
+    }
+   ],
    "source": [
     "tags_a = {\"python\", \"data\", \"ml\"}\n",
     "tags_b = {\"ml\", \"stats\", \"python\"}\n",
@@ -1637,10 +2125,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "id": "37f1a759",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alan 95\n",
+      "Ada 91\n",
+      "Grace 88\n"
+     ]
+    }
+   ],
    "source": [
     "students = [\n",
     "    {\"name\": \"Ada\", \"score\": 91},\n",
@@ -1677,10 +2175,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "id": "5d7cc80e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ada: 91.3\n",
+      "Grace: 87.7\n"
+     ]
+    }
+   ],
    "source": [
     "course = {\n",
     "    \"name\": \"Data Science Intensive\",\n",
@@ -1713,10 +2220,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "id": "93702267",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4]\n"
+     ]
+    }
+   ],
    "source": [
     "original = [1, 2, 3]\n",
     "alias = original          # alias is NOT a copy -- it's the same list\n",
@@ -1746,10 +2261,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "id": "fc4a78c6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    name  age  score\n",
+      "0    Ada   28     91\n",
+      "1  Grace   34     88\n",
+      "2   Alan   41     95\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -1776,10 +2302,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "id": "07839abf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18.0\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -1800,10 +2334,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "id": "2b4e1126",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ridge(random_state=0)\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import Ridge\n",
     "\n",
@@ -1840,10 +2382,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "id": "49729fc0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data science\n",
+      "DATA SCIENCE\n",
+      "['Data', 'Science']\n",
+      "Computer Science\n",
+      "Data\n",
+      "12\n"
+     ]
+    }
+   ],
    "source": [
     "text = \"Data Science\"\n",
     "\n",
@@ -1869,10 +2424,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "id": "252ffead",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'raw input line'\n",
+      "Data-Science-Intensive\n"
+     ]
+    }
+   ],
    "source": [
     "messy = \"   raw input line \\n\"\n",
     "clean = messy.strip()\n",
@@ -1897,10 +2461,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "id": "641c0c22",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model_v2 achieved 91.37% accuracy\n"
+     ]
+    }
+   ],
    "source": [
     "name = \"model_v2\"\n",
     "accuracy = 0.9137\n",
@@ -1927,10 +2499,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "id": "fccff7ec",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first line\n",
+      "second line\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "with open(\"notes.txt\", \"w\") as f:\n",
     "    f.write(\"first line\\n\")\n",
@@ -1962,10 +2544,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "5ae79d92",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first line\n",
+      "second line\n"
+     ]
+    }
+   ],
    "source": [
     "with open(\"notes.txt\", \"r\") as f:\n",
     "    for line in f:\n",
@@ -1991,10 +2582,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "id": "137fae52",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scores.csv\n",
+      "False\n",
+      ".csv\n"
+     ]
+    }
+   ],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -2024,7 +2625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "id": "e8c31160",
    "metadata": {},
    "outputs": [],
@@ -2047,10 +2648,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "id": "610ddd99",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['name', 'age', 'score']\n",
+      "[['Ada', '28', '91'], ['Grace', '34', '88'], ['Alan', '41', '95']]\n"
+     ]
+    }
+   ],
    "source": [
     "with open(\"scores.csv\", \"r\") as f:\n",
     "    lines = f.read().strip().split(\"\\n\")\n",
@@ -2076,10 +2686,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "id": "22e9cdec",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    name  age  score\n",
+      "0    Ada   28     91\n",
+      "1  Grace   34     88\n",
+      "2   Alan   41     95\n",
+      "name     object\n",
+      "age       int64\n",
+      "score     int64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -2103,10 +2728,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "id": "095da3c6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.0\n",
+      "cannot divide by zero, returning None instead\n",
+      "None\n"
+     ]
+    }
+   ],
    "source": [
     "def safe_divide(a, b):\n",
     "    try:\n",
@@ -2137,10 +2772,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "id": "12d651dd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "file not found: does_not_exist.txt\n",
+      "finished attempting to load the file\n",
+      "file did not contain a valid integer\n",
+      "finished attempting to load the file\n",
+      "finished attempting to load the file\n",
+      "42\n"
+     ]
+    }
+   ],
    "source": [
     "def load_and_parse(path):\n",
     "    try:\n",
@@ -2186,10 +2834,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "id": "8aab9b9c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "caught an error: values list cannot be empty\n"
+     ]
+    }
+   ],
    "source": [
     "def load_scores(values):\n",
     "    if not values:\n",
@@ -2217,10 +2873,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "id": "ecc12c20",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "caught a custom error: need at least 5 values, got 2\n"
+     ]
+    }
+   ],
    "source": [
     "class InsufficientDataError(Exception):\n",
     "    '''Raised when there isn't enough data to compute a reliable result.'''\n",
@@ -2269,10 +2933,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "id": "baef211a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.0\n",
+      "3.141592653589793\n"
+     ]
+    }
+   ],
    "source": [
     "import math\n",
     "\n",
@@ -2292,10 +2965,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "id": "6e16fb9b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.0\n"
+     ]
+    }
+   ],
    "source": [
     "from math import sqrt, pi\n",
     "\n",
@@ -2314,10 +2995,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "id": "b38cd226",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "all three imported with their standard aliases\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -2408,10 +3097,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "id": "57c090b2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running the main program\n",
+      "in this notebook, __name__ is: '__main__'\n"
+     ]
+    }
+   ],
    "source": [
     "def main():\n",
     "    print(\"running the main program\")\n",
@@ -2456,10 +3154,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "id": "49a81c54",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.4.6\n",
+      "1.9.0\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import sklearn\n",
@@ -2482,10 +3189,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "id": "1ee0e137",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlcelbwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAROJJREFUeJzt3QmcVfP/x/H3zLRv027aVJKoJEmUJSJGicQfJSISaVOS7FGytQlZUkmLXSlKskS0qCR+IVJJizZmKjUtc/+PzznumJlmuTNz566v5+Mxj3HOPTP3uMbc93yXzyfG4/F4BAAAECCxgXoiAAAAwgcAAAg4Rj4AAEBAET4AAEBAET4AAEBAET4AAEBAET4AAEBAET4AAEBAFVEISk1N1ZYtW1S2bFnFxMQE+3YAAIAPrG7pnj17VL16dcXGxoZX+LDgUatWrWDfBgAAyIdNmzapZs2a4RU+bMTDe/PlypUL9u0AAAAfJCcnO4MH3vfxsAof3qkWCx6EDwAAwktuSyZYcAoAAAKK8AEAAAKK8AEAAAKK8AEAAAKK8AEAAAKK8AEAAAKK8AEAAAKK8AEAAAKK8AEAAAIqJCucAgCAvDmS6tGy9bu1fc8BVS1bQi3qVlRcbGg2ZyV8AAAQ5ub9sFVDZ6/R1qQDaeeqxZfQQx0aKrFxtZALKIQPAADCPHjcPnWlPJnOb0s64Jwf37WZE0B8DSiBQPgAACDEHclmxMLOW6DIHDyMnbMxDXs8NVW6Y3ruASVQCB8AAISweTmMWMSXLJbhfGYWNuzx+2f9kGtAadswIWBTMOx2AQAgxKdUtmYKGN4RiwVrtvn0fXbvO5hrQLGRlUAhfAAAEIKO5DKlYt5btdlvz2dTOoFC+AAAIAQtW7871ymV3fsOqWLpYs7USVbsfMXSRX16PltLEiiEDwAAQtB2H0ciOjat7nzOHEC8x8Mub+ysEckpoNjjtog1UAgfAACEoKo+jkTYQlHbrZIQn/F6O7bz7ZpUdxan5hRQ7PFA1vtgtwsAACGoRd2KzoiELS7Nat1HzL8Bw7vt1kJIdgXEbButBZHMu2YSglTnI8bj8WT17xRUycnJio+PV1JSksqVKxfs2wEAIKi7XUz6N2vvGEVe63MUdoVTX9+/CR8AAISweSFUmdRf4YNpFwAAQlhi42o5TqnkmU14/P67VLu2goXwAQBAiIuLjVHLepUK/o127ZJuuUVatEhavVqqFpyRE3a7AAAQDT77TGrSRJo5U0pKkr7+Omi3QvgAACCSHTok3XefdMEF0pYt0gknSEuWSFdeGbRbYtoFAIAgOlKYO1B++03q0kVautQ9vvlmacwYqUwZBRPhAwCASNzJMm2adPvt0p49Uny89NJL0tVXKxQw7QIAQAh2rJ33w9b8fePkZOmGG6SuXd3gcdZZ0nffhUzwMIQPAABCsGPt0NlrnOvyZNky6dRTpddek2JjpYcflj7/PKjbags87XLo0CG9/vrrWrhwofPPp59+um655RaVKJGxnvxvv/2mZ599Vhs3blT9+vXVv39/JSQk+PveAQCI2I61W5MOONf5tMU2NVV68knpgQekw4elY4+Vpk93Rz1CUJ5GPlq1aqUFCxbozDPP1HnnnacXX3xRZ599tg4c+O8FXLdunZo3b64tW7aoY8eO+u6775zjHTt2FMb9AwAQsR1rt/ty3ebNUtu20pAhbvCw6RWbZgnR4JHnkY85c+bomGOOSTu+6KKLVLNmTX344Yfq1KmTc+6RRx7RcccdpxkzZigmJkbXXHONjj/+eI0cOVKPP/64//8NAACI0I61VXO77v33pe7d3eJhpUpJ48ZJN90kxQSuQ22hj3ykDx6mcuXKKlq0qFPL3WvevHm64oornOBhihUrpg4dOjjnAQCA0jrWZhcR7Lw9btdlaf9+6Y47pMsvd4NHs2bSypVuEAnx4FHgBafjxo1TbGysLrDCJZL++ecfbd++3RkNSa9WrVpav359tt8nJSXFCTDpPwAAiFRxsTHOdlqTOSp4j+3xLOt9/PCD1KKF9Pzz7vHAgW610gYNFC7yHT4+/vhj3XvvvRo1apQTLszBgwedz6Vs6CcdO/Y+lpURI0Y4XfC8H97vBwBApEpsXE3juzZTQnzGqRU7tvNH1fmwhnAWOE4/3Q0gNhthswpPPy0VL65wkq8iY7bbxRaTPvTQQ+rVq1fa+TJlyiguLk67d+/OcP2uXbtUvnz5bL/fkCFDNGDAgLRjG/kggAAAIl2irx1rd+50G8LNmuUeX3KJNHmyVLWqwlGew8cXX3yh9u3b65577tF9Vis+/TcrUkSNGjVydrikt2rVKp1yyinZfs/ixYs7HwAARJu43DrWfvqpdP31bl+WYsXcLbV9+4bF2g6/TLssWrRI7dq10+DBg/WA7SXOwg033KA333xTGzZscI4tiHz00UfOeQAAkIeGcLZ99sIL3eBx4oluEbF+/cI6eJgYj8cmkXxTqVIlZ+1G69atM5zv0qWL82Gs+FjXrl01d+5cnXzyyc6oR7du3fTcc8+l7YDJjU272NqPpKQklStXLq//TgAAhLd169yGcBY2zK23SqNGSaVLK5T5+v6dp2mXqVOn6siRI0edP8Ha8/7Ltt6+8cYb+vnnn/X77787NT7q1q2b1/sHACA6TZ0q2XpK68ti6yUnTJCuvFKRJE8jH4HCyAcAIOokJ7uhw7rRmnPPdYNIGO0A9fX9m8ZyAAAE29KlbkM4Cx5xcVYu3F1oGkbBo9C32gIAAD84ckR64gnpwQfdf7bus9YQrlWriH55CR8AAATD5s3uFtrPPnOPr7lGeuEFd51HhGPaBQCAQJs5U2rSxA0etoNl4kRpxoyoCB6GkQ8AAALFGsINGOCOcJjTTnOnWdLtGo0GjHwAABAI338vNW/+X/C46y63IVyUBQ/DyAcAAIXJKlo895wbNlJS3IZwU6ZIF10Uta874QMAgMJiDeG6d5dmz3aP27WTJk0K24Zw/sK0CwAAhWHBAndRqQUPawg3dqw0Z07UBw/CBwAA/nbwoDR4sDutsnWrdNJJbo+WMO9E609MuwAA4C+//ip17iwtX+4e9+zpNoQrVYrXOB3CBwAgahxJ9WjZ+t3avueAqpYtoRZ1KyouNsY/i0ptEWnv3tLevVKFCm5DuE6d/HHbEYfwAQCICvN+2Kqhs9doa9KBtHPV4kvooQ4Nldi4Wv6/cVKSdPvtbpEw07q12xCuZk0/3HVkYsEpACAqgsftU1dmCB5mW9IB57w9ni9LlkhNm7rBwxrCDRsmffIJwSMXhA8AQMRPtdiIhyeLx7zn7HG7znv94nW7NGvVZuez93zGb3pEGj5cOvtsacMGqU4d6csvpfvuc0MIcsS0CwAgotdz2LnMIx7pWbSwx+26pP0Hc5+a2bRJnuuvV8zChc7hjsuuVMXJExRXITr6svgD4QMAENHrOVIOp/r0PT5es02Tvtpw1AiJd2pmfNdmSvxliQ7e2F3Fkv/W3mIl9WDb2/TuiW1U7YUVBV87EkWYdgEARPR6jg079/n0fWau2pLt1EyJQwe0/+Zbnd0rFjxWVauv9jeO1buNL3BqdxR47UiUYeQDABCx6zlsE+2MZb8roVwJ/Zl8IMvr7JoKpYtq976DWT7HSdt/0zPvP6X6uzYpNSZGL7a4UqPOuU6H4ooe9Vx2L20bJjjTPYW2rTcCED4AAGHLl/Uc25JTdOeFJ2jMgrVOQEgfQLxR4IqmNfTKVxsyfbFHN66YrSGfT1LxI4f0Z5mKurP9AH1dp6l/1o5EMaZdAABhy0YVfFGncilnzUZCfIkM5+3Yzl/YMCHD+Yr/JOmVdx7Rw5+85ASPj49vocSbxmUbPDKvHSmUbb0RhJEPAEDYsukMX69rWa+SMyWS1VSITZHYyIQFhLPWf6tRH4xS1X1/KSWuqIa1uVlTT22vCmWKSfsOFWjtSOapmWhF+AAAhC0LD97QkN16DhvdsOuMveFbCMnMzj+ceLw23jZAty571zm3ttKx6nP53VpbpY5zPOzyxnr0gx9zfK6c1o5knpppmcV9RAumXQAAYctCg62jMJnHEbzH9niuowxr1+riHlemBY/XTm2nDt1G6+cqddKmZto1qZ7rc9naEX9OF0UqRj4AAGHNFnBaOMi8wDPBlwWe1hBu8mSpTx9p3z6pYkUdmfCKjm9yjp7MYpdKbs8VX7LY0QtXCzBdFKkIHwCAsGehILv1HNn6+2/pttukN95wj88/X3rtNcXVqKGW+Xyu9GtHfJkGilaEDwBARMhuPUeWvv5a6tJF2rjR7cXy6KPS3Xf73Jclp7UjNgJiu1qy29b7kC/TQBGONR8AgOhhDeEsaJx7rhs86taVvvpKGjLEbw3hvFMz2W3rTaTOByMfAIDgCWgV0N9/l7p2dbvPmuuuk55/XipXLjSmgaII0y4AgJBrBuf30YG335Z69HDXeZQp44aO669XyEwDRRmmXQAAIdcMzm9VQG0Hy623Sv/3f27waNFCWrWq0IMHckb4AACEVDM4Y4/bdQViIaN5c+nll53Os7rnHmnRIqlevYJ9XxQY4QMAEHLN4LxVQPPFaneMGSOdcYb0009StWrSggXSiBFS0f860SJ4WPMBAAgoX6t75qsK6J9/SjfdJM2d6x5fdpn0yitS5cp5/14oNIx8AABCthlcnnz0kXTKKW7wKFFCeu45aeZMgkcIInwAAILSDC67Tad2vlpeqoCmpEgDB0qJie7IR6NG0jffSL16uWs9EHIIHwCA8GwGZ37+WWrZUho1yj2+4w43eDRu7Oe7hj8RPgAAAVfgKqC2qNTWcjRrJn37rVSpkjRrlvTss1LJkoV78ygwFpwCAIIi31VArV5Hz57Sm2+6x23aSFOmSDV8a2eP4CN8AACCJs9VQK0PizWEs1LpRYpIw4ZJgwZJsQzkhxPCBwAg9B0+LA0fLj3yiJSa6hYKmz7drViKsEP4AACENus+aw3hrDqpueEGd21H2bLBvjPkE+NUAIDQ9dZbbu0OCx4WNqZNk159leAR5hj5AACEHmsI16+fu6PFWKl0m2Y57rhg3xn8gJEPAEBoWbnS3UJrwcOKhN13n/TllwSPCMLIBwCgUFhX2jxto7WFpNYQzrrPHjrkbp2dOlU67zz+C0UYwgcAwO/m/bBVQ2evydC91kqmW+XSLAuIWVn0bt3c/iymY0dpwgS3eBgiDtMuAAC/B4/bp67MEDzMtqQDznl7POMXzJOaNHGDhzWEGz9eevddgkcEI3wAAPw61WIjHp4sHvOes8ftOqch3J13SpdcIm3fLp18srR8uXTbbTSEi3CEDwCA39gaj8wjHpkDiD2+esFS6cwz3TUepndvadkytyMtIh5rPgAAfmOLS3Pk8eia1fN18tgJ0oH9UuXK0qRJ0qWX8l8hihA+AAB+Y7taslPuwF6NmDdO7X/+yj1x4YVuQ7hquXSwRcRh2gUA4De2ndZ2tWTeUHv6ph80d2IfJ3gcjo1T6hNPuAtMCR5RifABAPAbq+Nh22mNBZC41CO688tpen3GvaqxZ4fWV6imZdPnKPbuu+lEG8UIHwAAv7I6HuO7NlPT1CS9Pn2I+n09Q3GeVM1pdrF+nbtQra5J5BWPcqz5AAD4XeL/vtDFL/VSTFKSDpUpq/XDRuqSPrfkXOEUUYPwAQDwn717pb59nR0sTsxo2VJFp03TCXXr8iojDdMuAAD/WLHCbQhnW2etIdz990tffCERPJAJIx8AgIKxhnCjRkn33us2hKtZ020I17o1ryyyRPgAAOTftm1uQ7j5893jTp2kl1+WKlbkVUW2mHYBAOTPhx+6DeEseJQsKb34ovT22wQP5IqRDwBA3hw4IA0eLD3zjHtsAWTGDKmhW98DyA0jHwAA3/34o9sQzhs8bGfL0qUED+QJIx8AgNx5PO5ajv79pf37pSpV3F0t7dvz6iHPCB8AgJzt3i316CG9+6573Lat9OqrAevLciTVo2Xrdzsdc61xnfWPoVhZlIWPb7/9VuPHj9fKlSv16KOP6pJLLsnw+MSJE/X8889nOFe2bFl99tlnBb9bAEBgLVwode0q/fGHVLSo9Nhj0oABOqIYLVu3q9ADwbwftmro7DXamnQg7Zw1rrP+MVbGHVEQPp577jlNmDBBPXv21Msvv6xdu3Yddc2WLVuUkpKiSTYc532SIgywAEBYOXxYGjrUDRtWx6N+fXdR6WmnBSwQ2PPcPnWlPJnOb0s64Jy3/jEEkChYcHrDDTc4Ix+33XZbjteVLl1azZs3T/to2rRpQe8TABAoGzZI554rDRvmBo+bbpJWrkwLHvbGnz54pA8E9ri/plos4GQOHsZ7zh636xDh4cOmT3zx66+/6vzzz3emZB566CElJyfn9/4AAIH0+uvSKadIixdL5cq5ox0TJ0plygQ0ENgaj8wBJ/Pz2eN2HcKP3+dDihYtqm7duikxMVF///23RowYoenTpzsjJmXKlMnya2yaxj68CCsAEGB79rjbZidPdo9btpSmT5fq1MlXIGhZr1KBbsfWkvjzOkR4+LjzzjtVrFixtOM2bdro+OOPdxapDho0KMuvsYAy1OYWAQCB3zWyfLnUubMNW0uxsW5DuAcesAV7QQsEdn/+vA4RHj7SBw9TqVIlnXLKKVq9enW2XzNkyBANGDAgw8hHrVq1/H1rABC1slokWr1sMU3atVANxj3hLjC137vWEM7WewQ5EFgwskWstpYkq0kc21eTEO8GKISfgFQ43bp1a7ZTLqZ48eIqV65chg8AgH9ktUi0yt7devLlu9Vg9HA3eFx5pfTdd9kGj/SBILsNtXa+mp8CgY3I2O4Z7/fN/DzGHqfeR3jye/gYPnx42poNj8ejxx9/XL/88ouuvfZafz8VACAXWS0SbfPrMs2b2Ftnb1yl/UWK67ErBujIG29KFSqEVCCwbbS2ndZGONKzY7bZRtG0y7Jly9SrV6+04wcffFBjxoxRx44ddb/NEUoqX768GjRo4Ixe7N69WyVKlNCMGTPUunVr/989ACDn39vpFokWP3xQ93w+STetmO0c/6/qcep72SCtq1RL52/4y6dFot5AkHkKJ6GQCn/Z92vbMIEKpxEmxmPDEz7as2ePfv7556POV6lSRbVr1047Tk1N1W+//aZSpUqpWrVqionJWwq2kZP4+HglJSUxBQMABTBr1Wb1e32Vjt/5u8a9/6RO2rHBOf9K88v1ROsbdbBIUed47LVNdXnTGj5/X0qeoyDv30XyWufDioblJjY21tnhAgAIrqpliuu6bz/UA59OUInDB7WjVHkNatdfn9drXqBFoja1UtDttIhe1D0HgEi1a5fOvKuHWs6f6RwurNtMA9vfqZ2l/1vbwa4RBAPhAwAi0eefOw3hYjZvVmqRonrsnBs08fTLlRrz3z4Ddo0gorfaAgAKl63BWLxul97/ZoP+6DVAnjZtpM2bpQYNFLtsqZo/86iOKV8qw9ewawTBwsgHAIS43BZ3eguIFdm4Qc+8/5RqbnU3Bmzq1Fm1prxs3T6VKPm8a4TFpChshA8ACGG5ta/3FhDrsOZzDf/oOZU9uF/JxUvr3ot764P652j8+mQlNi7t8yLR3J4PCPhW20Bhqy0A/FeZNPMvae9YxXNdTtXId1eo19ujdeX/PnPOfVOjofp3uEub46umLSZdNLiNT4W/cns+CnvBX+/frPkAgBDkS/v66c+/o1fG9nSCx5GYWI0+q4uu7TLCCR55bTvvy/PZ43YdUFBMuwBACMqpfX2MJ1U9l76rgV++pqKpR/RHuSrOaMfymo3y3WU2p+fLHGSo74GCInwAQAjKLjBU3bNLoz8YqbM2up3C5zQ4W/cm9lZyieybd/pSQMyXgJKX64CcED4AIARlFRgu+HWpnvpwrCruT9Y/RYvroQt76pMz22vPP4ey/B55KSDma4XTvFZCBbLCmg8ACEHp29cXP5SioR+P1yvvPOoEjx+OqacO3cZq0TmXaVjHk/3SZTb982XFzlfzMcgAuSF8AEAI8ravP2HHBs2aMkDdVn7gnH/59I66suvT+q1STefxdk3803be+3z+CDJAbthqCwChyKogjB+vIwMGKi7lgNMQ7q72d2rhcadlWXfDX4XBqPOBQGy1JXwAQKjZuVO65RZp1izn0JN4iVY8PFKbi5crULDwFRVOUdjhgwWnABBKPv1Uuv56acsWqVgx6YknFNO3r5rHxqp5gG7Bl0qoQEEQPgAgFBw6JD34oBM2nCmXBg2kGTOkU08N9p0Bfkf4AIBgW7dO6tJFWrbMPe7RQxo92mkIB0QidrsAQDBNneqObljwKF9eeust6aWXCB6IaIx8AEAwJCdLvXpJ06a5x+ec4waRY4/lvwciHiMfABBoS5e6ox0WPOLipEcekT77jOCBqMHIBwAEypEj0pNPugtLDx+WatfWkanTtKzaidr+/baAbKMFQgHhAwACYfNmdwutjXCYa67Rgr4P64GFm7U1aUnaZVkVEAMiDdMuAFDYrFhYkyZu8LAdLJMmad59o9Tj/XVHtbHflnRAt09d6VQaBSIV4QMACsv+/e6i0o4dpd27pWbNpJUrdeSGbho650d5svgS77mhs9c4lUaBSET4AIDC8MMP0umnO/1ZHHfdJS1eLJ1wgtODJfOIR3oWOexxuw6IRKz5AAB/suqkzz3nho2UFOmYY6QpU6SLLkq7xJq/+cLX64BwQ/gAAH82hOveXZo92z1u316aOFGqWjXDZbarxRe+XgeEG6ZdAMBfDeFsUakFD2sIN3asjsx6X4v3xGnWqs1avG5X2hoO205ru1qy21Br5+1xuw6IRIx8AEBBHDzo1u2w+h025XLSSU5DuHlxVTX0yc8yrO1Iv43WPtuuFgsa6ZeVegOJPU69D0QqRj4AIL9+/VU666z/OtH27CktX+4EDwsWOW2jtQAyvmszJcRnnFqxYztPnQ9EMkY+ACCvLGi89pp0xx3S3r1ShQrShAlSp07O1MrQ2Yuz3UYb8+822rYNE5yAYZ9tV4stLqXCKaIF4QMA8iIpya3dMX26e9y6tdsQrmZN5zAv22hb1qvkTK3YZyCaMO0CAL5assRtCGfBwxrCDRsmffJJWvAwbKMFcsfIBwCkY9MmR02DeFKlxx+XHnrIbQ5Xp44bQFq2POq1YxstkDvCBwD8yxaC2nqM9NMmTbRHkz95RhWXL3ZPdO7sVi2Nj8/ydfNuo7XFpVmt+4j5d1Ep22gRzZh2AYB/g0fmHSoXr/1aU8b2cILH4VKlpVdflaZNyzZ4GFvDYdtkTeY6HmyjBVyEDwBRz92hsiZtpKLEoQMa/tGzevG9x1T+wF59l1BfnW9/Xke6Xi/FZFca7D9sowVyxrQLgKiXfofKSdt/0zPvP6X6uzY5xy+ccaVGntNVh+KKpu1Q8QXbaIHsET4ARD1nh4rHoxtXzNaQzyeq+JHD2l66gu68dKC+qtM0343e2EYLZI3wASDqVT+4V6+884guWPeN81osqHe67m7XX7tLZVzbQaM3wD8IHwCi2/z5at6tm2K2bVNKXFENP7+7pjS7NMPaDnaoAP5F+AAQvQ3h7rtPevppJ1zsqXeCrj63j36qWjfDZexQAfyP3S4Aos/atVKrVk7wcNx2m8qu/lb9BlxJozcgABj5ABBdDeEmT5b69JH27ZMqVpReeUXq2NF5OLFxKRq9AQFA+AAQHf7+2xnh0BtvuMfnned2pk3Xl8WwQwUofEy7AIh8X38tNW3qBg9rCDd8uLRgwVHBA0BgMPIBIHJZEzgLGo884v5z3brSjBnSGWf4v/lcbO6VTwG4CB8AItPvv0tdu0pffukeX3ed9PzzUrlyfm8+Z43krJ+LVTUFkDumXQBEnnfekU45xQ0eZcpIU6ZIU6f6JXhkbj5nrIOtnbfHAeSO8AEgctgOlltvla66yl1g2qKFtGqVdP31fm8+l573nD1u1wHIGeEDQGSwkNG8ufTyy2510nvukRYtkurVS7vEgsHidbs0a9Vm53NegkL65nNZse9kj9t1AHLGmg8A4V+7Y+xYafBgt2pp9eruFto2bfy6VsPXpnJ5bT4HRCNGPgCErz//lNq3l+680w0el10mffddlsGjoGs1fG0qR/M5IHeEDwDh6aOP3EWlc+dKJUpIzz0nzZwpVa5cKGs1bDutjZRkt6HWztvjdh2AnBE+ABSKgqyvyFFKijRwoJSY6I58NGokffON1KtXhk60/l6rYXU8bIrGZH4Wms8BecOaDwB+V2i1MH7+WercWfr2W/f4jjukp56SSpYMyFoNu/fxXZsd9e+WQJ0PIE8IHwD8yru+IvM4h3d9hb155zmA2KLSiROlvn2lf/6RKlVyj22NR4DXati9t22YQIVToAAIHwD8Jrf1FTY9YY/bm7fP5citXkfPntKbb7rHtpjUiobVqOHTl3vXalj4yeq+Yv4ducjLWg2azwEFw5oPAH7j91oYX33lLiq14FGkiDRihDR/vs/Bw7BWAwg9hA8AfuO39RWHD0tDh0rnnuv2aLFCYRZErHCYdaXNI+9aDRvhSM+O8zUNBKBAmHYB4Dd5XV+RZXfYTf82hLPqpMZKo9s22rJlC3RvrNUAQgfhA4Df5GV9RVY7Yq77fakenjNWRfcku2Fj/Hi3G62fsFYDCA2EDwB+411fYbtaLGh4sqmF8fGabRl2xJQ8eEAPffKSrl093zn+++RTVX7m29Jxx/FfB4hArPkA4Fe5ra+wnS7pd8Q0+nOd5rza3wkeqYrRcy2v1qVXj9CROnVDo6gZAL9j5AOA3+W0vsKCgU21xHhS1X35+7p74WQVP3JYW8tU0oBLB2px7SbS3sPO17asVym4Rc0ABH/kIzU1VXPnztVll12mmjVr6r333svyuo8//lht2rRRvXr1lJiYqKVLl/rrfgGECe/6isub1nA+e+t6WBipvO8vTXprqB74dIITPObXP1OXdB/nBo887pzxR9M4ACEcPsaMGaOxY8eqe/fu2rx5s/bt23fUNYsXL1b79u114YUXaubMmWrUqJETRH62ssgAgioUpiZOWPmV5k7so/PWr9CBIsV030W9dOsV9+nvkuXyvHPGX03jAARWjMdjdYt9c+TIEcX9u8c+JiZGr732mrralrh0bFTk4MGDmjdvXtq5xo0bq1WrVnrppZd8ep7k5GTFx8crKSlJ5cpl/IUEIH+CPjVhDeGGDJFGj3YOf6pSR306DNIvVWpnuSNm0eA2aaMlWW7J/XcKp/PLS3J96hk9zvR5CgdA/vn6/p2nNR/e4JGThQsX6sEHH8xwzqZe5syZk5enAhDq/Vby4qef3IZwq1Y5hxs736SO1S5VStHiuXaHzSk0pRxO9enpfZ3CARCGu1327NnjpJ6EhIQM54855hhnmiY7KSkpztel/wDgH4UxNeHz9I0NrE6YIJ12mhs8rCHc+++r9vSJGnNTy1wrjua2nmPDzqOnfgtS/AxAGO52sQWpzje1HgzpFC1a1Jmyyc6IESM01EopAwhqvxVfpiZ8nr756y+3Idxbb7nHF1zgNoSrXt2niqO+NKmbsex3JZQroT+T/dc0DkCYjXyULVtWxYsX186dOzOct+MqVapk+3VDhgxx5oe8H5s2bfLnbQFRzW/9VvKys+TLL92GcBY87I+RJ55wG8L9Gzxy2xHja2jalpyizi2OdY4z98jNagoHQASGj9jYWDVv3lxfWQOodL788ku1aNEi26+zwGILU9J/AAhOv5WCTN8Mm/m9Um3N13nnSfZHxPHHS19/Ld19t/2CKJTQVKdyKZrGAdFeZOyOO+5wtuJ++umnzhbbt956S4sWLdKCBQv8/VQA/NxvJSe5jURUT9qu0VOfVuzmNe6Jbt2kcePy3RAuL6HJRk1ymsIBEMbhw0LEtddem3bct29f3XPPPerSpYuefPJJ51znzp21YcMGdezY0VkDYqMa48eP1/nnn+//uwfgt34rub1R5zQS0f7HLzXio2dVLmWfDpUpq6IvviB16RLQ0ETTOCBC63zYrpQdO3Ycdb506dKqUKFChnOHDx/WX3/9pYoVK/q0RTc96nwAoVfnI6uaGqUO7tdDC17SNd9/7ByvrN5AmjZdzc5r5rd7ttCkbEJToW8RBlAo7995Ch+BQvgACkd2xbp8/dqzn/g0bSSi0bZf9czsp1Rv9+a0hnBvtLtJC++9yK/THUEvjgYguEXGAIS3gkxNeKdver22XLd8M1ODFk5RsdTD2lK2stMQbumxJ2t8xyZ+X2eR25ZcAOGH8AHAZ4mVY7Rs0UhV/nqhczzvhJYanNhXpRKqaHwhjkSwngOILIQPAL758EPpxhtVeccOeUqW1G/3DVNKu6v1QrmSjEQAyBPCB4DcG8INHiyNHeseN2mimBkzVK9hQ9XjtQMQ7CJjACLMjz9KZ5zxX/Do21daulRq2DDYdwYgjDHyAeBotgnu5Zel/v2l/fulypWlyZOl9u15tQAUGOEDiODtsfmye7d0663SO++4x23bSq++KlVjWysA/yB8ACEs4DUuFi6UunaV/vjD2lFLjz0mDRiQ574sAJATfqMAIcrnDrL+cPiw9MADUps2bvCoX99tCHfXXQQPAH5H+ABCkC8dZO1xu67A1q+Xzj1XGjZMSk2VbrpJWrlSat684N8bALJA+ABCUG4dZC1y2ON2XYHMmCE1bSotXixZKWQ7njhRKlOmYN8XAHLAmg8gBOXUQTY/1x1lzx5326ztYDEtW0rTp0t16uTv+wFAHjDyAYQg29Xiz+syWL5catbMDR62kNTWenzxBcEDQMAQPoAQZNtpbVdLdhtq7bw9btf5zNZzPPWU1KqV9OuvUs2a0mefSY88IhVhEBRA4BA+gBDk7SBrMgcQ77E97nO9j61bpYsvlu6+Wzp0SOrUSfruO3ehaSa2iHXxul2atWqz89kvi1oBIB3+3AFClNXxGN+12VF1PhLyWudjzhx3B8vOnVLJkm6p9FtukWJigl9XBEBUivF4rI5yaElOTlZ8fLySkpJUzlbgA1Es3xVODxxwRzrGjXOPbVeL7WY58cQc64pk/oXgfSYLQgQQAP54/2bkAwhxFjRa1quUty9as0bq3Flavdo9th4tjz8uFS+er7oiFkDs8bYNEwq3tDuAqED4ACJpdCR5vxp/8IaOe+wBxVhDuCpV3F0t7dr5ra5InoMQAGRC+ADCnHedxj/bduiJec+o3trFzvmdrVqr8juvSwkJwa8rAgDpsNsFCGPedRq1v1+meRN7K3HtYh2MLaJh59+sFmcP1LydnuDXFQGATBj5AMJ4qmXYzNUa8MVrumPxm4qVR+sq1lDfDoP0v4Tj87ROw1tXxJrWZRVXYv7dZZOnuiIAkA1GPoAgKkhNje8WrtQz4/upz+I3nODxepOL1KHbGCd45LX/i9/rigBADhj5AIKkQDU1pk/Xybf2VNF9e5VcvLSGXNxbH5x0ToHWafitrggA5ILwAQRBdjU1bNrDzmdbU8MawvXuLU2ZoqKSvqnRUP073KXN8VX9sk7DntOmafJVVwQAfET4AAIs3zU1vvnGrd2xbp3TEC71/vt1Z9GztGXvoSyfJ7/rNPJVVwQA8oA1H0CA5aWmRlpDuCeecBvCWfCoVUv6/HPFDh2q+zue7FzCOg0A4YTwAQRYnmpqbNkiXXSRdM890uHD0lVXuQ3hzjknwzoNG+FIz44phw4gVDHtAgSYr2swGiz7XLq3n7Rrl1SqlNsQ7uabj2oIxzoNAOGG8AEEWG41NUocStGwr6foxCdm+dQQzrBOA0A4YdoFCLCcamqcsGOjZk4ZoKuW/Bs8BgyQlizJMXgAQLghfABBcNRaDY9HXb/9ULOn3KkTd26UqlaV5s6VRo7MthMtAIQrpl2AIPGu1Vi54hfVvLuvqn3+0b8PJLqdaI85hv82ACIS4QMIoriFn+v0rl3dXS3Firlbavv2dep4AECk4jccEAyHDkn33itdcIEbPBo0cNd29O9P8AAQ8Rj5AALNCoV16SItW+Ye33KLNGaMVLo0/y0ARAVGPoBAmjpVOvVUN3iULy+99Zb08ssEDwBRhZEPIBCSk6U77nDDh7EKpfbPxx7L6w8g6jDyARS2pUvd0Q4LG7aQdOhQ6bPPCB4AohYjH0BhOXJEevJJ6cEH3b4stWtL06ZJZ53Faw4gqhE+gMKwebM811+vGBvhkLSzfUdVmDJRcRUr8HoDiHpMuwD+NmuWDjY62Qke+4qW0KBL+ql5o5t19osrNe+HrbzeAKIe4QPwl/373UWlHTuqWNJf+v6Yerr0xrF6q0lbpxOtNZK7fSoBBAAIH4A/fP+9dPrp0vPPO4cvnX6FOl3/tNZXrJF2ibeD7dDZa3QkNat+tgAQHVjzARSEx+MGjoEDpZQUHaxcVTe36a0v6zbL+nJJW5MOaNn63WpZrxKvPYCoxMgHkF87d0qXXy717u0ED7Vrp0/emJ9t8Ehv+54DvO4AohbhA8iPTz6RmjSRZs92G8JZefQ5c1S+dk2fvrxq2RK87gCiFuEDyGtDuHvukdq2lbZulU480S0i1q+fs6i0Rd2KqhZfQjHZfLmdt8ftOgCIVoQPwFe//uoWCLO297bW49ZbpeXLpaZN0y6Ji43RQx0aOv+cOYB4j+1xuw4AohXhA8iNBY0pU9wS6d98I1WoIL39tvTii1k2hEtsXE3juzZTQnzGqRU7tvP2OABEM3a7ADlJSpJ69ZKmT3ePzz3X6dFypEZNLVu3y1k4aus3bBol/WiGBYy2DROcXS3ZXQMA0YrwAWRnyRKpSxdp/XopLk56+GFpyBDN+3G7hk791Nky62XrOGw6Jf2ohgUNttMCwNGYdgGyagg3fLh09tlu8KhTR/riC+n++53gYVVK0wcPQ/VSAPAd4QNI748/pAsvdIKGE0KuvVZatUpq1cqpSmrVSbOqTUr1UgDwHeED8HrvPbd2x+efuwtJJ09213rExzsP2/qNzCMe2VUvBQBkjzUfwD//SAMGuLtXzGmnSTNmSPXr56sqKdVLASBnjHwguq1e7TaE8waPQYOkr78+KnjkpSop1UsBIGeED0Rv7Y5x46QWLaQ1a6SEBOnjj6Unn3TLpWeB6qUA4B+ED0SfHTukDh2kvn3dhnDt27sjILbQNAdULwUA/yB8ILrY6IYtKv3gA6l4cemZZ9zmcFWqOA/bjpbF63Zp1qrNzmc7To/qpQBQcCw4RXQ4eNDdPvvUU+7xSSdJr7/uBpF/zfthq7OVNrfiYVQvBYCCifF4bPI7tCQnJys+Pl5JSUkqV65csG8H4e6XX6TOnaUVK9zjnj2lUaOkUqUyBA8rHpb5fwZvMXR6sgCA/96/mXZB5LJcbbU6rCGcBY+KFaV335VeeCFD8KB4GAAEFtMuiAgWIDI0casYp7het7tTK6Z1a6chnGrWPOpr81I8jF4tAFBwhA+EvcxrNZpt/lHPfjBS1f/a5jaEe+QRafBg95+zQPEwAAgsv0+7PPbYYypSpEiGj8qVK/v7aYAMazUseMSmHlGfr2bozWmDneDxe/wxWvzqTOnee7MNHobiYQAQ5iMfqampat68uRYtWpR2LibGu2wP8J/0azWqJe/QmDkjdcamH5zHZjZsrQcv6qXSv5fUolSPU6MjO97iYdaZNqvV1/aVCfElnOsAAAVXaAtO0498xOXwVyeQX961Ghf//LXmTurjBI+9xUrqzvYD1L/DICUXL+1TozeKhwFABISP1atXq0KFCqpWrZouv/xyrbHy1YCf7dr+lx6bN04vznxM5Q/s1apq9dX+xrF6r3GbPK/poHgYAITxtEvVqlX14osvKjExUX///bfuv/9+nXXWWfr+++9VM4udBiYlJcX5SL9PGMjRd9+pzfX/p1LrflGqYvTiGVdq1DnX6VBc0Xw3hKN4GABESJGxgwcPqk6dOrrlllv0iO06yMLDDz+soUOHHnWeImM4iv24Wkn0u+92qpbuKFtJ/dvdqa/qNM12rcaiwW1yXPMBAIiwImPFihXTiSeeqF+symQ2hgwZ4tyo92PTpk2FfVsIR9u3S5deKvXv75ZL79BBqz/4XF/XaZpWidTLe2yl0QkeABBldT4OHTqktWvXqmnTo/8y9SpevLjzgSgvDFa3YvZBYf586YYbpD//dBvCjRwp9eqlC2JiNL5CpaN6siRk0ZMFABCh4aNz587q06ePTj31VO3cuVP33nuvdu/erR49evj7qRDGfG3i5oxwWJ0OCxumUSNpxgzp5JPzvFYjT2EHABA+4eO2227TAw88oKVLl6pUqVJq0aKFFi9erJOsiyiQQxM3q7Nh59OauK1d6zaEW7nSveD2290QUrLkUa+jhYicSp/7HHYAAIWOrrYIKBt9OPuJT7PtpeIsEi1XXF8d87ti+/WV9u1zG8JNnChdfnm+npOOtQAQWgtO6e2CgMqtiVvZA3t136wnFPvTl+6J88+XXntNqlEjX8+XW8daCzv2uE3bMAUDAIFR6LtdAF8Lfp32xxp9OKmvLv3pS6VaVdzHHpM+/jjfwSOvHWsBAIHByAcCKquCX3GpR3TH4jfV76sZivOkamP5BCVPeFUnX3lRgZ+PjrUAEHoIHwiozE3cqidv15jZT6vFH24J/vcana9nr+yv+Ve09cvz0bEWAEIP0y4IqPRN3Nr9tEhzJ/ZxgofTEO7SgRpw6UAN+r/T/bb+wht2svtudt4ep2MtAAQOIx8IuMS65fTFL9NUa9YM53hVtRPUt8MgHapTV+P9vPXVG3ZsC68FjfQLT6mCCgDBwVZbBNa337q1O37+WZ6YGG3u2U8ru/dTlYplC7XoF3U+AKDwsdUWRwlqhc/UVGnsWOmee9yqpdWrK2bqVNU8/3xl3evYv+hYCwChg2mXKAkVQf3L3/qx3HijNG+ee3zZZdIrr0iVKyuQcquCCgAIDMJHmPMlVPhczrwwfPSR2xDOOtKWKCGNHi317CnF0FMFAKIVu13CmDdUZC6i5Q0V9nhuFT6NPW7X+VVKijRwoJSY6AaPxo2l5cut+Q/BAwCiHOEjTPkaKpb8tivwFT5//llq2VIaNco97t1bWrbM7UgLAIh6TLuEKV/Lhi9et8uvlUBz5PG4DeD69pX++UeHKlTUiodGynNpB7UoXkJxBX8GAEAEIHyEKd/DgsevlUCz9ddf7lqOt95yDpcd30y9L+qn7VsrSS8voX09ACAN0y5hytew0PK4yoVf4XPRIqlpUyd4pBYposfPu1HXdHpY28tWynIdCgAguhE+wpSvZcPPrFcprZx55msLXOHz8GHp4Yel1q2l33+X5/jjdcutY/TCGVfJExMbuMWtAICwQviIgB4puYUK20Zr22kT4jOOlthxvrfZbtwonXeeNHSoW0CsWzcte/tjfVq2TrZfQvt6AIBhzUcY84aKzHU+ErIoHubXCp9vvindequUlCSVKye98IJTMn3bqs2BW9wKAAhbhI8wl5dQUeAKn3v3Sv36uTtazJlnStOnS3XrOoe0rwcA+ILwEQECUjZ85Uq3IdzatW6RsPvukx58UCpa9Kh1KLa4NKtVHTH/jsrQvh4AohtrPpAzW88xcqQ7ymHBo2ZN6bPPpEcfzRA88roOBQAQvQgfyN62bdIll0h33SUdOiRdcYX03Xfu7pZsFMriVgBARGHaBVn78EO3E+2OHVLJktKYMVKPHj71ZaF9PQAgJ4QPHN0QbvBgaexY97hJE2nGDKmhO53iK9rXAwCyw7QL/vPjj9IZZ/wXPKxHy9KleQ4eAADkhJEPuA3hJkxwt9Hu3y9VqSJNmiS1b8+rAwDwO8JHtNu92y0Y9s47zqHnwrZa8chobS5ZXlXX7cp/ITI/snLsfimOBgAICYSPaPbFF9J110l//OFsm/2p7z3qXuEcbZm1Ie2SallUSw0ka0SXuYJrsO8JAFAwrPmIRtYQzgqEnX++Gzzq19fXr72vS4qcoS17Dma4NJjdaO057bnTB49g3xMAoOAIHyHOphwWr9ulWas2O58L3BF2wwa3TocVCbMCYjfdpCPLV2jgb0WzrEqaVTdav99TFux72nP6ek8AgPDBtEsI8/uUwxtvSD17/tcQ7sUXpWuv1bJ1u44aXciuG23S/oMBmQax5/L1ngq9tDwAwK8Y+QhRfp1ysIZwN93kBA0neLRs6VYqteM8dJn9eM22gE2D+HpPdMgFgPBD+AhBfp1yWLFCatZMmjxZio2VHnjAXWhap07aJb52o525akvApkHokAsAkYvwEYLyMuWQLVvP8fTT7ijHL7/81xDukUekIhln27zdaLPbvGrnK5Yuqt37DhbsnvLAl3uyx+mQCwDhh/ARggo85bB1q5SYKA0a5DaE69TJnWY599wsL/elG+0VTWv49d5zQ4dcAIhchI8QVKAphw8+kE45Rfr4Y7ch3EsvSW+/LVWsmOP3yq0b7YUNE/x6776gQy4ARCZ2u4Qg75SDLeTMagVFzL+hIMOUw4EDbkO4Z55xjy2AWEO4k07y+Xlz6kZraznyfE9+QIdcAIg8hI8Q5J1ysB0k9qbuyWIaxB5PKzFuDeFs58rq1e5x//7SiBFSibyPQmTXjTbP9+RHdMgFgMjCtEuI8mnKwRrC2bTKaae5wcMawtm0y+jR+QoefrknAAByEePx2DtYaElOTlZ8fLySkpJUzophRbFsm6pZQ7gePaR333UvvOgi6dVXpYSE4N0TACCqJfv4/s20S4jLcsph4UKpa9e0hnB6/HF3qsXqeATrngAA8BHhI5zYtlmr0zF8uDvlcsIJ7qJSKyIGAECYIHyEi/XrpS5dpCVL3OPu3aWxY6UyZYJ9ZwAA5AnhIxzWPNjoxm232WSaFB/vLjK9+urCez4AAAoR4SPYnWZzsmeP1KePu5DUtGolTZ8u1a7t3+cBACCA2GpbSJ1mbXRk8bpdmrVqs/M5zw3XvvnGXcthwcMWkj74oLvQlOABAAhzjHz4qdOsTbrY41Yh1FrP53t0xNsQ7r77pMOHpVq1pGnTpHPOydN/WAAAQhUjH37uNPvsp7/6PDqSZUO4iy92y6Rb8LjqKrchHMEDABBBCB8+8rVb66Sv1mc7OmJsRCTLKZjZs6UmTaQFC6RSpaQJE6Q335QqVPD1FgEACAuEDx/52q317/2Hch0dsVEUYyFkyZrN+u3am6TLLpN27pSaNpVWrJBuvlmKoWooACDyED7y2Gk2uzhg58uXLOrzKIpNv1w/YKLizztbx70x2Tk//eyr9NEr70knnujrbQEAEHYIHz7ydnU1mQOI9/ims+r49L027NinRQOHaeJzvXTSjg3aUaq8uv3fUN131o267c3/5bwuBACAMEf48GNX195t6uc6OnJCkRSd0q+7hs1/XiUOH9TndU/TJd3HaeFxp+W+LgQAgAjAVtt8BBDbTptdhVMbHbFdLXaUPj7Y8ZkbV+ulBWNVduefSokroida36RJzTvIExOb5boQmrcBACIR4cPPXV29oyPp63wUOXJY9y9/U90WzlCMx6N1FWuq72WD9L9j6hV4dw0AAOGG8FHIoyN7f/xZZ97fV2W/W+E89ufVXXVpjY7aX6yEX3bXAAAQbljzUZijI0vmqW2XRDd4lC8vvfWWKs+YovJVyue4LsTWjdhUDgAAkYjwURis++wNN0hdu7rN4axCqVUqveoqn3bN2OOF2iUXAIAgInz427Jl0qmnSq+95jaEGzpU+uwz6dhjfd414/fuuAAAhBDWfPiLNYR78knpgQfcvizWfdYawp11Vr52zQAAEKkIH/6webM7zfLpp+7xNddIL7zgrvPI564ZAAAiFeEjHSvsleeRiPffl7p3l3btkkqXlsaNk268kb4sAABkg/DxLytpnr42h/7ddWKLP7Ncg7F/v3TXXdLzz7vHzZpJM2ZIJ5yQ3WsNAABYcPpf8LCqpOmDh9mWdMA5f1SvlR9+kFq0+C94DBwoff01wQMAAB/ERtOUyuJ1uzRr1Wbns7d3in22EY+sOqkc1WvF43EDx+mnuwHkmGOkefOkp5+WihcP6L8PAADhqtCmXTwej/bu3auyZcsqlKdU4ksWO2rEIz1vr5WVK37R6cMGuWs8zCWXSJMnS1WrBuJfAQCAiFEoIx+jR49W5cqVValSJVWrVk1TpkxRqE6pLFizLdfv0XLjd2rc/lw3eBQrJo0ZI33wAcEDAIBQGPl4++23dc899+i9997TxRdfrGnTpql79+6qW7euzrFKnwGU25SK7WN5b9XmbL/eGsINWDRVty15R7H2FSee6C4qbdq0UO8bAIBIFuOx+RE/at26tTPa8frrr6edO/vss1WjRg298cYbPn2P5ORkxcfHKykpSeXKlcv3vdjajs4vL8n1uoqli+mvfQczhJRj/9qqZ2Y/qaZbf3GOU3v0UOzo0e52WgAAkO/3b79Ou6SmpmrZsmVO2MgcSJYuXapA87Utfcem1Z3P3ooeV/zwqT6c3NcJHknFS+vbUS8r9qWXCB4AAITatIstMD1w4ICqVKmS4bwd79ixI9uvS0lJcT7SJyd/8LUtvZU5t4JiT731jXq/PVpXrPncOf9tnSZKevkVnXdhc7/cDwAA8PPIR0yMO3Zw5MiRDOcPHz6sWGuylo0RI0Y4wzTej1q1avnlfixQ2K4WX9rXJ+7dqAWv9XeCR2pcnH7vf4+arF1B8AAAIJTDh22rtY8///wzw3k7tnUg2RkyZIgzP+T92LRpk1/ux6f29e0aKO7xEbYwRTHr1zsN4WK/+ELHjh6huKIUgAUAIOS32tqOlgULFmQ4N3/+/Bx3uhQvXtxZmJL+w19yal8/sW01Jfa/XrrvPhuuka69Vlq1SmrVym/PDwAAMvL7n/a2zbZNmzYaNWqUOnTooMmTJ2vt2rWaYVtUgyTL9vXffaG4q6+Wdu92F5I++6zUrRsN4QAACLfwYSMcVuNj+PDhGjlypOrXr6+PPvpIDRu60x/Bkta+/p9/3F4s1vLenHaaW7ujfv2g3h8AANHC73U+/MFfdT6O8v337tTKmjXu8aBB0rBhbtVSAAAQkPfv6FlRaSMeF1wg2ZbfhATJSr63bRvsuwIAIOpETVdblSoljRwptW8vrV5N8AAAIEiia9rF2L/uv/VIAABAmJdXDwsEDwAAgir6wgcAAAgqwgcAAAgowgcAAAgowgcAAAgowgcAAAgowgcAAAgowgcAAAgowgcAAAgowgcAAAgowgcAAAgowgcAACB8AACAyFVEIcjbaNe64wEAgPDgfd/2vo+HVfjYs2eP87lWrVrBvhUAAJCP9/H4+PhsH4/x5BZPgiA1NVVbtmxR2bJlFRMT49dEZoFm06ZNKleunN++L3idg4WfaV7nSMLPc/i/zhYpLHhUr15dsbGx4TXyYTdcs2bNQvv+9mITPgofr3Pg8FrzOkcSfp7D+3XOacTDi90uAAAgoAgfAAAgoKIqfBQvXlwPPfSQ8xm8zpGAn2le50jCz3P0vM4hueAUAABErqga+QAAAMFH+AAAAAFF+AAAAAEVknU+CsOhQ4f01VdfKSkpSc2bN1eNGjWCfUsRyV7fFStWOIVmTjnlFFWuXDnYtxTRDh48qPfee08VKlTQRRddFOzbiUhW9NB+prdt26bTTz9dCQkJwb6liPT777/rhx9+UFxcnJo0aaJq1aoF+5YiwtatW/Xll1/qxBNPdF7XrKxdu1Zr1qxxXvMWLVr4tbhntjxRYOPGjZ4TTjjBU69ePc/555/vKVmypGfcuHHBvq2IM3DgQE/16tU9bdq08bRu3dpTqlQpz5gxY4J9WxGtf//+nmLFinlOO+20YN9KRPrll188J598sqd27dqejh07eho0aOB55ZVXgn1bEadfv37O7+XExETn90eJEiU8w4YNC/Zthf373lVXXeWpWbOmp2zZsp7Bgwdn+zukTJkynrZt23oSEhI855xzjic5ObnQ7y8qwkf79u09Z599tufgwYPO8WuvveaJi4vz/PTTT8G+tYjywgsveP7555+04+nTp3tiYmI833//fVDvK1J98MEHnpNOOsnTvXt3wkchOHDggPNHS6dOndJ+d6SkpHjmzp1bGE8XtVauXGk7Lj3z589POzdp0iTn3JYtW4J6b+Fs9erVnjfeeMP52W3UqFGW4WP27NnOe+E333zjHO/cudNz7LHHeu66665Cv7+IX/Oxa9cuzZ07V3369FHRokWdc126dFGVKlX0+uuvB/v2IkrPnj1VsmTJtONOnTo50y+rV68O6n1FIut91KNHD02bNi3Daw7/eeutt/Trr79q7Nixab87ihUrpsTERF5mP0pJSXE+16lTJ+3ccccdl+Ex5N3JJ5+sq6++Ou1nNytTp07Vueee6yxFMJUqVdKNN97onC9sEb/mw+axbM62cePGGXrHNGrUSN9//31Q7y3SffLJJ87n9K89Cs5+nrt27ap+/frp1FNP5SUtJF988YUaNmzorFuaP3++Mw/etGlT5w8X+M+ZZ56p22+/Xdddd51uvvlmHT58WC+88IKGDh2aIZDA/+w98IILLjgqtNj6pp07dxbqmr0i0bAA0lSsWDHDeUt4NiqCwmE/vLfddpszypTdIifkz/Dhw50RpbvuuouXsBBt377d+UPFFpnaQrwDBw44C09Hjx6tW2+9ldfej+w1njdvnt59911nc4AtpLYF6yj898es3hvN33//TfgoCG/52L1792Y4b8clSpQo0PdG1iwx286LevXqacKECbxMfrRhwwY98sgjeuyxx/Tmm28653755Rf99ddfzjRimzZtVLVqVV5zP7DfDzZl+MEHH6hdu3bOueeff169e/d2jguz83Y0+eyzz5wRj0WLFqlVq1bOOdvBdeWVVzqvv40+ofDeH7N6bzSF/f4Y8Ws+7A3Qu40rvY0bN6bNK8J/bDTpwgsvdLZ+zpkzh/UIfmZD//ZL2f4CnzlzpvOxfv16J3zYP1vwg/9+d9h6Gm/wMPba21/mrGPyn88//9wpfeANHqZjx47OqJNNfaFwf8azem+04FHYW50jPnxYwGjQoIGzeMzrf//7n/PRvn37oN5bpNm9e7cTPOLj4/Xhhx+qdOnSwb6liFO7dm1nhCP9h40y2c+5/TN/JfpPhw4dtH//fme0yevHH390PteqVcuPzxTd7LXcsWNHhmlwW+hrIY/RpcJlwdrWM3mXJ9h0ro2o2qJqq7dSmKKisZzNJdovkl69ejlJz1avn3TSSc5f5vAP+zGyFdO//fabnn766QzBwxZFWgBE4bBpgCVLlmj58uW8xH5mK/+XLl3q7JazNR+jRo1S69atnV1G8A8b5j/ttNOcUSbbMWehY9y4cc5aBJuKyWm3BrJnP682GmoGDRqkZs2aqXPnzs46Dvsj0fzzzz/Ogl977e1nfeHChc4fjvb7pLD/kImK8GFsmHrKlClOwmvZsqW6d+/OD7Wfd2DY4tKs2M6MSy+91J9Ph3QmTZrk/KVoC1HhX/brcfr06fr0009VqlQpZ1viVVddFZgKkFFk3759zs+x7b6wv7jtjfKGG25wtjYjf+y9zsJcZvXr19ejjz6adrxnzx6NHz/emQ2wqRZbTB2IJQlREz4AAEBoiPg1HwAAILQQPgAAQEARPgAAQEARPgAAQEARPgAAQEARPgAAQEARPgAAQEARPgAAQEARPgAAQEARPgAAQEARPgAAQEARPgAAgALp/wGnqbIgaon3ogAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np              # arrays and fast numerical computation\n",
     "import pandas as pd             # labeled tabular data (DataFrames)\n",
@@ -2541,10 +3259,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "id": "6394c8a6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rex\n",
+      "Rex says Woof!\n"
+     ]
+    }
+   ],
    "source": [
     "class Dog:\n",
     "    def __init__(self, name, breed):\n",
@@ -2581,10 +3308,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "id": "b08e1532",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Canis familiaris Canis familiaris\n",
+      "Rex Fido\n"
+     ]
+    }
+   ],
    "source": [
     "class Dog:\n",
     "    species = \"Canis familiaris\"     # class attribute -- shared by all dogs\n",
@@ -2624,10 +3360,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "id": "ab0e65f1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creature makes a sound\n",
+      "Whiskers says Meow!\n"
+     ]
+    }
+   ],
    "source": [
     "class Animal:\n",
     "    def __init__(self, name):\n",
@@ -2668,10 +3413,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "id": "6d130076",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Point(1, 2)\n",
+      "True\n"
+     ]
+    }
+   ],
    "source": [
     "class Point:\n",
     "    def __init__(self, x, y):\n",
@@ -2713,10 +3467,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "id": "dbe27832",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fitted parameters: [2.51494229 2.6046724 ]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -2798,10 +3560,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "id": "f438711b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing messy_example.py\n"
+     ]
+    }
+   ],
    "source": [
     "%%writefile messy_example.py\n",
     "def compute_average(values):\n",
@@ -2814,20 +3584,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "id": "36a69872",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mreformatted messy_example.py\u001b[0m\n",
+      "\n",
+      "\u001b[1mAll done! ✨ 🍰 ✨\u001b[0m\n",
+      "\u001b[34m\u001b[1m1 file \u001b[0m\u001b[1mreformatted\u001b[0m.\n"
+     ]
+    }
+   ],
    "source": [
     "!python3 -m black messy_example.py\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "id": "b0fdbd3f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "def compute_average(values):\n",
+      "    total = 0\n",
+      "    for i in range(len(values)):\n",
+      "        total = total + values[i]\n",
+      "    average = total / len(values)\n",
+      "    return average\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "with open(\"messy_example.py\") as f:\n",
     "    print(f.read())\n"
@@ -2851,10 +3646,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "id": "c53741e4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing bad_example.py\n"
+     ]
+    }
+   ],
    "source": [
     "%%writefile bad_example.py\n",
     "import numpy as np\n",
@@ -2873,10 +3676,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "id": "7f2f5148",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/vincent/Documents/netflix-writers/venv/bin/python3: No module named ruff\n"
+     ]
+    }
+   ],
    "source": [
     "!python3 -m ruff check bad_example.py\n"
    ]
@@ -2904,10 +3715,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "id": "792ed9a0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing typed_example.py\n"
+     ]
+    }
+   ],
    "source": [
     "%%writefile typed_example.py\n",
     "def z_score(x: float, mean: float, std: float) -> float:\n",
@@ -2918,10 +3737,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "id": "22648597",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "typed_example.py:4: \u001b[1m\u001b[31merror:\u001b[m Argument 3 to \u001b[m\u001b[1m\"z_score\"\u001b[m has incompatible type \u001b[m\u001b[1m\"str\"\u001b[m; expected \u001b[m\u001b[1m\"float\"\u001b[m  \u001b[m\u001b[33m[arg-type]\u001b[m\n",
+      "\u001b[1m\u001b[31mFound 1 error in 1 file (checked 1 source file)\u001b[m\n"
+     ]
+    }
+   ],
    "source": [
     "!python3 -m mypy typed_example.py\n"
    ]
@@ -2946,10 +3774,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "id": "527c33fb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing stats_utils.py\n"
+     ]
+    }
+   ],
    "source": [
     "%%writefile stats_utils.py\n",
     "def z_score(x, mean, std):\n",
@@ -2960,10 +3796,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "id": "0e6b302c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing test_stats_utils.py\n"
+     ]
+    }
+   ],
    "source": [
     "%%writefile test_stats_utils.py\n",
     "from stats_utils import z_score\n",
@@ -2982,10 +3826,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
    "id": "7ec2b396",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m============================= test session starts ==============================\u001b[0m\n",
+      "platform darwin -- Python 3.13.9, pytest-9.1.0, pluggy-1.6.0 -- /Users/vincent/Documents/netflix-writers/venv/bin/python3\n",
+      "cachedir: .pytest_cache\n",
+      "rootdir: /Users/vincent/Documents/netflix-writers\n",
+      "configfile: pyproject.toml\n",
+      "plugins: mock-3.15.1, anyio-4.13.0\n",
+      "collected 3 items                                                              \u001b[0m\n",
+      "\n",
+      "test_stats_utils.py::test_z_score_basic \u001b[32mPASSED\u001b[0m\u001b[32m                           [ 33%]\u001b[0m\n",
+      "test_stats_utils.py::test_z_score_zero_mean \u001b[32mPASSED\u001b[0m\u001b[32m                       [ 66%]\u001b[0m\n",
+      "test_stats_utils.py::test_z_score_raises_on_zero_std \u001b[32mPASSED\u001b[0m\u001b[32m              [100%]\u001b[0m\n",
+      "\n",
+      "\u001b[32m============================== \u001b[32m\u001b[1m3 passed\u001b[0m\u001b[32m in 0.01s\u001b[0m\u001b[32m ===============================\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
     "!python3 -m pytest test_stats_utils.py -v\n"
    ]
@@ -3054,10 +3918,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 92,
    "id": "9f0db2fb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7\n",
+      "assertion failed: n_train must be between 0 and n_total\n"
+     ]
+    }
+   ],
    "source": [
     "def train_test_split_ratio(n_total, n_train):\n",
     "    assert 0 < n_train < n_total, \"n_train must be between 0 and n_total\"\n",
@@ -3135,13 +4008,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.10"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,
diff --git a/notes.txt b/notes.txt
new file mode 100644
index 0000000..06fcdd7
--- /dev/null
+++ b/notes.txt
@@ -0,0 +1,2 @@
+first line
+second line
diff --git a/stats_utils.py b/stats_utils.py
new file mode 100644
index 0000000..6ab6f7a
--- /dev/null
+++ b/stats_utils.py
@@ -0,0 +1,4 @@
+def z_score(x, mean, std):
+    if std == 0:
+        raise ValueError("standard deviation cannot be zero")
+    return (x - mean) / std
diff --git a/team5 final project (2).pdf b/team5 final project (2).pdf
new file mode 100644
index 0000000..f5259cf
Binary files /dev/null and b/team5 final project (2).pdf differ
diff --git a/temp.txt b/temp.txt
new file mode 100644
index 0000000..2bd3bba
--- /dev/null
+++ b/temp.txt
@@ -0,0 +1,72 @@
+page 7
+
+First, let’s ask a simple question: What makes a show a real 'Hit'? 
+To be fair and scientific, we created three simple rules to find a hit:
+
+High Grade: 
+The show must have a very good score.
+
+Many People Rank: 
+A lot of people must vote and rate the show.
+
+Enough Watching: 
+People must spend a lot of time actually watching it.
+
+If a show passes all three rules, we call it a Hit!
+
+page   8
+
+"But why do these rules matter? 
+There is a paper that studied eWOM 
+which just means online talk, like reviews and comments on social media. 
+The paper shows that good online talk has a positive influence on how well a movie does. 
+
+
+
+page 9
+
+Here is another cool fact from another paper:
+When we look at movie , the volume of online talk—meaning how many people are talking—is actually more important than whether the talk is good or bad.
+In short, even if some comments are bad, having a lot of people talking about a show is already a big win!
+
+page 10
+
+So, how does Netflix measure if we really love a show?
+Netflix tells us that they measure engagement by views. 
+This is how they know which shows are keeping us glued to our screens!
+
+page 11
+
+On this page, let's talk about some simple math: Medium vs. Average. 
+Why do we use 'medium' for some things and 'average' for others?
+Netflix Viewing Hours: The numbers here are very uneven.
+A few super popular shows have billions of hours, while most others have very little. 
+So, using the medium is much fairer.
+TMDB Rating: The scores are mostly close to each other, so the average works perfectly.
+TMDB vote_count
+some unpopular films have low vote_count
+
+
+page 12
+
+What is the biggest difference between TMDB and IMDb? Think of them like this:
+TMDB is like the public. 
+It is made of comments from everyday people—like you and me—sharing what they like.
+IMDb is more like a strict professor. 
+The users there are very professional and judge movies much harder."
+
+page 13
+So, why did we choose TMDB instead of IMDb?
+The big reason is missing data. 
+IMDb loses a huge part of its data!
+If you look at this table, you can discover that about 80% of the rating data is lost (blank)! 
+We cannot make good plans with a database that is missing so many pieces, so TMDB is the much better choice.
+
+page 14
+Let’s look at a real example to see why we need Netflix viewing hours.
+Look at Stranger Things and The Seven Deadly Sins. They have very similar TMDB ratings (8.6 vs 8.4).
+Although their vote counts are very different, 
+if we only use ratings and votes, we still might miss how truly massive Stranger Things is.
+That is why we added netflix_viewing_hours to our selection rule. 
+It makes our hit-finding rule complete and accurate!
+
diff --git a/test_stats_utils.py b/test_stats_utils.py
new file mode 100644
index 0000000..4f36d07
--- /dev/null
+++ b/test_stats_utils.py
@@ -0,0 +1,12 @@
+from stats_utils import z_score
+import pytest
+
+def test_z_score_basic():
+    assert z_score(10, 5, 2) == 2.5
+
+def test_z_score_zero_mean():
+    assert z_score(0, 0, 1) == 0.0
+
+def test_z_score_raises_on_zero_std():
+    with pytest.raises(ValueError):
+        z_score(10, 5, 0)
diff --git a/typed_example.py b/typed_example.py
new file mode 100644
index 0000000..bad2c11
--- /dev/null
+++ b/typed_example.py
@@ -0,0 +1,4 @@
+def z_score(x: float, mean: float, std: float) -> float:
+    return (x - mean) / std
+
+result = z_score(10, 5, "2")   # passing a string where a float is expected