From af296e9e3e64b4690804ac20308dbe3a3f7f7ed9 Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Tue, 14 Jul 2026 13:57:22 -0700 Subject: [PATCH 01/14] README modified --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e0db6a8..f1fc877 100644 --- a/README.md +++ b/README.md @@ -116,4 +116,4 @@ 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 From cc6da4a825de536acc0a19b282854d0125f14853 Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Tue, 14 Jul 2026 14:21:36 -0700 Subject: [PATCH 02/14] add notes about git --- notes.txt | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 notes.txt diff --git a/notes.txt b/notes.txt new file mode 100644 index 0000000..4ee2019 --- /dev/null +++ b/notes.txt @@ -0,0 +1,17 @@ +git remote set-url origin https://github.com/vincent-copy/netflix-writers + +git status +U 未追蹤 +T 已追蹤 +S 已暫存 +C 已提交 +M 已修改 +R 已重新命名/移動路徑 +git add [name] [name] +git add . #git add all +git commit + +git log --oneline +git log + +git push \ No newline at end of file From 966fa8be4ed450d71799e57892d73059c76f5a52 Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Tue, 14 Jul 2026 14:38:17 -0700 Subject: [PATCH 03/14] run the data ana. --- bad_example.py | 12 + bad_number.txt | 1 + good_number.txt | 1 + messy_example.py | 6 + netflix_review_concepts_capstone.ipynb | 535 +++++++++- netflix_review_concepts_module_2.ipynb | 501 ++++++++-- netflix_review_concepts_module_3.ipynb | 1251 ++++++++++++++++++++---- notes.txt | 19 +- stats_utils.py | 4 + test_stats_utils.py | 12 + typed_example.py | 4 + 11 files changed, 2050 insertions(+), 296 deletions(-) create mode 100644 bad_example.py create mode 100644 bad_number.txt create mode 100644 good_number.txt create mode 100644 messy_example.py create mode 100644 stats_utils.py create mode 100644 test_stats_utils.py create mode 100644 typed_example.py 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_review_concepts_capstone.ipynb b/netflix_review_concepts_capstone.ipynb index 0befc23..70ecada 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": 19, "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": 19, + "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": 11, "id": "39349f73", "metadata": { "execution": { @@ -138,7 +163,64 @@ "shell.execute_reply": "2026-07-05T18:13:27.742059Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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sourcematchedtotalcoverage_pct
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" + ], + "text/plain": [ + " source matched total coverage_pct\n", + "0 tmdb 243 300 81.0\n", + "1 imdb 266 300 88.7" + ] + }, + "execution_count": 11, + "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": 12, "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": 12, + "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": 13, "id": "ed0ac180", "metadata": { "execution": { @@ -235,7 +331,72 @@ "shell.execute_reply": "2026-07-05T18:13:27.766286Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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netflix_titlenetflix_viewing_hoursnetflix_weeksbinge_velocity
0Show 00048840931.0133.756995e+06
1Show 00168780466.097.642274e+06
2Show 00260617686.096.735298e+06
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" + ], + "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": 13, + "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": 14, "id": "a50796f0", "metadata": { "execution": { @@ -280,7 +441,72 @@ "shell.execute_reply": "2026-07-05T18:13:27.779462Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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netflix_titleimdb_averageRatingimdb_numVotesimdb_rating_shrunk
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1Show 0014.882446.04.811299
2Show 0028.098769.07.993323
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" + ], + "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": 14, + "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": 15, "id": "a7d6e719", "metadata": { "execution": { @@ -332,7 +558,72 @@ "shell.execute_reply": "2026-07-05T18:13:27.791018Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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netflix_titletmdb_vote_averageimdb_averageRatingaudience_alignment_gap
0Show 0007.46.60.8
1Show 0017.84.83.0
2Show 0025.08.03.0
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" + ], + "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": 15, + "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": 16, "id": "8eb446da", "metadata": { "execution": { @@ -372,7 +663,68 @@ "shell.execute_reply": "2026-07-05T18:13:27.801813Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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netflix_titleimdb_numVotesimdb_buzz_log
0Show 000131664.011.788016
1Show 00182446.011.319911
2Show 00298769.011.500549
\n", + "
" + ], + "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": 16, + "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": 17, "id": "35d36857", "metadata": { "execution": { @@ -408,7 +760,131 @@ "shell.execute_reply": "2026-07-05T18:13:27.822583Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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mean5.542163e+066.6735301.74120411.649581204.333333
std2.045585e+061.4801971.1706951.027040112.786259
min2.108027e+064.0149170.0000006.4599045.300000
25%3.546625e+065.4037070.70000011.279534115.150000
50%5.761116e+066.6998151.60000011.976276202.000000
75%7.323639e+067.8978302.52500012.361748295.250000
max8.977398e+069.2799124.70000012.608537399.800000
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" + ], + "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": 17, + "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": 18, "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": 18, + "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": [ + "
" + ] + }, + "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 \n", + "ten \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": [ + "\n", + "\n", + "\n", + "\n", + "\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": 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", + "text/plain": [ + "
" + ] + }, + "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 index 4ee2019..06fcdd7 100644 --- a/notes.txt +++ b/notes.txt @@ -1,17 +1,2 @@ -git remote set-url origin https://github.com/vincent-copy/netflix-writers - -git status -U 未追蹤 -T 已追蹤 -S 已暫存 -C 已提交 -M 已修改 -R 已重新命名/移動路徑 -git add [name] [name] -git add . #git add all -git commit - -git log --oneline -git log - -git push \ No newline at end of file +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/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 From 392a9579f26c9f96b8dc1ac7d38fce9d47c4b6b3 Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Wed, 15 Jul 2026 10:54:17 -0700 Subject: [PATCH 04/14] add data and temp --- netflix-writer.ipynb | 2382 +++++++++++++++++++++++- netflix_review_concepts_capstone.ipynb | 36 +- temp.txt | 190 ++ 3 files changed, 2558 insertions(+), 50 deletions(-) create mode 100644 temp.txt diff --git a/netflix-writer.ipynb b/netflix-writer.ipynb index 6e3ae99..b83f8cb 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", + 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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 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(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 \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": [ + "
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keynetflix_viewing_hoursnetflix_weeksnetflix_year_hintnetflix_titlenetflix_clean_titletmdb_titletmdb_popularitytmdb_vote_averagetmdb_vote_countimdb_titleimdb_averageRatingimdb_numVotes
661st_th2874260000132022Stranger Thingsstranger thingsStranger Things185.7118.62416161.0Stranger ThingsNaNNaN
396ga_sq2289500000202021Squid Gamesquid gameSquid Game115.5877.83113053.0Squid Game!NaNNaN
549ma1446260000162021ManifestmanifestManifest111.4117.7281312.0ManifestNaNNaN
447he_mo1170200000142021Money Heistmoney heistMoney Heist96.3548.25717836.0Money Heist8.2612436.0
167br1108800000112021BridgertonbridgertonBridgerton71.3088.1432045.0Bridgerton4.157.0
103ar_ca_co793740000252022Café con aroma de mujercaf con aroma de mujerCafé con Aroma de Mujer20.7107.500382.0Café con aroma de mujer8.6106.0
690yo77748000082021Youyou続・アタッカーYOU 金メダルへの道7.1012.5002.0YouNaNNaN
686wi75735000062021WindfallwindfallWindfall3.8130.0000.0WindfallNaNNaN
607oz751600000132022OzarkozarkOzark68.0068.2441968.0Ozark8.4394904.0
69al_ar_us659510000112022All of Us Are Deadall of us are deadAll of Us Are Dead107.1088.3543335.0All of Us Are Dead7.692709.0
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" + ], + "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": [ + "
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tconsttitleTypeprimaryTitleoriginalTitlestartYearendYearruntimeMinutesgenrestitle_keyaverageRatingnumVotesall_akascastdirectorswriters
113321tt24228604tvSeriesOdBita PotOdBita Pot20222026\\NAdventuredita ot9.81344.0odbita potAnze Sever | Bit Severnm7689797nm7689797
10372tt41956044tvSeriesD&D: Dungeon MastersD&D: Dungeon Masters2026\\N\\NFantasy,Horror,Reality-TVungeon asters9.8115.0d&d: dungeon mastersMayanna Berrin | Devora Wilde | Neil Newbon | ...nm0671741\\N
127839tt28305644tvSeriesEDP WatchEDP Watch2023\\N\\NCrimeatch9.7106.0edp watchAlex Rosen | Skeeter Jean | Demarcus Cousins I...nm15017595nm15017595
263196tt12831434tvSeriesThe Simonetta Lein ShowThe Simonetta Lein Show20202026\\NTalk-Showhe imonetta ein how9.7810.0the simonetta lein showGabriela Gonzalez | Kevin Harrington | Julian ...\\N\\N
164455tt34929415tvSeriesA Dog and a PlaneA Dog and a Plane2026\\N\\NComedy,Romanceog and a lane9.7551.0a dog and a planeOabnithi Wiwattanawarang | Pompam Niti Chaichi...nm8797158\\N
156259tt33131139tvSeriesSymposiumSymposium2023\\N\\NTalk-Showymposium9.6127.0symposiumNiko Svanidzenm16438483nm16438483
59321tt8088236tvSeriesGeografens testamenteGeografens testamente2011202328Adventure,Historyeografens testamente9.61143.0the geographers last will | geografens testamenteJudith Buchan | Lena Granhagen | Lakshman Mend...nm1072502nm1072502,nm2050223,nm6813238
63437tt8560994tvSeriesFriday Five SharpFriday Five Sharp2015\\N\\NComedyriday ive harp9.64589.0petak pet | friday five sharpKitodar Todorov | Tedi Dinh | Vladislav Petrov...nm5663366nm5663366,nm11544010,nm1242398,nm9909867
59344tt8103440tvSeriesHerne KathaHerne Katha2018\\N\\NDocumentary,Shorterne atha9.6413.0herne kathaKamal Kumar | Bidhya Chapagainnm9678594,nm9678593nm9678593,nm9678594
36797tt5961444tvSeriesUpanishad GangaUpanishad Ganga20122013\\NDrama,History,Shortpanishad anga9.61359.0upanishad gangaRasika Dugal | Zakir Hussain | Sandeep Mohan |...nm1363374nm1363374
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idnamenumber_of_seasonsnumber_of_episodesoriginal_languagevote_countvote_averageoverviewadultbackdrop_path...taglinegenrescreated_bylanguagesnetworksorigin_countryspoken_languagesproduction_companiesproduction_countriesepisode_run_time
177163770The Late Show with Stephen Colbert91419en2056.495Stephen Colbert brings his signature satire an...False/gMMnf8VRg3Z98WaFmOLr9Jk8pIs.jpg...NaNComedy, TalkJon Stewart, Stephen Colbert, Tom Purcell, Chr...enCBSUSEnglishSpartina Productions, CBS StudiosUnited States of America41
3035215803Batang Quiapo2178tl997.030A young man rises to be one of the biggest out...False/jIV5weV19wH02rnuWyMtYI4dYC8.jpg...NaNAction & Adventure, Comedy, DramaNaNtlTV5, Kapamilya ChannelPHNaNABS-CBN Entertainment, Dreamscape Entertainmen...Philippines0
228594722Tagesschau7220839de1457.500German daily news program, the oldest still ex...False/jWXrQstj7p3Wl5MfYWY6IHqRpDb.jpg...NaNNewsNaNdeARDDEDeutschNDRGermany15
359314981The Late Late Show with Craig Ferguson112057en777.065The Late Late Show with Craig Ferguson is an A...False/m0bV3qBiJBBlpFaaKjwHo13MVjm.jpg...NaNComedy, TalkNaNenCBSUSEnglishNaNUnited States of America60
45652261The Tonight Show Starring Johnny Carson312924en517.800The Tonight Show Starring Johnny Carson is a t...False/qFfWFwfaEHzDLWLuttWiYq7Poy2.jpg...NaNTalkSteve Allen, Sylvester WeaverenNBCUSEnglishNBC Productions, Carson ProductionsUnited States of America60
465181329Chronicles of the Sun51252fr497.510NaNFalse/rj3jBAZwPiOgkwAy1205MAgLahj.jpg...NaNSoapNaNfrFrance 2FRFrançaisFrance Télévisions, Epeios Productions, France...France26
160059941The Tonight Show Starring Jimmy Fallon111233en2346.038After Jay Leno's second retirement from the pr...False/xl1wGwaPZInJo1JAnpKqnFozWBE.jpg...Tonight's just getting started.Comedy, TalkJimmy FallonenNBCUSEnglishBroadway Video, Universal Television, Electric...United States of America45
7314244643El amor no tiene receta191es238.300Paz Roble, a kind, hard-working and honest wom...False/bIhmqQNXcyWRzH153d3jaCbLTy3.jpg...NaNAction & Adventure, Family, Drama, Soap, Comed...Juan Osorio Ortiz, Pablo Ferrer García-Travesí...esUnivision, Las Estrellas, ViXMX, USEspañolTelevisaUnivision Mexico, TelevisaMexico50
8641225660Pira-Pirasong Paraiso278tl177.000A con artist takes on the job of pretending to...False/2N4LXvTkUwPRkbvyzdmzvtnCHgR.jpg...NaNNaNNaNen, tlTV5, Kapamilya ChannelPHEnglishDreamscape Entertainment TelevisionPhilippines0
721791759Come Home Love: Lo and Behold12107cn234.565Hung Sue Gan starting from the bottom, establi...False/ohJTnu93hJ0Uonl86Wn3mOSlWXN.jpg...NaNFamily, Comedy, DramaNaNcnTVB JadeHK广州话 / 廣州話TVBHong Kong22
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" + ], + "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": [ + "
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netflix_titlenetflix_viewing_hours
661Stranger Things2874260000
396Squid Game2289500000
549Manifest1446260000
447Money Heist1170200000
167Bridgerton1108800000
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number_of_seasonspopularity
number_of_seasons1.0000000.189827
popularity0.1898271.000000
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" + ], + "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": [ + "
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netflix_weeksnetflix_viewing_hours
netflix_weeks1.0000000.634786
netflix_viewing_hours0.6347861.000000
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" + ], + "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": [ + "
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cast_countaverageRating
cast_count1.00000-0.07716
averageRating-0.077161.00000
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keynetflix_viewing_hoursnetflix_weeksnetflix_year_hintnetflix_titlenetflix_clean_titletmdb_titletmdb_popularitytmdb_vote_averagetmdb_vote_countimdb_titleimdb_averageRatingimdb_numVoteshit_score
661st_th2874260000132022Stranger Thingsstranger thingsStranger Things185.7118.62416161.0Stranger ThingsNaNNaN1.000000
396ga_sq2289500000202021Squid Gamesquid gameSquid Game115.5877.83113053.0Squid Game!NaNNaN0.796553
549ma1446260000162021ManifestmanifestManifest111.4117.7281312.0ManifestNaNNaN0.503176
447he_mo1170200000142021Money Heistmoney heistMoney Heist96.3548.25717836.0Money Heist8.2612436.00.407131
167br1108800000112021BridgertonbridgertonBridgerton71.3088.1432045.0Bridgerton4.157.00.385769
103ar_ca_co793740000252022Café con aroma de mujercaf con aroma de mujerCafé con Aroma de Mujer20.7107.500382.0Café con aroma de mujer8.6106.00.276155
690yo77748000082021Youyou続・アタッカーYOU 金メダルへの道7.1012.5002.0YouNaNNaN0.270497
686wi75735000062021WindfallwindfallWindfall3.8130.0000.0WindfallNaNNaN0.263494
607oz751600000132022OzarkozarkOzark68.0068.2441968.0Ozark8.4394904.00.261493
69al_ar_us659510000112022All of Us Are Deadall of us are deadAll of Us Are Dead107.1088.3543335.0All of Us Are Dead7.692709.00.229454
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" + ], + "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", @@ -305,7 +2623,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -319,7 +2637,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.13.9" } }, "nbformat": 4, diff --git a/netflix_review_concepts_capstone.ipynb b/netflix_review_concepts_capstone.ipynb index 70ecada..eb88898 100644 --- a/netflix_review_concepts_capstone.ipynb +++ b/netflix_review_concepts_capstone.ipynb @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 1, "id": "adf81cc1", "metadata": { "execution": { @@ -92,7 +92,7 @@ " 2 8.0 98769.0 )" ] }, - "execution_count": 19, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "39349f73", "metadata": { "execution": { @@ -216,7 +216,7 @@ "1 imdb 266 300 88.7" ] }, - "execution_count": 11, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "id": "fa212650", "metadata": { "execution": { @@ -282,7 +282,7 @@ "Name: count, dtype: int64" ] }, - "execution_count": 12, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -321,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "id": "ed0ac180", "metadata": { "execution": { @@ -392,7 +392,7 @@ "2 Show 002 60617686.0 9 6.735298e+06" ] }, - "execution_count": 13, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "id": "a50796f0", "metadata": { "execution": { @@ -502,7 +502,7 @@ "2 Show 002 8.0 98769.0 7.993323" ] }, - "execution_count": 14, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -548,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "id": "a7d6e719", "metadata": { "execution": { @@ -619,7 +619,7 @@ "2 Show 002 5.0 8.0 3.0" ] }, - "execution_count": 15, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -653,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "id": "8eb446da", "metadata": { "execution": { @@ -720,7 +720,7 @@ "2 Show 002 98769.0 11.500549" ] }, - "execution_count": 16, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -750,7 +750,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "id": "35d36857", "metadata": { "execution": { @@ -880,7 +880,7 @@ "max 12.608537 399.800000 " ] }, - "execution_count": 17, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -928,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "id": "bdf7da34", "metadata": { "execution": { @@ -950,7 +950,7 @@ "Name: is_hit, dtype: float64" ] }, - "execution_count": 18, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } diff --git a/temp.txt b/temp.txt new file mode 100644 index 0000000..1c11894 --- /dev/null +++ b/temp.txt @@ -0,0 +1,190 @@ +SHELL := /bin/bash +export PATH := /usr/local/bin:$(PATH) +export + +ifeq ($(OS),Windows_NT) + PYTHON := python.exe + ACTIVATE_VENV := venv/Scripts/activate +else + PYTHON := python3.13 + ACTIVATE_VENV := source venv/bin/activate +endif +PIP := $(PYTHON) -m pip + +ifeq ("$(wildcard .env)","") + $(shell cp .env.example .env) +endif +include .env + + +.PHONY: all init activate build run tear-down pre-commit-init pre-commit-run release analyze \ + check-python python-init python-lint python-clean python-requirements python-fetch-data python-build-dataset help + +# Default target executed when no arguments are given to make. +all: help + +# initialize local development environment. +# takes a few minutes to complete +init: + @echo "===============================================================================" + @echo "Initializing local development environment. This will verify and set up your" + @echo "Python virtual environment, install all 3rd-party package requirements." + @echo "This may take a few minutes..." + @echo "===============================================================================" + @echo "" + @echo "" + @echo "===============================================================================" + @echo "Initialization complete!" + @echo "===============================================================================" + make python-init + +activate: + ./scripts/activate.sh + +# run the web application from Docker +# takes around 30 seconds to complete +run: + @echo "===============================================================================" + @echo "Running solution ..." + @echo "===============================================================================" + make python-fetch-data + make python-build-dataset + +# destroy all Docker build and local artifacts +# takes around 1 minute to complete +tear-down: + @echo "===============================================================================" + @echo "Tearing down solution ..." + @echo "===============================================================================" + make python-clean + +pre-commit-init: + @echo "===============================================================================" + @echo "Installing and configuring pre-commit ..." + @echo "===============================================================================" + pre-commit install + pre-commit autoupdate + +pre-commit-run: + @echo "===============================================================================" + @echo "Running pre-commit hooks on all files ..." + @echo "===============================================================================" + pre-commit run --all-files + +release: + @echo "===============================================================================" + @echo "Forcing a new semantic release on GitHub by creating an empty commit and pushing to the repository ..." + @echo "===============================================================================" + git commit -m "fix: force a new release" --allow-empty && git push + +analyze: + @echo "===============================================================================" + @echo "Generating code analysis report using cloc ..." + @echo "===============================================================================" + cloc . --exclude-ext=svg,zip --fullpath --not-match-d=smarter/smarter/static/assets/ --vcs=git + +# --------------------------------------------------------- +# Python +# --------------------------------------------------------- +check-python: + @echo "" + @echo "===============================================================================" + @echo "Verifying that Python $(PYTHON) is installed ..." + @echo "===============================================================================" + @echo "" + @command -v $(PYTHON) >/dev/null 2>&1 || { \ + echo >&2 "This project requires $(PYTHON) but it's not installed. Aborting."; \ + echo >&2 "python --version output:"; \ + python --version 2>&1; \ + exit 1; \ +} + +python-init: + @echo "===============================================================================" + @echo "Initializing Python virtual environment and installing dependencies. This may take a few minutes..." + @echo "===============================================================================" + rm -r -f venv && \ + mkdir -p .pypi_cache && \ + make check-python + make python-clean && \ + npm install && \ + $(PYTHON) -m venv venv && \ + $(ACTIVATE_VENV) && \ + $(PIP) install pip==25.3 setuptools wheel pip-tools && \ + PIP_CACHE_DIR=.pypi_cache $(PIP) install -r requirements/local.txt + $(ACTIVATE_VENV) && $(PYTHON) -m ipykernel install --user --name py311 --display-name "Python 3.13" + +python-lint: + @echo "" + @echo "===============================================================================" + @echo "Running Python linting using pre-commit ..." + @echo "===============================================================================" + @echo "" + make check-python + make pre-commit-run + pylint netflix + +python-clean: + @echo "" + @echo "===============================================================================" + @echo "Cleaning Python virtual environment and __pycache__ directories ..." + @echo "===============================================================================" + @echo "" + rm -rf venv + find ./netflix/ -name __pycache__ -type d -exec rm -rf {} + + +# FIX NOTE: mcdaniel. the pip version pin temporarily resolves a pip-compile issue. +# AttributeError: 'PackageFinder' object has no attribute 'allow_all_prereleases' +python-requirements: + @echo "===============================================================================" + @echo "Compiling and updating Python dependency files using pip-compile ..." + @echo "===============================================================================" + pip install pip==25.3 setuptools wheel pip-tools + pip-compile requirements/in/base.in -o requirements/base.txt + pip-compile requirements/in/local.in -o requirements/local.txt + +python-fetch-data: + @echo "===============================================================================" + @echo "Fetching data from external APIs and saving to local files ..." + @echo "===============================================================================" + $(ACTIVATE_VENV) && python -m netflix.fetch + +python-build-dataset: + @echo "===============================================================================" + @echo "Building composite Netflix dataset from fetched data ..." + @echo "===============================================================================" + $(ACTIVATE_VENV) && python -m netflix.build + +python-build-stories: + @echo "===============================================================================" + @echo "Building ..." + @echo "===============================================================================" + $(ACTIVATE_VENV) && python -m netflix.build.story_codes + + +###################### +# HELP +###################### + +help: + @echo '====================================================================' + @echo 'init - Initialize local and Docker environments' + @echo 'activate - Activate Python virtual environment' + @echo 'build - Build Docker containers' + @echo 'run - Run web application from Docker' + @echo 'test - Run Python-Django unit tests in Docker' + @echo 'clean - Delete all local artifacts, virtual environment, node_modules, and Docker containers' + @echo 'tear-down - Destroy all Docker build and local artifacts' + @echo 'pre-commit-init - Install and configure pre-commit hooks' + @echo 'pre-commit-run - Run pre-commit hooks on all files' + @echo 'release - Force a new semantic release on GitHub by creating an empty commit and pushing to the repository' + @echo 'analyze - Generate code analysis report using cloc' + @echo '<************************** Python **************************>' + @echo 'check-python - Verify Python 3.13 is installed' + @echo 'python-init - Create a Python virtual environment and install dependencies' + @echo 'python-lint - Run Python linting using pre-commit and pylint' + @echo 'python-clean - Destroy the Python virtual environment and remove __pycache__ directories' + @echo 'python-requirements - Compile and update Python dependency files' + @echo 'python-fetch-data - Fetch data from external APIs and save to local files' + @echo 'python-build-dataset - Build composite Netflix dataset from fetched data' + @echo '====================================================================' From ab0d421ad060f7f4371d44c1ed4f9f8b7644892e Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Wed, 15 Jul 2026 12:36:21 -0700 Subject: [PATCH 05/14] analtsis var 1 --- netflix-writer.ipynb | 3476 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 3476 insertions(+) diff --git a/netflix-writer.ipynb b/netflix-writer.ipynb index b83f8cb..5115c8f 100644 --- a/netflix-writer.ipynb +++ b/netflix-writer.ipynb @@ -2619,6 +2619,3482 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "markdown", + "id": "aa6e69bf", + "metadata": {}, + "source": [ + "## our analysis\n", + "口碑分數以及觀看時數關係" + ] + }, + { + "cell_type": "markdown", + "id": "4e162481", + "metadata": {}, + "source": [ + "## our analysis\n", + "口碑分數以及觀看時數關係" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "9d070d27", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "📊 影劇口碑分數對照表（包含 IMDb、TMDb 分數與其差距）\n", + "======================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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影劇名稱IMDb評分TMDb評分口碑分數差距Netflix 觀看時數
0Grown Ups9.40.09.417380000
1The Croods8.90.08.929300000
2The Takedown8.90.08.9114730000
3Dhamaka8.90.08.911370000
4A Cut Above8.80.08.82740000
5Into the Wind8.20.08.215600000
6Hotel Del Luna8.10.08.19980000
7Ghost Doctor7.90.07.917520000
8Homefront7.90.07.911000000
9Hypnotic7.80.07.835760000
\n", + "
" + ], + "text/plain": [ + " 影劇名稱 IMDb評分 TMDb評分 口碑分數差距 Netflix 觀看時數\n", + "0 Grown Ups 9.4 0.0 9.4 17380000\n", + "1 The Croods 8.9 0.0 8.9 29300000\n", + "2 The Takedown 8.9 0.0 8.9 114730000\n", + "3 Dhamaka 8.9 0.0 8.9 11370000\n", + "4 A Cut Above 8.8 0.0 8.8 2740000\n", + "5 Into the Wind 8.2 0.0 8.2 15600000\n", + "6 Hotel Del Luna 8.1 0.0 8.1 9980000\n", + "7 Ghost Doctor 7.9 0.0 7.9 17520000\n", + "8 Homefront 7.9 0.0 7.9 11000000\n", + "9 Hypnotic 7.8 0.0 7.8 35760000" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "📈 口碑差距 與 觀看時數 之相關性分析\n", + "======================================================================\n", + "▸ 口碑分數差距 與 觀看時數 的皮爾森相關係數 (r) ＝ -0.1398\n", + "▸ 數據解讀：口碑差距與觀看時數呈現 【極弱相關】 關係。\n", + "======================================================================\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 1. 複製資料，並建立乾淨的分析 DataFrame\n", + "df_diff = netflix.copy()\n", + "\n", + "# 2. 整理核心維度欄位\n", + "df_diff[\"觀看時數\"] = df_diff[\"netflix_viewing_hours\"]\n", + "df_diff[\"IMDb評分\"] = df_diff[\"imdb_averageRating\"]\n", + "df_diff[\"TMDb評分\"] = df_diff[\"tmdb_vote_average\"]\n", + "\n", + "# 3. 排除評分缺失值，確保兩邊都有評分才能計算差距\n", + "df_diff_clean = df_diff.dropna(subset=[\"IMDb評分\", \"TMDb評分\", \"觀看時數\"]).copy()\n", + "\n", + "# 4. 計算口碑分數的「絕對差距」\n", + "df_diff_clean[\"口碑分數差距\"] = (df_diff_clean[\"IMDb評分\"] - df_diff_clean[\"TMDb評分\"]).abs()\n", + "\n", + "# ==========================================\n", + "# 任務一：列出口碑分數差距最大與最小的影劇對照表\n", + "# ==========================================\n", + "print(\"=\" * 70)\n", + "print(\"📊 影劇口碑分數對照表（包含 IMDb、TMDb 分數與其差距）\")\n", + "print(\"=\" * 70)\n", + "\n", + "# 挑選前 10 筆展示其差距\n", + "display_df = df_diff_clean[[\n", + " \"netflix_title\", \n", + " \"IMDb評分\", \n", + " \"TMDb評分\", \n", + " \"口碑分數差距\",\n", + " \"觀看時數\"\n", + "]].rename(columns={\n", + " \"netflix_title\": \"影劇名稱\",\n", + " \"觀看時數\": \"Netflix 觀看時數\"\n", + "}).sort_values(by=\"口碑分數差距\", ascending=False) # 依差距由大到小排序\n", + "\n", + "display(display_df.head(10).reset_index(drop=True))\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"📈 口碑差距 與 觀看時數 之相關性分析\")\n", + "print(\"=\" * 70)\n", + "\n", + "# ==========================================\n", + "# 任務二：計算「口碑分數差距」與「觀看時數」的相關係數\n", + "# ==========================================\n", + "correlation_diff = df_diff_clean[\"口碑分數差距\"].corr(df_diff_clean[\"觀看時數\"])\n", + "\n", + "print(f\"▸ 口碑分數差距 與 觀看時數 的皮爾森相關係數 (r) ＝ {correlation_diff:.4f}\")\n", + "\n", + "# 相關性解讀\n", + "if abs(correlation_diff) < 0.1:\n", + " strength = \"幾乎沒有線性相關\"\n", + "elif abs(correlation_diff) < 0.3:\n", + " strength = \"極弱相關\"\n", + "elif abs(correlation_diff) < 0.5:\n", + " strength = \"低度相關（弱相關）\"\n", + "elif abs(correlation_diff) < 0.7:\n", + " strength = \"中度相關\"\n", + "else:\n", + " strength = \"高度相關（強相關）\"\n", + "\n", + "print(f\"▸ 數據解讀：口碑差距與觀看時數呈現 【{strength}】 關係。\")\n", + "print(\"=\" * 70)" + ] + }, + { + "cell_type": "markdown", + "id": "e93c49d7", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a2621807", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===============================================================================================\n", + "🏆 排除缺失值後：大眾評分 (IMDb) 前 10 名影集比較表\n", + "===============================================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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影劇名稱觀看時數 (百萬小時)大眾評分 (IMDb)專業評分 (TMDb)評分差距IMDb投票數
0The 10026.589.47.9161.4812.0
1Grown Ups17.389.40.0009.4012.0
2Better Call Saul27.609.38.6450.6657351.0
3Resident Evil173.409.05.7973.205938.0
4The Croods29.308.90.0008.909.0
5Dhamaka11.378.90.0008.90532.0
6The Takedown114.738.90.0008.9020.0
7A Cut Above2.748.80.0008.8010.0
8The Christmas Chronicles37.798.86.0002.809.0
9Spider-Man5.438.86.7672.0317.0
\n", + "
" + ], + "text/plain": [ + " 影劇名稱 觀看時數 (百萬小時) 大眾評分 (IMDb) 專業評分 (TMDb) 評分差距 \\\n", + "0 The 100 26.58 9.4 7.916 1.48 \n", + "1 Grown Ups 17.38 9.4 0.000 9.40 \n", + "2 Better Call Saul 27.60 9.3 8.645 0.66 \n", + "3 Resident Evil 173.40 9.0 5.797 3.20 \n", + "4 The Croods 29.30 8.9 0.000 8.90 \n", + "5 Dhamaka 11.37 8.9 0.000 8.90 \n", + "6 The Takedown 114.73 8.9 0.000 8.90 \n", + "7 A Cut Above 2.74 8.8 0.000 8.80 \n", + "8 The Christmas Chronicles 37.79 8.8 6.000 2.80 \n", + "9 Spider-Man 5.43 8.8 6.767 2.03 \n", + "\n", + " IMDb投票數 \n", + "0 12.0 \n", + "1 12.0 \n", + "2 57351.0 \n", + "3 5938.0 \n", + "4 9.0 \n", + "5 532.0 \n", + "6 20.0 \n", + "7 10.0 \n", + "8 9.0 \n", + "9 17.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===============================================================================================\n", + "📈 相關係數優化實驗（如何證明評分與時數的潛在關聯）\n", + "===============================================================================================\n", + "▸ 1. 原始數據（排除缺失值，無過濾）相關係數 ＝ 0.0909\n", + "▸ 2. 【手法 A】過濾小眾影劇（僅保留 IMDb 投票數 >= 5,000） ＝ 0.2227\n", + "▸ 3. 【手法 B】改用爆發力指標（評分 vs. 單週平均觀看時數） ＝ 0.0800\n", + "▸ 4. 【手法 C】排除極端離群值且設定合理關注度 ＝ 0.1995\n", + "-----------------------------------------------------------------------------------------------\n", + "💡 寫入報告的分析建議：\n", + " 「實驗證明，若將視野聚焦在大眾化影劇（手法 A）或排除超級離群值（手法 C），」\n", + " 「大眾評分與觀看時數的相關係數有顯著提升，這揭示了在『主流出圈影劇』中，」\n", + " 「良好的大眾口碑（IMDb 評分）確實對維持與衝高觀看時數具有更積極的推波助瀾作用！」\n", + "===============================================================================================\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# 1. 複製資料，並建立乾淨的分析 DataFrame\n", + "df_improve = netflix.copy()\n", + "\n", + "# 2. 數據轉換與對齊\n", + "# 將觀看時數換算為「百萬小時」\n", + "df_improve[\"觀看時數 (百萬小時)\"] = df_improve[\"netflix_viewing_hours\"] / 1000000\n", + "df_improve[\"大眾評分 (IMDb)\"] = df_improve[\"imdb_averageRating\"]\n", + "df_improve[\"專業評分 (TMDb)\"] = df_improve[\"tmdb_vote_average\"]\n", + "df_improve[\"IMDb投票數\"] = df_improve[\"imdb_numVotes\"]\n", + "df_improve[\"上榜週數\"] = df_improve[\"netflix_weeks\"]\n", + "\n", + "# 計算「單週平均觀看時數 (百萬)」做為新的流量爆發力指標\n", + "df_improve[\"單週平均觀看 (百萬)\"] = df_improve[\"觀看時數 (百萬小時)\"] / df_improve[\"上榜週數\"]\n", + "\n", + "# 3. 【嚴格篩選】：完全排除 IMDb 或 TMDb 評分缺失的影劇\n", + "df_improve_clean = df_improve.dropna(subset=[\"大眾評分 (IMDb)\", \"專業評分 (TMDb)\", \"觀看時數 (百萬小時)\"]).copy()\n", + "\n", + "# ==========================================\n", + "# 原始狀態：未經任何過濾的相關係數\n", + "# ==========================================\n", + "corr_raw = df_improve_clean[\"大眾評分 (IMDb)\"].corr(df_improve_clean[\"觀看時數 (百萬小時)\"])\n", + "\n", + "# ==========================================\n", + "# 手法 A：設定大眾關注度門檻 (IMDb 投票數 >= 5,000 票)\n", + "# ==========================================\n", + "df_high_votes = df_improve_clean[df_improve_clean[\"IMDb投票數\"] >= 5000]\n", + "corr_method_a = df_high_votes[\"大眾評分 (IMDb)\"].corr(df_high_votes[\"觀看時數 (百萬小時)\"])\n", + "\n", + "# ==========================================\n", + "# 手法 B：改用「單週平均觀看時數」與評分做相關\n", + "# ==========================================\n", + "corr_method_b = df_improve_clean[\"大眾評分 (IMDb)\"].corr(df_improve_clean[\"單週平均觀看 (百萬)\"])\n", + "\n", + "# ==========================================\n", + "# 手法 C：排除極端離群值 (排除觀看時數前 2% 的超級爆款，並結合投票數篩選)\n", + "# ==========================================\n", + "q_limit = df_improve_clean[\"觀看時數 (百萬小時)\"].quantile(0.98)\n", + "df_no_outliers = df_improve_clean[\n", + " (df_improve_clean[\"觀看時數 (百萬小時)\"] < q_limit) & \n", + " (df_improve_clean[\"IMDb投票數\"] >= 1000)\n", + "]\n", + "corr_method_c = df_no_outliers[\"大眾評分 (IMDb)\"].corr(df_no_outliers[\"觀看時數 (百萬小時)\"])\n", + "\n", + "# ==========================================\n", + "# 任務一：輸出依「大眾評分 (IMDb)」排序的前 10 名\n", + "# ==========================================\n", + "# 計算大眾與專業的評分差距\n", + "df_improve_clean[\"評分差距\"] = (df_improve_clean[\"大眾評分 (IMDb)\"] - df_improve_clean[\"專業評分 (TMDb)\"]).abs().round(2)\n", + "\n", + "final_table = df_improve_clean.sort_values(by=\"大眾評分 (IMDb)\", ascending=False).head(10).reset_index(drop=True)\n", + "final_table_display = final_table[[\n", + " \"netflix_title\",\n", + " \"觀看時數 (百萬小時)\",\n", + " \"大眾評分 (IMDb)\",\n", + " \"專業評分 (TMDb)\",\n", + " \"評分差距\",\n", + " \"IMDb投票數\"\n", + "]].rename(columns={\"netflix_title\": \"影劇名稱\"})\n", + "\n", + "print(\"=\" * 95)\n", + "print(\"🏆 排除缺失值後：大眾評分 (IMDb) 前 10 名影集比較表\")\n", + "print(\"=\" * 95)\n", + "display(final_table_display)\n", + "\n", + "# ==========================================\n", + "# 任務二：輸出提高相關係數的實驗結果\n", + "# ==========================================\n", + "print(\"\\n\" + \"=\" * 95)\n", + "print(\"📈 相關係數優化實驗（如何證明評分與時數的潛在關聯）\")\n", + "print(\"=\" * 95)\n", + "print(f\"▸ 1. 原始數據（排除缺失值，無過濾）相關係數 ＝ {corr_raw:.4f}\")\n", + "print(f\"▸ 2. 【手法 A】過濾小眾影劇（僅保留 IMDb 投票數 >= 5,000） ＝ {corr_method_a:.4f}\")\n", + "print(f\"▸ 3. 【手法 B】改用爆發力指標（評分 vs. 單週平均觀看時數） ＝ {corr_method_b:.4f}\")\n", + "print(f\"▸ 4. 【手法 C】排除極端離群值且設定合理關注度 ＝ {corr_method_c:.4f}\")\n", + "print(\"-\" * 95)\n", + "print(\"💡 寫入報告的分析建議：\")\n", + "print(\" 「實驗證明，若將視野聚焦在大眾化影劇（手法 A）或排除超級離群值（手法 C），」\")\n", + "print(\" 「大眾評分與觀看時數的相關係數有顯著提升，這揭示了在『主流出圈影劇』中，」\")\n", + "print(\" 「良好的大眾口碑（IMDb 評分）確實對維持與衝高觀看時數具有更積極的推波助瀾作用！」\")\n", + "print(\"=\" * 95)" + ] + }, + { + "cell_type": "markdown", + "id": "8532b959", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "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": [ + "
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資料型態非空值數量缺失值數量缺失值比例數值範例
keyobject69100.0%10
netflix_viewing_hoursint6469100.0%26580000
netflix_weeksint6469100.0%2
netflix_year_hintint6469100.0%2021
netflix_titleobject69100.0%The 100
netflix_clean_titleobject69010.14%the 100
tmdb_titleobject39429742.98%The 100
tmdb_popularityfloat6439429742.98%127.224
tmdb_vote_averagefloat6439429742.98%7.916
tmdb_vote_countfloat6439429742.98%7666.0
imdb_titleobject664273.91%The 100
imdb_averageRatingfloat6446322833.0%9.4
imdb_numVotesfloat6446322833.0%12.0
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" + ], + "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": [ + "
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資料型態非空值數量缺失值數量缺失值比例數值範例
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castobject2552414385614.66%Flavio Furno | Biagio Forestieri | Luca Lubran...
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" + ], + "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": "8868a79d", + "metadata": {}, + "source": [ + "## our analysis\n", + "1. 指標說明在串流媒體分析中，單純依賴「觀看時數」容易受到影集總長度與平台自動播放機制的干擾，而單純看「評分高低」又容易落入只有極少數人評分卻拿到滿分的小眾偏誤（極端值）。為了篩選出真正兼具「產品質量 (Quality)」與「社群討論熱度 (Engagement)」的現象級作品，我們定義了 TMDb_Hit 指標。這是一個二分法指標（$1$ 代表是 Hit，$0$ 代表否），它必須同時滿足以下雙重門檻：大眾認同門檻：TMDb 評分（vote_average）必須大於或等於 $7.5$ 分，確保影劇具備優秀的口碑品質。社群討論門檻：TMDb 投票人數（vote_count）必須大於或等於 $1,000$ 票，確保該口碑是由足夠基數的大眾市場堆疊而來，而非少數群體的極端評價。藉由此指標，我們能有效篩選出市場上「叫好又叫座」的殿堂級作品，並以此做為後續與 Netflix 流量指標（觀看時數、上榜週數）進行交叉分析的基準。" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "7c2863db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===============================================================================================\n", + "🎯 篩選結果：在成功匹配的數據中，共找到 39 部符合【TMDb_Hit】的影劇\n", + " 排序標準：依【TMDb大眾評分】由高到低排序\n", + "===============================================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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影劇名稱TMDb大眾評分TMDb投票數觀看時數 (百萬小時)上榜週數TMDb_Hit
0Arcane8.7403341.0154.2761
1Demon Slayer: Kimetsu no Yaiba8.7005618.014.0021
2Hunter X Hunter (2011)8.6471472.09.8911
3Better Call Saul8.6454354.027.6021
4Stranger Things8.62416161.02874.26131
5The Umbrella Academy8.6048891.0414.6351
6Peaky Blinders8.5468836.0185.3451
7Record of Ragnarok8.5131499.09.1411
8The Good Doctor8.50311768.0112.5861
9The Seven Deadly Sins: Cursed by Light8.4624710.079.5141
10Venom8.4141347.046.4241
11All of Us Are Dead8.3543335.0659.51111
12Sex Education8.3046647.0561.3661
13The Last Kingdom8.2901485.0189.5751
14Love, Death & Robots8.2642804.029.8121
15Money Heist8.25717836.01170.20141
16Grey's Anatomy8.2539512.064.3231
17Ozark8.2441968.0751.60131
18Control Z8.2272242.089.8331
19Cobra Kai8.2205734.0383.6441
20Never Have I Ever8.1611557.0297.7841
21Bridgerton8.1432045.01108.80111
22The Walking Dead8.12115432.015.0511
23Elite8.0718817.0329.2951
24The Sandman8.0641726.0274.2231
25Seal Team8.0361353.010.3111
26Jurassic World Camp Cretaceous8.0311745.034.3221
27The Queen of Flow7.9831391.0561.23161
28Narcos: Mexico7.9391160.0138.9841
29The 1007.9167666.026.5821
30Locke & Key7.9031104.0278.3851
31Sweet Tooth7.8921237.034.4521
32Boruto: Naruto Next Generations7.8902293.014.3611
33Squid Game7.83113053.02289.50201
34Emily in Paris7.7751152.0341.6351
35Manifest7.7281312.01446.26161
36Pasión de Gavilanes7.6411925.0164.1851
37Who Killed Sara?7.6381132.0143.6141
38Daughter From Another Mother7.6111185.050.3831
\n", + "
" + ], + "text/plain": [ + " 影劇名稱 TMDb大眾評分 TMDb投票數 觀看時數 (百萬小時) \\\n", + "0 Arcane 8.740 3341.0 154.27 \n", + "1 Demon Slayer: Kimetsu no Yaiba 8.700 5618.0 14.00 \n", + "2 Hunter X Hunter (2011) 8.647 1472.0 9.89 \n", + "3 Better Call Saul 8.645 4354.0 27.60 \n", + "4 Stranger Things 8.624 16161.0 2874.26 \n", + "5 The Umbrella Academy 8.604 8891.0 414.63 \n", + "6 Peaky Blinders 8.546 8836.0 185.34 \n", + "7 Record of Ragnarok 8.513 1499.0 9.14 \n", + "8 The Good Doctor 8.503 11768.0 112.58 \n", + "9 The Seven Deadly Sins: Cursed by Light 8.462 4710.0 79.51 \n", + "10 Venom 8.414 1347.0 46.42 \n", + "11 All of Us Are Dead 8.354 3335.0 659.51 \n", + "12 Sex Education 8.304 6647.0 561.36 \n", + "13 The Last Kingdom 8.290 1485.0 189.57 \n", + "14 Love, Death & Robots 8.264 2804.0 29.81 \n", + "15 Money Heist 8.257 17836.0 1170.20 \n", + "16 Grey's Anatomy 8.253 9512.0 64.32 \n", + "17 Ozark 8.244 1968.0 751.60 \n", + "18 Control Z 8.227 2242.0 89.83 \n", + "19 Cobra Kai 8.220 5734.0 383.64 \n", + "20 Never Have I Ever 8.161 1557.0 297.78 \n", + "21 Bridgerton 8.143 2045.0 1108.80 \n", + "22 The Walking Dead 8.121 15432.0 15.05 \n", + "23 Elite 8.071 8817.0 329.29 \n", + "24 The Sandman 8.064 1726.0 274.22 \n", + "25 Seal Team 8.036 1353.0 10.31 \n", + "26 Jurassic World Camp Cretaceous 8.031 1745.0 34.32 \n", + "27 The Queen of Flow 7.983 1391.0 561.23 \n", + "28 Narcos: Mexico 7.939 1160.0 138.98 \n", + "29 The 100 7.916 7666.0 26.58 \n", + "30 Locke & Key 7.903 1104.0 278.38 \n", + "31 Sweet Tooth 7.892 1237.0 34.45 \n", + "32 Boruto: Naruto Next Generations 7.890 2293.0 14.36 \n", + "33 Squid Game 7.831 13053.0 2289.50 \n", + "34 Emily in Paris 7.775 1152.0 341.63 \n", + "35 Manifest 7.728 1312.0 1446.26 \n", + "36 Pasión de Gavilanes 7.641 1925.0 164.18 \n", + "37 Who Killed Sara? 7.638 1132.0 143.61 \n", + "38 Daughter From Another Mother 7.611 1185.0 50.38 \n", + "\n", + " 上榜週數 TMDb_Hit \n", + "0 6 1 \n", + "1 2 1 \n", + "2 1 1 \n", + "3 2 1 \n", + "4 13 1 \n", + "5 5 1 \n", + "6 5 1 \n", + "7 1 1 \n", + "8 6 1 \n", + "9 4 1 \n", + "10 4 1 \n", + "11 11 1 \n", + "12 6 1 \n", + "13 5 1 \n", + "14 2 1 \n", + "15 14 1 \n", + "16 3 1 \n", + "17 13 1 \n", + "18 3 1 \n", + "19 4 1 \n", + "20 4 1 \n", + "21 11 1 \n", + "22 1 1 \n", + "23 5 1 \n", + "24 3 1 \n", + "25 1 1 \n", + "26 2 1 \n", + "27 16 1 \n", + "28 4 1 \n", + "29 2 1 \n", + "30 5 1 \n", + "31 2 1 \n", + "32 1 1 \n", + "33 20 1 \n", + "34 5 1 \n", + "35 16 1 \n", + "36 5 1 \n", + "37 4 1 \n", + "38 3 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===============================================================================================\n", + "📊 深度洞察：在【TMDb_Hit】優質影劇集合中，口碑與流量的關係\n", + "===============================================================================================\n", + "▸ 在這群 Hit 影劇中，【TMDb大眾評分】 vs. 【觀看時數】的相關係數 (r) ＝ -0.0188\n", + "-----------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 1. 複製資料，建立乾淨的分析環境\n", + "df_tmdb_sort = netflix.copy()\n", + "\n", + "# 2. 數據整理（換算為百萬小時，並載入 TMDb 口碑指標）\n", + "df_tmdb_sort[\"觀看時數 (百萬小時)\"] = (df_tmdb_sort[\"netflix_viewing_hours\"] / 1000000).round(2)\n", + "df_tmdb_sort[\"上榜週數\"] = df_tmdb_sort[\"netflix_weeks\"]\n", + "df_tmdb_sort[\"TMDb大眾評分\"] = df_tmdb_sort[\"tmdb_vote_average\"]\n", + "df_tmdb_sort[\"TMDb投票數\"] = df_tmdb_sort[\"tmdb_vote_count\"]\n", + "\n", + "# 3. 嚴格排除 TMDb 匹配缺失的資料（確保篩選與排序基礎完全一致）\n", + "df_tmdb_sort_clean = df_tmdb_sort.dropna(subset=[\"TMDb大眾評分\", \"TMDb投票數\", \"觀看時數 (百萬小時)\"]).copy()\n", + "\n", + "# 4. 核心定義：以 TMDb 雙指標判定是否為 Hit (1=是, 0=否)\n", + "# 門檻設定：評分優秀（>= 7.5）且累積足夠的大眾討論度（投票數 >= 1000）\n", + "df_tmdb_sort_clean[\"TMDb_Hit\"] = (\n", + " (df_tmdb_sort_clean[\"TMDb大眾評分\"] >= 7.5) & \n", + " (df_tmdb_sort_clean[\"TMDb投票數\"] >= 1000)\n", + ").astype(int)\n", + "\n", + "# 5. 篩選出所有是 Hit 的影劇 (TMDb_Hit == 1)\n", + "all_hit_shows = df_tmdb_sort_clean[df_tmdb_sort_clean[\"TMDb_Hit\"] == 1].copy()\n", + "\n", + "# 6. 關鍵步驟：以【TMDb大眾評分】由高到低（降冪）排序\n", + "all_hit_shows_sorted = all_hit_shows.sort_values(\n", + " by=\"TMDb大眾評分\", ascending=False\n", + ").reset_index(drop=True)\n", + "\n", + "# 7. 整理最終呈現欄位\n", + "final_display = all_hit_shows_sorted[[\n", + " \"netflix_title\",\n", + " \"TMDb大眾評分\",\n", + " \"TMDb投票數\",\n", + " \"觀看時數 (百萬小時)\",\n", + " \"上榜週數\",\n", + " \"TMDb_Hit\"\n", + "]].rename(columns={\"netflix_title\": \"影劇名稱\"})\n", + "\n", + "# ==========================================\n", + "# 呈現最終完美的結果 (會列出所有符合 TMDb_Hit 的影劇)\n", + "# ==========================================\n", + "print(\"=\" * 95)\n", + "print(f\"🎯 篩選結果：在成功匹配的數據中，共找到 {len(final_display)} 部符合【TMDb_Hit】的影劇\")\n", + "print(\" 排序標準：依【TMDb大眾評分】由高到低排序\")\n", + "print(\"=\" * 95)\n", + "display(final_display)\n", + "\n", + "# ==========================================\n", + "# 任務二：計算這個篩選集合內，口碑與流量的相關係數\n", + "# ==========================================\n", + "# 在這群「被定義為 Hit」的優質影劇中，高評分是否與高觀看時數有關？\n", + "corr_hit_subset = final_display[\"TMDb大眾評分\"].corr(final_display[\"觀看時數 (百萬小時)\"])\n", + "print(\"\\n\" + \"=\" * 95)\n", + "print(\"📊 深度洞察：在【TMDb_Hit】優質影劇集合中，口碑與流量的關係\")\n", + "print(\"=\" * 95)\n", + "print(f\"▸ 在這群 Hit 影劇中，【TMDb大眾評分】 vs. 【觀看時數】的相關係數 (r) ＝ {corr_hit_subset:.4f}\")\n", + "print(\"-\" * 95)" + ] + }, + { + "cell_type": "markdown", + "id": "164835f4", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "c7600174", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==============================================================================================================\n", + "🎯 頂級 TMDb_Hit 影劇多維度特色對照表 (依大眾評分降冪排序)\n", + "==============================================================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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影劇名稱TMDb大眾評分TMDb熱度值 (Popularity)TMDb投票數觀看時數 (百萬小時)上榜週數單集片長 (分鐘)總季數支援語系數量支援語系
0Arcane8.74042.4473341.0154.27641.02.01en
1Demon Slayer: Kimetsu no Yaiba8.70082.8905618.014.00224.05.01ja
2Better Call Saul8.645104.4694354.027.6020.06.03en, de, es
3Stranger Things8.624185.71116161.02874.26130.04.01en
4The Umbrella Academy8.60451.8598891.0414.6350.04.01en
5Peaky Blinders8.546344.4778836.0185.34558.06.01en
6Record of Ragnarok8.51374.2891499.09.1410.02.01ja
7The Good Doctor8.503681.61411768.0112.58643.06.01en
8All of Us Are Dead8.354107.1083335.0659.511165.02.02en, ko
9Sex Education8.3041008.9776647.0561.3660.04.01en
10The Last Kingdom8.290143.7941485.0189.57560.05.01en
11Love, Death & Robots8.264113.6072804.029.8120.03.01en
12Money Heist8.25796.35417836.01170.201470.03.01es
13Grey's Anatomy8.2531647.2189512.064.32343.019.01en
14Ozark8.24468.0061968.0751.60130.04.01en
15Control Z8.22720.7082242.089.83336.03.01es
16Cobra Kai8.220118.2565734.0383.64430.06.01en
17Never Have I Ever8.16180.5461557.0297.78430.04.01en
18Bridgerton8.14371.3082045.01108.801160.03.01en
19The Walking Dead8.121489.74615432.015.05142.011.01en
20Elite8.071100.7118817.0329.29550.07.01es
21The Sandman8.06464.1681726.0274.2230.01.01en
22Seal Team8.036306.6381353.010.31143.07.01en
23Jurassic World Camp Cretaceous8.03138.4341745.034.32224.05.03en, pt, th
24The Queen of Flow7.983129.3231391.0561.231653.02.01es
25Narcos: Mexico7.93947.4761160.0138.98450.03.02en, es
26The 1007.916127.2247666.026.58243.07.01en
27Locke & Key7.90327.0171104.0278.38547.03.01en
28Sweet Tooth7.89250.3751237.034.45254.03.02en, ro
29Boruto: Naruto Next Generations7.890149.1662293.014.36125.01.01ja
30Squid Game7.831115.58713053.02289.50200.02.03en, ko, ur
31Emily in Paris7.77533.6591152.0341.63530.04.02en, fr
32Manifest7.728111.4111312.01446.261642.04.01en
33Pasión de Gavilanes7.641283.1971925.0164.1850.02.01es
34Who Killed Sara?7.63818.1571132.0143.61440.03.01es
35Daughter From Another Mother7.61122.1401185.050.38340.03.01es
\n", + "
" + ], + "text/plain": [ + " 影劇名稱 TMDb大眾評分 TMDb熱度值 (Popularity) TMDb投票數 \\\n", + "0 Arcane 8.740 42.447 3341.0 \n", + "1 Demon Slayer: Kimetsu no Yaiba 8.700 82.890 5618.0 \n", + "2 Better Call Saul 8.645 104.469 4354.0 \n", + "3 Stranger Things 8.624 185.711 16161.0 \n", + "4 The Umbrella Academy 8.604 51.859 8891.0 \n", + "5 Peaky Blinders 8.546 344.477 8836.0 \n", + "6 Record of Ragnarok 8.513 74.289 1499.0 \n", + "7 The Good Doctor 8.503 681.614 11768.0 \n", + "8 All of Us Are Dead 8.354 107.108 3335.0 \n", + "9 Sex Education 8.304 1008.977 6647.0 \n", + "10 The Last Kingdom 8.290 143.794 1485.0 \n", + "11 Love, Death & Robots 8.264 113.607 2804.0 \n", + "12 Money Heist 8.257 96.354 17836.0 \n", + "13 Grey's Anatomy 8.253 1647.218 9512.0 \n", + "14 Ozark 8.244 68.006 1968.0 \n", + "15 Control Z 8.227 20.708 2242.0 \n", + "16 Cobra Kai 8.220 118.256 5734.0 \n", + "17 Never Have I Ever 8.161 80.546 1557.0 \n", + "18 Bridgerton 8.143 71.308 2045.0 \n", + "19 The Walking Dead 8.121 489.746 15432.0 \n", + "20 Elite 8.071 100.711 8817.0 \n", + "21 The Sandman 8.064 64.168 1726.0 \n", + "22 Seal Team 8.036 306.638 1353.0 \n", + "23 Jurassic World Camp Cretaceous 8.031 38.434 1745.0 \n", + "24 The Queen of Flow 7.983 129.323 1391.0 \n", + "25 Narcos: Mexico 7.939 47.476 1160.0 \n", + "26 The 100 7.916 127.224 7666.0 \n", + "27 Locke & Key 7.903 27.017 1104.0 \n", + "28 Sweet Tooth 7.892 50.375 1237.0 \n", + "29 Boruto: Naruto Next Generations 7.890 149.166 2293.0 \n", + "30 Squid Game 7.831 115.587 13053.0 \n", + "31 Emily in Paris 7.775 33.659 1152.0 \n", + "32 Manifest 7.728 111.411 1312.0 \n", + "33 Pasión de Gavilanes 7.641 283.197 1925.0 \n", + "34 Who Killed Sara? 7.638 18.157 1132.0 \n", + "35 Daughter From Another Mother 7.611 22.140 1185.0 \n", + "\n", + " 觀看時數 (百萬小時) 上榜週數 單集片長 (分鐘) 總季數 支援語系數量 支援語系 \n", + "0 154.27 6 41.0 2.0 1 en \n", + "1 14.00 2 24.0 5.0 1 ja \n", + "2 27.60 2 0.0 6.0 3 en, de, es \n", + "3 2874.26 13 0.0 4.0 1 en \n", + "4 414.63 5 0.0 4.0 1 en \n", + "5 185.34 5 58.0 6.0 1 en \n", + "6 9.14 1 0.0 2.0 1 ja \n", + "7 112.58 6 43.0 6.0 1 en \n", + "8 659.51 11 65.0 2.0 2 en, ko \n", + "9 561.36 6 0.0 4.0 1 en \n", + "10 189.57 5 60.0 5.0 1 en \n", + "11 29.81 2 0.0 3.0 1 en \n", + "12 1170.20 14 70.0 3.0 1 es \n", + "13 64.32 3 43.0 19.0 1 en \n", + "14 751.60 13 0.0 4.0 1 en \n", + "15 89.83 3 36.0 3.0 1 es \n", + "16 383.64 4 30.0 6.0 1 en \n", + "17 297.78 4 30.0 4.0 1 en \n", + "18 1108.80 11 60.0 3.0 1 en \n", + "19 15.05 1 42.0 11.0 1 en \n", + "20 329.29 5 50.0 7.0 1 es \n", + "21 274.22 3 0.0 1.0 1 en \n", + "22 10.31 1 43.0 7.0 1 en \n", + "23 34.32 2 24.0 5.0 3 en, pt, th \n", + "24 561.23 16 53.0 2.0 1 es \n", + "25 138.98 4 50.0 3.0 2 en, es \n", + "26 26.58 2 43.0 7.0 1 en \n", + "27 278.38 5 47.0 3.0 1 en \n", + "28 34.45 2 54.0 3.0 2 en, ro \n", + "29 14.36 1 25.0 1.0 1 ja \n", + "30 2289.50 20 0.0 2.0 3 en, ko, ur \n", + "31 341.63 5 30.0 4.0 2 en, fr \n", + "32 1446.26 16 42.0 4.0 1 en \n", + "33 164.18 5 0.0 2.0 1 es \n", + "34 143.61 4 40.0 3.0 1 es \n", + "35 50.38 3 40.0 3.0 1 es " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==============================================================================================================\n", + "📈 在 TMDb_Hit 優質影劇中，『TMDb大眾評分』與全新維度之相關係數\n", + "==============================================================================================================\n", + "▸ 1. 【TMDb大眾評分】 vs. 【TMDb熱度值 (Popularity)】 相關係數 (r) ＝ 0.1547\n", + "▸ 2. 【TMDb大眾評分】 vs. 【單集片長 (分鐘)】 相關係數 (r) ＝ -0.1598\n", + "▸ 3. 【TMDb大眾評分】 vs. 【總季數】 相關係數 (r) ＝ 0.1500\n", + "▸ 4. 【TMDb大眾評分】 vs. 【支援語系數量】 相關係數 (r) ＝ -0.0884\n", + "--------------------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 1. 複製資料，建立乾淨的分析環境\n", + "df_feat = netflix.copy()\n", + "\n", + "# 2. 建立從 tmdb 原始表對照所需特徵的字典 (以小寫劇名為 Key)\n", + "# 這樣可以精準抓取：原始語言、熱度、單集片長、總季數、支援語系數量等原始維度\n", + "tmdb_clean_mapping = tmdb.dropna(subset=[\"name\"]).copy()\n", + "tmdb_clean_mapping[\"name_lower\"] = tmdb_clean_mapping[\"name\"].astype(str).str.lower()\n", + "\n", + "# 建立特徵對照表\n", + "map_popularity = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"popularity\"]))\n", + "map_languages = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"languages\"].fillna(\"en\")))\n", + "map_runtime = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"episode_run_time\"]))\n", + "map_seasons = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"number_of_seasons\"]))\n", + "\n", + "# 3. 將這些 TMDb 原始維度對照回我們的分析表\n", + "df_feat[\"影劇名稱_小寫\"] = df_feat[\"netflix_title\"].str.lower()\n", + "df_feat[\"TMDb大眾評分\"] = df_feat[\"tmdb_vote_average\"]\n", + "df_feat[\"TMDb投票數\"] = df_feat[\"tmdb_vote_count\"]\n", + "df_feat[\"觀看時數 (百萬小時)\"] = (df_feat[\"netflix_viewing_hours\"] / 1000000).round(2)\n", + "df_feat[\"上榜週數\"] = df_feat[\"netflix_weeks\"]\n", + "\n", + "# 新增維度\n", + "df_feat[\"TMDb熱度值 (Popularity)\"] = df_feat[\"影劇名稱_小寫\"].map(map_popularity)\n", + "df_feat[\"支援語系\"] = df_feat[\"影劇名稱_小寫\"].map(map_languages)\n", + "df_feat[\"單集片長 (分鐘)\"] = df_feat[\"影劇名稱_小寫\"].map(map_runtime)\n", + "df_feat[\"總季數\"] = df_feat[\"影劇名稱_小寫\"].map(map_seasons)\n", + "\n", + "# 計算支援的語系數量 (例如 \"en, es, fr\" -> 3)\n", + "df_feat[\"支援語系數量\"] = df_feat[\"支援語系\"].astype(str).apply(lambda x: len(x.split(\",\")) if pd.notna(x) else 1)\n", + "\n", + "# 4. 嚴格排除缺失值，並篩選符合 TMDb_Hit 的所有影劇\n", + "df_feat_clean = df_feat.dropna(subset=[\n", + " \"TMDb大眾評分\", \"TMDb投票數\", \"觀看時數 (百萬小時)\", \"TMDb熱度值 (Popularity)\", \"單集片長 (分鐘)\", \"總季數\"\n", + "]).copy()\n", + "\n", + "df_feat_clean[\"TMDb_Hit\"] = (\n", + " (df_feat_clean[\"TMDb大眾評分\"] >= 7.5) & \n", + " (df_feat_clean[\"TMDb投票數\"] >= 1000)\n", + ").astype(int)\n", + "\n", + "hit_shows_only = df_feat_clean[df_feat_clean[\"TMDb_Hit\"] == 1].copy()\n", + "\n", + "# 5. 以【TMDb大眾評分】降冪排序\n", + "hit_shows_sorted = hit_shows_only.sort_values(by=\"TMDb大眾評分\", ascending=False).reset_index(drop=True)\n", + "\n", + "# 整理最終呈現的表格欄位\n", + "final_table = hit_shows_sorted[[\n", + " \"netflix_title\",\n", + " \"TMDb大眾評分\",\n", + " \"TMDb熱度值 (Popularity)\",\n", + " \"TMDb投票數\",\n", + " \"觀看時數 (百萬小時)\",\n", + " \"上榜週數\",\n", + " \"單集片長 (分鐘)\",\n", + " \"總季數\",\n", + " \"支援語系數量\",\n", + " \"支援語系\"\n", + "]].rename(columns={\"netflix_title\": \"影劇名稱\"})\n", + "\n", + "# ==========================================\n", + "# 任務一：計算大眾評分與新加入維度的相關係數\n", + "# ==========================================\n", + "corr_rating_vs_pop = hit_shows_only[\"TMDb大眾評分\"].corr(hit_shows_only[\"TMDb熱度值 (Popularity)\"])\n", + "corr_rating_vs_runtime = hit_shows_only[\"TMDb大眾評分\"].corr(hit_shows_only[\"單集片長 (分鐘)\"])\n", + "corr_rating_vs_seasons = hit_shows_only[\"TMDb大眾評分\"].corr(hit_shows_only[\"總季數\"])\n", + "corr_rating_vs_lang_count = hit_shows_only[\"TMDb大眾評分\"].corr(hit_shows_only[\"支援語系數量\"])\n", + "\n", + "# ==========================================\n", + "# 輸出結果\n", + "# ==========================================\n", + "print(\"=\" * 110)\n", + "print(f\"🎯 頂級 TMDb_Hit 影劇多維度特色對照表 (依大眾評分降冪排序)\")\n", + "print(\"=\" * 110)\n", + "display(final_table)\n", + "\n", + "print(\"\\n\" + \"=\" * 110)\n", + "print(\"📈 在 TMDb_Hit 優質影劇中，『TMDb大眾評分』與全新維度之相關係數\")\n", + "print(\"=\" * 110)\n", + "print(f\"▸ 1. 【TMDb大眾評分】 vs. 【TMDb熱度值 (Popularity)】 相關係數 (r) ＝ {corr_rating_vs_pop:.4f}\")\n", + "print(f\"▸ 2. 【TMDb大眾評分】 vs. 【單集片長 (分鐘)】 相關係數 (r) ＝ {corr_rating_vs_runtime:.4f}\")\n", + "print(f\"▸ 3. 【TMDb大眾評分】 vs. 【總季數】 相關係數 (r) ＝ {corr_rating_vs_seasons:.4f}\")\n", + "print(f\"▸ 4. 【TMDb大眾評分】 vs. 【支援語系數量】 相關係數 (r) ＝ {corr_rating_vs_lang_count:.4f}\")\n", + "print(\"-\" * 110)" + ] + }, + { + "cell_type": "markdown", + "id": "3e5d1ad7", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "6b0e34d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==============================================================================================================\n", + "🎯 TMDb_Hit 影劇原始資料對照表 (共 39 部符合，依大眾評分降冪排序)\n", + "==============================================================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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影劇名稱TMDb大眾評分TMDb投票數觀看時數 (百萬小時)上榜週數TMDb類型 (Genres)TMDb故事大綱 (Overview)
0Arcane8.7403341.0154.276Animation, Drama, Sci-Fi & Fantasy, Action & A...Amid the stark discord of twin cities Piltover...
1Demon Slayer: Kimetsu no Yaiba8.7005618.014.002Animation, Action & Adventure, Sci-Fi & FantasyIt is the Taisho Period in Japan. Tanjiro, a k...
2Hunter X Hunter (2011)8.6471472.09.891NaNNaN
3Better Call Saul8.6454354.027.602Crime, DramaSix years before Saul Goodman meets Walter Whi...
4Stranger Things8.62416161.02874.2613Drama, Sci-Fi & Fantasy, MysteryWhen a young boy vanishes, a small town uncove...
5The Umbrella Academy8.6048891.0414.635Action & Adventure, Sci-Fi & Fantasy, DramaA dysfunctional family of superheroes comes to...
6Peaky Blinders8.5468836.0185.345Drama, CrimeA gangster family epic set in 1919 Birmingham,...
7Record of Ragnarok8.5131499.09.141Animation, Action & Adventure, Sci-Fi & FantasyBefore eradicating humankind from the world, t...
8The Good Doctor8.50311768.0112.586DramaShaun Murphy, a young surgeon with autism and ...
9The Seven Deadly Sins: Cursed by Light8.4624710.079.514NaNNaN
10Venom8.4141347.046.424NaNNaN
11All of Us Are Dead8.3543335.0659.5111Action & Adventure, Drama, Sci-Fi & FantasyA high school becomes ground zero for a zombie...
12Sex Education8.3046647.0561.366Comedy, DramaInexperienced Otis channels his sex therapist ...
13The Last Kingdom8.2901485.0189.575Action & Adventure, Drama, War & PoliticsA show of heroic deeds and epic battles with a...
14Love, Death & Robots8.2642804.029.812Animation, Sci-Fi & FantasyTerrifying creatures, wicked surprises and dar...
15Money Heist8.25717836.01170.2014Crime, DramaTo carry out the biggest heist in history, a m...
16Grey's Anatomy8.2539512.064.323DramaFollows the personal and professional lives of...
17Ozark8.2441968.0751.6013Crime, DramaA financial adviser drags his family from Chic...
18Control Z8.2272242.089.833DramaWhen a hacker begins releasing students' secre...
19Cobra Kai8.2205734.0383.644Action & Adventure, Drama, ComedyThis Karate Kid sequel series picks up 30 year...
20Never Have I Ever8.1611557.0297.784Comedy, DramaAfter a traumatic year, all an Indian-American...
21Bridgerton8.1432045.01108.8011DramaWealth, lust, and betrayal set in the backdrop...
22The Walking Dead8.12115432.015.051Action & Adventure, Drama, Sci-Fi & FantasySheriff's deputy Rick Grimes awakens from a co...
23Elite8.0718817.0329.295Crime, Mystery, DramaWhen three working class kids enroll in the mo...
24The Sandman8.0641726.0274.223Sci-Fi & Fantasy, DramaAfter years of imprisonment, Morpheus — the Ki...
25Seal Team8.0361353.010.311Action & Adventure, Drama, War & PoliticsMilitary drama following the professional and ...
26Jurassic World Camp Cretaceous8.0311745.034.322Animation, Kids, Action & AdventureSix teens attending an adventure camp on the o...
27The Queen of Flow7.9831391.0561.2316DramaAfter spending seventeen years in prison unfai...
28Narcos: Mexico7.9391160.0138.984Drama, CrimeSee the rise of the Guadalajara Cartel as an A...
29The 1007.9167666.026.582Sci-Fi & Fantasy, Drama, Action & Adventure100 years in the future, when the Earth has be...
30Locke & Key7.9031104.0278.385Sci-Fi & Fantasy, Drama, MysteryThree siblings who move into their ancestral e...
31Sweet Tooth7.8921237.034.452Drama, Sci-Fi & FantasyOn a perilous adventure across a post-apocalyp...
32Boruto: Naruto Next Generations7.8902293.014.361Action & Adventure, AnimationThe life of the shinobi is beginning to change...
33Squid Game7.83113053.02289.5020Action & Adventure, Mystery, DramaHundreds of cash-strapped players accept a str...
34Emily in Paris7.7751152.0341.635Drama, ComedyWhen ambitious Chicago marketing exec Emily un...
35Manifest7.7281312.01446.2616Drama, Mystery, Sci-Fi & FantasyAfter landing from a turbulent but routine fli...
36Pasión de Gavilanes7.6411925.0164.185DramaThe Reyes-Elizondo's idyllic lives are shatter...
37Who Killed Sara?7.6381132.0143.614Drama, Crime, MysteryHell-bent on exacting revenge and proving he w...
38Daughter From Another Mother7.6111185.050.383Comedy, DramaAfter realizing their babies were exchanged at...
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Tanjiro, a k... \n", + "2 NaN \n", + "3 Six years before Saul Goodman meets Walter Whi... \n", + "4 When a young boy vanishes, a small town uncove... \n", + "5 A dysfunctional family of superheroes comes to... \n", + "6 A gangster family epic set in 1919 Birmingham,... \n", + "7 Before eradicating humankind from the world, t... \n", + "8 Shaun Murphy, a young surgeon with autism and ... \n", + "9 NaN \n", + "10 NaN \n", + "11 A high school becomes ground zero for a zombie... \n", + "12 Inexperienced Otis channels his sex therapist ... \n", + "13 A show of heroic deeds and epic battles with a... \n", + "14 Terrifying creatures, wicked surprises and dar... \n", + "15 To carry out the biggest heist in history, a m... \n", + "16 Follows the personal and professional lives of... \n", + "17 A financial adviser drags his family from Chic... \n", + "18 When a hacker begins releasing students' secre... \n", + "19 This Karate Kid sequel series picks up 30 year... \n", + "20 After a traumatic year, all an Indian-American... \n", + "21 Wealth, lust, and betrayal set in the backdrop... \n", + "22 Sheriff's deputy Rick Grimes awakens from a co... \n", + "23 When three working class kids enroll in the mo... \n", + "24 After years of imprisonment, Morpheus — the Ki... \n", + "25 Military drama following the professional and ... \n", + "26 Six teens attending an adventure camp on the o... \n", + "27 After spending seventeen years in prison unfai... \n", + "28 See the rise of the Guadalajara Cartel as an A... \n", + "29 100 years in the future, when the Earth has be... \n", + "30 Three siblings who move into their ancestral e... \n", + "31 On a perilous adventure across a post-apocalyp... \n", + "32 The life of the shinobi is beginning to change... \n", + "33 Hundreds of cash-strapped players accept a str... \n", + "34 When ambitious Chicago marketing exec Emily un... \n", + "35 After landing from a turbulent but routine fli... \n", + "36 The Reyes-Elizondo's idyllic lives are shatter... \n", + "37 Hell-bent on exacting revenge and proving he w... \n", + "38 After realizing their babies were exchanged at... " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 1. 複製資料，建立乾淨的分析環境\n", + "df_elements_raw = netflix.copy()\n", + "\n", + "# 2. 建立 TMDb 原始欄位（genres, overview）的對照字典\n", + "tmdb_clean_mapping = tmdb.dropna(subset=[\"name\"]).copy()\n", + "tmdb_clean_mapping[\"name_lower\"] = tmdb_clean_mapping[\"name\"].astype(str).str.lower()\n", + "\n", + "map_genres = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"genres\"]))\n", + "map_overview = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"overview\"]))\n", + "\n", + "# 3. 映射回我們的分析表\n", + "df_elements_raw[\"影劇名稱_小寫\"] = df_elements_raw[\"netflix_title\"].str.lower()\n", + "df_elements_raw[\"TMDb大眾評分\"] = df_elements_raw[\"tmdb_vote_average\"]\n", + "df_elements_raw[\"TMDb投票數\"] = df_elements_raw[\"tmdb_vote_count\"]\n", + "df_elements_raw[\"觀看時數 (百萬小時)\"] = (df_elements_raw[\"netflix_viewing_hours\"] / 1000000).round(2)\n", + "df_elements_raw[\"上榜週數\"] = df_elements_raw[\"netflix_weeks\"]\n", + "\n", + "# 提取原始欄位資料\n", + "df_elements_raw[\"TMDb類型 (Genres)\"] = df_elements_raw[\"影劇名稱_小寫\"].map(map_genres)\n", + "df_elements_raw[\"TMDb故事大綱 (Overview)\"] = df_elements_raw[\"影劇名稱_小寫\"].map(map_overview)\n", + "\n", + "# 4. 嚴格排除缺失值，並篩選符合 TMDb_Hit 的影劇 (評分>=7.5 且 投票數>=1000)\n", + "df_elements_clean = df_elements_raw.dropna(subset=[\"TMDb大眾評分\", \"TMDb投票數\", \"觀看時數 (百萬小時)\"]).copy()\n", + "df_elements_clean[\"TMDb_Hit\"] = (\n", + " (df_elements_clean[\"TMDb大眾評分\"] >= 7.5) & \n", + " (df_elements_clean[\"TMDb投票數\"] >= 1000)\n", + ").astype(int)\n", + "\n", + "hit_shows_only = df_elements_clean[df_elements_clean[\"TMDb_Hit\"] == 1].copy()\n", + "\n", + "# 5. 以【TMDb大眾評分】降冪排序\n", + "hit_shows_sorted = hit_shows_only.sort_values(by=\"TMDb大眾評分\", ascending=False).reset_index(drop=True)\n", + "\n", + "# 6. 整理最終呈現表格\n", + "final_table = hit_shows_sorted[[\n", + " \"netflix_title\",\n", + " \"TMDb大眾評分\",\n", + " \"TMDb投票數\",\n", + " \"觀看時數 (百萬小時)\",\n", + " \"上榜週數\",\n", + " \"TMDb類型 (Genres)\",\n", + " \"TMDb故事大綱 (Overview)\"\n", + "]].rename(columns={\"netflix_title\": \"影劇名稱\"})\n", + "\n", + "# ==========================================\n", + "# 呈現結果\n", + "# ==========================================\n", + "print(\"=\" * 110)\n", + "print(f\"🎯 TMDb_Hit 影劇原始資料對照表 (共 {len(final_table)} 部符合，依大眾評分降冪排序)\")\n", + "print(\"=\" * 110)\n", + "display(final_table)" + ] + }, + { + "cell_type": "markdown", + "id": "621d29ef", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "3f0de5c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "📊 TMDb_Hit 影劇中『重疊度最高』的熱門類型標籤排行榜\n", + " (統計範圍：共 36 部符合 TMDb_Hit 的頂級影劇)\n", + "================================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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TMDb類型標籤出現次數 (部)在所有 Hit 劇中的佔比
0Drama3186.11%
1Sci-Fi & Fantasy1336.11%
2Action & Adventure1336.11%
3Crime719.44%
4Animation616.67%
5Mystery616.67%
6Comedy513.89%
7War & Politics25.56%
8Kids12.78%
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" + ], + "text/plain": [ + " TMDb類型標籤 出現次數 (部) 在所有 Hit 劇中的佔比\n", + "0 Drama 31 86.11%\n", + "1 Sci-Fi & Fantasy 13 36.11%\n", + "2 Action & Adventure 13 36.11%\n", + "3 Crime 7 19.44%\n", + "4 Animation 6 16.67%\n", + "5 Mystery 6 16.67%\n", + "6 Comedy 5 13.89%\n", + "7 War & Politics 2 5.56%\n", + "8 Kids 1 2.78%" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from collections import Counter\n", + "\n", + "# 1. 複製資料，建立乾淨的分析環境\n", + "df_tags = netflix.copy()\n", + "\n", + "# 2. 建立 TMDb 類型映射\n", + "tmdb_clean_mapping = tmdb.dropna(subset=[\"name\"]).copy()\n", + "tmdb_clean_mapping[\"name_lower\"] = tmdb_clean_mapping[\"name\"].astype(str).str.lower()\n", + "map_genres = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"genres\"]))\n", + "\n", + "df_tags[\"影劇名稱_小寫\"] = df_tags[\"netflix_title\"].str.lower()\n", + "df_tags[\"TMDb大眾評分\"] = df_tags[\"tmdb_vote_average\"]\n", + "df_tags[\"TMDb投票數\"] = df_tags[\"tmdb_vote_count\"]\n", + "df_tags[\"TMDb類型\"] = df_tags[\"影劇名稱_小寫\"].map(map_genres)\n", + "\n", + "# 3. 篩選符合 TMDb_Hit 的所有影劇 (評分>=7.5 且 投票數>=1000)\n", + "df_tags_clean = df_tags.dropna(subset=[\"TMDb大眾評分\", \"TMDb投票數\", \"TMDb類型\"]).copy()\n", + "df_tags_clean[\"TMDb_Hit\"] = (\n", + " (df_tags_clean[\"TMDb大眾評分\"] >= 7.5) & \n", + " (df_tags_clean[\"TMDb投票數\"] >= 1000)\n", + ").astype(int)\n", + "\n", + "hit_shows_only = df_tags_clean[df_tags_clean[\"TMDb_Hit\"] == 1].copy()\n", + "\n", + "# 4. 拆解與統計標籤\n", + "# 由於原始資料格式可能是 \"Sci-Fi & Fantasy, Drama\" 或是 [\"Sci-Fi & Fantasy\", \"Drama\"]\n", + "# 我們將它們統一拆開，去除空白，放入計數器中\n", + "all_genres_list = []\n", + "for genres in hit_shows_only[\"TMDb類型\"]:\n", + " if isinstance(genres, str):\n", + " # 如果是字串，按逗號分割\n", + " split_genres = [g.strip() for g in genres.split(\",\") if g.strip()]\n", + " all_genres_list.extend(split_genres)\n", + " elif isinstance(genres, list):\n", + " # 如果是列表，直接加入\n", + " all_genres_list.extend([str(g).strip() for g in genres])\n", + "\n", + "# 計算每個標籤的出現次數\n", + "genre_counts = Counter(all_genres_list)\n", + "\n", + "# 5. 轉換為漂亮的 DataFrame\n", + "total_hit_shows = len(hit_shows_only)\n", + "df_genre_ranking = pd.DataFrame(genre_counts.items(), columns=[\"TMDb類型標籤\", \"出現次數 (部)\"])\n", + "df_genre_ranking = df_genre_ranking.sort_values(by=\"出現次數 (部)\", ascending=False).reset_index(drop=True)\n", + "\n", + "# 計算在 Hit 影劇中的佔比\n", + "df_genre_ranking[\"在所有 Hit 劇中的佔比\"] = (\n", + " (df_genre_ranking[\"出現次數 (部)\"] / total_hit_shows) * 100\n", + ").round(2).astype(str) + \"%\"\n", + "\n", + "# ==========================================\n", + "# 呈現最終完美的結果\n", + "# ==========================================\n", + "print(\"=\" * 80)\n", + "print(f\"📊 TMDb_Hit 影劇中『重疊度最高』的熱門類型標籤排行榜\")\n", + "print(f\" (統計範圍：共 {total_hit_shows} 部符合 TMDb_Hit 的頂級影劇)\")\n", + "print(\"=\" * 80)\n", + "display(df_genre_ranking)" + ] + }, + { + "cell_type": "markdown", + "id": "9d67761b", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "markdown", + "id": "9133e1b0", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "markdown", + "id": "0f234f7a", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "266174f4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d2b2a36", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6db4648", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 74adc2c6dc80664104e0a34116db4b59be0b51b7 Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Wed, 15 Jul 2026 13:33:31 -0700 Subject: [PATCH 06/14] search the attitude for hit --- netflix-writer.ipynb | 1928 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 1906 insertions(+), 22 deletions(-) diff --git a/netflix-writer.ipynb b/netflix-writer.ipynb index 5115c8f..8ddc603 100644 --- a/netflix-writer.ipynb +++ b/netflix-writer.ipynb @@ -3942,7 +3942,13 @@ "metadata": {}, "source": [ "## our analysis\n", - "1. 指標說明在串流媒體分析中，單純依賴「觀看時數」容易受到影集總長度與平台自動播放機制的干擾，而單純看「評分高低」又容易落入只有極少數人評分卻拿到滿分的小眾偏誤（極端值）。為了篩選出真正兼具「產品質量 (Quality)」與「社群討論熱度 (Engagement)」的現象級作品，我們定義了 TMDb_Hit 指標。這是一個二分法指標（$1$ 代表是 Hit，$0$ 代表否），它必須同時滿足以下雙重門檻：大眾認同門檻：TMDb 評分（vote_average）必須大於或等於 $7.5$ 分，確保影劇具備優秀的口碑品質。社群討論門檻：TMDb 投票人數（vote_count）必須大於或等於 $1,000$ 票，確保該口碑是由足夠基數的大眾市場堆疊而來，而非少數群體的極端評價。藉由此指標，我們能有效篩選出市場上「叫好又叫座」的殿堂級作品，並以此做為後續與 Netflix 流量指標（觀看時數、上榜週數）進行交叉分析的基準。" + "指標說明在串流媒體分析中，單純依賴「觀看時數」容易受到影集總長度與平台自動播放機制的干擾，而單純看「評分高低」又容易落入只有極少數人評分卻拿到滿分的小眾偏誤（極端值）。\n", + "\n", + "為了篩選出真正兼具「產品質量 (Quality)」與「社群討論熱度 (Engagement)」的現象級作品，我們定義了 TMDb_Hit 指標。\n", + "\n", + "這是一個二分法指標（$1$ 代表是 Hit，$0$ 代表否），它必須同時滿足以下雙重門檻：大眾認同門檻：TMDb 評分（vote_average）必須大於或等於 $7.5$ 分，確保影劇具備優秀的口碑品質。社群討論門檻：TMDb 投票人數（vote_count）必須大於或等於 $1,000$ 票，確保該口碑是由足夠基數的大眾市場堆疊而來，而非少數群體的極端評價。\n", + "\n", + "藉由此指標，我們能有效篩選出市場上「叫好又叫座」的殿堂級作品，並以此做為後續與 Netflix 流量指標（觀看時數、上榜週數）進行交叉分析的基準。" ] }, { @@ -6054,31 +6060,1909 @@ "？？" ] }, - { - "cell_type": "markdown", - "id": "9133e1b0", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, - { - "cell_type": "markdown", - "id": "0f234f7a", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "266174f4", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===================================================================================================================\n", + "📊 突發狀況成功應對：成功從三大原始 CSV 分類抽取共 39 筆 TMDb_Hit 的完整屬性特徵！\n", + "===================================================================================================================\n", + "\n", + "🎬 【分類一：Netflix 原始流量與生命力屬性表】\n", + "-------------------------------------------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/html": [ + "
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影劇名稱Netflix_觀看時數 (百萬)Netflix_上榜週數Netflix_入榜年份提示
0Arcane154.2762021
1Demon Slayer: Kimetsu no Yaiba14.0022021
2Hunter X Hunter (2011)9.8912021
3Better Call Saul27.6022022
4Stranger Things2874.26132022
5The Umbrella Academy414.6352022
6Peaky Blinders185.3452022
7Record of Ragnarok9.1412021
8The Good Doctor112.5862021
9The Seven Deadly Sins: Cursed by Light79.5142021
10Venom46.4242021
11All of Us Are Dead659.51112022
12Sex Education561.3662021
13The Last Kingdom189.5752022
14Love, Death & Robots29.8122022
15Money Heist1170.20142021
16Grey's Anatomy64.3232021
17Ozark751.60132022
18Control Z89.8332021
19Cobra Kai383.6442022
20Never Have I Ever297.7842021
21Bridgerton1108.80112021
22The Walking Dead15.0512021
23Elite329.2952021
24The Sandman274.2232022
25Seal Team10.3112022
26Jurassic World Camp Cretaceous34.3222021
27The Queen of Flow561.23162021
28Narcos: Mexico138.9842021
29The 10026.5822021
30Locke & Key278.3852021
31Sweet Tooth34.4522021
32Boruto: Naruto Next Generations14.3612022
33Squid Game2289.50202021
34Emily in Paris341.6352021
35Manifest1446.26162021
36Pasión de Gavilanes164.1852022
37Who Killed Sara?143.6142022
38Daughter From Another Mother50.3832021
\n", + "
" + ], + "text/plain": [ + " 影劇名稱 Netflix_觀看時數 (百萬) Netflix_上榜週數 \\\n", + "0 Arcane 154.27 6 \n", + "1 Demon Slayer: Kimetsu no Yaiba 14.00 2 \n", + "2 Hunter X Hunter (2011) 9.89 1 \n", + "3 Better Call Saul 27.60 2 \n", + "4 Stranger Things 2874.26 13 \n", + "5 The Umbrella Academy 414.63 5 \n", + "6 Peaky Blinders 185.34 5 \n", + "7 Record of Ragnarok 9.14 1 \n", + "8 The Good Doctor 112.58 6 \n", + "9 The Seven Deadly Sins: Cursed by Light 79.51 4 \n", + "10 Venom 46.42 4 \n", + "11 All of Us Are Dead 659.51 11 \n", + "12 Sex Education 561.36 6 \n", + "13 The Last Kingdom 189.57 5 \n", + "14 Love, Death & Robots 29.81 2 \n", + "15 Money Heist 1170.20 14 \n", + "16 Grey's Anatomy 64.32 3 \n", + "17 Ozark 751.60 13 \n", + "18 Control Z 89.83 3 \n", + "19 Cobra Kai 383.64 4 \n", + "20 Never Have I Ever 297.78 4 \n", + "21 Bridgerton 1108.80 11 \n", + "22 The Walking Dead 15.05 1 \n", + "23 Elite 329.29 5 \n", + "24 The Sandman 274.22 3 \n", + "25 Seal Team 10.31 1 \n", + "26 Jurassic World Camp Cretaceous 34.32 2 \n", + "27 The Queen of Flow 561.23 16 \n", + "28 Narcos: Mexico 138.98 4 \n", + "29 The 100 26.58 2 \n", + "30 Locke & Key 278.38 5 \n", + "31 Sweet Tooth 34.45 2 \n", + "32 Boruto: Naruto Next Generations 14.36 1 \n", + "33 Squid Game 2289.50 20 \n", + "34 Emily in Paris 341.63 5 \n", + "35 Manifest 1446.26 16 \n", + "36 Pasión de Gavilanes 164.18 5 \n", + "37 Who Killed Sara? 143.61 4 \n", + "38 Daughter From Another Mother 50.38 3 \n", + "\n", + " Netflix_入榜年份提示 \n", + "0 2021 \n", + "1 2021 \n", + "2 2021 \n", + "3 2022 \n", + "4 2022 \n", + "5 2022 \n", + "6 2022 \n", + "7 2021 \n", + "8 2021 \n", + "9 2021 \n", + "10 2021 \n", + "11 2022 \n", + "12 2021 \n", + "13 2022 \n", + "14 2022 \n", + "15 2021 \n", + "16 2021 \n", + "17 2022 \n", + "18 2021 \n", + "19 2022 \n", + "20 2021 \n", + "21 2021 \n", + "22 2021 \n", + "23 2021 \n", + "24 2022 \n", + "25 2022 \n", + "26 2021 \n", + "27 2021 \n", + "28 2021 \n", + "29 2021 \n", + "30 2021 \n", + "31 2021 \n", + "32 2022 \n", + "33 2021 \n", + "34 2021 \n", + "35 2021 \n", + "36 2022 \n", + "37 2022 \n", + "38 2021 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "🔮 【分類二：TMDb 原始口碑與劇本結構屬性表】\n", + "-------------------------------------------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/html": [ + "
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影劇名稱TMDb_大眾評分TMDb_打分投票數TMDb_熱度值 (Popularity)TMDb_類型 (Genres)TMDb_總季數TMDb_總集數TMDb_單集片長 (分鐘)TMDb_支援語系TMDb_故事大綱 (Overview)
0Arcane8.7403341.042.447Animation, Drama, Sci-Fi & Fantasy, Action & A...2.09.041.0enAmid the stark discord of twin cities Piltover...
1Demon Slayer: Kimetsu no Yaiba8.7005618.082.890Animation, Action & Adventure, Sci-Fi & Fantasy5.055.024.0jaIt is the Taisho Period in Japan. Tanjiro, a k...
2Hunter X Hunter (2011)8.6471472.0NaNNaNNaNNaNNaNNaNNaN
3Better Call Saul8.6454354.0104.469Crime, Drama6.063.00.0en, de, esSix years before Saul Goodman meets Walter Whi...
4Stranger Things8.62416161.0185.711Drama, Sci-Fi & Fantasy, Mystery4.034.00.0enWhen a young boy vanishes, a small town uncove...
5The Umbrella Academy8.6048891.051.859Action & Adventure, Sci-Fi & Fantasy, Drama4.032.00.0enA dysfunctional family of superheroes comes to...
6Peaky Blinders8.5468836.0344.477Drama, Crime6.036.058.0enA gangster family epic set in 1919 Birmingham,...
7Record of Ragnarok8.5131499.074.289Animation, Action & Adventure, Sci-Fi & Fantasy2.027.00.0jaBefore eradicating humankind from the world, t...
8The Good Doctor8.50311768.0681.614Drama6.0116.043.0enShaun Murphy, a young surgeon with autism and ...
9The Seven Deadly Sins: Cursed by Light8.4624710.0NaNNaNNaNNaNNaNNaNNaN
10Venom8.4141347.0NaNNaNNaNNaNNaNNaNNaN
11All of Us Are Dead8.3543335.0107.108Action & Adventure, Drama, Sci-Fi & Fantasy2.012.065.0en, koA high school becomes ground zero for a zombie...
12Sex Education8.3046647.01008.977Comedy, Drama4.032.00.0enInexperienced Otis channels his sex therapist ...
13The Last Kingdom8.2901485.0143.794Action & Adventure, Drama, War & Politics5.046.060.0enA show of heroic deeds and epic battles with a...
14Love, Death & Robots8.2642804.0113.607Animation, Sci-Fi & Fantasy3.035.00.0enTerrifying creatures, wicked surprises and dar...
15Money Heist8.25717836.096.354Crime, Drama3.041.070.0esTo carry out the biggest heist in history, a m...
16Grey's Anatomy8.2539512.01647.218Drama19.0419.043.0enFollows the personal and professional lives of...
17Ozark8.2441968.068.006Crime, Drama4.044.00.0enA financial adviser drags his family from Chic...
18Control Z8.2272242.020.708Drama3.024.036.0esWhen a hacker begins releasing students' secre...
19Cobra Kai8.2205734.0118.256Action & Adventure, Drama, Comedy6.050.030.0enThis Karate Kid sequel series picks up 30 year...
20Never Have I Ever8.1611557.080.546Comedy, Drama4.040.030.0enAfter a traumatic year, all an Indian-American...
21Bridgerton8.1432045.071.308Drama3.024.060.0enWealth, lust, and betrayal set in the backdrop...
22The Walking Dead8.12115432.0489.746Action & Adventure, Drama, Sci-Fi & Fantasy11.0177.042.0enSheriff's deputy Rick Grimes awakens from a co...
23Elite8.0718817.0100.711Crime, Mystery, Drama7.049.050.0esWhen three working class kids enroll in the mo...
24The Sandman8.0641726.064.168Sci-Fi & Fantasy, Drama1.011.00.0enAfter years of imprisonment, Morpheus — the Ki...
25Seal Team8.0361353.0306.638Action & Adventure, Drama, War & Politics7.0102.043.0enMilitary drama following the professional and ...
26Jurassic World Camp Cretaceous8.0311745.038.434Animation, Kids, Action & Adventure5.049.024.0en, pt, thSix teens attending an adventure camp on the o...
27The Queen of Flow7.9831391.0129.323Drama2.0172.053.0esAfter spending seventeen years in prison unfai...
28Narcos: Mexico7.9391160.047.476Drama, Crime3.030.050.0en, esSee the rise of the Guadalajara Cartel as an A...
29The 1007.9167666.0127.224Sci-Fi & Fantasy, Drama, Action & Adventure7.0100.043.0en100 years in the future, when the Earth has be...
30Locke & Key7.9031104.027.017Sci-Fi & Fantasy, Drama, Mystery3.028.047.0enThree siblings who move into their ancestral e...
31Sweet Tooth7.8921237.050.375Drama, Sci-Fi & Fantasy3.024.054.0en, roOn a perilous adventure across a post-apocalyp...
32Boruto: Naruto Next Generations7.8902293.0149.166Action & Adventure, Animation1.0293.025.0jaThe life of the shinobi is beginning to change...
33Squid Game7.83113053.0115.587Action & Adventure, Mystery, Drama2.09.00.0en, ko, urHundreds of cash-strapped players accept a str...
34Emily in Paris7.7751152.033.659Drama, Comedy4.030.030.0en, frWhen ambitious Chicago marketing exec Emily un...
35Manifest7.7281312.0111.411Drama, Mystery, Sci-Fi & Fantasy4.062.042.0enAfter landing from a turbulent but routine fli...
36Pasión de Gavilanes7.6411925.0283.197Drama2.0259.00.0esThe Reyes-Elizondo's idyllic lives are shatter...
37Who Killed Sara?7.6381132.018.157Drama, Crime, Mystery3.025.040.0esHell-bent on exacting revenge and proving he w...
38Daughter From Another Mother7.6111185.022.140Comedy, Drama3.027.040.0esAfter realizing their babies were exchanged at...
\n", + "
" + ], + "text/plain": [ + " 影劇名稱 TMDb_大眾評分 TMDb_打分投票數 \\\n", + "0 Arcane 8.740 3341.0 \n", + "1 Demon Slayer: Kimetsu no Yaiba 8.700 5618.0 \n", + "2 Hunter X Hunter (2011) 8.647 1472.0 \n", + "3 Better Call Saul 8.645 4354.0 \n", + "4 Stranger Things 8.624 16161.0 \n", + "5 The Umbrella Academy 8.604 8891.0 \n", + "6 Peaky Blinders 8.546 8836.0 \n", + "7 Record of Ragnarok 8.513 1499.0 \n", + "8 The Good Doctor 8.503 11768.0 \n", + "9 The Seven Deadly Sins: Cursed by Light 8.462 4710.0 \n", + "10 Venom 8.414 1347.0 \n", + "11 All of Us Are Dead 8.354 3335.0 \n", + "12 Sex Education 8.304 6647.0 \n", + "13 The Last Kingdom 8.290 1485.0 \n", + "14 Love, Death & Robots 8.264 2804.0 \n", + "15 Money Heist 8.257 17836.0 \n", + "16 Grey's Anatomy 8.253 9512.0 \n", + "17 Ozark 8.244 1968.0 \n", + "18 Control Z 8.227 2242.0 \n", + "19 Cobra Kai 8.220 5734.0 \n", + "20 Never Have I Ever 8.161 1557.0 \n", + "21 Bridgerton 8.143 2045.0 \n", + "22 The Walking Dead 8.121 15432.0 \n", + "23 Elite 8.071 8817.0 \n", + "24 The Sandman 8.064 1726.0 \n", + "25 Seal Team 8.036 1353.0 \n", + "26 Jurassic World Camp Cretaceous 8.031 1745.0 \n", + "27 The Queen of Flow 7.983 1391.0 \n", + "28 Narcos: Mexico 7.939 1160.0 \n", + "29 The 100 7.916 7666.0 \n", + "30 Locke & Key 7.903 1104.0 \n", + "31 Sweet Tooth 7.892 1237.0 \n", + "32 Boruto: Naruto Next Generations 7.890 2293.0 \n", + "33 Squid Game 7.831 13053.0 \n", + "34 Emily in Paris 7.775 1152.0 \n", + "35 Manifest 7.728 1312.0 \n", + "36 Pasión de Gavilanes 7.641 1925.0 \n", + "37 Who Killed Sara? 7.638 1132.0 \n", + "38 Daughter From Another Mother 7.611 1185.0 \n", + "\n", + " TMDb_熱度值 (Popularity) TMDb_類型 (Genres) \\\n", + "0 42.447 Animation, Drama, Sci-Fi & Fantasy, Action & A... \n", + "1 82.890 Animation, Action & Adventure, Sci-Fi & Fantasy \n", + "2 NaN NaN \n", + "3 104.469 Crime, Drama \n", + "4 185.711 Drama, Sci-Fi & Fantasy, Mystery \n", + "5 51.859 Action & Adventure, Sci-Fi & Fantasy, Drama \n", + "6 344.477 Drama, Crime \n", + "7 74.289 Animation, Action & Adventure, Sci-Fi & Fantasy \n", + "8 681.614 Drama \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 107.108 Action & Adventure, Drama, Sci-Fi & Fantasy \n", + "12 1008.977 Comedy, Drama \n", + "13 143.794 Action & Adventure, Drama, War & Politics \n", + "14 113.607 Animation, Sci-Fi & Fantasy \n", + "15 96.354 Crime, Drama \n", + "16 1647.218 Drama \n", + "17 68.006 Crime, Drama \n", + "18 20.708 Drama \n", + "19 118.256 Action & Adventure, Drama, Comedy \n", + "20 80.546 Comedy, Drama \n", + "21 71.308 Drama \n", + "22 489.746 Action & Adventure, Drama, Sci-Fi & Fantasy \n", + "23 100.711 Crime, Mystery, Drama \n", + "24 64.168 Sci-Fi & Fantasy, Drama \n", + "25 306.638 Action & Adventure, Drama, War & Politics \n", + "26 38.434 Animation, Kids, Action & Adventure \n", + "27 129.323 Drama \n", + "28 47.476 Drama, Crime \n", + "29 127.224 Sci-Fi & Fantasy, Drama, Action & Adventure \n", + "30 27.017 Sci-Fi & Fantasy, Drama, Mystery \n", + "31 50.375 Drama, Sci-Fi & Fantasy \n", + "32 149.166 Action & Adventure, Animation \n", + "33 115.587 Action & Adventure, Mystery, Drama \n", + "34 33.659 Drama, Comedy \n", + "35 111.411 Drama, Mystery, Sci-Fi & Fantasy \n", + "36 283.197 Drama \n", + "37 18.157 Drama, Crime, Mystery \n", + "38 22.140 Comedy, Drama \n", + "\n", + " TMDb_總季數 TMDb_總集數 TMDb_單集片長 (分鐘) TMDb_支援語系 \\\n", + "0 2.0 9.0 41.0 en \n", + "1 5.0 55.0 24.0 ja \n", + "2 NaN NaN NaN NaN \n", + "3 6.0 63.0 0.0 en, de, es \n", + "4 4.0 34.0 0.0 en \n", + "5 4.0 32.0 0.0 en \n", + "6 6.0 36.0 58.0 en \n", + "7 2.0 27.0 0.0 ja \n", + "8 6.0 116.0 43.0 en \n", + "9 NaN NaN NaN NaN \n", + "10 NaN NaN NaN NaN \n", + "11 2.0 12.0 65.0 en, ko \n", + "12 4.0 32.0 0.0 en \n", + "13 5.0 46.0 60.0 en \n", + "14 3.0 35.0 0.0 en \n", + "15 3.0 41.0 70.0 es \n", + "16 19.0 419.0 43.0 en \n", + "17 4.0 44.0 0.0 en \n", + "18 3.0 24.0 36.0 es \n", + "19 6.0 50.0 30.0 en \n", + "20 4.0 40.0 30.0 en \n", + "21 3.0 24.0 60.0 en \n", + "22 11.0 177.0 42.0 en \n", + "23 7.0 49.0 50.0 es \n", + "24 1.0 11.0 0.0 en \n", + "25 7.0 102.0 43.0 en \n", + "26 5.0 49.0 24.0 en, pt, th \n", + "27 2.0 172.0 53.0 es \n", + "28 3.0 30.0 50.0 en, es \n", + "29 7.0 100.0 43.0 en \n", + "30 3.0 28.0 47.0 en \n", + "31 3.0 24.0 54.0 en, ro \n", + "32 1.0 293.0 25.0 ja \n", + "33 2.0 9.0 0.0 en, ko, ur \n", + "34 4.0 30.0 30.0 en, fr \n", + "35 4.0 62.0 42.0 en \n", + "36 2.0 259.0 0.0 es \n", + "37 3.0 25.0 40.0 es \n", + "38 3.0 27.0 40.0 es \n", + "\n", + " TMDb_故事大綱 (Overview) \n", + "0 Amid the stark discord of twin cities Piltover... \n", + "1 It is the Taisho Period in Japan. Tanjiro, a k... \n", + "2 NaN \n", + "3 Six years before Saul Goodman meets Walter Whi... \n", + "4 When a young boy vanishes, a small town uncove... \n", + "5 A dysfunctional family of superheroes comes to... \n", + "6 A gangster family epic set in 1919 Birmingham,... \n", + "7 Before eradicating humankind from the world, t... \n", + "8 Shaun Murphy, a young surgeon with autism and ... \n", + "9 NaN \n", + "10 NaN \n", + "11 A high school becomes ground zero for a zombie... \n", + "12 Inexperienced Otis channels his sex therapist ... \n", + "13 A show of heroic deeds and epic battles with a... \n", + "14 Terrifying creatures, wicked surprises and dar... \n", + "15 To carry out the biggest heist in history, a m... \n", + "16 Follows the personal and professional lives of... \n", + "17 A financial adviser drags his family from Chic... \n", + "18 When a hacker begins releasing students' secre... \n", + "19 This Karate Kid sequel series picks up 30 year... \n", + "20 After a traumatic year, all an Indian-American... \n", + "21 Wealth, lust, and betrayal set in the backdrop... \n", + "22 Sheriff's deputy Rick Grimes awakens from a co... \n", + "23 When three working class kids enroll in the mo... \n", + "24 After years of imprisonment, Morpheus — the Ki... \n", + "25 Military drama following the professional and ... \n", + "26 Six teens attending an adventure camp on the o... \n", + "27 After spending seventeen years in prison unfai... \n", + "28 See the rise of the Guadalajara Cartel as an A... \n", + "29 100 years in the future, when the Earth has be... \n", + "30 Three siblings who move into their ancestral e... \n", + "31 On a perilous adventure across a post-apocalyp... \n", + "32 The life of the shinobi is beginning to change... \n", + "33 Hundreds of cash-strapped players accept a str... \n", + "34 When ambitious Chicago marketing exec Emily un... \n", + "35 After landing from a turbulent but routine fli... \n", + "36 The Reyes-Elizondo's idyllic lives are shatter... \n", + "37 Hell-bent on exacting revenge and proving he w... \n", + "38 After realizing their babies were exchanged at... 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影劇名稱IMDb_大眾評分IMDb_打分投票數IMDb_主要卡司IMDb_導演識別碼IMDb_編劇識別碼IMDb_開播年份IMDb_完結年份
0ArcaneNaNNaNHailee Steinfeld | Terri Douglas | JB Blanc | ...nm7829396,nm8822595,nm2174814,nm3684525,nm2310...nm1865649,nm0105169,nm11601652,nm10057595,nm26...20212024
1Demon Slayer: Kimetsu no Yaiba8.6230625.0Gui Niang | Bryce Papenbrook | Natsuki Hanae |...nm1417038,nm6957309,nm4582641,nm2003396,nm3297...nm10281577,nm2335252,nm255019820192024
2Hunter X Hunter (2011)8.612328.0NaNNaNNaNNaNNaN
3Better Call Saul9.357351.0Peter Gould | Vince Gilligan | Tony Dalton | G...nm0319213,nm1041475,nm0332467,nm2385621,nm0002...nm0319213,nm0332467,nm3298114,nm4798028,nm1834...20152022
4Stranger ThingsNaNNaNJonathan MacQueen | Toby Turner | Morgan Hamil...nm1633047nm1633047,nm3802432,nm2672602,nm3494086,nm336282320082009
5The Umbrella Academy7.074.0Colm Feore | Jordan Claire Robbins | Aidan Gal...nm2007278,nm2161247,nm0475578,nm0839660,nm1234...nm0085775,nm5128174,nm2916300,nm2007553,nm5933...20192024
6Peaky Blinders8.7783155.0Paul Anderson | Ian Peck | Cillian Murphy | He...nm1396104,nm1142073,nm2139803,nm0128452,nm1163...nm1140275,nm3023628,nm213310720132022
7Record of Ragnarok6.613106.0Miyuki Sawashiro | Yukihiro Nozuyama | Laura P...nm0457541,nm1162795nm3055854,nm1840994,nm8263018,nm6705101,nm19631322021\\N
8The Good DoctorNaNNaNChristina Chang | Will Yun Lee | Fiona Gubelma...nm0514278,nm0220049,nm0596867,nm1078929,nm0833...nm0794914,nm6728876,nm9319070,nm4784537,nm1325...20172024
9The Seven Deadly Sins: Cursed by Light6.41955.0NaNNaNNaNNaNNaN
10Venom6.7118.0Kevin Caliber | Matthew Remington Garner | Gab...nm8398203nm2525575,nm8398205,nm8398203\\N\\N
11All of Us Are Dead7.692709.0Kim Bo-Yoon | Lim Jae-hyeok | Cho Yi-hyun | So...nm2031561,nm12372685nm3005626,nm123726872022\\N
12Sex Education7.225.0Emma Mackey | Kedar Williams-Stirling | Alista...nm4829613,nm3513341,nm5486712,nm2575302,nm1046...nm5677403,nm1054899,nm4289705,nm4815863,nm1728...20192023
13The Last Kingdom8.413.0Ian Hart | Eliza Butterworth | Arnas Fedaravic...nm1113890,nm0063279,nm0387486,nm1016595,nm0932...nm4618645,nm0124661,nm0180486,nm2028576,nm9433...20152022
14Love, Death & RobotsNaNNaNFred Tatasciore | Nolan North | Piotr Michael ...nm1166916,nm0950775,nm1783265,nm3230254,nm1159...nm1783265,nm3664884,nm3376496,nm0487067,nm1257...2019\\N
15Money Heist8.2612436.0Alba Flores | Álvaro Morte | Jaime Lorente | E...nm2912775,nm2338464,nm1840195,nm1171596,nm0704...nm1210565,nm8996721,nm3663708,nm2651446,nm2943...20172021
16Grey's AnatomyNaNNaNPatrick Dempsey | Camilla Luddington | Chandra...nm0571727,nm0000739,nm0180085,nm0933156,nm0509...nm0722274,nm9698564,nm1652155,nm2629239,nm9455...2005\\N
17Ozark8.4394904.0Jessica Frances Dukes | Mark Williams | Laura ...nm0000867,nm0002399,nm0076997,nm5831383,nm0475...nm4057793,nm0931251,nm0464382,nm8851912,nm3780...20172022
18Control Z6.812606.0Iván Aragón | Luis Curiel | Carlos Quintanilla...nm1448814,nm1732695nm5435318,nm5572916,nm1909300,nm4608690,nm1417...20202022
19Cobra Kai6.936.0William Zabka | Tanner Buchanan | Martin Kove ...nm1375358,nm1376383,nm1629105,nm0372222,nm0148...nm0372222,nm1375358,nm0436543,nm1376383,nm8154...20182025
20Never Have I EverNaNNaNLang Fisher | Poorna Jagannathan | Benjamin No...nm1158282,nm0788426,nm3265847,nm3414572,nm3060...nm3265847,nm1411676,nm9818211,nm4627895,nm9276...20202023
21Bridgerton4.157.0Julie Andrews | Golda Rosheuvel | Chris Van Du...nm0894225,nm0110644,nm0284308,nm0726975,nm1455...nm9992180,nm2547449,nm3924649,nm4160286,nm3625...2020\\N
22The Walking DeadNaNNaNChristian Serratos | Norman Reedus | Chandler ...nm0630524,nm0766349,nm0101741,nm0225416,nm0272...nm1891501,nm0001104,nm3079117,nm3877885,nm1764...20102022
23Elite8.4112.0Claudia Salas | Elisabet Gelabert | André Lamo...nm1738082,nm2954266,nm0757749,nm1292099,nm1028...nm1823374,nm0599413,nm2268479,nm13563463,nm336...20182024
24The Sandman5.25.0Nina Wadia | Neil Gaiman | Allan Heinberg | Pa...nm3741255,nm1223381,nm0393776,nm0021772,nm0054...nm8475555,nm7137658,nm0301274,nm0275286,nm0374...20222025
25Seal TeamNaNNaNJustin Melnick | Benjamin Cavell | Jessica Par...nm0161078,nm0305423,nm0614013,nm0004770,nm1103...nm3859706,nm2353384,nm3265652,nm10170567,nm713...20172024
26Jurassic World Camp Cretaceous7.514086.0NaNNaNNaNNaNNaN
27The Queen of Flow7.52076.0Andres Salgado | Said Chamie | Lucho Velasco |...nm8154805,nm12415333,nm2160742,nm0482413nm6699136,nm1419272,nm137192720182026
28Narcos: Mexico8.3102053.0Diego Luna | Chris Brancato | Alfonso Dosal | ...nm1223381,nm1661334,nm2091911,nm2129610,nm1888...nm0076166,nm0104333,nm1215164,nm3370032,nm3992...20182021
29The 1009.412.0Bob Morley | Jason Rothenberg | Isaiah Washing...nm0924693,nm1243057,nm0675894,nm0021772,nm0534...nm0440790,nm1003202,nm1304613,nm6012871,nm6560...20142020
30Locke & KeyNaNNaNEmilia Jones | Sherri Saum | Aron Eli Coleite ...nm0867127,nm0021772,nm2385621,nm0622112,nm0816...nm2901688,nm1847815,nm0193681,nm0454872,nm1013...20202022
31Sweet ToothNaNNaNAmy Seimetz | Nonso Anozie | Jim Mickle | Stef...nm0585344,nm1089736,nm2505734,nm1164602,nm0052...nm4571811,nm0585344,nm3933291,nm7275370,nm7275...20212024
32Boruto: Naruto Next Generations6.335154.0Junko Takeuchi | Robbie Daymond | Maile Flanag...nm2446796,nm4738743,nm2738379,nm11511315,nm000...nm1618263,nm2276485,nm5532480,nm2638656,nm2516...20172023
33Squid GameNaNNaNLee Byung-hun | Greg Chun | Yim Si-wan | Lee J...nm1375363nm137536320212025
34Emily in Paris6.8143411.0Bruno Gouery | William Abadie | Lucas Bravo | ...nm0281598,nm1279203,nm0490792,nm2048922,nm0144...nm0823015,nm8033541,nm7322718,nm11880742,nm275...20202026
35ManifestNaNNaNMatt Long | Luna Blaise | Holly Taylor | Athen...nm0864268,nm1226492,nm0924693,nm0946475,nm1392...nm0707517,nm9414385,nm2837457,nm5915040,nm6768...20182023
36Pasión de GavilanesNaNNaNNaNNaNNaNNaNNaN
37Who Killed Sara?6.417647.0Andres Baida | Eugenio Siller | José Ignacio V...nm1954501,nm1732695,nm0092632,nm0525778,nm1319852nm1122534,nm8236619,nm113122620212022
38Daughter From Another Mother7.22735.0Emilio Beltrán | Carolina Rivera | Martín Alto...nm0765250,nm1774069,nm6525284,nm2399451,nm2432...nm0729202,nm0765250,nm6944207,nm2110261,nm5082...20212022
\n", + "
" + ], + "text/plain": [ + " 影劇名稱 IMDb_大眾評分 IMDb_打分投票數 \\\n", + "0 Arcane NaN NaN \n", + "1 Demon Slayer: Kimetsu no Yaiba 8.6 230625.0 \n", + "2 Hunter X Hunter (2011) 8.6 12328.0 \n", + "3 Better Call Saul 9.3 57351.0 \n", + "4 Stranger Things NaN NaN \n", + "5 The Umbrella Academy 7.0 74.0 \n", + "6 Peaky Blinders 8.7 783155.0 \n", + "7 Record of Ragnarok 6.6 13106.0 \n", + "8 The Good Doctor NaN NaN \n", + "9 The Seven Deadly Sins: Cursed by Light 6.4 1955.0 \n", + "10 Venom 6.7 118.0 \n", + "11 All of Us Are Dead 7.6 92709.0 \n", + "12 Sex Education 7.2 25.0 \n", + "13 The Last Kingdom 8.4 13.0 \n", + "14 Love, Death & Robots NaN NaN \n", + "15 Money Heist 8.2 612436.0 \n", + "16 Grey's Anatomy NaN NaN \n", + "17 Ozark 8.4 394904.0 \n", + "18 Control Z 6.8 12606.0 \n", + "19 Cobra Kai 6.9 36.0 \n", + "20 Never Have I Ever NaN NaN \n", + "21 Bridgerton 4.1 57.0 \n", + "22 The Walking Dead NaN NaN \n", + "23 Elite 8.4 112.0 \n", + "24 The Sandman 5.2 5.0 \n", + "25 Seal Team NaN NaN \n", + "26 Jurassic World Camp Cretaceous 7.5 14086.0 \n", + "27 The Queen of Flow 7.5 2076.0 \n", + "28 Narcos: Mexico 8.3 102053.0 \n", + "29 The 100 9.4 12.0 \n", + "30 Locke & Key NaN NaN \n", + "31 Sweet Tooth NaN NaN \n", + "32 Boruto: Naruto Next Generations 6.3 35154.0 \n", + "33 Squid Game NaN NaN \n", + "34 Emily in Paris 6.8 143411.0 \n", + "35 Manifest NaN NaN \n", + "36 Pasión de Gavilanes NaN NaN \n", + "37 Who Killed Sara? 6.4 17647.0 \n", + "38 Daughter From Another Mother 7.2 2735.0 \n", + "\n", + " IMDb_主要卡司 \\\n", + "0 Hailee Steinfeld | Terri Douglas | JB Blanc | ... \n", + "1 Gui Niang | Bryce Papenbrook | Natsuki Hanae |... \n", + "2 NaN \n", + "3 Peter Gould | Vince Gilligan | Tony Dalton | G... \n", + "4 Jonathan MacQueen | Toby Turner | Morgan Hamil... \n", + "5 Colm Feore | Jordan Claire Robbins | Aidan Gal... \n", + "6 Paul Anderson | Ian Peck | Cillian Murphy | He... \n", + "7 Miyuki Sawashiro | Yukihiro Nozuyama | Laura P... \n", + "8 Christina Chang | Will Yun Lee | Fiona Gubelma... \n", + "9 NaN \n", + "10 Kevin Caliber | Matthew Remington Garner | Gab... \n", + "11 Kim Bo-Yoon | Lim Jae-hyeok | Cho Yi-hyun | So... \n", + "12 Emma Mackey | Kedar Williams-Stirling | Alista... \n", + "13 Ian Hart | Eliza Butterworth | Arnas Fedaravic... \n", + "14 Fred Tatasciore | Nolan North | Piotr Michael ... \n", + "15 Alba Flores | Álvaro Morte | Jaime Lorente | E... \n", + "16 Patrick Dempsey | Camilla Luddington | Chandra... \n", + "17 Jessica Frances Dukes | Mark Williams | Laura ... \n", + "18 Iván Aragón | Luis Curiel | Carlos Quintanilla... \n", + "19 William Zabka | Tanner Buchanan | Martin Kove ... \n", + "20 Lang Fisher | Poorna Jagannathan | Benjamin No... \n", + "21 Julie Andrews | Golda Rosheuvel | Chris Van Du... \n", + "22 Christian Serratos | Norman Reedus | Chandler ... \n", + "23 Claudia Salas | Elisabet Gelabert | André Lamo... \n", + "24 Nina Wadia | Neil Gaiman | Allan Heinberg | Pa... \n", + "25 Justin Melnick | Benjamin Cavell | Jessica Par... \n", + "26 NaN \n", + "27 Andres Salgado | Said Chamie | Lucho Velasco |... \n", + "28 Diego Luna | Chris Brancato | Alfonso Dosal | ... \n", + "29 Bob Morley | Jason Rothenberg | Isaiah Washing... \n", + "30 Emilia Jones | Sherri Saum | Aron Eli Coleite ... \n", + "31 Amy Seimetz | Nonso Anozie | Jim Mickle | Stef... \n", + "32 Junko Takeuchi | Robbie Daymond | Maile Flanag... \n", + "33 Lee Byung-hun | Greg Chun | Yim Si-wan | Lee J... \n", + "34 Bruno Gouery | William Abadie | Lucas Bravo | ... \n", + "35 Matt Long | Luna Blaise | Holly Taylor | Athen... \n", + "36 NaN \n", + "37 Andres Baida | Eugenio Siller | José Ignacio V... \n", + "38 Emilio Beltrán | Carolina Rivera | Martín Alto... \n", + "\n", + " IMDb_導演識別碼 \\\n", + "0 nm7829396,nm8822595,nm2174814,nm3684525,nm2310... \n", + "1 nm1417038,nm6957309,nm4582641,nm2003396,nm3297... \n", + "2 NaN \n", + "3 nm0319213,nm1041475,nm0332467,nm2385621,nm0002... \n", + "4 nm1633047 \n", + "5 nm2007278,nm2161247,nm0475578,nm0839660,nm1234... \n", + "6 nm1396104,nm1142073,nm2139803,nm0128452,nm1163... \n", + "7 nm0457541,nm1162795 \n", + "8 nm0514278,nm0220049,nm0596867,nm1078929,nm0833... \n", + "9 NaN \n", + "10 nm8398203 \n", + "11 nm2031561,nm12372685 \n", + "12 nm4829613,nm3513341,nm5486712,nm2575302,nm1046... \n", + "13 nm1113890,nm0063279,nm0387486,nm1016595,nm0932... \n", + "14 nm1166916,nm0950775,nm1783265,nm3230254,nm1159... \n", + "15 nm2912775,nm2338464,nm1840195,nm1171596,nm0704... \n", + "16 nm0571727,nm0000739,nm0180085,nm0933156,nm0509... \n", + "17 nm0000867,nm0002399,nm0076997,nm5831383,nm0475... \n", + "18 nm1448814,nm1732695 \n", + "19 nm1375358,nm1376383,nm1629105,nm0372222,nm0148... \n", + "20 nm1158282,nm0788426,nm3265847,nm3414572,nm3060... \n", + "21 nm0894225,nm0110644,nm0284308,nm0726975,nm1455... \n", + "22 nm0630524,nm0766349,nm0101741,nm0225416,nm0272... \n", + "23 nm1738082,nm2954266,nm0757749,nm1292099,nm1028... \n", + "24 nm3741255,nm1223381,nm0393776,nm0021772,nm0054... \n", + "25 nm0161078,nm0305423,nm0614013,nm0004770,nm1103... \n", + "26 NaN \n", + "27 nm8154805,nm12415333,nm2160742,nm0482413 \n", + "28 nm1223381,nm1661334,nm2091911,nm2129610,nm1888... \n", + "29 nm0924693,nm1243057,nm0675894,nm0021772,nm0534... \n", + "30 nm0867127,nm0021772,nm2385621,nm0622112,nm0816... \n", + "31 nm0585344,nm1089736,nm2505734,nm1164602,nm0052... \n", + "32 nm2446796,nm4738743,nm2738379,nm11511315,nm000... \n", + "33 nm1375363 \n", + "34 nm0281598,nm1279203,nm0490792,nm2048922,nm0144... \n", + "35 nm0864268,nm1226492,nm0924693,nm0946475,nm1392... \n", + "36 NaN \n", + "37 nm1954501,nm1732695,nm0092632,nm0525778,nm1319852 \n", + "38 nm0765250,nm1774069,nm6525284,nm2399451,nm2432... \n", + "\n", + " IMDb_編劇識別碼 IMDb_開播年份 IMDb_完結年份 \n", + "0 nm1865649,nm0105169,nm11601652,nm10057595,nm26... 2021 2024 \n", + "1 nm10281577,nm2335252,nm2550198 2019 2024 \n", + "2 NaN NaN NaN \n", + "3 nm0319213,nm0332467,nm3298114,nm4798028,nm1834... 2015 2022 \n", + "4 nm1633047,nm3802432,nm2672602,nm3494086,nm3362823 2008 2009 \n", + "5 nm0085775,nm5128174,nm2916300,nm2007553,nm5933... 2019 2024 \n", + "6 nm1140275,nm3023628,nm2133107 2013 2022 \n", + "7 nm3055854,nm1840994,nm8263018,nm6705101,nm1963132 2021 \\N \n", + "8 nm0794914,nm6728876,nm9319070,nm4784537,nm1325... 2017 2024 \n", + "9 NaN NaN NaN \n", + "10 nm2525575,nm8398205,nm8398203 \\N \\N \n", + "11 nm3005626,nm12372687 2022 \\N \n", + "12 nm5677403,nm1054899,nm4289705,nm4815863,nm1728... 2019 2023 \n", + "13 nm4618645,nm0124661,nm0180486,nm2028576,nm9433... 2015 2022 \n", + "14 nm1783265,nm3664884,nm3376496,nm0487067,nm1257... 2019 \\N \n", + "15 nm1210565,nm8996721,nm3663708,nm2651446,nm2943... 2017 2021 \n", + "16 nm0722274,nm9698564,nm1652155,nm2629239,nm9455... 2005 \\N \n", + "17 nm4057793,nm0931251,nm0464382,nm8851912,nm3780... 2017 2022 \n", + "18 nm5435318,nm5572916,nm1909300,nm4608690,nm1417... 2020 2022 \n", + "19 nm0372222,nm1375358,nm0436543,nm1376383,nm8154... 2018 2025 \n", + "20 nm3265847,nm1411676,nm9818211,nm4627895,nm9276... 2020 2023 \n", + "21 nm9992180,nm2547449,nm3924649,nm4160286,nm3625... 2020 \\N \n", + "22 nm1891501,nm0001104,nm3079117,nm3877885,nm1764... 2010 2022 \n", + "23 nm1823374,nm0599413,nm2268479,nm13563463,nm336... 2018 2024 \n", + "24 nm8475555,nm7137658,nm0301274,nm0275286,nm0374... 2022 2025 \n", + "25 nm3859706,nm2353384,nm3265652,nm10170567,nm713... 2017 2024 \n", + "26 NaN NaN NaN \n", + "27 nm6699136,nm1419272,nm1371927 2018 2026 \n", + "28 nm0076166,nm0104333,nm1215164,nm3370032,nm3992... 2018 2021 \n", + "29 nm0440790,nm1003202,nm1304613,nm6012871,nm6560... 2014 2020 \n", + "30 nm2901688,nm1847815,nm0193681,nm0454872,nm1013... 2020 2022 \n", + "31 nm4571811,nm0585344,nm3933291,nm7275370,nm7275... 2021 2024 \n", + "32 nm1618263,nm2276485,nm5532480,nm2638656,nm2516... 2017 2023 \n", + "33 nm1375363 2021 2025 \n", + "34 nm0823015,nm8033541,nm7322718,nm11880742,nm275... 2020 2026 \n", + "35 nm0707517,nm9414385,nm2837457,nm5915040,nm6768... 2018 2023 \n", + "36 NaN NaN NaN \n", + "37 nm1122534,nm8236619,nm1131226 2021 2022 \n", + "38 nm0729202,nm0765250,nm6944207,nm2110261,nm5082... 2021 2022 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# ==============================================================================\n", + "# 1. 初始化資料與欄位映射準備\n", + "# ==============================================================================\n", + "# 建立 TMDb 原始表（tmdb.titles.v3）的對照字典，以小寫劇名為 Key 進行無缺失值安全抽取\n", + "tmdb_mapping = tmdb.dropna(subset=[\"name\"]).copy()\n", + "tmdb_mapping[\"name_lower\"] = tmdb_mapping[\"name\"].astype(str).str.lower()\n", + "\n", + "map_popularity = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"popularity\"]))\n", + "map_genres = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"genres\"]))\n", + "map_overview = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"overview\"]))\n", + "map_seasons = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"number_of_seasons\"]))\n", + "map_episodes = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"number_of_episodes\"]))\n", + "map_runtime = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"episode_run_time\"]))\n", + "map_languages = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"languages\"].fillna(\"en\")))\n", + "\n", + "# 建立 IMDb 原始表（imdb.titles.composite）的對照字典\n", + "imdb_mapping = imdb.dropna(subset=[\"primaryTitle\"]).copy()\n", + "imdb_mapping[\"title_lower\"] = imdb_mapping[\"primaryTitle\"].astype(str).str.lower()\n", + "\n", + "map_imdb_cast = dict(zip(imdb_mapping[\"title_lower\"], imdb_mapping[\"cast\"]))\n", + "map_imdb_directors = dict(zip(imdb_mapping[\"title_lower\"], imdb_mapping[\"directors\"]))\n", + "map_imdb_writers = dict(zip(imdb_mapping[\"title_lower\"], imdb_mapping[\"writers\"]))\n", + "map_imdb_start = dict(zip(imdb_mapping[\"title_lower\"], imdb_mapping[\"startYear\"]))\n", + "map_imdb_end = dict(zip(imdb_mapping[\"title_lower\"], imdb_mapping[\"endYear\"]))\n", + "\n", + "# ==============================================================================\n", + "# 2. 資料對齊與全屬性抽取\n", + "# ==============================================================================\n", + "df_all_attributes = netflix.copy()\n", + "df_all_attributes[\"影劇名稱_小寫\"] = df_all_attributes[\"netflix_title\"].str.lower()\n", + "\n", + "# --- [A 分類] 抽取 Netflix 原始屬性 ---\n", + "df_all_attributes[\"Netflix_觀看時數 (百萬)\"] = (df_all_attributes[\"netflix_viewing_hours\"] / 1000000).round(2)\n", + "df_all_attributes[\"Netflix_上榜週數\"] = df_all_attributes[\"netflix_weeks\"]\n", + "df_all_attributes[\"Netflix_入榜年份提示\"] = df_all_attributes[\"netflix_year_hint\"]\n", + "\n", + "# --- [B 分類] 抽取 TMDb 原始屬性 ---\n", + "df_all_attributes[\"TMDb_大眾評分\"] = df_all_attributes[\"tmdb_vote_average\"]\n", + "df_all_attributes[\"TMDb_打分投票數\"] = df_all_attributes[\"tmdb_vote_count\"]\n", + "df_all_attributes[\"TMDb_熱度值 (Popularity)\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_popularity)\n", + "df_all_attributes[\"TMDb_類型 (Genres)\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_genres)\n", + "df_all_attributes[\"TMDb_故事大綱 (Overview)\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_overview)\n", + "df_all_attributes[\"TMDb_總季數\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_seasons)\n", + "df_all_attributes[\"TMDb_總集數\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_episodes)\n", + "df_all_attributes[\"TMDb_單集片長 (分鐘)\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_runtime)\n", + "df_all_attributes[\"TMDb_支援語系\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_languages)\n", + "\n", + "# --- [C 分類] 抽取 IMDb 原始屬性 ---\n", + "df_all_attributes[\"IMDb_大眾評分\"] = df_all_attributes[\"imdb_averageRating\"]\n", + "df_all_attributes[\"IMDb_打分投票數\"] = df_all_attributes[\"imdb_numVotes\"]\n", + "df_all_attributes[\"IMDb_主要卡司\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_imdb_cast)\n", + "df_all_attributes[\"IMDb_導演識別碼\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_imdb_directors)\n", + "df_all_attributes[\"IMDb_編劇識別碼\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_imdb_writers)\n", + "df_all_attributes[\"IMDb_開播年份\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_imdb_start)\n", + "df_all_attributes[\"IMDb_完結年份\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_imdb_end)\n", + "\n", + "# ==============================================================================\n", + "# 3. 嚴格執行 TMDb_Hit 雙指標篩選與極致排序\n", + "# ==============================================================================\n", + "# 完全排除評分核心缺失值\n", + "df_clean_extract = df_all_attributes.dropna(subset=[\"TMDb_大眾評分\", \"TMDb_打分投票數\"]).copy()\n", + "\n", + "# 建立核心 Hit 判定欄位 (評分 >= 7.5 且 投票數 >= 1000)\n", + "df_clean_extract[\"TMDb_Hit\"] = (\n", + " (df_clean_extract[\"TMDb_大眾評分\"] >= 7.5) & \n", + " (df_clean_extract[\"TMDb_打分投票數\"] >= 1000)\n", + ").astype(int)\n", + "\n", + "# 精準抽取 39 筆 TMDb_Hit 資料\n", + "the_hit_data = df_clean_extract[df_clean_extract[\"TMDb_Hit\"] == 1].copy()\n", + "\n", + "# 依照 TMDb大眾評分 降冪排序（由高到低）\n", + "the_hit_data_sorted = the_hit_data.sort_values(by=\"TMDb_大眾評分\", ascending=False).reset_index(drop=True)\n", + "\n", + "# ==============================================================================\n", + "# 4. 依照 3 個 CSV 的屬性分類，進行表格分離與呈現\n", + "# ==============================================================================\n", + "print(\"=\" * 115)\n", + "print(f\"📊 突發狀況成功應對：成功從三大原始 CSV 分類抽取共 {len(the_hit_data_sorted)} 筆 TMDb_Hit 的完整屬性特徵！\")\n", + "print(\"=\" * 115)\n", + "\n", + "# --- 輸出分類一：Netflix 流量屬性表 ---\n", + "print(\"\\n🎬 【分類一：Netflix 原始流量與生命力屬性表】\")\n", + "print(\"-\" * 115)\n", + "display(the_hit_data_sorted[[\n", + " \"netflix_title\", \"Netflix_觀看時數 (百萬)\", \"Netflix_上榜週數\", \"Netflix_入榜年份提示\"\n", + "]].rename(columns={\"netflix_title\": \"影劇名稱\"}))\n", + "\n", + "# --- 輸出分類二：TMDb 口碑與劇本架構屬性表 ---\n", + "print(\"\\n🔮 【分類二：TMDb 原始口碑與劇本結構屬性表】\")\n", + "print(\"-\" * 115)\n", + "display(the_hit_data_sorted[[\n", + " \"netflix_title\", \"TMDb_大眾評分\", \"TMDb_打分投票數\", \"TMDb_熱度值 (Popularity)\", \n", + " \"TMDb_類型 (Genres)\", \"TMDb_總季數\", \"TMDb_總集數\", \"TMDb_單集片長 (分鐘)\", \n", + " \"TMDb_支援語系\", \"TMDb_故事大綱 (Overview)\"\n", + "]].rename(columns={\"netflix_title\": \"影劇名稱\"}))\n", + "\n", + "# --- 輸出分類三：IMDb 影迷與主創團隊屬性表 ---\n", + "print(\"\\n💎 【分類三：IMDb 原始重度影迷與主創團隊屬性表】\")\n", + "print(\"-\" * 115)\n", + "display(the_hit_data_sorted[[\n", + " \"netflix_title\", \"IMDb_大眾評分\", \"IMDb_打分投票數\", \"IMDb_主要卡司\", \n", + " \"IMDb_導演識別碼\", \"IMDb_編劇識別碼\", \"IMDb_開播年份\", \"IMDb_完結年份\"\n", + "]].rename(columns={\"netflix_title\": \"影劇名稱\"}))" + ] + }, + { + "cell_type": "markdown", + "id": "9133e1b0", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "markdown", + "id": "0f234f7a", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] }, { "cell_type": "code", From c0e2461adb0edf62cd6072f32437289573f8ae8b Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Wed, 15 Jul 2026 14:07:13 -0700 Subject: [PATCH 07/14] re define the hit --- netflix-writer.ipynb | 4587 ++---------------------------------------- 1 file changed, 167 insertions(+), 4420 deletions(-) diff --git a/netflix-writer.ipynb b/netflix-writer.ipynb index 8ddc603..15ef116 100644 --- a/netflix-writer.ipynb +++ b/netflix-writer.ipynb @@ -2629,530 +2629,13 @@ "口碑分數以及觀看時數關係" ] }, - { - "cell_type": "markdown", - "id": "4e162481", - "metadata": {}, - "source": [ - "## our analysis\n", - "口碑分數以及觀看時數關係" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "9d070d27", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================================================================\n", - "📊 影劇口碑分數對照表（包含 IMDb、TMDb 分數與其差距）\n", - "======================================================================\n" - ] - }, - { - "data": { - "text/html": [ - "
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影劇名稱IMDb評分TMDb評分口碑分數差距Netflix 觀看時數
0Grown Ups9.40.09.417380000
1The Croods8.90.08.929300000
2The Takedown8.90.08.9114730000
3Dhamaka8.90.08.911370000
4A Cut Above8.80.08.82740000
5Into the Wind8.20.08.215600000
6Hotel Del Luna8.10.08.19980000
7Ghost Doctor7.90.07.917520000
8Homefront7.90.07.911000000
9Hypnotic7.80.07.835760000
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" - ], - "text/plain": [ - " 影劇名稱 IMDb評分 TMDb評分 口碑分數差距 Netflix 觀看時數\n", - "0 Grown Ups 9.4 0.0 9.4 17380000\n", - "1 The Croods 8.9 0.0 8.9 29300000\n", - "2 The Takedown 8.9 0.0 8.9 114730000\n", - "3 Dhamaka 8.9 0.0 8.9 11370000\n", - "4 A Cut Above 8.8 0.0 8.8 2740000\n", - "5 Into the Wind 8.2 0.0 8.2 15600000\n", - "6 Hotel Del Luna 8.1 0.0 8.1 9980000\n", - "7 Ghost Doctor 7.9 0.0 7.9 17520000\n", - "8 Homefront 7.9 0.0 7.9 11000000\n", - "9 Hypnotic 7.8 0.0 7.8 35760000" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "======================================================================\n", - "📈 口碑差距 與 觀看時數 之相關性分析\n", - "======================================================================\n", - "▸ 口碑分數差距 與 觀看時數 的皮爾森相關係數 (r) ＝ -0.1398\n", - "▸ 數據解讀：口碑差距與觀看時數呈現 【極弱相關】 關係。\n", - "======================================================================\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# 1. 複製資料，並建立乾淨的分析 DataFrame\n", - "df_diff = netflix.copy()\n", - "\n", - "# 2. 整理核心維度欄位\n", - "df_diff[\"觀看時數\"] = df_diff[\"netflix_viewing_hours\"]\n", - "df_diff[\"IMDb評分\"] = df_diff[\"imdb_averageRating\"]\n", - "df_diff[\"TMDb評分\"] = df_diff[\"tmdb_vote_average\"]\n", - "\n", - "# 3. 排除評分缺失值，確保兩邊都有評分才能計算差距\n", - "df_diff_clean = df_diff.dropna(subset=[\"IMDb評分\", \"TMDb評分\", \"觀看時數\"]).copy()\n", - "\n", - "# 4. 計算口碑分數的「絕對差距」\n", - "df_diff_clean[\"口碑分數差距\"] = (df_diff_clean[\"IMDb評分\"] - df_diff_clean[\"TMDb評分\"]).abs()\n", - "\n", - "# ==========================================\n", - "# 任務一：列出口碑分數差距最大與最小的影劇對照表\n", - "# ==========================================\n", - "print(\"=\" * 70)\n", - "print(\"📊 影劇口碑分數對照表（包含 IMDb、TMDb 分數與其差距）\")\n", - "print(\"=\" * 70)\n", - "\n", - "# 挑選前 10 筆展示其差距\n", - "display_df = df_diff_clean[[\n", - " \"netflix_title\", \n", - " \"IMDb評分\", \n", - " \"TMDb評分\", \n", - " \"口碑分數差距\",\n", - " \"觀看時數\"\n", - "]].rename(columns={\n", - " \"netflix_title\": \"影劇名稱\",\n", - " \"觀看時數\": \"Netflix 觀看時數\"\n", - "}).sort_values(by=\"口碑分數差距\", ascending=False) # 依差距由大到小排序\n", - "\n", - "display(display_df.head(10).reset_index(drop=True))\n", - "\n", - "print(\"\\n\" + \"=\" * 70)\n", - "print(\"📈 口碑差距 與 觀看時數 之相關性分析\")\n", - "print(\"=\" * 70)\n", - "\n", - "# ==========================================\n", - "# 任務二：計算「口碑分數差距」與「觀看時數」的相關係數\n", - "# ==========================================\n", - "correlation_diff = df_diff_clean[\"口碑分數差距\"].corr(df_diff_clean[\"觀看時數\"])\n", - "\n", - "print(f\"▸ 口碑分數差距 與 觀看時數 的皮爾森相關係數 (r) ＝ {correlation_diff:.4f}\")\n", - "\n", - "# 相關性解讀\n", - "if abs(correlation_diff) < 0.1:\n", - " strength = \"幾乎沒有線性相關\"\n", - "elif abs(correlation_diff) < 0.3:\n", - " strength = \"極弱相關\"\n", - "elif abs(correlation_diff) < 0.5:\n", - " strength = \"低度相關（弱相關）\"\n", - "elif abs(correlation_diff) < 0.7:\n", - " strength = \"中度相關\"\n", - "else:\n", - " strength = \"高度相關（強相關）\"\n", - "\n", - "print(f\"▸ 數據解讀：口碑差距與觀看時數呈現 【{strength}】 關係。\")\n", - "print(\"=\" * 70)" - ] - }, - { - "cell_type": "markdown", - "id": "e93c49d7", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "a2621807", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "===============================================================================================\n", - "🏆 排除缺失值後：大眾評分 (IMDb) 前 10 名影集比較表\n", - "===============================================================================================\n" - ] - }, - { - "data": { - "text/html": [ - "
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影劇名稱觀看時數 (百萬小時)大眾評分 (IMDb)專業評分 (TMDb)評分差距IMDb投票數
0The 10026.589.47.9161.4812.0
1Grown Ups17.389.40.0009.4012.0
2Better Call Saul27.609.38.6450.6657351.0
3Resident Evil173.409.05.7973.205938.0
4The Croods29.308.90.0008.909.0
5Dhamaka11.378.90.0008.90532.0
6The Takedown114.738.90.0008.9020.0
7A Cut Above2.748.80.0008.8010.0
8The Christmas Chronicles37.798.86.0002.809.0
9Spider-Man5.438.86.7672.0317.0
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" - ], - "text/plain": [ - " 影劇名稱 觀看時數 (百萬小時) 大眾評分 (IMDb) 專業評分 (TMDb) 評分差距 \\\n", - "0 The 100 26.58 9.4 7.916 1.48 \n", - "1 Grown Ups 17.38 9.4 0.000 9.40 \n", - "2 Better Call Saul 27.60 9.3 8.645 0.66 \n", - "3 Resident Evil 173.40 9.0 5.797 3.20 \n", - "4 The Croods 29.30 8.9 0.000 8.90 \n", - "5 Dhamaka 11.37 8.9 0.000 8.90 \n", - "6 The Takedown 114.73 8.9 0.000 8.90 \n", - "7 A Cut Above 2.74 8.8 0.000 8.80 \n", - "8 The Christmas Chronicles 37.79 8.8 6.000 2.80 \n", - "9 Spider-Man 5.43 8.8 6.767 2.03 \n", - "\n", - " IMDb投票數 \n", - "0 12.0 \n", - "1 12.0 \n", - "2 57351.0 \n", - "3 5938.0 \n", - "4 9.0 \n", - "5 532.0 \n", - "6 20.0 \n", - "7 10.0 \n", - "8 9.0 \n", - "9 17.0 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "===============================================================================================\n", - "📈 相關係數優化實驗（如何證明評分與時數的潛在關聯）\n", - "===============================================================================================\n", - "▸ 1. 原始數據（排除缺失值，無過濾）相關係數 ＝ 0.0909\n", - "▸ 2. 【手法 A】過濾小眾影劇（僅保留 IMDb 投票數 >= 5,000） ＝ 0.2227\n", - "▸ 3. 【手法 B】改用爆發力指標（評分 vs. 單週平均觀看時數） ＝ 0.0800\n", - "▸ 4. 【手法 C】排除極端離群值且設定合理關注度 ＝ 0.1995\n", - "-----------------------------------------------------------------------------------------------\n", - "💡 寫入報告的分析建議：\n", - " 「實驗證明，若將視野聚焦在大眾化影劇（手法 A）或排除超級離群值（手法 C），」\n", - " 「大眾評分與觀看時數的相關係數有顯著提升，這揭示了在『主流出圈影劇』中，」\n", - " 「良好的大眾口碑（IMDb 評分）確實對維持與衝高觀看時數具有更積極的推波助瀾作用！」\n", - "===============================================================================================\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# 1. 複製資料，並建立乾淨的分析 DataFrame\n", - "df_improve = netflix.copy()\n", - "\n", - "# 2. 數據轉換與對齊\n", - "# 將觀看時數換算為「百萬小時」\n", - "df_improve[\"觀看時數 (百萬小時)\"] = df_improve[\"netflix_viewing_hours\"] / 1000000\n", - "df_improve[\"大眾評分 (IMDb)\"] = df_improve[\"imdb_averageRating\"]\n", - "df_improve[\"專業評分 (TMDb)\"] = df_improve[\"tmdb_vote_average\"]\n", - "df_improve[\"IMDb投票數\"] = df_improve[\"imdb_numVotes\"]\n", - "df_improve[\"上榜週數\"] = df_improve[\"netflix_weeks\"]\n", - "\n", - "# 計算「單週平均觀看時數 (百萬)」做為新的流量爆發力指標\n", - "df_improve[\"單週平均觀看 (百萬)\"] = df_improve[\"觀看時數 (百萬小時)\"] / df_improve[\"上榜週數\"]\n", - "\n", - "# 3. 【嚴格篩選】：完全排除 IMDb 或 TMDb 評分缺失的影劇\n", - "df_improve_clean = df_improve.dropna(subset=[\"大眾評分 (IMDb)\", \"專業評分 (TMDb)\", \"觀看時數 (百萬小時)\"]).copy()\n", - "\n", - "# ==========================================\n", - "# 原始狀態：未經任何過濾的相關係數\n", - "# ==========================================\n", - "corr_raw = df_improve_clean[\"大眾評分 (IMDb)\"].corr(df_improve_clean[\"觀看時數 (百萬小時)\"])\n", - "\n", - "# ==========================================\n", - "# 手法 A：設定大眾關注度門檻 (IMDb 投票數 >= 5,000 票)\n", - "# ==========================================\n", - "df_high_votes = df_improve_clean[df_improve_clean[\"IMDb投票數\"] >= 5000]\n", - "corr_method_a = df_high_votes[\"大眾評分 (IMDb)\"].corr(df_high_votes[\"觀看時數 (百萬小時)\"])\n", - "\n", - "# ==========================================\n", - "# 手法 B：改用「單週平均觀看時數」與評分做相關\n", - "# ==========================================\n", - "corr_method_b = df_improve_clean[\"大眾評分 (IMDb)\"].corr(df_improve_clean[\"單週平均觀看 (百萬)\"])\n", - "\n", - "# ==========================================\n", - "# 手法 C：排除極端離群值 (排除觀看時數前 2% 的超級爆款，並結合投票數篩選)\n", - "# ==========================================\n", - "q_limit = df_improve_clean[\"觀看時數 (百萬小時)\"].quantile(0.98)\n", - "df_no_outliers = df_improve_clean[\n", - " (df_improve_clean[\"觀看時數 (百萬小時)\"] < q_limit) & \n", - " (df_improve_clean[\"IMDb投票數\"] >= 1000)\n", - "]\n", - "corr_method_c = df_no_outliers[\"大眾評分 (IMDb)\"].corr(df_no_outliers[\"觀看時數 (百萬小時)\"])\n", - "\n", - "# ==========================================\n", - "# 任務一：輸出依「大眾評分 (IMDb)」排序的前 10 名\n", - "# ==========================================\n", - "# 計算大眾與專業的評分差距\n", - "df_improve_clean[\"評分差距\"] = (df_improve_clean[\"大眾評分 (IMDb)\"] - df_improve_clean[\"專業評分 (TMDb)\"]).abs().round(2)\n", - "\n", - "final_table = df_improve_clean.sort_values(by=\"大眾評分 (IMDb)\", ascending=False).head(10).reset_index(drop=True)\n", - "final_table_display = final_table[[\n", - " \"netflix_title\",\n", - " \"觀看時數 (百萬小時)\",\n", - " \"大眾評分 (IMDb)\",\n", - " \"專業評分 (TMDb)\",\n", - " \"評分差距\",\n", - " \"IMDb投票數\"\n", - "]].rename(columns={\"netflix_title\": \"影劇名稱\"})\n", - "\n", - "print(\"=\" * 95)\n", - "print(\"🏆 排除缺失值後：大眾評分 (IMDb) 前 10 名影集比較表\")\n", - "print(\"=\" * 95)\n", - "display(final_table_display)\n", - "\n", - "# ==========================================\n", - "# 任務二：輸出提高相關係數的實驗結果\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\" * 95)\n", - "print(\"📈 相關係數優化實驗（如何證明評分與時數的潛在關聯）\")\n", - "print(\"=\" * 95)\n", - "print(f\"▸ 1. 原始數據（排除缺失值，無過濾）相關係數 ＝ {corr_raw:.4f}\")\n", - "print(f\"▸ 2. 【手法 A】過濾小眾影劇（僅保留 IMDb 投票數 >= 5,000） ＝ {corr_method_a:.4f}\")\n", - "print(f\"▸ 3. 【手法 B】改用爆發力指標（評分 vs. 單週平均觀看時數） ＝ {corr_method_b:.4f}\")\n", - "print(f\"▸ 4. 【手法 C】排除極端離群值且設定合理關注度 ＝ {corr_method_c:.4f}\")\n", - "print(\"-\" * 95)\n", - "print(\"💡 寫入報告的分析建議：\")\n", - "print(\" 「實驗證明，若將視野聚焦在大眾化影劇（手法 A）或排除超級離群值（手法 C），」\")\n", - "print(\" 「大眾評分與觀看時數的相關係數有顯著提升，這揭示了在『主流出圈影劇』中，」\")\n", - "print(\" 「良好的大眾口碑（IMDb 評分）確實對維持與衝高觀看時數具有更積極的推波助瀾作用！」\")\n", - "print(\"=\" * 95)" - ] - }, { "cell_type": "markdown", "id": "8532b959", "metadata": {}, "source": [ - "## our analysis\n", - "？？" + "## our analysisy\n", + "資料表屬性總表" ] }, { @@ -3938,33 +3421,38 @@ }, { "cell_type": "markdown", - "id": "8868a79d", + "id": "e70defaf", "metadata": {}, "source": [ "## our analysis\n", - "指標說明在串流媒體分析中，單純依賴「觀看時數」容易受到影集總長度與平台自動播放機制的干擾，而單純看「評分高低」又容易落入只有極少數人評分卻拿到滿分的小眾偏誤（極端值）。\n", "\n", - "為了篩選出真正兼具「產品質量 (Quality)」與「社群討論熱度 (Engagement)」的現象級作品，我們定義了 TMDb_Hit 指標。\n", + "TMDb 評分門檻： 必須大於或等於全體有評分影劇的 前 $50\\%$（中位數 Median）。\n", "\n", - "這是一個二分法指標（$1$ 代表是 Hit，$0$ 代表否），它必須同時滿足以下雙重門檻：大眾認同門檻：TMDb 評分（vote_average）必須大於或等於 $7.5$ 分，確保影劇具備優秀的口碑品質。社群討論門檻：TMDb 投票人數（vote_count）必須大於或等於 $1,000$ 票，確保該口碑是由足夠基數的大眾市場堆疊而來，而非少數群體的極端評價。\n", + "TMDb 投票人數門檻： 必須大於或等於全體有投票人數的 前 $50\\%$（中位數 Median），確保口碑具備大眾基數。串流觀看量門檻： \n", "\n", - "藉由此指標，我們能有效篩選出市場上「叫好又叫座」的殿堂級作品，並以此做為後續與 Netflix 流量指標（觀看時數、上榜週數）進行交叉分析的基準。" + "觀看時數（netflix_viewing_hours）必須大於全體影劇的 平均值（Mean），確保在 Netflix 上確實引爆流量。" ] }, { "cell_type": "code", - "execution_count": 37, - "id": "7c2863db", + "execution_count": 50, + "id": "77a641db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "===============================================================================================\n", - "🎯 篩選結果：在成功匹配的數據中，共找到 39 部符合【TMDb_Hit】的影劇\n", - " 排序標準：依【TMDb大眾評分】由高到低排序\n", - "===============================================================================================\n" + "================================================================================\n", + "⚙️ 【The Hit】黃金篩選門檻定義：\n", + "--------------------------------------------------------------------------------\n", + "▸ 1. TMDb 評分門檻 (前 50% 中位數) >= 6.96 分\n", + "▸ 2. TMDb 投票數門檻 (前 50% 中位數)>= 25 票\n", + "▸ 3. Netflix 觀看時數門檻 (全體平均) > 69.19 百萬小時\n", + "================================================================================\n", + "\n", + "🎉 篩選完成！在 691 部影劇中，共有 71 部符合全新【The Hit】標準：\n", + "--------------------------------------------------------------------------------\n" ] }, { @@ -3989,533 +3477,201 @@ " \n", " \n", " 影劇名稱\n", - " TMDb大眾評分\n", - " TMDb投票數\n", + " TMDb 評分\n", + " TMDb 投票數\n", " 觀看時數 (百萬小時)\n", " 上榜週數\n", - " TMDb_Hit\n", " \n", " \n", " \n", " \n", " 0\n", - " Arcane\n", - " 8.740\n", - " 3341.0\n", - " 154.27\n", - " 6\n", - " 1\n", + " Stranger Things\n", + " 8.624\n", + " 16161.0\n", + " 2874.26\n", + " 13\n", " \n", " \n", " 1\n", - " Demon Slayer: Kimetsu no Yaiba\n", - " 8.700\n", - " 5618.0\n", - " 14.00\n", - " 2\n", - " 1\n", + " Squid Game\n", + " 7.831\n", + " 13053.0\n", + " 2289.50\n", + " 20\n", " \n", " \n", " 2\n", - " Hunter X Hunter (2011)\n", - " 8.647\n", - " 1472.0\n", - " 9.89\n", - " 1\n", - " 1\n", + " Manifest\n", + " 7.728\n", + " 1312.0\n", + " 1446.26\n", + " 16\n", " \n", " \n", " 3\n", - " Better Call Saul\n", - " 8.645\n", - " 4354.0\n", - " 27.60\n", - " 2\n", - " 1\n", - " \n", - " \n", - " 4\n", - " Stranger Things\n", - " 8.624\n", - " 16161.0\n", - " 2874.26\n", - " 13\n", - " 1\n", - " \n", - " \n", - " 5\n", - " The Umbrella Academy\n", - " 8.604\n", - " 8891.0\n", - " 414.63\n", - " 5\n", - " 1\n", - " \n", - " \n", - " 6\n", - " Peaky Blinders\n", - " 8.546\n", - " 8836.0\n", - " 185.34\n", - " 5\n", - " 1\n", - " \n", - " \n", - " 7\n", - " Record of Ragnarok\n", - " 8.513\n", - " 1499.0\n", - " 9.14\n", - " 1\n", - " 1\n", - " \n", - " \n", - " 8\n", - " The Good Doctor\n", - " 8.503\n", - " 11768.0\n", - " 112.58\n", - " 6\n", - " 1\n", - " \n", - " \n", - " 9\n", - " The Seven Deadly Sins: Cursed by Light\n", - " 8.462\n", - " 4710.0\n", - " 79.51\n", - " 4\n", - " 1\n", - " \n", - " \n", - " 10\n", - " Venom\n", - " 8.414\n", - " 1347.0\n", - " 46.42\n", - " 4\n", - " 1\n", - " \n", - " \n", - " 11\n", - " All of Us Are Dead\n", - " 8.354\n", - " 3335.0\n", - " 659.51\n", - " 11\n", - " 1\n", - " \n", - " \n", - " 12\n", - " Sex Education\n", - " 8.304\n", - " 6647.0\n", - " 561.36\n", - " 6\n", - " 1\n", - " \n", - " \n", - " 13\n", - " The Last Kingdom\n", - " 8.290\n", - " 1485.0\n", - " 189.57\n", - " 5\n", - " 1\n", - " \n", - " \n", - " 14\n", - " Love, Death & Robots\n", - " 8.264\n", - " 2804.0\n", - " 29.81\n", - " 2\n", - " 1\n", - " \n", - " \n", - " 15\n", " Money Heist\n", " 8.257\n", " 17836.0\n", " 1170.20\n", " 14\n", - " 1\n", - " \n", - " \n", - " 16\n", - " Grey's Anatomy\n", - " 8.253\n", - " 9512.0\n", - " 64.32\n", - " 3\n", - " 1\n", - " \n", - " \n", - " 17\n", - " Ozark\n", - " 8.244\n", - " 1968.0\n", - " 751.60\n", - " 13\n", - " 1\n", - " \n", - " \n", - " 18\n", - " Control Z\n", - " 8.227\n", - " 2242.0\n", - " 89.83\n", - " 3\n", - " 1\n", - " \n", - " \n", - " 19\n", - " Cobra Kai\n", - " 8.220\n", - " 5734.0\n", - " 383.64\n", - " 4\n", - " 1\n", - " \n", - " \n", - " 20\n", - " Never Have I Ever\n", - " 8.161\n", - " 1557.0\n", - " 297.78\n", - " 4\n", - " 1\n", " \n", " \n", - " 21\n", + " 4\n", " Bridgerton\n", " 8.143\n", " 2045.0\n", " 1108.80\n", " 11\n", - " 1\n", - " \n", - " \n", - " 22\n", - " The Walking Dead\n", - " 8.121\n", - " 15432.0\n", - " 15.05\n", - " 1\n", - " 1\n", - " \n", - " \n", - " 23\n", - " Elite\n", - " 8.071\n", - " 8817.0\n", - " 329.29\n", - " 5\n", - " 1\n", - " \n", - " \n", - " 24\n", - " The Sandman\n", - " 8.064\n", - " 1726.0\n", - " 274.22\n", - " 3\n", - " 1\n", " \n", " \n", - " 25\n", - " Seal Team\n", - " 8.036\n", - " 1353.0\n", - " 10.31\n", - " 1\n", - " 1\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 26\n", - " Jurassic World Camp Cretaceous\n", - " 8.031\n", - " 1745.0\n", - " 34.32\n", + " 66\n", + " On My Block\n", + " 8.461\n", + " 618.0\n", + " 80.88\n", " 2\n", - " 1\n", " \n", " \n", - " 27\n", - " The Queen of Flow\n", - " 7.983\n", - " 1391.0\n", - " 561.23\n", - " 16\n", - " 1\n", + " 67\n", + " Turning Point: 9/11 and the War on Terror\n", + " 7.287\n", + " 83.0\n", + " 80.68\n", + " 3\n", " \n", " \n", - " 28\n", - " Narcos: Mexico\n", - " 7.939\n", - " 1160.0\n", - " 138.98\n", + " 68\n", + " The Seven Deadly Sins: Cursed by Light\n", + " 8.462\n", + " 4710.0\n", + " 79.51\n", " 4\n", - " 1\n", - " \n", - " \n", - " 29\n", - " The 100\n", - " 7.916\n", - " 7666.0\n", - " 26.58\n", - " 2\n", - " 1\n", - " \n", - " \n", - " 30\n", - " Locke & Key\n", - " 7.903\n", - " 1104.0\n", - " 278.38\n", - " 5\n", - " 1\n", - " \n", - " \n", - " 31\n", - " Sweet Tooth\n", - " 7.892\n", - " 1237.0\n", - " 34.45\n", - " 2\n", - " 1\n", - " \n", - " \n", - " 32\n", - " Boruto: Naruto Next Generations\n", - " 7.890\n", - " 2293.0\n", - " 14.36\n", - " 1\n", - " 1\n", - " \n", - " \n", - " 33\n", - " Squid Game\n", - " 7.831\n", - " 13053.0\n", - " 2289.50\n", - " 20\n", - " 1\n", - " \n", - " \n", - " 34\n", - " Emily in Paris\n", - " 7.775\n", - " 1152.0\n", - " 341.63\n", - " 5\n", - " 1\n", - " \n", - " \n", - " 35\n", - " Manifest\n", - " 7.728\n", - " 1312.0\n", - " 1446.26\n", - " 16\n", - " 1\n", - " \n", - " \n", - " 36\n", - " Pasión de Gavilanes\n", - " 7.641\n", - " 1925.0\n", - " 164.18\n", - " 5\n", - " 1\n", " \n", " \n", - " 37\n", - " Who Killed Sara?\n", - " 7.638\n", - " 1132.0\n", - " 143.61\n", - " 4\n", - " 1\n", + " 69\n", + " Hospital Playlist\n", + " 8.490\n", + " 100.0\n", + " 72.06\n", + " 7\n", " \n", " \n", - " 38\n", - " Daughter From Another Mother\n", - " 7.611\n", - " 1185.0\n", - " 50.38\n", + " 70\n", + " I Am Georgina\n", + " 7.111\n", + " 293.0\n", + " 71.29\n", " 3\n", - " 1\n", " \n", " \n", "\n", + "

71 rows × 5 columns

\n", "" ], "text/plain": [ - " 影劇名稱 TMDb大眾評分 TMDb投票數 觀看時數 (百萬小時) \\\n", - "0 Arcane 8.740 3341.0 154.27 \n", - "1 Demon Slayer: Kimetsu no Yaiba 8.700 5618.0 14.00 \n", - "2 Hunter X Hunter (2011) 8.647 1472.0 9.89 \n", - "3 Better Call Saul 8.645 4354.0 27.60 \n", - "4 Stranger Things 8.624 16161.0 2874.26 \n", - "5 The Umbrella Academy 8.604 8891.0 414.63 \n", - "6 Peaky Blinders 8.546 8836.0 185.34 \n", - "7 Record of Ragnarok 8.513 1499.0 9.14 \n", - "8 The Good Doctor 8.503 11768.0 112.58 \n", - "9 The Seven Deadly Sins: Cursed by Light 8.462 4710.0 79.51 \n", - "10 Venom 8.414 1347.0 46.42 \n", - "11 All of Us Are Dead 8.354 3335.0 659.51 \n", - "12 Sex Education 8.304 6647.0 561.36 \n", - "13 The Last Kingdom 8.290 1485.0 189.57 \n", - "14 Love, Death & Robots 8.264 2804.0 29.81 \n", - "15 Money Heist 8.257 17836.0 1170.20 \n", - "16 Grey's Anatomy 8.253 9512.0 64.32 \n", - "17 Ozark 8.244 1968.0 751.60 \n", - "18 Control Z 8.227 2242.0 89.83 \n", - "19 Cobra Kai 8.220 5734.0 383.64 \n", - "20 Never Have I Ever 8.161 1557.0 297.78 \n", - "21 Bridgerton 8.143 2045.0 1108.80 \n", - "22 The Walking Dead 8.121 15432.0 15.05 \n", - "23 Elite 8.071 8817.0 329.29 \n", - "24 The Sandman 8.064 1726.0 274.22 \n", - "25 Seal Team 8.036 1353.0 10.31 \n", - "26 Jurassic World Camp Cretaceous 8.031 1745.0 34.32 \n", - "27 The Queen of Flow 7.983 1391.0 561.23 \n", - "28 Narcos: Mexico 7.939 1160.0 138.98 \n", - "29 The 100 7.916 7666.0 26.58 \n", - "30 Locke & Key 7.903 1104.0 278.38 \n", - "31 Sweet Tooth 7.892 1237.0 34.45 \n", - "32 Boruto: Naruto Next Generations 7.890 2293.0 14.36 \n", - "33 Squid Game 7.831 13053.0 2289.50 \n", - "34 Emily in Paris 7.775 1152.0 341.63 \n", - "35 Manifest 7.728 1312.0 1446.26 \n", - "36 Pasión de Gavilanes 7.641 1925.0 164.18 \n", - "37 Who Killed Sara? 7.638 1132.0 143.61 \n", - "38 Daughter From Another Mother 7.611 1185.0 50.38 \n", - "\n", - " 上榜週數 TMDb_Hit \n", - "0 6 1 \n", - "1 2 1 \n", - "2 1 1 \n", - "3 2 1 \n", - "4 13 1 \n", - "5 5 1 \n", - "6 5 1 \n", - "7 1 1 \n", - "8 6 1 \n", - "9 4 1 \n", - "10 4 1 \n", - "11 11 1 \n", - "12 6 1 \n", - "13 5 1 \n", - "14 2 1 \n", - "15 14 1 \n", - "16 3 1 \n", - "17 13 1 \n", - "18 3 1 \n", - "19 4 1 \n", - "20 4 1 \n", - "21 11 1 \n", - "22 1 1 \n", - "23 5 1 \n", - "24 3 1 \n", - "25 1 1 \n", - "26 2 1 \n", - "27 16 1 \n", - "28 4 1 \n", - "29 2 1 \n", - "30 5 1 \n", - "31 2 1 \n", - "32 1 1 \n", - "33 20 1 \n", - "34 5 1 \n", - "35 16 1 \n", - "36 5 1 \n", - "37 4 1 \n", - "38 3 1 " + " 影劇名稱 TMDb 評分 TMDb 投票數 觀看時數 (百萬小時) \\\n", + "0 Stranger Things 8.624 16161.0 2874.26 \n", + "1 Squid Game 7.831 13053.0 2289.50 \n", + "2 Manifest 7.728 1312.0 1446.26 \n", + "3 Money Heist 8.257 17836.0 1170.20 \n", + "4 Bridgerton 8.143 2045.0 1108.80 \n", + ".. ... ... ... ... \n", + "66 On My Block 8.461 618.0 80.88 \n", + "67 Turning Point: 9/11 and the War on Terror 7.287 83.0 80.68 \n", + "68 The Seven Deadly Sins: Cursed by Light 8.462 4710.0 79.51 \n", + "69 Hospital Playlist 8.490 100.0 72.06 \n", + "70 I Am Georgina 7.111 293.0 71.29 \n", + "\n", + " 上榜週數 \n", + "0 13 \n", + "1 20 \n", + "2 16 \n", + "3 14 \n", + "4 11 \n", + ".. ... \n", + "66 2 \n", + "67 3 \n", + "68 4 \n", + "69 7 \n", + "70 3 \n", + "\n", + "[71 rows x 5 columns]" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "===============================================================================================\n", - "📊 深度洞察：在【TMDb_Hit】優質影劇集合中，口碑與流量的關係\n", - "===============================================================================================\n", - "▸ 在這群 Hit 影劇中，【TMDb大眾評分】 vs. 【觀看時數】的相關係數 (r) ＝ -0.0188\n", - "-----------------------------------------------------------------------------------------------\n" - ] } ], "source": [ "import pandas as pd\n", "\n", - "# 1. 複製資料，建立乾淨的分析環境\n", - "df_tmdb_sort = netflix.copy()\n", + "# 1. 讀取 Netflix 複合資料集 (確保路徑正確)\n", + "# 備註：此資料表已在前面的步驟 fuzzy match 併入了 tmdb 屬性\n", + "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\n", "\n", - "# 2. 數據整理（換算為百萬小時，並載入 TMDb 口碑指標）\n", - "df_tmdb_sort[\"觀看時數 (百萬小時)\"] = (df_tmdb_sort[\"netflix_viewing_hours\"] / 1000000).round(2)\n", - "df_tmdb_sort[\"上榜週數\"] = df_tmdb_sort[\"netflix_weeks\"]\n", - "df_tmdb_sort[\"TMDb大眾評分\"] = df_tmdb_sort[\"tmdb_vote_average\"]\n", - "df_tmdb_sort[\"TMDb投票數\"] = df_tmdb_sort[\"tmdb_vote_count\"]\n", + "# 2. 為了確保統計指標不受 NaN (缺失值) 影響，先建立一個用來計算門檻的乾淨 Data\n", + "# (排除沒有配對到 TMDb 或沒有評分數據的影劇)\n", + "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\n", "\n", - "# 3. 嚴格排除 TMDb 匹配缺失的資料（確保篩選與排序基礎完全一致）\n", - "df_tmdb_sort_clean = df_tmdb_sort.dropna(subset=[\"TMDb大眾評分\", \"TMDb投票數\", \"觀看時數 (百萬小時)\"]).copy()\n", + "# 3. 計算三大核心指標的篩選門檻\n", + "tmdb_rating_p50 = df_valid_tmdb[\"tmdb_vote_average\"].median() # TMDb 評分前 50% (中位數)\n", + "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median() # TMDb 投票數前 50% (中位數)\n", + "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean() # Netflix 觀看時數平均值\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"⚙️ 【The Hit】黃金篩選門檻定義：\")\n", + "print(\"-\" * 80)\n", + "print(f\"▸ 1. TMDb 評分門檻 (前 50% 中位數) >= {tmdb_rating_p50:.2f} 分\")\n", + "print(f\"▸ 2. TMDb 投票數門檻 (前 50% 中位數)>= {int(tmdb_votes_p50)} 票\")\n", + "print(f\"▸ 3. Netflix 觀看時數門檻 (全體平均) > {netflix_hours_mean / 1000000:.2f} 百萬小時\")\n", + "print(\"=\" * 80)\n", "\n", - "# 4. 核心定義：以 TMDb 雙指標判定是否為 Hit (1=是, 0=否)\n", - "# 門檻設定：評分優秀（>= 7.5）且累積足夠的大眾討論度（投票數 >= 1000）\n", - "df_tmdb_sort_clean[\"TMDb_Hit\"] = (\n", - " (df_tmdb_sort_clean[\"TMDb大眾評分\"] >= 7.5) & \n", - " (df_tmdb_sort_clean[\"TMDb投票數\"] >= 1000)\n", - ").astype(int)\n", + "# 4. 套用三重條件進行「The Hit」篩選\n", + "hit_condition = (\n", + " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", + " (df[\"tmdb_vote_count\"] >= tmdb_votes_p50) &\n", + " (df[\"netflix_viewing_hours\"] > netflix_hours_mean)\n", + ")\n", "\n", - "# 5. 篩選出所有是 Hit 的影劇 (TMDb_Hit == 1)\n", - "all_hit_shows = df_tmdb_sort_clean[df_tmdb_sort_clean[\"TMDb_Hit\"] == 1].copy()\n", + "df_hits = df[hit_condition].copy()\n", "\n", - "# 6. 關鍵步驟：以【TMDb大眾評分】由高到低（降冪）排序\n", - "all_hit_shows_sorted = all_hit_shows.sort_values(\n", - " by=\"TMDb大眾評分\", ascending=False\n", - ").reset_index(drop=True)\n", + "# 為了方便呈現，換算觀看時數為「百萬小時」\n", + "df_hits[\"netflix_viewing_hours_million\"] = (df_hits[\"netflix_viewing_hours\"] / 1000000).round(2)\n", "\n", - "# 7. 整理最終呈現欄位\n", - "final_display = all_hit_shows_sorted[[\n", - " \"netflix_title\",\n", - " \"TMDb大眾評分\",\n", - " \"TMDb投票數\",\n", - " \"觀看時數 (百萬小時)\",\n", - " \"上榜週數\",\n", - " \"TMDb_Hit\"\n", - "]].rename(columns={\"netflix_title\": \"影劇名稱\"})\n", + "# 5. 依觀看時數降冪排序，並選出重點欄位展示\n", + "display_cols = [\n", + " \"netflix_title\", \n", + " \"tmdb_vote_average\", \n", + " \"tmdb_vote_count\", \n", + " \"netflix_viewing_hours_million\",\n", + " \"netflix_weeks\"\n", + "]\n", "\n", - "# ==========================================\n", - "# 呈現最終完美的結果 (會列出所有符合 TMDb_Hit 的影劇)\n", - "# ==========================================\n", - "print(\"=\" * 95)\n", - "print(f\"🎯 篩選結果：在成功匹配的數據中，共找到 {len(final_display)} 部符合【TMDb_Hit】的影劇\")\n", - "print(\" 排序標準：依【TMDb大眾評分】由高到低排序\")\n", - "print(\"=\" * 95)\n", - "display(final_display)\n", + "df_hits_sorted = df_hits.sort_values(by=\"netflix_viewing_hours\", ascending=False).reset_index(drop=True)\n", "\n", - "# ==========================================\n", - "# 任務二：計算這個篩選集合內，口碑與流量的相關係數\n", - "# ==========================================\n", - "# 在這群「被定義為 Hit」的優質影劇中，高評分是否與高觀看時數有關？\n", - "corr_hit_subset = final_display[\"TMDb大眾評分\"].corr(final_display[\"觀看時數 (百萬小時)\"])\n", - "print(\"\\n\" + \"=\" * 95)\n", - "print(\"📊 深度洞察：在【TMDb_Hit】優質影劇集合中，口碑與流量的關係\")\n", - "print(\"=\" * 95)\n", - "print(f\"▸ 在這群 Hit 影劇中，【TMDb大眾評分】 vs. 【觀看時數】的相關係數 (r) ＝ {corr_hit_subset:.4f}\")\n", - "print(\"-\" * 95)" + "print(f\"\\n🎉 篩選完成！在 {len(df)} 部影劇中，共有 {len(df_hits_sorted)} 部符合全新【The Hit】標準：\")\n", + "print(\"-\" * 80)\n", + "display(df_hits_sorted[display_cols].rename(columns={\n", + " \"netflix_title\": \"影劇名稱\",\n", + " \"tmdb_vote_average\": \"TMDb 評分\",\n", + " \"tmdb_vote_count\": \"TMDb 投票數\",\n", + " \"netflix_viewing_hours_million\": \"觀看時數 (百萬小時)\",\n", + " \"netflix_weeks\": \"上榜週數\"\n", + "}))" ] }, { "cell_type": "markdown", - "id": "164835f4", + "id": "d8caa9b0", "metadata": {}, "source": [ "## our analysis\n", @@ -4524,3454 +3680,45 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "c7600174", + "execution_count": null, + "id": "d1c4b8cc", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==============================================================================================================\n", - "🎯 頂級 TMDb_Hit 影劇多維度特色對照表 (依大眾評分降冪排序)\n", - "==============================================================================================================\n" - ] - }, - { - "data": { - "text/html": [ - "
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影劇名稱TMDb大眾評分TMDb熱度值 (Popularity)TMDb投票數觀看時數 (百萬小時)上榜週數單集片長 (分鐘)總季數支援語系數量支援語系
0Arcane8.74042.4473341.0154.27641.02.01en
1Demon Slayer: Kimetsu no Yaiba8.70082.8905618.014.00224.05.01ja
2Better Call Saul8.645104.4694354.027.6020.06.03en, de, es
3Stranger Things8.624185.71116161.02874.26130.04.01en
4The Umbrella Academy8.60451.8598891.0414.6350.04.01en
5Peaky Blinders8.546344.4778836.0185.34558.06.01en
6Record of Ragnarok8.51374.2891499.09.1410.02.01ja
7The Good Doctor8.503681.61411768.0112.58643.06.01en
8All of Us Are Dead8.354107.1083335.0659.511165.02.02en, ko
9Sex Education8.3041008.9776647.0561.3660.04.01en
10The Last Kingdom8.290143.7941485.0189.57560.05.01en
11Love, Death & Robots8.264113.6072804.029.8120.03.01en
12Money Heist8.25796.35417836.01170.201470.03.01es
13Grey's Anatomy8.2531647.2189512.064.32343.019.01en
14Ozark8.24468.0061968.0751.60130.04.01en
15Control Z8.22720.7082242.089.83336.03.01es
16Cobra Kai8.220118.2565734.0383.64430.06.01en
17Never Have I Ever8.16180.5461557.0297.78430.04.01en
18Bridgerton8.14371.3082045.01108.801160.03.01en
19The Walking Dead8.121489.74615432.015.05142.011.01en
20Elite8.071100.7118817.0329.29550.07.01es
21The Sandman8.06464.1681726.0274.2230.01.01en
22Seal Team8.036306.6381353.010.31143.07.01en
23Jurassic World Camp Cretaceous8.03138.4341745.034.32224.05.03en, pt, th
24The Queen of Flow7.983129.3231391.0561.231653.02.01es
25Narcos: Mexico7.93947.4761160.0138.98450.03.02en, es
26The 1007.916127.2247666.026.58243.07.01en
27Locke & Key7.90327.0171104.0278.38547.03.01en
28Sweet Tooth7.89250.3751237.034.45254.03.02en, ro
29Boruto: Naruto Next Generations7.890149.1662293.014.36125.01.01ja
30Squid Game7.831115.58713053.02289.50200.02.03en, ko, ur
31Emily in Paris7.77533.6591152.0341.63530.04.02en, fr
32Manifest7.728111.4111312.01446.261642.04.01en
33Pasión de Gavilanes7.641283.1971925.0164.1850.02.01es
34Who Killed Sara?7.63818.1571132.0143.61440.03.01es
35Daughter From Another Mother7.61122.1401185.050.38340.03.01es
\n", - "
" - ], - "text/plain": [ - " 影劇名稱 TMDb大眾評分 TMDb熱度值 (Popularity) TMDb投票數 \\\n", - "0 Arcane 8.740 42.447 3341.0 \n", - "1 Demon Slayer: Kimetsu no Yaiba 8.700 82.890 5618.0 \n", - "2 Better Call Saul 8.645 104.469 4354.0 \n", - "3 Stranger Things 8.624 185.711 16161.0 \n", - "4 The Umbrella Academy 8.604 51.859 8891.0 \n", - "5 Peaky Blinders 8.546 344.477 8836.0 \n", - "6 Record of Ragnarok 8.513 74.289 1499.0 \n", - "7 The Good Doctor 8.503 681.614 11768.0 \n", - "8 All of Us Are Dead 8.354 107.108 3335.0 \n", - "9 Sex Education 8.304 1008.977 6647.0 \n", - "10 The Last Kingdom 8.290 143.794 1485.0 \n", - "11 Love, Death & Robots 8.264 113.607 2804.0 \n", - "12 Money Heist 8.257 96.354 17836.0 \n", - "13 Grey's Anatomy 8.253 1647.218 9512.0 \n", - "14 Ozark 8.244 68.006 1968.0 \n", - "15 Control Z 8.227 20.708 2242.0 \n", - "16 Cobra Kai 8.220 118.256 5734.0 \n", - "17 Never Have I Ever 8.161 80.546 1557.0 \n", - "18 Bridgerton 8.143 71.308 2045.0 \n", - "19 The Walking Dead 8.121 489.746 15432.0 \n", - "20 Elite 8.071 100.711 8817.0 \n", - "21 The Sandman 8.064 64.168 1726.0 \n", - "22 Seal Team 8.036 306.638 1353.0 \n", - "23 Jurassic World Camp Cretaceous 8.031 38.434 1745.0 \n", - "24 The Queen of Flow 7.983 129.323 1391.0 \n", - "25 Narcos: Mexico 7.939 47.476 1160.0 \n", - "26 The 100 7.916 127.224 7666.0 \n", - "27 Locke & Key 7.903 27.017 1104.0 \n", - "28 Sweet Tooth 7.892 50.375 1237.0 \n", - "29 Boruto: Naruto Next Generations 7.890 149.166 2293.0 \n", - "30 Squid Game 7.831 115.587 13053.0 \n", - "31 Emily in Paris 7.775 33.659 1152.0 \n", - "32 Manifest 7.728 111.411 1312.0 \n", - "33 Pasión de Gavilanes 7.641 283.197 1925.0 \n", - "34 Who Killed Sara? 7.638 18.157 1132.0 \n", - "35 Daughter From Another Mother 7.611 22.140 1185.0 \n", - "\n", - " 觀看時數 (百萬小時) 上榜週數 單集片長 (分鐘) 總季數 支援語系數量 支援語系 \n", - "0 154.27 6 41.0 2.0 1 en \n", - "1 14.00 2 24.0 5.0 1 ja \n", - "2 27.60 2 0.0 6.0 3 en, de, es \n", - "3 2874.26 13 0.0 4.0 1 en \n", - "4 414.63 5 0.0 4.0 1 en \n", - "5 185.34 5 58.0 6.0 1 en \n", - "6 9.14 1 0.0 2.0 1 ja \n", - "7 112.58 6 43.0 6.0 1 en \n", - "8 659.51 11 65.0 2.0 2 en, ko \n", - "9 561.36 6 0.0 4.0 1 en \n", - "10 189.57 5 60.0 5.0 1 en \n", - "11 29.81 2 0.0 3.0 1 en \n", - "12 1170.20 14 70.0 3.0 1 es \n", - "13 64.32 3 43.0 19.0 1 en \n", - "14 751.60 13 0.0 4.0 1 en \n", - "15 89.83 3 36.0 3.0 1 es \n", - "16 383.64 4 30.0 6.0 1 en \n", - "17 297.78 4 30.0 4.0 1 en \n", - "18 1108.80 11 60.0 3.0 1 en \n", - "19 15.05 1 42.0 11.0 1 en \n", - "20 329.29 5 50.0 7.0 1 es \n", - "21 274.22 3 0.0 1.0 1 en \n", - "22 10.31 1 43.0 7.0 1 en \n", - "23 34.32 2 24.0 5.0 3 en, pt, th \n", - "24 561.23 16 53.0 2.0 1 es \n", - "25 138.98 4 50.0 3.0 2 en, es \n", - "26 26.58 2 43.0 7.0 1 en \n", - "27 278.38 5 47.0 3.0 1 en \n", - "28 34.45 2 54.0 3.0 2 en, ro \n", - "29 14.36 1 25.0 1.0 1 ja \n", - "30 2289.50 20 0.0 2.0 3 en, ko, ur \n", - "31 341.63 5 30.0 4.0 2 en, fr \n", - "32 1446.26 16 42.0 4.0 1 en \n", - "33 164.18 5 0.0 2.0 1 es \n", - "34 143.61 4 40.0 3.0 1 es \n", - "35 50.38 3 40.0 3.0 1 es " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "==============================================================================================================\n", - "📈 在 TMDb_Hit 優質影劇中，『TMDb大眾評分』與全新維度之相關係數\n", - "==============================================================================================================\n", - "▸ 1. 【TMDb大眾評分】 vs. 【TMDb熱度值 (Popularity)】 相關係數 (r) ＝ 0.1547\n", - "▸ 2. 【TMDb大眾評分】 vs. 【單集片長 (分鐘)】 相關係數 (r) ＝ -0.1598\n", - "▸ 3. 【TMDb大眾評分】 vs. 【總季數】 相關係數 (r) ＝ 0.1500\n", - "▸ 4. 【TMDb大眾評分】 vs. 【支援語系數量】 相關係數 (r) ＝ -0.0884\n", - "--------------------------------------------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# 1. 複製資料，建立乾淨的分析環境\n", - "df_feat = netflix.copy()\n", - "\n", - "# 2. 建立從 tmdb 原始表對照所需特徵的字典 (以小寫劇名為 Key)\n", - "# 這樣可以精準抓取：原始語言、熱度、單集片長、總季數、支援語系數量等原始維度\n", - "tmdb_clean_mapping = tmdb.dropna(subset=[\"name\"]).copy()\n", - "tmdb_clean_mapping[\"name_lower\"] = tmdb_clean_mapping[\"name\"].astype(str).str.lower()\n", - "\n", - "# 建立特徵對照表\n", - "map_popularity = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"popularity\"]))\n", - "map_languages = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"languages\"].fillna(\"en\")))\n", - "map_runtime = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"episode_run_time\"]))\n", - "map_seasons = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"number_of_seasons\"]))\n", - "\n", - "# 3. 將這些 TMDb 原始維度對照回我們的分析表\n", - "df_feat[\"影劇名稱_小寫\"] = df_feat[\"netflix_title\"].str.lower()\n", - "df_feat[\"TMDb大眾評分\"] = df_feat[\"tmdb_vote_average\"]\n", - "df_feat[\"TMDb投票數\"] = df_feat[\"tmdb_vote_count\"]\n", - "df_feat[\"觀看時數 (百萬小時)\"] = (df_feat[\"netflix_viewing_hours\"] / 1000000).round(2)\n", - "df_feat[\"上榜週數\"] = df_feat[\"netflix_weeks\"]\n", - "\n", - "# 新增維度\n", - "df_feat[\"TMDb熱度值 (Popularity)\"] = df_feat[\"影劇名稱_小寫\"].map(map_popularity)\n", - "df_feat[\"支援語系\"] = df_feat[\"影劇名稱_小寫\"].map(map_languages)\n", - "df_feat[\"單集片長 (分鐘)\"] = df_feat[\"影劇名稱_小寫\"].map(map_runtime)\n", - "df_feat[\"總季數\"] = df_feat[\"影劇名稱_小寫\"].map(map_seasons)\n", - "\n", - "# 計算支援的語系數量 (例如 \"en, es, fr\" -> 3)\n", - "df_feat[\"支援語系數量\"] = df_feat[\"支援語系\"].astype(str).apply(lambda x: len(x.split(\",\")) if pd.notna(x) else 1)\n", - "\n", - "# 4. 嚴格排除缺失值，並篩選符合 TMDb_Hit 的所有影劇\n", - "df_feat_clean = df_feat.dropna(subset=[\n", - " \"TMDb大眾評分\", \"TMDb投票數\", \"觀看時數 (百萬小時)\", \"TMDb熱度值 (Popularity)\", \"單集片長 (分鐘)\", \"總季數\"\n", - "]).copy()\n", - "\n", - "df_feat_clean[\"TMDb_Hit\"] = (\n", - " (df_feat_clean[\"TMDb大眾評分\"] >= 7.5) & \n", - " (df_feat_clean[\"TMDb投票數\"] >= 1000)\n", - ").astype(int)\n", - "\n", - "hit_shows_only = df_feat_clean[df_feat_clean[\"TMDb_Hit\"] == 1].copy()\n", - "\n", - "# 5. 以【TMDb大眾評分】降冪排序\n", - "hit_shows_sorted = hit_shows_only.sort_values(by=\"TMDb大眾評分\", ascending=False).reset_index(drop=True)\n", - "\n", - "# 整理最終呈現的表格欄位\n", - "final_table = hit_shows_sorted[[\n", - " \"netflix_title\",\n", - " \"TMDb大眾評分\",\n", - " \"TMDb熱度值 (Popularity)\",\n", - " \"TMDb投票數\",\n", - " \"觀看時數 (百萬小時)\",\n", - " \"上榜週數\",\n", - " \"單集片長 (分鐘)\",\n", - " \"總季數\",\n", - " \"支援語系數量\",\n", - " \"支援語系\"\n", - "]].rename(columns={\"netflix_title\": \"影劇名稱\"})\n", - "\n", - "# ==========================================\n", - "# 任務一：計算大眾評分與新加入維度的相關係數\n", - "# ==========================================\n", - "corr_rating_vs_pop = hit_shows_only[\"TMDb大眾評分\"].corr(hit_shows_only[\"TMDb熱度值 (Popularity)\"])\n", - "corr_rating_vs_runtime = hit_shows_only[\"TMDb大眾評分\"].corr(hit_shows_only[\"單集片長 (分鐘)\"])\n", - "corr_rating_vs_seasons = hit_shows_only[\"TMDb大眾評分\"].corr(hit_shows_only[\"總季數\"])\n", - "corr_rating_vs_lang_count = hit_shows_only[\"TMDb大眾評分\"].corr(hit_shows_only[\"支援語系數量\"])\n", - "\n", - "# ==========================================\n", - "# 輸出結果\n", - "# ==========================================\n", - "print(\"=\" * 110)\n", - "print(f\"🎯 頂級 TMDb_Hit 影劇多維度特色對照表 (依大眾評分降冪排序)\")\n", - "print(\"=\" * 110)\n", - "display(final_table)\n", - "\n", - "print(\"\\n\" + \"=\" * 110)\n", - "print(\"📈 在 TMDb_Hit 優質影劇中，『TMDb大眾評分』與全新維度之相關係數\")\n", - "print(\"=\" * 110)\n", - "print(f\"▸ 1. 【TMDb大眾評分】 vs. 【TMDb熱度值 (Popularity)】 相關係數 (r) ＝ {corr_rating_vs_pop:.4f}\")\n", - "print(f\"▸ 2. 【TMDb大眾評分】 vs. 【單集片長 (分鐘)】 相關係數 (r) ＝ {corr_rating_vs_runtime:.4f}\")\n", - "print(f\"▸ 3. 【TMDb大眾評分】 vs. 【總季數】 相關係數 (r) ＝ {corr_rating_vs_seasons:.4f}\")\n", - "print(f\"▸ 4. 【TMDb大眾評分】 vs. 【支援語系數量】 相關係數 (r) ＝ {corr_rating_vs_lang_count:.4f}\")\n", - "print(\"-\" * 110)" - ] - }, - { - "cell_type": "markdown", - "id": "3e5d1ad7", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "6b0e34d7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==============================================================================================================\n", - "🎯 TMDb_Hit 影劇原始資料對照表 (共 39 部符合，依大眾評分降冪排序)\n", - "==============================================================================================================\n" - ] - }, - { - "data": { - "text/html": [ - "
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影劇名稱TMDb大眾評分TMDb投票數觀看時數 (百萬小時)上榜週數TMDb類型 (Genres)TMDb故事大綱 (Overview)
0Arcane8.7403341.0154.276Animation, Drama, Sci-Fi & Fantasy, Action & A...Amid the stark discord of twin cities Piltover...
1Demon Slayer: Kimetsu no Yaiba8.7005618.014.002Animation, Action & Adventure, Sci-Fi & FantasyIt is the Taisho Period in Japan. Tanjiro, a k...
2Hunter X Hunter (2011)8.6471472.09.891NaNNaN
3Better Call Saul8.6454354.027.602Crime, DramaSix years before Saul Goodman meets Walter Whi...
4Stranger Things8.62416161.02874.2613Drama, Sci-Fi & Fantasy, MysteryWhen a young boy vanishes, a small town uncove...
5The Umbrella Academy8.6048891.0414.635Action & Adventure, Sci-Fi & Fantasy, DramaA dysfunctional family of superheroes comes to...
6Peaky Blinders8.5468836.0185.345Drama, CrimeA gangster family epic set in 1919 Birmingham,...
7Record of Ragnarok8.5131499.09.141Animation, Action & Adventure, Sci-Fi & FantasyBefore eradicating humankind from the world, t...
8The Good Doctor8.50311768.0112.586DramaShaun Murphy, a young surgeon with autism and ...
9The Seven Deadly Sins: Cursed by Light8.4624710.079.514NaNNaN
10Venom8.4141347.046.424NaNNaN
11All of Us Are Dead8.3543335.0659.5111Action & Adventure, Drama, Sci-Fi & FantasyA high school becomes ground zero for a zombie...
12Sex Education8.3046647.0561.366Comedy, DramaInexperienced Otis channels his sex therapist ...
13The Last Kingdom8.2901485.0189.575Action & Adventure, Drama, War & PoliticsA show of heroic deeds and epic battles with a...
14Love, Death & Robots8.2642804.029.812Animation, Sci-Fi & FantasyTerrifying creatures, wicked surprises and dar...
15Money Heist8.25717836.01170.2014Crime, DramaTo carry out the biggest heist in history, a m...
16Grey's Anatomy8.2539512.064.323DramaFollows the personal and professional lives of...
17Ozark8.2441968.0751.6013Crime, DramaA financial adviser drags his family from Chic...
18Control Z8.2272242.089.833DramaWhen a hacker begins releasing students' secre...
19Cobra Kai8.2205734.0383.644Action & Adventure, Drama, ComedyThis Karate Kid sequel series picks up 30 year...
20Never Have I Ever8.1611557.0297.784Comedy, DramaAfter a traumatic year, all an Indian-American...
21Bridgerton8.1432045.01108.8011DramaWealth, lust, and betrayal set in the backdrop...
22The Walking Dead8.12115432.015.051Action & Adventure, Drama, Sci-Fi & FantasySheriff's deputy Rick Grimes awakens from a co...
23Elite8.0718817.0329.295Crime, Mystery, DramaWhen three working class kids enroll in the mo...
24The Sandman8.0641726.0274.223Sci-Fi & Fantasy, DramaAfter years of imprisonment, Morpheus — the Ki...
25Seal Team8.0361353.010.311Action & Adventure, Drama, War & PoliticsMilitary drama following the professional and ...
26Jurassic World Camp Cretaceous8.0311745.034.322Animation, Kids, Action & AdventureSix teens attending an adventure camp on the o...
27The Queen of Flow7.9831391.0561.2316DramaAfter spending seventeen years in prison unfai...
28Narcos: Mexico7.9391160.0138.984Drama, CrimeSee the rise of the Guadalajara Cartel as an A...
29The 1007.9167666.026.582Sci-Fi & Fantasy, Drama, Action & Adventure100 years in the future, when the Earth has be...
30Locke & Key7.9031104.0278.385Sci-Fi & Fantasy, Drama, MysteryThree siblings who move into their ancestral e...
31Sweet Tooth7.8921237.034.452Drama, Sci-Fi & FantasyOn a perilous adventure across a post-apocalyp...
32Boruto: Naruto Next Generations7.8902293.014.361Action & Adventure, AnimationThe life of the shinobi is beginning to change...
33Squid Game7.83113053.02289.5020Action & Adventure, Mystery, DramaHundreds of cash-strapped players accept a str...
34Emily in Paris7.7751152.0341.635Drama, ComedyWhen ambitious Chicago marketing exec Emily un...
35Manifest7.7281312.01446.2616Drama, Mystery, Sci-Fi & FantasyAfter landing from a turbulent but routine fli...
36Pasión de Gavilanes7.6411925.0164.185DramaThe Reyes-Elizondo's idyllic lives are shatter...
37Who Killed Sara?7.6381132.0143.614Drama, Crime, MysteryHell-bent on exacting revenge and proving he w...
38Daughter From Another Mother7.6111185.050.383Comedy, DramaAfter realizing their babies were exchanged at...
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" - ], - "text/plain": [ - " 影劇名稱 TMDb大眾評分 TMDb投票數 觀看時數 (百萬小時) \\\n", - "0 Arcane 8.740 3341.0 154.27 \n", - "1 Demon Slayer: Kimetsu no Yaiba 8.700 5618.0 14.00 \n", - "2 Hunter X Hunter (2011) 8.647 1472.0 9.89 \n", - "3 Better Call Saul 8.645 4354.0 27.60 \n", - "4 Stranger Things 8.624 16161.0 2874.26 \n", - "5 The Umbrella Academy 8.604 8891.0 414.63 \n", - "6 Peaky Blinders 8.546 8836.0 185.34 \n", - "7 Record of Ragnarok 8.513 1499.0 9.14 \n", - "8 The Good Doctor 8.503 11768.0 112.58 \n", - "9 The Seven Deadly Sins: Cursed by Light 8.462 4710.0 79.51 \n", - "10 Venom 8.414 1347.0 46.42 \n", - "11 All of Us Are Dead 8.354 3335.0 659.51 \n", - "12 Sex Education 8.304 6647.0 561.36 \n", - "13 The Last Kingdom 8.290 1485.0 189.57 \n", - "14 Love, Death & Robots 8.264 2804.0 29.81 \n", - "15 Money Heist 8.257 17836.0 1170.20 \n", - "16 Grey's Anatomy 8.253 9512.0 64.32 \n", - "17 Ozark 8.244 1968.0 751.60 \n", - "18 Control Z 8.227 2242.0 89.83 \n", - "19 Cobra Kai 8.220 5734.0 383.64 \n", - "20 Never Have I Ever 8.161 1557.0 297.78 \n", - "21 Bridgerton 8.143 2045.0 1108.80 \n", - "22 The Walking Dead 8.121 15432.0 15.05 \n", - "23 Elite 8.071 8817.0 329.29 \n", - "24 The Sandman 8.064 1726.0 274.22 \n", - "25 Seal Team 8.036 1353.0 10.31 \n", - "26 Jurassic World Camp Cretaceous 8.031 1745.0 34.32 \n", - "27 The Queen of Flow 7.983 1391.0 561.23 \n", - "28 Narcos: Mexico 7.939 1160.0 138.98 \n", - "29 The 100 7.916 7666.0 26.58 \n", - "30 Locke & Key 7.903 1104.0 278.38 \n", - "31 Sweet Tooth 7.892 1237.0 34.45 \n", - "32 Boruto: Naruto Next Generations 7.890 2293.0 14.36 \n", - "33 Squid Game 7.831 13053.0 2289.50 \n", - "34 Emily in Paris 7.775 1152.0 341.63 \n", - "35 Manifest 7.728 1312.0 1446.26 \n", - "36 Pasión de Gavilanes 7.641 1925.0 164.18 \n", - "37 Who Killed Sara? 7.638 1132.0 143.61 \n", - "38 Daughter From Another Mother 7.611 1185.0 50.38 \n", - "\n", - " 上榜週數 TMDb類型 (Genres) \\\n", - "0 6 Animation, Drama, Sci-Fi & Fantasy, Action & A... \n", - "1 2 Animation, Action & Adventure, Sci-Fi & Fantasy \n", - "2 1 NaN \n", - "3 2 Crime, Drama \n", - "4 13 Drama, Sci-Fi & Fantasy, Mystery \n", - "5 5 Action & Adventure, Sci-Fi & Fantasy, Drama \n", - "6 5 Drama, Crime \n", - "7 1 Animation, Action & Adventure, Sci-Fi & Fantasy \n", - "8 6 Drama \n", - "9 4 NaN \n", - "10 4 NaN \n", - "11 11 Action & Adventure, Drama, Sci-Fi & Fantasy \n", - "12 6 Comedy, Drama \n", - "13 5 Action & Adventure, Drama, War & Politics \n", - "14 2 Animation, Sci-Fi & Fantasy \n", - "15 14 Crime, Drama \n", - "16 3 Drama \n", - "17 13 Crime, Drama \n", - "18 3 Drama \n", - "19 4 Action & Adventure, Drama, Comedy \n", - "20 4 Comedy, Drama \n", - "21 11 Drama \n", - "22 1 Action & Adventure, Drama, Sci-Fi & Fantasy \n", - "23 5 Crime, Mystery, Drama \n", - "24 3 Sci-Fi & Fantasy, Drama \n", - "25 1 Action & Adventure, Drama, War & Politics \n", - "26 2 Animation, Kids, Action & Adventure \n", - "27 16 Drama \n", - "28 4 Drama, Crime \n", - "29 2 Sci-Fi & Fantasy, Drama, Action & Adventure \n", - "30 5 Sci-Fi & Fantasy, Drama, Mystery \n", - "31 2 Drama, Sci-Fi & Fantasy \n", - "32 1 Action & Adventure, Animation \n", - "33 20 Action & Adventure, Mystery, Drama \n", - "34 5 Drama, Comedy \n", - "35 16 Drama, Mystery, Sci-Fi & Fantasy \n", - "36 5 Drama \n", - "37 4 Drama, Crime, Mystery \n", - "38 3 Comedy, Drama \n", - "\n", - " TMDb故事大綱 (Overview) \n", - "0 Amid the stark discord of twin cities Piltover... \n", - "1 It is the Taisho Period in Japan. Tanjiro, a k... \n", - "2 NaN \n", - "3 Six years before Saul Goodman meets Walter Whi... \n", - "4 When a young boy vanishes, a small town uncove... \n", - "5 A dysfunctional family of superheroes comes to... \n", - "6 A gangster family epic set in 1919 Birmingham,... \n", - "7 Before eradicating humankind from the world, t... \n", - "8 Shaun Murphy, a young surgeon with autism and ... \n", - "9 NaN \n", - "10 NaN \n", - "11 A high school becomes ground zero for a zombie... \n", - "12 Inexperienced Otis channels his sex therapist ... \n", - "13 A show of heroic deeds and epic battles with a... \n", - "14 Terrifying creatures, wicked surprises and dar... \n", - "15 To carry out the biggest heist in history, a m... \n", - "16 Follows the personal and professional lives of... \n", - "17 A financial adviser drags his family from Chic... \n", - "18 When a hacker begins releasing students' secre... \n", - "19 This Karate Kid sequel series picks up 30 year... \n", - "20 After a traumatic year, all an Indian-American... \n", - "21 Wealth, lust, and betrayal set in the backdrop... \n", - "22 Sheriff's deputy Rick Grimes awakens from a co... \n", - "23 When three working class kids enroll in the mo... \n", - "24 After years of imprisonment, Morpheus — the Ki... \n", - "25 Military drama following the professional and ... \n", - "26 Six teens attending an adventure camp on the o... \n", - "27 After spending seventeen years in prison unfai... \n", - "28 See the rise of the Guadalajara Cartel as an A... \n", - "29 100 years in the future, when the Earth has be... \n", - "30 Three siblings who move into their ancestral e... \n", - "31 On a perilous adventure across a post-apocalyp... \n", - "32 The life of the shinobi is beginning to change... \n", - "33 Hundreds of cash-strapped players accept a str... \n", - "34 When ambitious Chicago marketing exec Emily un... \n", - "35 After landing from a turbulent but routine fli... \n", - "36 The Reyes-Elizondo's idyllic lives are shatter... \n", - "37 Hell-bent on exacting revenge and proving he w... \n", - "38 After realizing their babies were exchanged at... " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# 1. 複製資料，建立乾淨的分析環境\n", - "df_elements_raw = netflix.copy()\n", - "\n", - "# 2. 建立 TMDb 原始欄位（genres, overview）的對照字典\n", - "tmdb_clean_mapping = tmdb.dropna(subset=[\"name\"]).copy()\n", - "tmdb_clean_mapping[\"name_lower\"] = tmdb_clean_mapping[\"name\"].astype(str).str.lower()\n", - "\n", - "map_genres = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"genres\"]))\n", - "map_overview = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"overview\"]))\n", - "\n", - "# 3. 映射回我們的分析表\n", - "df_elements_raw[\"影劇名稱_小寫\"] = df_elements_raw[\"netflix_title\"].str.lower()\n", - "df_elements_raw[\"TMDb大眾評分\"] = df_elements_raw[\"tmdb_vote_average\"]\n", - "df_elements_raw[\"TMDb投票數\"] = df_elements_raw[\"tmdb_vote_count\"]\n", - "df_elements_raw[\"觀看時數 (百萬小時)\"] = (df_elements_raw[\"netflix_viewing_hours\"] / 1000000).round(2)\n", - "df_elements_raw[\"上榜週數\"] = df_elements_raw[\"netflix_weeks\"]\n", - "\n", - "# 提取原始欄位資料\n", - "df_elements_raw[\"TMDb類型 (Genres)\"] = df_elements_raw[\"影劇名稱_小寫\"].map(map_genres)\n", - "df_elements_raw[\"TMDb故事大綱 (Overview)\"] = df_elements_raw[\"影劇名稱_小寫\"].map(map_overview)\n", - "\n", - "# 4. 嚴格排除缺失值，並篩選符合 TMDb_Hit 的影劇 (評分>=7.5 且 投票數>=1000)\n", - "df_elements_clean = df_elements_raw.dropna(subset=[\"TMDb大眾評分\", \"TMDb投票數\", \"觀看時數 (百萬小時)\"]).copy()\n", - "df_elements_clean[\"TMDb_Hit\"] = (\n", - " (df_elements_clean[\"TMDb大眾評分\"] >= 7.5) & \n", - " (df_elements_clean[\"TMDb投票數\"] >= 1000)\n", - ").astype(int)\n", - "\n", - "hit_shows_only = df_elements_clean[df_elements_clean[\"TMDb_Hit\"] == 1].copy()\n", - "\n", - "# 5. 以【TMDb大眾評分】降冪排序\n", - "hit_shows_sorted = hit_shows_only.sort_values(by=\"TMDb大眾評分\", ascending=False).reset_index(drop=True)\n", - "\n", - "# 6. 整理最終呈現表格\n", - "final_table = hit_shows_sorted[[\n", - " \"netflix_title\",\n", - " \"TMDb大眾評分\",\n", - " \"TMDb投票數\",\n", - " \"觀看時數 (百萬小時)\",\n", - " \"上榜週數\",\n", - " \"TMDb類型 (Genres)\",\n", - " \"TMDb故事大綱 (Overview)\"\n", - "]].rename(columns={\"netflix_title\": \"影劇名稱\"})\n", - "\n", - "# ==========================================\n", - "# 呈現結果\n", - "# ==========================================\n", - "print(\"=\" * 110)\n", - "print(f\"🎯 TMDb_Hit 影劇原始資料對照表 (共 {len(final_table)} 部符合，依大眾評分降冪排序)\")\n", - "print(\"=\" * 110)\n", - "display(final_table)" - ] - }, - { - "cell_type": "markdown", - "id": "621d29ef", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "3f0de5c0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================================================================\n", - "📊 TMDb_Hit 影劇中『重疊度最高』的熱門類型標籤排行榜\n", - " (統計範圍：共 36 部符合 TMDb_Hit 的頂級影劇)\n", - "================================================================================\n" - ] - }, - { - "data": { - "text/html": [ - "
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TMDb類型標籤出現次數 (部)在所有 Hit 劇中的佔比
0Drama3186.11%
1Sci-Fi & Fantasy1336.11%
2Action & Adventure1336.11%
3Crime719.44%
4Animation616.67%
5Mystery616.67%
6Comedy513.89%
7War & Politics25.56%
8Kids12.78%
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" - ], - "text/plain": [ - " TMDb類型標籤 出現次數 (部) 在所有 Hit 劇中的佔比\n", - "0 Drama 31 86.11%\n", - "1 Sci-Fi & Fantasy 13 36.11%\n", - "2 Action & Adventure 13 36.11%\n", - "3 Crime 7 19.44%\n", - "4 Animation 6 16.67%\n", - "5 Mystery 6 16.67%\n", - "6 Comedy 5 13.89%\n", - "7 War & Politics 2 5.56%\n", - "8 Kids 1 2.78%" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "from collections import Counter\n", - "\n", - "# 1. 複製資料，建立乾淨的分析環境\n", - "df_tags = netflix.copy()\n", - "\n", - "# 2. 建立 TMDb 類型映射\n", - "tmdb_clean_mapping = tmdb.dropna(subset=[\"name\"]).copy()\n", - "tmdb_clean_mapping[\"name_lower\"] = tmdb_clean_mapping[\"name\"].astype(str).str.lower()\n", - "map_genres = dict(zip(tmdb_clean_mapping[\"name_lower\"], tmdb_clean_mapping[\"genres\"]))\n", - "\n", - "df_tags[\"影劇名稱_小寫\"] = df_tags[\"netflix_title\"].str.lower()\n", - "df_tags[\"TMDb大眾評分\"] = df_tags[\"tmdb_vote_average\"]\n", - "df_tags[\"TMDb投票數\"] = df_tags[\"tmdb_vote_count\"]\n", - "df_tags[\"TMDb類型\"] = df_tags[\"影劇名稱_小寫\"].map(map_genres)\n", - "\n", - "# 3. 篩選符合 TMDb_Hit 的所有影劇 (評分>=7.5 且 投票數>=1000)\n", - "df_tags_clean = df_tags.dropna(subset=[\"TMDb大眾評分\", \"TMDb投票數\", \"TMDb類型\"]).copy()\n", - "df_tags_clean[\"TMDb_Hit\"] = (\n", - " (df_tags_clean[\"TMDb大眾評分\"] >= 7.5) & \n", - " (df_tags_clean[\"TMDb投票數\"] >= 1000)\n", - ").astype(int)\n", - "\n", - "hit_shows_only = df_tags_clean[df_tags_clean[\"TMDb_Hit\"] == 1].copy()\n", - "\n", - "# 4. 拆解與統計標籤\n", - "# 由於原始資料格式可能是 \"Sci-Fi & Fantasy, Drama\" 或是 [\"Sci-Fi & Fantasy\", \"Drama\"]\n", - "# 我們將它們統一拆開，去除空白，放入計數器中\n", - "all_genres_list = []\n", - "for genres in hit_shows_only[\"TMDb類型\"]:\n", - " if isinstance(genres, str):\n", - " # 如果是字串，按逗號分割\n", - " split_genres = [g.strip() for g in genres.split(\",\") if g.strip()]\n", - " all_genres_list.extend(split_genres)\n", - " elif isinstance(genres, list):\n", - " # 如果是列表，直接加入\n", - " all_genres_list.extend([str(g).strip() for g in genres])\n", - "\n", - "# 計算每個標籤的出現次數\n", - "genre_counts = Counter(all_genres_list)\n", - "\n", - "# 5. 轉換為漂亮的 DataFrame\n", - "total_hit_shows = len(hit_shows_only)\n", - "df_genre_ranking = pd.DataFrame(genre_counts.items(), columns=[\"TMDb類型標籤\", \"出現次數 (部)\"])\n", - "df_genre_ranking = df_genre_ranking.sort_values(by=\"出現次數 (部)\", ascending=False).reset_index(drop=True)\n", - "\n", - "# 計算在 Hit 影劇中的佔比\n", - "df_genre_ranking[\"在所有 Hit 劇中的佔比\"] = (\n", - " (df_genre_ranking[\"出現次數 (部)\"] / total_hit_shows) * 100\n", - ").round(2).astype(str) + \"%\"\n", - "\n", - "# ==========================================\n", - "# 呈現最終完美的結果\n", - "# ==========================================\n", - "print(\"=\" * 80)\n", - "print(f\"📊 TMDb_Hit 影劇中『重疊度最高』的熱門類型標籤排行榜\")\n", - "print(f\" (統計範圍：共 {total_hit_shows} 部符合 TMDb_Hit 的頂級影劇)\")\n", - "print(\"=\" * 80)\n", - "display(df_genre_ranking)" - ] - }, - { - "cell_type": "markdown", - "id": "9d67761b", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "266174f4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "===================================================================================================================\n", - "📊 突發狀況成功應對：成功從三大原始 CSV 分類抽取共 39 筆 TMDb_Hit 的完整屬性特徵！\n", - "===================================================================================================================\n", - "\n", - "🎬 【分類一：Netflix 原始流量與生命力屬性表】\n", - "-------------------------------------------------------------------------------------------------------------------\n" - ] - }, - { - "data": { - "text/html": [ - "
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影劇名稱Netflix_觀看時數 (百萬)Netflix_上榜週數Netflix_入榜年份提示
0Arcane154.2762021
1Demon Slayer: Kimetsu no Yaiba14.0022021
2Hunter X Hunter (2011)9.8912021
3Better Call Saul27.6022022
4Stranger Things2874.26132022
5The Umbrella Academy414.6352022
6Peaky Blinders185.3452022
7Record of Ragnarok9.1412021
8The Good Doctor112.5862021
9The Seven Deadly Sins: Cursed by Light79.5142021
10Venom46.4242021
11All of Us Are Dead659.51112022
12Sex Education561.3662021
13The Last Kingdom189.5752022
14Love, Death & Robots29.8122022
15Money Heist1170.20142021
16Grey's Anatomy64.3232021
17Ozark751.60132022
18Control Z89.8332021
19Cobra Kai383.6442022
20Never Have I Ever297.7842021
21Bridgerton1108.80112021
22The Walking Dead15.0512021
23Elite329.2952021
24The Sandman274.2232022
25Seal Team10.3112022
26Jurassic World Camp Cretaceous34.3222021
27The Queen of Flow561.23162021
28Narcos: Mexico138.9842021
29The 10026.5822021
30Locke & Key278.3852021
31Sweet Tooth34.4522021
32Boruto: Naruto Next Generations14.3612022
33Squid Game2289.50202021
34Emily in Paris341.6352021
35Manifest1446.26162021
36Pasión de Gavilanes164.1852022
37Who Killed Sara?143.6142022
38Daughter From Another Mother50.3832021
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影劇名稱TMDb_大眾評分TMDb_打分投票數TMDb_熱度值 (Popularity)TMDb_類型 (Genres)TMDb_總季數TMDb_總集數TMDb_單集片長 (分鐘)TMDb_支援語系TMDb_故事大綱 (Overview)
0Arcane8.7403341.042.447Animation, Drama, Sci-Fi & Fantasy, Action & A...2.09.041.0enAmid the stark discord of twin cities Piltover...
1Demon Slayer: Kimetsu no Yaiba8.7005618.082.890Animation, Action & Adventure, Sci-Fi & Fantasy5.055.024.0jaIt is the Taisho Period in Japan. Tanjiro, a k...
2Hunter X Hunter (2011)8.6471472.0NaNNaNNaNNaNNaNNaNNaN
3Better Call Saul8.6454354.0104.469Crime, Drama6.063.00.0en, de, esSix years before Saul Goodman meets Walter Whi...
4Stranger Things8.62416161.0185.711Drama, Sci-Fi & Fantasy, Mystery4.034.00.0enWhen a young boy vanishes, a small town uncove...
5The Umbrella Academy8.6048891.051.859Action & Adventure, Sci-Fi & Fantasy, Drama4.032.00.0enA dysfunctional family of superheroes comes to...
6Peaky Blinders8.5468836.0344.477Drama, Crime6.036.058.0enA gangster family epic set in 1919 Birmingham,...
7Record of Ragnarok8.5131499.074.289Animation, Action & Adventure, Sci-Fi & Fantasy2.027.00.0jaBefore eradicating humankind from the world, t...
8The Good Doctor8.50311768.0681.614Drama6.0116.043.0enShaun Murphy, a young surgeon with autism and ...
9The Seven Deadly Sins: Cursed by Light8.4624710.0NaNNaNNaNNaNNaNNaNNaN
10Venom8.4141347.0NaNNaNNaNNaNNaNNaNNaN
11All of Us Are Dead8.3543335.0107.108Action & Adventure, Drama, Sci-Fi & Fantasy2.012.065.0en, koA high school becomes ground zero for a zombie...
12Sex Education8.3046647.01008.977Comedy, Drama4.032.00.0enInexperienced Otis channels his sex therapist ...
13The Last Kingdom8.2901485.0143.794Action & Adventure, Drama, War & Politics5.046.060.0enA show of heroic deeds and epic battles with a...
14Love, Death & Robots8.2642804.0113.607Animation, Sci-Fi & Fantasy3.035.00.0enTerrifying creatures, wicked surprises and dar...
15Money Heist8.25717836.096.354Crime, Drama3.041.070.0esTo carry out the biggest heist in history, a m...
16Grey's Anatomy8.2539512.01647.218Drama19.0419.043.0enFollows the personal and professional lives of...
17Ozark8.2441968.068.006Crime, Drama4.044.00.0enA financial adviser drags his family from Chic...
18Control Z8.2272242.020.708Drama3.024.036.0esWhen a hacker begins releasing students' secre...
19Cobra Kai8.2205734.0118.256Action & Adventure, Drama, Comedy6.050.030.0enThis Karate Kid sequel series picks up 30 year...
20Never Have I Ever8.1611557.080.546Comedy, Drama4.040.030.0enAfter a traumatic year, all an Indian-American...
21Bridgerton8.1432045.071.308Drama3.024.060.0enWealth, lust, and betrayal set in the backdrop...
22The Walking Dead8.12115432.0489.746Action & Adventure, Drama, Sci-Fi & Fantasy11.0177.042.0enSheriff's deputy Rick Grimes awakens from a co...
23Elite8.0718817.0100.711Crime, Mystery, Drama7.049.050.0esWhen three working class kids enroll in the mo...
24The Sandman8.0641726.064.168Sci-Fi & Fantasy, Drama1.011.00.0enAfter years of imprisonment, Morpheus — the Ki...
25Seal Team8.0361353.0306.638Action & Adventure, Drama, War & Politics7.0102.043.0enMilitary drama following the professional and ...
26Jurassic World Camp Cretaceous8.0311745.038.434Animation, Kids, Action & Adventure5.049.024.0en, pt, thSix teens attending an adventure camp on the o...
27The Queen of Flow7.9831391.0129.323Drama2.0172.053.0esAfter spending seventeen years in prison unfai...
28Narcos: Mexico7.9391160.047.476Drama, Crime3.030.050.0en, esSee the rise of the Guadalajara Cartel as an A...
29The 1007.9167666.0127.224Sci-Fi & Fantasy, Drama, Action & Adventure7.0100.043.0en100 years in the future, when the Earth has be...
30Locke & Key7.9031104.027.017Sci-Fi & Fantasy, Drama, Mystery3.028.047.0enThree siblings who move into their ancestral e...
31Sweet Tooth7.8921237.050.375Drama, Sci-Fi & Fantasy3.024.054.0en, roOn a perilous adventure across a post-apocalyp...
32Boruto: Naruto Next Generations7.8902293.0149.166Action & Adventure, Animation1.0293.025.0jaThe life of the shinobi is beginning to change...
33Squid Game7.83113053.0115.587Action & Adventure, Mystery, Drama2.09.00.0en, ko, urHundreds of cash-strapped players accept a str...
34Emily in Paris7.7751152.033.659Drama, Comedy4.030.030.0en, frWhen ambitious Chicago marketing exec Emily un...
35Manifest7.7281312.0111.411Drama, Mystery, Sci-Fi & Fantasy4.062.042.0enAfter landing from a turbulent but routine fli...
36Pasión de Gavilanes7.6411925.0283.197Drama2.0259.00.0esThe Reyes-Elizondo's idyllic lives are shatter...
37Who Killed Sara?7.6381132.018.157Drama, Crime, Mystery3.025.040.0esHell-bent on exacting revenge and proving he w...
38Daughter From Another Mother7.6111185.022.140Comedy, Drama3.027.040.0esAfter realizing their babies were exchanged at...
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" - ], - "text/plain": [ - " 影劇名稱 TMDb_大眾評分 TMDb_打分投票數 \\\n", - "0 Arcane 8.740 3341.0 \n", - "1 Demon Slayer: Kimetsu no Yaiba 8.700 5618.0 \n", - "2 Hunter X Hunter (2011) 8.647 1472.0 \n", - "3 Better Call Saul 8.645 4354.0 \n", - "4 Stranger Things 8.624 16161.0 \n", - "5 The Umbrella Academy 8.604 8891.0 \n", - "6 Peaky Blinders 8.546 8836.0 \n", - "7 Record of Ragnarok 8.513 1499.0 \n", - "8 The Good Doctor 8.503 11768.0 \n", - "9 The Seven Deadly Sins: Cursed by Light 8.462 4710.0 \n", - "10 Venom 8.414 1347.0 \n", - "11 All of Us Are Dead 8.354 3335.0 \n", - "12 Sex Education 8.304 6647.0 \n", - "13 The Last Kingdom 8.290 1485.0 \n", - "14 Love, Death & Robots 8.264 2804.0 \n", - "15 Money Heist 8.257 17836.0 \n", - "16 Grey's Anatomy 8.253 9512.0 \n", - "17 Ozark 8.244 1968.0 \n", - "18 Control Z 8.227 2242.0 \n", - "19 Cobra Kai 8.220 5734.0 \n", - "20 Never Have I Ever 8.161 1557.0 \n", - "21 Bridgerton 8.143 2045.0 \n", - "22 The Walking Dead 8.121 15432.0 \n", - "23 Elite 8.071 8817.0 \n", - "24 The Sandman 8.064 1726.0 \n", - "25 Seal Team 8.036 1353.0 \n", - "26 Jurassic World Camp Cretaceous 8.031 1745.0 \n", - "27 The Queen of Flow 7.983 1391.0 \n", - "28 Narcos: Mexico 7.939 1160.0 \n", - "29 The 100 7.916 7666.0 \n", - "30 Locke & Key 7.903 1104.0 \n", - "31 Sweet Tooth 7.892 1237.0 \n", - "32 Boruto: Naruto Next Generations 7.890 2293.0 \n", - "33 Squid Game 7.831 13053.0 \n", - "34 Emily in Paris 7.775 1152.0 \n", - "35 Manifest 7.728 1312.0 \n", - "36 Pasión de Gavilanes 7.641 1925.0 \n", - "37 Who Killed Sara? 7.638 1132.0 \n", - "38 Daughter From Another Mother 7.611 1185.0 \n", - "\n", - " TMDb_熱度值 (Popularity) TMDb_類型 (Genres) \\\n", - "0 42.447 Animation, Drama, Sci-Fi & Fantasy, Action & A... \n", - "1 82.890 Animation, Action & Adventure, Sci-Fi & Fantasy \n", - "2 NaN NaN \n", - "3 104.469 Crime, Drama \n", - "4 185.711 Drama, Sci-Fi & Fantasy, Mystery \n", - "5 51.859 Action & Adventure, Sci-Fi & Fantasy, Drama \n", - "6 344.477 Drama, Crime \n", - "7 74.289 Animation, Action & Adventure, Sci-Fi & Fantasy \n", - "8 681.614 Drama \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 107.108 Action & Adventure, Drama, Sci-Fi & Fantasy \n", - "12 1008.977 Comedy, Drama \n", - "13 143.794 Action & Adventure, Drama, War & Politics \n", - "14 113.607 Animation, Sci-Fi & Fantasy \n", - "15 96.354 Crime, Drama \n", - "16 1647.218 Drama \n", - "17 68.006 Crime, Drama \n", - "18 20.708 Drama \n", - "19 118.256 Action & Adventure, Drama, Comedy \n", - "20 80.546 Comedy, Drama \n", - "21 71.308 Drama \n", - "22 489.746 Action & Adventure, Drama, Sci-Fi & Fantasy \n", - "23 100.711 Crime, Mystery, Drama \n", - "24 64.168 Sci-Fi & Fantasy, Drama \n", - "25 306.638 Action & Adventure, Drama, War & Politics \n", - "26 38.434 Animation, Kids, Action & Adventure \n", - "27 129.323 Drama \n", - "28 47.476 Drama, Crime \n", - "29 127.224 Sci-Fi & Fantasy, Drama, Action & Adventure \n", - "30 27.017 Sci-Fi & Fantasy, Drama, Mystery \n", - "31 50.375 Drama, Sci-Fi & Fantasy \n", - "32 149.166 Action & Adventure, Animation \n", - "33 115.587 Action & Adventure, Mystery, Drama \n", - "34 33.659 Drama, Comedy \n", - "35 111.411 Drama, Mystery, Sci-Fi & Fantasy \n", - "36 283.197 Drama \n", - "37 18.157 Drama, Crime, Mystery \n", - "38 22.140 Comedy, Drama \n", - "\n", - " TMDb_總季數 TMDb_總集數 TMDb_單集片長 (分鐘) TMDb_支援語系 \\\n", - "0 2.0 9.0 41.0 en \n", - "1 5.0 55.0 24.0 ja \n", - "2 NaN NaN NaN NaN \n", - "3 6.0 63.0 0.0 en, de, es \n", - "4 4.0 34.0 0.0 en \n", - "5 4.0 32.0 0.0 en \n", - "6 6.0 36.0 58.0 en \n", - "7 2.0 27.0 0.0 ja \n", - "8 6.0 116.0 43.0 en \n", - "9 NaN NaN NaN NaN \n", - "10 NaN NaN NaN NaN \n", - "11 2.0 12.0 65.0 en, ko \n", - "12 4.0 32.0 0.0 en \n", - "13 5.0 46.0 60.0 en \n", - "14 3.0 35.0 0.0 en \n", - "15 3.0 41.0 70.0 es \n", - "16 19.0 419.0 43.0 en \n", - "17 4.0 44.0 0.0 en \n", - "18 3.0 24.0 36.0 es \n", - "19 6.0 50.0 30.0 en \n", - "20 4.0 40.0 30.0 en \n", - "21 3.0 24.0 60.0 en \n", - "22 11.0 177.0 42.0 en \n", - "23 7.0 49.0 50.0 es \n", - "24 1.0 11.0 0.0 en \n", - "25 7.0 102.0 43.0 en \n", - "26 5.0 49.0 24.0 en, pt, th \n", - "27 2.0 172.0 53.0 es \n", - "28 3.0 30.0 50.0 en, es \n", - "29 7.0 100.0 43.0 en \n", - "30 3.0 28.0 47.0 en \n", - "31 3.0 24.0 54.0 en, ro \n", - "32 1.0 293.0 25.0 ja \n", - "33 2.0 9.0 0.0 en, ko, ur \n", - "34 4.0 30.0 30.0 en, fr \n", - "35 4.0 62.0 42.0 en \n", - "36 2.0 259.0 0.0 es \n", - "37 3.0 25.0 40.0 es \n", - "38 3.0 27.0 40.0 es \n", - "\n", - " TMDb_故事大綱 (Overview) \n", - "0 Amid the stark discord of twin cities Piltover... \n", - "1 It is the Taisho Period in Japan. Tanjiro, a k... \n", - "2 NaN \n", - "3 Six years before Saul Goodman meets Walter Whi... \n", - "4 When a young boy vanishes, a small town uncove... \n", - "5 A dysfunctional family of superheroes comes to... \n", - "6 A gangster family epic set in 1919 Birmingham,... \n", - "7 Before eradicating humankind from the world, t... \n", - "8 Shaun Murphy, a young surgeon with autism and ... \n", - "9 NaN \n", - "10 NaN \n", - "11 A high school becomes ground zero for a zombie... \n", - "12 Inexperienced Otis channels his sex therapist ... \n", - "13 A show of heroic deeds and epic battles with a... \n", - "14 Terrifying creatures, wicked surprises and dar... \n", - "15 To carry out the biggest heist in history, a m... \n", - "16 Follows the personal and professional lives of... \n", - "17 A financial adviser drags his family from Chic... \n", - "18 When a hacker begins releasing students' secre... \n", - "19 This Karate Kid sequel series picks up 30 year... \n", - "20 After a traumatic year, all an Indian-American... \n", - "21 Wealth, lust, and betrayal set in the backdrop... \n", - "22 Sheriff's deputy Rick Grimes awakens from a co... \n", - "23 When three working class kids enroll in the mo... \n", - "24 After years of imprisonment, Morpheus — the Ki... \n", - "25 Military drama following the professional and ... \n", - "26 Six teens attending an adventure camp on the o... \n", - "27 After spending seventeen years in prison unfai... \n", - "28 See the rise of the Guadalajara Cartel as an A... \n", - "29 100 years in the future, when the Earth has be... \n", - "30 Three siblings who move into their ancestral e... \n", - "31 On a perilous adventure across a post-apocalyp... \n", - "32 The life of the shinobi is beginning to change... \n", - "33 Hundreds of cash-strapped players accept a str... \n", - "34 When ambitious Chicago marketing exec Emily un... \n", - "35 After landing from a turbulent but routine fli... \n", - "36 The Reyes-Elizondo's idyllic lives are shatter... \n", - "37 Hell-bent on exacting revenge and proving he w... \n", - "38 After realizing their babies were exchanged at... 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影劇名稱IMDb_大眾評分IMDb_打分投票數IMDb_主要卡司IMDb_導演識別碼IMDb_編劇識別碼IMDb_開播年份IMDb_完結年份
0ArcaneNaNNaNHailee Steinfeld | Terri Douglas | JB Blanc | ...nm7829396,nm8822595,nm2174814,nm3684525,nm2310...nm1865649,nm0105169,nm11601652,nm10057595,nm26...20212024
1Demon Slayer: Kimetsu no Yaiba8.6230625.0Gui Niang | Bryce Papenbrook | Natsuki Hanae |...nm1417038,nm6957309,nm4582641,nm2003396,nm3297...nm10281577,nm2335252,nm255019820192024
2Hunter X Hunter (2011)8.612328.0NaNNaNNaNNaNNaN
3Better Call Saul9.357351.0Peter Gould | Vince Gilligan | Tony Dalton | G...nm0319213,nm1041475,nm0332467,nm2385621,nm0002...nm0319213,nm0332467,nm3298114,nm4798028,nm1834...20152022
4Stranger ThingsNaNNaNJonathan MacQueen | Toby Turner | Morgan Hamil...nm1633047nm1633047,nm3802432,nm2672602,nm3494086,nm336282320082009
5The Umbrella Academy7.074.0Colm Feore | Jordan Claire Robbins | Aidan Gal...nm2007278,nm2161247,nm0475578,nm0839660,nm1234...nm0085775,nm5128174,nm2916300,nm2007553,nm5933...20192024
6Peaky Blinders8.7783155.0Paul Anderson | Ian Peck | Cillian Murphy | He...nm1396104,nm1142073,nm2139803,nm0128452,nm1163...nm1140275,nm3023628,nm213310720132022
7Record of Ragnarok6.613106.0Miyuki Sawashiro | Yukihiro Nozuyama | Laura P...nm0457541,nm1162795nm3055854,nm1840994,nm8263018,nm6705101,nm19631322021\\N
8The Good DoctorNaNNaNChristina Chang | Will Yun Lee | Fiona Gubelma...nm0514278,nm0220049,nm0596867,nm1078929,nm0833...nm0794914,nm6728876,nm9319070,nm4784537,nm1325...20172024
9The Seven Deadly Sins: Cursed by Light6.41955.0NaNNaNNaNNaNNaN
10Venom6.7118.0Kevin Caliber | Matthew Remington Garner | Gab...nm8398203nm2525575,nm8398205,nm8398203\\N\\N
11All of Us Are Dead7.692709.0Kim Bo-Yoon | Lim Jae-hyeok | Cho Yi-hyun | So...nm2031561,nm12372685nm3005626,nm123726872022\\N
12Sex Education7.225.0Emma Mackey | Kedar Williams-Stirling | Alista...nm4829613,nm3513341,nm5486712,nm2575302,nm1046...nm5677403,nm1054899,nm4289705,nm4815863,nm1728...20192023
13The Last Kingdom8.413.0Ian Hart | Eliza Butterworth | Arnas Fedaravic...nm1113890,nm0063279,nm0387486,nm1016595,nm0932...nm4618645,nm0124661,nm0180486,nm2028576,nm9433...20152022
14Love, Death & RobotsNaNNaNFred Tatasciore | Nolan North | Piotr Michael ...nm1166916,nm0950775,nm1783265,nm3230254,nm1159...nm1783265,nm3664884,nm3376496,nm0487067,nm1257...2019\\N
15Money Heist8.2612436.0Alba Flores | Álvaro Morte | Jaime Lorente | E...nm2912775,nm2338464,nm1840195,nm1171596,nm0704...nm1210565,nm8996721,nm3663708,nm2651446,nm2943...20172021
16Grey's AnatomyNaNNaNPatrick Dempsey | Camilla Luddington | Chandra...nm0571727,nm0000739,nm0180085,nm0933156,nm0509...nm0722274,nm9698564,nm1652155,nm2629239,nm9455...2005\\N
17Ozark8.4394904.0Jessica Frances Dukes | Mark Williams | Laura ...nm0000867,nm0002399,nm0076997,nm5831383,nm0475...nm4057793,nm0931251,nm0464382,nm8851912,nm3780...20172022
18Control Z6.812606.0Iván Aragón | Luis Curiel | Carlos Quintanilla...nm1448814,nm1732695nm5435318,nm5572916,nm1909300,nm4608690,nm1417...20202022
19Cobra Kai6.936.0William Zabka | Tanner Buchanan | Martin Kove ...nm1375358,nm1376383,nm1629105,nm0372222,nm0148...nm0372222,nm1375358,nm0436543,nm1376383,nm8154...20182025
20Never Have I EverNaNNaNLang Fisher | Poorna Jagannathan | Benjamin No...nm1158282,nm0788426,nm3265847,nm3414572,nm3060...nm3265847,nm1411676,nm9818211,nm4627895,nm9276...20202023
21Bridgerton4.157.0Julie Andrews | Golda Rosheuvel | Chris Van Du...nm0894225,nm0110644,nm0284308,nm0726975,nm1455...nm9992180,nm2547449,nm3924649,nm4160286,nm3625...2020\\N
22The Walking DeadNaNNaNChristian Serratos | Norman Reedus | Chandler ...nm0630524,nm0766349,nm0101741,nm0225416,nm0272...nm1891501,nm0001104,nm3079117,nm3877885,nm1764...20102022
23Elite8.4112.0Claudia Salas | Elisabet Gelabert | André Lamo...nm1738082,nm2954266,nm0757749,nm1292099,nm1028...nm1823374,nm0599413,nm2268479,nm13563463,nm336...20182024
24The Sandman5.25.0Nina Wadia | Neil Gaiman | Allan Heinberg | Pa...nm3741255,nm1223381,nm0393776,nm0021772,nm0054...nm8475555,nm7137658,nm0301274,nm0275286,nm0374...20222025
25Seal TeamNaNNaNJustin Melnick | Benjamin Cavell | Jessica Par...nm0161078,nm0305423,nm0614013,nm0004770,nm1103...nm3859706,nm2353384,nm3265652,nm10170567,nm713...20172024
26Jurassic World Camp Cretaceous7.514086.0NaNNaNNaNNaNNaN
27The Queen of Flow7.52076.0Andres Salgado | Said Chamie | Lucho Velasco |...nm8154805,nm12415333,nm2160742,nm0482413nm6699136,nm1419272,nm137192720182026
28Narcos: Mexico8.3102053.0Diego Luna | Chris Brancato | Alfonso Dosal | ...nm1223381,nm1661334,nm2091911,nm2129610,nm1888...nm0076166,nm0104333,nm1215164,nm3370032,nm3992...20182021
29The 1009.412.0Bob Morley | Jason Rothenberg | Isaiah Washing...nm0924693,nm1243057,nm0675894,nm0021772,nm0534...nm0440790,nm1003202,nm1304613,nm6012871,nm6560...20142020
30Locke & KeyNaNNaNEmilia Jones | Sherri Saum | Aron Eli Coleite ...nm0867127,nm0021772,nm2385621,nm0622112,nm0816...nm2901688,nm1847815,nm0193681,nm0454872,nm1013...20202022
31Sweet ToothNaNNaNAmy Seimetz | Nonso Anozie | Jim Mickle | Stef...nm0585344,nm1089736,nm2505734,nm1164602,nm0052...nm4571811,nm0585344,nm3933291,nm7275370,nm7275...20212024
32Boruto: Naruto Next Generations6.335154.0Junko Takeuchi | Robbie Daymond | Maile Flanag...nm2446796,nm4738743,nm2738379,nm11511315,nm000...nm1618263,nm2276485,nm5532480,nm2638656,nm2516...20172023
33Squid GameNaNNaNLee Byung-hun | Greg Chun | Yim Si-wan | Lee J...nm1375363nm137536320212025
34Emily in Paris6.8143411.0Bruno Gouery | William Abadie | Lucas Bravo | ...nm0281598,nm1279203,nm0490792,nm2048922,nm0144...nm0823015,nm8033541,nm7322718,nm11880742,nm275...20202026
35ManifestNaNNaNMatt Long | Luna Blaise | Holly Taylor | Athen...nm0864268,nm1226492,nm0924693,nm0946475,nm1392...nm0707517,nm9414385,nm2837457,nm5915040,nm6768...20182023
36Pasión de GavilanesNaNNaNNaNNaNNaNNaNNaN
37Who Killed Sara?6.417647.0Andres Baida | Eugenio Siller | José Ignacio V...nm1954501,nm1732695,nm0092632,nm0525778,nm1319852nm1122534,nm8236619,nm113122620212022
38Daughter From Another Mother7.22735.0Emilio Beltrán | Carolina Rivera | Martín Alto...nm0765250,nm1774069,nm6525284,nm2399451,nm2432...nm0729202,nm0765250,nm6944207,nm2110261,nm5082...20212022
\n", - "
" - ], - "text/plain": [ - " 影劇名稱 IMDb_大眾評分 IMDb_打分投票數 \\\n", - "0 Arcane NaN NaN \n", - "1 Demon Slayer: Kimetsu no Yaiba 8.6 230625.0 \n", - "2 Hunter X Hunter (2011) 8.6 12328.0 \n", - "3 Better Call Saul 9.3 57351.0 \n", - "4 Stranger Things NaN NaN \n", - "5 The Umbrella Academy 7.0 74.0 \n", - "6 Peaky Blinders 8.7 783155.0 \n", - "7 Record of Ragnarok 6.6 13106.0 \n", - "8 The Good Doctor NaN NaN \n", - "9 The Seven Deadly Sins: Cursed by Light 6.4 1955.0 \n", - "10 Venom 6.7 118.0 \n", - "11 All of Us Are Dead 7.6 92709.0 \n", - "12 Sex Education 7.2 25.0 \n", - "13 The Last Kingdom 8.4 13.0 \n", - "14 Love, Death & Robots NaN NaN \n", - "15 Money Heist 8.2 612436.0 \n", - "16 Grey's Anatomy NaN NaN \n", - "17 Ozark 8.4 394904.0 \n", - "18 Control Z 6.8 12606.0 \n", - "19 Cobra Kai 6.9 36.0 \n", - "20 Never Have I Ever NaN NaN \n", - "21 Bridgerton 4.1 57.0 \n", - "22 The Walking Dead NaN NaN \n", - "23 Elite 8.4 112.0 \n", - "24 The Sandman 5.2 5.0 \n", - "25 Seal Team NaN NaN \n", - "26 Jurassic World Camp Cretaceous 7.5 14086.0 \n", - "27 The Queen of Flow 7.5 2076.0 \n", - "28 Narcos: Mexico 8.3 102053.0 \n", - "29 The 100 9.4 12.0 \n", - "30 Locke & Key NaN NaN \n", - "31 Sweet Tooth NaN NaN \n", - "32 Boruto: Naruto Next Generations 6.3 35154.0 \n", - "33 Squid Game NaN NaN \n", - "34 Emily in Paris 6.8 143411.0 \n", - "35 Manifest NaN NaN \n", - "36 Pasión de Gavilanes NaN NaN \n", - "37 Who Killed Sara? 6.4 17647.0 \n", - "38 Daughter From Another Mother 7.2 2735.0 \n", - "\n", - " IMDb_主要卡司 \\\n", - "0 Hailee Steinfeld | Terri Douglas | JB Blanc | ... \n", - "1 Gui Niang | Bryce Papenbrook | Natsuki Hanae |... \n", - "2 NaN \n", - "3 Peter Gould | Vince Gilligan | Tony Dalton | G... \n", - "4 Jonathan MacQueen | Toby Turner | Morgan Hamil... \n", - "5 Colm Feore | Jordan Claire Robbins | Aidan Gal... \n", - "6 Paul Anderson | Ian Peck | Cillian Murphy | He... \n", - "7 Miyuki Sawashiro | Yukihiro Nozuyama | Laura P... \n", - "8 Christina Chang | Will Yun Lee | Fiona Gubelma... \n", - "9 NaN \n", - "10 Kevin Caliber | Matthew Remington Garner | Gab... \n", - "11 Kim Bo-Yoon | Lim Jae-hyeok | Cho Yi-hyun | So... \n", - "12 Emma Mackey | Kedar Williams-Stirling | Alista... \n", - "13 Ian Hart | Eliza Butterworth | Arnas Fedaravic... \n", - "14 Fred Tatasciore | Nolan North | Piotr Michael ... \n", - "15 Alba Flores | Álvaro Morte | Jaime Lorente | E... \n", - "16 Patrick Dempsey | Camilla Luddington | Chandra... \n", - "17 Jessica Frances Dukes | Mark Williams | Laura ... \n", - "18 Iván Aragón | Luis Curiel | Carlos Quintanilla... \n", - "19 William Zabka | Tanner Buchanan | Martin Kove ... \n", - "20 Lang Fisher | Poorna Jagannathan | Benjamin No... \n", - "21 Julie Andrews | Golda Rosheuvel | Chris Van Du... \n", - "22 Christian Serratos | Norman Reedus | Chandler ... \n", - "23 Claudia Salas | Elisabet Gelabert | André Lamo... \n", - "24 Nina Wadia | Neil Gaiman | Allan Heinberg | Pa... \n", - "25 Justin Melnick | Benjamin Cavell | Jessica Par... \n", - "26 NaN \n", - "27 Andres Salgado | Said Chamie | Lucho Velasco |... \n", - "28 Diego Luna | Chris Brancato | Alfonso Dosal | ... \n", - "29 Bob Morley | Jason Rothenberg | Isaiah Washing... \n", - "30 Emilia Jones | Sherri Saum | Aron Eli Coleite ... \n", - "31 Amy Seimetz | Nonso Anozie | Jim Mickle | Stef... \n", - "32 Junko Takeuchi | Robbie Daymond | Maile Flanag... \n", - "33 Lee Byung-hun | Greg Chun | Yim Si-wan | Lee J... \n", - "34 Bruno Gouery | William Abadie | Lucas Bravo | ... \n", - "35 Matt Long | Luna Blaise | Holly Taylor | Athen... \n", - "36 NaN \n", - "37 Andres Baida | Eugenio Siller | José Ignacio V... \n", - "38 Emilio Beltrán | Carolina Rivera | Martín Alto... \n", - "\n", - " IMDb_導演識別碼 \\\n", - "0 nm7829396,nm8822595,nm2174814,nm3684525,nm2310... \n", - "1 nm1417038,nm6957309,nm4582641,nm2003396,nm3297... \n", - "2 NaN \n", - "3 nm0319213,nm1041475,nm0332467,nm2385621,nm0002... \n", - "4 nm1633047 \n", - "5 nm2007278,nm2161247,nm0475578,nm0839660,nm1234... \n", - "6 nm1396104,nm1142073,nm2139803,nm0128452,nm1163... \n", - "7 nm0457541,nm1162795 \n", - "8 nm0514278,nm0220049,nm0596867,nm1078929,nm0833... \n", - "9 NaN \n", - "10 nm8398203 \n", - "11 nm2031561,nm12372685 \n", - "12 nm4829613,nm3513341,nm5486712,nm2575302,nm1046... \n", - "13 nm1113890,nm0063279,nm0387486,nm1016595,nm0932... \n", - "14 nm1166916,nm0950775,nm1783265,nm3230254,nm1159... \n", - "15 nm2912775,nm2338464,nm1840195,nm1171596,nm0704... \n", - "16 nm0571727,nm0000739,nm0180085,nm0933156,nm0509... \n", - "17 nm0000867,nm0002399,nm0076997,nm5831383,nm0475... \n", - "18 nm1448814,nm1732695 \n", - "19 nm1375358,nm1376383,nm1629105,nm0372222,nm0148... \n", - "20 nm1158282,nm0788426,nm3265847,nm3414572,nm3060... \n", - "21 nm0894225,nm0110644,nm0284308,nm0726975,nm1455... \n", - "22 nm0630524,nm0766349,nm0101741,nm0225416,nm0272... \n", - "23 nm1738082,nm2954266,nm0757749,nm1292099,nm1028... \n", - "24 nm3741255,nm1223381,nm0393776,nm0021772,nm0054... \n", - "25 nm0161078,nm0305423,nm0614013,nm0004770,nm1103... \n", - "26 NaN \n", - "27 nm8154805,nm12415333,nm2160742,nm0482413 \n", - "28 nm1223381,nm1661334,nm2091911,nm2129610,nm1888... \n", - "29 nm0924693,nm1243057,nm0675894,nm0021772,nm0534... \n", - "30 nm0867127,nm0021772,nm2385621,nm0622112,nm0816... \n", - "31 nm0585344,nm1089736,nm2505734,nm1164602,nm0052... \n", - "32 nm2446796,nm4738743,nm2738379,nm11511315,nm000... \n", - "33 nm1375363 \n", - "34 nm0281598,nm1279203,nm0490792,nm2048922,nm0144... \n", - "35 nm0864268,nm1226492,nm0924693,nm0946475,nm1392... \n", - "36 NaN \n", - "37 nm1954501,nm1732695,nm0092632,nm0525778,nm1319852 \n", - "38 nm0765250,nm1774069,nm6525284,nm2399451,nm2432... \n", - "\n", - " IMDb_編劇識別碼 IMDb_開播年份 IMDb_完結年份 \n", - "0 nm1865649,nm0105169,nm11601652,nm10057595,nm26... 2021 2024 \n", - "1 nm10281577,nm2335252,nm2550198 2019 2024 \n", - "2 NaN NaN NaN \n", - "3 nm0319213,nm0332467,nm3298114,nm4798028,nm1834... 2015 2022 \n", - "4 nm1633047,nm3802432,nm2672602,nm3494086,nm3362823 2008 2009 \n", - "5 nm0085775,nm5128174,nm2916300,nm2007553,nm5933... 2019 2024 \n", - "6 nm1140275,nm3023628,nm2133107 2013 2022 \n", - "7 nm3055854,nm1840994,nm8263018,nm6705101,nm1963132 2021 \\N \n", - "8 nm0794914,nm6728876,nm9319070,nm4784537,nm1325... 2017 2024 \n", - "9 NaN NaN NaN \n", - "10 nm2525575,nm8398205,nm8398203 \\N \\N \n", - "11 nm3005626,nm12372687 2022 \\N \n", - "12 nm5677403,nm1054899,nm4289705,nm4815863,nm1728... 2019 2023 \n", - "13 nm4618645,nm0124661,nm0180486,nm2028576,nm9433... 2015 2022 \n", - "14 nm1783265,nm3664884,nm3376496,nm0487067,nm1257... 2019 \\N \n", - "15 nm1210565,nm8996721,nm3663708,nm2651446,nm2943... 2017 2021 \n", - "16 nm0722274,nm9698564,nm1652155,nm2629239,nm9455... 2005 \\N \n", - "17 nm4057793,nm0931251,nm0464382,nm8851912,nm3780... 2017 2022 \n", - "18 nm5435318,nm5572916,nm1909300,nm4608690,nm1417... 2020 2022 \n", - "19 nm0372222,nm1375358,nm0436543,nm1376383,nm8154... 2018 2025 \n", - "20 nm3265847,nm1411676,nm9818211,nm4627895,nm9276... 2020 2023 \n", - "21 nm9992180,nm2547449,nm3924649,nm4160286,nm3625... 2020 \\N \n", - "22 nm1891501,nm0001104,nm3079117,nm3877885,nm1764... 2010 2022 \n", - "23 nm1823374,nm0599413,nm2268479,nm13563463,nm336... 2018 2024 \n", - "24 nm8475555,nm7137658,nm0301274,nm0275286,nm0374... 2022 2025 \n", - "25 nm3859706,nm2353384,nm3265652,nm10170567,nm713... 2017 2024 \n", - "26 NaN NaN NaN \n", - "27 nm6699136,nm1419272,nm1371927 2018 2026 \n", - "28 nm0076166,nm0104333,nm1215164,nm3370032,nm3992... 2018 2021 \n", - "29 nm0440790,nm1003202,nm1304613,nm6012871,nm6560... 2014 2020 \n", - "30 nm2901688,nm1847815,nm0193681,nm0454872,nm1013... 2020 2022 \n", - "31 nm4571811,nm0585344,nm3933291,nm7275370,nm7275... 2021 2024 \n", - "32 nm1618263,nm2276485,nm5532480,nm2638656,nm2516... 2017 2023 \n", - "33 nm1375363 2021 2025 \n", - "34 nm0823015,nm8033541,nm7322718,nm11880742,nm275... 2020 2026 \n", - "35 nm0707517,nm9414385,nm2837457,nm5915040,nm6768... 2018 2023 \n", - "36 NaN NaN NaN \n", - "37 nm1122534,nm8236619,nm1131226 2021 2022 \n", - "38 nm0729202,nm0765250,nm6944207,nm2110261,nm5082... 2021 2022 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# ==============================================================================\n", - "# 1. 初始化資料與欄位映射準備\n", - "# ==============================================================================\n", - "# 建立 TMDb 原始表（tmdb.titles.v3）的對照字典，以小寫劇名為 Key 進行無缺失值安全抽取\n", - "tmdb_mapping = tmdb.dropna(subset=[\"name\"]).copy()\n", - "tmdb_mapping[\"name_lower\"] = tmdb_mapping[\"name\"].astype(str).str.lower()\n", - "\n", - "map_popularity = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"popularity\"]))\n", - "map_genres = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"genres\"]))\n", - "map_overview = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"overview\"]))\n", - "map_seasons = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"number_of_seasons\"]))\n", - "map_episodes = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"number_of_episodes\"]))\n", - "map_runtime = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"episode_run_time\"]))\n", - "map_languages = dict(zip(tmdb_mapping[\"name_lower\"], tmdb_mapping[\"languages\"].fillna(\"en\")))\n", - "\n", - "# 建立 IMDb 原始表（imdb.titles.composite）的對照字典\n", - "imdb_mapping = imdb.dropna(subset=[\"primaryTitle\"]).copy()\n", - "imdb_mapping[\"title_lower\"] = imdb_mapping[\"primaryTitle\"].astype(str).str.lower()\n", - "\n", - "map_imdb_cast = dict(zip(imdb_mapping[\"title_lower\"], imdb_mapping[\"cast\"]))\n", - "map_imdb_directors = dict(zip(imdb_mapping[\"title_lower\"], imdb_mapping[\"directors\"]))\n", - "map_imdb_writers = dict(zip(imdb_mapping[\"title_lower\"], imdb_mapping[\"writers\"]))\n", - "map_imdb_start = dict(zip(imdb_mapping[\"title_lower\"], imdb_mapping[\"startYear\"]))\n", - "map_imdb_end = dict(zip(imdb_mapping[\"title_lower\"], imdb_mapping[\"endYear\"]))\n", - "\n", - "# ==============================================================================\n", - "# 2. 資料對齊與全屬性抽取\n", - "# ==============================================================================\n", - "df_all_attributes = netflix.copy()\n", - "df_all_attributes[\"影劇名稱_小寫\"] = df_all_attributes[\"netflix_title\"].str.lower()\n", - "\n", - "# --- [A 分類] 抽取 Netflix 原始屬性 ---\n", - "df_all_attributes[\"Netflix_觀看時數 (百萬)\"] = (df_all_attributes[\"netflix_viewing_hours\"] / 1000000).round(2)\n", - "df_all_attributes[\"Netflix_上榜週數\"] = df_all_attributes[\"netflix_weeks\"]\n", - "df_all_attributes[\"Netflix_入榜年份提示\"] = df_all_attributes[\"netflix_year_hint\"]\n", - "\n", - "# --- [B 分類] 抽取 TMDb 原始屬性 ---\n", - "df_all_attributes[\"TMDb_大眾評分\"] = df_all_attributes[\"tmdb_vote_average\"]\n", - "df_all_attributes[\"TMDb_打分投票數\"] = df_all_attributes[\"tmdb_vote_count\"]\n", - "df_all_attributes[\"TMDb_熱度值 (Popularity)\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_popularity)\n", - "df_all_attributes[\"TMDb_類型 (Genres)\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_genres)\n", - "df_all_attributes[\"TMDb_故事大綱 (Overview)\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_overview)\n", - "df_all_attributes[\"TMDb_總季數\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_seasons)\n", - "df_all_attributes[\"TMDb_總集數\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_episodes)\n", - "df_all_attributes[\"TMDb_單集片長 (分鐘)\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_runtime)\n", - "df_all_attributes[\"TMDb_支援語系\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_languages)\n", - "\n", - "# --- [C 分類] 抽取 IMDb 原始屬性 ---\n", - "df_all_attributes[\"IMDb_大眾評分\"] = df_all_attributes[\"imdb_averageRating\"]\n", - "df_all_attributes[\"IMDb_打分投票數\"] = df_all_attributes[\"imdb_numVotes\"]\n", - "df_all_attributes[\"IMDb_主要卡司\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_imdb_cast)\n", - "df_all_attributes[\"IMDb_導演識別碼\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_imdb_directors)\n", - "df_all_attributes[\"IMDb_編劇識別碼\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_imdb_writers)\n", - "df_all_attributes[\"IMDb_開播年份\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_imdb_start)\n", - "df_all_attributes[\"IMDb_完結年份\"] = df_all_attributes[\"影劇名稱_小寫\"].map(map_imdb_end)\n", - "\n", - "# ==============================================================================\n", - "# 3. 嚴格執行 TMDb_Hit 雙指標篩選與極致排序\n", - "# ==============================================================================\n", - "# 完全排除評分核心缺失值\n", - "df_clean_extract = df_all_attributes.dropna(subset=[\"TMDb_大眾評分\", \"TMDb_打分投票數\"]).copy()\n", - "\n", - "# 建立核心 Hit 判定欄位 (評分 >= 7.5 且 投票數 >= 1000)\n", - "df_clean_extract[\"TMDb_Hit\"] = (\n", - " (df_clean_extract[\"TMDb_大眾評分\"] >= 7.5) & \n", - " (df_clean_extract[\"TMDb_打分投票數\"] >= 1000)\n", - ").astype(int)\n", - "\n", - "# 精準抽取 39 筆 TMDb_Hit 資料\n", - "the_hit_data = df_clean_extract[df_clean_extract[\"TMDb_Hit\"] == 1].copy()\n", - "\n", - "# 依照 TMDb大眾評分 降冪排序（由高到低）\n", - "the_hit_data_sorted = the_hit_data.sort_values(by=\"TMDb_大眾評分\", ascending=False).reset_index(drop=True)\n", - "\n", - "# ==============================================================================\n", - "# 4. 依照 3 個 CSV 的屬性分類，進行表格分離與呈現\n", - "# ==============================================================================\n", - "print(\"=\" * 115)\n", - "print(f\"📊 突發狀況成功應對：成功從三大原始 CSV 分類抽取共 {len(the_hit_data_sorted)} 筆 TMDb_Hit 的完整屬性特徵！\")\n", - "print(\"=\" * 115)\n", - "\n", - "# --- 輸出分類一：Netflix 流量屬性表 ---\n", - "print(\"\\n🎬 【分類一：Netflix 原始流量與生命力屬性表】\")\n", - "print(\"-\" * 115)\n", - "display(the_hit_data_sorted[[\n", - " \"netflix_title\", \"Netflix_觀看時數 (百萬)\", \"Netflix_上榜週數\", \"Netflix_入榜年份提示\"\n", - "]].rename(columns={\"netflix_title\": \"影劇名稱\"}))\n", - "\n", - "# --- 輸出分類二：TMDb 口碑與劇本架構屬性表 ---\n", - "print(\"\\n🔮 【分類二：TMDb 原始口碑與劇本結構屬性表】\")\n", - "print(\"-\" * 115)\n", - "display(the_hit_data_sorted[[\n", - " \"netflix_title\", \"TMDb_大眾評分\", \"TMDb_打分投票數\", \"TMDb_熱度值 (Popularity)\", \n", - " \"TMDb_類型 (Genres)\", \"TMDb_總季數\", \"TMDb_總集數\", \"TMDb_單集片長 (分鐘)\", \n", - " \"TMDb_支援語系\", \"TMDb_故事大綱 (Overview)\"\n", - "]].rename(columns={\"netflix_title\": \"影劇名稱\"}))\n", - "\n", - "# --- 輸出分類三：IMDb 影迷與主創團隊屬性表 ---\n", - "print(\"\\n💎 【分類三：IMDb 原始重度影迷與主創團隊屬性表】\")\n", - "print(\"-\" * 115)\n", - "display(the_hit_data_sorted[[\n", - " \"netflix_title\", \"IMDb_大眾評分\", \"IMDb_打分投票數\", \"IMDb_主要卡司\", \n", - " \"IMDb_導演識別碼\", \"IMDb_編劇識別碼\", \"IMDb_開播年份\", \"IMDb_完結年份\"\n", - "]].rename(columns={\"netflix_title\": \"影劇名稱\"}))" - ] + "outputs": [], + "source": [] }, { - "cell_type": "markdown", - "id": "9133e1b0", + "cell_type": "code", + "execution_count": null, + "id": "2a4af732", "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] + "outputs": [], + "source": [] }, { - "cell_type": "markdown", - "id": "0f234f7a", + "cell_type": "code", + "execution_count": null, + "id": "afd8cfa3", "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", "execution_count": null, - "id": "1d2b2a36", + "id": "0d64c4aa", "metadata": {}, "outputs": [], "source": [] }, + { + "cell_type": "markdown", + "id": "0f234f7a", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, { "cell_type": "code", "execution_count": null, From 3879636b8c281b487f0b9044265bbc495ed51bec Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Wed, 15 Jul 2026 14:07:55 -0700 Subject: [PATCH 08/14] re define the hit ver 2 --- netflix-writer.ipynb | 651 ++++++++++++++++++++++++++++++++++++++----- 1 file changed, 587 insertions(+), 64 deletions(-) diff --git a/netflix-writer.ipynb b/netflix-writer.ipynb index 15ef116..24b5a61 100644 --- a/netflix-writer.ipynb +++ b/netflix-writer.ipynb @@ -3435,7 +3435,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "id": "77a641db", "metadata": {}, "outputs": [ @@ -3450,9 +3450,8 @@ "▸ 2. TMDb 投票數門檻 (前 50% 中位數)>= 25 票\n", "▸ 3. Netflix 觀看時數門檻 (全體平均) > 69.19 百萬小時\n", "================================================================================\n", - "\n", - "🎉 篩選完成！在 691 部影劇中，共有 71 部符合全新【The Hit】標準：\n", - "--------------------------------------------------------------------------------\n" + "🎉 篩選完成！在 691 部影劇中，共有 71 部完全符合條件：\n", + "================================================================================\n" ] }, { @@ -3525,12 +3524,492 @@ " 11\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 5\n", + " Café con aroma de mujer\n", + " 7.500\n", + " 382.0\n", + " 793.74\n", + " 25\n", + " \n", + " \n", + " 6\n", + " Ozark\n", + " 8.244\n", + " 1968.0\n", + " 751.60\n", + " 13\n", + " \n", + " \n", + " 7\n", + " All of Us Are Dead\n", + " 8.354\n", + " 3335.0\n", + " 659.51\n", + " 11\n", + " \n", + " \n", + " 8\n", + " Virgin River\n", + " 8.032\n", + " 448.0\n", + " 624.25\n", + " 6\n", + " \n", + " \n", + " 9\n", + " Sex Education\n", + " 8.304\n", + " 6647.0\n", + " 561.36\n", + " 6\n", + " \n", + " \n", + " 10\n", + " The Queen of Flow\n", + " 7.983\n", + " 1391.0\n", + " 561.23\n", + " 16\n", + " \n", + " \n", + " 11\n", + " The Umbrella Academy\n", + " 8.604\n", + " 8891.0\n", + " 414.63\n", + " 5\n", + " \n", + " \n", + " 12\n", + " Extraordinary Attorney Woo\n", + " 8.482\n", + " 585.0\n", + " 404.03\n", + " 7\n", + " \n", + " \n", + " 13\n", + " Cobra Kai\n", + " 8.220\n", + " 5734.0\n", + " 383.64\n", + " 4\n", + " \n", + " \n", + " 14\n", + " Emily in Paris\n", + " 7.775\n", + " 1152.0\n", + " 341.63\n", + " 5\n", + " \n", + " \n", + " 15\n", + " Elite\n", + " 8.071\n", + " 8817.0\n", + " 329.29\n", + " 5\n", + " \n", + " \n", + " 16\n", + " The Lincoln Lawyer\n", + " 7.806\n", + " 183.0\n", + " 305.27\n", + " 6\n", + " \n", + " \n", + " 17\n", + " Hometown Cha-Cha-Cha\n", + " 8.233\n", + " 442.0\n", + " 300.58\n", + " 16\n", + " \n", + " \n", + " 18\n", + " Never Have I Ever\n", + " 8.161\n", + " 1557.0\n", + " 297.78\n", + " 4\n", + " \n", + " \n", + " 19\n", + " Sex/Life\n", + " 7.138\n", + " 1200.0\n", + " 282.10\n", + " 7\n", + " \n", + " \n", + " 20\n", + " Business Proposal\n", + " 8.422\n", + " 281.0\n", + " 279.11\n", + " 13\n", + " \n", + " \n", + " 21\n", + " Locke & Key\n", + " 7.903\n", + " 1104.0\n", + " 278.38\n", + " 5\n", + " \n", + " \n", + " 22\n", + " The Sandman\n", + " 8.064\n", + " 1726.0\n", + " 274.22\n", + " 3\n", + " \n", + " \n", + " 23\n", + " The Marked Heart\n", + " 7.817\n", + " 232.0\n", + " 272.75\n", + " 7\n", + " \n", + " \n", + " 24\n", + " Vikings: Valhalla\n", + " 7.851\n", + " 671.0\n", + " 265.55\n", + " 5\n", + " \n", + " \n", + " 25\n", + " Love Is Blind\n", + " 7.100\n", + " 54.0\n", + " 256.89\n", + " 5\n", + " \n", + " \n", + " 26\n", + " Dark Desire\n", + " 7.300\n", + " 4181.0\n", + " 202.94\n", + " 5\n", + " \n", + " \n", + " 27\n", + " Hustle\n", + " 7.708\n", + " 137.0\n", + " 198.31\n", + " 6\n", + " \n", + " \n", + " 28\n", + " My Name\n", + " 8.283\n", + " 715.0\n", + " 194.14\n", + " 5\n", + " \n", + " \n", + " 29\n", + " The Last Kingdom\n", + " 8.290\n", + " 1485.0\n", + " 189.57\n", + " 5\n", + " \n", + " \n", + " 30\n", + " Peaky Blinders\n", + " 8.546\n", + " 8836.0\n", + " 185.34\n", + " 5\n", + " \n", + " \n", + " 31\n", + " Lost in Space\n", + " 7.115\n", + " 96.0\n", + " 185.18\n", + " 4\n", + " \n", + " \n", + " 32\n", + " Carter\n", + " 7.400\n", + " 30.0\n", + " 183.04\n", + " 10\n", + " \n", + " \n", + " 33\n", + " Sweet Magnolias\n", + " 7.800\n", + " 206.0\n", + " 182.94\n", + " 5\n", + " \n", + " \n", + " 34\n", + " All American\n", + " 8.245\n", + " 414.0\n", + " 180.66\n", + " 5\n", + " \n", + " \n", + " 35\n", + " The Five Juanas\n", + " 7.419\n", + " 246.0\n", + " 177.85\n", + " 6\n", + " \n", + " \n", + " 36\n", + " Alba\n", + " 7.000\n", + " 167.0\n", + " 175.64\n", + " 6\n", + " \n", + " \n", + " 37\n", + " Raising Dion\n", + " 7.696\n", + " 388.0\n", + " 172.87\n", + " 4\n", + " \n", + " \n", + " 38\n", + " Twenty Five Twenty One\n", + " 8.597\n", + " 98.0\n", + " 171.61\n", + " 10\n", + " \n", + " \n", + " 39\n", + " Anatomy of a Scandal\n", + " 7.071\n", + " 120.0\n", + " 171.02\n", + " 5\n", + " \n", + " \n", + " 40\n", + " Pasión de Gavilanes\n", + " 7.641\n", + " 1925.0\n", + " 164.18\n", + " 5\n", + " \n", + " \n", + " 41\n", + " Collision\n", + " 7.300\n", + " 26.0\n", + " 161.63\n", + " 5\n", + " \n", + " \n", + " 42\n", + " Selling Sunset\n", + " 7.100\n", + " 39.0\n", + " 158.78\n", + " 4\n", + " \n", + " \n", + " 43\n", + " Arcane\n", + " 8.740\n", + " 3341.0\n", + " 154.27\n", + " 6\n", + " \n", + " \n", + " 44\n", + " Good Girls\n", + " 7.998\n", + " 509.0\n", + " 153.14\n", + " 6\n", + " \n", + " \n", + " 45\n", + " Who Killed Sara?\n", + " 7.638\n", + " 1132.0\n", + " 143.61\n", + " 4\n", + " \n", + " \n", + " 46\n", + " Midnight Mass\n", + " 7.525\n", + " 664.0\n", + " 142.28\n", + " 4\n", + " \n", + " \n", + " 47\n", + " Welcome to Eden\n", + " 7.090\n", + " 233.0\n", + " 140.34\n", + " 5\n", + " \n", + " \n", + " 48\n", + " Narcos: Mexico\n", + " 7.939\n", + " 1160.0\n", + " 138.98\n", + " 4\n", + " \n", + " \n", + " 49\n", + " The King's Affection\n", + " 8.351\n", + " 232.0\n", + " 138.54\n", + " 10\n", + " \n", + " \n", + " 50\n", + " Juvenile Justice\n", + " 7.947\n", + " 85.0\n", + " 134.56\n", + " 7\n", + " \n", + " \n", + " 51\n", + " Archive 81\n", + " 7.219\n", + " 442.0\n", + " 129.47\n", + " 3\n", + " \n", + " \n", + " 52\n", + " Our Beloved Summer\n", + " 8.500\n", + " 74.0\n", + " 128.89\n", + " 8\n", + " \n", + " \n", + " 53\n", + " Alchemy of Souls\n", + " 8.622\n", + " 411.0\n", + " 127.69\n", + " 8\n", + " \n", + " \n", + " 54\n", + " The Good Doctor\n", + " 8.503\n", + " 11768.0\n", + " 112.58\n", + " 6\n", + " \n", + " \n", + " 55\n", + " The Snitch Cartel: Origins\n", + " 7.885\n", + " 283.0\n", + " 112.14\n", + " 6\n", + " \n", + " \n", + " 56\n", + " Sintonia\n", + " 8.200\n", + " 179.0\n", + " 109.43\n", + " 2\n", + " \n", + " \n", + " 57\n", + " Our Blues\n", + " 8.800\n", + " 43.0\n", + " 105.15\n", + " 9\n", + " \n", + " \n", + " 58\n", + " First Kill\n", + " 7.952\n", + " 270.0\n", + " 104.61\n", + " 3\n", + " \n", + " \n", + " 59\n", + " The Chestnut Man\n", + " 7.584\n", + " 455.0\n", + " 104.21\n", + " 5\n", + " \n", + " \n", + " 60\n", + " Forecasting Love and Weather\n", + " 7.631\n", + " 61.0\n", + " 100.85\n", + " 8\n", + " \n", + " \n", + " 61\n", + " Money Heist: Korea - Joint Economic Area\n", + " 7.900\n", + " 601.0\n", + " 98.37\n", + " 3\n", + " \n", + " \n", + " 62\n", + " True Story\n", + " 7.171\n", + " 82.0\n", + " 90.82\n", + " 3\n", + " \n", + " \n", + " 63\n", + " Control Z\n", + " 8.227\n", + " 2242.0\n", + " 89.83\n", + " 3\n", + " \n", + " \n", + " 64\n", + " The Silent Sea\n", + " 7.800\n", + " 527.0\n", + " 89.77\n", + " 3\n", + " \n", + " \n", + " 65\n", + " Another Self\n", + " 7.400\n", + " 30.0\n", + " 88.59\n", + " 4\n", " \n", " \n", " 66\n", @@ -3574,37 +4053,81 @@ " \n", " \n", "\n", - "

71 rows × 5 columns

\n", "" ], "text/plain": [ - " 影劇名稱 TMDb 評分 TMDb 投票數 觀看時數 (百萬小時) \\\n", - "0 Stranger Things 8.624 16161.0 2874.26 \n", - "1 Squid Game 7.831 13053.0 2289.50 \n", - "2 Manifest 7.728 1312.0 1446.26 \n", - "3 Money Heist 8.257 17836.0 1170.20 \n", - "4 Bridgerton 8.143 2045.0 1108.80 \n", - ".. ... ... ... ... \n", - "66 On My Block 8.461 618.0 80.88 \n", - "67 Turning Point: 9/11 and the War on Terror 7.287 83.0 80.68 \n", - "68 The Seven Deadly Sins: Cursed by Light 8.462 4710.0 79.51 \n", - "69 Hospital Playlist 8.490 100.0 72.06 \n", - "70 I Am Georgina 7.111 293.0 71.29 \n", - "\n", - " 上榜週數 \n", - "0 13 \n", - "1 20 \n", - "2 16 \n", - "3 14 \n", - "4 11 \n", - ".. ... \n", - "66 2 \n", - "67 3 \n", - "68 4 \n", - "69 7 \n", - "70 3 \n", - "\n", - "[71 rows x 5 columns]" + " 影劇名稱 TMDb 評分 TMDb 投票數 觀看時數 (百萬小時) 上榜週數\n", + "0 Stranger Things 8.624 16161.0 2874.26 13\n", + "1 Squid Game 7.831 13053.0 2289.50 20\n", + "2 Manifest 7.728 1312.0 1446.26 16\n", + "3 Money Heist 8.257 17836.0 1170.20 14\n", + "4 Bridgerton 8.143 2045.0 1108.80 11\n", + "5 Café con aroma de mujer 7.500 382.0 793.74 25\n", + "6 Ozark 8.244 1968.0 751.60 13\n", + "7 All of Us Are Dead 8.354 3335.0 659.51 11\n", + "8 Virgin River 8.032 448.0 624.25 6\n", + "9 Sex Education 8.304 6647.0 561.36 6\n", + "10 The Queen of Flow 7.983 1391.0 561.23 16\n", + "11 The Umbrella Academy 8.604 8891.0 414.63 5\n", + "12 Extraordinary Attorney Woo 8.482 585.0 404.03 7\n", + "13 Cobra Kai 8.220 5734.0 383.64 4\n", + "14 Emily in Paris 7.775 1152.0 341.63 5\n", + "15 Elite 8.071 8817.0 329.29 5\n", + "16 The Lincoln Lawyer 7.806 183.0 305.27 6\n", + "17 Hometown Cha-Cha-Cha 8.233 442.0 300.58 16\n", + "18 Never Have I Ever 8.161 1557.0 297.78 4\n", + "19 Sex/Life 7.138 1200.0 282.10 7\n", + "20 Business Proposal 8.422 281.0 279.11 13\n", + "21 Locke & Key 7.903 1104.0 278.38 5\n", + "22 The Sandman 8.064 1726.0 274.22 3\n", + "23 The Marked Heart 7.817 232.0 272.75 7\n", + "24 Vikings: Valhalla 7.851 671.0 265.55 5\n", + "25 Love Is Blind 7.100 54.0 256.89 5\n", + "26 Dark Desire 7.300 4181.0 202.94 5\n", + "27 Hustle 7.708 137.0 198.31 6\n", + "28 My Name 8.283 715.0 194.14 5\n", + "29 The Last Kingdom 8.290 1485.0 189.57 5\n", + "30 Peaky Blinders 8.546 8836.0 185.34 5\n", + "31 Lost in Space 7.115 96.0 185.18 4\n", + "32 Carter 7.400 30.0 183.04 10\n", + "33 Sweet Magnolias 7.800 206.0 182.94 5\n", + "34 All American 8.245 414.0 180.66 5\n", + "35 The Five Juanas 7.419 246.0 177.85 6\n", + "36 Alba 7.000 167.0 175.64 6\n", + "37 Raising Dion 7.696 388.0 172.87 4\n", + "38 Twenty Five Twenty One 8.597 98.0 171.61 10\n", + "39 Anatomy of a Scandal 7.071 120.0 171.02 5\n", + "40 Pasión de Gavilanes 7.641 1925.0 164.18 5\n", + "41 Collision 7.300 26.0 161.63 5\n", + "42 Selling Sunset 7.100 39.0 158.78 4\n", + "43 Arcane 8.740 3341.0 154.27 6\n", + "44 Good Girls 7.998 509.0 153.14 6\n", + "45 Who Killed Sara? 7.638 1132.0 143.61 4\n", + "46 Midnight Mass 7.525 664.0 142.28 4\n", + "47 Welcome to Eden 7.090 233.0 140.34 5\n", + "48 Narcos: Mexico 7.939 1160.0 138.98 4\n", + "49 The King's Affection 8.351 232.0 138.54 10\n", + "50 Juvenile Justice 7.947 85.0 134.56 7\n", + "51 Archive 81 7.219 442.0 129.47 3\n", + "52 Our Beloved Summer 8.500 74.0 128.89 8\n", + "53 Alchemy of Souls 8.622 411.0 127.69 8\n", + "54 The Good Doctor 8.503 11768.0 112.58 6\n", + "55 The Snitch Cartel: Origins 7.885 283.0 112.14 6\n", + "56 Sintonia 8.200 179.0 109.43 2\n", + "57 Our Blues 8.800 43.0 105.15 9\n", + "58 First Kill 7.952 270.0 104.61 3\n", + "59 The Chestnut Man 7.584 455.0 104.21 5\n", + "60 Forecasting Love and Weather 7.631 61.0 100.85 8\n", + "61 Money Heist: Korea - Joint Economic Area 7.900 601.0 98.37 3\n", + "62 True Story 7.171 82.0 90.82 3\n", + "63 Control Z 8.227 2242.0 89.83 3\n", + "64 The Silent Sea 7.800 527.0 89.77 3\n", + "65 Another Self 7.400 30.0 88.59 4\n", + "66 On My Block 8.461 618.0 80.88 2\n", + "67 Turning Point: 9/11 and the War on Terror 7.287 83.0 80.68 3\n", + "68 The Seven Deadly Sins: Cursed by Light 8.462 4710.0 79.51 4\n", + "69 Hospital Playlist 8.490 100.0 72.06 7\n", + "70 I Am Georgina 7.111 293.0 71.29 3" ] }, "metadata": {}, @@ -3614,12 +4137,10 @@ "source": [ "import pandas as pd\n", "\n", - "# 1. 讀取 Netflix 複合資料集 (確保路徑正確)\n", - "# 備註：此資料表已在前面的步驟 fuzzy match 併入了 tmdb 屬性\n", + "# 1. 讀取 Netflix 複合資料集\n", "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\n", "\n", - "# 2. 為了確保統計指標不受 NaN (缺失值) 影響，先建立一個用來計算門檻的乾淨 Data\n", - "# (排除沒有配對到 TMDb 或沒有評分數據的影劇)\n", + "# 2. 排除沒有配對到 TMDb 或沒有評分數據的影劇，以計算乾淨的 P50 門檻\n", "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\n", "\n", "# 3. 計算三大核心指標的篩選門檻\n", @@ -3627,14 +4148,6 @@ "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median() # TMDb 投票數前 50% (中位數)\n", "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean() # Netflix 觀看時數平均值\n", "\n", - "print(\"=\" * 80)\n", - "print(\"⚙️ 【The Hit】黃金篩選門檻定義：\")\n", - "print(\"-\" * 80)\n", - "print(f\"▸ 1. TMDb 評分門檻 (前 50% 中位數) >= {tmdb_rating_p50:.2f} 分\")\n", - "print(f\"▸ 2. TMDb 投票數門檻 (前 50% 中位數)>= {int(tmdb_votes_p50)} 票\")\n", - "print(f\"▸ 3. Netflix 觀看時數門檻 (全體平均) > {netflix_hours_mean / 1000000:.2f} 百萬小時\")\n", - "print(\"=\" * 80)\n", - "\n", "# 4. 套用三重條件進行「The Hit」篩選\n", "hit_condition = (\n", " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", @@ -3644,29 +4157,39 @@ "\n", "df_hits = df[hit_condition].copy()\n", "\n", - "# 為了方便呈現，換算觀看時數為「百萬小時」\n", + "# 5. 數據單位轉換\n", "df_hits[\"netflix_viewing_hours_million\"] = (df_hits[\"netflix_viewing_hours\"] / 1000000).round(2)\n", "\n", - "# 5. 依觀看時數降冪排序，並選出重點欄位展示\n", - "display_cols = [\n", - " \"netflix_title\", \n", - " \"tmdb_vote_average\", \n", - " \"tmdb_vote_count\", \n", - " \"netflix_viewing_hours_million\",\n", - " \"netflix_weeks\"\n", - "]\n", - "\n", + "# 6. 依觀看時數由高到低（降冪）排序\n", "df_hits_sorted = df_hits.sort_values(by=\"netflix_viewing_hours\", ascending=False).reset_index(drop=True)\n", "\n", - "print(f\"\\n🎉 篩選完成！在 {len(df)} 部影劇中，共有 {len(df_hits_sorted)} 部符合全新【The Hit】標準：\")\n", - "print(\"-\" * 80)\n", - "display(df_hits_sorted[display_cols].rename(columns={\n", + "# 7. 顯示設定優化：強制 Pandas 展開所有 Rows 與 Columns，不出現任何「...」\n", + "pd.set_option('display.max_rows', None) # 不省略任何列 (Rows)\n", + "pd.set_option('display.max_columns', None) # 不省略任何欄 (Columns)\n", + "pd.set_option('display.width', 1000) # 避免寬度不夠自動換行\n", + "\n", + "# 選擇要展示的欄位，並將欄位改為中文名稱\n", + "display_cols = {\n", " \"netflix_title\": \"影劇名稱\",\n", " \"tmdb_vote_average\": \"TMDb 評分\",\n", " \"tmdb_vote_count\": \"TMDb 投票數\",\n", " \"netflix_viewing_hours_million\": \"觀看時數 (百萬小時)\",\n", " \"netflix_weeks\": \"上榜週數\"\n", - "}))" + "}\n", + "\n", + "# 8. 印出篩選報告與完整清單\n", + "print(\"=\" * 80)\n", + "print(\"⚙️ 【The Hit】黃金篩選門檻定義：\")\n", + "print(\"-\" * 80)\n", + "print(f\"▸ 1. TMDb 評分門檻 (前 50% 中位數) >= {tmdb_rating_p50:.2f} 分\")\n", + "print(f\"▸ 2. TMDb 投票數門檻 (前 50% 中位數)>= {int(tmdb_votes_p50)} 票\")\n", + "print(f\"▸ 3. Netflix 觀看時數門檻 (全體平均) > {netflix_hours_mean / 1000000:.2f} 百萬小時\")\n", + "print(\"=\" * 80)\n", + "print(f\"🎉 篩選完成！在 {len(df)} 部影劇中，共有 {len(df_hits_sorted)} 部完全符合條件：\")\n", + "print(\"=\" * 80)\n", + "\n", + "# 直接呼叫與顯示 DataFrame (在 Jupyter 中會渲染成精美的無省略 HTML 表格)\n", + "display(df_hits_sorted[list(display_cols.keys())].rename(columns=display_cols))" ] }, { From 2dc6d7807bb8d91b031d581bd1ae772af61ec535 Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Wed, 15 Jul 2026 14:13:32 -0700 Subject: [PATCH 09/14] re define the hit ver 3 --- netflix-writer.ipynb | 1474 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 1472 insertions(+), 2 deletions(-) diff --git a/netflix-writer.ipynb b/netflix-writer.ipynb index 24b5a61..72a7d08 100644 --- a/netflix-writer.ipynb +++ b/netflix-writer.ipynb @@ -4206,8 +4206,1478 @@ "execution_count": null, "id": "d1c4b8cc", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "📊 【The Hit】篩選結果：共找到 71 部影集\n", + "================================================================================\n", + "\n", + "🔎 【屬性總表 (Attributes Schema)】\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/html": [ + "
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資料型態 (Dtype)非空值數量 (Non-Null)缺失值數量 (Null)缺失值比例 (%)
keyobject7100.0%
netflix_viewing_hoursint647100.0%
netflix_weeksint647100.0%
netflix_year_hintint647100.0%
netflix_titleobject7100.0%
netflix_clean_titleobject7100.0%
tmdb_titleobject7100.0%
tmdb_popularityfloat647100.0%
tmdb_vote_averagefloat647100.0%
tmdb_vote_countfloat647100.0%
imdb_titleobject7100.0%
imdb_averageRatingfloat64472433.8%
imdb_numVotesfloat64472433.8%
\n", + "
" + ], + "text/plain": [ + " 資料型態 (Dtype) 非空值數量 (Non-Null) 缺失值數量 (Null) 缺失值比例 (%)\n", + "key object 71 0 0.0%\n", + "netflix_viewing_hours int64 71 0 0.0%\n", + "netflix_weeks int64 71 0 0.0%\n", + "netflix_year_hint int64 71 0 0.0%\n", + "netflix_title object 71 0 0.0%\n", + "netflix_clean_title object 71 0 0.0%\n", + "tmdb_title object 71 0 0.0%\n", + "tmdb_popularity float64 71 0 0.0%\n", + "tmdb_vote_average float64 71 0 0.0%\n", + "tmdb_vote_count float64 71 0 0.0%\n", + "imdb_title object 71 0 0.0%\n", + "imdb_averageRating float64 47 24 33.8%\n", + "imdb_numVotes float64 47 24 33.8%" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "🎬 【這 71 部影集的所有屬性完整資料】 (依觀看時數降冪排序)\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/html": [ + "
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keynetflix_viewing_hoursnetflix_weeksnetflix_year_hintnetflix_titlenetflix_clean_titletmdb_titletmdb_popularitytmdb_vote_averagetmdb_vote_countimdb_titleimdb_averageRatingimdb_numVotes
0st_th2874260000132022Stranger Thingsstranger thingsStranger Things185.7118.62416161.0Stranger ThingsNaNNaN
1ga_sq2289500000202021Squid Gamesquid gameSquid Game115.5877.83113053.0Squid Game!NaNNaN
2ma1446260000162021ManifestmanifestManifest111.4117.7281312.0ManifestNaNNaN
3he_mo1170200000142021Money Heistmoney heistMoney Heist96.3548.25717836.0Money Heist8.2612436.0
4br1108800000112021BridgertonbridgertonBridgerton71.3088.1432045.0Bridgerton4.157.0
5ar_ca_co793740000252022Café con aroma de mujercaf con aroma de mujerCafé con Aroma de Mujer20.7107.500382.0Café con aroma de mujer8.6106.0
6oz751600000132022OzarkozarkOzark68.0068.2441968.0Ozark8.4394904.0
7al_ar_us659510000112022All of Us Are Deadall of us are deadAll of Us Are Dead107.1088.3543335.0All of Us Are Dead7.692709.0
8ri_vi62425000062021Virgin Rivervirgin riverVirgin River54.1928.032448.0Virgin River7.460100.0
9ed_se56136000062021Sex Educationsex educationSex Education1008.9778.3046647.0Sex Education7.225.0
10fl_qu561230000162021The Queen of Flowthe queen of flowThe Queen of Flow129.3237.9831391.0The Queen of Flow7.52076.0
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69ho_pl7206000072021Hospital Playlisthospital playlistHospital Playlist21.8268.490100.0Hospital Playlist8.711014.0
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NaN NaN\n", + "2 ma 1446260000 16 2021 Manifest manifest Manifest 111.411 7.728 1312.0 Manifest NaN NaN\n", + "3 he_mo 1170200000 14 2021 Money Heist money heist Money Heist 96.354 8.257 17836.0 Money Heist 8.2 612436.0\n", + "4 br 1108800000 11 2021 Bridgerton bridgerton Bridgerton 71.308 8.143 2045.0 Bridgerton 4.1 57.0\n", + "5 ar_ca_co 793740000 25 2022 Café con aroma de mujer caf con aroma de mujer Café con Aroma de Mujer 20.710 7.500 382.0 Café con aroma de mujer 8.6 106.0\n", + "6 oz 751600000 13 2022 Ozark ozark Ozark 68.006 8.244 1968.0 Ozark 8.4 394904.0\n", + "7 al_ar_us 659510000 11 2022 All of Us Are Dead all of us are dead All of Us Are Dead 107.108 8.354 3335.0 All of Us Are Dead 7.6 92709.0\n", + "8 ri_vi 624250000 6 2021 Virgin River virgin river Virgin River 54.192 8.032 448.0 Virgin River 7.4 60100.0\n", + "9 ed_se 561360000 6 2021 Sex Education sex education Sex Education 1008.977 8.304 6647.0 Sex Education 7.2 25.0\n", + "10 fl_qu 561230000 16 2021 The Queen of 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9_po_tu 80680000 3 2021 Turning Point: 9/11 and the War on Terror turning point 9 11 the war on terror Turning Point: 9/11 and the War on Terror 9.194 7.287 83.0 Turning Point: 9/11 and the War on Terror 7.9 13266.0\n", + "68 de_se_si 79510000 4 2021 The Seven Deadly Sins: Cursed by Light the seven deadly sins cursed by light The Seven Deadly Sins 10.059 8.462 4710.0 The Seven Deadly Sins: Cursed by Light 6.4 1955.0\n", + "69 ho_pl 72060000 7 2021 Hospital Playlist hospital playlist Hospital Playlist 21.826 8.490 100.0 Hospital Playlist 8.7 11014.0\n", + "70 am_ge_i 71290000 3 2022 I Am Georgina i am georgina I Am Georgina 11.560 7.111 293.0 I Am Georgina 4.1 4667.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 1. 讀取資料集 (請確保此路徑與你之前的步驟一致)\n", + "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\n", + "\n", + "# 2. 計算篩選門檻 (TMDb 評分前 50%, TMDb 投票數前 50%, 觀看時數大於平均值)\n", + "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\n", + "\n", + "tmdb_rating_p50 = df_valid_tmdb[\"tmdb_vote_average\"].median()\n", + "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median()\n", + "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean()\n", + "\n", + "# 3. 篩選出這 71 部 The Hit 影集\n", + "hit_condition = (\n", + " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", + " (df[\"tmdb_vote_count\"] >= tmdb_votes_p50) &\n", + " (df[\"netflix_viewing_hours\"] > netflix_hours_mean)\n", + ")\n", + "df_hits = df[hit_condition].copy()\n", + "\n", + "# 4. 關鍵設定：強制 Pandas 展開所有欄位與資料，絕不使用 \"...\" 省略\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.max_rows', None)\n", + "pd.set_option('display.width', 1000)\n", + "\n", + "print(\"=\" * 80)\n", + "print(f\"📊 【The Hit】篩選結果：共找到 {len(df_hits)} 部影集\")\n", + "print(\"=\" * 80)\n", + "\n", + "# ─── 步驟 A：輸出這 71 部影集擁有的「所有屬性（欄位）總表」 ───\n", + "print(\"\\n🔎 【屬性總表 (Attributes Schema)】\")\n", + "print(\"-\" * 80)\n", + "\n", + "# 建立屬性分析表\n", + "schema_df = pd.DataFrame({\n", + " \"資料型態 (Dtype)\": df_hits.dtypes,\n", + " \"非空值數量 (Non-Null)\": df_hits.notnull().sum(),\n", + " \"缺失值數量 (Null)\": df_hits.isnull().sum(),\n", + " \"缺失值比例 (%)\": (df_hits.isnull().sum() / len(df_hits) * 100).round(2).astype(str) + \"%\"\n", + "})\n", + "display(schema_df)\n", + "\n", + "# ─── 步驟 B：完整展示這 71 部影集的所有欄位數據（不省略） ───\n", + "print(\"\\n🎬 【這 71 部影集的所有屬性完整資料】 (依觀看時數降冪排序)\")\n", + "print(\"-\" * 80)\n", + "\n", + "# 將觀看時數降冪排序，並在 Jupyter 中渲染成完整的 HTML 表格\n", + "df_hits_sorted = df_hits.sort_values(by=\"netflix_viewing_hours\", ascending=False).reset_index(drop=True)\n", + "display(df_hits_sorted)" + ] }, { "cell_type": "code", From 1485401582f4a26a74a5269b08c99797a0780773 Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Wed, 15 Jul 2026 14:25:44 -0700 Subject: [PATCH 10/14] re define the hit ver 4 --- netflix-writer.ipynb | 2500 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 2476 insertions(+), 24 deletions(-) diff --git a/netflix-writer.ipynb b/netflix-writer.ipynb index 72a7d08..3385e52 100644 --- a/netflix-writer.ipynb +++ b/netflix-writer.ipynb @@ -4201,6 +4201,15 @@ "？？" ] }, + { + "cell_type": "markdown", + "id": "e07f5503", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, { "cell_type": "code", "execution_count": null, @@ -5679,30 +5688,6 @@ "display(df_hits_sorted)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a4af732", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "afd8cfa3", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0d64c4aa", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "0f234f7a", @@ -5712,6 +5697,2473 @@ "？？" ] }, + { + "cell_type": "code", + "execution_count": 54, + "id": "2a4af732", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========================================================================================================================\n", + "🚀 【The Hit 影集】 7 大核心屬性 Min-Max 標準化分數總表\n", + " * 數值範圍皆在 0.0 ~ 1.0 之間（1.0 代表該屬性在 71 部影集中表現最強，0.0 為最弱）\n", + "========================================================================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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影劇名稱觀看時數_Norm上榜週數_NormTMDb熱度_NormTMDb評分_NormTMDb投票數_NormIMDb評分_NormIMDb投票數_Norm
0Stranger Things1.00000.47830.17930.90220.9060NaNNaN
1Squid Game0.79140.78260.10940.46170.7314NaNNaN
2Manifest0.49050.60870.10530.40440.0722NaNNaN
3Money Heist0.39210.52170.09030.69831.00000.89130.7820
4Bridgerton0.37010.39130.06530.63500.11340.00000.0001
5Café con aroma de mujer0.25771.00000.01490.27780.02000.97830.0001
6Ozark0.24270.47830.06200.69110.10900.93480.5042
7All of Us Are Dead0.20990.39130.10100.75220.18580.76090.1184
8Virgin River0.19730.17390.04820.57330.02370.71740.0767
9Sex Education0.17480.17391.00000.72440.37180.67390.0000
10The Queen of Flow0.17480.60870.12310.54610.07660.73910.0026
11The Umbrella Academy0.12250.13040.04590.89110.49780.63040.0001
12Extraordinary Attorney Woo0.11870.21740.03700.82330.03140.95650.0498
13Cobra Kai0.11140.08700.11210.67780.32050.60870.0000
14Emily in Paris0.09640.13040.02780.43060.06320.58700.1831
15Elite0.09200.13040.09460.59500.49360.93480.0001
16The Lincoln Lawyer0.08350.17390.04650.44780.0088NaNNaN
17Hometown Cha-Cha-Cha0.08180.60870.02950.68500.02340.91300.0380
18Never Have I Ever0.08080.08700.07450.64500.0860NaNNaN
19Sex/Life0.07520.21740.11800.07670.0659NaNNaN
20Business Proposal0.07410.47830.08250.79000.0143NaNNaN
21Locke & Key0.07390.13040.02120.50170.0605NaNNaN
22The Sandman0.07240.04350.05820.59110.09550.23910.0000
23The Marked Heart0.07190.21740.02960.45390.01160.54350.0061
24Vikings: Valhalla0.06930.13040.05330.47280.03620.71740.0916
25Love Is Blind0.06620.13040.04060.05560.0016NaNNaN
26Dark Desire0.04700.13040.08450.16670.23330.84780.0000
27Hustle0.04530.17390.02490.39330.0062NaNNaN
28My Name0.04380.13040.01970.71280.03870.80430.0575
29The Last Kingdom0.04220.13040.13760.71670.08190.93480.0000
30Peaky Blinders0.04070.13040.33760.85890.49471.00001.0000
31Lost in Space0.04060.08700.40490.06390.0039NaNNaN
32Carter0.03990.34780.00570.22220.0002NaNNaN
33Sweet Magnolias0.03980.13040.02000.44440.01010.69570.0364
34All American0.03900.13040.13450.69170.0218NaNNaN
35The Five Juanas0.03800.17390.01280.23280.01240.60870.0033
36Alba0.03720.17390.00700.00000.0079NaNNaN
37Raising Dion0.03620.08700.01330.38670.02030.86960.0001
38Twenty Five Twenty One0.03580.34780.02590.88720.00400.97830.0290
39Anatomy of a Scandal0.03560.13040.00740.03940.00530.63040.0516
40Pasión de Gavilanes0.03310.13040.27650.35610.1066NaNNaN
41Collision0.03220.13040.00000.16670.0000NaNNaN
42Selling Sunset0.03120.08700.00370.05560.00070.52170.0119
43Arcane0.02960.17390.03650.96670.1861NaNNaN
44Good Girls0.02920.17390.04640.55440.02710.89130.0008
45Who Killed Sara?0.02580.08700.01230.35440.06210.50000.0225
46Midnight Mass0.02530.08700.01620.29170.03580.71740.0000
47Welcome to Eden0.02460.13040.03460.05000.0116NaNNaN
48Narcos: Mexico0.02410.08700.04150.52170.06370.91300.1303
49The King's Affection0.02400.34780.06380.75060.01160.84780.0111
50Juvenile Justice0.02260.21740.00840.52610.0033NaNNaN
51Archive 810.02080.04350.01420.12170.02340.69570.0871
52Our Beloved Summer0.02050.26090.02460.83330.00270.93480.0003
53Alchemy of Souls0.02010.26090.05520.90110.02161.00000.0380
54The Good Doctor0.01470.17390.67370.83500.6593NaNNaN
55The Snitch Cartel: Origins0.01460.17390.03620.49170.01440.41300.0004
56Sintonia0.01360.00000.01650.66670.0086NaNNaN
57Our Blues0.01210.30430.00721.00000.00100.97830.0071
58First Kill0.01190.04350.01530.52890.0137NaNNaN
59The Chestnut Man0.01170.13040.00990.32440.02410.76090.0873
60Forecasting Love and Weather0.01050.26090.01110.35060.00200.65220.0071
61Money Heist: Korea - Joint Economic Area0.00970.04350.04040.50000.03230.39130.0137
62True Story0.00700.04350.00990.09500.0031NaNNaN
63Control Z0.00660.04350.01490.68170.12440.58700.0161
64The Silent Sea0.00660.04350.01920.44440.0281NaNNaN
65Another Self0.00620.08700.00480.22220.00020.69570.0338
66Turning Point: 9/11 and the War on Terror0.00340.04350.00340.15940.00320.82610.0169
67On My Block0.00340.00000.01110.81170.03320.63040.0000
68The Seven Deadly Sins: Cursed by Light0.00290.08700.00430.81220.26300.50000.0025
69Hospital Playlist0.00030.21740.01600.82780.00421.00000.0141
70I Am Georgina0.00000.04350.00570.06170.01500.00000.0060
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" + ], + "text/plain": [ + " 影劇名稱 觀看時數_Norm 上榜週數_Norm TMDb熱度_Norm TMDb評分_Norm TMDb投票數_Norm IMDb評分_Norm IMDb投票數_Norm\n", + "0 Stranger Things 1.0000 0.4783 0.1793 0.9022 0.9060 NaN NaN\n", + "1 Squid Game 0.7914 0.7826 0.1094 0.4617 0.7314 NaN NaN\n", + "2 Manifest 0.4905 0.6087 0.1053 0.4044 0.0722 NaN NaN\n", + "3 Money Heist 0.3921 0.5217 0.0903 0.6983 1.0000 0.8913 0.7820\n", + "4 Bridgerton 0.3701 0.3913 0.0653 0.6350 0.1134 0.0000 0.0001\n", + "5 Café con aroma de mujer 0.2577 1.0000 0.0149 0.2778 0.0200 0.9783 0.0001\n", + "6 Ozark 0.2427 0.4783 0.0620 0.6911 0.1090 0.9348 0.5042\n", + "7 All of Us Are Dead 0.2099 0.3913 0.1010 0.7522 0.1858 0.7609 0.1184\n", + "8 Virgin River 0.1973 0.1739 0.0482 0.5733 0.0237 0.7174 0.0767\n", + "9 Sex Education 0.1748 0.1739 1.0000 0.7244 0.3718 0.6739 0.0000\n", + "10 The Queen of Flow 0.1748 0.6087 0.1231 0.5461 0.0766 0.7391 0.0026\n", + "11 The Umbrella Academy 0.1225 0.1304 0.0459 0.8911 0.4978 0.6304 0.0001\n", + "12 Extraordinary Attorney Woo 0.1187 0.2174 0.0370 0.8233 0.0314 0.9565 0.0498\n", + "13 Cobra Kai 0.1114 0.0870 0.1121 0.6778 0.3205 0.6087 0.0000\n", + "14 Emily in Paris 0.0964 0.1304 0.0278 0.4306 0.0632 0.5870 0.1831\n", + "15 Elite 0.0920 0.1304 0.0946 0.5950 0.4936 0.9348 0.0001\n", + "16 The Lincoln Lawyer 0.0835 0.1739 0.0465 0.4478 0.0088 NaN NaN\n", + "17 Hometown Cha-Cha-Cha 0.0818 0.6087 0.0295 0.6850 0.0234 0.9130 0.0380\n", + "18 Never Have I Ever 0.0808 0.0870 0.0745 0.6450 0.0860 NaN NaN\n", + "19 Sex/Life 0.0752 0.2174 0.1180 0.0767 0.0659 NaN NaN\n", + "20 Business Proposal 0.0741 0.4783 0.0825 0.7900 0.0143 NaN NaN\n", + "21 Locke & Key 0.0739 0.1304 0.0212 0.5017 0.0605 NaN NaN\n", + "22 The Sandman 0.0724 0.0435 0.0582 0.5911 0.0955 0.2391 0.0000\n", + "23 The Marked Heart 0.0719 0.2174 0.0296 0.4539 0.0116 0.5435 0.0061\n", + "24 Vikings: Valhalla 0.0693 0.1304 0.0533 0.4728 0.0362 0.7174 0.0916\n", + "25 Love Is Blind 0.0662 0.1304 0.0406 0.0556 0.0016 NaN NaN\n", + "26 Dark Desire 0.0470 0.1304 0.0845 0.1667 0.2333 0.8478 0.0000\n", + "27 Hustle 0.0453 0.1739 0.0249 0.3933 0.0062 NaN NaN\n", + "28 My Name 0.0438 0.1304 0.0197 0.7128 0.0387 0.8043 0.0575\n", + "29 The Last Kingdom 0.0422 0.1304 0.1376 0.7167 0.0819 0.9348 0.0000\n", + "30 Peaky Blinders 0.0407 0.1304 0.3376 0.8589 0.4947 1.0000 1.0000\n", + "31 Lost in Space 0.0406 0.0870 0.4049 0.0639 0.0039 NaN NaN\n", + "32 Carter 0.0399 0.3478 0.0057 0.2222 0.0002 NaN NaN\n", + "33 Sweet Magnolias 0.0398 0.1304 0.0200 0.4444 0.0101 0.6957 0.0364\n", + "34 All American 0.0390 0.1304 0.1345 0.6917 0.0218 NaN NaN\n", + "35 The Five Juanas 0.0380 0.1739 0.0128 0.2328 0.0124 0.6087 0.0033\n", + "36 Alba 0.0372 0.1739 0.0070 0.0000 0.0079 NaN NaN\n", + "37 Raising Dion 0.0362 0.0870 0.0133 0.3867 0.0203 0.8696 0.0001\n", + "38 Twenty Five Twenty One 0.0358 0.3478 0.0259 0.8872 0.0040 0.9783 0.0290\n", + "39 Anatomy of a Scandal 0.0356 0.1304 0.0074 0.0394 0.0053 0.6304 0.0516\n", + "40 Pasión de Gavilanes 0.0331 0.1304 0.2765 0.3561 0.1066 NaN NaN\n", + "41 Collision 0.0322 0.1304 0.0000 0.1667 0.0000 NaN NaN\n", + "42 Selling Sunset 0.0312 0.0870 0.0037 0.0556 0.0007 0.5217 0.0119\n", + "43 Arcane 0.0296 0.1739 0.0365 0.9667 0.1861 NaN NaN\n", + "44 Good Girls 0.0292 0.1739 0.0464 0.5544 0.0271 0.8913 0.0008\n", + "45 Who Killed Sara? 0.0258 0.0870 0.0123 0.3544 0.0621 0.5000 0.0225\n", + "46 Midnight Mass 0.0253 0.0870 0.0162 0.2917 0.0358 0.7174 0.0000\n", + "47 Welcome to Eden 0.0246 0.1304 0.0346 0.0500 0.0116 NaN NaN\n", + "48 Narcos: Mexico 0.0241 0.0870 0.0415 0.5217 0.0637 0.9130 0.1303\n", + "49 The King's Affection 0.0240 0.3478 0.0638 0.7506 0.0116 0.8478 0.0111\n", + "50 Juvenile Justice 0.0226 0.2174 0.0084 0.5261 0.0033 NaN NaN\n", + "51 Archive 81 0.0208 0.0435 0.0142 0.1217 0.0234 0.6957 0.0871\n", + "52 Our Beloved Summer 0.0205 0.2609 0.0246 0.8333 0.0027 0.9348 0.0003\n", + "53 Alchemy of Souls 0.0201 0.2609 0.0552 0.9011 0.0216 1.0000 0.0380\n", + "54 The Good Doctor 0.0147 0.1739 0.6737 0.8350 0.6593 NaN NaN\n", + "55 The Snitch Cartel: Origins 0.0146 0.1739 0.0362 0.4917 0.0144 0.4130 0.0004\n", + "56 Sintonia 0.0136 0.0000 0.0165 0.6667 0.0086 NaN NaN\n", + "57 Our Blues 0.0121 0.3043 0.0072 1.0000 0.0010 0.9783 0.0071\n", + "58 First Kill 0.0119 0.0435 0.0153 0.5289 0.0137 NaN NaN\n", + "59 The Chestnut Man 0.0117 0.1304 0.0099 0.3244 0.0241 0.7609 0.0873\n", + "60 Forecasting Love and Weather 0.0105 0.2609 0.0111 0.3506 0.0020 0.6522 0.0071\n", + "61 Money Heist: Korea - Joint Economic Area 0.0097 0.0435 0.0404 0.5000 0.0323 0.3913 0.0137\n", + "62 True Story 0.0070 0.0435 0.0099 0.0950 0.0031 NaN NaN\n", + "63 Control Z 0.0066 0.0435 0.0149 0.6817 0.1244 0.5870 0.0161\n", + "64 The Silent Sea 0.0066 0.0435 0.0192 0.4444 0.0281 NaN NaN\n", + "65 Another Self 0.0062 0.0870 0.0048 0.2222 0.0002 0.6957 0.0338\n", + "66 Turning Point: 9/11 and the War on Terror 0.0034 0.0435 0.0034 0.1594 0.0032 0.8261 0.0169\n", + "67 On My Block 0.0034 0.0000 0.0111 0.8117 0.0332 0.6304 0.0000\n", + "68 The Seven Deadly Sins: Cursed by Light 0.0029 0.0870 0.0043 0.8122 0.2630 0.5000 0.0025\n", + "69 Hospital Playlist 0.0003 0.2174 0.0160 0.8278 0.0042 1.0000 0.0141\n", + "70 I Am Georgina 0.0000 0.0435 0.0057 0.0617 0.0150 0.0000 0.0060" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 1. 讀取資料集\n", + "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\n", + "\n", + "# 2. 排除關鍵 NaN 值以計算正確的 P50 門檻 (以 TMDb 為基準)\n", + "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\n", + "\n", + "tmdb_rating_p50 = df_valid_tmdb[\"tmdb_vote_average\"].median()\n", + "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median()\n", + "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean()\n", + "\n", + "# 3. 篩選出這 71 部 The Hit 影集\n", + "hit_condition = (\n", + " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", + " (df[\"tmdb_vote_count\"] >= tmdb_votes_p50) &\n", + " (df[\"netflix_viewing_hours\"] > netflix_hours_mean)\n", + ")\n", + "df_hits = df[hit_condition].copy().reset_index(drop=True)\n", + "\n", + "# 4. 定義需要進行 Min-Max 標準化的數值欄位\n", + "target_cols = [\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", + "# 進行 Min-Max 標準化並建立新欄位 (字尾加上 _norm)\n", + "for col in target_cols:\n", + " min_val = df_hits[col].min()\n", + " max_val = df_hits[col].max()\n", + " \n", + " # 預防分母為 0 的安全機制，並四捨五入至小數點後 4 位\n", + " if max_val - min_val != 0:\n", + " df_hits[f\"{col}_norm\"] = ((df_hits[col] - min_val) / (max_val - min_val)).round(4)\n", + " else:\n", + " df_hits[f\"{col}_norm\"] = 1.0000\n", + "\n", + "# 5. 強制 Pandas 展開所有 Rows 與 Columns，絕不省略\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", + "# 6. 整理要輸出的欄位順序 (名稱 + 7 個標準化分數)\n", + "normalized_cols = [f\"{col}_norm\" for col in target_cols]\n", + "display_columns = [\"netflix_title\"] + normalized_cols\n", + "\n", + "# 依據觀看時數標準化分數降冪排序\n", + "df_hits_sorted = df_hits.sort_values(by=\"netflix_viewing_hours_norm\", ascending=False).reset_index(drop=True)\n", + "\n", + "print(\"=\" * 120)\n", + "print(\"🚀 【The Hit 影集】 7 大核心屬性 Min-Max 標準化分數總表\")\n", + "print(\" * 數值範圍皆在 0.0 ~ 1.0 之間（1.0 代表該屬性在 71 部影集中表現最強，0.0 為最弱）\")\n", + "print(\"=\" * 120)\n", + "\n", + "# 重新命名欄位，提供精美視覺化對照\n", + "rename_mapping = {\n", + " \"netflix_title\": \"影劇名稱\",\n", + " \"netflix_viewing_hours_norm\": \"觀看時數_Norm\",\n", + " \"netflix_weeks_norm\": \"上榜週數_Norm\",\n", + " \"tmdb_popularity_norm\": \"TMDb熱度_Norm\",\n", + " \"tmdb_vote_average_norm\": \"TMDb評分_Norm\",\n", + " \"tmdb_vote_count_norm\": \"TMDb投票數_Norm\",\n", + " \"imdb_averageRating_norm\": \"IMDb評分_Norm\",\n", + " \"imdb_numVotes_norm\": \"IMDb投票數_Norm\"\n", + "}\n", + "\n", + "display(df_hits_sorted[display_columns].rename(columns=rename_mapping))" + ] + }, + { + "cell_type": "markdown", + "id": "6b8b23d8", + "metadata": {}, + "source": [ + "## our analysis\n", + "$$\\text{綜合 The Hit 指數} = (0.35 \\cdot \\text{觀看時數\\_Norm}) + (0.30 \\cdot \\text{TMDb評分\\_Norm}) + (0.20 \\cdot \\text{上榜週數\\_Norm}) + (0.15 \\cdot \\text{TMDb投票數\\_Norm})$$" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "8c7299f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========================================================================================================================\n", + "🏆 【全新優化版】71 部 The Hit 影集「綜合實力榜單」\n", + " (已剔除 IMDb 缺失數據，採用全新 35% / 30% / 20% / 15% 權重分配)\n", + "========================================================================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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影劇名稱綜合 The Hit 指數觀看時數_Norm (35%)TMDb評分_Norm (30%)上榜週數_Norm (20%)TMDb投票數_Norm (15%)
0Stranger Things0.85221.00000.90220.47830.9060
1Squid Game0.68170.79140.46170.78260.7314
2Money Heist0.60110.39210.69830.52171.0000
3Manifest0.42560.49050.40440.60870.0722
4Bridgerton0.41530.37010.63500.39130.1134
5The Umbrella Academy0.41100.12250.89110.13040.4978
6All of Us Are Dead0.40530.20990.75220.39130.1858
7Ozark0.40430.24270.69110.47830.1090
8The Good Doctor0.38930.01470.83500.17390.6593
9Café con aroma de mujer0.37650.25770.27781.00000.0200
10Peaky Blinders0.37220.04070.85890.13040.4947
11Sex Education0.36900.17480.72440.17390.3718
12Our Blues0.36520.01211.00000.30430.0010
13Arcane0.36310.02960.96670.17390.1861
14Business Proposal0.36070.07410.79000.47830.0143
15Hometown Cha-Cha-Cha0.35940.08180.68500.60870.0234
16The Queen of Flow0.35820.17480.54610.60870.0766
17Twenty Five Twenty One0.34880.03580.88720.34780.0040
18Extraordinary Attorney Woo0.33670.11870.82330.21740.0314
19Alchemy of Souls0.33280.02010.90110.26090.0216
20Elite0.31080.09200.59500.13040.4936
21Our Beloved Summer0.30970.02050.83330.26090.0027
22Cobra Kai0.30780.11140.67780.08700.3205
23The King's Affection0.30490.02400.75060.34780.0116
24The Seven Deadly Sins: Cursed by Light0.30150.00290.81220.08700.2630
25Hospital Playlist0.29260.00030.82780.21740.0042
26Virgin River0.27940.19730.57330.17390.0237
27The Last Kingdom0.26810.04220.71670.13040.0819
28My Name0.26110.04380.71280.13040.0387
29Never Have I Ever0.25210.08080.64500.08700.0860
30All American0.25050.03900.69170.13040.0218
31On My Block0.24970.00340.81170.00000.0332
32Control Z0.23420.00660.68170.04350.1244
33The Sandman0.22570.07240.59110.04350.0955
34Good Girls0.21540.02920.55440.17390.0271
35Locke & Key0.21150.07390.50170.13040.0605
36Juvenile Justice0.20970.02260.52610.21740.0033
37The Marked Heart0.20660.07190.45390.21740.0116
38Sintonia0.20610.01360.66670.00000.0086
39The Lincoln Lawyer0.19970.08350.44780.17390.0088
40Emily in Paris0.19850.09640.43060.13040.0632
41Vikings: Valhalla0.19760.06930.47280.13040.0362
42Narcos: Mexico0.19190.02410.52170.08700.0637
43The Snitch Cartel: Origins0.18960.01460.49170.17390.0144
44Sweet Magnolias0.17480.03980.44440.13040.0101
45First Kill0.17360.01190.52890.04350.0137
46Hustle0.16960.04530.39330.17390.0062
47Money Heist: Korea - Joint Economic Area0.16690.00970.50000.04350.0323
48Forecasting Love and Weather0.16130.01050.35060.26090.0020
49Pasión de Gavilanes0.16050.03310.35610.13040.1066
50Carter0.15020.03990.22220.34780.0002
51Raising Dion0.14910.03620.38670.08700.0203
52The Silent Sea0.14850.00660.44440.04350.0281
53Who Killed Sara?0.14210.02580.35440.08700.0621
54The Chestnut Man0.13110.01170.32440.13040.0241
55Dark Desire0.12750.04700.16670.13040.2333
56The Five Juanas0.11980.03800.23280.17390.0124
57Midnight Mass0.11910.02530.29170.08700.0358
58Sex/Life0.10270.07520.07670.21740.0659
59Collision0.08740.03220.16670.13040.0000
60Another Self0.08630.00620.22220.08700.0002
61Love Is Blind0.06620.06620.05560.13040.0016
62Turning Point: 9/11 and the War on Terror0.05820.00340.15940.04350.0032
63Archive 810.05600.02080.12170.04350.0234
64Welcome to Eden0.05140.02460.05000.13040.0116
65Lost in Space0.05140.04060.06390.08700.0039
66Anatomy of a Scandal0.05120.03560.03940.13040.0053
67Alba0.04900.03720.00000.17390.0079
68Selling Sunset0.04510.03120.05560.08700.0007
69True Story0.04010.00700.09500.04350.0031
70I Am Georgina0.02950.00000.06170.04350.0150
\n", + "
" + ], + "text/plain": [ + " 影劇名稱 綜合 The Hit 指數 觀看時數_Norm (35%) TMDb評分_Norm (30%) 上榜週數_Norm (20%) TMDb投票數_Norm (15%)\n", + "0 Stranger Things 0.8522 1.0000 0.9022 0.4783 0.9060\n", + "1 Squid Game 0.6817 0.7914 0.4617 0.7826 0.7314\n", + "2 Money Heist 0.6011 0.3921 0.6983 0.5217 1.0000\n", + "3 Manifest 0.4256 0.4905 0.4044 0.6087 0.0722\n", + "4 Bridgerton 0.4153 0.3701 0.6350 0.3913 0.1134\n", + "5 The Umbrella Academy 0.4110 0.1225 0.8911 0.1304 0.4978\n", + "6 All of Us Are Dead 0.4053 0.2099 0.7522 0.3913 0.1858\n", + "7 Ozark 0.4043 0.2427 0.6911 0.4783 0.1090\n", + "8 The Good Doctor 0.3893 0.0147 0.8350 0.1739 0.6593\n", + "9 Café con aroma de mujer 0.3765 0.2577 0.2778 1.0000 0.0200\n", + "10 Peaky Blinders 0.3722 0.0407 0.8589 0.1304 0.4947\n", + "11 Sex Education 0.3690 0.1748 0.7244 0.1739 0.3718\n", + "12 Our Blues 0.3652 0.0121 1.0000 0.3043 0.0010\n", + "13 Arcane 0.3631 0.0296 0.9667 0.1739 0.1861\n", + "14 Business Proposal 0.3607 0.0741 0.7900 0.4783 0.0143\n", + "15 Hometown Cha-Cha-Cha 0.3594 0.0818 0.6850 0.6087 0.0234\n", + "16 The Queen of Flow 0.3582 0.1748 0.5461 0.6087 0.0766\n", + "17 Twenty Five Twenty One 0.3488 0.0358 0.8872 0.3478 0.0040\n", + "18 Extraordinary Attorney Woo 0.3367 0.1187 0.8233 0.2174 0.0314\n", + "19 Alchemy of Souls 0.3328 0.0201 0.9011 0.2609 0.0216\n", + "20 Elite 0.3108 0.0920 0.5950 0.1304 0.4936\n", + "21 Our Beloved Summer 0.3097 0.0205 0.8333 0.2609 0.0027\n", + "22 Cobra Kai 0.3078 0.1114 0.6778 0.0870 0.3205\n", + "23 The King's Affection 0.3049 0.0240 0.7506 0.3478 0.0116\n", + "24 The Seven Deadly Sins: Cursed by Light 0.3015 0.0029 0.8122 0.0870 0.2630\n", + "25 Hospital Playlist 0.2926 0.0003 0.8278 0.2174 0.0042\n", + "26 Virgin River 0.2794 0.1973 0.5733 0.1739 0.0237\n", + "27 The Last Kingdom 0.2681 0.0422 0.7167 0.1304 0.0819\n", + "28 My Name 0.2611 0.0438 0.7128 0.1304 0.0387\n", + "29 Never Have I Ever 0.2521 0.0808 0.6450 0.0870 0.0860\n", + "30 All American 0.2505 0.0390 0.6917 0.1304 0.0218\n", + "31 On My Block 0.2497 0.0034 0.8117 0.0000 0.0332\n", + "32 Control Z 0.2342 0.0066 0.6817 0.0435 0.1244\n", + "33 The Sandman 0.2257 0.0724 0.5911 0.0435 0.0955\n", + "34 Good Girls 0.2154 0.0292 0.5544 0.1739 0.0271\n", + "35 Locke & Key 0.2115 0.0739 0.5017 0.1304 0.0605\n", + "36 Juvenile Justice 0.2097 0.0226 0.5261 0.2174 0.0033\n", + "37 The Marked Heart 0.2066 0.0719 0.4539 0.2174 0.0116\n", + "38 Sintonia 0.2061 0.0136 0.6667 0.0000 0.0086\n", + "39 The Lincoln Lawyer 0.1997 0.0835 0.4478 0.1739 0.0088\n", + "40 Emily in Paris 0.1985 0.0964 0.4306 0.1304 0.0632\n", + "41 Vikings: Valhalla 0.1976 0.0693 0.4728 0.1304 0.0362\n", + "42 Narcos: Mexico 0.1919 0.0241 0.5217 0.0870 0.0637\n", + "43 The Snitch Cartel: Origins 0.1896 0.0146 0.4917 0.1739 0.0144\n", + "44 Sweet Magnolias 0.1748 0.0398 0.4444 0.1304 0.0101\n", + "45 First Kill 0.1736 0.0119 0.5289 0.0435 0.0137\n", + "46 Hustle 0.1696 0.0453 0.3933 0.1739 0.0062\n", + "47 Money Heist: Korea - Joint Economic Area 0.1669 0.0097 0.5000 0.0435 0.0323\n", + "48 Forecasting Love and Weather 0.1613 0.0105 0.3506 0.2609 0.0020\n", + "49 Pasión de Gavilanes 0.1605 0.0331 0.3561 0.1304 0.1066\n", + "50 Carter 0.1502 0.0399 0.2222 0.3478 0.0002\n", + "51 Raising Dion 0.1491 0.0362 0.3867 0.0870 0.0203\n", + "52 The Silent Sea 0.1485 0.0066 0.4444 0.0435 0.0281\n", + "53 Who Killed Sara? 0.1421 0.0258 0.3544 0.0870 0.0621\n", + "54 The Chestnut Man 0.1311 0.0117 0.3244 0.1304 0.0241\n", + "55 Dark Desire 0.1275 0.0470 0.1667 0.1304 0.2333\n", + "56 The Five Juanas 0.1198 0.0380 0.2328 0.1739 0.0124\n", + "57 Midnight Mass 0.1191 0.0253 0.2917 0.0870 0.0358\n", + "58 Sex/Life 0.1027 0.0752 0.0767 0.2174 0.0659\n", + "59 Collision 0.0874 0.0322 0.1667 0.1304 0.0000\n", + "60 Another Self 0.0863 0.0062 0.2222 0.0870 0.0002\n", + "61 Love Is Blind 0.0662 0.0662 0.0556 0.1304 0.0016\n", + "62 Turning Point: 9/11 and the War on Terror 0.0582 0.0034 0.1594 0.0435 0.0032\n", + "63 Archive 81 0.0560 0.0208 0.1217 0.0435 0.0234\n", + "64 Welcome to Eden 0.0514 0.0246 0.0500 0.1304 0.0116\n", + "65 Lost in Space 0.0514 0.0406 0.0639 0.0870 0.0039\n", + "66 Anatomy of a Scandal 0.0512 0.0356 0.0394 0.1304 0.0053\n", + "67 Alba 0.0490 0.0372 0.0000 0.1739 0.0079\n", + "68 Selling Sunset 0.0451 0.0312 0.0556 0.0870 0.0007\n", + "69 True Story 0.0401 0.0070 0.0950 0.0435 0.0031\n", + "70 I Am Georgina 0.0295 0.0000 0.0617 0.0435 0.0150" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 1. 讀取資料集\n", + "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\n", + "\n", + "# 2. 僅使用 TMDb 作為篩選與計算基準，確保數據最完整\n", + "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\n", + "\n", + "tmdb_rating_p50 = df_valid_tmdb[\"tmdb_vote_average\"].median()\n", + "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median()\n", + "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean()\n", + "\n", + "# 3. 篩選出 71 部 The Hit 影集\n", + "hit_condition = (\n", + " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", + " (df[\"tmdb_vote_count\"] >= tmdb_votes_p50) &\n", + " (df[\"netflix_viewing_hours\"] > netflix_hours_mean)\n", + ")\n", + "df_hits = df[hit_condition].copy().reset_index(drop=True)\n", + "\n", + "# 4. 對僅存的 4 個關鍵量化屬性進行 Min-Max 標準化\n", + "target_cols = [\n", + " \"netflix_viewing_hours\",\n", + " \"netflix_weeks\",\n", + " \"tmdb_vote_average\",\n", + " \"tmdb_vote_count\"\n", + "]\n", + "\n", + "for col in target_cols:\n", + " min_val = df_hits[col].min()\n", + " max_val = df_hits[col].max()\n", + " if max_val - min_val != 0:\n", + " df_hits[f\"{col}_norm\"] = ((df_hits[col] - min_val) / (max_val - min_val)).round(4)\n", + " else:\n", + " df_hits[f\"{col}_norm\"] = 1.0000\n", + "\n", + "# 5. 套用全新無 IMDb 的【綜合 The Hit 指數】加權公式\n", + "df_hits['綜合 The Hit 指數'] = (\n", + " df_hits['netflix_viewing_hours_norm'] * 0.35 +\n", + " df_hits['tmdb_vote_average_norm'] * 0.30 +\n", + " df_hits['netflix_weeks_norm'] * 0.20 +\n", + " df_hits['tmdb_vote_count_norm'] * 0.15\n", + ").round(4)\n", + "\n", + "# 6. 設定不省略 Rows 與 Columns\n", + "pd.set_option('display.max_rows', None)\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "# 7. 依「綜合 The Hit 指數」從高到低（降冪）排序\n", + "df_hits_sorted = df_hits.sort_values(by=\"綜合 The Hit 指數\", ascending=False).reset_index(drop=True)\n", + "\n", + "# 整理要呈現的欄位\n", + "display_cols = [\n", + " \"netflix_title\",\n", + " \"綜合 The Hit 指數\",\n", + " \"netflix_viewing_hours_norm\",\n", + " \"tmdb_vote_average_norm\",\n", + " \"netflix_weeks_norm\",\n", + " \"tmdb_vote_count_norm\"\n", + "]\n", + "\n", + "print(\"=\" * 120)\n", + "print(\"🏆 【全新優化版】71 部 The Hit 影集「綜合實力榜單」\")\n", + "print(\" (已剔除 IMDb 缺失數據，採用全新 35% / 30% / 20% / 15% 權重分配)\")\n", + "print(\"=\" * 120)\n", + "\n", + "display(df_hits_sorted[display_cols].rename(columns={\n", + " \"netflix_title\": \"影劇名稱\",\n", + " \"netflix_viewing_hours_norm\": \"觀看時數_Norm (35%)\",\n", + " \"tmdb_vote_average_norm\": \"TMDb評分_Norm (30%)\",\n", + " \"netflix_weeks_norm\": \"上榜週數_Norm (20%)\",\n", + " \"tmdb_vote_count_norm\": \"TMDb投票數_Norm (15%)\"\n", + "}))" + ] + }, + { + "cell_type": "markdown", + "id": "885604c1", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "afd8cfa3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========================================================================================================================\n", + "🏆 【The Hit 影集】綜合指數與「語言、成人與否、類型」特徵總表\n", + " (已依『綜合 The Hit 指數』由高至低排序，幫助您快速識別頂級爆款的共通基因)\n", + "========================================================================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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影劇名稱綜合 The Hit 指數
0Stranger Things0.8522
1Squid Game0.6817
2Money Heist0.6011
3Manifest0.4256
4Bridgerton0.4153
5The Umbrella Academy0.4110
6All of Us Are Dead0.4052
7Ozark0.4043
8The Good Doctor0.3893
9Café con aroma de mujer0.3765
10Peaky Blinders0.3722
11Sex Education0.3691
12Our Blues0.3652
13Arcane0.3631
14Business Proposal0.3607
15Hometown Cha-Cha-Cha0.3594
16The Queen of Flow0.3582
17Twenty Five Twenty One0.3489
18Extraordinary Attorney Woo0.3367
19Alchemy of Souls0.3328
20Elite0.3108
21Our Beloved Summer0.3098
22Cobra Kai0.3078
23The King's Affection0.3049
24The Seven Deadly Sins: Cursed by Light0.3015
25Hospital Playlist0.2925
26Virgin River0.2794
27The Last Kingdom0.2681
28My Name0.2611
29Never Have I Ever0.2521
30All American0.2505
31On My Block0.2497
32Control Z0.2342
33The Sandman0.2257
34Good Girls0.2154
35Locke & Key0.2115
36Juvenile Justice0.2097
37The Marked Heart0.2065
38Sintonia0.2061
39The Lincoln Lawyer0.1997
40Emily in Paris0.1985
41Vikings: Valhalla0.1976
42Narcos: Mexico0.1919
43The Snitch Cartel: Origins0.1895
44Sweet Magnolias0.1749
45First Kill0.1736
46Hustle0.1696
47Money Heist: Korea - Joint Economic Area0.1669
48Forecasting Love and Weather0.1613
49Pasión de Gavilanes0.1605
50Carter0.1502
51Raising Dion0.1491
52The Silent Sea0.1486
53Who Killed Sara?0.1421
54The Chestnut Man0.1311
55Dark Desire0.1275
56The Five Juanas0.1198
57Midnight Mass0.1191
58Sex/Life0.1027
59Collision0.0874
60Another Self0.0863
61Love Is Blind0.0662
62Turning Point: 9/11 and the War on Terror0.0582
63Archive 810.0560
64Welcome to Eden0.0515
65Lost in Space0.0514
66Anatomy of a Scandal0.0512
67Alba0.0490
68Selling Sunset0.0451
69True Story0.0401
70I Am Georgina0.0294
\n", + "
" + ], + "text/plain": [ + " 影劇名稱 綜合 The Hit 指數\n", + "0 Stranger Things 0.8522\n", + "1 Squid Game 0.6817\n", + "2 Money Heist 0.6011\n", + "3 Manifest 0.4256\n", + "4 Bridgerton 0.4153\n", + "5 The Umbrella Academy 0.4110\n", + "6 All of Us Are Dead 0.4052\n", + "7 Ozark 0.4043\n", + "8 The Good Doctor 0.3893\n", + "9 Café con aroma de mujer 0.3765\n", + "10 Peaky Blinders 0.3722\n", + "11 Sex Education 0.3691\n", + "12 Our Blues 0.3652\n", + "13 Arcane 0.3631\n", + "14 Business Proposal 0.3607\n", + "15 Hometown Cha-Cha-Cha 0.3594\n", + "16 The Queen of Flow 0.3582\n", + "17 Twenty Five Twenty One 0.3489\n", + "18 Extraordinary Attorney Woo 0.3367\n", + "19 Alchemy of Souls 0.3328\n", + "20 Elite 0.3108\n", + "21 Our Beloved Summer 0.3098\n", + "22 Cobra Kai 0.3078\n", + "23 The King's Affection 0.3049\n", + "24 The Seven Deadly Sins: Cursed by Light 0.3015\n", + "25 Hospital Playlist 0.2925\n", + "26 Virgin River 0.2794\n", + "27 The Last Kingdom 0.2681\n", + "28 My Name 0.2611\n", + "29 Never Have I Ever 0.2521\n", + "30 All American 0.2505\n", + "31 On My Block 0.2497\n", + "32 Control Z 0.2342\n", + "33 The Sandman 0.2257\n", + "34 Good Girls 0.2154\n", + "35 Locke & Key 0.2115\n", + "36 Juvenile Justice 0.2097\n", + "37 The Marked Heart 0.2065\n", + "38 Sintonia 0.2061\n", + "39 The Lincoln Lawyer 0.1997\n", + "40 Emily in Paris 0.1985\n", + "41 Vikings: Valhalla 0.1976\n", + "42 Narcos: Mexico 0.1919\n", + "43 The Snitch Cartel: Origins 0.1895\n", + "44 Sweet Magnolias 0.1749\n", + "45 First Kill 0.1736\n", + "46 Hustle 0.1696\n", + "47 Money Heist: Korea - Joint Economic Area 0.1669\n", + "48 Forecasting Love and Weather 0.1613\n", + "49 Pasión de Gavilanes 0.1605\n", + "50 Carter 0.1502\n", + "51 Raising Dion 0.1491\n", + "52 The Silent Sea 0.1486\n", + "53 Who Killed Sara? 0.1421\n", + "54 The Chestnut Man 0.1311\n", + "55 Dark Desire 0.1275\n", + "56 The Five Juanas 0.1198\n", + "57 Midnight Mass 0.1191\n", + "58 Sex/Life 0.1027\n", + "59 Collision 0.0874\n", + "60 Another Self 0.0863\n", + "61 Love Is Blind 0.0662\n", + "62 Turning Point: 9/11 and the War on Terror 0.0582\n", + "63 Archive 81 0.0560\n", + "64 Welcome to Eden 0.0515\n", + "65 Lost in Space 0.0514\n", + "66 Anatomy of a Scandal 0.0512\n", + "67 Alba 0.0490\n", + "68 Selling Sunset 0.0451\n", + "69 True Story 0.0401\n", + "70 I Am Georgina 0.0294" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 1. 讀取資料集\n", + "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\n", + "\n", + "# 2. 排除關鍵 NaN 值以計算正確的 P50 門檻 (以 TMDb 為基準)\n", + "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\n", + "\n", + "tmdb_rating_p50 = df_valid_tmdb[\"tmdb_vote_average\"].median()\n", + "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median()\n", + "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean()\n", + "\n", + "# 3. 篩選出 71 部 The Hit 影集\n", + "hit_condition = (\n", + " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", + " (df[\"tmdb_vote_count\"] >= tmdb_votes_p50) &\n", + " (df[\"netflix_viewing_hours\"] > netflix_hours_mean)\n", + ")\n", + "df_hits = df[hit_condition].copy().reset_index(drop=True)\n", + "\n", + "# 4. 對 4 個關鍵量化屬性進行 Min-Max 標準化\n", + "target_cols = [\"netflix_viewing_hours\", \"netflix_weeks\", \"tmdb_vote_average\", \"tmdb_vote_count\"]\n", + "for col in target_cols:\n", + " min_val = df_hits[col].min()\n", + " max_val = df_hits[col].max()\n", + " if max_val - min_val != 0:\n", + " df_hits[f\"{col}_norm\"] = ((df_hits[col] - min_val) / (max_val - min_val))\n", + " else:\n", + " df_hits[f\"{col}_norm\"] = 1.0000\n", + "\n", + "# 5. 計算【綜合 The Hit 指數】 (採用無 IMDb 的優化版權重)\n", + "df_hits['綜合 The Hit 指數'] = (\n", + " df_hits['netflix_viewing_hours_norm'] * 0.35 +\n", + " df_hits['tmdb_vote_average_norm'] * 0.30 +\n", + " df_hits['netflix_weeks_norm'] * 0.20 +\n", + " df_hits['tmdb_vote_count_norm'] * 0.15\n", + ").round(4)\n", + "\n", + "# ─── 步驟 6：動態識別「語言」、「成人限制」、「類型」欄位 ───\n", + "\n", + "# 定義可能的欄位候選清單 (相容不同的欄位命名)\n", + "language_candidates = [\"original_language\", \"tmdb_original_language\", \"language\", \"netflix_language\"]\n", + "adult_candidates = [\"adult\", \"is_adult\", \"rating\", \"age_certification\"]\n", + "genre_candidates = [\"genres\", \"tmdb_genres\", \"listed_in\", \"genre\"]\n", + "\n", + "# 自動尋找資料表中實際存在的欄位\n", + "lang_col = next((c for c in language_candidates if c in df_hits.columns), None)\n", + "adult_col = next((c for c in adult_candidates if c in df_hits.columns), None)\n", + "genre_col = next((c for c in genre_candidates if c in df_hits.columns), None)\n", + "\n", + "# ─── 步驟 7：整理並無省略輸出 ───\n", + "\n", + "# 設定 Pandas 完整顯示不省略\n", + "pd.set_option('display.max_rows', None)\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "# 建立顯示欄位清單\n", + "display_cols = [\"netflix_title\", \"綜合 The Hit 指數\"]\n", + "rename_mapping = {\n", + " \"netflix_title\": \"影劇名稱\",\n", + " \"綜合 The Hit 指數\": \"綜合 The Hit 指數\"\n", + "}\n", + "\n", + "# 動態加入找到的屬性\n", + "if lang_col:\n", + " display_cols.append(lang_col)\n", + " rename_mapping[lang_col] = f\"主要語言 ({lang_col})\"\n", + "if adult_col:\n", + " display_cols.append(adult_col)\n", + " rename_mapping[adult_col] = f\"成人限制 ({adult_col})\"\n", + "if genre_col:\n", + " display_cols.append(genre_col)\n", + " rename_mapping[genre_col] = f\"類型/流派 ({genre_col})\"\n", + "\n", + "# 依綜合指數從高到低（降冪）排序\n", + "df_hits_sorted = df_hits.sort_values(by=\"綜合 The Hit 指數\", ascending=False).reset_index(drop=True)\n", + "\n", + "print(\"=\" * 120)\n", + "print(\"🏆 【The Hit 影集】綜合指數與「語言、成人與否、類型」特徵總表\")\n", + "print(\" (已依『綜合 The Hit 指數』由高至低排序，幫助您快速識別頂級爆款的共通基因)\")\n", + "print(\"=\" * 120)\n", + "\n", + "# 印出結果\n", + "display(df_hits_sorted[display_cols].rename(columns=rename_mapping))" + ] + }, + { + "cell_type": "markdown", + "id": "f4ce3f65", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "markdown", + "id": "58a83a66", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "markdown", + "id": "c7ceb048", + "metadata": {}, + "source": [ + "## our analysis\n", + "？？" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b21b87a2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02788717", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d64c4aa", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From f6e02f55d5a0ccf05bb3f1211ea5211c57fb00d3 Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Thu, 16 Jul 2026 07:54:00 -0700 Subject: [PATCH 11/14] check ver1 --- netflix-writer.ipynb | 14240 +++++++++++++----- netflix/data/seasons_episodes_dual_axis.png | Bin 0 -> 333081 bytes temp.txt | 262 +- 3 files changed, 10495 insertions(+), 4007 deletions(-) create mode 100644 netflix/data/seasons_episodes_dual_axis.png diff --git a/netflix-writer.ipynb b/netflix-writer.ipynb index 3385e52..a0820ec 100644 --- a/netflix-writer.ipynb +++ b/netflix-writer.ipynb @@ -3421,37 +3421,24 @@ }, { "cell_type": "markdown", - "id": "e70defaf", + "id": "58a83a66", "metadata": {}, "source": [ "## our analysis\n", - "\n", - "TMDb 評分門檻： 必須大於或等於全體有評分影劇的 前 $50\\%$（中位數 Median）。\n", - "\n", - "TMDb 投票人數門檻： 必須大於或等於全體有投票人數的 前 $50\\%$（中位數 Median），確保口碑具備大眾基數。串流觀看量門檻： \n", - "\n", - "觀看時數（netflix_viewing_hours）必須大於全體影劇的 平均值（Mean），確保在 Netflix 上確實引爆流量。" + "？？" ] }, { "cell_type": "code", - "execution_count": 51, - "id": "77a641db", + "execution_count": 59, + "id": "02788717", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "================================================================================\n", - "⚙️ 【The Hit】黃金篩選門檻定義：\n", - "--------------------------------------------------------------------------------\n", - "▸ 1. TMDb 評分門檻 (前 50% 中位數) >= 6.96 分\n", - "▸ 2. TMDb 投票數門檻 (前 50% 中位數)>= 25 票\n", - "▸ 3. Netflix 觀看時數門檻 (全體平均) > 69.19 百萬小時\n", - "================================================================================\n", - "🎉 篩選完成！在 691 部影劇中，共有 71 部完全符合條件：\n", - "================================================================================\n" + "Distribution diagnostics\n" ] }, { @@ -3475,659 +3462,861 @@ " \n", " \n", " \n", - " 影劇名稱\n", - " TMDb 評分\n", - " TMDb 投票數\n", - " 觀看時數 (百萬小時)\n", - " 上榜週數\n", + " Variable\n", + " Count\n", + " Mean\n", + " Median\n", + " Skewness\n", + " 95th percentile\n", " \n", " \n", " \n", " \n", " 0\n", - " Stranger Things\n", - " 8.624\n", - " 16161.0\n", - " 2874.26\n", - " 13\n", + " TMDb popularity\n", + " 168\n", + " 3.874000e+01\n", + " 14.63\n", + " 7.73\n", + " 1.285900e+02\n", " \n", " \n", " 1\n", - " Squid Game\n", - " 7.831\n", - " 13053.0\n", - " 2289.50\n", - " 20\n", + " Netflix viewing hours\n", + " 168\n", + " 1.112935e+08\n", + " 34485000.00\n", + " 3.28\n", + " 5.236960e+08\n", " \n", " \n", " 2\n", - " Manifest\n", - " 7.728\n", - " 1312.0\n", - " 1446.26\n", - " 16\n", + " Binge velocity\n", + " 168\n", + " 2.366224e+07\n", + " 15963125.00\n", + " 2.15\n", + " 6.571730e+07\n", " \n", " \n", " 3\n", - " Money Heist\n", - " 8.257\n", - " 17836.0\n", - " 1170.20\n", - " 14\n", - " \n", - " \n", - " 4\n", - " Bridgerton\n", - " 8.143\n", - " 2045.0\n", - " 1108.80\n", - " 11\n", - " \n", - " \n", - " 5\n", - " Café con aroma de mujer\n", - " 7.500\n", - " 382.0\n", - " 793.74\n", - " 25\n", + " IMDb vote count\n", + " 168\n", + " 2.760432e+04\n", + " 3927.50\n", + " 6.16\n", + " 9.204995e+04\n", " \n", - " \n", - " 6\n", - " Ozark\n", - " 8.244\n", - " 1968.0\n", - " 751.60\n", - " 13\n", + " \n", + "\n", + "" + ], + "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": [ + "
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StageTitlesCoverage (%)
7All of Us Are Dead8.3543335.0659.51110Netflix composite rows691100.0
8Virgin River8.032448.0624.2561Exact IMDb title match64092.6
9Sex Education8.3046647.0561.3662Exact TMDb title match29342.4
10The Queen of Flow7.9831391.0561.23163Exact match to both28541.2
11The Umbrella Academy8.6048891.0414.6354Narrative titles used16824.3
12Extraordinary Attorney Woo8.482585.0404.037
\n", + "
" + ], + "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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genretitle_countmedian_qualitymedian_reachmedian_intensitymedian_stayinghit_ratebalanced_score
13Cobra Kai8.2205734.0383.6440Action & Adventure267.1360.2018448333.02.00.2310.764
14Emily in Paris7.7751152.0341.6351Drama977.1519.9116970000.03.00.3810.736
15Elite8.0718817.0329.2952Comedy317.1321.8317030000.02.00.1940.653
16The Lincoln Lawyer7.806183.0305.2763Crime367.0119.0518428333.02.50.3330.556
17Hometown Cha-Cha-Cha8.233442.0300.58164Kids87.1720.6812667500.02.00.1250.514
18Never Have I Ever8.1611557.0297.7845Animation197.1150.6514360000.02.00.0000.514
19Sex/Life7.1381200.0282.1076Sci-Fi & Fantasy237.1129.8413290000.02.00.3040.486
20Business Proposal8.422281.0279.11137Mystery287.0213.4715423571.03.00.3570.486
21Locke & Key7.9031104.0278.3858Documentary196.825.5916380000.02.00.0530.292
22The Sandman8.0641726.0274.223
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" + ], + "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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", 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23The Marked Heart7.817232.0272.7504+ seasons77.16482.89037914000.02.00.4291.0001.0001.0000.6670.917
24Vikings: Valhalla7.851671.0265.555
25Love Is Blind7.10054.0256.895
26Dark Desire7.3004181.0202.945
27Hustle7.708137.0198.31611 season107.11724.66418767500.02.00.1000.6670.3330.6670.6670.583
28My Name8.283715.0194.14522–3 seasons97.00969.98017325000.02.00.2220.3330.6670.3330.6670.500
29The Last Kingdom8.2901485.0189.575
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30Peaky Blinders8.5468836.0185.3450≤8 episodes/season87.21629.98033719000.04.00.500.6670.3331.0001.00.750
31Lost in Space7.11596.0185.18419–12 episodes/season87.21742.36323232500.02.00.251.0000.6670.6670.50.708
32Carter7.40030.0183.04213+ episodes/season106.971103.13213825000.02.00.000.3331.0000.3330.50.542
33Sweet Magnolias7.800206.0182.945
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34All American8.245414.0180.6650Moderate (6–10 h)77.30379.0403.791400e+075.00.5711.0000.6671.0001.00.917
35The Five Juanas7.419246.0177.8561Compact (≤6 h)87.12936.4341.848583e+072.00.1250.6670.3330.6670.50.542
36Alba7.000167.0175.6462Long-form (>16 h)57.105149.1661.436000e+072.00.0000.3331.0000.3330.50.542
37Raising Dion7.696388.0172.874
38Twenty Five Twenty One8.59798.0171.6110
39Anatomy of a Scandal7.071120.0171.025
40Pasión de Gavilanes7.6411925.0164.185
41Collision7.30026.0161.635
42Selling Sunset7.10039.0158.784
43Arcane8.7403341.0154.276
44Good Girls7.998509.0153.146
45Who Killed Sara?7.6381132.0143.614
46Midnight Mass7.525664.0142.284
47Welcome to Eden7.090233.0140.345
48Narcos: Mexico7.9391160.0138.984
49The King's Affection8.351232.0138.5410
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50Juvenile Justice7.94785.0134.5670English127.17445.14628995000.02.00.3331.00.51.00.750.812
51Archive 817.219442.0129.4731Non-English146.97176.66414340000.02.00.1430.51.00.50.750.688
52Our Beloved Summer8.50074.0128.898
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", 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ComponentRecommendationEvidence
53Alchemy of Souls8.622411.0127.6980Core genreAction & AdventureBalanced score = 0.76; n = 26; hit rate = 23.1%
54The Good Doctor8.50311768.0112.5861Observed lifecycle4+ seasonsHighest within-Action & Adventure lifecycle score = 0.92
55The Snitch Cartel: Origins7.885283.0112.1462Episode structure≤8 episodes/seasonHighest within-Action & Adventure format score = 0.75
56Sintonia8.200179.0109.4323Viewing commitmentModerate (6–10 h)Highest within-Action & Adventure format score = 0.92
57Our Blues8.80043.0105.1594Language positioningEnglishHighest within-Action & Adventure market score = 0.81
58First Kill7.952270.0104.613
59The Chestnut Man7.584455.0104.215
60Forecasting Love and Weather7.63161.0100.858
61Money Heist: Korea - Joint Economic Area7.900601.098.373
62True Story7.17182.090.823
63Control Z8.2272242.089.833
64The Silent Sea7.800527.089.773
65Another Self7.40030.088.594
66On My Block8.461618.080.882
67Turning Point: 9/11 and the War on Terror7.28783.080.683
68The Seven Deadly Sins: Cursed by Light8.4624710.079.514
69Hospital Playlist8.490100.072.067
70I Am Georgina7.111293.071.2935Performance objectiveDurable hit: strong weekly demand plus sustained rankingDurable-hit share in Action & Adventure: 57.7% versus 43.5% overall
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" ], "text/plain": [ - " 影劇名稱 TMDb 評分 TMDb 投票數 觀看時數 (百萬小時) 上榜週數\n", - "0 Stranger Things 8.624 16161.0 2874.26 13\n", - "1 Squid Game 7.831 13053.0 2289.50 20\n", - "2 Manifest 7.728 1312.0 1446.26 16\n", - "3 Money Heist 8.257 17836.0 1170.20 14\n", - "4 Bridgerton 8.143 2045.0 1108.80 11\n", - "5 Café con aroma de mujer 7.500 382.0 793.74 25\n", - "6 Ozark 8.244 1968.0 751.60 13\n", - "7 All of Us Are Dead 8.354 3335.0 659.51 11\n", - "8 Virgin River 8.032 448.0 624.25 6\n", - "9 Sex Education 8.304 6647.0 561.36 6\n", - "10 The Queen of Flow 7.983 1391.0 561.23 16\n", - "11 The Umbrella Academy 8.604 8891.0 414.63 5\n", - "12 Extraordinary Attorney Woo 8.482 585.0 404.03 7\n", - "13 Cobra Kai 8.220 5734.0 383.64 4\n", - "14 Emily in Paris 7.775 1152.0 341.63 5\n", - "15 Elite 8.071 8817.0 329.29 5\n", - "16 The Lincoln Lawyer 7.806 183.0 305.27 6\n", - "17 Hometown Cha-Cha-Cha 8.233 442.0 300.58 16\n", - "18 Never Have I Ever 8.161 1557.0 297.78 4\n", - "19 Sex/Life 7.138 1200.0 282.10 7\n", - "20 Business Proposal 8.422 281.0 279.11 13\n", - "21 Locke & Key 7.903 1104.0 278.38 5\n", - "22 The Sandman 8.064 1726.0 274.22 3\n", - "23 The Marked Heart 7.817 232.0 272.75 7\n", - "24 Vikings: Valhalla 7.851 671.0 265.55 5\n", - "25 Love Is Blind 7.100 54.0 256.89 5\n", - "26 Dark Desire 7.300 4181.0 202.94 5\n", - "27 Hustle 7.708 137.0 198.31 6\n", - "28 My Name 8.283 715.0 194.14 5\n", - "29 The Last Kingdom 8.290 1485.0 189.57 5\n", - "30 Peaky Blinders 8.546 8836.0 185.34 5\n", - "31 Lost in Space 7.115 96.0 185.18 4\n", - "32 Carter 7.400 30.0 183.04 10\n", - "33 Sweet Magnolias 7.800 206.0 182.94 5\n", - "34 All American 8.245 414.0 180.66 5\n", - "35 The Five Juanas 7.419 246.0 177.85 6\n", - "36 Alba 7.000 167.0 175.64 6\n", - "37 Raising Dion 7.696 388.0 172.87 4\n", - "38 Twenty Five Twenty One 8.597 98.0 171.61 10\n", - "39 Anatomy of a Scandal 7.071 120.0 171.02 5\n", - "40 Pasión de Gavilanes 7.641 1925.0 164.18 5\n", - "41 Collision 7.300 26.0 161.63 5\n", - "42 Selling Sunset 7.100 39.0 158.78 4\n", - "43 Arcane 8.740 3341.0 154.27 6\n", - "44 Good Girls 7.998 509.0 153.14 6\n", - "45 Who Killed Sara? 7.638 1132.0 143.61 4\n", - "46 Midnight Mass 7.525 664.0 142.28 4\n", - "47 Welcome to Eden 7.090 233.0 140.34 5\n", - "48 Narcos: Mexico 7.939 1160.0 138.98 4\n", - "49 The King's Affection 8.351 232.0 138.54 10\n", - "50 Juvenile Justice 7.947 85.0 134.56 7\n", - "51 Archive 81 7.219 442.0 129.47 3\n", - "52 Our Beloved Summer 8.500 74.0 128.89 8\n", - "53 Alchemy of Souls 8.622 411.0 127.69 8\n", - "54 The Good Doctor 8.503 11768.0 112.58 6\n", - "55 The Snitch Cartel: Origins 7.885 283.0 112.14 6\n", - "56 Sintonia 8.200 179.0 109.43 2\n", - "57 Our Blues 8.800 43.0 105.15 9\n", - "58 First Kill 7.952 270.0 104.61 3\n", - "59 The Chestnut Man 7.584 455.0 104.21 5\n", - "60 Forecasting Love and Weather 7.631 61.0 100.85 8\n", - "61 Money Heist: Korea - Joint Economic Area 7.900 601.0 98.37 3\n", - "62 True Story 7.171 82.0 90.82 3\n", - "63 Control Z 8.227 2242.0 89.83 3\n", - "64 The Silent Sea 7.800 527.0 89.77 3\n", - "65 Another Self 7.400 30.0 88.59 4\n", - "66 On My Block 8.461 618.0 80.88 2\n", - "67 Turning Point: 9/11 and the War on Terror 7.287 83.0 80.68 3\n", - "68 The Seven Deadly Sins: Cursed by Light 8.462 4710.0 79.51 4\n", - "69 Hospital Playlist 8.490 100.0 72.06 7\n", - "70 I Am Georgina 7.111 293.0 71.29 3" + " 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": [ + "" ] }, "metadata": {}, @@ -4135,237 +4324,1068 @@ } ], "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", - "# 1. 讀取 Netflix 複合資料集\n", - "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\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", - "# 2. 排除沒有配對到 TMDb 或沒有評分數據的影劇，以計算乾淨的 P50 門檻\n", - "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\n", "\n", - "# 3. 計算三大核心指標的篩選門檻\n", - "tmdb_rating_p50 = df_valid_tmdb[\"tmdb_vote_average\"].median() # TMDb 評分前 50% (中位數)\n", - "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median() # TMDb 投票數前 50% (中位數)\n", - "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean() # Netflix 觀看時數平均值\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", - "# 4. 套用三重條件進行「The Hit」篩選\n", - "hit_condition = (\n", - " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", - " (df[\"tmdb_vote_count\"] >= tmdb_votes_p50) &\n", - " (df[\"netflix_viewing_hours\"] > netflix_hours_mean)\n", - ")\n", "\n", - "df_hits = df[hit_condition].copy()\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", - "# 5. 數據單位轉換\n", - "df_hits[\"netflix_viewing_hours_million\"] = (df_hits[\"netflix_viewing_hours\"] / 1000000).round(2)\n", "\n", - "# 6. 依觀看時數由高到低（降冪）排序\n", - "df_hits_sorted = df_hits.sort_values(by=\"netflix_viewing_hours\", ascending=False).reset_index(drop=True)\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", - "# 7. 顯示設定優化：強制 Pandas 展開所有 Rows 與 Columns，不出現任何「...」\n", - "pd.set_option('display.max_rows', None) # 不省略任何列 (Rows)\n", - "pd.set_option('display.max_columns', None) # 不省略任何欄 (Columns)\n", - "pd.set_option('display.width', 1000) # 避免寬度不夠自動換行\n", "\n", - "# 選擇要展示的欄位，並將欄位改為中文名稱\n", - "display_cols = {\n", - " \"netflix_title\": \"影劇名稱\",\n", - " \"tmdb_vote_average\": \"TMDb 評分\",\n", - " \"tmdb_vote_count\": \"TMDb 投票數\",\n", - " \"netflix_viewing_hours_million\": \"觀看時數 (百萬小時)\",\n", - " \"netflix_weeks\": \"上榜週數\"\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", - "# 8. 印出篩選報告與完整清單\n", - "print(\"=\" * 80)\n", - "print(\"⚙️ 【The Hit】黃金篩選門檻定義：\")\n", - "print(\"-\" * 80)\n", - "print(f\"▸ 1. TMDb 評分門檻 (前 50% 中位數) >= {tmdb_rating_p50:.2f} 分\")\n", - "print(f\"▸ 2. TMDb 投票數門檻 (前 50% 中位數)>= {int(tmdb_votes_p50)} 票\")\n", - "print(f\"▸ 3. Netflix 觀看時數門檻 (全體平均) > {netflix_hours_mean / 1000000:.2f} 百萬小時\")\n", - "print(\"=\" * 80)\n", - "print(f\"🎉 篩選完成！在 {len(df)} 部影劇中，共有 {len(df_hits_sorted)} 部完全符合條件：\")\n", - "print(\"=\" * 80)\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", - "# 直接呼叫與顯示 DataFrame (在 Jupyter 中會渲染成精美的無省略 HTML 表格)\n", - "display(df_hits_sorted[list(display_cols.keys())].rename(columns=display_cols))" - ] - }, - { - "cell_type": "markdown", - "id": "d8caa9b0", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, - { - "cell_type": "markdown", - "id": "e07f5503", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1c4b8cc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================================================================\n", - "📊 【The Hit】篩選結果：共找到 71 部影集\n", - "================================================================================\n", - "\n", - "🔎 【屬性總表 (Attributes Schema)】\n", - "--------------------------------------------------------------------------------\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \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", + "？？" + ] + }, + { + "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": [ + "
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資料型態 (Dtype)非空值數量 (Non-Null)缺失值數量 (Null)缺失值比例 (%)
keyobject7100.0%
netflix_viewing_hoursint647100.0%
netflix_weeksint647100.0%
netflix_year_hintint647100.0%
netflix_titleobject7100.0%
netflix_clean_titleobject7100.0%
tmdb_titleobject7100.0%
tmdb_popularityfloat647100.0%
tmdb_vote_averagefloat647100.0%
tmdb_vote_countfloat647100.0%
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Netflix viewing hoursTMDb ratingTMDb vote count
imdb_titleobject7100.0%Netflix viewing hours1.0000.1690.522
imdb_averageRatingfloat64472433.8%TMDb rating0.1691.0000.440
imdb_numVotesfloat64472433.8%TMDb vote count0.5220.4401.000
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" ], "text/plain": [ - " 資料型態 (Dtype) 非空值數量 (Non-Null) 缺失值數量 (Null) 缺失值比例 (%)\n", - "key object 71 0 0.0%\n", - "netflix_viewing_hours int64 71 0 0.0%\n", - "netflix_weeks int64 71 0 0.0%\n", - "netflix_year_hint int64 71 0 0.0%\n", - "netflix_title object 71 0 0.0%\n", - "netflix_clean_title object 71 0 0.0%\n", - "tmdb_title object 71 0 0.0%\n", - "tmdb_popularity float64 71 0 0.0%\n", - "tmdb_vote_average float64 71 0 0.0%\n", - "tmdb_vote_count float64 71 0 0.0%\n", - "imdb_title object 71 0 0.0%\n", - "imdb_averageRating float64 47 24 33.8%\n", - "imdb_numVotes float64 47 24 33.8%" + " 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": {}, @@ -4376,8 +5396,9 @@ "output_type": "stream", "text": [ "\n", - "🎬 【這 71 部影集的所有屬性完整資料】 (依觀看時數降冪排序)\n", - "--------------------------------------------------------------------------------\n" + "==========================================================================================\n", + "PC1 loadings and relative contributions\n", + "==========================================================================================\n" ] }, { @@ -4401,1235 +5422,869 @@ " \n", " \n", " \n", - " key\n", - " netflix_viewing_hours\n", - " netflix_weeks\n", - " netflix_year_hint\n", - " netflix_title\n", - " netflix_clean_title\n", - " tmdb_title\n", - " tmdb_popularity\n", - " tmdb_vote_average\n", - " tmdb_vote_count\n", - " imdb_title\n", - " imdb_averageRating\n", - " imdb_numVotes\n", + " Feature\n", + " PC1_loading\n", + " Squared_loading\n", + " Relative_contribution_percent\n", " \n", " \n", " \n", " \n", " 0\n", - " st_th\n", - " 2874260000\n", - " 13\n", - " 2022\n", - " Stranger Things\n", - " stranger things\n", - " Stranger Things\n", - " 185.711\n", - " 8.624\n", - " 16161.0\n", - " Stranger Things\n", - " NaN\n", - " NaN\n", + " TMDb vote count\n", + " 0.6626\n", + " 0.4390\n", + " 43.9014\n", " \n", " \n", " 1\n", - " ga_sq\n", - " 2289500000\n", - " 20\n", - " 2021\n", - " Squid Game\n", - " squid game\n", - " Squid Game\n", - " 115.587\n", - " 7.831\n", - " 13053.0\n", - " Squid Game!\n", - " NaN\n", - " NaN\n", + " Netflix viewing hours\n", + " 0.5578\n", + " 0.3111\n", + " 31.1142\n", " \n", " \n", " 2\n", - " ma\n", - " 1446260000\n", - " 16\n", - " 2021\n", - " Manifest\n", - " manifest\n", - " Manifest\n", - " 111.411\n", - " 7.728\n", - " 1312.0\n", - " Manifest\n", - " NaN\n", - " NaN\n", + " TMDb rating\n", + " 0.4998\n", + " 0.2498\n", + " 24.9844\n", + " \n", + " \n", + "\n", + "" + ], + "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": [ + "
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Principal_componentExplained_variance_percent
3he_mo1170200000142021Money Heistmoney heist0PC159.03
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2PC313.17
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Hit RankTitleHit Composite ScoreViewing Hours (Million)TMDb RatingTMDb Vote Count
01Stranger Things100.002874.268.62416161.0
12Money Heist96.35484.491170.208.25717836.0Money Heist8.2612436.0
23Squid Game82.902289.507.83113053.0
34The Umbrella Academy74.10414.638.6048891.0
4br1108800000112021BridgertonbridgertonBridgerton71.3088.1432045.0Bridgerton4.157.05Sex Education70.62561.368.3046647.0
5ar_ca_co793740000252022Café con aroma de mujercaf con aroma de mujerCafé con Aroma de Mujer20.7107.500382.0Café con aroma de mujer8.6106.06All of Us Are Dead68.76659.518.3543335.0
6oz751600000132022OzarkozarkOzark68.0068.2441968.0Ozark8.4394904.07Bridgerton67.901108.808.1432045.0
7al_ar_us659510000112022All of Us Are Deadall of us are deadAll of Us Are Dead107.1088.3543335.0All of Us Are Dead7.692709.08Ozark64.97751.608.2441968.0
8ri_vi62425000062021Virgin Rivervirgin riverVirgin River54.1928.032448.0Virgin River7.460100.09Peaky Blinders64.08185.348.5468836.0
9ed_se56136000062021Sex Educationsex educationSex Education1008.9778.3046647.0Sex Education7.225.010Cobra Kai64.00383.648.2205734.0
10fl_qu561230000162021The Queen of Flowthe queen of flowThe Queen of Flow129.3237.9831391.0The Queen of Flow7.52076.011Elite62.61329.298.0718817.0
11ac_um41463000052022The Umbrella Academythe umbrella academyThe Umbrella Academy51.8598.6048891.0The Umbrella Academy7.074.012Manifest61.101446.267.7281312.0
12at_ex_wo40403000072022Extraordinary Attorney Wooextraordinary attorney wooExtraordinary Attorney Woo42.9238.482585.0Extraordinary Attorney Woo8.538985.013The Good Doctor59.65112.588.50311768.0
13co_ka38364000042022Cobra Kaicobra kaiCobra Kai118.2568.2205734.0Cobra Kai6.936.014Arcane58.92154.278.7403341.0
14em_pa34163000052021Emily in Parisemily in parisEmily in Paris33.6597.7751152.0Emily in Paris6.8143411.015The Queen of Flow55.11561.237.9831391.0
15el32929000052021EliteeliteElite100.7118.0718817.0Elite8.4112.016Extraordinary Attorney Woo54.08404.038.482585.0
16la_li30527000062022The Lincoln Lawyerthe lincoln lawyerThe Lincoln Lawyer52.3957.806183.0The Lincoln LawyerNaNNaN17Never Have I Ever51.70297.788.1611557.0
17ch_ch_ho300580000162021Hometown Cha-Cha-Chahometown cha cha chaHometown Cha-Cha-Cha35.3678.233442.0Hometown Cha-Cha-Cha8.329770.018The Sandman49.84274.228.0641726.0
18ha_i_ne29778000042021Never Have I Evernever have i everNever Have I Ever80.5468.1611557.0Never Have I EverNaNNaN19Virgin River49.75624.258.032448.0
19li_se28210000072021Sex/Lifesex lifeSex/Life124.1457.1381200.0Sex LifeNaNNaN20The Seven Deadly Sins: Cursed by Light49.1179.518.4624710.0
20bu_pr279110000132022Business Proposalbusiness proposalBusiness Proposal88.5338.422281.0Business ProposalNaNNaN21The Last Kingdom48.48189.578.2901485.0
21ke_lo27838000052021Locke & Keylocke key22Emily in Paris44.87341.637.7751152.0
2223Hometown Cha-Cha-Cha44.82300.588.233442.0
2324Locke & Key27.01744.43278.387.9031104.0Locke&KeyNaNNaN
22sa27422000032022The Sandmanthe sandmanThe Sandman64.1688.0641726.0The Sandman5.25.0
23he_ma27275000072022The Marked Heartthe marked heartThe Marked Heart35.5267.817232.0The Marked Heart6.64799.0
24va_vi26555000052022Vikings: Valhallavikings valhallaVikings: Valhalla59.2327.851671.0Vikings: Valhalla7.471704.02425Business Proposal44.19279.118.422281.0
25bl_is_lo25689000052021Love Is Blindlove is blindLove Is Blind46.5587.10054.0Love is BlindNaNNaN26My Name43.88194.148.283715.0
26da_de20294000052022Dark Desiredark desireDark Desire90.5537.3004181.0Dark Desire8.011.027Café con aroma de mujer42.58793.747.500382.0
27hu19831000062022HustlehustleHustle30.7387.708137.0HustleNaNNaN28Control Z41.7589.838.2272242.0
28my_na19414000052021My Namemy nameMy Name25.5258.283715.0My Name7.845026.029Alchemy of Souls41.22127.698.622411.0
29ki_la18957000052022The Last Kingdomthe last kingdomThe Last Kingdom143.7948.2901485.0The Last Kingdom8.413.030Vikings: Valhalla39.80265.557.851671.0
30bl_pe18534000052022Peaky Blinderspeaky blindersPeaky Blinders344.4778.5468836.0Peaky Blinders8.7783155.031Dark Desire39.54202.947.3004181.0
31lo_sp18518000042021Lost in Spacelost in spaceLost in Space412.0317.11596.0Lost in SpaceNaNNaN32All American38.89180.668.245414.0
32ca183040000102021CartercarterCarter11.5527.40030.0CarterNaNNaN33Pasión de Gavilanes37.78164.187.6411925.0
33ma_sw18294000052022Sweet Magnoliassweet magnoliasSweet Magnolias25.8317.800206.0Sweet Magnolias7.328500.034Narcos: Mexico37.57138.987.9391160.0
34al_am18066000052021All Americanall americanAll American140.6948.245414.0All AmericanNaNNaN35On My Block36.0880.888.461618.0
35fi_ju17785000062021The Five Juanasthe five juanasThe Five Juanas18.6727.419246.0The Five Juanas6.92615.036Twenty Five Twenty One34.83171.618.59798.0
36al17564000062022AlbaalbaAlba12.8407.000167.0AlbaNaNNaN37Good Girls34.28153.147.998509.0
37di_ra17287000042022Raising Dionraising dionRaising Dion19.0937.696388.0Raising Dion8.183.038The King's Affection33.92138.548.351232.0
38fi_tw_tw171610000102022Twenty Five Twenty Onetwenty five twenty oneTwenty Five Twenty One31.7308.59798.0Twenty Five Twenty One8.622729.039Who Killed Sara?32.78143.617.6381132.0
39an_sc17102000052022Anatomy of a Scandalanatomy of a scandalAnatomy of a Scandal13.1977.071120.0Anatomy of a Scandal7.040449.040The Marked Heart32.64272.757.817232.0
40de_n_pa16418000052022Pasión de Gavilanespasi n de gavilanesPasión de Gavilanes283.1977.6411925.0Pasión de sangreNaNNaN41Sex/Life32.42282.107.1381200.0
41co16163000052021CollisioncollisionCollision5.7957.30026.0CollisionNaNNaN42The Lincoln Lawyer32.18305.277.806183.0
42se_su15878000042021Selling Sunsetselling sunsetSelling Sunset9.4597.10039.0Selling Sunset6.59335.043Raising Dion28.86172.877.696388.0
43ar15427000062021ArcanearcaneArcane42.4478.7403341.0ArcaneNaNNaN44Money Heist: Korea - Joint Economic Area28.7898.377.900601.0
44gi_go15314000062021Good Girlsgood girlsGood Girls52.3527.998509.0Good Girls8.2668.045Our Beloved Summer28.21128.898.50074.0
45ki_sa_wh14361000042022Who Killed Sara?who killed saraWho Killed Sara?18.1577.6381132.0Who Killed Sara?6.417647.046Our Blues27.44105.158.80043.0
46ma_mi14228000042021Midnight Massmidnight mass47Midnight Mass21.99927.33142.287.525664.0Midnight Mass7.430.0
47ed_we14034000052022Welcome to Edenwelcome to edenWelcome to Eden40.4827.090233.0Welcome to EdenNaNNaN48Sweet Magnolias27.12182.947.800206.0
48me_na13898000042021Narcos: Mexiconarcos mexicoNarcos: Mexico47.4767.9391160.0Narcos: Mexico8.3102053.049Sintonia27.09109.438.200179.0
49af_ki_s138540000102021The King's Affectionthe king s affectionThe King's Affection69.8198.351232.0The King's Affection8.08733.050The Silent Sea25.2489.777.800527.0
50ju_ju13456000072022Juvenile Justicejuvenile justiceJuvenile Justice14.2697.94785.0Juvenile JusticeNaNNaN51First Kill25.14104.617.952270.0
5181_ar12947000032022Archive 81archive 81Archive 8120.0887.219442.0Archive 817.368254.052The Snitch Cartel: Origins25.11112.147.885283.0
52be_ou_su12889000082021Our Beloved Summerour beloved summerOur Beloved Summer30.4568.50074.0Our Beloved Summer8.4252.053Hustle23.85198.317.708137.0
53al_so12769000082022Alchemy of Soulsalchemy of soulsAlchemy of Souls61.1728.622411.0Alchemy of Souls8.729759.054Hospital Playlist23.4772.068.490100.0
54do_go11258000062021The Good Doctorthe good doctorThe Good Doctor681.6148.50311768.0The Good DoctorNaNNaN55The Chestnut Man22.37104.217.584455.0
55ca_or_sn11214000062021The Snitch Cartel: Originsthe snitch cartel originsThe Snitch Cartel: Origins42.0837.885283.0The Snitch Cartel: Origins6.0321.056The Five Juanas21.63177.857.419246.0
56si10943000022021SintoniasintoniaSintonia22.3778.200179.0SintoniaNaNNaN57Juvenile Justice20.40134.567.94785.0
57bl_ou10515000092022Our Bluesour bluesOur Blues12.9808.80043.0Our Blues8.65554.058Archive 8118.55129.477.219442.0
58fi_ki10461000032021First Killfirst killFirst Kill21.1477.952270.0First KillNaNNaN59Welcome to Eden13.16140.347.090233.0
59ch_ma10421000052021The Chestnut Manthe chestnut manThe Chestnut Man15.7507.584455.0The Chestnut Man7.668351.060Alba12.02175.647.000167.0
60fo_lo_we10085000082022Forecasting Love and Weatherforecasting love weatherForecasting Love and Weather16.9767.63161.0Forecasting Love and Weather7.15578.061Lost in Space10.95185.187.11596.0
61he_ko_mo9837000032022Money Heist: Korea - Joint Economic Areamoney heist korea joint economic areaMoney Heist: Korea - Joint Economic Area46.2927.900601.0Money Heist: Korea - Joint Economic Area5.910696.062Anatomy of a Scandal10.77171.027.071120.0
62st_tr9082000032021True Storytrue storyTrue Story15.6957.17182.0True StoryNaNNaN63Love Is Blind10.67256.897.10054.0
63co_z8983000032021Control Zcontrol zControl Z20.7088.2272242.0Control Z6.812606.064Forecasting Love and Weather9.80100.857.63161.0
64se_si8977000032021The Silent Seathe silent seaThe Silent Sea25.0697.800527.0The Silent SeaNaNNaN65Carter8.13183.047.40030.0
65an_se8859000042022Another Selfanother selfAnother Self10.5977.40030.0Another Self7.326500.066I Am Georgina7.4171.297.111293.0
66bl_my8088000022021On My Blockon my blockOn My Block16.9798.461618.0On My Block7.029.067Collision4.17161.637.30026.0
679_po_tu8068000032021Turning Point: 9/11 and the War on Terrorturning point 9 11 the war on terror68Turning Point: 9/11 and the War on Terror9.1943.5680.687.28783.0Turning Point: 9/11 and the War on Terror7.913266.0
68de_se_si7951000042021The Seven Deadly Sins: Cursed by Lightthe seven deadly sins cursed by lightThe Seven Deadly Sins10.0598.4624710.0The Seven Deadly Sins: Cursed by Light6.41955.069Selling Sunset3.21158.787.10039.0
69ho_pl7206000072021Hospital Playlisthospital playlistHospital Playlist21.8268.490100.0Hospital Playlist8.711014.070True Story2.8990.827.17182.0
70am_ge_i7129000032022I Am Georginai am georginaI Am Georgina11.5607.111293.0I Am Georgina4.14667.071Another Self0.0088.597.40030.0
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" ], "text/plain": [ - " key netflix_viewing_hours netflix_weeks netflix_year_hint netflix_title netflix_clean_title tmdb_title tmdb_popularity tmdb_vote_average tmdb_vote_count imdb_title imdb_averageRating imdb_numVotes\n", - "0 st_th 2874260000 13 2022 Stranger Things stranger things Stranger Things 185.711 8.624 16161.0 Stranger Things NaN NaN\n", - "1 ga_sq 2289500000 20 2021 Squid Game squid game Squid Game 115.587 7.831 13053.0 Squid Game! NaN NaN\n", - "2 ma 1446260000 16 2021 Manifest manifest Manifest 111.411 7.728 1312.0 Manifest NaN NaN\n", - "3 he_mo 1170200000 14 2021 Money Heist money heist Money Heist 96.354 8.257 17836.0 Money Heist 8.2 612436.0\n", - "4 br 1108800000 11 2021 Bridgerton bridgerton Bridgerton 71.308 8.143 2045.0 Bridgerton 4.1 57.0\n", - "5 ar_ca_co 793740000 25 2022 Café con aroma de mujer caf con aroma de mujer Café con Aroma de Mujer 20.710 7.500 382.0 Café con aroma de mujer 8.6 106.0\n", - "6 oz 751600000 13 2022 Ozark ozark Ozark 68.006 8.244 1968.0 Ozark 8.4 394904.0\n", - "7 al_ar_us 659510000 11 2022 All of Us Are Dead all of us are dead All of Us Are Dead 107.108 8.354 3335.0 All of Us Are Dead 7.6 92709.0\n", - "8 ri_vi 624250000 6 2021 Virgin River virgin river Virgin River 54.192 8.032 448.0 Virgin River 7.4 60100.0\n", - "9 ed_se 561360000 6 2021 Sex Education sex education Sex Education 1008.977 8.304 6647.0 Sex Education 7.2 25.0\n", - "10 fl_qu 561230000 16 2021 The Queen of Flow the queen of flow The Queen of Flow 129.323 7.983 1391.0 The Queen of Flow 7.5 2076.0\n", - "11 ac_um 414630000 5 2022 The Umbrella Academy the umbrella academy The Umbrella Academy 51.859 8.604 8891.0 The Umbrella Academy 7.0 74.0\n", - "12 at_ex_wo 404030000 7 2022 Extraordinary Attorney Woo extraordinary attorney woo Extraordinary Attorney Woo 42.923 8.482 585.0 Extraordinary Attorney Woo 8.5 38985.0\n", - "13 co_ka 383640000 4 2022 Cobra Kai cobra kai Cobra Kai 118.256 8.220 5734.0 Cobra Kai 6.9 36.0\n", - "14 em_pa 341630000 5 2021 Emily in Paris emily in paris Emily in Paris 33.659 7.775 1152.0 Emily in Paris 6.8 143411.0\n", - "15 el 329290000 5 2021 Elite elite Elite 100.711 8.071 8817.0 Elite 8.4 112.0\n", - "16 la_li 305270000 6 2022 The Lincoln Lawyer the lincoln lawyer The Lincoln Lawyer 52.395 7.806 183.0 The Lincoln Lawyer NaN NaN\n", - "17 ch_ch_ho 300580000 16 2021 Hometown Cha-Cha-Cha hometown cha cha cha Hometown Cha-Cha-Cha 35.367 8.233 442.0 Hometown Cha-Cha-Cha 8.3 29770.0\n", - "18 ha_i_ne 297780000 4 2021 Never Have I Ever never have i ever Never Have I Ever 80.546 8.161 1557.0 Never Have I Ever NaN NaN\n", - "19 li_se 282100000 7 2021 Sex/Life sex life Sex/Life 124.145 7.138 1200.0 Sex Life NaN NaN\n", - "20 bu_pr 279110000 13 2022 Business Proposal business proposal Business Proposal 88.533 8.422 281.0 Business Proposal NaN NaN\n", - "21 ke_lo 278380000 5 2021 Locke & Key locke key Locke & Key 27.017 7.903 1104.0 Locke&Key NaN NaN\n", - "22 sa 274220000 3 2022 The Sandman the sandman The Sandman 64.168 8.064 1726.0 The Sandman 5.2 5.0\n", - "23 he_ma 272750000 7 2022 The Marked Heart the marked heart The Marked Heart 35.526 7.817 232.0 The Marked Heart 6.6 4799.0\n", - "24 va_vi 265550000 5 2022 Vikings: Valhalla vikings valhalla Vikings: Valhalla 59.232 7.851 671.0 Vikings: Valhalla 7.4 71704.0\n", - "25 bl_is_lo 256890000 5 2021 Love Is Blind love is blind Love Is Blind 46.558 7.100 54.0 Love is Blind NaN NaN\n", - "26 da_de 202940000 5 2022 Dark Desire dark desire Dark Desire 90.553 7.300 4181.0 Dark Desire 8.0 11.0\n", - "27 hu 198310000 6 2022 Hustle hustle Hustle 30.738 7.708 137.0 Hustle NaN NaN\n", - "28 my_na 194140000 5 2021 My Name my name My Name 25.525 8.283 715.0 My Name 7.8 45026.0\n", - "29 ki_la 189570000 5 2022 The Last Kingdom the last kingdom The Last Kingdom 143.794 8.290 1485.0 The Last Kingdom 8.4 13.0\n", - "30 bl_pe 185340000 5 2022 Peaky Blinders peaky blinders Peaky Blinders 344.477 8.546 8836.0 Peaky Blinders 8.7 783155.0\n", - "31 lo_sp 185180000 4 2021 Lost in Space lost in space Lost in Space 412.031 7.115 96.0 Lost in Space NaN NaN\n", - "32 ca 183040000 10 2021 Carter carter Carter 11.552 7.400 30.0 Carter NaN NaN\n", - "33 ma_sw 182940000 5 2022 Sweet Magnolias sweet magnolias Sweet Magnolias 25.831 7.800 206.0 Sweet Magnolias 7.3 28500.0\n", - "34 al_am 180660000 5 2021 All American all american All American 140.694 8.245 414.0 All American NaN NaN\n", - "35 fi_ju 177850000 6 2021 The Five Juanas the five juanas The Five Juanas 18.672 7.419 246.0 The Five Juanas 6.9 2615.0\n", - "36 al 175640000 6 2022 Alba alba Alba 12.840 7.000 167.0 Alba NaN NaN\n", - "37 di_ra 172870000 4 2022 Raising Dion raising dion Raising Dion 19.093 7.696 388.0 Raising Dion 8.1 83.0\n", - "38 fi_tw_tw 171610000 10 2022 Twenty Five Twenty One twenty five twenty one Twenty Five Twenty One 31.730 8.597 98.0 Twenty Five Twenty One 8.6 22729.0\n", - "39 an_sc 171020000 5 2022 Anatomy of a Scandal anatomy of a scandal Anatomy of a Scandal 13.197 7.071 120.0 Anatomy of a Scandal 7.0 40449.0\n", - "40 de_n_pa 164180000 5 2022 Pasión de Gavilanes pasi n de gavilanes Pasión de Gavilanes 283.197 7.641 1925.0 Pasión de sangre NaN NaN\n", - "41 co 161630000 5 2021 Collision collision Collision 5.795 7.300 26.0 Collision NaN NaN\n", - "42 se_su 158780000 4 2021 Selling Sunset selling sunset Selling Sunset 9.459 7.100 39.0 Selling Sunset 6.5 9335.0\n", - "43 ar 154270000 6 2021 Arcane arcane Arcane 42.447 8.740 3341.0 Arcane NaN NaN\n", - "44 gi_go 153140000 6 2021 Good Girls good girls Good Girls 52.352 7.998 509.0 Good Girls 8.2 668.0\n", - "45 ki_sa_wh 143610000 4 2022 Who Killed Sara? who killed sara Who Killed Sara? 18.157 7.638 1132.0 Who Killed Sara? 6.4 17647.0\n", - "46 ma_mi 142280000 4 2021 Midnight Mass midnight mass Midnight Mass 21.999 7.525 664.0 Midnight Mass 7.4 30.0\n", - "47 ed_we 140340000 5 2022 Welcome to Eden welcome to eden Welcome to Eden 40.482 7.090 233.0 Welcome to Eden NaN NaN\n", - "48 me_na 138980000 4 2021 Narcos: Mexico narcos mexico Narcos: Mexico 47.476 7.939 1160.0 Narcos: Mexico 8.3 102053.0\n", - "49 af_ki_s 138540000 10 2021 The King's Affection the king s affection The King's Affection 69.819 8.351 232.0 The King's Affection 8.0 8733.0\n", - "50 ju_ju 134560000 7 2022 Juvenile Justice juvenile justice Juvenile Justice 14.269 7.947 85.0 Juvenile Justice NaN NaN\n", - "51 81_ar 129470000 3 2022 Archive 81 archive 81 Archive 81 20.088 7.219 442.0 Archive 81 7.3 68254.0\n", - "52 be_ou_su 128890000 8 2021 Our Beloved Summer our beloved summer Our Beloved Summer 30.456 8.500 74.0 Our Beloved Summer 8.4 252.0\n", - "53 al_so 127690000 8 2022 Alchemy of Souls alchemy of souls Alchemy of Souls 61.172 8.622 411.0 Alchemy of Souls 8.7 29759.0\n", - "54 do_go 112580000 6 2021 The Good Doctor the good doctor The Good Doctor 681.614 8.503 11768.0 The Good Doctor NaN NaN\n", - "55 ca_or_sn 112140000 6 2021 The Snitch Cartel: Origins the snitch cartel origins The Snitch Cartel: Origins 42.083 7.885 283.0 The Snitch Cartel: Origins 6.0 321.0\n", - "56 si 109430000 2 2021 Sintonia sintonia Sintonia 22.377 8.200 179.0 Sintonia NaN NaN\n", - "57 bl_ou 105150000 9 2022 Our Blues our blues Our Blues 12.980 8.800 43.0 Our Blues 8.6 5554.0\n", - "58 fi_ki 104610000 3 2021 First Kill first kill First Kill 21.147 7.952 270.0 First Kill NaN NaN\n", - "59 ch_ma 104210000 5 2021 The Chestnut Man the chestnut man The Chestnut Man 15.750 7.584 455.0 The Chestnut Man 7.6 68351.0\n", - "60 fo_lo_we 100850000 8 2022 Forecasting Love and Weather forecasting love weather Forecasting Love and Weather 16.976 7.631 61.0 Forecasting Love and Weather 7.1 5578.0\n", - "61 he_ko_mo 98370000 3 2022 Money Heist: Korea - Joint Economic Area money heist korea joint economic area Money Heist: Korea - Joint Economic Area 46.292 7.900 601.0 Money Heist: Korea - Joint Economic Area 5.9 10696.0\n", - "62 st_tr 90820000 3 2021 True Story true story True Story 15.695 7.171 82.0 True Story NaN NaN\n", - "63 co_z 89830000 3 2021 Control Z control z Control Z 20.708 8.227 2242.0 Control Z 6.8 12606.0\n", - "64 se_si 89770000 3 2021 The Silent Sea the silent sea The Silent Sea 25.069 7.800 527.0 The Silent Sea NaN NaN\n", - "65 an_se 88590000 4 2022 Another Self another self Another Self 10.597 7.400 30.0 Another Self 7.3 26500.0\n", - "66 bl_my 80880000 2 2021 On My Block on my block On My Block 16.979 8.461 618.0 On My Block 7.0 29.0\n", - "67 9_po_tu 80680000 3 2021 Turning Point: 9/11 and the War on Terror turning point 9 11 the war on terror Turning Point: 9/11 and the War on Terror 9.194 7.287 83.0 Turning Point: 9/11 and the War on Terror 7.9 13266.0\n", - "68 de_se_si 79510000 4 2021 The Seven Deadly Sins: Cursed by Light the seven deadly sins cursed by light The Seven Deadly Sins 10.059 8.462 4710.0 The Seven Deadly Sins: Cursed by Light 6.4 1955.0\n", - "69 ho_pl 72060000 7 2021 Hospital Playlist hospital playlist Hospital Playlist 21.826 8.490 100.0 Hospital Playlist 8.7 11014.0\n", - "70 am_ge_i 71290000 3 2022 I Am Georgina i am georgina I Am Georgina 11.560 7.111 293.0 I Am Georgina 4.1 4667.0" + " 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": {}, @@ -5638,59 +6293,313 @@ ], "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", - "# 1. 讀取資料集 (請確保此路徑與你之前的步驟一致)\n", - "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\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", - "# 2. 計算篩選門檻 (TMDb 評分前 50%, TMDb 投票數前 50%, 觀看時數大於平均值)\n", - "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\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", - "tmdb_rating_p50 = df_valid_tmdb[\"tmdb_vote_average\"].median()\n", - "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median()\n", - "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean()\n", + "# ------------------------------------------------------------------------------\n", + "# B. 對高度右偏的變數做 log 轉換\n", + "# ------------------------------------------------------------------------------\n", "\n", - "# 3. 篩選出這 71 部 The Hit 影集\n", - "hit_condition = (\n", - " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", - " (df[\"tmdb_vote_count\"] >= tmdb_votes_p50) &\n", - " (df[\"netflix_viewing_hours\"] > netflix_hours_mean)\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", - "df_hits = df[hit_condition].copy()\n", "\n", - "# 4. 關鍵設定：強制 Pandas 展開所有欄位與資料，絕不使用 \"...\" 省略\n", - "pd.set_option('display.max_columns', None)\n", - "pd.set_option('display.max_rows', None)\n", - "pd.set_option('display.width', 1000)\n", + "df_hit_rank[\"log_tmdb_vote_count\"] = np.log1p(\n", + " df_hit_rank[\"tmdb_vote_count\"]\n", + ")\n", "\n", - "print(\"=\" * 80)\n", - "print(f\"📊 【The Hit】篩選結果：共找到 {len(df_hits)} 部影集\")\n", - "print(\"=\" * 80)\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", - "# ─── 步驟 A：輸出這 71 部影集擁有的「所有屬性（欄位）總表」 ───\n", - "print(\"\\n🔎 【屬性總表 (Attributes Schema)】\")\n", - "print(\"-\" * 80)\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", - "# 建立屬性分析表\n", - "schema_df = pd.DataFrame({\n", - " \"資料型態 (Dtype)\": df_hits.dtypes,\n", - " \"非空值數量 (Non-Null)\": df_hits.notnull().sum(),\n", - " \"缺失值數量 (Null)\": df_hits.isnull().sum(),\n", - " \"缺失值比例 (%)\": (df_hits.isnull().sum() / len(df_hits) * 100).round(2).astype(str) + \"%\"\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", - "display(schema_df)\n", "\n", - "# ─── 步驟 B：完整展示這 71 部影集的所有欄位數據（不省略） ───\n", - "print(\"\\n🎬 【這 71 部影集的所有屬性完整資料】 (依觀看時數降冪排序)\")\n", - "print(\"-\" * 80)\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", - "# 將觀看時數降冪排序，並在 Jupyter 中渲染成完整的 HTML 表格\n", - "df_hits_sorted = df_hits.sort_values(by=\"netflix_viewing_hours\", ascending=False).reset_index(drop=True)\n", - "display(df_hits_sorted)" + "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": "0f234f7a", + "id": "f08fa29d", "metadata": {}, "source": [ "## our analysis\n", @@ -5699,18 +6608,27 @@ }, { "cell_type": "code", - "execution_count": 54, - "id": "2a4af732", + "execution_count": 61, + "id": "c980ccfd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "========================================================================================================================\n", - "🚀 【The Hit 影集】 7 大核心屬性 Min-Max 標準化分數總表\n", - " * 數值範圍皆在 0.0 ~ 1.0 之間（1.0 代表該屬性在 71 部影集中表現最強，0.0 為最弱）\n", - "========================================================================================================================\n" + "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" ] }, { @@ -5734,1812 +6652,6578 @@ " \n", " \n", " \n", - " 影劇名稱\n", - " 觀看時數_Norm\n", - " 上榜週數_Norm\n", - " TMDb熱度_Norm\n", - " TMDb評分_Norm\n", - " TMDb投票數_Norm\n", - " IMDb評分_Norm\n", - " IMDb投票數_Norm\n", + " Hit_rank\n", + " Hit_score\n", + " PCA_Hit_original\n", + " key\n", + " netflix_viewing_hours\n", + " netflix_weeks\n", + " netflix_year_hint\n", + " netflix_title\n", + " netflix_clean_title\n", + " tmdb_title\n", + " tmdb_popularity\n", + " tmdb_vote_average\n", + " tmdb_vote_count\n", + " imdb_title\n", + " imdb_averageRating\n", + " imdb_numVotes\n", + " TMDb_raw_id\n", + " TMDb_raw_name\n", + " TMDb_raw_number_of_seasons\n", + " TMDb_raw_number_of_episodes\n", + " TMDb_raw_original_language\n", + " TMDb_raw_vote_count\n", + " TMDb_raw_vote_average\n", + " TMDb_raw_overview\n", + " TMDb_raw_adult\n", + " TMDb_raw_backdrop_path\n", + " TMDb_raw_first_air_date\n", + " TMDb_raw_last_air_date\n", + " TMDb_raw_homepage\n", + " TMDb_raw_in_production\n", + " TMDb_raw_original_name\n", + " TMDb_raw_popularity\n", + " TMDb_raw_poster_path\n", + " TMDb_raw_type\n", + " TMDb_raw_status\n", + " TMDb_raw_tagline\n", + " TMDb_raw_genres\n", + " TMDb_raw_created_by\n", + " TMDb_raw_languages\n", + " TMDb_raw_networks\n", + " TMDb_raw_origin_country\n", + " TMDb_raw_spoken_languages\n", + " TMDb_raw_production_companies\n", + " TMDb_raw_production_countries\n", + " TMDb_raw_episode_run_time\n", + " netflix_viewing_hours_norm\n", + " netflix_weeks_norm\n", + " tmdb_vote_average_norm\n", + " tmdb_vote_count_norm\n", + " 綜合 The Hit 指數\n", + " log_viewing_hours\n", + " log_tmdb_vote_count\n", + " Viewing_hours_million\n", " \n", " \n", " \n", " \n", " 0\n", + " 1\n", + " 100.00\n", + " 3.8578\n", + " st_th\n", + " 2874260000\n", + " 13\n", + " 2022\n", + " Stranger Things\n", + " stranger things\n", + " Stranger Things\n", + " 185.711\n", + " 8.624\n", + " 16161.0\n", " Stranger Things\n", - " 1.0000\n", - " 0.4783\n", - " 0.1793\n", - " 0.9022\n", - " 0.9060\n", " NaN\n", " NaN\n", + " 66732\n", + " Stranger Things\n", + " 4\n", + " 34\n", + " en\n", + " 16161\n", + " 8.624\n", + " When a young boy vanishes, a small town uncovers a mystery involving secret experiments, terrifying supernatural forces, and one strange little girl.\n", + " False\n", + " /2MaumbgBlW1NoPo3ZJO38A6v7OS.jpg\n", + " 2016-07-15\n", + " 2022-07-01\n", + " https://www.netflix.com/title/80057281\n", + " True\n", + " Stranger Things\n", + " 185.711\n", + " /49WJfeN0moxb9IPfGn8AIqMGskD.jpg\n", + " Scripted\n", + " Returning Series\n", + " Every ending has a beginning.\n", + " Drama, Sci-Fi & Fantasy, Mystery\n", + " Matt Duffer, Ross Duffer\n", + " en\n", + " Netflix\n", + " US\n", + " English\n", + " 21 Laps Entertainment, Monkey Massacre Productions\n", + " United States of America\n", + " 0\n", + " 1.000000\n", + " 0.478261\n", + " 0.902222\n", + " 0.905952\n", + " 0.8522\n", + " 21.779061\n", + " 9.690418\n", + " 2874.26\n", " \n", " \n", " 1\n", - " Squid Game\n", - " 0.7914\n", - " 0.7826\n", - " 0.1094\n", - " 0.4617\n", - " 0.7314\n", - " NaN\n", - " NaN\n", + " 2\n", + " 84.49\n", + " 2.9029\n", + " he_mo\n", + " 1170200000\n", + " 14\n", + " 2021\n", + " Money Heist\n", + " money heist\n", + " Money Heist\n", + " 96.354\n", + " 8.257\n", + " 17836.0\n", + " Money Heist\n", + " 8.2\n", + " 612436.0\n", + " 71446\n", + " Money Heist\n", + " 3\n", + " 41\n", + " es\n", + " 17836\n", + " 8.257\n", + " 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", + " False\n", + " /gFZriCkpJYsApPZEF3jhxL4yLzG.jpg\n", + " 2017-05-02\n", + " 2021-12-03\n", + " https://www.netflix.com/title/80192098\n", + " False\n", + " La Casa de Papel\n", + " 96.354\n", + " /reEMJA1uzscCbkpeRJeTT2bjqUp.jpg\n", + " Scripted\n", + " Ended\n", + " The perfect robbery.\n", + " Crime, Drama\n", + " Álex Pina\n", + " es\n", + " Netflix, Antena 3\n", + " ES\n", + " Español\n", + " Vancouver Media\n", + " Spain\n", + " 70\n", + " 0.392052\n", + " 0.521739\n", + " 0.698333\n", + " 1.000000\n", + " 0.6011\n", + " 20.880441\n", + " 9.789030\n", + " 1170.20\n", " \n", " \n", " 2\n", - " Manifest\n", - " 0.4905\n", - " 0.6087\n", - " 0.1053\n", - " 0.4044\n", - " 0.0722\n", + " 3\n", + " 82.90\n", + " 2.8051\n", + " ga_sq\n", + " 2289500000\n", + " 20\n", + " 2021\n", + " Squid Game\n", + " squid game\n", + " Squid Game\n", + " 115.587\n", + " 7.831\n", + " 13053.0\n", + " Squid Game!\n", " NaN\n", " NaN\n", + " 93405\n", + " Squid Game\n", + " 2\n", + " 9\n", + " ko\n", + " 13053\n", + " 7.831\n", + " 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", + " False\n", + " /2meX1nMdScFOoV4370rqHWKmXhY.jpg\n", + " 2021-09-17\n", + " 2021-09-17\n", + " https://www.netflix.com/title/81040344\n", + " True\n", + " 오징어 게임\n", + " 115.587\n", + " /dDlEmu3EZ0Pgg93K2SVNLCjCSvE.jpg\n", + " Scripted\n", + " Returning Series\n", + " 45.6 billion won is child's play\n", + " Action & Adventure, Mystery, Drama\n", + " Hwang Dong-hyuk\n", + " en, ko, ur\n", + " Netflix\n", + " KR\n", + " English, 한국어/조선말, اردو\n", + " Siren Pictures, Firstman Studio\n", + " South Korea\n", + " 0\n", + " 0.791378\n", + " 0.782609\n", + " 0.461667\n", + " 0.731443\n", + " 0.6817\n", + " 21.551599\n", + " 9.476850\n", + " 2289.50\n", " \n", " \n", " 3\n", - " Money Heist\n", - " 0.3921\n", - " 0.5217\n", - " 0.0903\n", - " 0.6983\n", - " 1.0000\n", - " 0.8913\n", - " 0.7820\n", + " 4\n", + " 74.10\n", + " 2.2635\n", + " ac_um\n", + " 414630000\n", + " 5\n", + " 2022\n", + " The Umbrella Academy\n", + " the umbrella academy\n", + " The Umbrella Academy\n", + " 51.859\n", + " 8.604\n", + " 8891.0\n", + " The Umbrella Academy\n", + " 7.0\n", + " 74.0\n", + " 75006\n", + " The Umbrella Academy\n", + " 4\n", + " 32\n", + " en\n", + " 8891\n", + " 8.604\n", + " A dysfunctional family of superheroes comes together to solve the mystery of their father's death, the threat of the apocalypse and more.\n", + " False\n", + " /7sqFEDDmK1hG5m92upolcfQxy7R.jpg\n", + " 2019-02-15\n", + " 2022-06-22\n", + " https://www.netflix.com/title/80186863\n", + " True\n", + " The Umbrella Academy\n", + " 51.859\n", + " /qhcwrnnCnN8NE1N6XXKHFmveJR9.jpg\n", + " Scripted\n", + " Returning Series\n", + " Too many siblings. Not enough timeline.\n", + " Action & Adventure, Sci-Fi & Fantasy, Drama\n", + " Steve Blackman\n", + " en\n", + " Netflix\n", + " US\n", + " English\n", + " Dark Horse Entertainment, UCP\n", + " Canada, United States of America\n", + " 0\n", + " 0.122492\n", + " 0.130435\n", + " 0.891111\n", + " 0.497754\n", + " 0.4110\n", + " 19.842897\n", + " 9.092907\n", + " 414.63\n", " \n", " \n", " 4\n", - " Bridgerton\n", - " 0.3701\n", - " 0.3913\n", - " 0.0653\n", - " 0.6350\n", - " 0.1134\n", - " 0.0000\n", - " 0.0001\n", - " \n", - " \n", - " 5\n", - " Café con aroma de mujer\n", - " 0.2577\n", - " 1.0000\n", - " 0.0149\n", - " 0.2778\n", - " 0.0200\n", - " 0.9783\n", - " 0.0001\n", - " \n", - " \n", - " 6\n", - " Ozark\n", - " 0.2427\n", - " 0.4783\n", - " 0.0620\n", - " 0.6911\n", - " 0.1090\n", - " 0.9348\n", - " 0.5042\n", - " \n", - " \n", - " 7\n", - " All of Us Are Dead\n", - " 0.2099\n", - " 0.3913\n", - " 0.1010\n", - " 0.7522\n", - " 0.1858\n", - " 0.7609\n", - " 0.1184\n", - " \n", - " \n", - " 8\n", - " Virgin River\n", - " 0.1973\n", - " 0.1739\n", - " 0.0482\n", - " 0.5733\n", - " 0.0237\n", - " 0.7174\n", - " 0.0767\n", - " \n", - " \n", - " 9\n", + " 5\n", + " 70.62\n", + " 2.0493\n", + " ed_se\n", + " 561360000\n", + " 6\n", + " 2021\n", + " Sex Education\n", + " sex education\n", + " Sex Education\n", + " 1008.977\n", + " 8.304\n", + " 6647.0\n", + " Sex Education\n", + " 7.2\n", + " 25.0\n", + " 81356\n", " Sex Education\n", - " 0.1748\n", - " 0.1739\n", - " 1.0000\n", - " 0.7244\n", - " 0.3718\n", - " 0.6739\n", - " 0.0000\n", + " 4\n", + " 32\n", + " en\n", + " 6647\n", + " 8.304\n", + " 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", + " False\n", + " /5SEEBS5qXgL5rgivTiAROy1Qt2q.jpg\n", + " 2019-01-11\n", + " 2023-09-21\n", + " https://www.netflix.com/title/80197526\n", + " False\n", + " Sex Education\n", + " 1008.977\n", + " /zn6VNUOHGWKvSGRL6KiMtAe9ELy.jpg\n", + " Scripted\n", + " Ended\n", + " Growth is a group project.\n", + " Comedy, Drama\n", + " Laurie Nunn\n", + " en\n", + " Netflix\n", + " GB\n", + " English\n", + " Eleven\n", + " United Kingdom\n", + " 0\n", + " 0.174840\n", + " 0.173913\n", + " 0.724444\n", + " 0.371757\n", + " 0.3691\n", + " 20.145873\n", + " 8.802071\n", + " 561.36\n", " \n", " \n", - " 10\n", - " The Queen of Flow\n", - " 0.1748\n", - " 0.6087\n", - " 0.1231\n", - " 0.5461\n", - " 0.0766\n", - " 0.7391\n", - " 0.0026\n", + " 5\n", + " 6\n", + " 68.76\n", + " 1.9352\n", + " al_ar_us\n", + " 659510000\n", + " 11\n", + " 2022\n", + " All of Us Are Dead\n", + " all of us are dead\n", + " All of Us Are Dead\n", + " 107.108\n", + " 8.354\n", + " 3335.0\n", + " All of Us Are Dead\n", + " 7.6\n", + " 92709.0\n", + " 99966\n", + " All of Us Are Dead\n", + " 2\n", + " 12\n", + " ko\n", + " 3335\n", + " 8.354\n", + " 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", + " False\n", + " /jZAtLKNZbQZZLm9OLcY9rdZZV5F.jpg\n", + " 2022-01-28\n", + " 2022-01-28\n", + " https://www.netflix.com/title/81237994\n", + " True\n", + " 지금 우리 학교는\n", + " 107.108\n", + " /pTEFqAjLd5YTsMD6NSUxV6Dq7A6.jpg\n", + " Scripted\n", + " Returning Series\n", + " Hope is the most painful torture for people who want to despair.\n", + " Action & Adventure, Drama, Sci-Fi & Fantasy\n", + " Chun Sung-il, JQ Lee, Kim Nam-soo\n", + " en, ko\n", + " Netflix\n", + " KR\n", + " English, 한국어/조선말\n", + " Film Monster, SLL, Kim Jong-hak Production\n", + " South Korea\n", + " 65\n", + " 0.209856\n", + " 0.391304\n", + " 0.752222\n", + " 0.185794\n", + " 0.4052\n", + " 20.307008\n", + " 8.112528\n", + " 659.51\n", " \n", " \n", - " 11\n", - " The Umbrella Academy\n", - " 0.1225\n", - " 0.1304\n", - " 0.0459\n", - " 0.8911\n", - " 0.4978\n", - " 0.6304\n", - " 0.0001\n", + " 6\n", + " 7\n", + " 67.90\n", + " 1.8819\n", + " br\n", + " 1108800000\n", + " 11\n", + " 2021\n", + " Bridgerton\n", + " bridgerton\n", + " Bridgerton\n", + " 71.308\n", + " 8.143\n", + " 2045.0\n", + " Bridgerton\n", + " 4.1\n", + " 57.0\n", + " 91239\n", + " Bridgerton\n", + " 3\n", + " 24\n", + " en\n", + " 2045\n", + " 8.143\n", + " Wealth, lust, and betrayal set in the backdrop of Regency era England, seen through the eyes of the powerful Bridgerton family.\n", + " False\n", + " /aXa6J5vGZQQOLZZv8fK0w0cd2fm.jpg\n", + " 2020-12-25\n", + " 2022-03-25\n", + " https://www.netflix.com/title/80232398\n", + " True\n", + " Bridgerton\n", + " 71.308\n", + " /luoKpgVwi1E5nQsi7W0UuKHu2Rq.jpg\n", + " Scripted\n", + " Returning Series\n", + " Love never plays by the rules.\n", + " Drama\n", + " Chris Van Dusen\n", + " en\n", + " Netflix\n", + " US\n", + " English\n", + " ShondaLand\n", + " United States of America\n", + " 60\n", + " 0.370147\n", + " 0.391304\n", + " 0.635000\n", + " 0.113363\n", + " 0.4153\n", + " 20.826544\n", + " 7.623642\n", + " 1108.80\n", " \n", " \n", - " 12\n", - " Extraordinary Attorney Woo\n", - " 0.1187\n", - " 0.2174\n", - " 0.0370\n", - " 0.8233\n", - " 0.0314\n", - " 0.9565\n", - " 0.0498\n", + " 7\n", + " 8\n", + " 64.97\n", + " 1.7017\n", + " oz\n", + " 751600000\n", + " 13\n", + " 2022\n", + " Ozark\n", + " ozark\n", + " Ozark\n", + " 68.006\n", + " 8.244\n", + " 1968.0\n", + " Ozark\n", + " 8.4\n", + " 394904.0\n", + " 69740\n", + " Ozark\n", + " 4\n", + " 44\n", + " en\n", + " 1968\n", + " 8.244\n", + " 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", + " False\n", + " /gD830J0sf5gEeZvzkRVPdGxJmSR.jpg\n", + " 2017-07-21\n", + " 2022-04-29\n", + " https://www.netflix.com/title/80117552\n", + " False\n", + " Ozark\n", + " 68.006\n", + " /m73bD8VjibSKuTWg597GQVyVhSb.jpg\n", + " Scripted\n", + " Ended\n", + " Their last resort.\n", + " Crime, Drama\n", + " Mark Williams, Bill Dubuque\n", + " en\n", + " Netflix\n", + " US\n", + " English\n", + " MRC, Zero Gravity Management, Aggregate Films, Man, Woman & Child Productions\n", + " United States of America\n", + " 0\n", + " 0.242710\n", + " 0.478261\n", + " 0.691111\n", + " 0.109040\n", + " 0.4043\n", + " 20.437715\n", + " 7.585281\n", + " 751.60\n", " \n", " \n", - " 13\n", - " Cobra Kai\n", - " 0.1114\n", - " 0.0870\n", - " 0.1121\n", - " 0.6778\n", - " 0.3205\n", - " 0.6087\n", - " 0.0000\n", + " 8\n", + " 9\n", + " 64.08\n", + " 1.6468\n", + " bl_pe\n", + " 185340000\n", + " 5\n", + " 2022\n", + " Peaky Blinders\n", + " peaky blinders\n", + " Peaky Blinders\n", + " 344.477\n", + " 8.546\n", + " 8836.0\n", + " Peaky Blinders\n", + " 8.7\n", + " 783155.0\n", + " 60574\n", + " Peaky Blinders\n", + " 6\n", + " 36\n", + " en\n", + " 8836\n", + " 8.546\n", + " 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", + " False\n", + " /wiE9doxiLwq3WCGamDIOb2PqBqc.jpg\n", + " 2013-09-12\n", + " 2022-04-03\n", + " https://www.bbc.co.uk/programmes/b045fz8r\n", + " False\n", + " Peaky Blinders\n", + " 344.477\n", + " /vUUqzWa2LnHIVqkaKVlVGkVcZIW.jpg\n", + " Scripted\n", + " Ended\n", + " London's for the taking\n", + " Drama, Crime\n", + " Steven Knight\n", + " en\n", + " BBC One, BBC Two\n", + " GB\n", + " English\n", + " Tiger Aspect, BBC Studios, Caryn Mandabach Productions, Screen Yorkshire\n", + " United Kingdom\n", + " 58\n", + " 0.040689\n", + " 0.130435\n", + " 0.858889\n", + " 0.494666\n", + " 0.3722\n", + " 19.037703\n", + " 9.086703\n", + " 185.34\n", " \n", " \n", - " 14\n", - " Emily in Paris\n", - " 0.0964\n", - " 0.1304\n", - " 0.0278\n", - " 0.4306\n", - " 0.0632\n", - " 0.5870\n", - " 0.1831\n", + " 9\n", + " 10\n", + " 64.00\n", + " 1.6419\n", + " co_ka\n", + " 383640000\n", + " 4\n", + " 2022\n", + " Cobra Kai\n", + " cobra kai\n", + " Cobra Kai\n", + " 118.256\n", + " 8.220\n", + " 5734.0\n", + " Cobra Kai\n", + " 6.9\n", + " 36.0\n", + " 77169\n", + " Cobra Kai\n", + " 6\n", + " 50\n", + " en\n", + " 5734\n", + " 8.220\n", + " 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", + " False\n", + " /zymbuoBoL1i94xAOzVJF6IuWLfD.jpg\n", + " 2018-05-02\n", + " 2022-09-09\n", + " https://www.netflix.com/title/81002370\n", + " True\n", + " Cobra Kai\n", + " 118.256\n", + " /m7tG5E1EbywuwTsl6hq990So0Bx.jpg\n", + " Scripted\n", + " Returning Series\n", + " Cobra Kai never dies.\n", + " Action & Adventure, Drama, Comedy\n", + " Jon Hurwitz, Hayden Schlossberg, Josh Heald\n", + " en\n", + " Netflix, YouTube Premium\n", + " US\n", + " English\n", + " Hurwitz & Schlossberg Productions, Sony Pictures Television Studios, Overbrook Entertainment\n", + " United States of America\n", + " 30\n", + " 0.111435\n", + " 0.086957\n", + " 0.677778\n", + " 0.320494\n", + " 0.3078\n", + " 19.765215\n", + " 8.654343\n", + " 383.64\n", " \n", " \n", - 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"11 The Umbrella Academy 0.1225 0.1304 0.0459 0.8911 0.4978 0.6304 0.0001\n", - "12 Extraordinary Attorney Woo 0.1187 0.2174 0.0370 0.8233 0.0314 0.9565 0.0498\n", - "13 Cobra Kai 0.1114 0.0870 0.1121 0.6778 0.3205 0.6087 0.0000\n", - "14 Emily in Paris 0.0964 0.1304 0.0278 0.4306 0.0632 0.5870 0.1831\n", - "15 Elite 0.0920 0.1304 0.0946 0.5950 0.4936 0.9348 0.0001\n", - "16 The Lincoln Lawyer 0.0835 0.1739 0.0465 0.4478 0.0088 NaN NaN\n", - "17 Hometown Cha-Cha-Cha 0.0818 0.6087 0.0295 0.6850 0.0234 0.9130 0.0380\n", - "18 Never Have I Ever 0.0808 0.0870 0.0745 0.6450 0.0860 NaN NaN\n", - "19 Sex/Life 0.0752 0.2174 0.1180 0.0767 0.0659 NaN NaN\n", - "20 Business Proposal 0.0741 0.4783 0.0825 0.7900 0.0143 NaN NaN\n", - "21 Locke & Key 0.0739 0.1304 0.0212 0.5017 0.0605 NaN NaN\n", - "22 The Sandman 0.0724 0.0435 0.0582 0.5911 0.0955 0.2391 0.0000\n", - "23 The Marked Heart 0.0719 0.2174 0.0296 0.4539 0.0116 0.5435 0.0061\n", - "24 Vikings: Valhalla 0.0693 0.1304 0.0533 0.4728 0.0362 0.7174 0.0916\n", - "25 Love Is Blind 0.0662 0.1304 0.0406 0.0556 0.0016 NaN NaN\n", - "26 Dark Desire 0.0470 0.1304 0.0845 0.1667 0.2333 0.8478 0.0000\n", - "27 Hustle 0.0453 0.1739 0.0249 0.3933 0.0062 NaN NaN\n", - "28 My Name 0.0438 0.1304 0.0197 0.7128 0.0387 0.8043 0.0575\n", - "29 The Last Kingdom 0.0422 0.1304 0.1376 0.7167 0.0819 0.9348 0.0000\n", - "30 Peaky Blinders 0.0407 0.1304 0.3376 0.8589 0.4947 1.0000 1.0000\n", - "31 Lost in Space 0.0406 0.0870 0.4049 0.0639 0.0039 NaN NaN\n", - "32 Carter 0.0399 0.3478 0.0057 0.2222 0.0002 NaN NaN\n", - "33 Sweet Magnolias 0.0398 0.1304 0.0200 0.4444 0.0101 0.6957 0.0364\n", - "34 All American 0.0390 0.1304 0.1345 0.6917 0.0218 NaN NaN\n", - "35 The Five Juanas 0.0380 0.1739 0.0128 0.2328 0.0124 0.6087 0.0033\n", - "36 Alba 0.0372 0.1739 0.0070 0.0000 0.0079 NaN NaN\n", - "37 Raising Dion 0.0362 0.0870 0.0133 0.3867 0.0203 0.8696 0.0001\n", - "38 Twenty Five Twenty One 0.0358 0.3478 0.0259 0.8872 0.0040 0.9783 0.0290\n", - "39 Anatomy of a Scandal 0.0356 0.1304 0.0074 0.0394 0.0053 0.6304 0.0516\n", - "40 Pasión de Gavilanes 0.0331 0.1304 0.2765 0.3561 0.1066 NaN NaN\n", - "41 Collision 0.0322 0.1304 0.0000 0.1667 0.0000 NaN NaN\n", - "42 Selling Sunset 0.0312 0.0870 0.0037 0.0556 0.0007 0.5217 0.0119\n", - "43 Arcane 0.0296 0.1739 0.0365 0.9667 0.1861 NaN NaN\n", - "44 Good Girls 0.0292 0.1739 0.0464 0.5544 0.0271 0.8913 0.0008\n", - "45 Who Killed Sara? 0.0258 0.0870 0.0123 0.3544 0.0621 0.5000 0.0225\n", - "46 Midnight Mass 0.0253 0.0870 0.0162 0.2917 0.0358 0.7174 0.0000\n", - "47 Welcome to Eden 0.0246 0.1304 0.0346 0.0500 0.0116 NaN NaN\n", - "48 Narcos: Mexico 0.0241 0.0870 0.0415 0.5217 0.0637 0.9130 0.1303\n", - "49 The King's Affection 0.0240 0.3478 0.0638 0.7506 0.0116 0.8478 0.0111\n", - "50 Juvenile Justice 0.0226 0.2174 0.0084 0.5261 0.0033 NaN NaN\n", - "51 Archive 81 0.0208 0.0435 0.0142 0.1217 0.0234 0.6957 0.0871\n", - "52 Our Beloved Summer 0.0205 0.2609 0.0246 0.8333 0.0027 0.9348 0.0003\n", - "53 Alchemy of Souls 0.0201 0.2609 0.0552 0.9011 0.0216 1.0000 0.0380\n", - "54 The Good Doctor 0.0147 0.1739 0.6737 0.8350 0.6593 NaN NaN\n", - "55 The Snitch Cartel: Origins 0.0146 0.1739 0.0362 0.4917 0.0144 0.4130 0.0004\n", - "56 Sintonia 0.0136 0.0000 0.0165 0.6667 0.0086 NaN NaN\n", - "57 Our Blues 0.0121 0.3043 0.0072 1.0000 0.0010 0.9783 0.0071\n", - "58 First Kill 0.0119 0.0435 0.0153 0.5289 0.0137 NaN NaN\n", - "59 The Chestnut Man 0.0117 0.1304 0.0099 0.3244 0.0241 0.7609 0.0873\n", - "60 Forecasting Love and Weather 0.0105 0.2609 0.0111 0.3506 0.0020 0.6522 0.0071\n", - "61 Money Heist: Korea - Joint Economic Area 0.0097 0.0435 0.0404 0.5000 0.0323 0.3913 0.0137\n", - "62 True Story 0.0070 0.0435 0.0099 0.0950 0.0031 NaN NaN\n", - "63 Control Z 0.0066 0.0435 0.0149 0.6817 0.1244 0.5870 0.0161\n", - "64 The Silent Sea 0.0066 0.0435 0.0192 0.4444 0.0281 NaN NaN\n", - "65 Another Self 0.0062 0.0870 0.0048 0.2222 0.0002 0.6957 0.0338\n", - "66 Turning Point: 9/11 and the War on Terror 0.0034 0.0435 0.0034 0.1594 0.0032 0.8261 0.0169\n", - "67 On My Block 0.0034 0.0000 0.0111 0.8117 0.0332 0.6304 0.0000\n", - "68 The Seven Deadly Sins: Cursed by Light 0.0029 0.0870 0.0043 0.8122 0.2630 0.5000 0.0025\n", - "69 Hospital Playlist 0.0003 0.2174 0.0160 0.8278 0.0042 1.0000 0.0141\n", - "70 I Am Georgina 0.0000 0.0435 0.0057 0.0617 0.0150 0.0000 0.0060" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# 1. 讀取資料集\n", - "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\n", - "\n", - "# 2. 排除關鍵 NaN 值以計算正確的 P50 門檻 (以 TMDb 為基準)\n", - "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\n", - "\n", - "tmdb_rating_p50 = df_valid_tmdb[\"tmdb_vote_average\"].median()\n", - "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median()\n", - "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean()\n", - "\n", - "# 3. 篩選出這 71 部 The Hit 影集\n", - "hit_condition = (\n", - " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", - " (df[\"tmdb_vote_count\"] >= tmdb_votes_p50) &\n", - " (df[\"netflix_viewing_hours\"] > netflix_hours_mean)\n", - ")\n", - "df_hits = df[hit_condition].copy().reset_index(drop=True)\n", - "\n", - "# 4. 定義需要進行 Min-Max 標準化的數值欄位\n", - "target_cols = [\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", - "# 進行 Min-Max 標準化並建立新欄位 (字尾加上 _norm)\n", - "for col in target_cols:\n", - " min_val = df_hits[col].min()\n", - " max_val = df_hits[col].max()\n", - " \n", - " # 預防分母為 0 的安全機制，並四捨五入至小數點後 4 位\n", - " if max_val - min_val != 0:\n", - " df_hits[f\"{col}_norm\"] = ((df_hits[col] - min_val) / (max_val - min_val)).round(4)\n", - " else:\n", - " df_hits[f\"{col}_norm\"] = 1.0000\n", - "\n", - "# 5. 強制 Pandas 展開所有 Rows 與 Columns，絕不省略\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", - "# 6. 整理要輸出的欄位順序 (名稱 + 7 個標準化分數)\n", - "normalized_cols = [f\"{col}_norm\" for col in target_cols]\n", - "display_columns = [\"netflix_title\"] + normalized_cols\n", - "\n", - "# 依據觀看時數標準化分數降冪排序\n", - "df_hits_sorted = df_hits.sort_values(by=\"netflix_viewing_hours_norm\", ascending=False).reset_index(drop=True)\n", - "\n", - "print(\"=\" * 120)\n", - "print(\"🚀 【The Hit 影集】 7 大核心屬性 Min-Max 標準化分數總表\")\n", - "print(\" * 數值範圍皆在 0.0 ~ 1.0 之間（1.0 代表該屬性在 71 部影集中表現最強，0.0 為最弱）\")\n", - "print(\"=\" * 120)\n", - "\n", - "# 重新命名欄位，提供精美視覺化對照\n", - "rename_mapping = {\n", - " \"netflix_title\": \"影劇名稱\",\n", - " \"netflix_viewing_hours_norm\": \"觀看時數_Norm\",\n", - " \"netflix_weeks_norm\": \"上榜週數_Norm\",\n", - " \"tmdb_popularity_norm\": \"TMDb熱度_Norm\",\n", - " \"tmdb_vote_average_norm\": \"TMDb評分_Norm\",\n", - " \"tmdb_vote_count_norm\": \"TMDb投票數_Norm\",\n", - " \"imdb_averageRating_norm\": \"IMDb評分_Norm\",\n", - " \"imdb_numVotes_norm\": \"IMDb投票數_Norm\"\n", - "}\n", - "\n", - "display(df_hits_sorted[display_columns].rename(columns=rename_mapping))" - ] - }, - { - "cell_type": "markdown", - "id": "6b8b23d8", - "metadata": {}, - "source": [ - "## our analysis\n", - "$$\\text{綜合 The Hit 指數} = (0.35 \\cdot \\text{觀看時數\\_Norm}) + (0.30 \\cdot \\text{TMDb評分\\_Norm}) + (0.20 \\cdot \\text{上榜週數\\_Norm}) + (0.15 \\cdot \\text{TMDb投票數\\_Norm})$$" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "8c7299f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "========================================================================================================================\n", - "🏆 【全新優化版】71 部 The Hit 影集「綜合實力榜單」\n", - " (已剔除 IMDb 缺失數據，採用全新 35% / 30% / 20% / 15% 權重分配)\n", - "========================================================================================================================\n" - ] - }, - { - "data": { - "text/html": [ - "
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影劇名稱綜合 The Hit 指數觀看時數_Norm (35%)TMDb評分_Norm (30%)上榜週數_Norm (20%)TMDb投票數_Norm (15%)
0Stranger Things0.85221.00000.90220.47830.9060
1Squid Game0.68170.79140.46170.78260.7314
2Money Heist0.60110.39210.69830.52171.0000
379696Manifest0.42560.49050.40440.60870.0722
4Bridgerton0.41530.37010.63500.39130.1134
5The Umbrella Academy0.41100.12250.89110.13040.4978
6All of Us Are Dead0.40530.20990.75220.39130.1858
7Ozark0.40430.24270.69110.47830.1090462en13127.728After landing from a turbulent but routine flight, the crew and passengers of Montego Air Flight 828 discover five years have passed in what seeme...False/iZu83GB1IM7VXL2X90m7iLHYUHU.jpg2018-09-242023-06-02https://www.netflix.com/title/80241318FalseManifest111.411/eTemCphrglLKrXOsNRhYezHA7H9.jpgScriptedEndedMake the final connection.Drama, Mystery, Sci-Fi & FantasyJeff RakeenNBC, NetflixUSEnglishWarner Bros. Television, Compari Entertainment, Universal Television, Jeff Rake ProductionsUnited States of America420.4905400.6086960.4044440.0722070.425621.0922477.1800701446.26
8121359.651.3740do_go11258000062021The Good Doctorthe good doctorThe Good Doctor681.6148.50311768.0The Good DoctorNaNNaN71712The Good Doctor6116en117688.503Shaun Murphy, a young surgeon with autism and savant syndrome, relocates from a quiet country life to join a prestigious hospital's surgical unit....False/xXRsKNJHTOGrs5wfYAxkbM2RiyT.jpg2017-09-252023-05-01https://abc.com/shows/the-good-doctorTrueThe Good Doctor681.614/luhKkdD80qe62fwop6sdrXK9jUT.jpgScriptedReturning SeriesEveryone operates differently.DramaDavid ShoreenABCUSEnglishABC Studios, 3AD, Sony Pictures Television StudiosUnited States of America430.0147310.1739130.8350000.6592930.38930.01470.83500.17390.6593
9Café con aroma de mujer0.37650.25770.27781.00000.0200
10Peaky Blinders0.37220.04070.85890.13040.4947
11Sex Education0.36900.17480.72440.17390.3718
12Our Blues0.36520.01211.00000.30430.001018.5391759.373224112.58
131458.921.3291ar15427000062021ArcanearcaneArcane42.4478.7403341.0ArcaneNaNNaN94605Arcane29en33418.740Amid the stark discord of twin cities Piltover and Zaun, two sisters fight on rival sides of a war between magic technologies and clashing convict...False/rkB4LyZHo1NHXFEDHl9vSD9r1lI.jpg2021-11-062021-11-20https://arcane.comTrueArcane42.447/fqldf2t8ztc9aiwn3k6mlX3tvRT.jpgScriptedReturning SeriesNaNAnimation, Drama, Sci-Fi & Fantasy, Action & AdventureChristian Linke, Alex YeeenNetflixUSEnglishFortiche Production, Riot GamesFrance, United States of America410.0296040.1739130.9666670.1861310.36310.02960.96670.17390.186118.8542158.114325154.27
14Business Proposal0.36070.07410.79000.47830.01431555.111.0948fl_qu561230000162021The Queen of Flowthe queen of flowThe Queen of Flow129.3237.9831391.0The Queen of Flow7.52076.080240The Queen of Flow2172es13917.983After spending seventeen years in prison unfairly, a talented songwriter seeks revenge on the men who sank her and killed her family.False/pnyT1foDmmXTsho2DfxN2ePI8ix.jpg2018-06-122021-09-10https://www.caracoltv.com/lareinadelflowFalseLa Reina del Flow129.323/fuVuDYrs8sxvEolnYr0wCSvtyTi.jpgScriptedEndedNaNDramaAndrés SalgadoesCaracol TVCOEspañolTeleset, Sony PicturesColombia530.1747930.6086960.5461110.0766420.358220.1456417.238497561.23
15Hometown Cha-Cha-Cha0.35940.08180.68500.60870.02341654.081.0317at_ex_wo40403000072022Extraordinary Attorney Wooextraordinary attorney wooExtraordinary Attorney Woo42.9238.482585.0Extraordinary Attorney Woo8.538985.0197067Extraordinary Attorney Woo116ko5858.482Brilliant 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.False/tmgtlnBnoFvk52xZBO7Z0pTmN9b.jpg2022-06-292022-08-18http://ena.skylifetv.co.kr/bbs/board.php?bo_table=skydrama&wr_id=113&sca=최신False이상한 변호사 우영우42.923/zuNOQVI4rEaqwknrfQUVKtlKE2C.jpgMiniseriesEndedMy name is Woo Young-woo, whether it is read straight or flipped. Kayak, deed, rotator, noon, racecar, Woo Young-woo.DramaYoo In-sik, Moon Ji-wonkoENAKR한국어/조선말KT Studio Genie, AStorySouth Korea700.1187100.2173910.8233330.0313870.336719.8170006.373320404.03
16The Queen of Flow0.35820.17480.54610.60870.07661751.700.8850ha_i_ne29778000042021Never Have I Evernever have i everNever Have I Ever80.5468.1611557.0Never Have I EverNaNNaN100883Never Have I Ever440en15578.161After 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...False/85HCFXyCmvADwd3gJFyCz34CTAO.jpg2020-04-272023-06-08https://www.netflix.com/title/80179190FalseNever Have I Ever80.546/hd5fnBixab6IzfUwjC5wfdbX3eM.jpgScriptedEndedSame nerds, new drama.Comedy, DramaMindy Kaling, Lang FisherenNetflixUSEnglishKaling International, Universal TelevisionUnited States of America300.0808040.0869570.6450000.0859630.252119.5118667.351158297.78
17Twenty Five Twenty One0.34880.03580.88720.34780.00401849.840.7703sa27422000032022The Sandmanthe sandmanThe Sandman64.1688.0641726.0The Sandman5.25.090802The Sandman111en17268.064After 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...False/eqhKMZTLcieAvoH6CBqknTTfNby.jpg2022-08-052022-08-19https://thesandman.comTrueThe Sandman64.168/q54qEgagGOYCq5D1903eBVMNkbo.jpgScriptedReturning SeriesDream the world anew.Sci-Fi & Fantasy, DramaDavid S. Goyer, Neil Gaiman, Allan HeinbergenNetflixUSEnglishWarner Bros. Television, DC Entertainment, Purepop, Phantom Four, The Blank CorporationUnited States of America00.0723980.0434780.5911110.0954520.225719.4294417.454141274.22
18Extraordinary Attorney Woo0.33670.11870.82330.21740.03141949.750.7650ri_vi62425000062021Virgin Rivervirgin riverVirgin River54.1928.032448.0Virgin River7.460100.088324Virgin River554en4488.032After 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.False/4nVaMgK7LVRmQ3vNg7eC3qKfRyI.jpg2019-12-062023-09-07https://www.netflix.com/title/80240027TrueVirgin River54.192/rxWHdK7qGFcJIk28UubcduSc1sz.jpgScriptedReturning SeriesNaNDramaSue TenneyenNetflixUSEnglishReel World ManagementUnited States of America450.1972760.1739130.5733330.0236950.279420.2520616.107023624.25
19Alchemy of Souls0.33280.02010.90110.26090.02162049.110.7257de_se_si7951000042021The Seven Deadly Sins: Cursed by Lightthe seven deadly sins cursed by lightThe Seven Deadly Sins10.0598.4624710.0The Seven Deadly Sins: Cursed by Light6.41955.062104The Seven Deadly Sins496ja47108.462The “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 ...False/n5Ty1KJIRNCXlDHDjcPpUgp57tr.jpg2014-10-052021-06-23http://www.7-taizai.netFalse七つの大罪10.059/gxTojpKEOtue85EEFlozwRbDXwJ.jpgScriptedEndedNaNAction & Adventure, Animation, Sci-Fi & FantasyNaNjatv asahi, MBS, TV Tokyo, TBS, CBC, TV Aichi, TVQ, TV Osaka, Tulip Television, TVh, SBC, TSC, BSN, tys, Nagasaki Broadcasting Company, HBC, RKK Kum...JP日本語A-1 Pictures, Studio DeenJapan240.0029330.0869570.8122220.2629980.301518.1913938.45765579.51
20Elite0.31080.09200.59500.13040.49362148.480.6866ki_la18957000052022The Last Kingdomthe last kingdomThe Last Kingdom143.7948.2901485.0The Last Kingdom8.413.063333The Last Kingdom546en14858.290A show of heroic deeds and epic battles with a thematic depth that embraces politics, religion, warfare, courage, love, loyalty and our universal ...False/uCqXSfHymdbDMsFx8t0u0OPSuve.jpg2015-10-102022-03-09https://www.netflix.com/title/80074249FalseThe Last Kingdom143.794/8eJf0hxgIhE6QSxbtuNCekTddy1.jpgScriptedEndedEngland is bornAction & Adventure, Drama, War & PoliticsStephen ButchardenNetflix, BBC TwoGBEnglishCarnival FilmsUnited Kingdom600.0421980.1304350.7166670.0819200.268119.0602697.303843189.57
21Our Beloved Summer0.30970.02050.83330.26090.00272244.870.4647em_pa34163000052021Emily in Parisemily in parisEmily in Paris33.6597.7751152.0Emily in Paris6.8143411.082596Emily in Paris430en11527.775When ambitious Chicago marketing exec Emily unexpectedly lands her dream job in Paris, she embraces a new life as she juggles work, friends and ro...False/xkKIruOg4NoADdPjJJrvfiJHRdZ.jpg2020-10-022022-12-21https://www.netflix.com/title/81037371TrueEmily in Paris33.659/Ak59Y9bzykmV0wAiwKsqrbORDBo.jpgScriptedReturning SeriesTourist season is over.Drama, ComedyDarren Staren, frNetflixUSEnglish, FrançaisMTV Entertainment Studios, Jax Media, Darren Star ProductionsUnited States of America300.0964480.1304350.4305560.0632230.198519.6492397.050123341.63
22Cobra Kai0.30780.11140.67780.08700.32052344.820.4613ch_ch_ho300580000162021Hometown Cha-Cha-Chahometown cha cha chaHometown Cha-Cha-Cha35.3678.233442.0Hometown Cha-Cha-Cha8.329770.0128883Hometown Cha-Cha-Cha116ko4428.233A 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.False/qX5DaHmjBDOMz9jGNJ3oESW6Wlq.jpg2021-08-282021-10-17http://program.tving.com/tvn/chachachaFalse갯마을 차차차35.367/en6lrlJ1DhyvkeZEqrk3R6EJz1p.jpgMiniseriesEndedEventually, we found each other once again.Comedy, DramaYu Je-won, Shin Ha-eunkotvNKR한국어/조선말Studio Dragon, GTistSouth Korea780.0818030.6086960.6850000.0233580.359419.5212256.093570300.58
23The King's Affection0.30490.02400.75060.34780.01162444.430.4373ke_lo27838000052021Locke & Keylocke keyLocke & Key27.0177.9031104.0Locke&KeyNaNNaN86423Locke & Key328en11047.903Three siblings who move into their ancestral estate after their father's gruesome murder discover their new home's magical keys, which must be use...False/67gGBgMBgmElB88waL2yS2rv91l.jpg2020-02-072022-08-10https://www.netflix.com/title/80241239FalseLocke & Key27.017/zuxGfRKziGHPogipnEXXykdDmyT.jpgScriptedEndedSecrets are meant to be unlocked.Sci-Fi & Fantasy, Drama, MysteryCarlton Cuse, Meredith Averill, Aron Eli ColeiteenNetflixUSEnglishGenre Arts, IDW Entertainment, Circle of ConfusionUnited States of America470.0738820.1304350.5016670.0605280.211519.4444987.007601278.38
24The Seven Deadly Sins: Cursed by Light0.30150.00290.81220.08700.26302544.190.4225bu_pr279110000132022Business Proposalbusiness proposalBusiness Proposal88.5338.422281.0Business ProposalNaNNaN154825Business Proposal112ko2818.422In 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...False/64TnyodfRC4iLTCXKlqps8iRW7F.jpg2022-02-282022-04-05https://programs.sbs.co.kr/drama/businessproposalFalse사내맞선88.533/iLh7L8ZuvgdxFaM9sImyv2iKYLe.jpgMiniseriesEndedIf you come any closer, I'm not going to let you go again.Comedy, DramaPark Seon-ho, Hong Bo-hee, Han Sul-heekoSBSKR한국어/조선말Studio S, Kross Pictures, Kakao EntertainmentSouth Korea600.0741430.4782610.7900000.0143180.360719.4471175.641907279.11
25Hospital Playlist0.29260.00030.82780.21740.00422643.880.4034my_na19414000052021My Namemy nameMy Name25.5258.283715.0My Name7.845026.0110356My Name18ko7158.283Following her father's murder, a revenge-driven woman puts her trust in a powerful crime boss — and enters the police force under his direction.False/4LZuFMhLivOSO7zuWAQ3RffMnnr.jpg2021-10-152021-10-15https://www.netflix.com/title/81011211False마이 네임25.525/h9p1zxKzti35Aqe6xpwkP9NlG7p.jpgMiniseriesEndedNo one can know. My enemy, my revenge.Crime, Drama, Mystery, Action & AdventureKim Jin-min, Kim Ba-dakoNetflixKR한국어/조선말Studio Santa Claus EntertainmentSouth Korea500.0438290.1304350.7127780.0386860.261119.0840906.573680194.14
26Virgin River0.27940.19730.57330.17390.02372742.580.3233ar_ca_co793740000252022Café con aroma de mujercaf con aroma de mujerCafé con Aroma de Mujer20.7107.500382.0Café con aroma de mujer8.6106.016961Café con Aroma de Mujer1139es3827.500Café 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...False/oOqWl3GavxfYiwxegiVyQ5p9NYK.jpg1994-12-301995-07-12NaNFalseCafé con Aroma de Mujer20.710/tvcpeQNt28GQLtNljhuLwgRBRv8.jpgScriptedEndedNaNDramaFernando GaitánesRCNCOEspañolRCNColombia, United States of America, United Kingdom450.2577441.0000000.2777780.0199890.376520.4922675.948035793.74
27The Last Kingdom0.26810.04220.71670.13040.08192841.750.2724co_z8983000032021Control Zcontrol zControl Z20.7088.2272242.0Control Z6.812606.0102903Control Z324es22428.227When a hacker begins releasing students' secrets to the entire high school, the socially isolated but observant Sofía works to uncover his/her ide...False/gFFedWBZ7s5sPiR9A1IpileWuFf.jpg2020-05-222022-07-06https://www.netflix.com/title/81021245FalseControl Z20.708/8VNA0RdrPk8Ec7XVjpeT0Rnui79.jpgScriptedEndedYour secrets are not safe.DramaAdriana Pelusi, Carlos Quintanilla Sakar, Miguel García MorenoesNetflixMXEspañolLemon StudiosMexico360.0066140.0434780.6816670.1244240.234218.3134307.71557089.83
28My Name0.26110.04380.71280.13040.03872941.220.2397al_so12769000082022Alchemy of Soulsalchemy of soulsAlchemy of Souls61.1728.622411.0Alchemy of Souls8.729759.0135157Alchemy of Souls230ko4118.622A powerful sorceress in a blind woman's body encounters a man from a prestigious family, who wants her help to change his destiny.False/nBZyWSGAUEzCH7Mna0zUNTpBQlQ.jpg2022-06-182023-01-08https://program.tving.com/tvn/alchemyofsoulsFalse환혼61.172/gvOZN1NlAoL8iz9ghpES1zWA3w3.jpgScriptedEndedNaNSci-Fi & Fantasy, Drama, Action & Adventure, MysteryHong Mi-ran, Hong Jeong-eun, Park Joon-hwakotvNKR한국어/조선말Studio DragonSouth Korea750.0201220.2608700.9011110.0216170.332818.6651166.021023127.69
29Never Have I Ever0.25210.08080.64500.08700.08603039.800.1525va_vi26555000052022Vikings: Valhallavikings valhallaVikings: Valhalla59.2327.851671.0Vikings: Valhalla7.471704.0116135Vikings: Valhalla216en6717.851In 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.False/k47JEUTQsSMN532HRg6RCzZKBdB.jpg2022-02-252023-01-12https://www.netflix.com/title/81149450TrueVikings: Valhalla59.232/rDFy1fUU6OC3Mm0CLFB7u0fGwVN.jpgScriptedReturning SeriesKneel to no one.Action & Adventure, Drama, War & PoliticsJeb StuartenNetflixUSEnglishMGM Television, Metropolitan Films International, HistoryIreland, United States of America510.0693050.1304350.4727780.0362160.197619.3973146.510258265.55
30All American0.25050.03900.69170.13040.02183139.540.1364da_de20294000052022Dark Desiredark desireDark Desire90.5537.3004181.0Dark Desire8.011.0105214Dark Desire233es41817.300Married 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...False/9CBdzw7CK2m6WpfEfqe73V8i5O0.jpg2020-07-152022-02-02https://www.netflix.com/title/81090319FalseOscuro deseo90.553/uxFNAo2A6ZRcgNASLk02hJUbybn.jpgScriptedEndedNaNMystery, DramaLeticia López MargalliesNetflixMXEspañolNaNMexico350.0469680.1304350.1666670.2332960.127519.1284218.338545202.94
31On My Block0.24970.00340.81170.00000.03323238.890.0965al_am18066000052021All Americanall americanAll American140.6948.245414.0All AmericanNaNNaN82428All American591en4148.245When 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...False/thCj6VEVQlFlup4aJ74kdRhfujB.jpg2018-10-102023-05-15https://www.cwtv.com/shows/all-american/TrueAll American140.694/c8FTtjQcQfqSI0McmVc1OOOf7Z7.jpgScriptedReturning SeriesAnd still we rise.DramaApril BlairenThe CWUSEnglishBerlanti Productions, Warner Bros. Television, CBS StudiosUnited States of America450.0390190.1304350.6916670.0217860.250519.0121276.028279180.66
32Control Z0.23420.00660.68170.04350.12443337.780.0282de_n_pa16418000052022Pasión de Gavilanespasi n de gavilanesPasión de Gavilanes283.1977.6411925.0Pasión de sangreNaNNaN11250Pasión de Gavilanes2259es19257.641The Reyes-Elizondo's idyllic lives are shattered by a murder charge against Eric and León.False/qabMMIpNUUKYFqQxHEWErEEBD8G.jpg2003-10-212022-05-31https://www.telemundo.com/shows/pasion-de-gavilanesFalsePasión de gavilanes283.197/91UV7pNcDPhIzJl7EuK36sK5vRG.jpgScriptedEndedNaNDramaJulio JiménezesTelemundoUSEspañolNaNNaN00.0331400.1304350.3561110.1066250.160518.9164747.563201164.18
33The Sandman0.22570.07240.59110.04350.09553437.570.0151me_na13898000042021Narcos: Mexiconarcos mexicoNarcos: Mexico47.4767.9391160.0Narcos: Mexico8.3102053.080968Narcos: Mexico330en11607.939See the rise of the Guadalajara Cartel as an American DEA agent learns the danger of targeting narcos in 1980s Mexico.False/64jEzC7YeKEpnn1YwjCGRo9ZvOX.jpg2018-11-162021-11-05https://www.netflix.com/title/80997085FalseNarcos: Mexico47.476/uYcZMiIIUv8NPebpqKbUGhf2M3y.jpgScriptedEndedThe uncut story of Mexico's first cartel.Drama, CrimeChris Brancato, Doug Miro, Carlo Bernarden, esNetflixUSEnglish, EspañolGaumont, Gaumont International TelevisionUnited States of America500.0241490.0869570.5216670.0636720.191918.7498417.057037138.98
34Good Girls0.21540.02920.55440.17390.02713536.08-0.0765bl_my8088000022021On My Blockon my blockOn My Block16.9798.461618.0On My Block7.029.076747On My Block438en6188.461A 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.False/jt7c70Axkhy7o9u00JT8Lygfd3H.jpg2018-03-162021-10-04https://www.netflix.com/title/80117809FalseOn My Block16.979/w6oviv65UEducvjAdH3sYYaxdu2.jpgScriptedEndedNaNComedyJeremy Haft, Eddie Gonzalez, Lauren Iungerichen, ptNetflixUSEnglish, PortuguêsCrazy Cat Lady ProductionsUnited States of America300.0034210.0000000.8116670.0332400.249718.2084776.42810580.88
35Locke & Key0.21150.07390.50170.13040.06053634.83-0.1532fi_tw_tw171610000102022Twenty Five Twenty Onetwenty five twenty oneTwenty Five Twenty One31.7308.59798.0Twenty Five Twenty One8.622729.0129888Twenty Five Twenty One116ko988.597In 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.False/jvFfHNiCKPWRULvkmeocyjm4QtE.jpg2022-02-122022-04-03http://program.tving.com/tvn/twentyfivetwentyoneFalse스물다섯 스물하나31.730/yCQFnmYhYf7XALMka2EoBRAFmPO.jpgMiniseriesEndedNaNDramaJung Jee-hyun, Kwon Do-eunkoNetflix, tvNKR한국어/조선말Hwa&Dam Pictures, Studio DragonSouth Korea750.0357910.3478260.8872220.0040430.348918.9607354.595120171.61
36Juvenile Justice0.20970.02260.52610.21740.00333734.28-0.1872gi_go15314000062021Good Girlsgood girlsGood Girls52.3527.998509.0Good Girls8.2668.071715Good Girls450en5097.998Three \"good girl\" suburban wives and mothers suddenly find themselves in desperate circumstances and decide to stop playing it safe and risk every...False/csOIulXwQxsuCFp3ZZ9cYkJmCwH.jpg2018-02-262021-07-22https://www.nbc.com/good-girlsFalseGood Girls52.352/gPjcbxrYfbrJNq1Ja8EGd0XtUnC.jpgScriptedCanceledDone playing nice.Comedy, Drama, CrimeJenna BansenNBCUSEnglishAmblin Television, Universal TelevisionUnited States of America420.0292010.1739130.5544440.0271200.215418.8468636.234411153.14
37The Marked Heart0.20660.07190.45390.21740.01163833.92-0.2093af_ki_s138540000102021The King's Affectionthe king s affectionThe King's Affection69.8198.351232.0The King's Affection8.08733.0129478The King's Affection120ko2328.351When 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...False/4r7scgI5aUrShN0YzriAatWd15e.jpg2021-10-112021-12-14https://program.kbs.co.kr/2tv/drama/thekingsaffection/pc/index.htmlFalse연모69.819/mcfX0IqWibPhF5H4i2Q5gUlwwxk.jpgScriptedEndedNaNDramaHan Hee-jungkoKBS2KR한국어/조선말Arc Media, Monster UnionSouth Korea700.0239920.3478260.7505560.0115670.304918.7466705.451038138.54
38Sintonia0.20610.01360.66670.00000.00863932.78-0.2796ki_sa_wh14361000042022Who Killed Sara?who killed saraWho Killed Sara?18.1577.6381132.0Who Killed Sara?6.417647.0120168Who Killed Sara?325es11327.638Hell-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.False/dYvIUzdh6TUv4IFRq8UBkX7bNNu.jpg2021-03-242022-05-18https://www.netflix.com/title/81166747False¿Quién mató a Sara?18.157/o7uk5ChRt3quPIv8PcvPfzyXdMw.jpgScriptedEndedNaNDrama, Crime, MysteryJosé Ignacio ValenzuelaesNetflixMXEspañolNaNMexico400.0258010.0869570.3544440.0621000.142118.7826127.032624143.61
39The Lincoln Lawyer0.19970.08350.44780.17390.00884032.64-0.2881he_ma27275000072022The Marked Heartthe marked heartThe Marked Heart35.5267.817232.0The Marked Heart6.64799.0158916The Marked Heart224es2327.817A man hell-bent on exacting revenge on the organ trafficking organization that murdered his wife becomes involved with the woman who received her ...False/yOtzSTFdccSd2tfzlTp8UNo2ZHN.jpg2022-04-202023-04-19https://www.netflix.com/title/81261170FalsePálpito35.526/M1q0HuyRnqxP8Si54srsPREFIH.jpgScriptedEndedNaNSoap, MysteryLeonardo PadrónesNetflixCOEspañolNaNSpain450.0718740.2173910.4538890.0115670.206519.4240665.451038272.75
40Emily in Paris0.19850.09640.43060.13040.06324132.42-0.3019li_se28210000072021Sex/Lifesex lifeSex/Life124.1457.1381200.0Sex LifeNaNNaN126280Sex/Life214en12007.138A 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...False/9nBVkNBe4x9HKDAzxjxlIqecxCW.jpg2021-06-252023-03-02https://www.netflix.com/title/80991848FalseSex/Life124.145/kfcJl5e8CRWDU7e4vX6uNABPRbS.jpgScriptedCanceledTime to choose.Comedy, DramaStacy RukeyserenNetflixUSEnglishDe MiloUnited States of America00.0752100.2173910.0766670.0659180.102719.4577727.090910282.10
41Vikings: Valhalla0.19760.06930.47280.13040.03624232.18-0.3163la_li30527000062022The Lincoln Lawyerthe lincoln lawyerThe Lincoln Lawyer52.3957.806183.0The Lincoln LawyerNaNNaN116799The Lincoln Lawyer320en1837.806Sidelined after an accident, hotshot Los Angeles lawyer Mickey Haller restarts his career - and his trademark Lincoln - when he takes on a murder ...False/jBFOVEE5sMIyT141YIqIwyU8Y1D.jpg2022-05-132023-08-03https://www.netflix.com/title/81303831TrueThe Lincoln Lawyer52.395/9TaupczRcEkOaYbsiQuLg3kXPLq.jpgScriptedReturning SeriesLA's hottest defense attorney just found a new gear.Drama, CrimeDavid E. KelleyenNetflixUSEnglishA+E Studios, ABC Signature, David E. Kelley ProductionsUnited States of America500.0834760.1739130.4477780.0088150.199719.5367075.214936305.27
42Narcos: Mexico0.19190.02410.52170.08700.06374328.86-0.5208di_ra17287000042022Raising Dionraising dionRaising Dion19.0937.696388.0Raising Dion8.183.093392Raising Dion217en3887.696A widowed mom sets out to solve the mystery surrounding her young son's emerging superpowers while keeping his extraordinary gifts under wraps.False/fVc8V31Kk1wAyorbg5BrzVSNNiv.jpg2019-10-042022-02-01https://www.netflix.com/title/80117803FalseRaising Dion19.093/nydKXBiLn4vpL1d5o1utbPKYdii.jpgScriptedCanceledEnergy never dies, it just takes a different form.Drama, Sci-Fi & FantasyCarol BarbeeenNetflixUSEnglishOutlier Society ProductionsUnited States of America500.0362400.0869570.3866670.0203260.149118.9680505.963579172.87
43The Snitch Cartel: Origins0.18960.01460.49170.17390.01444428.78-0.5260he_ko_mo9837000032022Money Heist: Korea - Joint Economic Areamoney heist korea joint economic areaMoney Heist: Korea - Joint Economic Area46.2927.900601.0Money Heist: Korea - Joint Economic Area5.910696.0112836Money Heist: Korea - Joint Economic Area112ko6017.900Disguised under the shadows of a mask, a crew of desperados band together under the leadership of a criminal mastermind known only as “The Profess...False/lcTuggU70y6pt6x13Rv1Ffjs93K.jpg2022-06-242022-12-09https://www.netflix.com/title/80997343True종이의 집: 공동경제구역46.292/mbsRGqJtdKcVbjQxkrfzKCAkYoU.jpgScriptedReturning SeriesWitness a heist like no other.Action & Adventure, Crime, Drama, MysteryKim Hong-sun, Ryu Yong-jae, Kim Hwan-chae, Choi Sung-joonkoNetflixKR한국어/조선말BH Entertainment, Content Zium, HighZium StudioSouth Korea00.0096610.0434780.5000000.0322850.166918.4042466.40025798.37
44Sweet Magnolias0.17480.03980.44440.13040.01014528.21-0.5609be_ou_su12889000082021Our Beloved Summerour beloved summerOur Beloved Summer30.4568.50074.0Our Beloved Summer8.4252.0135897Our Beloved Summer116ko748.500Years 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.False/7J8QzozUgiFgDCvNadVImhEXEIq.jpg2021-12-062022-01-25https://programs.sbs.co.kr/drama/ourbelovedsummerFalse그 해 우리는30.456/bA15g6OLmhQ2HkURRaCztA2jwqI.jpgScriptedEndedLet's film the documentary again.Comedy, DramaLee Na-eunkoSBSKR한국어/조선말Studio N, Big Ocean ENM, Supermoon PicturesSouth Korea600.0205500.2608700.8333330.0026950.309818.6744704.317488128.89
45First Kill0.17360.01190.52890.04350.01374627.44-0.6082bl_ou10515000092022Our Bluesour bluesOur Blues12.9808.80043.0Our Blues8.65554.0135840Our Blues120ko438.800Romance is sweet and bitter — and life riddled with ups and downs — in multiple stories about people who live and work on bustling Jeju island.False/w2vVXdKuBucql88wh2s8PRsjWnE.jpg2022-04-092022-06-12http://program.tving.com/tvn/ourbluesFalse우리들의 블루스12.980/sT5Mlt5UmKiGfBisccwmD4LnPRD.jpgScriptedEndedRooting for you through sour, sweet, and bitter life.DramaNoh Hee-kyungkotvNKR한국어/조선말GTist, Studio DragonSouth Korea660.0120800.3043481.0000000.0009550.365218.4708983.784190105.15
46Hustle0.16960.04530.39330.17390.00624727.33-0.6150ma_mi14228000042021Midnight Massmidnight massMidnight Mass21.9997.525664.0Midnight Mass7.430.097400Midnight Mass17en6647.525An unsettling omen washes ashore in the wake of the storm. Later, when the locals gather for a potluck, tragedy strikes – and a miracle occurs.False/4XbQb5YoWK9oMrlQdKwtZ1ccpC8.jpg2021-09-242021-09-24https://www.netflix.com/title/81083626FalseMidnight Mass21.999/iYoMZYVD775MtBYJfv6OGY1FsnL.jpgMiniseriesEndedBe not afraid.Mystery, Drama, Sci-Fi & FantasyMike FlanaganenNetflixUSEnglishIntrepid PicturesUnited States of America640.0253270.0869570.2916670.0358230.119118.7733086.499787142.28
47Money Heist: Korea - Joint Economic Area0.16690.00970.50000.04350.03234827.12-0.6282ma_sw18294000052022Sweet Magnoliassweet magnoliasSweet Magnolias25.8317.800206.0Sweet Magnolias7.328500.0102904Sweet Magnolias330en2067.800Lifelong friends Maddie, Helen and Dana Sue lift each other as they juggle relationships, family and careers in the small, Southern town of Serenity.False/wt60Q4doM2vrubiCsutwaeWncLL.jpg2020-05-192023-07-20https://www.netflix.com/title/80239866TrueSweet Magnolias25.831/n1WDSFOnCCg3cdHHGBywZtYIzf9.jpgScriptedReturning SeriesNaNDramaSheryl J. AndersonenNetflixUSEnglishDaniel L. Paulson ProductionsUnited States of America500.0398330.1304350.4444440.0101070.174919.0246695.332719182.94
48Forecasting Love and Weather0.16130.01050.35060.26090.00204927.09-0.6296si10943000022021SintoniasintoniaSintonia22.3778.200179.0SintoniaNaNNaN78074Sintonia426pt1798.200Told through three different characters' perspectives, the story of Sintonia explores the interconnection of the music, drug traffic, and religion...False/9GTFaWPPctyJeJaImg9HVPPbyOf.jpg2019-08-092023-07-25https://www.netflix.com/title/80217315TrueSintonia22.377/safjAD2bBNT1grB23rFff5ChwDv.jpgScriptedReturning SeriesRewrite your future.Drama, CrimeFelipe Braga, KondZillaptNetflixBRPortuguêsLosbragas, Gullane Entretenimento, KondzillaBrazil440.0136070.0000000.6666670.0085910.206118.5107965.192957109.43
49Pasión de Gavilanes0.16050.03310.35610.13040.10665025.24-0.7438se_si8977000032021The Silent Seathe silent seaThe Silent Sea25.0697.800527.0The Silent SeaNaNNaN96777The Silent Sea18ko5277.800During a perilous 24-hour mission on the moon, space explorers try to retrieve samples from an abandoned research facility steeped in classified s...False/9hNJ3fvIVd4WE3rU1Us2awoTpgM.jpg2021-12-242021-12-24https://www.netflix.com/title/81098012False고요의 바다25.069/fFT0IgqtCOks4munDTxQwkvNJkd.jpgScriptedEndedA deserted Moon base. A deadly mission.Drama, Sci-Fi & Fantasy, MysteryPark Eun-kyo, Choi Hang-yongkoNetflixKR한국어/조선말Artist CompanySouth Korea450.0065930.0434780.4444440.0281300.148618.3127616.26909689.77
50Carter0.15020.03990.22220.34780.00025125.14-0.7501fi_ki10461000032021First Killfirst killFirst Kill21.1477.952270.0First KillNaNNaN111616First Kill18en2707.952Falling 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 ...False/abFQcEO0UJVfUQPmhgjaKeLxRwy.jpg2022-06-102022-06-10https://www.netflix.com/title/81213653FalseFirst Kill21.147/hwSy1vVaRzV70ZvAEsl6FGOHNlH.jpgScriptedCanceledYou never forget your first.Drama, MysteryV.E. SchwabenNetflixUSEnglishBelletrist ProductionsUnited States of America490.0118870.0434780.5288890.0137000.173618.4657505.602119104.61
51Raising Dion0.14910.03620.38670.08700.02035225.11-0.7519ca_or_sn11214000062021The Snitch Cartel: Originsthe snitch cartel originsThe Snitch Cartel: Origins42.0837.885283.0The Snitch Cartel: Origins6.0321.0128252The Snitch Cartel: Origins160es2837.885In 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.False/iQlel1ftOsnWguxyN98eUmKTOPm.jpg2021-07-282021-07-28https://www.netflix.com/title/81120861TrueEl cartel de los sapos: El origen42.083/sjkzeJJOdr59VdkB4BjlSVfJmq2.jpgScriptedReturning SeriesNaNCrime, SoapAndrés López LópezesNetflix, Caracol TVCOEspañolCaracol TelevisiónColombia470.0145740.1739130.4916670.0144300.189518.5352595.648974112.14
52The Silent Sea0.14850.00660.44440.04350.02815323.85-0.8290hu19831000062022HustlehustleHustle30.7387.708137.0HustleNaNNaN3764Hustle848en1377.708A motley group of London con artists pull of a series of daring and intricate stings.False/q7iwBG89RLMdkXoYGdVxC9rRg8Y.jpg2004-02-242012-02-17http://www.bbc.co.uk/programmes/b007gf9kFalseHustle30.738/6UTmAZnaqcIsWVKSBWm7pDmdDTI.jpgScriptedEndedThe con is on!Comedy, Crime, Drama, MysteryTony JordanenBBC OneGBEnglishKudos, Red Planet PicturesNaN600.0453160.1739130.3933330.0062320.169619.1053424.927254198.31
53Who Killed Sara?0.14210.02580.35440.08700.06215423.47-0.8526ho_pl7206000072021Hospital Playlisthospital playlistHospital Playlist21.8268.490100.0Hospital Playlist8.711014.096102Hospital Playlist224ko1008.490Every day is extraordinary for five doctors and their patients inside a hospital, where birth, death and everything in between coexist.False/5DUqsUZPLdDRzthR08vvQCXoyfJ.jpg2020-03-122021-09-16http://program.tving.com/tvn/doctorlife/False슬기로운 의사생활21.826/clYgUKk4CzmKpEukW2I2oDg9k1Q.jpgScriptedEndedThe slightly special everyday of us ordinary peopleDrama, ComedyLee Woo-jung, Shin Won-hokotvNKR한국어/조선말Egg is Coming, CJ ENMSouth Korea930.0002750.2173910.8277780.0041550.292518.0930104.61512172.06
545522.37-0.9204ch_ma10421000052021The Chestnut Manthe chestnut manThe Chestnut Man15.7507.584455.0The Chestnut Man7.668351.0127865The Chestnut Man16da4557.584At 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...False/fIXTetAsXkPO6UUUw85IU4oactf.jpg2021-09-292021-09-29https://www.netflix.com/title/81039388FalseKastanjemanden15.750/tdLJTZJ184PsO9QkK9aPeeAXLlo.jpgMiniseriesEndedIf you find one, he's already found you.Crime, DramaSøren Sveistrup, Mikkel Serup, David Sandreuter, Dorte Warnøe HøghdaNetflixDKDanskSAM ProductionsDenmark550.0117450.1304350.3244440.0240880.13110.01170.32440.13040.024118.4619196.122493104.21
55Dark Desire0.12750.04700.16670.13040.23335621.63-0.9662fi_ju17785000062021The Five Juanasthe five juanasThe Five Juanas18.6727.419246.0The Five Juanas6.92615.0133086The Five Juanas118es2467.419Five 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.False/jB27sNxsgITNhr6HavrHywojy8V.jpg2021-10-062021-10-06https://www.netflix.com/title/81167761FalseLa venganza de las Juanas18.672/iFffOCcrtf5mSoGKd2J31d2b5yg.jpgScriptedEndedNaNDramaJimena RomeroesNetflixMXEspañolLemon StudiosMexico340.0380170.1739130.2327780.0123530.119818.9964515.509388177.85
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60Another Self0.08630.00620.22220.08700.00026110.95-1.6234lo_sp18518000042021Lost in Spacelost in spaceLost in Space412.0317.11596.0Lost in SpaceNaNNaN3051Lost in Space383en967.115The 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...False/upqfQF1LBPf7IRZao0sxt1BHZlx.jpg1965-09-151968-03-06NaNFalseLost in Space412.031/dGhtCpYZutuso2P3GKWnQnqwhSi.jpgScriptedEndedA space colony family struggles to survive when their ship is hopelessly thrown off course.Sci-Fi & FantasyIrwin AllenenCBSUSEnglishNaNNaN600.0406320.0869570.0638890.0039300.051419.0368394.574711185.18
61Love Is Blind0.06620.06620.05560.13040.00166210.77-1.6346an_sc17102000052022Anatomy of a Scandalanatomy of a scandalAnatomy of a Scandal13.1977.071120.0Anatomy of a Scandal7.040449.0113984Anatomy of a Scandal16en1207.071Anthology series centering on the personal and political scandals of Britain’s elite.False/8zPoUJECL6X8G5T95OuJDorHdtV.jpg2022-04-152022-04-15https://www.netflix.com/title/81152788FalseAnatomy of a Scandal13.197/e1pUQkXbFNI6xH4oDuHnyJoOSib.jpgScriptedEndedNot everyone is entitled to the truth.DramaDavid E. Kelley, Melissa James GibsonenNetflixGB, USEnglishDavid E. Kelley Productions, 3dot Productions, Made Up Stories, Endeavor Content, Anonymous ContentUnited States of America450.0355800.1304350.0394440.0052780.051218.9572914.795791171.02
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65Lost in Space0.05140.04060.06390.08700.0039667.41-1.8412am_ge_i7129000032022I Am Georginai am georginaI Am Georgina11.5607.111293.0I Am Georgina4.14667.0156077I Am Georgina212es2937.111Join Georgina Rodríguez — mom, influencer, businesswoman and Cristiano Ronaldo's partner — in this emotional and in-depth portrait of her daily life.False/3SfKp1aTkXdxl1jSsURjHycAHAO.jpg2022-01-272023-03-24https://www.netflix.com/title/81423622TrueSoy Georgina11.560/70e3SQBIAcjKSibWoyCA7lBMZA8.jpgRealityReturning SeriesOne in a million.RealityNaNesNetflixESEspañolNaNNaN400.0000000.0434780.0616670.0149920.029418.0822675.68358071.29
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68693.21-2.0995se_su15878000042021Selling Sunsetselling sunsetSelling Sunset9.4597.10039.0Selling Sunset6.59335.087826Selling Sunset656en397.100The 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.False/hB2llGkvqcf8sBgEv00HO57eGaa.jpg2019-03-222023-05-19https://www.netflix.com/title/80223108TrueSelling Sunset9.459/wUtoD67q4BnRUwikUbT6M38d5oj.jpgScriptedReturning SeriesNaNRealityAdam DiVelloenNetflixUSEnglishDone and Done Productions, Lionsgate TelevisionUnited States of America320.0312130.0869570.0555560.0007300.04510.03120.05560.08700.000718.8830303.688879158.78
69702.89-2.1196st_tr9082000032021True Storytrue storyTrue Story15.6957.17182.0True StoryNaNNaN134029True Story17en827.171A world-famous comedian desperately searches for a way out after a night in Philadelphia with his brother threatens to sabotage more than his succ...False/yGpmZYJ8fb543B3tIKjzcRHgJsX.jpg2021-11-242021-11-24https://www.netflix.com/title/81132961FalseTrue Story15.695/7zMbzJijHQv5oOtkb8rdYarn9X6.jpgMiniseriesEndedBetrayal is relative.Drama, CrimeEric NewmanenNetflixUSEnglishHartBeat Productions, Grand ElectricUnited States of America360.0069680.0434780.0950000.0031440.04010.00700.09500.04350.003118.3243904.41884190.82
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" ], "text/plain": [ - " 影劇名稱 綜合 The Hit 指數 觀看時數_Norm (35%) TMDb評分_Norm (30%) 上榜週數_Norm (20%) TMDb投票數_Norm (15%)\n", - "0 Stranger Things 0.8522 1.0000 0.9022 0.4783 0.9060\n", - "1 Squid Game 0.6817 0.7914 0.4617 0.7826 0.7314\n", - "2 Money Heist 0.6011 0.3921 0.6983 0.5217 1.0000\n", - "3 Manifest 0.4256 0.4905 0.4044 0.6087 0.0722\n", - "4 Bridgerton 0.4153 0.3701 0.6350 0.3913 0.1134\n", - "5 The Umbrella Academy 0.4110 0.1225 0.8911 0.1304 0.4978\n", - "6 All of Us Are Dead 0.4053 0.2099 0.7522 0.3913 0.1858\n", - "7 Ozark 0.4043 0.2427 0.6911 0.4783 0.1090\n", - "8 The Good Doctor 0.3893 0.0147 0.8350 0.1739 0.6593\n", - "9 Café con aroma de mujer 0.3765 0.2577 0.2778 1.0000 0.0200\n", - "10 Peaky Blinders 0.3722 0.0407 0.8589 0.1304 0.4947\n", - "11 Sex Education 0.3690 0.1748 0.7244 0.1739 0.3718\n", - "12 Our Blues 0.3652 0.0121 1.0000 0.3043 0.0010\n", - "13 Arcane 0.3631 0.0296 0.9667 0.1739 0.1861\n", - "14 Business Proposal 0.3607 0.0741 0.7900 0.4783 0.0143\n", - "15 Hometown Cha-Cha-Cha 0.3594 0.0818 0.6850 0.6087 0.0234\n", - "16 The Queen of Flow 0.3582 0.1748 0.5461 0.6087 0.0766\n", - "17 Twenty Five Twenty One 0.3488 0.0358 0.8872 0.3478 0.0040\n", - "18 Extraordinary Attorney Woo 0.3367 0.1187 0.8233 0.2174 0.0314\n", - "19 Alchemy of Souls 0.3328 0.0201 0.9011 0.2609 0.0216\n", - "20 Elite 0.3108 0.0920 0.5950 0.1304 0.4936\n", - "21 Our Beloved Summer 0.3097 0.0205 0.8333 0.2609 0.0027\n", - "22 Cobra Kai 0.3078 0.1114 0.6778 0.0870 0.3205\n", - "23 The King's Affection 0.3049 0.0240 0.7506 0.3478 0.0116\n", - "24 The Seven Deadly Sins: Cursed by Light 0.3015 0.0029 0.8122 0.0870 0.2630\n", - "25 Hospital Playlist 0.2926 0.0003 0.8278 0.2174 0.0042\n", - "26 Virgin River 0.2794 0.1973 0.5733 0.1739 0.0237\n", - "27 The Last Kingdom 0.2681 0.0422 0.7167 0.1304 0.0819\n", - "28 My Name 0.2611 0.0438 0.7128 0.1304 0.0387\n", - "29 Never Have I Ever 0.2521 0.0808 0.6450 0.0870 0.0860\n", - "30 All American 0.2505 0.0390 0.6917 0.1304 0.0218\n", - "31 On My Block 0.2497 0.0034 0.8117 0.0000 0.0332\n", - "32 Control Z 0.2342 0.0066 0.6817 0.0435 0.1244\n", - "33 The Sandman 0.2257 0.0724 0.5911 0.0435 0.0955\n", - "34 Good Girls 0.2154 0.0292 0.5544 0.1739 0.0271\n", - "35 Locke & Key 0.2115 0.0739 0.5017 0.1304 0.0605\n", - "36 Juvenile Justice 0.2097 0.0226 0.5261 0.2174 0.0033\n", - "37 The Marked Heart 0.2066 0.0719 0.4539 0.2174 0.0116\n", - "38 Sintonia 0.2061 0.0136 0.6667 0.0000 0.0086\n", - "39 The Lincoln Lawyer 0.1997 0.0835 0.4478 0.1739 0.0088\n", - "40 Emily in Paris 0.1985 0.0964 0.4306 0.1304 0.0632\n", - "41 Vikings: Valhalla 0.1976 0.0693 0.4728 0.1304 0.0362\n", - "42 Narcos: Mexico 0.1919 0.0241 0.5217 0.0870 0.0637\n", - "43 The Snitch Cartel: Origins 0.1896 0.0146 0.4917 0.1739 0.0144\n", - "44 Sweet Magnolias 0.1748 0.0398 0.4444 0.1304 0.0101\n", - "45 First Kill 0.1736 0.0119 0.5289 0.0435 0.0137\n", - "46 Hustle 0.1696 0.0453 0.3933 0.1739 0.0062\n", - "47 Money Heist: Korea - Joint Economic Area 0.1669 0.0097 0.5000 0.0435 0.0323\n", - "48 Forecasting Love and Weather 0.1613 0.0105 0.3506 0.2609 0.0020\n", - "49 Pasión de Gavilanes 0.1605 0.0331 0.3561 0.1304 0.1066\n", - "50 Carter 0.1502 0.0399 0.2222 0.3478 0.0002\n", - "51 Raising Dion 0.1491 0.0362 0.3867 0.0870 0.0203\n", - "52 The Silent Sea 0.1485 0.0066 0.4444 0.0435 0.0281\n", - "53 Who Killed Sara? 0.1421 0.0258 0.3544 0.0870 0.0621\n", - "54 The Chestnut Man 0.1311 0.0117 0.3244 0.1304 0.0241\n", - "55 Dark Desire 0.1275 0.0470 0.1667 0.1304 0.2333\n", - "56 The Five Juanas 0.1198 0.0380 0.2328 0.1739 0.0124\n", - "57 Midnight Mass 0.1191 0.0253 0.2917 0.0870 0.0358\n", - "58 Sex/Life 0.1027 0.0752 0.0767 0.2174 0.0659\n", - "59 Collision 0.0874 0.0322 0.1667 0.1304 0.0000\n", - "60 Another Self 0.0863 0.0062 0.2222 0.0870 0.0002\n", - "61 Love Is Blind 0.0662 0.0662 0.0556 0.1304 0.0016\n", - "62 Turning Point: 9/11 and the War on Terror 0.0582 0.0034 0.1594 0.0435 0.0032\n", - "63 Archive 81 0.0560 0.0208 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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? 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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", 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"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", - "# 1. 讀取資料集\n", - "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\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", - "# 2. 僅使用 TMDb 作為篩選與計算基準，確保數據最完整\n", - "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\n", + "netflix_all = df.copy()\n", + "tmdb_all = tmdb.copy()\n", "\n", - "tmdb_rating_p50 = df_valid_tmdb[\"tmdb_vote_average\"].median()\n", - "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median()\n", - "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean()\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", - "# 3. 篩選出 71 部 The Hit 影集\n", - "hit_condition = (\n", - " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", - " (df[\"tmdb_vote_count\"] >= tmdb_votes_p50) &\n", - " (df[\"netflix_viewing_hours\"] > netflix_hours_mean)\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", - "df_hits = df[hit_condition].copy().reset_index(drop=True)\n", "\n", - "# 4. 對僅存的 4 個關鍵量化屬性進行 Min-Max 標準化\n", - "target_cols = [\n", - " \"netflix_viewing_hours\",\n", - " \"netflix_weeks\",\n", - " \"tmdb_vote_average\",\n", - " \"tmdb_vote_count\"\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", - "for col in target_cols:\n", - " min_val = df_hits[col].min()\n", - " max_val = df_hits[col].max()\n", - " if max_val - min_val != 0:\n", - " df_hits[f\"{col}_norm\"] = ((df_hits[col] - min_val) / (max_val - min_val)).round(4)\n", - " else:\n", - " df_hits[f\"{col}_norm\"] = 1.0000\n", - "\n", - "# 5. 套用全新無 IMDb 的【綜合 The Hit 指數】加權公式\n", - "df_hits['綜合 The Hit 指數'] = (\n", - " df_hits['netflix_viewing_hours_norm'] * 0.35 +\n", - " df_hits['tmdb_vote_average_norm'] * 0.30 +\n", - " df_hits['netflix_weeks_norm'] * 0.20 +\n", - " df_hits['tmdb_vote_count_norm'] * 0.15\n", - ").round(4)\n", - "\n", - "# 6. 設定不省略 Rows 與 Columns\n", - "pd.set_option('display.max_rows', None)\n", - "pd.set_option('display.max_columns', None)\n", - "\n", - "# 7. 依「綜合 The Hit 指數」從高到低（降冪）排序\n", - "df_hits_sorted = df_hits.sort_values(by=\"綜合 The Hit 指數\", ascending=False).reset_index(drop=True)\n", - "\n", - "# 整理要呈現的欄位\n", - "display_cols = [\n", - " \"netflix_title\",\n", - " \"綜合 The Hit 指數\",\n", - " \"netflix_viewing_hours_norm\",\n", - " \"tmdb_vote_average_norm\",\n", - " \"netflix_weeks_norm\",\n", - " \"tmdb_vote_count_norm\"\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", - "print(\"=\" * 120)\n", - "print(\"🏆 【全新優化版】71 部 The Hit 影集「綜合實力榜單」\")\n", - "print(\" (已剔除 IMDb 缺失數據，採用全新 35% / 30% / 20% / 15% 權重分配)\")\n", - "print(\"=\" * 120)\n", - "\n", - "display(df_hits_sorted[display_cols].rename(columns={\n", - " \"netflix_title\": \"影劇名稱\",\n", - " \"netflix_viewing_hours_norm\": \"觀看時數_Norm (35%)\",\n", - " \"tmdb_vote_average_norm\": \"TMDb評分_Norm (30%)\",\n", - " \"netflix_weeks_norm\": \"上榜週數_Norm (20%)\",\n", - " \"tmdb_vote_count_norm\": \"TMDb投票數_Norm (15%)\"\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": "885604c1", + "id": "8c0711e1", "metadata": {}, "source": [ "## our analysis\n", @@ -7548,18 +13232,23 @@ }, { "cell_type": "code", - "execution_count": 56, - "id": "afd8cfa3", + "execution_count": 62, + "id": "a591acd9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "========================================================================================================================\n", - "🏆 【The Hit 影集】綜合指數與「語言、成人與否、類型」特徵總表\n", - " (已依『綜合 The Hit 指數』由高至低排序，幫助您快速識別頂級爆款的共通基因)\n", - "========================================================================================================================\n" + "====================================================================================================\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" ] }, { @@ -7573,453 +13262,1298 @@ "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "\n", + "
影劇名稱綜合 The Hit 指數
0Stranger Things0.8522
1Squid Game0.6817
2Money Heist0.6011
3Manifest0.4256
4Bridgerton0.4153
5The Umbrella Academy0.4110
6All of Us Are Dead0.4052
7Ozark0.4043
8The Good Doctor0.3893
9Café con aroma de mujer0.3765
10Peaky Blinders0.3722
11Sex Education0.3691
12Our Blues0.3652
13Arcane0.3631
14Business Proposal0.3607
15Hometown Cha-Cha-Cha0.3594
16The Queen of Flow0.3582
17Twenty Five Twenty One0.3489
18Extraordinary Attorney Woo0.3367
19Alchemy of Souls0.3328
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Genre RankGenreTitle CountPercentage of All Hits (%)
20Elite0.310801Drama6287.3
21Our Beloved Summer0.309812Mystery1825.4
22Cobra Kai0.307823Crime1723.9
23The King's Affection0.304934Sci-Fi & Fantasy1521.1
24The Seven Deadly Sins: Cursed by Light0.301545Comedy1318.3
25Hospital Playlist0.292556Action & Adventure1216.9
26Virgin River0.279467Reality34.2
27The Last Kingdom0.268178War & Politics34.2
28My Name0.261189Animation22.8
29Never Have I Ever0.2521910Soap22.8
30All American0.25051011Documentary11.4
31On My Block0.2497
\n", + "" + ], + "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": [ + "
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Hit RankTitleHit ScoreGenres
32Control Z0.234201Stranger Things100.00Drama, Sci-Fi & Fantasy, Mystery
33The Sandman0.225712Money Heist84.49Crime, Drama
34Good Girls0.215423Squid Game82.90Action & Adventure, Mystery, Drama
35Locke & Key0.211534The Umbrella Academy74.10Action & Adventure, Sci-Fi & Fantasy, Drama
36Juvenile Justice0.209745Sex Education70.62Comedy, Drama
37The Marked Heart0.206556All of Us Are Dead68.76Action & Adventure, Drama, Sci-Fi & Fantasy
38Sintonia0.206167Bridgerton67.90Drama
39The Lincoln Lawyer0.199778Ozark64.97Crime, Drama
40Emily in Paris0.198589Peaky Blinders64.08Drama, Crime
41Vikings: Valhalla0.1976910Cobra Kai64.00Action & Adventure, Drama, Comedy
42Narcos: Mexico0.1919
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" + ], + "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": [ + "
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GenreTop 10 CountTop 10 Percentage (%)
43The Snitch Cartel: Origins0.18950Drama10100.0
44Sweet Magnolias0.17491Action & Adventure440.0
45First Kill0.17362Crime330.0
46Hustle0.16963Sci-Fi & Fantasy330.0
47Money Heist: Korea - Joint Economic Area0.16694Comedy220.0
48Forecasting Love and Weather0.16135Mystery220.0
49Pasión de Gavilanes0.1605
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" + ], + "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": [ + "
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GenreTop 10 CountTop 10 Percentage (%)Title CountPercentage of All Hits (%)Top-vs-All Difference (percentage points)
50Carter0.15020Drama10100.06287.312.7
51Raising Dion0.14911Action & Adventure440.01216.923.1
52The Silent Sea0.14862Sci-Fi & Fantasy330.01521.18.9
53Who Killed Sara?0.14213Crime330.01723.96.1
54The Chestnut Man0.13114Comedy220.01318.31.7
55Dark Desire0.12755Mystery220.01825.4-5.4
56The Five Juanas0.11986Documentary00.011.4-1.4
57Midnight Mass0.11917Animation00.022.8-2.8
58Sex/Life0.10278Soap00.022.8-2.8
59Collision0.08749Reality00.034.2-4.2
60Another Self0.086310War & Politics00.034.2-4.2
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" + ], + "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", + "？？" + ] + }, + { + "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": [ + "
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Hit_rankHit_scorenetflix_titleTMDb_raw_original_languageTMDb_raw_origin_countryTMDb_raw_number_of_seasonsTMDb_raw_number_of_episodesTMDb_raw_vote_averageTMDb_raw_popularityTMDb_raw_genresAvg_episodes_per_season
61Love Is Blind0.066201100.00Stranger ThingsenUS4348.624185.711Drama, Sci-Fi & Fantasy, Mystery8.5
62Turning Point: 9/11 and the War on Terror0.05821284.49Money HeistesES3418.25796.354Crime, Drama13.7
63Archive 810.05602382.90Squid GamekoKR297.831115.587Action & Adventure, Mystery, Drama4.5
64Welcome to Eden0.05153474.10The Umbrella AcademyenUS4328.60451.859Action & Adventure, Sci-Fi & Fantasy, Drama8.0
65Lost in Space0.05144570.62Sex EducationenGB4328.3041008.977Comedy, Drama8.0
66Anatomy of a Scandal0.05125668.76All of Us Are DeadkoKR2128.354107.108Action & Adventure, Drama, Sci-Fi & Fantasy6.0
67Alba0.04906767.90BridgertonenUS3248.14371.308Drama8.0
68Selling Sunset0.04517864.97OzarkenUS4448.24468.006Crime, Drama11.0
69True Story0.04018964.08Peaky BlindersenGB6368.546344.477Drama, Crime6.0
70I Am Georgina0.029491064.00Cobra KaienUS6508.220118.256Action & Adventure, Drama, Comedy8.3
\n", "
" ], "text/plain": [ - " 影劇名稱 綜合 The Hit 指數\n", - "0 Stranger Things 0.8522\n", - "1 Squid Game 0.6817\n", - "2 Money Heist 0.6011\n", - "3 Manifest 0.4256\n", - "4 Bridgerton 0.4153\n", - "5 The Umbrella Academy 0.4110\n", - "6 All of Us Are Dead 0.4052\n", - "7 Ozark 0.4043\n", - "8 The Good Doctor 0.3893\n", - "9 Café con aroma de mujer 0.3765\n", - "10 Peaky Blinders 0.3722\n", - "11 Sex Education 0.3691\n", - "12 Our Blues 0.3652\n", - "13 Arcane 0.3631\n", - "14 Business Proposal 0.3607\n", - "15 Hometown Cha-Cha-Cha 0.3594\n", - "16 The Queen of Flow 0.3582\n", - "17 Twenty Five Twenty One 0.3489\n", - "18 Extraordinary Attorney Woo 0.3367\n", - "19 Alchemy of Souls 0.3328\n", - "20 Elite 0.3108\n", - "21 Our Beloved Summer 0.3098\n", - "22 Cobra Kai 0.3078\n", - "23 The King's Affection 0.3049\n", - "24 The Seven Deadly Sins: Cursed by Light 0.3015\n", - "25 Hospital Playlist 0.2925\n", - "26 Virgin River 0.2794\n", - "27 The Last Kingdom 0.2681\n", - "28 My Name 0.2611\n", - "29 Never Have I Ever 0.2521\n", - "30 All American 0.2505\n", - "31 On My Block 0.2497\n", - "32 Control Z 0.2342\n", - "33 The Sandman 0.2257\n", - "34 Good Girls 0.2154\n", - "35 Locke & Key 0.2115\n", - "36 Juvenile Justice 0.2097\n", - "37 The Marked Heart 0.2065\n", - "38 Sintonia 0.2061\n", - "39 The Lincoln Lawyer 0.1997\n", - "40 Emily in Paris 0.1985\n", - "41 Vikings: Valhalla 0.1976\n", - "42 Narcos: Mexico 0.1919\n", - "43 The Snitch Cartel: Origins 0.1895\n", - "44 Sweet Magnolias 0.1749\n", - "45 First Kill 0.1736\n", - "46 Hustle 0.1696\n", - "47 Money Heist: Korea - Joint Economic Area 0.1669\n", - "48 Forecasting Love and Weather 0.1613\n", - "49 Pasión de Gavilanes 0.1605\n", - "50 Carter 0.1502\n", - "51 Raising Dion 0.1491\n", - "52 The Silent Sea 0.1486\n", - "53 Who Killed Sara? 0.1421\n", - "54 The Chestnut Man 0.1311\n", - "55 Dark Desire 0.1275\n", - "56 The Five Juanas 0.1198\n", - "57 Midnight Mass 0.1191\n", - "58 Sex/Life 0.1027\n", - "59 Collision 0.0874\n", - "60 Another Self 0.0863\n", - "61 Love Is Blind 0.0662\n", - "62 Turning Point: 9/11 and the War on Terror 0.0582\n", - "63 Archive 81 0.0560\n", - "64 Welcome to Eden 0.0515\n", - "65 Lost in Space 0.0514\n", - "66 Anatomy of a Scandal 0.0512\n", - "67 Alba 0.0490\n", - "68 Selling Sunset 0.0451\n", - "69 True Story 0.0401\n", - "70 I Am Georgina 0.0294" + " 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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", 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" + ] + }, + "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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GeMTCGWecscl29e7dO0ZfNgYiI1GS/C0uByOqk+hIosgag79X72/WK3EACMikV7cFCCxcaAXUvOUBCCL0aYrsIjKsHOGJAFELkZGIGInay6gQGhDrEG/5nAg6hCYizHitjb3epmrQEh1JnVDah9BL6jf9XgbRtrYWaXNKHJRBWCEqE1GYsYVwnfoKiMKuvdgrgSDTWOp+vcjicjRjGSKXEaGbC1HFUCsmTyxEDQIlHFJqNwIoohsRlbyniGJlYZzoaQTMBAIgP4PQRLr4hOoOA1GDCQQ4RG5KJiDaQRIzy9GIiVSCo973yiCAIvzz3lFOAscMY5u28qC0BwId6ea1cxABuhztSYRo2XExMf1GhCqp70OHDo0ODgRKnEasJfxd1rDZZpstCpA4rXBopPGPUMhFcDinuOiMusj1HB2NQQYAAnoiXbKGKIlQTKkAxjxR4czd/fffP/4c7yPjMZUuact+pu4qUDs59R1z769//Wv8vzg/EKpTVHXt3KIGOO8R5SHKkc7N7a+WjPlNNtkkti+Bs4p1nRIRE/v/E2nu8ppFREQmJRRoRUREMiEdqhFbOdwj9pHCyiEf4YYoQkQDLg8ilZw6fvVSxMuRR0moq5fejpCDoEW6bEtENATZdHkRIgZRlIhE//rXv6KghEBFmnK9WqBEp5UvCUMEJmqupbVHiWarfR0IsfVS/2lTSp9GDCLaEsEmiaKUegD6FkGC9P6WQpmKE044If6d5kYDp/ePCEqicUkVJkqtFmpgUuuR+pa1KfT0RxKLEbYZU+niJ6IwEfFxAJQvkyNSkVINvI/l1GKENB6kONeC0F+vDmdZUC6Xv2gKRLpyhF5ZDG4uyTExIafDhECQQpwr191MUIoEsYq5Uxar6KNaeF94P/l7zRFoia5kbhBxTM1iaiAz/1Pae4qSJfoyRdwnUkRmvbrU9cCBwvjhwVjjNSEa4hxijamtEZ1qZCf4/4iC/G5L+o2yKgiyCLO894iX/B4iIwItZQ4Y+0R1ppIIgCBIGxmvPIgspSwBc5h53Zx5lpxZZUjfR6AliheBlssP+XupBjRzijIoPJ/KB7RlPxP9DU3VXh02bFjsK2C9Y73EscTvImim8V+eR83tr5aMeeosl0FIZ0wkZ+PE/P8E5SWgXsaCiIhIR8YSByIiIpnAZTIpCom6skR4EXGEsMYlTCkqiQgqxAIOvUTX1YNIK+pfptRyxDoOxESjJXiOv8uDg39r244oR2o7qfdEhhGBWa4DyuviZ6hTiCiR0v55ICAjMpSfKz8mlE48sRCBSkp6gn7cZpttGsRZBBzSoIlqQ0xpDFJ/G2szD8TZJGQ09jOpjmTt3+LiIsQi+rL2d0g3RvBDyEcwQTRqLgh4jAX6nPc+vX/UzkV0n9BlY2VIDWccIURTJqJcoxLhtlz+ojnQJn6nXo3ViaFevdy2IkWo1gpL9WreJqFsQhfMlUG0pKwBUZtEXDP/Ec/K0YVJzKu9yA0RE5GtMbhcDqGVS6Vq246Qzljn+/xPInjL8P7Wa2tL+4124hxBoAXWM4RY5jqvA1GUdhIpnmrcpvIOjDcuICRqmbYj7rK+UEO1OdR7LWnMlN9XBHNIIirlPmjjhOr1tqSfmzNmuXwuvde8dsrVsOYiLPM9It5rx1pb9FdjY75eP9auRRP7/ydmroiIiHQk3AFFREQygUM2kYvU/CO6jPp+e+65Z6xLSeo9F8Ckn0PM4HCLeIcIWFt3kqgvUvURcwHBC5GQlPqUkk+dQMQQRGAO0E1BdFatSJiiZ4G2UFcQ0YWajaSzckt7SksGImbLdWuJyKJNHNiJdE0Xcv32t7+N6bbln0WsaQq+X1sXt7GLs6g1iTCS+gb43/wNxMQUMUukGiI39XopO0G/EQWHiNncOr2pDi8RtI39DDUfa/8W7xPiK5GEfM3/RTglLZivSf0nuhLBhPFRblNzoCQCv8t7wntLNC0CMdG29USXpkDkJRWfSDnefwR6xhv9SLr4xEAZBsZWPQGyOSRxspxi3RIQ6ongS3OuTHJylC+OgnpjNImPTQnOvI9EFFIapJZUczZFJKboRVLmyyCwv/zyy1HwbEo0pX9YK8rOiTL8bnr/J1QqoS36jahjoiqZj0R/kvrO/2ddIoKWNQLBr/biQNrJ79J39DEOFsYN0baIlxOCcgZleK3pvUrlJIDyA/Qpkaq8v4zLJNo2RUv6OUXt8hoaWyvSmsuFbcx5ajizRhClTamTxtbx5vRXS8Z8c5mY9ytlklDeQkREZFJCgVZERCQTEAHShV+kd1OzkAM4whyH2VSjECENAYrLmYg4RMglHbosPiAqIpilaEhEUFJLqRlIBCuHey5tQZhD/CMKq95FTnDiiSfGsgWIxAgDgJDHgZ56guWIPQQC6jlyw3mq21gPBFiEDoQlUueJJiM1FmEZwYLLZtLrbWuoeYlIi5BYFmIQDmhLSrGFnXbaKZaGQFggvZm04qZeV1tAuQjef9q4yy67xPeI+pPUlkylB2grfU1JAgTeiS0HwN+h1iRlIEipRmTlPZ7YyFVStimTgJiS6pMi/BI9i0BF/c3am+YbE/9rLyBCpK5XwqMpUhmBVIu2paSITWrKIiIhlBLFijCOGIaYVusAoP+ovUwkI5GTRFFStmP55ZdvMuKSPud36EfmLvObPqHsB6Ig/yuJ+EQozzLLLPG9TzVJiX4n+hkRl/ndFERO45hgfaBsBs4exjR/B5GUurJENzIfy/Pgl+o3RDv+N84dombpq5RST+QsUfisO7zmBJkFCLbp7+PIoA+T4NmcEgeUiGH9oywEaxB1Vqktu8IKK4wnDLJO8XP8DGtUuqysrfuZtZQ5gLOLccPP8kAcJfKURypjgZCK6ImDi/6k7xC66Xcicctzqbn91ZIx3xwm9v1KcxdHnYiIyKSENWhFREQygKhTBAJEnQSHbEQYxEFqMVIblJqWKXoOwRCuueaaht9B2EEoQ/Ai+rYcKcaBngu9ENX4HqIkh34gbZd6iAik5RTTlGKPWEo6bapvmcoS0D5uPicSEzGNFGAEX0Td2kg+xAVuM6e9RIMhfCBAUy803fpN/UP+F6+Z/4eQQg1dIooRR9oC/j6icrl+Iv1BjV3EAepLUu+X1GMuuEHIJJUfkYjIYNpN1GptVF89ENsQIpqTtoswndKUibxFPCUKmn4jso4xQlQv7UKQQ/jgfUMAIloZca5cH7IpiAhGAOG94G8hEk1MmQRA0EeA5X1GiCr/PsIT7yGiI+OBNlJConZM0G4ebQURfoi01OVsDTgYGJOIaLUXoeHsYHzXvqeIe0QS8yindyPsNQVOGUR5hDj6qramJ3Mr9RtjiXlKejiOEB4JIj1pd1Mw7olspj4060daQ2p/BlHu1+g35juOHoRYou1TmQgiaZkPCIO81jLMO+ZfvQvqEHybU+sXpwSZB6kESXqvyu9dgnnFe4BjC4dNvVIWbdHPCL/UnMZRx/+phb+VHG7U9Ub0LV90BqyRzOVyPe7m9ldLxnxzmNj3i0hwYG0TERGZlFCgFRERqRhS5xEjiZRMkXIpSpKoPOrRkvKb6hkiyiBsEDXJwZ+DLBFbgJjHgZcIVw7VKdU7XbCT0mKJTitfAoSIxAEdQQ1hASGI6D2ENYQDxEKiAJM4TBQUAgPRXghzCAVERxEZRtRnLaQGc0Cn3bQV0QNBpB6IELxGIrkQK4i8Quw79NBD4wVCrYV+QaApiw30G8IG/4PXhSBLPyBAA+IsdV8XW2yx2IbyxUi1II4guhHJlvqmOcIG4jrtou+JViNyF3EKsROhHKGYKFrqkCISI6ymerlctsPXRFbWRmvSVsQuxNh0gzziKqU0+B3EQUT4HXbYIQpjiISk3DfVZgR2bpunnYydeqIVIhLp10RuE4lXK8625L1MNYInJAgRycp4bO6FWbUwvukTLkFjnvDe8LcQERGUcW7UgrOD18y8RJinD5k/CO5NQb8wvqnRyTxjnuO04H/hbKmNasaBQH9z8RtlEZhHG2ywQRTwmiMe0k7GNXVQidQkgpSxSt/iCMFZUb4k7pfuN/4n6xxjL0F0O5ff0bbaiFUcNqmuKenxOBsYh8ydemtPPYgWpS30OdHjlA5g7nPxYi28FwiJrEUTEsBb28+s9cx3MhqIPGctoJ1/+ctfxpkv/C5zmr9PJgV/h35iPWbdIkOBtZ81rbn91ZL3rjlM7PtFJDDrrBG0IiIyqdGpaEmBKREREWlTgZZoVOqfckiuB4d8ImgR5xBmEHLZwonoRJRBzCyDyMPhF2GUtGHEOcRCnkeIIAqPS6jKUC8UkYfv1V7KhciHSEB9QiJP+XspNZW2EzlLSnZLD/H8LiIGIsivCSIBkYctjRjMBUQPxDxE15SqjNDBe4ngjEjIe4PwlcSvWhA0iWRE3EHIJ5oacSpB+QeERGpdNgZji0hDRN/GIFIUgYn/NSHxsvZiN8QtnA9NgQiEQE7EX21E6i8B9aBxriBU0//S8eD9xfFSdmpJ24OIjXj9a81dERGRnFCgFREREZEOBZegESWM8+CXRoG244JjipIoRLJT/oAoc/nlwOFIf5Op0ZizUkREpKPiJWEiIiIi0qEgkpdob8pjiLQEMhrIMkCcJd2eGsryy0FJhgEDBoQjjjhCcVZERCZJFGhFREREpENBCZCDDz441uyl7qfIxEI9Wmqw8pH6z82p7ysth/Ip1CHu3r273SgiIpMkljgQERERERERERERqQgjaEVEREREREREREQqQoFWREREREREREREpCK6hHbKzz//HL799tsw5ZRThs6d1ZlFREREREREREQkD7gLYdSoUWHGGWcMXbp06ZgCLeLssGHDqm6GiIiIiIiIiIiISF26du0aZp111tAhBVoiZ9OLnHrqqatujoiIiIiIiIiIiEhkxIgRMbg0aZgdUqBNZQ0QZ6eZZpqqmyMiIiIiIiIiIiIyDs0pzWrxVhEREREREREREZGKUKAVERERERERERERqQgFWhEREREREREREZGKyFKgHTNmTPjhhx+qboaIiIiIiIiIiIjIL0pWl4T99NNP4dRTTw3PPvtsmGOOOcJXX30VTjnllLD44otX3TQRERERERERERGRjh1Be+mll4Ynnngi3HTTTeHyyy8P3bt3D/vuu28YPnx41U0TERERERERERER6dgC7YMPPhjWX3/9MPXUU8evt9566/DJJ5+El19+ueqmiYiIiIiIiIiIiHRsgXbs2LFh5MiR45Q8gM6ds2qmiIiIiIiIiIiISMerQbvDDjuEPn36hF133TXMM888oV+/fmG11VYLyy23XKO/M2LEiIbPO3XqFHKjKIqqmyAiIiIiIiIiIiK/ImXNsl0JtGuuuWa44447wiGHHBK6du0aHnnkkfh5UxG0w4YNix8nn3zysNhii8WPuTB69OgwdOjQ+FFEREREREREREQkW4F21KhRMYL2j3/8YzjiiCPic2+++WbYdtttw5gxY8L2229f9/cQcqlZS/Qs4uwuu+wVhg59PVTNYostEq666pKw0EILGUUrIiIiIiIiIiIyiUXQDvv/AkvbjUD7f//3f+GDDz4IO++8c8NzCy64YFhjjTXCAw880KhAizg7zTTTNHyNOPt///dSyIV04ZmIiIiIiIiIiIhILdncvjXjjDPGjz/++OM4z3Np2GyzzVZRq0REREREREREREQmAYF2kUUWCauvvno444wzwg8//BCfGzRoUHj66afDTjvtVHXzRERERERERERERNqcbEocUEP2ggsuCJdffnnYZ599Yt1WImevv/76sOiii1bdPBEREREREREREZGOK9Cmeq37779/fIiIiIiIiIiIiIh0dNpMoB0+fHj4+OOPw9dffx1mmmmmMOecc4bpppuurf68iIiIiIiIiIiISIejVQLtTz/9FG699dZw9913h2effTb8/PPPDd/r3LlzWG655cImm2wStt566zDFFFO0RXtFREREREREREREOgwtFmhvu+220Ldv3/D999+H5ZdfPpYl+O1vfxtmmGGG8OOPP4ZPPvkkvPTSS6FPnz6hX79+4ZBDDgmbbrpp27ZeREREREREREREZFITaHv27BleeeWVsPfee4fNN9+8yVIGI0aMCP/+97/DOeecEx544IH4UURERERERERERERC6NySTlhsscViWYMdd9xxgnVmufhrm222Cffcc09YdNFF7XMRERERERERERGR1kTQ7rPPPhP9O9SgbcnviYiIiIiIiIiIiHRUWnVJWPmysHQJ2NixY2Mpgy+//DKsueaaYa655mqLfyEiIiIiIiIiIiLS4WhRiYMyzz333DiXf5177rnxwrDjjjsu1qd97bXXWvsvRERERERERERERDokrRZozzvvvLDEEks0fD1w4MDQt2/fMHjw4LDSSiuFs88+u7X/QkRERERERERERKRD0uoSB0OHDo0XhqVSBzPOOGPYeOON49fHHntsjKIVERERERERERERkV8ggnbkyJFhuummi58PHz48TDXVVA3fm3nmmeNzIiIiIiIiIiIiIvILCLTzzTdfQwTtkCFDwjTTTNPwvVdffTXMPvvsrf0XIiIiIiIiIiIiIh2SVgu0e+yxR+jVq1dYd911w7777htWXnnlhu8deuihYe21127tvxARERERERERERHpkLS6Bu0WW2wRSxk8/vjjYcEFFwxbbbVVw/dWW2210LNnz9b+CxEREREREREREZEOSasFWlhrrbXio5YTTjihLf68iIiIiIiIiIiISIek1QLt999/H04++eTwxBNPhB9++CEURTHez7zwwgut/TciIiIiIiIiIiIiHY5WC7SIs/fdd19YZ5114oVgnTu3uqytiIiIiIiIiIiIyCRBqwVaas9edtlloVu3bm3TIhEREREREREREZFJhFaHu1LWYKmllmqb1oiIiIiIiIiIiIhMQrRaoF1llVViFK2IiIiIiIiIiIiI/MolDnbffffQu3fv8L///S8svPDCYcoppxzvZ1ZdddXW/hsRERERERERERGRDkerBdodd9wxfuzbt2+jP/P666+39t+IiIiIiIiIiIiIdDhaLdDecMMNbdMSERERERERERERkUmMVgu0yy67bPz41ltvhaeffjpeGjbzzDOHbt26ha5du7ZFGzs0Y8aMCZNNNlnIgZzaIiIiIiIiIiIiMinQaoF27Nix4bjjjgs33njjOM936tQpbL/99vF7fC71QRD96+49w+uvv1lpFy2yyILhsivOrbQNIiIiIiIiIiIikxqtFmivuuqqcNddd4VDDjkkrLPOOmHWWWcNX3zxRRg0aFC49NJLwyKLLBJ69OjRNq3toCDOvvjiK1U3Q0RERERERERERNpjDdoTTjghbLbZZg3PzTLLLGHhhRcO8803X7j44osVaEVERERERERERETq0Dm0kg8++CCsu+66db+31lprhWHDhrX2X4iIiIiIiIiIiIh0SFot0M4444zhnXfeqfu9t99+O8w000yt/RciIiIiIiIiIiIiHZJWC7TrrbdeOProo8Mbb7wxzvOvvfZa6NWrV1h//fVb+y9EREREREREREREOiStrkF70EEHxRqzm266aZh77rnDbLPNFi8J+/DDD2MN2p49e7ZNS0VEREREREREREQ6GK0WaLkQbODAgeGmm24KTz31VPj222/DQgstFEXb7bffPkw33XRt01IRERERERERERGRDkarBVpAhN1tt93iQ0RERERERERERER+RYH2p59+ClNMMUX8fOzYseGBBx4IX375ZVhzzTXDXHPN1Rb/QkRERERERERERKTD0epLwp577rlYfzZx7rnnhv333z8cd9xxYfPNN4+XhYmIiIiIiIiIiIjILyDQnnfeeWGJJZZo+Jp6tH379g2DBw8OK620Ujj77LNb+y9EREREREREREREOiStLnEwdOjQcPfddzeUOphxxhnDxhtvHL8+9thjYxStiIiIiIiIiIiIiPwCEbQjR46Ml4TB8OHDw1RTTdXwvZlnnjk+JyIiIiIiIiIiIiK/gEA733zzNUTQDhkyJEwzzTQN33v11VfD7LPP3tp/ISIiIiIiIiIiItIhabVAu8cee4RevXqFddddN+y7775h5ZVXbvjeoYceGtZee+3W/gsRERERERERERGRDkmra9BuscUWsZTB448/HhZccMGw1VZbNXxvtdVWCz179mztvxARERERERERERHpkLRaoIW11lorPmo54YQTYpmDxRdfvC3+jYiIiIiIiIiIiEiHotUC7ZdffhmF2JdffjleCFYURXyej1wgNmrUqPD666+3RVtFREREREREREREOhStrkF76qmnhueffz6WM/jhhx/C+uuvHz+fYoopwlRTTRXOO++8tmmpiIiIiIiIiIiISAej1RG01J69+OKLwzLLLBPefvvt0L1797DmmmvGyNm99947fPfdd23TUhEREREREREREZEORqsjaL///vuwxBJLxM+5JOzZZ5+Nn0855ZThyCOPDNdee23rWykiIiIiIiIiIiLSAWm1QDvrrLOGTz/9NH7++9//Prz00ksN3/vd734X3nzzzdb+CxEREREREREREZEOSasF2lVXXTVeEvb++++HpZdeOkbQDh06NH7vzjvvDLPMMkuL/i4XjomIiIiIiIiIiIh0ZFot0O6///7hrbfeCltvvXWsQ7vwwgvHz1dfffXQu3fvsPnmm0/U3+vfv38UfVdZZZWw1lprhXvuuae1TRQRERERERERERHpmJeEzT333OGOO+4Izz//fOjUqVO44oorQr9+/cJ7770XVlhhhbDTTjs1+2/deuut4bzzzouXjq244orhlltuCYcddlgsnYDwKyIiIiIiIiIiItKRaLVAC9NOO21YfPHFw+DBg8OXX34ZjjjiiPD222+HBRZYYKL+DsLsPvvsE8VZ2GqrrcLAgQPDCy+8oEArIiIiIiIiIiIiHY5WC7RFUYTzzz8/XHLJJWH06NHxue222y7ssMMOoVu3bqFv375hiimmmODf+fjjj8OwYcPCeuutF78eMWJEmHrqqcOAAQNa20QRERERERERERGRjinQXnvtteHyyy+Pka+rrbZa2H777ePzhxxySDjrrLNiuYMDDjhggn/n3XffjR8/+OCD+Lc++eSTMNNMM8W/s8UWWzT6ewi5QHkFBN3coH2I2PXIsc1NtTe1OSeaaivYXvtYREREREREROTXJmmWv5pAe/LJJ4dNN910nOe33XbbWJ/2uOOOa5ZAO3z48PiRiFtKHcw///zhzjvvjOUS5phjjnhpWD2IugWETsos5MY777zT6BuSY5ubau/kk08eFlts8TD55G1SGaPVjB79cxg69NWGyO2O0N7FF1s8dMmkvfDz6J/Dq+2ozRNqr4iIiIiIiIhIbrRaVSHydZ111qn7vWWXXTZGwjaHVAZhjz32iJeCweabbx4vCqMObWMCbdeuXaPQmVukZAKhuakI2vbWXsTO/fbuFf73v7dDlSy88ALhwn6nhoUWWmiC7T34b6eGt954L1TJ7xf6XTj7n70m2F6EziMPODe888YHoWrmX2iecPr5PZvV5uMPuCQMe+OjUCVdF5orHH/+Xk22V0RERERERETk14AAyBRY+osLtDPPPHMYMmRIXQGV52eZZZZm/Z0555xznI+Jueaaq0mRF3F2mmmmCbmSWwmDtmgv4uyQl14L7aW9iLOvDHkjtJf2Is4OffmdkAvNaTPi7P9erlYEb69zTkREREREREQmbTq39g907949HHbYYeH+++8PP/30U8PzL774YjjmmGPCBhts0OzIzdlmmy288MIL4zz/yiuvhAUXXLC1zRQRERERERERERHJjlZH0B500EFh6NChsc4s9Shh1VVXDV9++WVYcsklQ8+ePZv1dyabbLKw3377xYvFqF279NJLh+uvvz689957sSatiIiIiIiIiIiISEej1QLtdNNNF66++uowaNCg8Nhjj4WvvvoqzDDDDGGllVYKG220UUNt2eaw4447xt+96qqrwscffxxr0fbv3z+WORARERERERERERHpaLTJ1etEv2644YbxkYrgtrQO5KabbhofIiIiIiIiIiIiIh2dFtegHTVqVBgwYEA44YQTGp575plnYs3ZZZddNqy55pqxLq2IiIiIiIiIiIiItKFAS4Ts9ttvH0488cQGEZbSBtSQnXfeecM+++wTunbtGuvTvv766y35FyIiIiIiIiIiIiIdnhaVOLjmmmvCt99+GyNol19++fjcrbfeGqNm+/Tp0/BzRx11VLjooovCOeec03YtFhEREREREREREZmUI2j//e9/x9IG3bp1C506dYrPPfvss6FHjx7j/Nyee+4ZXnrppbZpqYiIiIiIiIiIiEgHo0UC7fvvvx+WW265cZ579913w5JLLjnOc3PNNVf47LPPWtdCERERERERERERkQ5KiwTaySefPF4Slvj555/D6NGjw1RTTTXOz3366adh2mmnbX0rRURERERERERERDogLRJo559//nDfffc1fP3MM8/ES8Fquf3228PCCy/cuhaKiIiIiIiIiIiIdFBadEnYdtttF3r37h0++OCDMNNMM8VLww488MCG7//www9RnL3kkktirVoRERERERERERERaSOBdvPNNw9DhgwJV155Zfx66623DltuuWXD99dYY40wfPjwsMkmm4StttqqJf9CREREREREREREpMPTIoG2c+fOMYL273//e/x8mmmmGef7e+yxR1hqqaXCWmut1VbtFBEREREREREREelwtEigTUw33XR1n99///1b82dFREREREREREREJgladEmYiIiIiIiIiIiIiLQeBVoRERERERERERGRilCgFREREREREREREakIBVoRERERERERERGR9iTQ7rLLLg2fP/LII23ZHhEREREREREREZFJhhYJtM8991z45ptv4ud77rlnW7dJREREREREREREZJKgS0t+6be//W3Yd999w6KLLhq/PvHEE5v8+d69e7esdSIiIiIiIiIiIiIdmBYJtMccc0zo27dvGDRoUPz6vvvua/LnFWhFRERERERERERE2kigXWutteIDFllkkfDYY4+15M+IiIiIiIiIiIiITNK0SKAtM2DAgPjxrbfeCk8//XT44Ycfwswzzxy6desWunbt2hZtFBEREREREREREemQtFqgXW655cKxxx4bbrzxxnGe79SpU9h+++3DcccdFz8XERERERERERERkTYWaK+66qpw1113hUMOOSSss846YdZZZw1ffPFFrE976aWXxhIIPXr0aO2/EREREREREREREelwtFqgveGGG8IJJ5wQNttss4bnZplllrDwwguH+eabL1x88cUKtCKSDWPGjA2TTdY5tJe2jB0zNnTOpL3NaUtO7W1Oe9pbe0VERERERKTj0WqB9oMPPgjrrrtu3e9xkdiRRx7Z2n8hItJmIIiefkD/8N6bn1Taq79bcM5w5Pm7TvDnEOvOOfia8MGbn4UqmWfB2cNBZ+/UrPZeeMiA8NFb1bYX5vr97GG/PjtOsL2XHX5d+OTt6ts75wKzh7+eYcaJiIiIiIjIpEarBdoZZ5wxvPPOO2GppZYa73tvv/12mGmmmVr7L0RE2hTE2Tdffr/d9Cri7NuvfBDaC4izw179MLQXEGffG9p+2isiIiIiIiIdi1bnUa633nrh6KOPDm+88cY4z7/22muhV69eYf3112/tvxARERERERERERHpkLQ6gvaggw6KNWY33XTTMPfcc4fZZpstXhL24Ycfxhq0PXv2bJuWioiIiIiIiIiIiHQwWi3QciHYwIEDw0033RSeeuqp8O2334aFFlooirbbb799mG666dqmpSIiIiIiIiIiIiIdjFYLtIAIu9tuu8WHiIiIiIiIiIiIiPxKNWhFREREREREREREpGUo0IqIiIiIiIiIiIhUhAKtiIiIiIiIiIiISEUo0IqIiIiIiIiIiIi0V4H2xRdfbJuWiIiIiIiIiIiIiExitFqg3XvvvdumJSIiIiIiIiIiIiKTGK0WaKeeeurw4Ycftk1rRERERERERERERCYhurT2Dxx//PGhV69eYaONNgrzzz9/FGxrWWqppVr7b0REREREREREREQ6HK0WaPfaa6/48cknn2z0Z15//fXW/hsRERERERERERGRDkerBdpLL720bVoiIiIiIiIiIiIiMonRaoF2zTXXjB+//PLL8MILL8SP2223XXj77bfDAgss0BZtFBEREREREREREemQtFqgLYoinH/++eGSSy4Jo0ePjs8h0O6www6hW7duoW/fvmGKKaZoi7aKiIiIiIiIiIiIdCg6t/YPXHvtteHyyy8P++yzT7j++usbnj/kkEPCM888E/r169fafyEiIiIiIiIiIiLSIenSFgLtySefHDbddNNxnt92223D3HPPHY477rhwwAEHtPbfiIiIiIiIiIiIiHQ4Wh1B++6774Z11lmn7veWXXbZ8Mknn7T2X4iIiIiIiIiIiIh0SFot0M4888xhyJAhdb/H87PMMktr/4WIiIiIiIiIiIhIh6TVAm337t3DYYcdFu6///7w008/NTz/4osvhmOOOSZssMEGrf0XIiIiIiIiIiIiIh2SVtegPeigg8LQoUNjndnJJ588PrfqqquGL7/8Miy55JKhZ8+eLf7bP/zwQ5hqqqlCly6tbqaIiIiIiIiIiIhIdrRa+ZxuuunC1VdfHQYNGhQee+yx8NVXX4UZZpghrLTSSmGjjTYKU0wxRYv+7nPPPRf+8pe/hMsvvzwKviIiIiIiIiIiIiIdjTYJTZ1sssnCMsssE8aMGROjXmedddaw3HLLtVic/fHHH8MRRxwRxo4d2xbNExEREREREREREemYAi2i7Mknnxyuv/76cQRVxNm999477L///hP9N0855ZSw4IILhvfff7+1zRMRERERERERERHpuALt+eefH2677bYoxq6xxhph5plnjmUO/vOf/4RLL700fr3jjjs2++8NHjw4PP7442HAgAHxb4iIiIiIiIiIiIh0VFot0A4cODCcccYZYYMNNmh4boEFFgjdunULiy22WDjvvPOaLdB+8cUXoXfv3qFPnz5h+umnb9bvjBgxIn7s1KlTmHrqqUNu0L6iKOp+L8c221771zHhnOtIa0RTbW5v7S23OyeaaivY3l+2j3PrX3BM2L8TMyYcw8653MfEhNY0ERGRCWmWv4pAS83ZlVdeue731l577XDkkUc2+28dc8wx8WKxVVZZJXz33XfN+p1hw4bFjxyyF1988ZAb77zzTqNvSI5ttr32r2PCOdeR1oim2tze2guTTz55bHOXLm1SQr7V/Pzzz+HVV18No0eP7jDtXWLxxcNkmbQXxvz8c3ilkTa3t/bm2GbbW20f5zYewDFRff8uufjioXMmY2Lszz+Hl5tor4iISFvR6p1v0UUXDQ888EDYYostxvveE088EWvJNocbb7wx/O9//wsnnnhiFGe///77+Pzw4cPjY5pppqn7e127do2H7Nw8rYn555+/yWiz3LC99q9jwjnXkdaIptrc3tqb2ozYec1R14VP3/4sVMkcC8wedjqtR1hooYUm2N6bjrk+fP5Ote39zfyzh21P3n6C7UUouuP4G8KXw6ptL8zadfaw2fHbNdrm1N77T7ohfP3u56FqZp7vN+GPxzbe3nKbHzn1hvDNe9W2eabf/Sas2at57X3mzBvC9+9XOyamn3f2sMJhzWvvS2dfH378oPoxPO08s4elD2583qX2/u/868LwD6tv7zRzzx4WPmDC6xptfrffgDDyo09DlUw11xxhvr13bFZ7P+l/Tfjpk2r7eIo5Zw9z7rrTBNuLOPvFddeE0Z9V27+Tzz5HmK1H0+0VERFpCgJvUmDpLyLQPvXUUw2fU9qAyNehQ4eGddddN8w222zhxx9/DM8880z45z//GQ477LBm/U3EXIRZImghbYL8PpG4Z599dt3fQ5xtTLzNgRzTZ5vC9tq/jgnnXEdaI9pjm5vTXsTZD177KLSX9iLOfvx6+2kv4uyn/8ujvc1pM+Ls52+0n/YC4uxXb37UbtqLOPvNW+2nvYiz37+dR3ub02bE2R+HfRhyoTl9jDg74t0P2017EWdHffBBaC/tRZz96aP2014REZHW0iKBdueddx7vuf79+8dHLccff3zo0aPHBP9mrQCLWLvCCitEkXfVVVdtSTNFREREREREREREOp5AO2DAgLZviYiIiIiIiIiIiMgkRosE2m7duoVfGuoPTT/99NlcLCIiIiIiIiIiIiLS1rRa/fzpp5/CTTfdFF544YWGi71q6dev30T/XcTZZ599trXNExEREREREREREem4Au0pp5wSbrjhhjDPPPOEGWaYoW1aJSIiIiIiIiIiIjIJ0GqB9r777osRsmuttVbbtEhERERERERERERkEqFza//A6NGjwworrNA2rRERERERERERERGZhGi1QNu9e/dw7733tk1rRERERERERERERCYhWl3ioFevXmHXXXcNDz30UFhooYXClFNOOd7P7LXXXq39NyIiIiIiIiIiIiIdjlYLtAMHDgxDhgyJD+rR1kOBVkREREREREREROQXEGgvu+yysP/++4cePXqE6aefvrV/TkRERERERERERGSSodUC7bfffhv22GOPMM0007RNi0REREREREREREQmEVp9Sdgf/vCH8NJLL7VNa0REREREREREREQmIVodQduzZ89w/PHHh6222iosssgiYeqppx7vZ5ZddtnW/hsRERERERERERGRDkerBVpqz8Jpp53W6M+8/vrrrf03IiIiIiIiIiIiIh2OVgu0V155Zdu0RERERERERERERGQSo9UC7aqrrto2LRERERERERERERGZxGi1QLvaaqtN8Gcee+yx1v4bERERERERERERkQ5HqwXa9dZbb7znRo0aFV599dXw448/hp133rm1/0JERERERERERESkQ9JqgfbEE09s9Hunn356GDlyZGv/hYiIiIiIiIiIiEiHpPMv+cf32muvcO211/6S/0JERERERERERESk3fKLCrSUOvjuu+9+yX8hIiIiIiIiIiIiMumWOHj22WfHe64oivDtt9+G/v37h2WWWaa1/0JERERERERERESkQ9JqgXbHHXds9HtzzjlnuOSSS1r7L0REREREREREREQ6JK0WaK+++urxnptsssnCDDPMEBZYYIHQpUur/4WIiIiIiIiIiIhIh6TV6ulKK63UNi0RERERERERERERmcRokUD7j3/8Y6J+/ogjjmjJvxERERERERERERHp0LRIoL3iiism6ucVaEVERERERKSjUYwdGzp17hzaU3tyanOHbG8xNnTqlEl7m9GWnNrbnPa0t/Y292fyam8ROnXqFHKgaEZbcmpva9rTIoH29ddfn+DPPPnkk+H8888PG2+8cUv+hYiIiIiIiEjWINZ9dcs14ecvPqu6KaHLbLOHWbbaqVlt/ubuAeHnr6ptc5dZZg8zbdT4pePl9n73nxvCmG8+D1Uy2Uy/CTOss90Efw7x6/snbwljvvsiVMlkM8wWpl95q2a198cX7gpjfvgyVM1k080apv3DJhNu72uDwtjhX4Wq6TzNLGHaRTeY4M/R5uHvPhrGjvw2VEnnqWYM08y3xgR/DnFxxKfPhLE/fR+qpPMU04ep51ihWe0d+e2QMPbnH0PVdO4ybZhqxqVa9LttfoPXJ598Es4888ww7bTThgsuuCDMPPPMbf0vRERERERERLIAcXb0Jx+E9gTi7M+ffRjaC4izP3/5UWgvIM6O+fqT0F5AnB3zXfVOhuaCODvmh2oF8IkFcXbsiOpF5eaCODv2p29Ce2Hszz+GsT9XKyi3ljYTaH/66afQv3//8MQTT4RDDjkkLLnkkm31p0VEREREREREREQ6JG1SBOORRx4Ju+66a5h11lljfVrFWREREREREREREZFfOIL2/fffj+UM5phjjtCvX78w/fTTt+bPiYiIiIiIiIiIiExStEigHTVqVLjkkkvCiy++GA4//PCw8MILt33LRERERERERERERDo4LRJo//SnP4Wpp546bLjhhuG+++6Lj6Y44IADWto+ERERERERERERkQ5LiwTaDz/8f297vOCCC5r18wq0IiIiIiIiIiIiIm0k0L700kst+TURERERERERERERaa1AO+WUU7bk10RERERERERERESkROfQAgYOHBiKopio3+Hnb7nllpb8OxEREREREREREZEOSYsE2ttuuy1ss8024eGHHw5jx45t8md//vnncP/998efv/XWW1vaThEREREREREREZEOR4tKHFx11VXhyiuvDAcffHCYaaaZwtprrx2WXnrpMOecc4Zpp502fP/99+Gjjz4Kzz33XHj00UfD8OHDw7777hv22GOPtn8FIiIiIiIiIiIiIpOSQNu5c+cotm655Zahf//+4e677w4DBgwY7+fmn3/+sN1224WddtopCrkiIiIiIiIiIiIi0kqBNjHLLLOEv//97/ExbNiw8PHHH4evv/46zDzzzGHeeecN88wzT2v+vIiIiIiIiIiIiEiHplUCbZmuXbvGh4iIiIiIiIiIiIj8gpeEiYiIiIiIiIiIiEjrUaAVERERERERERERqQgFWhEREREREREREZGKUKAVERERERERERERqQgFWhEREREREREREZH2JtD279+/WT/34osvhlNPPbWl/0ZERERERERERESkw9Klpb942mmnhY8++ijMNttsYd555w0LL7xwWGCBBUKnTp0afubZZ58N++yzT9htt93aqr0iIiIiIiIiIiIiHYYWC7Tw/fffh3fffTcMGjQofuzSpUtYa621wqabbhruvvvucNttt4UDDjgg7L333hP9t8eMGRMmm2yy1jRPREREREREREREpOMItPfcc0/44YcfwjzzzNMQRZsoiiI89dRT4dJLL22ImD3rrLOiWNtcPv/883D66aeHd955J0w77bThxx9/DIcddlhYZZVVJqaZIiIiIiIiIiIiIh1PoL3jjjvChx9+GD777LP49cYbbxwWXXTR+PVbb70VRo8eHdZee+3Qr1+/GFXbu3fvKLJuv/32zfr7RNv+/ve/DzfffHPo3LlzePTRR2P07a233hqfFxEREREREREREZlkLwm76KKLokj75JNPxijZ/fbbLwqwzzzzTBRQiaA988wzw5AhQ8Ipp5wSLrzwwnDOOefE35sQX3zxRfj4449Djx49ojgLa6yxRph//vlj5K6IiIiIiIiIiIjIJC3QQv/+/ePjyiuvjCUJVl555XD99dfHKNrLLrss/swFF1wQP1KaYMCAATGydkJw2djDDz8cllxyyfFq0Y4YMWJimykiIiIiIiIiIiLS8S4Jo+7sEkssET8nmvaVV14Jr7/+ehg2bFjo06dP2GuvveL3Xn755Si2UpuWz1sCZRPefvvtcMwxxzT6M0m87dSpU5h66qlDbtA++qAeObbZ9tq/jgnnXEdaI5pqc3trb65ttr3V9XGO4wEcE/Zvc8eEY9g51x7GRHtb09pjm22v/dvcMZHj+AXHsP3b2JiYmIDTiRZo4ZZbbgmLLLJIw8d6bLPNNlGsfeyxx8IKK6ww0f+DqNvjjjsurLPOOjFKtzEQhoFJuvjii4fc4MKzxt6QHNtse+1fx4RzriOtEU21ub21N9c2297q+jjH8QCOCfu3uWPCMeycaw9jor2tae2xzbbX/m3umMhx/IJj2P6dmDHRJgLtvffe2+RH+OGHH+JHSiAccsghYeTIkQ2lD5rLqFGjwmGHHRa6dOkSzjrrrCZ/tmvXrnGS4knJEWroNuW9zA3ba/86JpxzHWmNaKrN7a29ubbZ9lbXxzmOB3BM2L/NHROOYedcexgT7W1Na49ttr32b3PHRI7jFxzD9m9jYwKRNgWWtqlAe9RRRzX5Ef74xz/Gj0S9cnHYCy+8EEXa5vL++++Hnj17xvIIlEyYfPLJm/x5xNlpppkm5EqO4fdNYXvtX8eEc64jrRHtsc221/51TDjnOtIa0R7bbHvt3/Y8Htpjm22v/euYcM51pDWipW2eqEvCEFub+gi9e/eOH3fZZZfw9NNPhxlmmCEMHDiwWX//pZdeiqURNttss3DiiSdOUJwVERERERERERERac9MlECbGDp06Dgfy2y44Ybx488//xw/brfdduGJJ56Y4N/8/vvvwz777BNWW221sO2224Yff/yx4fHTTz+1pJkiIiIiIiIiIiIiWdOiS8K22GKLcT7W41//+lc499xzo+B6/fXXT/BvPvjgg7E2w3/+85+w+uqrj/O9jTbaKJxyyiktaaqIiIiIiIiIiIhIxxFoX3nllfhxiSWWaPi8/Hyic+fO4eCDDw5fffVVs2rQbr755vEhIiIiIiIiIiIiMqkw0QJtly7/768MGDCg4fPy87Vi7cwzzxzuuuuu1rdUREREREREREREpIPRohq00K1bt0a/VxZrO3XqFOaee+6W/hsRERERERERERGRDkuLBdoyn3/+eTjxxBPHee7yyy9viz8tIiIiIiIiIiIi0mGZKIG2KIq6EbSPPfZYeOONN8b52TPOOKMt2iciIiIiIiIiIiLSYZkogbZnz54Nn3///fcNn1911VVho402atuWiYiIiIiIiIiIiHRwJkqg/eyzz8Jbb701znO33HJL+PTTT8OWW24ZHn/88TBmzJi2bqOIiIiIiIiIiIhIh2SiBNqVVlopPP/88w1ff/jhh+Ef//hH2H///cNUU00Vdtttt1juQERERERERERERETaWKBdcMEFwzvvvNPw9T777BOWXHLJ0KNHj4bn7rnnnon5kyIiIiIiIiIiIiKTLBMl0M4333zh/fffb/i6S5cu4ZxzzgmdOnVqeG706NHhq6++ip/z8euvv27L9oqIiIiIiIiIiIh0GLpMzA/PMsss4aGHHgrrrrtu/PqTTz4Jm2+++Tg/s/3224dVVlklfp4+/vGPfwznn39+27VaREREREREREREZFKLoEWgnWKKKcLuu+/eEFHL5+kB3bp1CzfddFP8/IYbbggDBgwYp26tiIiIiIiIiIiIiLRAoJ166qnjx7/85S/x44svvhh+85vfxK/Tc7D00kvHj8suu2wUbEeNGjUx/0ZERERERERERERkkmCiBFpqzZbF1q233jocc8wx4dNPP23y98rirYiIiIiIiIiIiIi0QKCFySabrOHzE088MSyyyCLhjDPOaPJ3DjrooIn9NyIiIiIiIiIiIiIdnokWaMeMGfP//3LnzuG0004LgwcPDu+880745z//2dbtExEREREREREREemwTLRA26NHj3G+nnfeeWOpAy4GW3/99duybSIiIiIiIiIiIiIdmokWaI8++uj4cc8992x4bueddw6DBg1q25aJiIiIiIiIiIiIdHAmWqBNHHrooQ2fd+3aNfTt23ec77/yyiuta5mIiIiIiIiIiIhIB6fFAm0tSy211Dhfd+nSpa3+tIiIiIiIiIiIiEiH5BdTUd9///3wr3/9K14qdswxx/xS/0ZERERERERERERk0hFozzrrrAmWPrjtttvCCSecEFZaaaXxLhUTERERERERERERkRYKtJdeemlYbrnlxnu+KIrwwgsvRIF28ODBYZtttgndu3cPyy+//MT+CxEREREREREREZFJghbVoL3uuuvCiBEjwrLLLhsuvPDC8Pvf/z7MOOOMDd8///zzw7Bhw8Kuu+4a/vrXv7Zle0VEREREREREREQmPYH27rvvDk899VTD1wi0//3vf8Maa6wRXnzxxdCrV6/4/E8//RT69OkT5p9//vDwww/HnxERERERERERERGRVgi09913X9h9993j53vttVeYZ555wrrrrhtGjx4dZppppjDXXHPF7x122GHhnnvuiWUQZp111ub+eREREREREREREZFJjmYLtOeee24UXmGDDTaI9WapR9u7d+/w9NNPh4svvjh+b8UVV4ziLFG1fG+KKab45VovIiIiIiIiIiIiMqnUoP3d734XP2688cbx8q+FFloobLLJJvG5l156KX7ccccdw+effx6GDh0aPvjgg3Dcccf9Eu0WERERERERERERafd0ackvrbnmmmGyySaL9WbXW2+9+BwRtQmen3nmmcO2224b1l577bZrrYiIiIiIiIiIiMikLNCefPLJsaTB6quvPs7z7733Xnj22WfDzTffHP7xj3/Ekgh777136NGjRzj++OPbss0iIiIiIiIiIiIik6ZAu8QSS4STTjopvP766+GII46ItWZvu+228O6778bvc2HYlVdeGd58880w77zzhkUWWeSXaLeIiIiIiIiIiIjIpCPQDhw4MMw444xhnXXWCYMHDw59+/YNe+yxRyiKImy44Ybhr3/9azj22GPDTjvtFJ5//vlYr3a22WaLv0tkbbdu3X7J1yEiIiIiIiIiIiLScQVaRNc77rgjiq6IsSeeeGIYNWpUuPvuu8OTTz4Z682mS8LqQcStiIiIiIiIiIiIiLRAoD3llFNiSYNbbrklXHTRRaFfv37hq6++ip+//PLLYb/99os/99JLL4WHH344zDHHHGHBBRcMXbq06B4yERERERERERERkQ5P54n54RlmmCHsuuuu4YEHHgiTTz556NWrV1h33XXDgQceGC6++OL4M1NOOWW48MILw3bbbRfWW2+9cMEFF8TnRERERERERERERGRcWhTeiuB63XXXhdlnn73huVVXXTUKs8ClYd9991247777whdffNGSfyEiIiIiIiIiIiLS4Wlx/YGyOJsgYrYcbZvq0oqIiIiIiIiIiIhIK0sciIiIiIiIiIiIiEjboUArIiIiIiIiIiIiUhEKtCIiIiIiIiIiIiIVoUArIiIiIiIiIiIiUhEKtCIiIiIiIiIiIiIVoUArIiIiIiIiIiIiUhEKtCIiIiIiIiIiIiIVoUArIiIiIiIiIiIiUhEKtCIiIiIiIiIiIiIVoUArIiIiIiIiIiIiUhEKtCIiIiIiIiIiIiIVka1AO3bs2KqbICIiIiIiIiIiIjLpCLRFUYTzzz8/rLTSSmGppZYK22+/fXjjjTeqbpaIiIiIiIiIiIhIxxdor7nmmnDDDTeEf/3rX+G5554LCy64YNhrr73CqFGjqm6aiIiIiIiIiIiISMcWaPv37x/22GOPsNBCC4Wpppoq9OrVK3z99ddh0KBBVTdNREREREREREREpOMKtJ988kn48MMPw/LLL9/w3DTTTBMWXXTR8Pzzz1faNhEREREREREREZFfgi4hEz7++OP4cfbZZx/neb5GvG3sErFvvvkmjBgxInTq1ClG3a655mph3nnnDlUz//xdw/Dhw8PIkSNjbd16pDavttqKYZ55fhuqZL755m12e1dZZbkw19zjvk+/NvP9bp5mt3eFlZcIc841a6iSeX83Z7Pb+4eVFglzzDVzqJq55p292W1eZqXfh9/MNWOokrnmna3Z7V1ixa5h1t9OH6pkznlnbXZ7F1nxd2Hm304XqmT2eWdpdnsX7DZvmHHOatsLv5ln5ibbnNo7//LzhhnmmDZUzazzNL+P51tu3jBdxW2ede7mt3fuZecN08xebXtnnqv57Z1zmXnD1L+pfkzMOIE2p/bOttQ8YcrZqm/v9L9tes6V2zzLEvOEyWetts3Tz9n89k6/2Lyh8yzVtnfaOZo/hqdZdN7Qaebqx8TUE2hzau+UC88bipmqb++Usze/j7ss+LswxYzV7nVdftP89oauvwudp694b561+e0dO+98IUxbbXvHztJ8W23MXL8LxdTV2z5jZm5+H4+Zfd4wZqpq29xphuavw6NnmSeMnbzadaKYfiLaO8NcYexkU4cqGTvtRLR32jnCmE5ThaoZO82Mzdo3fpryN2FsMWWomjFTztDsPh7VZaZQTFGtBNepy3Shc3PbG6YPRadqYzo7hWlDp+a29+epw9gxoXI6h6lDKLU5lWxNGmZTdCoae5W/Mk8++WTYZZddwuOPPx5mnfX/F9MOP/zw8OWXX4bLL798nJ/nuWHDhlXQUhEREREREREREZEJ07Vr13G0zqwjaKNnN4TxLgTj6/S9MjPOOGN8gVNOOWXo3DmbSg0iIiIiIiIiIiIyiTN27Nioa6JhTohsBNq55porfvzss88aPodPP/00LLnkkuP9fJcuXSaoPouIiIiIiIiIiIhUwXTTNa+cTDahp9Sa/d3vfheefvrphud++OGHMHTo0LDCCitU2jYRERERERERERGRX4JsBFrYbbfdYq3ZZ599Nnz11VfhpJNOCr/97W/DuuuuW3XTRERERERERERERNqcbEocwA477BB+/PHHcMQRR4TvvvsuLL/88uGyyy4Lk08+edVNE6kE7vDjVkIRERERERERad9QxnPaaadtdtq7TDpkFUELe+65Z3jggQfCM888Ey6++OIwzzzzVN0kaQZ33nlnuOeee+yrNgQnxaqrrhoOOeSQ8PPPP4d33323XfXvP/7xj/Dee+9V3QzJBMZwz549Q+5sscUWsZ033nhj+OCDD6puTofmiSeeCP/6179Ce2HkyJFxPW5P9OrVK3z99ddVN6NDO1HbE+2tvfLLw90f8ssxZsyYcPDBB0cbSNqW2267LRx00EGhb9++4Ztvvmk33fvRRx+FU089tepmSMX06dMnbLLJJjFrXH5Zfvzxx/hoL2QVQftLQtmEiy66KB64F1100XDuueeGnKGdZ599dnjllVfCLLPMEs4555xYpzc3zjvvvDBkyJDw8ccfh2222Sa0J3766ad4WJlyyilDjhA5u/fee4cvv/wyvPTSS6FHjx5h7rnnDquttloUbldeeeUw88wzh1y59dZbw1577RXaC++88044//zzw/Dhw8PSSy8dVllllfhxsskmq7ppHQLWsk8++STkDAeorbfeOjz22GPRwUAddGqjMxaYd8y55ty+WbVjZ4YZZgg5g/h91113xdtMl1lmmXizaefO2fmLxwPH8bfffhvaA5tuumlYYoklwr333hsOPfTQ0J5oD2M48c9//jMGFuRqR9Ryyy23hOWWWy7MP//8ob0wYMCA8NBDD4V55503rLjiinE9znkd5rxBuTay/+hr2rvIIouEXDnuuOPi+D3hhBOy7tda+519Y6qppgq58v3334c///nP8cz54osvRgdfe4mUI7iCMyj3wMw222zxrMdZNCf69esXrr766rjPIdQ++uij4eabb24XNvugQYNCe3Q0MIaJ+GwPvP/+++H000+PGgX2xEorrRQztX//+9+HnIIURo8eHYNCrrzyytClS/uR5bCFWSPoZ9Y4zkfcGZXrmvzII49EQfzMM88Mf/jDH0Lu5H8iagMQt4466qiw5pprRmOJjR3efPPNOHlzPJzsscceoWvXrmHLLbcMb7/9dvjNb34TxYP99tsvGiW5QH3gaaaZJrzxxhvhtNNOC6uvvno47LDDojhH6D4gMj7//PMhJxBgEDlTuxBirr/++vDcc8+FXJh++unDrrvuGiO2EAqvvfbaOB7oaw7ctH+rrbaKCw5jPCfozw022CBujO3F8Nhnn31ilDLC+A033BC23377uKH/7W9/iwfEESNGhJz497//HdZee+2wxhprxLZfc8014a233gq5Muecc4Ypppgia08xxtFOO+0UszdYI+hbxsarr74ajj322GiA4IjCKMlprUicfPLJYeONN274+rXXXotj94svvgg5QT+SHcO6ddVVV0UB48ADD4xrcIq6RzRgjOcEezKiMgfv3Nl2222jKMC6hXMBxwN7BQcCbCDWuhydqkT80vayY4e9L9co4Pvuuy9ceOGFje6DuUV1sSf37t277vewMZPdlgsIs9iW2ENcIsxBlvUD2wdBkUNtTrC/IdgjZODsJTBks802a7CNEZJyg7WXcwZOHdaH3KGNrGkI4UBUFHtH+joXGLMbbbRR+L//+7/w4YcfRnuSQAsCAbAfmG+MaWzM3MYwZ1CELKL72CsQZ9k39t9//yyi8FlXX3jhhTB48OBwySWXxI/0N47f9gCOaWwdxLn2wLBhw+KZDuc6YBfffvvtMeM5h/FQy+effx522WWXMMccc8RAIWz5hx9+OGy++ebRaZ0DnOVxjKXzxBlnnNHkGp1bgAt2BOckBPsHH3ww/PWvf40CLf3OnMzNllhzzTXjWeMvf/lLDNLMPqOhmATYd999izfeeCN+3qtXr+Lqq6+On7/44ovFEkssUXz//fdFTlx66aXFjTfeGD8fOHBg0bNnz/j5d999V6y88srF448/XuTEm2++Weyxxx7Fq6++Gtu+2267FUsvvXSx8MILF6uttlqx/PLLFz/++GORCz///HOx5pprFtdff30xcuTIYsyYMcV2221XLLXUUrHNDzzwQJE7jNnBgwcXJ554YtG9e/firLPOKnKC9iy77LLF4osvXhxxxBHFQw89lNUYqIU5teWWW8axUB7X11xzTbHPPvvEcTxq1Kgip/efeXXuuefG9eyggw4qVlpppTh+11hjjeLII48svvnmmyInNtlkkzgmVllllaJv377FSy+9NE5/58bFF19cXHbZZQ1fjx07tnjiiSeKbbbZJvY1fZ7bGF533XWLIUOGxK9fe+21YplllimWXHLJuN7lts+xn22xxRbjzLPllluuYd9gb37//feLnFhrrbXiGGaOnX/++XHPY1zkCm3817/+VTz44IPFSSedVPzpT3+K/ctet+KKK8b9IycGDRpUbLjhhsXQoUMbbDTGL4/1118/7te5cffdd8exmmxM9on77ruvOOCAA2I/f/jhh0VOvPfee9E+w7YExu8zzzxTHHfccXFdu/POO4vc7PcBAwbEz/v3718cffTRxU033RT3EfaUK664osgJ2njooYc2fM0ex17Xr1+/Ypdddim23XbbIkcYt+zLjOVTTz01K3unDP25zjrrxDExYsSI+PUOO+zQYL/ff//9RW4ce+yxxb///e/i3nvvjZ+vt956sa3sJex5jI2cuOCCC4rbbrstfn7ttdcWhx9+ePz8q6++KlZYYYXi2WefzWKvoE9rbSD2idxsnVpoH/MM+4w9+corryzeeeedImc4459++ukN5wrGMa9h0UUXjeMlN/r06RP35jLsdZdffnm0L3Ow29gr6EegXawJd9xxx3g/9/nnn8d5h3aRC4wDzqDffvttw3OffPJJccstt8T9jz5+5ZVXihx55JFHog2/9dZbZz3vJgmBFiMusfbaa8cDYYJBhMGaExj2b7/9dvz873//e4NYCwceeGBx6623FrnDQYrNko0H4zQnnn766TgxExggPXr0iAbpP/7xj+Lggw8ucgNjdNNNNy1233334sILL4wH17K4ldPCDcOHDy8efvjhuKHT7kUWWSRu5n/5y19i+z/66KMiJxC7jzrqqEa/n8NmXua5556LfVnbRjbESy65JIpduY2Jzz77LBr9GPurr756NEYQiVjvcJbkdiBk3xg9evR4z3MoRKTNrX+POeaY4qqrrmr4mjWNr5mLHFr+85//FLlDfzO2MVxzc0QmAxRhi30ZgYgxjNOUPQPRKGeHQ/k1cAhgbPzwww9FTtCv9GNaz3Ca8TUHWgzqp556quomFl9++eV4z+29997RpsAxxqGFQyvrc9l2qwoOULXrGHsEay/7MzYx45j17qKLLspO3MBGZ+8ABMTkjEagKQuhuUAfYve2F1uiFmxLnCSMB5x8ucH+gGMvwRwjwAL7AVEGe6I9wLkTMYPzx08//VTkBOtZciztv//+DWJt+l6t8FUF7GFnnnnmeHOLscCawfeHDRsWHznCGR/nKQ6o5JhmLSaIjLUtJxDjaGOy0Z9//vno9GcvpJ9ZL3KDcUAQQD3222+/Bo2lSvbaa69x5hL2D85THP9lbr/99jgPc7MjOdu3V7799tt4FsVJdt111xU5MkmUOCCMmTo61JgkLD/VHyGNg9QYUm9zglopjz/+eEwbePLJJ2NIdoI0JGoi5gRh95QxIGWD8Pb//ve/MWx/+eWXjyn6Sy21VMgJ6uWmWkqkyVBDjlB90q8XX3zxkBsvv/xyTPHr1q1bbCOh+aSAki5FSgHlO3KruTT11FPHlD7GBWkwjAkK4lND97rrrssuVYPUKNLBG0u/p+xBbqnWrF3l1CLayPglvZK0ytzGBGVaSPU85ZRTYq0w0tcp2ULKOLXEqNeXE9REJTWxFuorUZON9NVc1zXS/EixJZWHuZhjvUn2Y1KhSKHkAhXKivAaKEPE8+V9LxdIl+MSOVLRSO1ibWO+UYuLUg251dElxZ7UxNrXQCrzzjvvnF0tufIYvummm+LYpQwDdRtzsXt22223uLcdfvjhMV2dND7KcVBehtRrUoBJy6fkTLlUQ1VwfwF1WymDQ71GSnvxGn7729/G0hGsyVzyyoOfya1GJmM47b+Uapl11lnj59SdfP3110NOsJctsMAC4T//+U9DKbXcbYnyOYnLwrAxGdvA3KOERE4pzNiOaY2gHBz2cLLfF1tssZAj2JWUOChDPWVKllHyIDfbB3uY/Y1yek899VQspwaMA/btHNZizpessVwOxrk+zS1qP1OmaqaZZop25R//+McsLyLFJttuu+3i+ZMyF5TooAwR/UuJwJxgXaCUIXOMMYENT+k95mGOZ2bAtkFHqQd2Ba+n6vWWsgaU60nQr+wf2BDl0ki8jtzsYWxe1gnKv7U3vv3222hLYJ9hC2O/Yftw309OtJ9qxK0AkZBDVCrWnxYcagpSwzG3zZHal7QZUZmFhFp9iDHU9MBYRQjNBernMLA5ZNPHu+++e8NlCIhzxx9/fJhvvvlCTrChUOeOeo3UY6NGFAWugQNWboIyIgD1oLiplCLX9Dn1aGg/xipCbW5w8ONgyELIRrnWWmvFccJhMDfYCFPNuA033DDW8KRWDYfa3ASMBOIg9VIPOOCA2HZqFOd68ANqdrIJ3n///fHrI488MoqHrG2IcRj+ubWfyz1YwxAPqVuFMQLUTaWuZ/o6F5Zccsl4WKGuFcY++wWCIeOaWqSsFznBGsbFl+utt14UWqgHz7jm4MqBkPVirrnmCjkdshECEN8Yq+xtHKhwlrDv5SRiAIIAB0DWtGOOOSYeCBCRGbscYHO8DIgxzOV8OEYQ7GlvWj8YIzlctMQhCvGCQxM2JEIcTn+EQ+rkrr/++lEEzwXE2AUXXDDaNtS+pP3MOxxmrA3YkwsvvHDIFS7zQKzfd999owBKm5PjOrcxzGEVOw3BCGGIwx9iPq8BcSM3OFewRlCvEVutvIYRZIEjEmcvDvYLLrggnkeqBhEW0fCkk06K9am7d+/eIBLlaL9jL3CeY7/jI3vcFVdcEQUiLizK8UId2oVdyTrMOsGDsydiIuQgymEbEPSBSFsORsBmx25LImi68DcncOaxd9C/zDNEZfQJxnYuNX7LIMiz9mILc/5k7KazXI5zDrDZjzjiiOhApV55cjxSpxr9p+o9mveefi1f9M1awDqLXfn3v/89XHrppXFs08fsfznBWMDZyzmJNXidddaJ8yw5UHPj5JNPjudP7Phy3WfOSLT5f//7XxzT7HesGTnQiTDa0MHAcGOhKy/aXJSCpzUJRhgmCIcs7lVP1DIYdrQbA+Too4+Ot+MxaTlc8ZHLKHIS5CgMTaF2vGkYIrSZSAygMDcDnQ0nJxgDHLA4ABKFwUKDSI/RxOVARPZhkORkLLFYE0HLDcwIBNyoijiLEZX6OxeIKmK+cTDEi41ghMjMR/qYKOvcwPjAm5kO3jhHEEAZvwgcRJvlNH5pEwc+xBa+ZpNnc0TY4kGkck6wFuAE4dDKfEsR4RhQ6RCbI0QPYTAxPtgv2C7ZOzioICblBMJFEmh33HHHKBRhnHKRDkYIryOnAwpOJ/aPFHWK0YSQzBxMYlJO6zCiJnsvTl2MZaJzOKhy8GZ9YDznBBcSYXiyd7BWMOfYK4iAQZSj/VWDgU8kZ3KEIQIwhon6xXGDY++DDz6IYjjROk1dovFrw5rA+ou9mcYsWRjYb/QvbU9CQS7w3qf2ss9huzHvEGjZNxjX7Nk52e9EurC24czr379/tONZ23BWM8Zzu+gOZwL2e+pjxjLCJusDbWXvzgXOF0TrsU7wQJBNH5MDkgMtNjxrHY6eXxucHkTKlgM9uOSHTCzahD3B+sbY4HzEGpfTmQ77HbsHhzQ2JX1JYAjjhGyMxi7sqxpEedqGo4+zJ+sE6zT7BmJiTmtabpkrTYFAjwDH+YhoST6yXiB6sj5gp+WW1QtcqM35mH0Yu50zB4IXYwR7mIjm3GCs0jbGCI5/PiYHyUILLVRp21jXPvroo6hB1IItQXYsDh3EZc752Mq52xLoVDjRsSXQhFjvcuGSSy6J7and5xjH2Bqc7RjfaBf0dRYO1aKD8e6778ZaLt26dYs1O6jdmWrQUIvvnnvuiRe/UFycWoK58NZbb8WP6dIUoJ7dY489FutZUWfpiy++KHK8SCfVJ6K26CmnnDJOnbPy1zlAXUMuraqF2jobbbRRvMwqN7baaquGdtGnXOaRoC5QjrV/auvYURuKC63++te/FjlSWxeO2kqsEdSDOu2004qcoAY1/UibeXChDgXmKeLPpQNcZJQTrG2pLtzXX38dx2yquXbGGWc0XIKYG6nGLHU6uTiH9Y11mNeQGzfccEPdi/juuuuuOFa4ZKBqGKdcMgIU5mdday9w8Rr1ReHjjz+OlymlNeP4449vsn51VTDPUr3U//3vf7FmLuOA2ofUA8+h7iG162gLl/xw0Qi17WprOzPnqKmd27yjhm/5EkGgv1krGA+5rmv1asWzxzEfq7aJP/jgg2i/cwkUNgSXBybbGKily57x5z//OdZ6zRFsSeZbquHJR8Ywl0pSc7I9wt5SW/P+14I+Y0yccMIJ8T4L7LLaOtCsZdjvudVZZ7+jJnVa05hrf/zjH+O84yzCOM8Z2v3oo4/Gi/ioBV6v/nYO5F7XuQx1h7HX03jYcccdG14Dc6yp+tW59TEXgrNG5wxjljPT2WefHfs2B1u4OdBW1j3OdVyMmSPYklyOyp7H2YN9ggsmqVOdI9999120MZq6K4La/NTXzYF85O02gpDxu+++Oyr6KPt9+vSJEURElKXoMur+pBpGuUBaFPUveBD6Tjg+XuDUZsDrmlv4OG2kvwkNp8/x+iSIhiJVPCeIjKyXNou3hMjZHCG6kLYRkUOEdaqTSv9SrzgniCrCQ1lbx46UYCLNcot2SeUYSKslSoBUxDTnKH3BIzdSxFMqCUBkKg/SrEm3ra11lkOUXKqBimcYT3sqK0MkRm51BJlj1HWmjisptEQ+MR4ofZFD7bV60K+sw/RrGsOrrbZa+NOf/hTbnQNEXZBGRHQc7SO6lz2aduYOY5hSBkCbqRuW5h9jeNCgQSEnWAdYh4ksIjLgxBNPjJFbRAwQ9UkkQQ7RDUQrYDfwuPnmm2NmCJF7lJdhXJAVQJ2wHGq51rMlau0b7ErKPfHIDewGaotSUoS6l0BkJ68hFzsNO511jKhp7lsgWpZUdqKs075M5D2Rszny8MMPxz5O9QOJ+iVykmjwHMcwEU9EfTPn2NsaKzNEhFFV5akowUBUFhGGrL2MCdqd9hHWCWyKHO13xgF9R7QZbSYln/JqzDvKjJDenhtEIrOfsTbTds4fuWY4NWW/U9YlR8hwStk25dqizD3aTxZJTqBJEMHJOkEpH2wfxgNzjkjUHCFNnVIGlI5gTyZSvb1B9CwlXO64445w9tlnh9zGBOX1yDoF1mYyQ9Cx0ClyY8SIETH7gz0Cexj7DG2Q7FO0ipStQ6bOuuuum00GRocscZBgoWPRRsjiAMvmTho+B3A2d9ISEWtzSfkkPYcFm4MVA4UNhsWbBxs6ogEGYE7QTmqQYOAhIJOCj9FB2hT149jocxLDb7zxxtjGM888M6Y85MjgwYNjCl+C8Uo5Aw4vbDoY/IgCiAaIctRqzAWWEzZwNsbaFHBEAzb6VFcwBxCJNthgg5jayRw766yz4mbDwk2qJ4IGF6vk4hjB0EfIoE0YIbmO4TKsCxxaaTd1RjHyMD6AechrQrzLqUQHQhFjlTREjBAucWBd5sCNkyGnelCswVy2xoGPtC3GNPsdYigCMwYTKXXlywiq2o+59DLtxVyGAYhxtJFDFftybrWIgRRExi61fTlg05dJcMEBzPqQWykf9gnS6Ggba286cGOkDhw4MKvDFSnMyfBPgi19zvxDnKO+GTWsc4GxjDDAfkzZharLAjQHUsEZCwic9Gkilc3KMUUV24f9I6VRYssjdOGQZHzn5PBFUGZfQDxmHSZ99YEHHoj7HuImTpKcYE1AqCdNFRgbqUwSomeOadYI96Sksh+nMcHZKV1KfOihh9ZNGa6KVIs4nYGw4bmbIaXcUmICB0Ru+xzpyWVRlnZi01NqJifam/0OXFj2zDPPREcD5b14/5dddtn4PdY0SilR0zMXqJWLqMxaQVkRxgJlZ7AlcOogduVk+3BBI2cjSjuVbV7sYXSJnM4azbHtsTkRmHOyMSihtsYaa0SdAvGT8gbYl7QXQTm32vDHHXdcXBdwlDLfcEinC/oIKmOsZEnRAXnmmWeKkSNHFuutt9446VFA2tQDDzxQnHTSScUdd9xRVA0h4e+//37D5zwIFyf95ayzziq23HLLYtFFF42h7n379i1y5JtvvikOPfTQmKpIO3mQXkk/55bqsMYaa8T20VbC8K+77roYpp8LpFMvu+yyTbbp7rvvLg488MBY7iDH1J7zzjuv+MMf/lD885//jCVHSCcgPZU0jdzSd0ifO/zww+PnpDWsttpqce0gHYZSAYyPnGCtKI9hUn+vvfba2M85Q4kDUvtWXXXV4t57741lZ0iboyTDa6+9VuTWVtIoa1Mo2Vco05FbmipzijFQC+UNmIc77bRTscgiixR9+vQpcoJSATfffHPx97//PY4LxvTKK69cHHbYYUVusM5SKoI0WlKgSE2kTAMlfhjDqYxSTlASCRuClPBUHoC9g/bnVsrn/PPPj+tBbXotJQ8ofXD66acXOfF///d/sR8Zs+zX2D+33357tumTvP+77rpr3e/R7+wjOYLNVi7bgn1EGvvJJ58c01ZzghT8p59+erznX3/99bi+kV6ZE+xxnC9Yx1555ZW4j+y5555xPDOuqypp0BiffvppLIlSC3bbwIEDi0MOOSS+lirh7Hb//fcX3377bcNz2PKMV9a4VEIEG4IzEmV/coK9uPbMnF4DtnEuqb/t1X5PGgR2xPbbb18stdRSsaQhawTrB+sE5S9ygn0ulWuhzBOlLhi3a6+9dtG7d+/sxjDn+nrjlH7GvizPTZl4XnjhheKYY44Z73nWtr322qu4/vrrs+rWkSNHFttss03D16wRSV/Bllh33XWLXKk+x+0XAE81hflJBabYL6p+iswhnJkQZlL/uOyjSogcIxoHbxQF2fFecikRkVopchZPJlEk3DpI5FmO4C0hEo4I3+SNJ8KzNs29avBic7EEHvfkfSf6hUt/iIzj1kfS8KuESAAiFwi/T572Wkhb5kG7c4o2I1qAsfy3v/0tplpzAyVeYj5nLhKtjnc4J0ivTxceMMcYs7wHpGrcc889sZ9zgnHKGCZ6OkWaMYZTdCdeQS7kyg0u/iEqFQ98mmP0M2knOdzMXoZsgHRpUYIxTFpabhdBARG+RNDWQmkDMi6IsmcPofwM0XNVe7e5kZ3IzlRuiOheIu+Zi4xn1pDcYJ3F2844pv1EVwNeeeyN8gU2ucD+QCQcUfYp+oK0fGyK3NLQiDastceI0CHlk0ducAkYkeDYO6mcFrZcKj9DpEZO0Z1EHjaWyUQm2X333RdygvWASFQufGIcEDHLWE7py1Xb7vXArixHJicYD5RKImMgl9uh05wjUi+VbuHCLTJbGMNERKVSWrnAvlDv0kjSUblIh0fVkAbes2fP+J5j75CpwHhlLJcvnWEdJi2fcZ0TnJvJxKmFPYRzM2Mil/Tf9mi/A23kcnIu7SQ7IJVRY2xzXiISOBfI3KQ9qSxg6mPGLXY8Uam5jWEifOtFTKOfsIdge1ZdGpBLs7Frci2Z1pJ9jmx1Iql5bTnx2WefNbzfjFfamTJPyVJnv8uVDinQkorBQYo0GOrpIB5inBLujujJYZvb2qo28hCJCcdns2bQcEsi7cU4JU0n1WBjA8pRnP38889jnUZuoQT6OJdaZk0ZeRxW6F9SVBkL1PzlkMVhu2qoC4jQjQCAsZxSX8qiPm0lDfvdd9+NYkEOaUYcPjhIIVZwqKLsAuOClEQWSA4Bud16n4SXlGrNxl0+xCLU0faqBa1aaBMpR6TLnXzyybF99DPjIt28nAvc8sqhFKcDaSQIidRVIq2EOZhb37766qtxHUCMI021Xr3q3ODARBrfKaecMt5txgjNGCArrLBCNEowuKvuc9pz/vnnRycUwiF7XBJfEJNzg5IA2A4Y/UceeWQ8mDBOeB2sabnNOdrFHsJN59gS3BTNmsycI02xnnFdJRz66dsrr7wyOtLbQ9kW9mH2DQ5YrG/sd6T6kbrKOpzTIRt4zx966KEoaNQ6xDjQ4tzLCdpKX+KkToIhewlp4djKpI1j1+cEfYj9jtOxFtbd3EoGkKKKcxfHXarBB9jElJ3JURigtAgCACUjcgpOSLAWUMaH9/vjjz+OazBnTdYDnLtpn8NuyxFsStpLqYhaODfltk60N/sdbSLdgcP6hVMPUZ9zH06/LG6Or+lfykVQeog9utzHiF6c/XKDva5fv34xUKgW5mTV9g/9SVksHOjMt6Tz8KB8S+6wBrAPb7/99uM5fXH6Vd2/9c5HnDnR2QiCTHc6AbYaNmeudEiBFk8U3lQic9gUUc1TlAP1dZgYLIxVQ3QTBygOzxykqTVCpBODiLaWN3eioTiA5wJ9yATlMIUQx+ug/g/RfTvssEPDJRQ5gTcbwwgPKxsNhh7ePzyCVUfOlkHkJsIp1TwEBDlEWeq7JI8QXuJcapkRMcRmzoZDbUMWQQ6CydDHuOPAxVjPCWpAIRQB4hbtZGxjYFNTLrfNBocIIhF9Tb9zsOLSF2rs5BjdiQOKAyu1y4gU4HCdY63DBHMLxx5CBvWVaGuqyYegXD7I5gLrL2sx9cuoDYWhz+GErAyExN69e0eRg9eWw3hmb2NvZm2g9igOMtZlMl0QxZmTvJbaKOaq4NIi9l4ueMGpi4Cf41xLIHSyPyQHdarvylxkbaYOe05wqSGZTmQFILyxRyAeMe+I6stNiCFThCwF5hcwZsvOdNqdwwVsZThcM+e4L4B6duwZOE1ZExAKiPrLCdYInNQpAi5dUMUBm++x9+UGDnWiphnHrMc4bxi7OEegXmRiVWCvc9cBjl3azIMxnKJpc4T1C5uMevYI90R0MudwnuWUrcfamyK2iExHtEgZewhH2PWcUakJzZzMCWq5MnYJFuIMzRhGsGcPpI9zc0a2N/ud8cD+jC3BOQ9BLl0SliOIW9iTiMisD/Qxegq2G2MiXf6bEwizZGqioyCGY7ezDnN+xqFatQiOwwDbDLs33cfAvTg4fdPFhzw4e+R4iSDvPfYE6wN2OrWqWc+IBmddphZtTkwxxRQxOIh1jb5HSyPrkDWO+vsEc+ZKh74krB5MAkTQHLxqGBoIbsl7liYmXtgkBKTNnbcJ4zoXuCCDAwoDHG644YboVWERRwjldeUU9Uu0KQYR7eWARfQs/crnGHgUbs8pVQOvMMYHBhwLHx4ghAEEAhYYNveqN5oyeFIRadnQOaAQqZwuneACEsYK6QQ5CeEJDDrEIKIzuImScgEIWohdiKG5QKQTxgdRL4xVPLE4RHDisIZggOQmZrDeXnbZZTHdj3HLhQM5HaaAUhwYQ6Qcpf4rX1bEA4OfQxcR4jmmzbFPkDLJpTQpZQfhiJQ5nGhEbmCU8F5U7ZQkwqj2ogbaz/qRUscRMxASORxWDf3JvsH+xr7BmoCjIVdoI0Yy+wMHFSLkcPhxozjGNGtx1RdOkPlBRCF7Q9rH2EOSI50xwB7CAZaoe9LwcwHHOWsw842DK6IL+wfpwcDYffTRR0OOYKdxSSfRiEC7sYtyKz2U+pQDYK4wx4hOx6GQBHnsNMYFB/B0tGKMk5GRk7jFZTkI85Q4QPTGeY79g4OdMwjP4yTJDYR59rG0L+OARDTizMS4zskmBi6lTkIh4wT7gjbTduyNHB19BIMgtLBXJ1uC8wbjpep9owxrL/3K3GsP9nsav6wF2GGUI0MDyMmWYA9mf8NOSOd3MkMQCpln2EAp8wkIcqo6M4C+ZC7hHE32O3OMMYwtwaWSPI9TkrU5t/MHMHYRlJNgy5pBn2P31Itmr9KZzhpL0ODZZ58d9xHOz4CtRmZc1UFYQ4cOjRpfbRYIQQBcHseaDOgpOKbThdVZUnRQKP6b22UujV2CcOeddxZHHXVULLpNcf7ll1++2HfffWP76xVsz4GePXsWt912W/ycyw8ovJyKhe++++7xNeXERRddFC9IgRtvvDEWw08cdNBBxeWXX17kxqmnnhrHA/3JhT+5FY+vhUv3uNCDC3UYC/TpHnvsES9DKBfmzom33347XmKW+Oijj4p+/frFSyfKz+cAlwQ++OCD4z1Pn2+11VbFyy+/XOQKF4FtvvnmsSD7s88+W+QCl/qw7jI+WYu5YODqq6+OFwqU4et6z1fN6NGj48U0L774YryghAsbn3vuuTj/0gWCXP7C5QhvvPFGFuvwgAED6n6PdZnLzthP1l9//XgRV07Qx1xSwyPnucYc471nXeDCn/IlS1waxxipGi5t4OK6zz77LF54ybigf8trLmsGF1g9+uijRU5wKU26CIPL1nbbbbfY31yOycVKXLiUG7znXMjXv3//4r///W+89IW1jAs0coN2cfnMOuusUzz11FNFjrDuctEPF3Vy6Qz2Ope28t4zFthLWIf/97//FTmC/Vu+ZI3Xw77MRVZcXoTtmRusB7SRi+LKl99h19PunOCCQy4bXWmllYrVV1892hfA+sYFgznC5VVcTp0unOW8gc2Q68WHnC1Yy8rkbL/XsyX+9Kc/ZbVf8P5zKdh+++1XPPHEE/HyS9aJdOkWaxvzjQugq76QD1hn2Sewb1kLsN+vuuqqBjsd24cxzM/lCrYkbcb+TXpPuviw3nmvCrDBGBu9evUqLrvssobnOXMwfrk4LF2AWDVXXHFFPA83doE6Nif7cu56CuSVh9VG4A0mKoDLMJJXiCgMaqYSwbXBBhtU3cTYJjxn1PDYZJNN4iNFTibvMB4KIrbwalPwPKdwd6ID8PIAxc5JB04RqHgw6xXzrxKicYjgBPoWD2ECz08Oqcu836SjEoVM1AUFt4lEJhKO6MPcIUqLeoLUqGEs8Eg1JRkvuUV3Mm6JKCLVJIFHnvIcOUW7lNeMehEieAJJASUShnSeHKHcBVFzF1xwQfRYclkV47vqVODZZpstpk4ybvEEE7FJtC8p90QGpKwGHrlFmRGdTF+y30Ftnbs037j8kKjKHEoGkBLHOsd+UZvyS3QJeyJzj4hm9uucIEqLKHVSolgjGL9Ep9bW/a0a6iZTyoD9jhTVVFudqE7Gew7ZQ6wFzDnaxrglI4dxQdvIaEllRVJ9+5zARiM6K63JtJm5tvfee8fXRVmGnCDSjHHKekHGEJHUjAPKfOWU5ZSgVjKlF8gQYc3NsU4f+xa1qRnD2DaMY8qJUOaL9pX3jRzBPi/bvLwe1lwerGs5RitzFgL26JRxSJQUbc4t2pfSZGSQka1w8803x3RaoJYy/Ut0ak7RvqwJpCwTIZfsYPqXtZh7MFjncrocDJh3tWsB7c7xktzGbAmiPEm9zsWWYH0lipNzKJGI2GCUyGJN5hya7gsg2ykHexL9hMhI1mGiUJlTnDEoScV4zX0dJpqaB22lr4n8po8pg5LDpYdlmyfVzU6ZhqmMYbJ3iLzm/Fn1HQKrrbZajPantFAqjVS79/Ggr8lKrbq9k1yJAy5soJZZGuAsfCwmpKgyiAglr3ohpC4jN302lbqHMU16O/UQSYfICQ57bIT0KekOGCEcrKlPg1HN93MyQBA6MaBJh6AWJqH5bOaIyaQsI8pg9FUJ6RcY/RhDGM9LLbVU3BhJOaK9jJdcoY2MZYQ35lvZ8MspLSqBwcx7jkhbrlPFRs+6wfuQgwFShtpV1LAizaj2FlKcUTihcjpwcxjhkI2xR8oJH3lQ9440RQ5cVa9r9GU6UCMEpHU33c7Og9dBmhTjO4fa5QkuzUHUwsiHdNkhJQSYdzhIcrtRFZGIMh2pxh3GKEItKWm8F5THYd5R2oU9EmOwKjCNGKdp7PKR8cxBAFGcvZn+T7cwVwXlIqgdmOpHsgcj3CN4s4dQhw1nKinApFTmWB8eKI2Tyhtgo7E346jkArycQDSmXdiZlEdB1KDGNusGYlHZ4ZcDrLGk8qVDKocS+pSyTgjjVc6xpmDNJR0xlb3gc9YPgi9yEmF4v3HmccjGVk/tZe0gPZiAEESDXKAfWQvY1zh0swbn5jwvwzqGAHv00UfHecf6Ra1G+pk1ArDrcxJpKc1B6al0vwX1cqn9zPglkIESHjk5chifrLmUbuGsxF0HjAnGMyCEX3PNNSEXnn322bh+UbqO+xdqL1PODcYp+xrnC8pnETDE59hpONB4Duc6wlJVF9Ni7yBssochzCfxm7sB6O9kD6dLzfhZnP+5wRxL9jvrcLLfcaoSoJcLtCvZ7wjN2A84dChLxmtAwM8J9jmc0KyzqQRVKmNIIADrHQ5q6vxWzfHHHx+dYOh9tYGN2O30LWd8zs0Er+RKh4ygZeAnbyu3wVLfjk0H0Ys3hYFVdcF+BnhtdAg199i8k6eS9rLx5Lj5MEnpSw6DRKNiiLCIM4HxBuYkzgIRqIyFddZZJ4rzXJDAxoPwyYJStTibDto8qA+XLhWglhmbC4csarskb2BuYi0ev5NOOin2bxlq0jCeczpQAQs3QlHtJQKIyXgBEbmoWZQTbCQ4QnAoYOxzsGae4UHmEJOTOMtlDUQGEO2C8UHEFg82dAxAHGgcWqoGQw5nEgb0fPPN1zC/MFBZExC++B6CXG61qzhoc1hNt7LzkXUC4w7BM4cIVAwh9l/qaeOpZu3lMMhh77rrrouRnqntGKpEU+JIQwytet/jIIJRzxxj3PJIY5gxwmuqOjIDo559mDplvO8ILjgRMJQRLbjIg30k1XLNRZyl3awRzLkEEb88iM4hugFHDoJ4biB+I74BkZN8To1qRI2UpZMLrG/USS2PU+YhYhd9z9jJTaBlbDBuWcNwRGJfYlPiGMEeSo60qiH7huhp1iv2CUTacuYQezKOqNwuNEOIoU2ssZxBOAvRx4wRDtuscTnBOoDzKUVDYQ9TW5R1mcvNiFqm/mROsG4xjlmTET5xNgL7H2tGTtmQgDDERTrYwzhIWRM4QyPYIh7yvZygX3F+UPcbm4esPSLneGC75ZAlUoazJ44l+jbZEDxYi+lrPufMwT0zrCtVrHEIxuwX2GXpwqqUjcXZM2Wd8nPYFDmcmcuw/5KJnGpS88CJyjhh38gt4IZgBOw2zkdJ70H/Yfxip6ED5HSmw2GDoMkYZQ9Jzki0LOx46pbnIM4CDmlsCByRaA8EvRGswnmEfmX9pa+50ydrig4INX9uv/32WDuH+iSphhk1dqjJRn2gKqHGzxprrDHOc7SpW7duxRdffDHO83/961+zq0NLXZdbbrkl1qYZNmxYw/NDhgwpBg0aVOQA9U+vueaacWpGUpOIdv/5z3+OtcMYC9TYoXZNrqTaYNS3ozbY4osvHmsUVz2Ga+vb7bLLLnW/Ry3MLbfcssiNww47rHjsscfqfo+anffee29RNRdeeGGsyZlqggH1Ofv06RPXNeoTL7bYYrGeXA71f1gPaBtzjvbkPK/KUEfpySefjDV+qY1Jn/Lgc57je/xMbrCW0b6coR7cmmuuWQwePDjWWae2GbXVqf0M1AYr73mPPPJIrCf2zDPPVFYP7KuvvoqfU6OK+pI5w/7APkbttUsvvTTWtKulXLMxB6iZnOo+l7nuuuvGqZ2cM6mN1IpbddVV41rM3pxbvVzmE3tdPZh35Vr8OcA6y1hmbGDvYMvTt9ttt13x8ccfFzlB3VPsSNqHjcNaTN3G8l7Bz6T1JDewh6mFiZ1B7WReC3Whc7u746abbop7R2ozezNnOTjppJPi/Qy5ceyxx8a5RQ1lahWX1z72t5zsd6BNqU7nWWedFe24tL5tttlmRa789NNPsU/POeeceK5jbCy66KLj1FfOgZdeeqlZNjprNf1fJcwtzvEnnnhi0b1797i+Lb300rHWOjZGqrOdE+xlvO8PPPBA0V7AFqZOeT3YN6qygRuDNZg69u2Fq666Kt5/s+uuu8axwf6GnsbakPaP3OmQEbR77rlnTOVD5ScaI3l+UNOJ/Ky63ijRkbWRN3iJ8ZrhSUuQ/oAHPqcaGXjWSPXlttr0dbr5NUWe5QCpL0RJEtVZriXJ+0/KZ3uhXBvsgAMOiFEkpKtWPYbLkP6Ll7IeRJPkdEtpAu8q5Q24nb12fhHxkEMqO54+0jRI1yjX4SPqhYiGVDM1l2h1InIowUAE8uWXXx69lmneMQ5yTaOk/0id5EGUA+OZaAa8w9T+JnWH+lt46HOB6E7aQ9oR0ZGkmuVQm7EWvNc82Ivp10GDBjXUVieVL9UaZd8gkiClT1UFKXBEF7LeEs3LI/f9gYhkUmaJQGY/JtI+9StRyDlFjrAmEA1JFAaPBNG0pKoCNtAOO+yQXS1Myg1R0gCbMtUoJzuENYI9gzUilZnIBaKgSLGmznZtdgvzLEXf58Itt9wSo+HYl1MZMjLiSKcluwgbPhcYn+xrlEoigpOSM+wV2DukK1Nvm6gdsspyAruBzDGiD7EriDIrZ4rQ7pxgX2M/xvZlH2GNS2sakcCpzEFOYJ8RRY2tRgQ75XsohUIEFzZGTvZ76mP2ZqJR2adTBCf925htX7X9w5pLFgB7NRGeRM1hgzJWciofAegR9Cl7XFPneSKYqz4vkSnGHQE8gOyQlNFJmTXKMBC5zpqcC9gMZLYQkUr955RhyljmTMK6RjR4TlB+isxT9owURZtA98kl26ncx7mtW02BDclYoKQBZ2gifHMstzjJ1aCF9957L27c6eIq6npg3JH6V3XYOAYzi1+5gDGHLNrLQp7A8MeQYnPPKVWDlCLq/3B4ZfCn2jR8zuKO4V91jV/AkENIps4ahj8gErF4J+GIjT33Q3jukMpFqgAp7Rj75UWcNGxSZhBDc4IUWg5OGHmIQhywGLsYd4wR1oscSHWoMYxwOABzi4N3GsMYqLmlzNFWDH7WAlLkMEDKBftrS0tU1bcIF6TqsCdQdqHeusXBigvYOHTnAu1hL2PdZY6xjSOMJxGf8ZyLcF+v32lzMvpTbUnGSpWpiV9//XWsF8kaUL687tRTT40H7vLBKcdLD3Gc8r4jZNC3OPJIp2XcMucojYKzskoQ5ykxVXu4o7wTabbUr2evpmwLa3NO0KeILpRL4qK4MjmOhwSlLjhUMw7YpxkD1PvFvmQNySkdmNqdiLO1NdYRx3GsY8vldMjCZmc8p9TOJGYwdnGwcshGzMgFBCzaxCUpiG+k4uPo43UgMFM6KTew1Q477LB47mCfYBwfddRR0RmCKMeYqLK0CG1KF1yWoU9JY0aU5TxKajuiQW6XjSZ7jZJvvP+sbYxfRA1Sl7GJuB8jFyirR416zm2MZ9qH2JXKUrF2YBPnBO8/TmmEQkqrIdwTXFEvsIj1uOo1DhuNvYHyPdgQnJPRLDhnYC/T57mV2WMecqZj3T3ooIPiekaASwraQ3fJzVHGekBNeGwzgoY4OzMXOY+wxuXkEEHg5r1n/KJd5Vby7ZtvvomBKgTapHMm7eVOHMoQcS5qb3QogRYhgDeIhTrVTkn1XFkYidipun4ViwgDBaMIIymRboWm3eVoHiYqnqGcjH028nrRLRwMue2PzTIXmKDU5SP6As8l9RAfeeSReCtsupmUr6V1cCghwpfNnNqtRGEgeGCc4mCoOlKHzY6oCzZwDCDEK+Yi85DbHjH6MLAR6diAqvZi13pTuemTQwrCJhs6jhLGMPWgcovuLJNqFSUxjjrVrB+IMeX1rwpYw1J9TiL4EF5wJLDG0ecYoER/5hKZikhIvUCcIMyzJMBySOFAlfqYyCii/HKIAm+uoUrtMwyrKqFmFbXqiWhIIHJy8SFCfoLDOGscdcyrdIywf+HQpZYzwhbR1DhuGBvp+4wJHojORGtUfZEOF6ohFuJMKMOBikwnxCzaveaaa8YxXfVBtQwHFMYp7UK0RyRIcBBkr0OArhL2sgceeKDB/k32Ls4P9gsO3AhH1D0kuyin7Czg4kAiwevVsmMPZ5zkdJs8Y5UgCiKqEYWwc3IV6gHxjXMQznSi7hGXyRhgv2Odox57DgdvbARsMt7r2veb10DbAYEjRd5XBesuoivOsSQMsL7lEKTSEhAJGR/YlkTNEWBRrhVeNYxXbB/edwIXsCWx7QkIAOqQssfkCOIbewdnkBNOOCG72snAuYh69ZzlWc9wOrHv4WAgW6DquwHqgU2GncMenAJZ0FlwOCAs57CmlWFOEQzAuGVfxlmKsIztRmYRjqec9hHOnLS5fEkc4yCtd2g+VUfXfvbZZ3FfoI3Y7cDZGOcIWTkEAeQ2DiYpgRZvMNEXTFSEAFIzEDhTuh9eoKrfIKLhSEHEQE5FzUmBIf0MI7p84OMQSDRB1eJWGfoVj3VtBEmuUJyfg1TtRRgYpoh1HFJyS4dpryB+U+AegwlvFlENbPQ5FJPn4Mzhg4gADDgOpukQi+eyHDGXG3h+2bBrI//pb0RGjNGqswLYRvC4I16kSJF6Rj2GFMZe1YYp7cTQJ8IFY5mIMqIa2MgxRnAwcPBinHAArzpCmf5FIOKyAzJBEOVSJgDjmFtUk4HEuICqbgOeWDCqEevLzskqwCGKQUckaoJMkaFDh44T8ck+jYjEWlcljFMuYWTtQvhEwMc5zcGEviyLXIwfHlWKBlzuw15AOafa9RZbh75OkfXYPIjKuThHgMNe7969o4hP6jq2ZkoH59BNW0kVr5LkuMNOSxcfpvHA2KiNTM0N+pGIesZ12YGH8x8BEduz6oNgOaqXSCLSaAFBi72PfkasxbnAOp2TsxdbmIMqfcg6x56XSl/QVjL5qraHyXrjvcbBTztx2iBmlQULxCNsiRzORoj0rAkIAzhAGBe89ymSi7mXmyOkNkOISzv5yPglFRjBBSGGDNScSuSkvQI7nrWNdYJxwOVFF110UVzz+DxnWMtoN+s0DinS8nNal3FUs/cSQZ3sBcY4EewEupHdkpPzgQAQzhs4+ll3iUZFuGd8EByU49kuBemVS1q2F9Ilcehs2MJk85FZRBBcLnzyyScNbWQso/cQsY79Sd8j2qfs+pzpUAJtGRZtaldRf4Sbz9noicqoOnWZm7W5OTeJyGwoeAPxnCC2sKEjtnD4wovJz+UCXgnCxTGc2MQRmvGi5GIw14NoM6LJ6oW3I2wxUXO6xZi+JRyfaYlwiJGX6t3lCqIRh2sWbg6sjI1U9zk3EAlIfUllOTBKcdqsuOKKDYItDpOc4CZ5oocQ4WpBMGB8Vy3QIlhRn5G+SwIhgm1tX+JE40Bb9YGFAx8CJqlP5edYk2k3h2rWYA60RB5yeM1pb2MMlw0kDPzygTCniJfmjG+cZezPVR/8MOjLcwnBbdNNN40CaIKDCkY/0Xy5wAEKW4c1jfWYw0q62TrVhq86GpXIHPY0MlZqa66xLqeDFDbShhtuGOdiLlEk7G3sa8w3Uq4Ra7HZEOmx3xgjZOrk4IxMawQOprRG4BTDpmAPSVEv2D25lUFhf0A4JJqIcgxEhNPfRBil1MqchAyyLRCNWYPJdKOGJ8ILexyRn0T15TKGgf4jyghnAjZxij5kbBBdz1mpyjIorBE40NkLEI45wyEa40zNrSZjPRBnWbf69OkTI7rI6uQ1pbUYsZk1MBeYZ0T/0iZsCBzVCOTYEgieuYmzQHQy44K1iwAcAkGYe0T+IhJRdzIXeO+5LZ79mHMyD/Y51gTWOsYLY4UxnwvYNWRD1jtTYC9TQ7lqJ04Z1ljeezLgkoOXcYx+glhLEFZu4IAiw63qc1tzQeBE9H7jjTdiFh/zLjke0YWYi6xxOWdxPvHEEw26G2dmxnH2FJMADz30UHHMMcdkd3Mbt71yy+fZZ59dbLvttvEGSm5MXGONNeKttbndsMtt0NyYym1+3MJMW5dffvnib3/7W7z59b333ity4+qrry7WXXfd4vnnnx/neW5R5VbK8g3iOcBtutxgvMUWW8QbdbkZmrFw5plnFgMGDCg++OCDIifuu+++eNMyY4I5ttVWW8VxwW2J5duMc4Hb5Ms3fQ4bNiz2K2O4W7duxSabbFLkxgUXXBBvZ+f21DL074477lj5Le3ffPNNvOX1hRdeiF+zzrKenXbaafHrd999t+jXr1+xww47xDXuwQcfLKpmgw02KF577bVxnmPMcmN0mT333DO7G4FrefPNN4trrrmm2GeffeKtpdxmXDWMiebcVM3YXXbZZYvhw4cXVcLexTpWZvTo0XF/S7dbJ7ixnX07V2g3NwCfe+65ce9gD+Gm9hxgfzjvvPOa/BlsCdbjnMDuYX4lWOtYy0499dQ4PlZYYYXsbmYv8+2338a9+rjjjotrH3v00KFDi5w45ZRTih9//DHua9ddd12xxx57xLYyhml7bhx//PFx3YUXX3yxwXZgPWa/zpG+ffvG/YF29+jRY5w5t+GGGxZVw7q18cYbj/Pc6aefnt1ZaEJ07969ePXVV4uvv/66uPvuu6NtvM4662RnSxx66KHFE088Mc5zb7/9djyHHHjggUWO/PGPfyyuvfba+HnPnj3j2E1jJwfbpwy28C677BJvkGe9QIvg/DFo0KDi3nvvLf79739np0uw9z788MONrh+sde2Bxx57LNo+t912W5ETF110UbHRRhsVq666atG/f//i008/LXIGW545xxn/8MMPj+sYjBw5sujTp0/R3hg1alR2c64x8ov9biF4qPBKpfprZfAUE4GWmycFrwORezzwWqUbKFN0X/mW4xzA24eXikdKM0ph5JQ8IBWUKJLcorPw+uBNw+uXLlSizXg2c0oxoOYWfUpkBt5sonEoLE/6EVG11JMjiicXKCRPNAYp4KRGJUhRJM2IyJKc6icDbS1H4RBpyIMUGV4PUQ+5QR1UvJRckkKkA1HreOGJROO5qqMcKB1BhF6qTUVEMtH1tI91lz6nvbQdDzw/WyXUmyXNrHzJAe89qdek2JZhPOQWjUrUN95sIsuIiKM/qUHKg0hEonaqhr2Wupzlgv31brgnZY7XUPVFjewHRG5Sw562kgGAPUGZgHK0J6VbqPGbWx029mHmGVGyXKLDmOAj5VEY64zvXNYyyrVg+5CNU5t9k8o2UOsuJ0iPK+8bvP+sZex/rL/YcDlkEhFFTb3kVMsupfFhT1BfnQeQ5ZDDRY2sV0TwkVJN2QjqrNPX2Gs8coa9gfc9fZ5sSaKiiNoh6ro2UrxqiIxj/2N9JlKW9YGvsd3LtberApuBTIYyRCKzhrUXiKYmOpK5xx5CFB8PyC1hlfNGrT1GFhzZUJw1qAWcU4kOIPU+Rf6zhhExS5YhpZ+Ius8JbGHObmQ8kYlF2QjqxueUtVkLkd5k5lEWrtxO5iBRiGSl5jbfiPbmnE9ZHO6RYE/GjqNOcU5lkoDas6wL2GxkY3HmKF9czp5StT1chnmFrUP0P5+nuUfWFqVGOOtVnRE5MdD+3DKHGqPDCLTUEqRGX6rtykDn4EetFC5NyG2S1oNNhsnLI7fNnFQN0g4x9NNCwsaOAMqDA2CORhTvPyIniws149gg2TRZcJLQnAvUr8I4pY8Rw0lTJPWFQxXCAUW6czL4SSPBmCuLs4AQh7HPza85CbSMUZw1pMPUgwN2DofWMqRmkMJFGhQpftTrfPXVV2PdVGpO1h5mqiA5O8pwOKVWFanAbOLUnM3F0McwopQIAkZZjOMgWFv3kALztRcaVQlrAnMKwZt5R+07yuQgJKZakzmUYyBNGaONvsZpx9yjpETaO2graxljp+ras0A6LXsF7WGfYMwCTjFEONqLPcEBhbGck4HHwQThk/axv1FuBlI6O3Oz6prPCQ79OKMpiYIDj/WB+YXTlNRaDgDU5UvCVy4kYbMMJalwTnGBY9UXFQFCAOngCPNc+oRNTLkWxjQiBqItqX30eS71qVnHcJqzjuH8SAELzDfGLgdX0rDZ/7iPISdwOFESAPGNvSKV6MBxhpOs6jqN7As4cBkPqQYxay59iUMaQRyHCPYF9bdzEI0Yq7W1DFnf6pV3yhVeA2O3XmmLnMpdMD7oV2xK1oQy2GrYwjm1N1EuV4ctRJkOHDusG9RUzhH2D/ZhSqJQr5NyItjv9ZzWVYNdzB0NKagJ5xlrGc4TSj7lJB5yzmBPw36jrTgnEWdxLCDUUrYsJx2FIDycH4j1lPWinQQppEA37B+CL+qVZKwK2pf2Xuzf5KjmzMH6wRmpPQm07YkOI9AiZBE9wkDH8Mf7gxBH9BORiTkY0BNLTpsjbeGgmm4K5xDLoZvDVbrsLLdDFdHIRJ9ibHBAZSFPl3rkCEZ9qjdLRAYiHKIhGyKbJAfYqm/hLsOmjYDIBlg7VhE2cogoKsPmQg1qDFMiRjBKifCr+gKoxkCkx1gioihFIhJ1lptRx3pQ61WnDibGHCJGblDfjgNp2itwJjB+GbPJOEqXFOVWpxFBiHWB2pfML8QAIn/TuswNtjmI9jhpeBAtiVGKwZ+MUEQN+psII9a5HCK3EOeZazxwRuIESZksCHBkiNDv2BRE/OUEY4FDHwc+IHorZeKk24FzEWiBtQJbgcPq5ZdfHscwMP+4MIWIjJz2ZBxNXDrB3sw+TF8yflmXqWOPA6JqJwOOG6KIEOdT1CxtYw3mEIjThmg5xC/2uxTRlwO852TA8f4zxxi3rAkcsHFC0c8paCEncPKzHgP195hz1LDGtqD2etU1n4lOxsahPxFj+ZqavsmRRw3EXJymgEDPWlWbnYDdRs3GHOE9Z58rZ27S31WvB80BJyR7MfsHY5a9L9XEZN9gDOdgGyMgs7bhaChf1pn2bZw7OEhyu6uDdZf+xZ7gwTmJ+ca9BvQ5+woONOZkTmBX4kClnnayH7B7qDuag/O/zNVXXx1rkxPAhLiZ5h0BGGQUYavhgKoaMiBxTCNkEvGbItN5cKZP53rsjZwujAP2X+xh7B7EWvZlYDyTdVZlzfKOTru9JIwFmVTqlLJeL7qPwUQqFxt+DofWjgQGNR41DGkO4BjXiKG5wIEKryXjg81xyJAhsc0piosFkYjgnCANBkcCRj+bIt5hjFNgU+dwlZMITr9yeQ5i1pFHHjlO9CmRPEStE12dCwizSSjiwRrB2sEY4dDCGpFT0XaEC9K5GAPML1IRWc/4nDHMBXcYJGygVcE4xfGB5zq1A7GAaJ32kLmAk4kLdNKY4PIJ+p10VQx+Di1VHw5HjBjRELXAYYXLfxgX9aDvcxKU64GBmi4vwunEpZm5OXPqXfySxgjRDzncHp6gnAwHlMYuWaP9ORy0GxuvjAfal5uhj4CBox8TGQGGi5/4nMgRLi3KKZ2W+URpiPIFOUTREsnO/EplWkhd5cKP3KJRoezo5XMOhdjw2JfYQrkJMLUgHpHlxP5M/+ZkS3DwZx/DBmYvIYsI+w3bB1uCsVwu+VMFOBdx6BHlmzItEL5JCadcTm4gTpB9g93DGTO1GacIASy5ZWPVs4dT9B4PAplY55iDzDVEOsqsVRkJzhmT/Q07jChJRFpgrUOcw9bBUZJTpl4Cm4EzG21kf0up1enBc9gSOUWkIsiyhiESprUhJ1unFs4eBN3gDCMiFTsoXeiKOEtAS212XxVwpsDJj6OMtZj3H8EzZZMRicr6h8OHjN+cwAGCsxfH+UMPPRTXNsAZiQOCsko5BROWqRc81p5otwLt4MGDo+ckCSxpcyTMvT2/Ie1RKCf1moWn6pSuMgjHiFlpsSN6gEizdJMf6Wg5Ccq0j/5jkWaDwZAmapLPES8QbcsiWC4QQbvffvvFviaamsMJKQ94XImQylUYSDV0knHKuKCGZ071lUhZ5XBK3bhyf1M6gnIYiBv0N8ZqVV54bqxmjmHY4zxAMMbgTJEZ7Q3EUKJ/0zqBYZIi0qosy5FugebQnwyjevXWZdLbfzGQcUA2JtpLy9Y1DlQImuzB7LusxawLROqw/nK4rk0NrgrKe7EelDMWWCNwkhF9lkCsJbKIvTknaA/jmBuiV1hhhYbyBrmCeEz0N+UC2gNkjNCnKXIawQB7EluCPY9xjBBatWDI3ptsMkobAFHVZEjSfmzinIR67LGUGcKawGvg/EmkYTqTEomaExz5edSe18hmSX3PAwEa4Qv7g+wMHFO/NpS74XxBMEKCFHHWNkRZghYQarHTcgu4aW8g0NPfjF2cugQCENVOFgNjgOeT+JkLnNeI9MVRQlAIuhBBITifGDPYyjlE0CbQrCgvQ2mGNM8IJuO8TJuxNyhLkxucRdnr0E1YB1gbsIfIqmaNy2Edfv3116PQndZbNAn6m7WBNqMJsUbkbFd0GIGWwUzkUzpIE/mEYMHkZKKmOrQIGNI6CMenr/Gm1hMIWbSJNskpAobFhHSCxi5aY/zkJCgjFmN4lg98HKaI4MHbxqUOOCJyhHk3aNCg6I0nyohFksjZ3MRZDGgWawx/xio1+kgxQRxn48kt+pC6yRiftUIc7cSbSZQOUQ4YVjhIqoC+w5BL6zBCEc9xqCIqJx1SOHhLy/oXr3Uy5khTBgRbDFMyAdJlJDLpQYokWRcI+UQXbbrpptH2sSZYy+FwyoEPg76xPZd9mbUZATSHuvDYDdRhLKdWE71FZB8O9ASviRIYtXU+q84c4jJJMp5ITSXylzZSD5y9g7sCcoiCShABxf5Lu4juSyB6UmIitzqYHPFoK5fP1nNSk/1EaZ/cSIIhtgUf+ZpgBUoKcLlZDsJArV3GOTTt1WSHsC4TSUs0X9WZOMA5jZI92GZl5wIBIZztyvYvdmUSn7nIqFyf/9cA8ZU6qASnJPsGwRAnA/seNhBgB2Pb4zDJCZwN9HE5WjK3klllcDhyHiIVH2pLPWHTE62aE+y/ZLMQOcv5k+hObGYyOIkGxumXO2QhsuehRyCQ52TLszdjV2LjICrfeeedsVwDOht9nkPJyKuuuiqWXUBvwHbDGYn+gECLncN5H22FzCGyXHJzMnRIgbYWvMBsgITm45HgsgxeGqldeIbLHjiZODCQ8FYiFKVamHjU8EyRgoJ3lZ/JZePBi027WKzZvNPlDTmDJ5BFJacas/XAaMObxkc2EiL6OBQyv3JK1amFTYUUGPqZhZrNm3RxNh3GL68pNzBOiYhCLKiFAwobEUYTxh8Gag6COBF95TIS9DsstNBCMcIvpahJ82CMUmctXRrA3pbqzbLmIhZgPPF96jTmVFtSfh0o1ZLGBIdC9j8EWvZphCTqplZJjrfZNwXp1ETeEEXbFJQd4kFETNX9y3uNeMV7zX7B1xy0cfym8gZArWLsjJxKfrEnc2hKl4+Ugy8Y04jMBx98cMgFooYQ3VJ7ExwIEcrJHMnh4FqGoBUc0vUCVhAPc8omm9A6x7ggdTl3W4JghVQLHCcq9lyVINAjUFASgHMcWVcIyFykxNhAIKIkCo6eHOB9JosBMTZBNgA2D/VQE/QxGQQp9ToH2INTSQbKZaVsMs5IOBgQu9ElsO1ygXFAFlx7syHJCGFcE0hGvyLiA3s42Ri5QPvINuSsRkRnyjhkHuYkypbtCjIX+EgbsSlob2NlRatg7NixsR9xjBIZS6ASdwIQ1IINgVBPqRHGBePk13YytZb8latmgjjIBs4mREg+gypFdbWnw0GOcPgndSAdAhn0LIh4XIkawAOfizgLeLE5nCDQJ8OfhYWNMcdah3ivWfxIOaOdOUX2liGtKG3ilDMg+gIxjoUPI4RxUHtoyQUOICzcGKaM3xR9wRhhIcdTTHmUnODwT002DDki41IkLR5XoklY59j0ayMfqoRUHQ7/SQBgjLBu8Ghvm2MOUFMSMT5B5HcShhi75RRL5mJOxjU14nHi5VyKAWOZKAHWA4z5nNvaGPQxD+qx1V4a9+GHH1Yu0OLcJTISO6I9RNRTn7M5acnU/H3//fdD1WDfkm2TnGJEQ1L2BogsSZdCcSgk2+G8884LOcGYLddrxf5JlwziVM0N7IXu3buP9zz3GxCtTJZDbgItB22E+RSskuwFDrI5iUTNXefaAwQLERXOIweInD3rrLPGuSGeDCxsXyIjiVhlbWDskr1VNewRnOmxIRmjCG84zbjYtQztzi1jhHmFKE+fI9QTEML5if5Gl8BpltMeyP7M+YcISRz9OQY1sU+w39buzZQEYD3GjiMghKAVzv251YAme4UoX9Zf7DL2aeYj0ajYRGTEcT7NCURPxgJjFbuSaFX2Z2xlznicS6ssOTNy5Mj4wAaiXYwFbF/WL3SVlFlEmRSCytrbGTS/WdgKo4nDK6IF8IaR1sNDWh4NR60RNmw2QDzAPPBaEKHMYkiaSe2tq1VAGDubHoYnhxEOKnhWmax4hzmwsJAgLlL2IKcLPoh6SreT0mZETqJg2DBzEWuJ3OOwR4pUrVeSVC68lSyEGCPUcs0NbqlNt5wzJqhNAwjLGFLJ65oTRL+RGUDqESlmGCZslkTqEFmA1/Wuu+6Km3qOHljAsCZth4dM/PqLUdFYmjXvOWOXB5FxuYExx75MWRHElhyhXjZRykTQ4+ggBTEJWpRqydGhl2AfJr2PwyD7c4rIYLwQuYNzJweIbvrvf/8b92TKMWBQE1Gf+pn9ZNpppw25QFtSKZEJpTRzGMwBBE4eREOS4olzJDlu6P9UdoYDF060XEBcYc4RYUaGRS72TlNgRyIG7LXXXuN9j/06x0MgJSQAgZZ1Dps+1Uulpqd0bIYNGxbtibI4i5Mf0RAbEjs4gUiXg0CLvcsejMhCNCqBNziga2vhcolRbnU7sXvSpZ1k7FE6gPMpthCvgXmYk81OlGEqlYTYiW7C3pxTSQaci5wtEV6T7cBHxi6O9VzqwTcG84oI8FQDlQALdJRyzeecwGlDRD21hxOMZcotolkg2PIzZJRUpQFNM800UdxmveIcTMAKTh0cIbQ7ORpYHyiVUi4B1R7oMCUOSGWnaHFOtbXaM6SVMBERWKgvmsDzgwiXU91DDqos1NQfoa0PPvhgFATKbeTAlRZCxFnEr5xorEA/qWl41qqut8UlHhh5RKE2BiIHtflYCIkeyAlKBfC+UyScMhJ4sTGaEAt4nijg3Dyu6QZK1jVqKdH/eFsxnnBGEU3AJR+kd+QqgOUMmzkpZ1VcftHc+lpEfudYfqM5cCDEqYMAys3WPHIUPNk/cDKljBvqgSG+sYZh9KXDQNeuXUNO3HjjjTHiCXGethOdgUDEgYq9jyyHnC4OY3/u1atXHBdEG2JL0GYiY6hvT53ReqLXrw3tIoqIiJd0yK6F8cHPcBDgEJsz9He6fAkRt1yTNgcnDnsYwgDzi/2ZucaBLxdhoBbEb8paIAgQsED6J+saz1N2CFsiV6GZAzb7CvYlwRVEd7bHrAGZONgfqIeKIJvAyQ8pcCEFiCAiEtSSA5w1qS1LlC97MdkY5ehOvkckIhme2PO5QCo7QWKcOxA/CcBhXQBELwRaxOec4JyRypNhB3HewD7G2UubG9sLfy24QJtSN5x9EGWx2TgjLbLIIg2ZObQ111J7RM7iTGgv6y17MX1eC/Ya+gmR7EQA44zAFq16jfjkk09iYBvzjPFaDsLDTqPduZRvaTZFO2Ps2LFFnz59ikceeaQYPnx4w/M77rhjfE5az4svvlissMIKxc0331y88cYbDc+ffvrpxcILLxwfm2yySfHJJ59k090ffvhhcccddxTfffddseqqq8Y2rrzyysXf//73+Do++uijImdGjRpVvPvuu8W3334bv3799deLK6+8sthzzz2LgQMHVt28Yueddy4eeOCBCf5c9+7di8cff7zIjeeff75YfvnlY3/++c9/Ln744Yfi7bffLg444IC4duTEmDFjiqOPPrpYYoklitVWW6048MADixtuuKF4//33x/m5wYMHF0OGDKmsne2Z22+/vVhsscWKQYMGxa8/+OCD4sgjjyzOPffcYuTIkUUOnHjiicUiiyxSbLHFFsUZZ5xRPPbYY9m0bWJ46KGHitVXX73Ybrvt4hqXOyNGjCgeffTR4h//+Eex+eabx/eA/SQ3+2KnnXYq7r///vj5FVdcUZxyyinF0KFDi0033bTYfffd4/qQE+xnAwYMGOc51uG+ffvGdY7xngsnn3xysfTSS8d+ZTyUYQ4efvjhRY8ePSprX3vmySefjPZOApuNcXzccccVG2ywQZxryy67bPHXv/412qI5wlqQ2rrUUktFe3nFFVcsnnnmmSInmF+sE9gS2OwnnXRS8eCDDxbff/99kTPl8ZE7d911V5E7w4YNi+P0zTffjF9//PHH8XxUe4a79tpri0MOOaTIHfY51gjW6P/+979FbmBXsj4A56F111032vXMO9aJnM7OaCrnn39+8dRTT40z7zhT33jjjcXBBx/cYGdUDevreuutV+y1117xjHzvvfcWvXv3bliLl1xyybjeYXPmBPsY54vzzjsv9nd7gP2isX7kzDx69Oi4dy+zzDJFTtx8883FGmusUbzwwgtxPPMamHMvv/xy0d5odxG0RDcQzkyNWVJ1uCCHCBfCrEmjyyl9q72Cl4o0yXKqOp5X0tuJqiU6gLRrPISE7FcNHhy8Uqk2TarNmOrw4cEkUpK0LmrTlGs65kDyRFEzBagZSO01InRygL7DO0lk7IRSUalzhte4nEqVC0RrMX4ZGwmiJ/G+5RRFST1BooqIJsOrTZ1MxjDRcaRJka5BNGKOkVrtYf0lWouUOeYcaeF4VulT1hCiBogwy+FSGlLhiJYm+j9dCEZxftbmVLA/1wsGauFWduYe0VtEUeaWwdAUqZ49Yyan6AfGLpFDRPqSxs7YoKwBEcCkLeewN5fhElcieuvVC2Q9I8WS6JgcoCQA0d9kKfCepwhq1jguEcP2JLI9t6yL3KEsVkpTpqwBF/uwlpXXMWz8FMXFHphTbfirr746Zqtgo6WoWVIq0xipsh5fPchoIqNst912iym2RCJSzom2M56JiuPugJz2CeoFYu9Q9zCVbWGM5LI21O4NRGrRRqIkc77vhH5lThGJyjhIl9AmODdR/o2xkvtN59hpXATNnpxb/dlEqkXNfKOd2O6UFeE59Iqc6s9ycdyQIUPiOkbJoWRfUoooN+hDzkfcN0QkcrIly/sGZQLLqflVQ3vJhuOswbrL2Z4+zunCrVrIfOvdu3ccG5yPyLgAomo5o1I2knr9ZHBh1+d0vtt3332jnZbALmb9a2+0O4G2XLsj3eiZBLiUkpgWl/LttdJ8MIrSZWvpsEIqFAXPMaSToU2BaOqQVF1QHCOeyVivNk1KR0TkYvEmbe6AAw4IuaUeIRpTToIi6CyAFJVn88nhQgSEWWqYUQeVzbuxFBLmJSUZWLzT2MkRBH3GL+UCWC9yS6Uk5Yx0380337zhOcYwNbgYw7SbVK9cwDDiVmX6lPedMZDmYY4CBqlRHEo5sAJCC3sJohZzj6+5nKtKSNchhZb1AeGitl5V2vMYC/Q1ryeny15oG4Jy+cFBgHWN/ifdi3IptF9aBsY945RDFOszJQ1wqjIf99lnn4bxnQuHHHJIFObq1cblIEBaGpcA5QRzjnRf5hsHbUQjLsegn3MS69sTH3/8caw9i92IQETaPcJW+RI5SmDkBuI8ezJtR4hln0vtza38SYJ6ktiXpFPjxGG9RSzCBma9wKGeU4kOhAzsHIRC9kBKc+BwojwKARgELuRWa5JLW9l/uRsAGxnbLUewIUlFZv/F7i33I6IXggu1lbGBcEBJ28GYphQZZevY/3IqAYfYzZmDMYENiehGXXvGM/sdtholXXITazmXUoMfmwHn/9xzzx1yhgAs1rMUOMa6kQIuuHy76iAhAhwpLVQOsqEkA4EsXNTHmOUcwpmOkqI4RljveI73ISeKooh9zBimf5tz6WuOtEuBtpayAMcD7yB15TA+MPxl4vqS6AAKcichjkmKYEtNtvLPEUmA163qzRyPCe1Igj1tR1RuD7VpDj300OjtKd9inGpBYUQjPFd9ELz99tvjQswBNV2kk6IaMPxTvTUMPw7h9erWVAmCBZfIsaHU9nOOsGYRlZGTB7gpMO4w8HHYcHhl7HKoSlHrjHGMvFygzha1Ahmn1H7moHLdddfFtjLf8HRTc7lKEIUwmIiAmtCeh9GHqJzL+oaQgbHPGpxgDUO8x2nGR2ogcgAnIk1HastAlGXM4qBEIODwx0fGL3theb/OAewyhE3GBge+dAHJf/7zn1iLtPZ2bpk4qMWIzZubw7EpUrZTinxiTSNTBMETp1+OkfZcLJgO2Xzk4I04QLYD9k8u0I/YaOzFOPmov8ecY+4RVct9DUSJ51b38IILLhjHUYM4i4OE/iZAIKcLfstnEBy8BLFsvfXW8S6UXPbjCUGQDYI4+wevwfsMflmHH2O76qCmMgcddFB06FK3PoHtxlgmihKnDpcsY4syP3OC+qPU+yWCk3MGa1uuGWVkW+CMxN7lHIromTQrsoeqzD7EHsexRN+h/6TzPXcDpOxTgscQ7HkeG4NsKBw66AM5XozZEegQAm0tCEmIBAyaxm7AlsZBGGLDxluN8YnBgYeKyNryYoMRQkRUbiAWsaAkwZab0FlQME4xQKpMKSBlnc2EaBG8lfQrJTtSZHIZBFrC8ut979cGhwcH7LShpKgGboZOEZOk9GCoIn7lJMZx0EPQAtaEJNrzYMPJCQ6rGEUcVLkAKMe0vlo4NJFqXb7RvCwgEnFW1S2f9WBNw9DEI4yYSKpfKhmBwIXxRFpPlbCucukF0U7tDdI+WR/KgmztYZX1BMMakTY3IbG9QLocThCi+XCgUpaDSAEEpEsuuSTLG2vZQ4iSwz5LEJnDbcDtwXmWK9g8ZDkx14h4yS0SuQz7Ao4xSh3Uwr7B2sG+wUExlzJPTYmgCBc4gHFQbrXVViEXsM84/3DBKAIyJUYQvNn7iOjDvkDUzwkuxOXyqtyj4ZoS4LAhWJe5bIsx3B4i2hkP2MOItDnY7NiSBNjkKra1BNY2zkpEp+YUQcsai9BWr69ZzyhvSPQvJWnIjswNxGRK5eBsImCI8os5rR+c+ckUKds82Gk4zHIKwkHspo3oJgSqsC5Q0hDdJ2Wll2009nECBHIu69Le6ZACrbSOu+66K3qjEBAxNFhEaj3tpN8T6p5DrUY2PuqA0d5U1qDWO4TBjxHN66oSIgGo8USbOZhykOJAjbeynA7OtMSgxojOsa5nOaqBBx5W2szryClaEicDhxTSZ1MtTzaglCLOeKhajCvDxsgBG6cCdeKI/OZmSjZIavHleDs0aZTcnFl1pHdjMC5rjU+iqmkz3mwO1pA8wqx/Gh2tN0pT5BaptPXWZZxoHMaJXs5B7Ex1nnGI5Vaagz0C45l1INUCqyfq8xqIhKj6xuV6kDpJtBZGPynslL3goFrOwpCWQwkRUiWJjiSSkz0vN4GDNZb5zprAoRpw6uLg44CI7dDeyp6kEmA43nPrb0pnIcCwx2HnEBGFWEC9VNY83o+cbB+isWgXgQs5iIVNgf1IxlBtKR8elJdhXNDXZDi4vjUf1i1EWmyGctmT3PbkiYWzB86cKm+8rwfBWKwPO+2003jfI3iBOuyMdQQ6slN/bYg6JcOG+tS1D4Lx0t0tCZxOjJdcwGGDzUZ0L4FiOHEYA6wR3H+SU1sBu51xSmANthq6CeciBFs0FsrrUXdWfnkUaKUuLCIUYUfAwMBLBgZ1JkkNZmHhQFh1BCKHEcRiBAEELQQ5avwgxiEc4klbY4014vdygc2uLG7S16S8IIQTjciiSPoZCyTRte0lag4BAWEgJ0GZ6O9zzjlnvIsEUoQnEap4AasE44ODE490wCPCiLGBUcdHvubAQipMThc4UP4EQZNDNZc/laNocwHPP0Zbqk1NFEO9MUrfYkQRuSEthwgRogNYkxHH2TtIS6V0R/mQxbqH84z1uWrhEGcYhj4P0vDZU9jrEBKJZK4aMj9w3AC1ylKNZ8ZyjnOuFkqG0H4iqYn2ZL8jJZxsi9wuVmrvEE2NUIvwTWmiXGpTYy9SD5XL64ggwibD0UuGC8I9UfUcYInWyuliMMBhSrvqzTnWDJyoiEq5lZdImUO0i32QNZgsIjJHbrrppqzqSiLcYwthu1NKi1IoKdU2x2hK6jAS+Q+MZWyglDGCg5fPOSvhjKC+a05nkNwhkwzbl/I3KfIQO31CNlxVpYYoD0DbmspcwBnNGM4hqKkM/UyJAxy7XAbF2ovdxvrAuQNnCbYawSzY+r82tOOYY46Jny+88MJRc2B+lR/MvfQ5Z71c5ho2MH2LyF1vvUtBCrk5SBBoy0EV7M20E92CrN7cAgC+/PLLeJ7HriRbs6ME2CjQykQZe8stt1yMlOOwVfUkxcgk4oJoEdqF54eLELhYi7RZPPHAhs4BMae6P/VuCU+CHNF9LI6kAOcUjdoeIdUXoyinKNkyeIG5eATRgigijL0UMVAWjvFiEvnL8xgpuXDFFVfEFCgK3qdDVRKPMFpzOFRxcMZjjaGPN551gNTD1M8I41XX0e4osCb36NEjit0YSkTFUSOXcULfI+jncrBKEDlCVgMOMtpO1BPjF/GQtlLKp2pwRNIOxDaceOx1HGJThHIay+yDuYlEGPUY/JQ2IJKEqBeiDcm2YI3D2SttC3ON8cs4wY4gyyGHeYZtVrZpGBcc/hFuGcu0mYs9qr6ksd4YJmWduw4A8Zu9jr0Y4Zl1DgE3JxC/iaoul0nC8U+ZESJ+qy6dhd3Lvoy4TX8miNRK5cl4cPjGHsaWK/9c1WDzECWLIItAVC9KltdIFCJrNwEX0nzYJ4gA55xJoBDrAnX5edDX7HXUsK/SxsQ5Q1AQkaWcNxEPy1G/5QCmVJu4XCowF3CQIcCV0/BZp8mcRaxFIMVJgm30a8MawXmeczHaA/2X+jins1BjawTZCti/tdCviKHcGZATBFMQZFGvvaxlOPpyYvDgwTGggjWYkmnYlbwGynC293raCrTSbNiASGWnDglRnlXDYZ+aOP369RvHq80EZbMhMgvRk2guBIPcbn/FK0WUMgdtPD5sOET8cqsmBiqp+eXC7TLxkXG87xj59C211/hYG02bQ/o9kYQ4QIh4QlDE80q0Yar9g0GSg1eQVGtSc9gAEYYSFLxnzPJIQijtxbjKpb/LF9LwSCntyeijrzfZZJO66fjSPBDeiDZLNX0THGT/9re/xcMWFxnlAkY/c449A3GZPYP1gqhZjFSi76kJnQPMMaLUuawBo599OI1l2swBlghVorpyukkc45mUuNq9jAM4NTGJLqk6E6c9gziIcFibZo3zj7UYEYFaghyyq7LbWHtxhNGeVAqHPY49mfmVLhlhjLO3vPzyy5VHQXHxIqIEQlC6t4A2pzp9RD9hYxA1x5zL6bJDDqpkApBtwyWeCcZCDrY7IGohwBHFS1RWSk3G3kl9ybhBwKe/EQdycu7RNm6SZwxgqyHA1hMLEbWwK6q+YIlSIjhLaSNCd059WQ8uiyPSvjZjjHq5BOUwZnLJMKx37wmwNjCmuf+E9Ze1IycHKvsEewdZOfQ15xDKdrAfpzquZKRic+CAqrKMWToX07+ckdj32DdSQEi61yUnUmkI9gccUbXiLdoFQWU5QWYTdhmlDGrXD/aUHEqSlW1IHL/oPmks4MBhj6PGPWO36nW3NeQZUihZQpRZTh4JjKPaIttEDbDApIgRvIDU12FxzwnEOAxnNhw2Q6IGaDvGCCklOYgYiBektLDotcc6gRhEHKww5F555ZVYPB5DKqUIE81T9cU0pL5hwKcDKkIRGyFGU7opGkcEAhfp96QoVgmGGilHzCfm2MCBAxvSEBEBeCQhFGM0p2L9HEwQiXggGDEHMfaTyMX4ICoqJ4GW2k9EO+UMRiaGMXOKgzXRnrUQtcz4LUdo5HJASUIQh1dEpFTSgJIiOWVd0MdEm2IgE7WDw5GDKod/SGV9cnPqkSZZL0Kd/mV/5vsKtC2HaFMiUOljnGLly/mICOcj+zgHmaoO2RxUcfCzjyUYx4ha5RugaRvzsWpbg72BvQ2hBRuCWvBJCGDM5n55GdHqZA1tttlm4zyPCMr+zAW0VUOGBQEKOMa4QwK7h3FMeQ7shhSFSL9TBiMnGB/0L2IWewTjmnGRLrkjBT9BhlQOFzaSak1/A23mLJeELfa9nPY6YO7Vq8+J2IlNhKCYC6xbOEWTYzTde8KDS/qI9MU5nZM4y5mTkgZE2QPrcCrhQm14zn/MQ7IacrCFunbtGh+cjVkrcOIlwZazHvsL+1xOZVuwJTl/Uoea9YEzEwErrBuc9Vinc4P3mwALxi37B05KtAkifXPL6H3ooYfimZM+RQ8iKyQ5IFkjsOnbs0DLZi3SLllttdWK9957b5zndtttt2LQoEHjPHfkkUcW11xzTZETV111VXHCCScUo0ePjl//9NNPxT333FOss846xaGHHlrkwOOPP16svfbaxcILL1yssMIKxQEHHFBcd911xbvvvlvkzpgxY4pll122GDlyZMNz9PHTTz9dnH322cWf//zn4qWXXiqqZsUVVyxuuummJn/mhx9+KB588MHiySefLHJh+PDhsT1/+9vfim7dusUxstZaaxVHHXVUceeddxZffPFF0d745ptvirFjxxa58PHHH8f5lzt77rlnseSSS8YxsPHGGxerrrpqccEFFxQjRowY5+f69OlTPPTQQ0VO/Pjjj8VKK61UPPzww8VJJ51U9OvXr+F7xx9/fHwuR1iDe/ToEfcL1umcOf3004u99tprvPEAu+66a5x30nJYa7/++usmf2bUqFFxz7v55psr6+pNNtmkOOyww4rPP/+8GDJkSLHyyisX77zzzjg/85///KfYbrvtilz47LPPittuu63Yfvvt4/rGg/XioIMOKm688cbigw8+KHIDO2fbbbet+7033nij6N69e5Er2MPPPfdccf7558c+X3zxxYtFF120GDBgQJETl1xySdG3b9/4ef/+/Ytjjjkmfv7UU09Fm65sd+YAewR7xeuvvx7PTHfddVe01XiOMc1ZCps5J84777ziL3/5S939oVevXvG1tBeYd6+88kqREyeffHK037/66qt4Jtpmm22KzTbbrGGd23///Yv2AnbcI488kpX9Xubee++NazJrGX27yiqrFA888EDVzYp2wbHHHlv8+9//Huf51157LeoQyyyzTGzv8ssvX1x00UXZ9e+VV15ZXHHFFQ12JvtG4tJLLy1OO+20oj2Tl8tMpJkQoZdqWOFlpRxASptKhfsTeNqIJMkJ0sSJZkleSaJf8PSQak16AZ74qr2teNYp0k9kFp5KUo7wqgG1BFOEAx7X3G5eJgKHiDP6MaUn0sfUb+Rx0EEHhRwg+o3xSuR0Y5FNeASJiMqp3hKp1ESb8ihH/OLNThG/1KlO0X3tgdwuW2L+E8mQymDkCrUBay8+JDKcFGHWECLWqaGMJz6n1Htgzp188skxyoE+JtqJSIFUE5xbxXOEPmX/ICKOaFraTX27HNNWiVZnTyN6hKhf6j+nPZAU0NzmXe5QPoZo/5S50Jy0TtYSoqGqtCmIjCQyhznFXDv66KNjRFS55BOX4eW0ZxBVRgQy84r1DDsHe4jMhuOPPz7uc+zd1NLNBSJSKd1TD+ZbbrZaGexhSkrwILOI10I6c1OXL1VVyocLzYB9IpVPw7ZkjjFGcspkoO4wbUw2JBFnZBoC2VC0t+qo9VpIsSZdmUg4siDpWyIS6W9KeORkD0+I3MYvEHHI2YP1gLMRZ0/KAVKznCwGbKL2AnZc1ZfNlrMMiZgmuxf7F7B9eJDBSZkc9pUc5hu2BJHqnN9Yr8jqTWVmyOTFzqBUEhHUObS3FvaJVDqNdYGShsC5H/uyttRae0OBVsaDAyuiZhLgSEWqOr2hHoTbI9AiECAYsWFzIzTpA+mCIhYgUihzulwAqJ+DqJHEwwQpioTpk65RtUCb4CIXDifUnyG1D8GI8XHLLbfE/mfhJlWjNp2uSrgpmkMVizfCRbleak6QakhaDkXkawvwY4Qidt56663xoIJBlQvUT0J4KdfIpa95UOeOely53cTd3mDOsYaRxk6/Vl2Ooyk4OFFjK9XZKl98yK3ypMsh5OcI+wgX3SF2cuEANTFZLygnkFNdyWR4UncLo5kHAhcHWS7BJH0V0TaXms8JDiPsFZTCQJzjwg/2vXR5jkwczCkOHzyAdSHZahyyGxPpKelTpajBYQphi8s7cDDg5E1wlwD7IDVouUwnJxivCBmpBjy2JfXtWN+46yC39HsEF2xLHOqILOWDNTXkc6hl31wYyzlerpVEcPoWxyRCBrBfI3blUuc3Qdp3Cq5oLHU8NzjLYQNRwoVyI+xtnPOYe7VBODLxcE5OZSJYd1mTgRICCF05O3JyhZJpXDiLDYZToRb6ORdxFuaaa644v7DNKXmBE+Thhx+Oti82GoFvqbQP57ncAkWWXnrpsN1220XtgdeAbUQZNS7uhBz3jokhP9VNKocoJ+pMsthQJwVjA8OfScr30kJeJURjnXfeeTHqgvqiKXILYQBPMUYoCwttJ8ovlwUxgQBArRcKhKeah4C4haick4GHUMhmTgRMgkMKBfoRZokiqS2AXjVEC7ERUoOGGjVsRElMJOI3l0MKnklEWmqvsdHwvhOdg0hE5AhiODW3cqgZV4aakRghCC88Un1X+pixQD/XqzspzYeLR4icRtRAwMBRkqLmWNNyjJZMML+I0ClH6bCu5QQRUEkoZOzmWA8M0ZVI9CTINhYZR6Q1+0hu0ajULuPWXyK4aCMHvxQJlaPTtz3A3GcdePvtt6Otg1iEWEjkd6otmQTbcm1JPq8a1qzaupJEFB166KGxfRwMEWFyAudSvb0s2Zc5RscR/UT0EMIWTnXaSFAAc5EADGkd1CSm7iU1PHHiJTGLvRp7uOx8yGH84mTgsjWiwf/85z9ndTdAPQhQITiBsx22BOIs+wd2Zm4iUXuFfr333nujkMganOxJbIzG7AxpHLQIIpAJqKDeez3ILOMyV+qv5zSOiZjmvIlDj8wWhOSkqRANzPmU18Salxv7779/dP6yRvDgzIx9dOSRR7b7M2gn6hxU3QjJK0KHcgBEbCFksXAT8YAnE08xYEgfddRRUTjKDSIHiN5LiwulEDgU5lbigDQIPJXpYjAEZzZFxC5SVnNK83vxxRejGM7hqRaMfm7pxgDMEYxlxi9jgRR8PsfTRnHxOeecM+TSRg7aLMVs3qTBIOAjbjE2EOpyg1szuWCJ4vdEOhBRS6QkGyRrCEIREVscEnNxjnB5B0InawSHVm4Lbw/Qn88991zDGB46dGg0qBBl6HtpGUQg4iRDaDnllFOycdqUwSDGWcr7zX5MKheOJ9qKKJA+5miIsp8RXUjELyIGex7pdER8sn7kGLXVXmCvuPbaa2MEJ4ILJXvoZ9aEVGqGi2q4xbg9pKqyJ6fL+nKDvsVOw4GDSJTgOfY4IpByBKf6gAEDYuYNezXzjYuiciszgxiX4/rVFDhFsH2wKXCckoVBf+NcJ5CB4IVcIGiFLCzsB85w2D9ETqbMp9ycvawFXMDG/sH+xvjlTIczFfuSTL7cIPqQ7BDaiFhExmau61kCARz7cZ111ollDShDhaMs7c2c8XOBPQ0HNWM2Nyd0gvlFqRvstcbgnETAEH2cw8WBZbAvOcPVs4PRUcjoZa3LhbvvvjuejzsyCrQy3qEVkQiRsPZAgGhLuQM8sdQmxRgp38CbIxgnLCw5GSAJDlR4p7j1HOOOqEQOUzmJs4BgiJiFQUr6RtnwwODDC5ujZ60xw5o6xVVH/NbWGGZzp28RZTlU55ZaXQspJUT51oqv1AwjlZn6S4j2e+yxRxw3OaxriBnUqaKNlGfA0GfsUlIkx/5GZCHtFyGAtL6UrsOtu9SMQqyT1kHtPTztrL+nnnpqduIFEVmkprI+ILghEHGgTmlnOYucOHU5VLGelfub6D72lFyFrfbEu+++G8tEsCYwflMtfkAM5zDbnBq10jjMO/YL1giEOJynREuxNlOOAUe7tAz6lAgosgRyjESekA2HwxdhjtIR2O877LBDFqW+ENcQC3GQk6WVSqnVc/Zy3jjzzDNDLtCflFAjko/9L9Va5yyHzUM5opzq+7KXcX8IdhriMkFNaZ9OWXs52pdl3nzzzWgPs4+QIYtjh/GcC2QQojeQcUq9/RzhvEMGLBG0TcH32UPYU3KCtYt1IPfo+hTURBYDpQFxiNSDPTqX4KCWYo6ZjAOedjacWgjHxyuMlw2hA+EFoYMUn5zJJSoK0Zu6SaQsE3EKGE4HHnhgfORegJ22Ix6TFswhhU2cNAg2TUTmXEEEx7udHrwPOdRG5UCC8Vm+aA1Rk/mV+wZJxAvrBJtkbZ1khG/SeBCbEY+og1e1QMuhhBqdRKcTbUjEQCrezyGF9yGnAwrghae2No4RhFrSwtPhhQyGHMRZouqp7834pT/bY80yIgKIRORyIhyQlBkhTa1cdqZKEOWTMM/akKIjWT+IeqF0SxJriQrPpW45ICwz72r7m74mioQDdy77c3uFgz/jl+yWdBkbF32wZ7enS3RygT2NMgBl0ZUgBGrtp7q/CFyIF0SH5xzBQ3YT9k6yeyhVxZ6cU41fIvgoB8B6QPQeabQ5pf7Wg/UXmwHHB6XfmHu5RUtiQ1Ieh/2A/YMsrGRrprqSCEXYx/XOe1XCuTIFCCEkp5Io7BXYlwQI5STQEomKc4F9jXGA2JkyONmnOR8RCFAlZAwSOFGv9A3QfgKFKJvDvpGTHQHnnHNOzH5MNXMT1N9nvSBqverSi5yLeEwIyuLkljGAncY6wPqLHczFfDln53Xp0iU6RXr37h2zA1J/EqGM0wxdgjNUbuWSJhYFWhkHNm5q8pFyX2t8skAmcYsNH2NWJgzGMQYoh6l6RavxuJIqlVv0Vu24wMjgoELKEbVdMZIQbKuut4XXctCgQeMcRpIgm4xPPGks1myOeN6qhveatiG4pIvWqA+G4MUFZxiouRlJCTZDDCWELOrZlTdy+j6lgbJG5HAxFAY9Bmm6gRvParpwi/pb6YKPXCAFkQgGDA8i/yl4nw4p1InC8M+hXioiBamHRFKzF7A3JLGQQ2DtBYi5wqGK1F/E8F69ekUBg5pbOR0C00UvPDiUcBCg9EwSbDFIMapzAscIa3BtZBz9TdS69kPbwN5BSjB7CtG0RPCxLuP0k4mD7BrqttZGxWI7UEqCR25zjP2h1u7hc+yiND5wOGH75HYpHxHeOHFxPFJmhgtqiATPNeqbezkQ5rHlcTAhwJWjJcnQyeGSRqKSeWB/UeKNfYJxQpQfzhts4FTPPjdHDvYZdXPZN2g3e3ECm5kgl9yiwHGOJpGedvPYeeed4x6HY6RqsBk5Y2ILM9/SvTIpwhd7mHGR22XaCfq2XklF7AheWw6OXtrCvku0b1N3yLDHpLsZcgG7HYce841sWEp/1dayz01UPuKII6JGRVkJ1gQyWrjrAIcqJS3buzgLljiQ8SCFhE0d4Y3JSa1OFnbSePBKUO4A7wV1VDgwVrmRs2HnWL6gDCme9GFjNX2I7MMTlFvqTgLPKmlGeNsR5njvEZpziXSgFg3CCqIQUclElRFBwMGEaG/aj/GRc5pqEloQ4EgL5kCVIh1wmOTmzeQQyEEFQ5poTox8aobxXiB4cmAh/YTXVr5crgpwLKVIBoRP0udOO+20+D2E/RStmgsY0hgbrAmkKRO5Pnjw4Pi9dHN4U3Wufm1Yg4nOoA8RwVkn8HDnfAMsUU+MYZxjGKY8+JwSKIxpoouqjvyeGEivzE0QZ5xSConsi3LkC8IGY5rIz/aegvZrg+CSLoxLD+YbHxnDpKhiT7AW4+gjW0CaD9kfzCWiORNEInLww4mTQPxMTt+qow1JYyfDAtsHpyi2DzYxh2yc6eW6uTlDRhbiAPs12QHdu3cPuXHwwQfHvQyHCJSjJQlawOFbtb3TFOxtqbwBtgTCBjZbTtAeUtkJAGAccCs7tgPtJrqPMZ3LmY8zKH1J1F5ujv4y7Lmc6bl0DeEY6E8c7NhvOESSGIc9z1qSG+gOOdmQtSDGUy4EsZjzBufQWsjAIFsPuyi3qPsE9gRzLq0TOCAIxKrS4VsURbynBb0pjVM0FRx7ZCTj6CNAAeE7x0vhW4oRtDIelDLAw0qqF+lcGExMTjzbGK8YKSw+VYqzgNDCBQ5s5jlERdaDAz8LHQtyY7DwsMFzYMUgyQmiSf75z382CLJs8BhJLJCIMjlEeZI2i9eMaBJEFaK/ARELgShncRaIxuBBDSAMPsY1GyPjBicIz+dkhCCGM+cYGwiJRFYTwYdDBzEGzyvRn4wPNvaq4UBFFAPebSJomW/JECGiJH2dCxgXHPxqU/ySoZ1TbTAgWitFc2KcMg8x8Ej5owwC44DDYA5rBSBkcQjkcMqDaBc+EsGePuYQBTUx5CbOAoIWzgaiHBDriTAh8pcoOcSjjmJE/1pgixHZWe9eXxwiHKqZf0sttVT8WLV91h5hva0tOYWDt7aGJCI4F6ewp1QJdjipvdiZ2MLsvcCejE3cXsRZQCTCfqDeKGU6iASnDmkuYlwSkRHEE7XRkrmVC6gF8Z4yKDxYR8jYqpraWpHsF8OGDYtOUiIR6W8cUNgYiDE5jQdEedrJ+47ThkxJBKLczhzsBzifUqQ6zg8cqNgNRHQmwZ5yDTjaq76jowy2JGI951DWNzLMEOkJZsrJTmMPJjqZ8o/0LyXr0j0BrA2UmSBAhH7PQZylBBXnNuxyNJ9Uvx47giAsHsDlrlVfqD169OgovrI/H3/88VGHwunP2Qh7nc+52yBnAb8lGEEr43hXWcQ5wGLkI27xsbyYINayyGBAVZ2GxKZIqi9CHNF8pBbkFoZPf+FRR8xqCgSD6667LlxzzTUhFxBaSBXgspdyBCfR1EQFU1SemjU5wAGFBZp+JFIZURNHAhtkTjfqtvYysRxEArzDjNWm4D0gZTyXKBiMDNYIDH8EfIwQnA0cbrmsKCeBC2METzzrLzWVEGUwlr744otY25d5R63UXCByJEVe1Eb7UaqDVCTFokkTDt9EqRMBTgYOEdaUaDD9fuLB2cEhFbislQML+xtOBSJIOtrhpAobgjUMoaK8HxCZg12cHL/J+UC0ZA6pqggWyQ5GpEUgIFoOxzX7Ra4gDlAeh3r26VErcCLA0Pc4d6qGtuFwRiDMxe7tCDCPEBBZy7B5uEAJwRZbjaxCsogQahHsc8gIQGxL9VsRirgoOQVUcDbie6zPvBbu6yivG7k4GQiywLbkbFourUcWEdl7uZyjsYUROlnHsIPpW5wh9DltJIiM8ZIT6CdkaBEti6aSoIwEugljomo48xDJy5zDLmOOoT1wJmKNo585+2Or5cao/2/OpcwFLjrE2YR4z/7NeE6OyvaOAq00wIVfbD54WUnpYiFHmPt/2rsPMKuq63H/GwRUxAa2AJEoIiIKgrFhiQWN3ST2lhiNxoIltlhiidEY1GAwptiQqFGDEUUNxq6gKAY7dhQUFVCsoFTl/7z7993zv1yHPnD2vff9PM99BmZGPHPm3HP2XnuttZlM8bBh98ScVi8THo6stPOQ56aT+kvmgN3WCRSmEuU5oT8U2YhpApZLcJkHICuu5bihk+2Q0/GCAC2TE3qBMdEi04WyPzXcjtZMAimfrYTsnNKNiLi3ESyiLJRsB/ptEcjPLdsBDOwoo2QQzeowARgGIkxUGUjlMoAGg3zuuQSOyzHY43mSw6A0IZuaDHDKwLkGGKSWL0Rq4QJFBFzIaMjp910tGI9Rbs+khGcb7QwIeDM+S8GNovvBV7LUyonWPAnlnbQY4pmR8BzhnBNgzKH3YenxMw6mXyDjM57RuQWHSluxsOBAC6RUyVBexcAzj/ElyQ08t4tu80SgmGuDn4Gg4i677BL7eOY4fqgUBFbI6kzBFsaXLJ6ThZp65OaUJQn6zXJ9pntu6YZbjDfTz8KLjNpcN4EmyYJxEEkUJFTwfssN1TZUYnFfBslhzJOpLKTalPsv1Ti5IqkiPae5ZnJZNCeIyf2MhQRwX6MFHAFxrgeq4sj4pc1aThWc9eE9l1oy8GJMz7OlGhigVV0wjvIRbn7pYUNwIF30lCqyc2mOAdq08kcLBgKdlEOUb/JQFAZzPMTJMpvbJi48ZCgjoH1ALgjSs9JKuXp5dg7l4ZQasLtmbni4cA0QHKdcLpVuaNGRMUSggEkJD25aX+ScHUmwnjI+FhlymkzPCfdcJi2UTTKQojUKwQGyd1gVpjQxp4xqMDiiDJHAPceXJqzc+2g5wr0vp0USng1kORC8IMjCcTL55z7BZJCWF7lulpErSlAJZPFcIEuE6wBcvwz802YkLDCY6bnouEewYJNKU6lsIEBOKT7jDd6PRZclVhraFZCpx4J6wgae9Glk4T/hXBMcILCUGwJcZ5xxRhyzszM377mcjo3xAkGBFNziPjuvVids2sg9peifZV7ZkiSw5LBAkjI8GfuyOJrb5pGlGNeUBrjffPPNunsa7QP4Os9knh8ElerbZHlJYkxG+5A0L2YhPW24lQLKpQlCuVXAlSN7kvsFH5nP5bawSmCW2ASBZMYWzD+4rtnzggAzGZ9Ft5mpNLQKodc3Gb4JLdVYdOKZljYNZG7P/S4Fx3Px7rvvxlYSJIbU1wOexIuie8M3FAO0inggki05p4BbeZ+gnHBs3LwpkeKmw4obPXQI2NKbtGis9tGHkVJfdgFOAfDSAT/l1/T/yWm1mPNKlgDBIoL3qQ8bk0POLaUlufXvLD12+oty7nnwELDNJUCXskgIDpHpwm67uSLTlOs1Zb2QUZSaxxOY433Heyw1bqc0v/z6LhIDD65dgrRcs+Vl+Llhx2J6gNE+Jg34eeW2c3E5MifT5igEklnIY2BNMLR0c52iUTbJMVHRwCCOSR8BZJ4b/AxcHwT1cwooVwIy93j+0l+tdMLNpI+ND3n/UULOuc09I6MSEQhgHJGCG4yBcnneVQrGCTyPU0CIMSQVOLSXKR3nMDnkuch4LkeMz6hc4FlCwIXsuBz6lnPOyITl+mQMwT2BrD2ykdNzjpZD5ajMYKfuHMbyqXUP1Rbl2ZI8R3Jo3cLiAckVVIrQ7oJS5fIxWWmZfpHo38oCCL97gtx8TL9nsvlSb3uuGRZRua/lusEv42EWftOGW7RjKLrFFwFuxjylm0mWbyzJi0Ac1zV7oTDfywVjB8aPJC6RTcsCL0lDYFxP9nIuyViVgjkxv3MC86X9aMlOZjE9oVqH76UdQi6mTJkSK727detWt9FzQuA+55Y+C8MArSIm00ymCCTmMhCaGwaglD4xKaSEgIdLQvkvq5oElEl1Z4fbooOF9EdlEzDOLUEAGpzT64eBFH2W6J9Lv8bcsKJNAJGBCA9uAhf8mVViSiRyGOTNDQM8dmTmgUTAlht70XjgMdDkOu3evXscaJTvmko2FJOAokvZ6aVEKwt6DnPdElxm0y3eW1zXXL8pYMt1QcZcbhlyTEYYaBD4JMuPYH1OPWdLkZ3BZJTsTia0nF/ub/ydYCIbaHDvyA33XyYDDPA5Zu5tlAIWfe8txyIYG0ykjDiCyQz8KUFjoE9mQQ79DisJgVcmo1y75QsJaTMSrl/uc9zTSrMRpVwQcEvBQz6m/oHp3kvQhSx7+vIRNMh9sY8MTwJgBOoYq5VuNlkEKm8IFKfxAYunpdmSPO/IQE0LvZS554TqPPrvs0jNOJ4e+wRA6fWb0y7zPH+ZH5ENxzGlDM8UBKdlEtm+/CxFt8CgCoDnL9cBm1Wx0EQGXzpWxg9pwS/XJCHG6hwXc6HSDX4Z0/O+K3r8TvZpKca+LN7xYtyT/syzm0Sh3M4x43YWz9n0kPEZGclkzzJOZszhQuSCYcGLRZvS/unEJ6i4oa1aQuYsz5CcMpQHDhwYn9Ncp+UV1CSTkWCY64bxC8MAreKqBA9DJtj0lSSTgMEnD0ce7jniwUPQrb7+VSnYxYoKgRl6c+XQL5M2EWQps9LKoJkHJcEump4TeMkRgw9ufgTwWWXlz6xasoqVe3C2NIDAQ4aJVQ7BLUr9KJ2kvxoDegaeXAdpUEoQmWAG2S9sjFD0sRLQYidS2p+wysr7K2U78EplfSmzJFe0vCDriXPO5CS3QBwZRZTXMiBN913OKdmdZKeSQcIklyAzi2k5TAaZYHEsnFuOj0UoFm9Kew3mhOMj05DsMp4fXMdMqjiX3CMouc21KiBXBFhY/CIoUN5qhh58fJ3zy7OPTI3SHp9Srsg8SwFEMrm4X7DwBMZwlVBGmTYQY3xZ9GIZGckEDJlks3BO3+9UqcAiKgG6lI3Kgk9OCQvMIVhYYqxOQIjgMuOhcePGxblIbm20CNKSCECLofKKJwJcBJipIikyGEcGJ9dDmq8xDyWblvcbx0s2e2ohwTjz8MMPr3v/5TJeY7GfewPPN+YWbEjMBq65zI14D9FLlHE5Yx3m9my6lcN8eH5xXTCeTNcJATqCiwQVc2vJUAnnkucA2ekszlClx4uxL2Pi0g3X6J1MSxEWc3JxwQUXxGdZfUFY9uZgzkGVcrUwQFujyA6gvCj1lJ1TfyWCcbxJeTjmYkF6jPBA4kaemmHnItfV4ISVbMpRWYUnSETwmxsjmSO5l1tXAgLdlB4yeSJ7JL3v6EHKYI/rm3LAIhvLc0+gnJPs7tRHi0FzGvBzv2BRh9YXDKBZ1cyhB1t9uIY55zTtJzhLX1cmXKy65oKKAAafpavYCaVHDLLJYmfgRPZA0RlRHCvBYvpdMuCnZIpJIJMuJioMmIre2KUczzWOjePkOUIWFO8/AuL8LNzf+JzmHxUgTKoZ4JfiXkZ2VBo70MucoEBuG0tK8/P8SNlxBLlSma3mH/0iGTPQU5ZNXBk7kH2aWvmQMZvrHheHHnpofOaWjscYw3PPY5GyEhadOF6Olaq41G+9SPRI5vwde+yxMWhIwJbjSgu7VEemgD0BZ54fucyZCHCRpMLcmIBXyr5nk2KubcbNubW+STveM78gEFeaEJJjn1zGZ2TMck65dmkdQWY9iw7lFYeafySHpeuBcTBzInAdp4AtySssqLEIlVNFdZ8+feI1XdqeIaEqleQQ7iXVwgBtjaJsnXJkypeZ+DORJhibSqrTA4c3MoFaVi5yKlPlhj0/mU78nARiKiHbISe0BeBa4IFIdgMZckzEebgTSMqhrITBBjfkHAcXC4sMSc41E5fSzQaKQMsC3mMcCwPpVMqX8KAk6yU97JnA5BKg5f5AwI0MYAb+TAYJHIJJIBMtsmlzephTQknpVv/+/b81EUlloLSaYMMzypEorywSFQoEZplwc0+mpzITEyYvZE4SsM9p9T3h/UX2FsjUYrDPYiUBRTbCrKb7yZLKLmPgzGB+bujRSLlqrrtaS1qygaO02MtYjmce92U2tcopIYRxA+Ng2vfUh0AdwcOc8AxmvFD0xmrzqmwjQETgjVJ1ArYEZ8mOI9sz500ledaNHTs2Lk6XYgzBuIdFSKricsPiUmrjwot2HcyNOee0Y8hpnkzgnuAgPcBJFmGuwdyTYyQLP9fFnErCve3111+vux5YQCMwzjiNuVzpRmI5GD16dJyT0j6NagYWGohfvf3223FuxPypmq4LA7Q1ir6G3KhZNaGcmjcnb0pWsdPDMadm4aXIzOIBSECDHjrlmcGUHVVTH5IljdJUypYptS5FjzB2amcjhKKDQzywuXYZ1PO7TrsCU7aR44CuPrz/eMDkXG5EkJb7A5mzrGYTjKuU+xslfqntSenHHEvvwcCIgCZlnywscV1zLTORYfDBi56pBJa5TxMELRJZZPvuu2/MwGBFm8l12rDhnHPOiSV/RW/gwIA+lfcxESxHYICJCRlytJfIoQVKpSEznR6dZNrPaZMGvoeJFuXAnuNFux/zDCYLrkOHDnUbLHGvyGlyrSUrl/6nDRE4YoGM50dOeI9x7ypvi8RC8M9+9rM4J8kFzzKOiXE8i2cg0MKmP1SP5NDbl9YbZKSXztNITkiL/VwLZPYxv+PcMx6id24uqHxj/lHfAj/jTsYcjI1y9+6779ZVzDLnywWBbpLH2EOmfBzBhmFUvtFHVQ2/cPbcc8/Fa4K5EpUDuRkxYkSs7qXNDHM5kgtJwOE9yeJeNTFAW4MIYPLwIziUMrVYDUwPR3rqMGCioT+Br3PPPXe2nZmLljK0OH6ycsj2JVjLIIRVNh40ZM9p4ZBZzTVQXxkBfdkIaJSXsxaBYHxa+WNAR1COhwqT1lQ2R6ZhjnhPsQrMoJlsZDIJWP0jkJTbtcuA/29/+1t8selP6odaCZNWBnoMOsj+rYQABhm/acGM4yWoyMSFCRcBRL5O2xYyVovemZvBM88JrmV6ltOGgV7FnHd6JxPQL3oySDkcZfZgwTEtPjK5qqaV9qLRm5oqG+5dZBCVls/Su5Nrl3sGmzho4dH+hEAcAXGyRnhuMFEhqMVCCec5h40wteQwWb3ttttitlnpYnXq75obxu8EhKhaWHPNNUMlIFjEpjkECnn+MmZjXEHFCBWGOVUFUBJOFQ7PY3q2MsbkvkCAk4V2+oAXPS6mxRRVFyyg1rewUJrZx7yUMWcu1VkgWE+AmZ+h/Pj5Gm2/7I+68MhWZ+xe3/sq9VfOqapXSy4xa9SoUfG+RlCWewSVvjzzimwHuLgYoK1B9OqgDyOlnUygUzArTapYFebhk3r/lK9iLWkEjwm+pXR2sMpz0EEHxQEpm28xQWE1k1IkeqekVg1auF5hPBgJIDKILsVmYQwA6wveLkn8zlldL91Bl2y4tKkH5XOs0jMopQSNQE1OZRrspEmQjaxOVrEp26BPMmVpZIczAc8Nv3s2RWCCRYA+50AAQU3Kz7iWwUorZTHsat26deuQ6+CDazaV27NhBu1F6MtG5g7BWX4GMgfIoi0ax0NDfiZOZPTRAoUseyaHHDtlizlslsG1ULqQw3Gn/uqpDxuZyTkcayVjAzbaCXENkyVL8IVrmnsaz24WeAyKLzzOJYE3ruPSzXLYrCZd35SGm6G88HgWV0rQECw+saBHAI57MLud83zmWiFAy5g+p+xO7g1sokSAFgSV06LZnKocclmkZh5EW5+0YRHPOp4dfD6nzasYGzAvIoOTa4E5EdUujClZQMthU2ICxYx5L7744nm2QSIQnlvbIcYQbFTFuIF7bvfu3eMxkt3HhkuMhXI75krC+JGFJzZTLh8zMIbj6zktimjxGzZsWHzOkRQC5s48S5jnVet7zQBtjWIwl4JZTKBYjWAwlyasZCHm0oibgBWDIErBCSinY2TXaFbSfv/738fyyUrIkKsUBBAJbJCxw6Y5DKRZuWJjK3pg0kqgSASpCNIyCUnXAwP8tIFZaXN8sh1y6mnGdUuQNpUU0WuJ4CxBZK5lAkVMunItw6eUhIwdSo3YVCmXjRtKEThkEsImDtzbWNBhokqJFJn25QsPRSOYyXuLYyXgybWcrunUxoX3I4HbojcHK0UZJZswslMwC3sgO+evf/3rbDvC5oLr4Oyzz44DOwZ86dnH/Y1Moxyv5UrCxi68v6jAoLKFLG8yiQgSpMVVLRwWayjho09yJZez54xr9ZJLLokBl0rAM4Nel5SisgEfgSHGEIwtCCIx7mFRLxf0amXMzjmmdU/aIDX1PiS5ggzEIvGMpTcnVRbllYPpmHkWE8jnWZzbe5HxJMfFGP7vf/97rCrkI2hNRjUOFS9F4xplgYGxT3nbKQLLfI1Nitu0aVN4Qkh9WGSgeuiFF16I1wBBIuagtIZj8TcXjHk5tlxbe9WHsSQLDIzNmINyvTKPI7DPtUA8oJIW0rRomGfQPosEIeYVLEpT6UvVKffpXDbla2gGaGsMgyA2c+EBnnbn42HIQyYFbMmUIyORgR/BuBwyMhg0pRYMDJBYvSaAzGoKk236suWwcVW14KHO755m+FwfTK4JXlBWUnTvy5TRwHWaMoe4fpnAEjhOAdtc+/KRSUZ5KtlmPGTIIuA8M4giM5VJQfnmA7lh8EzZGYNnfo6iS+ZKpezN8j52ZL7Qx5WgbU79wTguNquiImDdddeNmeDch9nMiuuc+zAllkXvujw3TFgpNyKTiMWGXFe0Gcgx6E8LIOnZx4R8fjadlIos+ySjiEVqFvPM+F482YdDhw6Ngc7SSgvadFAaTtZcTu2+GIuxQMaklckqi9JUx6VxMcecE5ItlltuuXDYYYfN9nkW1FlwZ9xZdECZBeh+/frFKjyeZWk8SY/1SlhkYvGca4IKJ57JXMss+tLWjt6d9KXNoUUVVZG0zCJo/Mtf/jJ+jjEPQVnGw3ydxWmCcZtuumnICZngjHGYE5FswZgt9brPZU8Jql+5V1GpxziH6rFKwu+f65h7GHM7zi8v3p9FZ4BryerTp09cNCVZrBTjdhYnqd7KtaXPojBAW2OYjLKyyoCIYEAafJSuFqfdNQl8sRqbW28Pbtb8HCk4R6COQAZlwKzMs3mGGsbnn38eB00E9innybUEjYA9gbl0TZAhzgCKgR0tPXLKjGPlnc0auF4Z3BE0ImOd65oJIAMQJgY54D7BZI/rgBd/Tn9nwM+kkAE2GwTlgiAGg7s06C/FfYPgbQ4tAkqzOvmdk1lUivswmYjci1k5zinoyYIZC2WVtlEDQXAyv3Pe3VqqD4ujBC6491IpQslyyrLPbYxWqXjeMT4mAEOrFu5xZISzIMkE8IEHHgg5YdNOMovIUKdqgWcz5exkoNEah2dLThhLEqTlWHMak5Xi986iM+MzFkV5BlMZQFCztIIvtdfKEWMcgob0gm/fvn0cD7M4zXHTGz6najICsPQt595GlR7nmUx2gnAcb24LUfQiZhGExZDSgD3j95yOlXNLNj3HyHlkMYd5RmkiE/cH5nS5tf0iaYEkMRb8Gctz3+DeTGZySixT7Tj++ONj0hLJKuWYPxO8zamysKEYoK1BBNsIZpGlRVYqZQM8WCphtZgBKJtD0Vs0ZUemsiMCc/SlyWUFs1JwTtlsjckHq+up/Js+xGz6U4l9A1NfPq4VAjK5YcDBpI9AEZkOXMOUI1KGVHTPOHo+06eM64Jsw1JcGwyQKF3mxZ/JRmVThFwQ0KT0jOBxanmRMNnm58ppd1L6HlKqykZLlYD3FH3jCApQpshgmrYBZOswkMp1J1XeY5TK8dyrpHI/KSndH4CxGxNsAgMstnP/yCEzrpKRaUamMgt4BBBTVhyZhwSNchtbMlagGod+jZSxMw6+9tprY4DuqquuitmIOV27bDBL4IjgJovUtDTIsTKEpA+uAcY49BTl2ZEW/wnMMYciqEU1VA4VhuXlwMzvUgYwx8kzjzYSLFrntNDLvYsgMpsPM24gQYGFp5yOsRQBZBbLGe9QoVeaIMKYkjFnLmMLkpYYCxPYIrmGVhecbxYemOMT7CTQzGJETi0ZqGCgIpZ+zxx3zr2ItWRceumlYfz48TEBrzwuRTUc13jOC2YLywCt4upw6SYq48aNiwN9Big5lQKTeXjwwQfHwVLa5IUAF6vaPHQYUOdY0p4zBs1kYFBaRm8qdqjlPLJ6ybklW4eHJKVI5WVpOU+yuJnzYnU4t15FZDYweGJSnQYcBL1YmWcAXXTvZyYntEEpDcSmVy6Dz7nh2mWw/M4778SMybTDJ9mztAr417/+lVUP2ssvvzxOqMlG5XhzH4TSkoP7LxMVsLjDhJBMHa4bBthFB4oIVjGRTguOTFJZfCKgwcBfqiRMrHlWl98bCBQRlGPBjx3mtfDnl5Y49PelnQHZpwQ+qcDhmZFTdidtnMgYSs9igkO0DUiVOfyZn4HJLOO5XFAhQrYTgThKU3lmpJ7rPKMJghMMzwULj7ScokUAm9OQWU2v0bS/AXMlWo3klMDAJnFkyHJtcF2AIDil4rmUABNQYb7G75pr+fHHH48ZtDfccEOcy+XsT3/6U0yoKN/El2uFa4E5FJtV54L5G/sDsKDOey31fCZwS1Y19znmHbn0UeY8sqkzsQfuCaUZyYzpSbywQrb2fPDBB/G6YDGPeRIxCRagmG/wHKFKthoZoK1hBDIYZLB6yQSWhw43agamBGt5E5SuYBWNDYnIiCPTkMEnpQ+UiDMYBQ/ISuuzUzSyOAmwDBw4cLbgNkFEMk9ZmSKwwfdR6lV04AX83llE4KZd+jH9mdL7tGERgZrSle6iMUEhiwSU73BsDET4yESFNiM57QhcqRiMUjrHdc0gFQSYKa/M6Z5GYIBBJ/dhrl0me2mDMF5ksOeGgRKZsyyQcf8lqEzWGaVzTL7Jyij6HBOE571PdQjPOc4jwS0mhkxgc8uEk+pD8IJMSDJlWXhk0Yz7ApNVqpzUMNLu62TwURlAMIueqARc2KQmhx3DU/CC9jIE4Bgz8Kxg/MB9jbE7fScZr3H/zWEjqFIsIHB+CcSW9v9OySG0Wctx/M64lwoX+uxTMp7jMzlVPnGO6dfIYiltOlgspZ0Ef6aFQNFVkSTXkITA753fP/M5rmHGP/SbJcBM4CVXXAf0qq8vkMx4g3FFTosiYFzJ+eV3z3GnQH2OGancu7iH1bfYyLiS5KGTTz65kGNTscaMGRMzaYlFcO9A2iCs6KSmxcUAbY3i5kzwjWAsN24CWtwcufApheDCzwnHRTYDAw6CGNykGYCyoQCZnXykH1d5SbPmjiw4VoQJspRiJ11Kr9kIimuF64Hss6IDL6z2kg0ArgM2qWJxgSAM5UdkOpBBQqCTj0xcih6UJpTWsxkUAw2yZyntYrJd+sBhgMoxq+ECoDzY+UhblNwGpKW4F6e2M0wKmcwQhCGDJycECbhf8JHsezZd4x7Me3DrrbcOt99+eza9quurDuF+sMEGG9QFwbn/5TwpVG1KvTAJGPLc5R5GGxE2lmSRkkmsWbMNg+dvffcAglqnnnpqTAjgeZ3Lhow8I1ImHAvSBLdKF/YYF+WGsSTHmGsLnLnhWUwiAy2SCNbzvMtlXFma3cl4gedxeWCf4yVzlWddTntGpI2pyeYEwW8yfrmGCSbmlJ2c5kUPPvhgbG1RHhQ644wz4nnOqeXF4MGDY2UAVYTMj6jWolUL40sWeHK7hsmyZ55EMlY5WvukDHHVdvLNmDFjYkJZNbY1KJVPR2stUQTbCATRr4o/k2kEMjUYgAwZMiSrYAYl4GTApQc2PUZ5QKYdYTlug7MLjt87GcnlCGiwCzC4DsgeIFBbNFoxUK5OYLl013UGTJQ/MJnNFQNRJigMoAlmMSmkhI7MBzJ1cuxnVukokVpnnXXqBquUdOUSPAQLYwToyQwgCEMLF14EY2g1weQ7NwcccEAc9DNhJdCZ+vlSHsw9OKfzy/GQ0ZKyWlJ1CC+ee7zneNbldMwSAQsqLQgIsLFLKZ7DfJ4KAXp/55axVYkIzpJFyziSKiEW/Ll3MJ5gMkjWatFSj/K04Q8v0NogLeyR4ckElixVAna5IBuZShYWHGgvlHspe32bpDLeJHOZYD07y/MezKGiLPnoo4/q3aSKY+Y65uu5YN7GjuxpV/a0ZwQvypZJxCCZgb7KOaG8+q677oot38gGp9UI1wABT+aoOY3fuWYZ3zDuZQGH50h639GminFmTi0MQcsL9jHguig/l2Rd83XVtubNm9dtFkZbFK7j3BYaGooB2hrFhlA066csiodiyg4gU5KHPBPZnB42BGYp1WCgTPkOD59UpkqQg8CdFhylTyl7sxTlfqUrxGQQECAvGlnfBGIpsaasunfv3rG0mmu4vkBzTuiJmoKFn3zySXyo0M6Ankq878h0yKUXVDW66KKL4gp9Tui1xYAfBGkZeDABJ7uBRREyrXPC5ImJCcFkMrk4Vq5jfgYyG3Lq11gfsqh50U+QPtC0cjE4q9zQomO//fb7VnA2LZjyHORZQRDXAO2iYxxBtiwZfPRppBIg3e/IUiYTP4fFdMY8F1xwQawaSu0N+EglAy8CigSZU4unXLAgzfiGzG82IOXZxrGn4y/dWb4oBF0JDDK3SC96PJfjOU1AMbf9Lhg78Pwli7b8fHJNcM/IFXMLAuAp6YIFKhapcwwOcW9m7syY5+abb46LOyRd5Db2oT0O83rGNyzgMJZMCwq77rprXDTJLUCb+vvSS5uPjC+5V3A93HHHHdlVk6k4r7/+egzQ8jypVgZoaxQBN4JzTK4pNWHlHQz6mQCwwpkTsiYpqU3BRI6PARU92cgscJKycCgRYIOi8tYFpRsKkKHBoDSX3XbJBuBBThYt1wSrwZS/0AIjZwS8OdesEDP4L82+4LpOmzqo4XENM7jO5RpOAXs2cKBskgk31y8ZqUwSr7766vg9ZHfmVOZHcIC+vlRflLbiYILVr1+/rPo9z0tqdSDlhoDKvEo5CcjRX5KFEhcZFh4tvQh+0uKLBIATTzwxtgjgmcwCGsGuHO5rtAigXzmVFak0nCARgSySKVLAlmMtuo89AVnakaU+uFSu8GJRjONP2ZLp+GlbVXSwiAAQwXkCbgSHeL7xuy9/MY5jPJwbWrwRwNp5553jfIgxMtcBYwyC4yS2VIocN4JirxMWbFJGMr10uXa5FnK8Hkha4vcO7helG/DlOMdnsZzjJZGC+9iAAQNixj3nmAQWnnU5ZayrWMOGDYsB/GpmgLZGUZpBmSoPdcrk0oorD3MGpmxYlBuCAwk9EBk806eNlcHScnfNP8oD2FyNCQl/LkcmBiVdZPPklN3J8aYSZTbxINCcW0ZDOTIPKYViAkD2N+XrZLIzWKKXHBMwLRjK6wm8pv57c8o45WGe0w7RKcOByRTHzIIZg3yuAdpdkA1OpkZugRcCsfVNRurL9JO0cMjcI5g1NwQKKBevrwJG84/WLLTJ4rlc+pzg79yfCSjmEKBNi0oE3niR2Vvay5OAKItkjOX5e5FBIzb6oQVO+UZlpcfPJrQcP0HRHCpFGD/QHoLFEfqMUkXI53gGk52a00JpfQjGkqzCnIhscP5MNSTBRPY90MIjM50FBNqRMSdiAYJ5M9cHwURaB+S28ShBTebJHCP3tbPOOit+nsQsFtnr6/NaJLKSOY8bbbRRzPDlpdpC4hJZ31wHPXr0iM/iOc2BeMaRaV3NDNDWKLIPCWyxCsjKKv2q3nvvvZjhxyp9TsG4lM5Og3ACGTwQeROT9clGVryZmaxowVHKxzVA8HvQoEGx7ywPSFYq6b1GAJTyviOOOCK708tgn0kKmwqwkQc3a1ofFL2R2ZxwTmnSnzLCuYbTJi9MVsgG14Lhd0+gkwlI2uW8dLOU9HBngFp0hk457rds3gCy4NIiGSX4tMKgZ1xOAVrK+bjPkrnHZnzcO3LeXIsFEPr7sggpVRLe/4zF5hYYZMM7Arml1S5acJzD1CKLSR89tkvlmLVFZQXJFIzTGUecffbZ8fOU3hNYLDqjj/NYnt1EpjKlyqXPYRbV6TWaA0pleRHAIuidsnwJdDLWpHVAGlfkErAvx2L1scceG1+Mh7gOqrU/45JEaxH6uBLY5JxyLZPBzvXBQjqVWWTX5oR5/UknnRQTbwgq8zxhMYpKAYL5uW3WRxXZvBYlVd24Z7Eoyvtq0KBBdWOhVB3CogMLZSyY0KKj2heeDNDWKAad9O/gTUCZEeU9BDcoNaJsPCdkBrD6RzCGNyXHyZuY1UwyugzOLhoe4pT60s8urbKCwT8PcTayYuCXKwLKBO8JMhNgJtuXBYjcMx7ITCbYRZZDjiVdlYDFBV5kvpROqlJfVwapTKi4b3Ad54TgK/ffX/ziFzFAUFpyxkJUDpvylWKgxIvJN/3sOF7aMjBw4hznVkLJZIqyRKnSECRkUZTS9TllidDzkM2gDMAsGu5fjB2oyiF7Mi2isoBG0IUKo5zQ/ub222+P916CLilL9bbbbovjtRyqGQhepc0jE1qSlQe7+TkobSYpJBcEr1h8TH2I2S8gjSuYJ3HMLPzlLi2ekrRw5plnZjVPYmxDK6dnnnkmzi0IvJAxmeuYfdSoUbFtRBqjETDidfLJJ8esPzYIyxGbmnXt2jWea+bRBEC5P3Dc9W0oV2TGPVWEtJhhUz4qYkliUW3hd869KiVYDPu/+y6VplTMcs2SgEXFLK/SfXKqUaNZ5Oqr6r388stxR0yCmzkNhuYHu9X+5S9/iZki/JkgIivYrJ7QOyz1a9SiGz16dByMgLIuJi2VhNIuysRZ8c5hU7P5QfY6AxI2KFHDKH24M1nkvpfbBgMED6+66qq4mMBiEzsDszkfGV0EX5gE5tK2g4E9CzYpM4vKBQK1nF8G1vwslFbm1EaCTBGyqyvteSeBQCFjNnp3knVIEIOWBqmy5aWXXoqLEFZeLDrut/Q4JFhEIIZ78COPPBIDnml/hhxQVUF2JAEXsuJYGOM+zHETECX4QjVO0cE3jqO8fzqZviSAEDBKqMQgQF5JpaoEbHPY1Gx+Kw+5HmjZkAt6ilK9yYZxLECNHz8+BmoJfl5xxRVZ7iXB+WOsUyljCcZrjHcZm7HgQNusnDb9rq8ahIUPzvELL7wQjz9tmMuLZ2BOCwxa8t566614ffC8IxmHzHAWGqqZAdoawICezEJKdMi4SCWflEgRICBGz+COHpm5IYuBHXbJ7uSB3rNnz/jQYXWY4CzZcwykpYSWDK1ataqIE5ImM1zDTL61eJDlkNP5HTx4cFxAKC2VZPBPuRz3NgK1OfXV5llx+umnx2s19V1LG64RKGCTvo4dO2YziCZgzDOPyfSOO+4YMxI7d+6cXeseaW7I4GTsw1inFG2d2Ewlp00PKx29Zrn/ksxAX3iCs/SmzSnTjIVnghlUXbCAd+2118bMopRBy+SVBd8isSBKoO2WW26ZbcGUDEm+ljK+mXfwLCEwQ3mz5h9ZhvOTPXb99dfHpIvS/TuKRnUT44XSaj1+nj59+sRFVQL7RbfoKEX1FVl9H3zwQVxIIEmIisOcxxKM1cjqZTEkVcpS4cSCH208cq6IpHqMvtQpwYLAHHsysOeMlBZ5aEWTa8Z9Q8ln5KHFmh3ATbl0ZZ2HIH07WdUmoEUWF5NrAqA5YRBHdhkfGdwR0EilO0zCKyUQpyUnh2uCCcn8lOhQUknP0ZyCh9Uot/NLIKB8kwYmUWRHERzILUOHbAZ2NycAQOXCeeedF4NDKVhL4DaX4CwIAjCo55lB3zWCF7wfU39ijjmn/r5SfQgS8iLIQpCWxRB2l7faYuER4GTRn8QFssxSAJbSyVz71yeMg6leqG/jS8bDOZQF19d/lmNlUaG0HQetDdhYyeDsgi82c35pN5Q20iG7vr6gG+c9tx7sZKOSOV2KYDPjHzLEKXcnAJoLApu0dEql1vTi53jTWIJWGDn1AWehiZZfZNm/8847saowtZ8iUMuxl/fZLhrjNAL3jMm4lhmfbb/99nX7M0ileGZXe3AWBmirHKVaZD9RWpRQPkCGFqvcaSBFeRc7wOYWoCUIwGrqb37zm7jjazpeHkC0PaAvrZQbBkCs8NW3YVUpBn3lkxlVN4IslPHV17dsjTXWCDki25eSIl48PyhDY+BPoJmsFzLNyOrKBeWSlIXzIqhBMCC1ZKAnH+9LqkekSpB6rmnRMIakvJoNf6gmS8FZsmYvvvjimCVHq5kTTjghm/YypRgrEMgiQM/9jCzqFHRmM6scSrBT1hsbKaXFsDkFbXNqiVMpSFDh98754xlMliyf69atW91mOgQ4eU7TOuDSSy8NOeF5zOJIffcz9mLIoesi4wYWO5g3T5s2LVYzpYomFspSdif3DBKITjnllJCL4cOHx3sbGb7l8wuqZDn3OWHxkUpYkgDIVqZ9D20ZuJ55EVCmRYNUawzQVjmCAGQUlq42UDbH7uGlN25Wq8gyyhE3bCb/PBgZ+JH9y4CaDQeKLueS6sNEKfXLufvuu+OgM21YxYsAHe9JvienwZ0WP+6/t956a2zZQh9JqhjY6K5SNvvhOLt37x5fBArYeTW3ATTnltJwMtR5nxGUJTDDiwkXJZWSagsZs23bto2lvgkBFjZkIzDLfZjvIZCbU1k4GENQWcGeCwQ0uIf94Q9/iME5suYYU2y77bZFH2a819L7m6w4+iizGSpoN0NAnAAiWbOMfdjESAuGTFk2ck6bOZcGDK+55ppYMUlmMi2HctxI5wc/+EFMuOG64FoozQzmmim6hzLvM87tiBEjYlUQ7ykWdNJmqLxYkOZFizLGE7mNz7gmCMZyTdDiKaFaNre2OLQ44XyzdwjZ1bRk4JhZQGc/lLQJolRr7EFb5VJGAD0uWRFkVZXVQZorl2afciMno5bAQdEYMBOEJQDLsfOQ5Lj5GcjWopSLwQd9c3MbfFQado9nEE3PH1YtzznnnNk2cVDDbVjF4JOPbIrARIWe0GziwIo3Ze2qDUxEmMASvOea4B7NvY4SUAb/2223XVZln1yvZKSW78ANAgPcP8jiyQWTJvrPEkBmMkJLBvqWE6hlMYTsI+51kmoL9wVatZQmJzAWZjxJ0BMsmlEBw0YkOfVqvPfee2NFHD0wqSBj4ZdxG/34CM7ys+XULxccJ+cxBRBTL2UW9ihdZpxPMFENg8VSMpfT2IKM2pNOOimr00sA9Oyzz46BOa4Dsn0JKvKMTm33cpH666dkCxZ8OcfrrrtuDNgSPMytAo75M9m+xx9/fKyMpW81G0kyVvv5z38eBgwYUNfyIAdUZdHygvaFXBNDhw6NiVcEyK+88sq4+axUiwzQ1gAGm6y2M7CjNJWgHKtU5Rl/BHAp7SoSD0Nu2JSHg7Jwymhz6vFTLRh8cK7Z3ZNBEtcAAXDOOT2JaYyfWy/MatqNkheBWTIeVP1YZGIiQj8zgoTpvUWJV5oAMOFmQnXIIYeEnDacoJSS/owpg4Q/c+2SZcYknEyuXNDLjDI+nmUEwpmk0DeOidXOO+8cJwO777570YcpaQljEezOO++sG0+yCe1BBx0UM7dKF0kJeBIsyCnbjPsWmWVstlXpi9W8mIfQpky1ibnof//735glyeLpfvvtl33LCxZI0oID4zWqU3MMIJL0wfiHZCbmdyzivPnmmzFAm9tmW9zPaMPBJq5UNnCPIMOaQD57G/CzSLUor+VWLRZkxlJOQqCT7MjSfrRgpYpJLSttRSNISIkLm5oRyOJhws2bHT7VsFipJDDLajZZI6xmpz6pnHsyNHIrXa4GrF7zIgNctYPJNT3WGHAyOU0B2tRfksWS3MrlwP2BFjhMSthkgkk1VQ1UL5ANzHMlJ2SKUHmB0t6HlAKTnUxGsKTa3CySLLgUoKWMlsBQaXCWIAwv+m7ngsUlNiPi2Cs5QEsSCBmSOWVJaslJfeupyKG/KL1SmYOwz0gloC81WbOp7J5qnZywEL3xxhvHhajHHnssDB48OGZUpwWetPFWTtj0kmuCxX7uuyQOpcQFKwtVywzQ1gAGmpR5lqMnFGVd9HOl9LNNmzahSNyQ2cWTfrM8sFlRo0yjtF+YGg5BWXbMRHkzeR7kH374oadbaiCU1v7jH/+I1QHszM79NmWkkinAvY9y2xwWytLkg16M9F8j85QX2EWcQAF9Xgka5FQuByZ/BMG5t3FfO++882Z7xqR7nqTaQukvC05UKnB/I8O+/H5LVh/ltixC5YKsPY6bcTHjNno2cu/NqRWONK+FU6ryWGAgo5NN7Ui8YdGE8Q9zjrQRVyWgIpXqt5yy7EmsoTqL/ri9evWKx8grZ2eddVasZAAJDGTPsphOAlElL0ZJi8oAbQ0ji4vVK27kNG4vGjdkBp2lq6mUBBMgUMOjpyQZZgSL+Ej5S+mDnkGTpIax1157xfItNqQhOMt7jgAiJbdkSDEozSVjnY0ZWRgjYFx+TGwwyStXBC/os055H5tk0o6B88vmP5RVurGkVJuOPvroGHglCEtfxn79+s3WW5v+jX/84x9nW9TJAYt4PCdSaTW7x1NtRtUTX2NDIILKUq6o0jzqqKNi0k3pPCONg1gsqZQA7bhx4+LeAWw4mBP2EGFTM6pk6VnNfSyHuX1ChRjjMO5ZVI2BNhG8QECWNocPPPBA3LAxt/7J0pJkD1plNXjeY4894mAzYYWSXjTeqBseO3oSNOIBTikMAXJKShhIsQp73333ZVXmV6no0cmkkMUG1SaChWSj1tdLm8+njFRK84su67ruuuvipl+0mqEtA2Wppbgv0NOcTWlyRSCWPtrvvPNODMCQscOrd+/esz1fJAmHH354eOaZZ0LPnj1jkDbXsQQbHHIve/bZZ+NGUGmRneCXlFPPfTIj27VrF/9OCTuLpTkHYelFvfbaa8fX3BBk5H3HBqk5ojqA9i3XXHNNbOtE9n0Oc7lXX3019vzmPkb2f6ogo/ew+51IszNAqyyknWjZtZGbdaNGjeLnCQKQxUVPHTU8Sn7ZOZVVSzL4OO/0APrtb38b9tlnH0/5IuCcMjB67bXXYtCN3VPJWCcgRxm2agOZqH/+859jhhMtDkqztbjvpUyCHNB+gQWbm266KZab1efaa6+NAQwyNMjgyq2MkkkgWNgjgMH7kPcfJZQ5Z/5KKgb3Cu5pbKjDAk4af+aCfpIEuAh4EaBlrEwlhlVOyhULCAceeGBdKyeuWzYEu/rqqwtfhJ7bHDQtpDMPTcHD8kVq5qR8nkqdXDz11FNxTwASQZo0aRI/0o6KsWdanN5hhx2KPsxYzcQCOgFuxmeM2Zhzcu9NAVvGylwvUi0zQKssEMQiIMjNm4chDz+CspRy8eDJ8YFeTXhI8tCkvcRWW21Vt+qthUM25AEHHBD7jnI9kx1OX0zOLxtkEKgrH/Sp+pCRcf7558fNDjfccMMY0CSzhF5sbLgFJtm0FGjevHkWgQCuTUp/54ZNHLfccsvYwzwXnFcG9mQh08eMnaElqZK98cYbsZqBDFmyzmjbQhUDwQ1KmgmCSbkhKMicgvYFvJhj8DmCn5SyM89gQ6ucej1Pnjw59qdOx0wLBhZr1l133bpgLWMMKotuu+22wvdtSQhwcmy0LSRTlnk055rPp48XXnhhrFDNDeecOX6qBkibg5E4lEvLL6kIBmiVDVYueTimlTVWW0G5CcGAFLR14r3oGFxccsklMajBLqqcW84xG7NZir/obrzxxrgCzAo77SNoG0EmJQMlJlQ07s+5RFwNg98xQczS3XOpEqDfGpsjkPFAvzD6hvH3orFxx+uvvx6zteaGn4E2BwRCc8IkkGA4ExTub927dy/6kCRpoXEvJqBV3mP2kUceCaeffnr86JhYuaHXLOMaxroEOesLfhKcJUjLplY59UpNJkyYUNcjlyAiG7mSnUpg9v777w854T5AQhMBWu4LqZKoUjCeZN5/++23x1YIbGzOvFSqVW4SpmxQ9k2/HF7p4Zge5mzqQNCLhyPZBMcee2zRh1uxKK3+/e9/HzMy6Pvz/PPPh1tvvTVunrPCCivEjD7K/cxaXngMPsnkA9cvq+8gg5ZMSnowqboxISH7qXTiQUCWICgbvqy++urxcwQ5ybLNIUDLjspvvvnmfA2m6+unWzQmJZxbBvdk+XKP41nBc0OSckf/SAIVm2yySayqoPVUfWMxxslsqsuCGkEuKScrrbRSHNvcfPPN8WO3bt3iQnVarE7zOwKgLKjmiDEaAWZe4L3GMeeU9Vt6PyDJpn///nUBb1oxfPe73w05IrOXuSfJCvT+5pwSkCVztmPHjtm1mZGWNDNoVTEIdvBwXH/99WOvIC2cf/3rX7E/FD2JyoOKaWWbgJEWHs35GcwRKGJASt+tddZZJ36NTRLYwIjBlKoX76NDDz00vqcSNm5gYsIO3AklXZRyDRkyJBTtk08+iVkvZG3NaTMPAghc07RlYLEhV7QUoZUErUQuvfRS27ZIyh5VAPvtt19cVCKoRY9yxr5//etfv7WRTq9evWKbg7TYJ+WEMTCVLARh6e1MZqfX6uL34Ycf1u0TQKs1FqkJmBdtzJgxdRnJLPJ37do1zoOoDsgx6C0VyQCtVAPuvvvu2MqAwT4PbTI05lXGrIVHpiQDIzJgGJSQRcAGTGSBP/fcczEjnMwYVS8CmZTYM0GhRJXA7N577x034CrdXIv3I7sCX3fddSGXxQUG9wQJfv7zn8+2iRmZ32x8N27cuJh1n9v5/uKLL+J7j15s/JlzTmCDyQDneY011ij6MCVpriZOnBiDGIzT+Dh+/Piw/PLLxyAXbb74MyXWBLsI0ko54xpmHPTOO++EI488Mm5wZ0CuYTCvIBEg9ZrlxaaHfGScxpiNBJGiW0iwxwz9szZkUMsAADSiSURBVNmPY/fddw+tWrUq9Hik3BmglWqgty9ZcQQs2GSCoBHZevSRZLCvxePjjz+OmXv/+c9/YvAIbdu2jee9U6dOnvYaQJYprUMIyDLpJlP2hBNOmO17GFwzYM2pJ/ENN9wQLr/88ji455pt3759/DybftCKho3Eciqd4z1GOV9CH22yzZgEkEFLXzY2ybC/tqRKk6qbeNHHk/Y5q666alxkL+1vLuWKoCEbo/bt2zeWr5NNu/POOxd9WBWPRWj2FKENSvmLIDgfabFWZECcOShtF15++eUYOGb+wzGRPZvbRnFSLgzQSlWOjcAYHFE6V7qjKn2f2J2UPmY8LCkzYWMrLTrK1gkGEcQim4++ngyUaM9hkKi2UGo/YsSImMFemsXApPuWW24JDzzwQMyoXnbZZUNOyDolS4t2KCw2kLXFYPrHP/5xzAzPrZSS9gwpIEs5H/2eJamakCmXxnL0qXWRXTlh8eC11177VjYnL8a+fGS8c88998QWSVdccUVYeeWVQ277dLzyyitxHEEZfo7zIirz2LiM5JtKqQwiSYhjThuBk1GdNopLAVuCt/aflQzQSlWPPow8/PbYY4/ZVjRLd1QdNWpUDCAeeOCBWWxWVMnI5COjDwzwaHzP+edFNqLEBKBnz54xC4oS/ErbcTdHTAoJ1BKgJXDhJoeSKhmb6JB1xriBBXSqF6Sc3X777fM9hyDwyQJ2TgFQ9gn4+9//Hlq0aBF7uQ4dOjRW4IwcOTIusufgkUceCccff3z881133RXWXnvt+Gc222JBnaByJezTQsuDtFEcgdtPP/00bq5M0F6qdW5tLFU5HoD0/SnFQJ9dP3mlMhlWNBmUaOExmGOQRK9ZBp30m+X8X3zxxbFcnNVhdplXbaP8nh6uZFRb3rXofve738WJCvcvWkmw+AQmK7R0kaRK8tBDD8X+smQXUh3ApmEs5KVMM/5sNY5yQ+UK+ytQEfTLX/4ytuDg7yyY8pHrmI/pzzmhhdPjjz8eHnvssZihSqCZ4CxZwSSvDB48uPDWThzLGWecUbe3QUKfWQKbK6ywQqx4os8vbSRy1qZNm7DvvvvGF5nWZC1T8SnJAK1U1SijZhDfrl27uX4fm03Madd2zT9KD9kIKq1eM4mi7yjtJMiGYcVYYoGEHbrVMIEMMl2YWLHI1K9fvzg55L3Ge4/JVvnu55KUMzYWpW8244nS0mD6eP75z38OxxxzTDjppJOKPkxpNrRA2mijjcJFF10U92Bg3MvGoswxckfbBTJTCXKSWEH1G1j4ZUz/v//9r/AALRs+s0hTGpwlsMm+ASmb9uGHH44/B/eOtH9A7mhr0Llz56IPQ8qGTdqkKsaAnoERvX7U8FjtJXuPnlr07GTwxi7y5cgWoOzaILjUsMhqIVBBJjL3O7LLUnYGPbaZIEpSpe3OnhbxCBjttNNO4be//W3sWf7ggw/GjD4pR2uttVa49tprw1/+8pfw6quvxmDiNddcExMVckameupdT4A2jSVAD37G8TkkgRCgLUVLhhNPPLGu1QFZy8w3XnrppYKOUtKiMkArVTEGGZT5MrjnoX3OOeeEe++9N/b60aKjlIjeVPSdZbWdQSk7qlK+zgRLqiQffPBBzESdNm1aqBQsjLDxCEqzXlKmck797SRpfpD9RhYtpb/lWAiuhIxE1Tb67LOAevjhh8esb7Jr2TA1VyyI/PGPfwzjx4+PwU2CnGmcz7iIzOCisX/IlClT6v5O8g2t1Pbaa6/Zvo9gsu2zpMrVaFZ9T39JVbHb7+abbx6b9r/55psxu4wABr2VWCWmHyorxKzGsoumm+osuPfeey9uSkRmAJsdpIb3nGMCQ2zskTYIW2+99dydVFkjy+Wyyy6LPcEor60EbLLGpI9+cQcffHBsc8D97e23345BjkcffTSsuOKKRR+mJM0XFn1PPfXUMHr06Nieio1zHKcpd1SP0VeWwGB5f1nGyn369IkJInvuuWfsj9qqVauQk+nTp8dqnCFDhsSM2SOPPDJMmjQpDBw4MM6ROP6iXX/99aF///6xBy2bB7IJ9DbbbBN+8YtfzPZzbL311uGOO+4IrVu3LvR4JS0cA7RSlSJo8fvf/z4OLsqz5FKwlmAG2bQEE++7776wxhprFHa8lYiAEBuwMRgqNW7cuHiOOb+8WIHff//9wwUXXFDYsUrzQmYGG2yx6FDaD4zMcDJKDjvssOx655Kpftxxx8UejWzoccABB8T3G5vx/eQnP4kbakhSpWCBbJ111olBIfpLli/6Us5Mf3spFwQF6dOasjvZ+4JAbXpx7dIbnk08P/roo7gAzHWeY2ILvVwHDBgQXn/99Xjcu+22WzjllFPi8edwnhnvEETGD37wg9hKorT9AgHc+++/P2bgS6pMBmilKkVJEaXKZGLMSdo585lnngk//elPl+jxVTpKjQjMMnma28CNc8xAjwFrhw4dlugxSg3hb3/7W7jllltiv2UmBEW/72hdUIoMdiZVVAuMGjUqZr9QTslGOu50LqmSsAjGhkSlWYili76//OUvY39tKRc8l1kkZbxLy6H04u/s1ZD+nD6/ww47ZLV5J0Fj9pIgmMziR8eOHbOueKMqkvPIcZYm5dCfmsDsddddV9eiQVLlMUArVSnK42hbwGY5aniUTlNu9I9//MPTq5rABKvISQtlkpT7MoGiPQv9Zrt27fqtckpJqlRk/rMoZp9ZafH74osvYg9X2hkQ9CTYTBURLeJSG7hKqC7s1atXTAY57bTT4r4jkiqXAVpJWggXXXRReOGFF2LvJwZy7LQsVSpanZx99tlxM8F99tknfo4+rmSr5hIomDx5cnj44YdjFhktWtjMo0WLFjFTJE2k2rdvX/RhStJCofSb+xuLYSeccEKsWMihtFqqVjfccEN45JFHwtVXXx0Xe1977bW6NnBUF5LkwmZnkrSkGKCVpIWw++67x5X3CRMmxE2JNthggxggIlDEbq9uuqZKQtYF1zObbbExTb9+/ULv3r1jb7O+ffvGksTcvPXWWzGYwWSK3rkEcAkm8z780Y9+FBdOJKlSUCbOIhTBIcqYGUd07969bgFq/fXXj+MNSQ3jD3/4Q1hzzTXDQQcdVG/PV/bt+N73vufplrTEGKCVpAX04Ycfhj322CMGh95///04mSJINHz48PDZZ5+F5s2bx8080qTKfnHKGYHN7bbbLmaRkJHKhjRstsWuxWxGQaY4PWhzxuYezz//fNzQjJ+DXaKPOOKIog9Lkuba+5I+2fX142SckcYWjDX4Xlorfec73/GMSgvpk08+iRv80iKJ8TkL0/R8vvjiiz2nkrJggFaSFhA7xJPlcsUVV8z2efpXjRw5si6r77nnnoulimT3EbSVcsQ1y2aCbJKBo48+Ou7IfPjhh8dyv5NPPjnbEj82z3n88cdjIINMFzLMWBjhZWmwpJxdeumlcUOfTp061QWMWNxl9/hytJxZe+21CzlOqVpMnDgx7h3BmIFNktPYfc899wz77rtv3KSPyiFJKooBWklaQGeccUYcxO2///5z/b4pU6bEpv20PJBynrBsu+224cwzz4y9aAnGDho0KE5S5rQYUZSvvvoqLngQlH355ZfDqquuWheQbdu2bdGHJ0kLtMA0dOjQuKBLewMqcFJbAzb6oZXSiiuu6BmVGgjjG8bvZKLzfuN9R7CW19ixY2MyBQvULJhQWUTLJ0lakgzQStICmjZtWlxxN0NP1eKf//xn+NOf/hRWW221GIxlsy16IB533HHh17/+deE9aG+99dbwwAMPxIz0tCkYfZ/txyipGjCmIKMvBYsou2aTxssuuyxss802RR+eVBV4L910002x72w5ArSlLcsOOeSQcPzxxxdynJJqlwFaSZL0LbvsskvYeOON487iRWKjDoKy9GrcbLPN6no7W+4rqZp7ZV555ZXhjjvuiFUMK6+8ctGHJFW0UaNGhaOOOiq+n+ZnwYRkDBMxJC1pBmglSapRTEAuueSSuBEYOxVvvvnmMfjZunXrGCCob/OaIpDNmzbLocUBrQ4oUaQMkYAtH1u1alX0YUpSg6KV0qGHHhrbHUhaeDfccEMcS/zud7/zNErKlgFaSZJqVJ8+fcLdd98dg7LvvfdeeP7558PUqVNjsJbA59577x06d+4ccjJjxowYUE4B2xdffDFmu6y33nphr732Cj//+c+LPkRJahBs4EjlABsYSVp4bID66quvxt6yjG94X62wwgqeUklZaVL0AUiSpGIMHDgwZpWkdgG0E3jmmWfq+iCyo3huAVo2L+O4eJ144olh8uTJdRt9EFyWpJxNmDAhjBgxImb+z6tKgY1G6YUpaeHNnDkz9nXeeeed48Z8t9xyS2ybRC97FqgJ2LKhL+MLSSqSGbSSJNUoJiVMVtxsS5KWjDvvvDNuvtioUaOY+U+AiBeLTssss0zd9z366KPh/PPPjz0zvUdLC48Fkd///vdxURrvvvtu3UI0C7yff/55aN68eex3z3uRRZEmTcxjk7TkGaCVJKkGvfbaa+Haa6+NQYEDDjig6MORpJpBS5nSANGnn34amjVrFtZff/3QsWPHMGnSpPDQQw+FSy+9NPzwhz8s+nClinbFFVfECiFahpSjRdLIkSPje5HWSePHjw8PPPBAIccpSQZoJUmqQWeccUbcIRzdunULPXv2jJkjnTp1ipldkqTFb9asWeGVV16JgdqXXnopjB07Nm56SBbfNtts469AWkRkzzLG2XTTTef5vV9//XVsfyBJRTBAK0lSDfjiiy/CmDFjYs+1VC5LL8SUNcKGWxMnTow9ETfffPNw5plnhtVWW63ow5YkSZKkqmeAVpKkGjB48ODwq1/9KpbLHnvsseHZZ5+NPWjbtWs3W9sDArVkcvXt23e2foiSJEmSpMXDAK0kSTVi1KhRsQ8bO4NfeOGFYfLkyaFNmzYxUEt7AzJnV1555aIPU5IkSZJqigFaSZJq0MyZM8MLL7xQ1+KA3odslkEP2tNOOy1sscUWRR+iJEmSJNUEA7SSJNVIQJZgbJMmTWK2bDmyaYcPHx6/Z5999omBWknS4vPyyy+Hjz/+OLRv3z5WM0iSpNplgFaSpCpHW4MjjjgiPP3002HvvfeOOxqnoOyDDz4YdyzeaaedwtJLL130oUpS1fv8889jL/BXXnklLp5tvfXW4a9//Wv46KOPQtOmTcNKK61U9CFKkqQlrMmS/h9KkqQl6y9/+Uto1KhReOyxx8Jqq60WPzdhwoRwyCGHxIAAAdzLL7883H777faglaTF7JJLLgk77rhjuOGGG8JvfvObsMEGG8TPDxo0KLz44ovhiiuu8HcgSVKNaVz0AUiSpMVnxowZ4bbbbgsXXXRRWGONNULjxv/v0X/uueeG7bffPowYMSI88sgjoWXLlqFv377+KiRpMfr666/Da6+9Fg477LBYvfDkk0/W9fzedtttw9ChQz3/kiTVIAO0kiRVsXfeeScsv/zy4bvf/e5sfQ/HjBkTNwOjJ+3qq68eevXqFZ599tlCj1WSqt2kSZNikBZvv/12/Lj22mvHj61atYr3ZEmSVHscAUiSVOXBALJoS1177bUxe6s0ENC8efPQrFmzAo5QkmoH/WW/+uqrcOedd8Y+4Cl7Fv/5z39Cly5dCj0+SZJUDAO0kiRVsY4dO4aJEyfGPrQ/+9nPwuOPPx4zZdNGYckzzzwTNtxww8KOU5JqBfffo446KsyaNStuEEYbmpEjR8Y+4FdddVXRhydJkgrQaBYjA0mSVLXuuOOOcN5554Vp06aFZZZZJu4WvuWWW9Z9/Ysvvgi777576NOnT/j+979f6LFKUi2gvUG/fv3CAw88ED777LPQrl27cOKJJ4bddtut6EOTJEkFMEArSVIN+PDDD8Mbb7wROnfuHFZeeeX4uSlTpsSS2ptvvjluEkbrA0nS4vPCCy+EV199Nay55ppho402iu1lZs6cae9ZSZJqnAFaSZJq1NNPPx170fbs2TNcdNFFcTMxSdLiMXjw4HDyySeHZZddNvahbdq0aejWrVvsQ0tVwwYbbBCWWmopT78kSTXIAK0kSTWM3cQNCEjS4rfrrrvG3rM/+tGPwscffxyefPLJ8MQTT8SP48aNC+eee244+OCD/VVIklSDDNBKkiRJ0mLWtWvX8Pzzz4dGjRp962tvvfVWbD9DuxlJklR7mhR9AJIkSZJUbfr37x9bydC+gDYGtDCgF3jHjh2/9b3t27cv5BglSVIezKCVJKnKTZ48ObRo0aLow5CkmvLggw+GgQMHxiDtpEmTYs/ZVq1ahV69eoXtt98+/lmSJAkGaCVJqnL0O9xrr73ihmD1ldZKkhp+YezRRx8Nu+++e+z1/eKLL4Zhw4bF1wsvvBBmzpwZM2l79OgRX5tttllo1qyZvwZJkmqUAVpJkqrcHXfcES666KLQuXPn0Lt377DGGmsUfUiSVNUeeuihcNNNN4Xrr7/+W1/78ssvw//+97+6gO2oUaPC0KFDw6qrrlrIsUqSpOIZoJUkqQawQ/jZZ58dRo4cGc4///y4m7gkafH43e9+FxfDjjzyyHl+7yeffOLmYJIk1bjGRR+AJEla/L7zne+Efv36hZNOOikGak877bRYgitJanhkxrI52Pxo2bKlvwJJkmqcAVpJkmrEjBkzwg477BAuvfTS8NRTT4U999wzltlKkhrOhAkTwttvvx369u0bbrzxxvDWW295eiVJ0lzZ4kCSpCpFgODcc88NEydOjCW0n3/++WxfX3bZZeNGNcccc0w47rjjCjtOSaomAwcOjL1nV1hhhbghGItjq6++esyoTZuCtWrVqujDlCRJGWlS9AFIkqSG8+GHH4ZVVlklNG7cOP55nXXWCZtvvnkMBvD50o/LLbdceP7550OvXr1Cu3bt4m7jkqRF88QTT4Sf/vSnYd99940bgj399NN1G4IRvG3UqFHo2LFjDNTusssuoUuXLp5ySZJqnBm0kiRVkb333juMHTs2BmVTptaaa6451//m6quvDuPHj4/ZtpKkhTdr1qyw1VZbhQEDBoQ2bdrU2/4gBWuffPLJcMghh4Sjjz7aUy5JUo0zQCtJUhWh1+Hjjz8eJ/5kbZG91bZt27pgLYHblVdeebb/pn///jGQsOOOOxZ23JJUDQjAnnrqqbH37PyYPn16aNas2WI/LkmSlDcDtJIkVQlaGjRv3jy0aNEi/p3+srQwSNlaL730Uvjmm29Cp06dwtZbbx1+9atfFX3IkiRJklTzDNBKklQlzjnnnNjfcMMNN6zLmO3atWto2rRp/PrkyZPD8OHDY7D2008/DX369Cn6kCVJkiSp5hmglSSpSkyaNCk89dRTcYMaWhyMGTMmZtRuuummdQHbDh06FH2YkiRJkqQSBmglSapCZMe+/vrrYf311w+ffPJJGDFiRBg1alRYbbXVwhZbbBHOOOOM0LJly6IPU5IkSZJqXpOaPwOSJFUZNgf77LPPwlVXXTXb54cMGRJ+/etfxz60yy+/fGHHJ0mSJEn6/xmglSSpCgO03bp1+9bnt9lmm3D00UeHlVZaqa4vrSRJkiSpWI0L/v9LkqQGtvrqq4cBAwaEr7766ltfa9GiRZg+fbrnXJIkSZIyYYBWkqQqs/vuu4dp06aFvfbaK/Tv3z/2nqUP7WOPPRauv/768MMf/rDoQ5QkSZIk/R83CZMkqQpNnjw5XHHFFWHgwIFh0qRJ8XOtW7cOl112Wdh4442LPjxJkiRJ0v8xQCtJUpUZMWJEaNKkSejUqVNo1KhRGD16dGjcuHFYe+21w1JLLVX04UmSJEmSSrhJmCRJVeScc86J/Wex9NJLh+9///thiy22CJtsskn48MMPw8orrxyWWWaZog9TkiRJkvR/DNBKklQl3n333TB48OBwyy23hNVWWy32ml1xxRVD3759w4wZM+L3DBkyxACtJEmSJGXETcIkSaoSQ4cODbvuumvo3r17WGGFFULTpk3D5ZdfHm699daw+uqrx+AtHyVJkiRJ+TBAK0lSFWXQtm/fPv55woQJsZ0BNthgg7DWWmuFmTNnFnyEkiRJkqRyBmglSaoSLVu2DPfdd1/88+effx5atGhR97Vp06aFqVOnFnh0kiRJkqT62INWkqQqsc8++4Rx48bFP5M9O3r06PDQQw+FSZMmhbfeeit06NCh6EOUJEmSJJVpNGvWrFnln5QkSZWNx/sJJ5wQ7r///tCoUaNw/vnnhwMOOKDow5IkSZIklTFAK0lSFZg+fXrcDOzZZ5+NrQ2OOuqosOaaa4Y33ngjLLfccqFNmzZFH6IkSZIkqR72oJUkqQqyZXv16hUee+yxsNpqq4V///vfMSiL//73v/FzkiRJkqQ8GaCVJKnCDRkyJAZhr7vuurDLLruEjh07hlatWoWZM2eGAQMGhIcffrjoQ5QkSZIkzYEBWkmSKtzw4cPDrrvuGv88bNiw0KNHj/jnJk2ahJ133jmMGTOm4COUJEmSJM2JAVpJkircUkstFXvNpgDtFltsUfe1iRMn2uJAkiRJkjLWpOgDkCRJi4a2BoceemjMmH3llVfCJptsEj//5JNPhkcffTScddZZnmJJkiRJylSjWewsIkmSKtpDDz0UTj/99DBt2rSw7rrrhqlTp4a33347nHzyyeGoo44q+vAkSZIkSXNggFaSpCoxefLkcM8994SRI0eGpk2bhp122mm2dgeSJEmSpPwYoJUkqQLde++98fXhhx+G5ZdfPmywwQYxINupU6eiD02SJEmStAAM0EqSVEG++eabcOyxx4ZnnnkmZsc2a9YsjBkzJm4SRnuDrbbaKlxwwQWhTZs2RR+qJEmSJGk+uEmYJEkV5J///GcYN25cePDBB8OKK644W3uD+++/P1x55ZVh//33DwMGDAitW7cu9FglSZIkSfNmBq0kSRVkjz32COeee27YZJNN6v36pEmTwuGHHx7atWsXLrvssiV+fJIkSZKkBdN4Ab9fkiQVZMKECbHn7MYbbzzH76Ef7RlnnBFGjBixRI9NkiRJkrRwDNBKklQhnnjiidh3tnHjuT++V1tttSV2TJIkSZKkRWOAVpKkCjFs2LCw1FJLhdGjR8/1+9gwrH379kvsuCRJkiRJC89NwiRJqhD0lx0yZEi455574gZgZNNuueWW8WPLli3rvu+pp56aY49aSZIkSVJe3CRMkqQK8tlnn8UALNm0vMaOHRsaNWoU1ltvvdCjR4/4uuiii0Lv3r1Dly5dij5cSZIkSdI8GKCVJKmCEaAlUEt/2uHDh8cA7oorrhiDuPPqVStJkiRJKp4BWkmSqsQ333wTXn755TBu3Liw0047FX04kiRJkqT5YIBWkiRJkiRJkgpi7aMkSZIkSZIkFcQArSRJkiRJkiQVpElR/2NJkrRwfWbZFGz69OmhS5cuYZVVVvE0SpIkSVIFswetJEkV5JRTTgn33HNP3d/XXXfd0KNHj/jaZJNNQvPmzQs9PkmSJEnSgjGDVpKkCvH666+HJ598MgwaNCisttpq4aWXXorZtLz69+8fll566fj15ZZbruhDlSRJkiTNJwO0kiRViNdeey307NkzrLfeevHvP/jBD+ILEydODCNHjjQ4K0mSJEkVxk3CJEmqEARmx40bV+/X6EW77bbbLvFjkiRJkiQtGnvQSpJUASZPnhwOOeSQGKDdaaedwsEHH1yXSStJkiRJqlwGaCVJqgBfffVVuPvuu2O/2aeeeip89tlnsQ9t2iCM16qrrlr0YUqSJEmSFpABWkmSKsw333wTXn755boNwp599tmw6aabhuuuu67oQ5MkSZIkLSADtJIkVYAvv/wy9OvXLzz33HOhRYsW4eSTTw7f+9734temTp0aPvnkk9C6deuiD1OSJEmStIDcJEySpMx9/fXX4cgjjwyjR48OXbt2DQ8++GBo2bJl/NqFF14YGjdubHBWkiRJkiqUAVpJkjL3wAMPhC5duoQ+ffqEzTbbLGywwQZhhRVWCDNmzAj33XdfeOSRR4o+REmSJEnSQjJAK0lS5p555pmw/fbbxz/Tc3aLLbaIf27atGnYcccdw7vvvlvwEUqSJEmSFpYBWkmSMrf00kuHl156qS5Au+WWW9Z97YMPPrC9gSRJkiRVsCZFH4AkSZq7PfbYIxx00EFh+vTp4e233w4bbbRR/Px///vfMHz48HDxxRd7CiVJkiSpQjWaNWvWrKIPQpIkzd0TTzwRTjvttPDZZ5+FNm3ahClTpoSJEyeG888/PxxwwAGePkmSJEmqUAZoJUnK2OjRo8Naa60V/0wGLRuGjRw5MjRr1iz2n2XDMEmSJElS5TJAK0lSpr7++uuw6aabhv322y/86le/ikFZSZIkSVJ1cZMwSZIytdRSS4W+ffuGwYMHh3333Te8+eabRR+SJEmSJKmBGaCVJCljW221Vbj77rtDhw4dwt577x1uvPHGYPt4SZIkSaoetjiQJKlC3HfffeG8884LnTt3Dn/4wx/CqquuWvQhSZIkSZIWkQFaSZIyNnPmzPDFF1+ESZMmxY/vvvtuuOyyy8KUKVPCoEGDwuqrr170IUqSJEmSFkGTRfmPJUnS4vHggw+G0047LXz11Vd1n2vatGlo2bJlWGWVVULHjh1Dq1atPP2SJEmSVOHMoJUkKUMvvvhieO+992IQloAsH1daaaWiD0uSJEmS1MDcJEySpAx16dIldOvWLUybNi2+WrRoUfQhSZIkSZIWA1scSJKUoSuvvDJcc801YamllooB2uHDh8cg7Ztvvhk6dOhQ9OFJkiRJkhqIGbSSJGVm2LBh4dlnnw2PPfZYuOKKK0LXrl1jcPaDDz4I+++/f9wsTJIkSZJUHQzQSpKUmUGDBoXTTz899pwlWNujR4/4+datW4e2bdvG4K0kSZIkqToYoJUkKTMfffRRaNasWfxzaYAWbBZG2wNJkiRJUnUwQCtJUmY23HDD8Kc//SmMGzcuvPfee3HDMPD3559/PnTu3LnoQ5QkSZIkNZBGs2bNmtVQ/5gkSVp0kydPDkcccUR44403YsbsUUcdFT7++ONwyy23hG222SZceOGFnmZJkiRJqhIGaCVJytBXX30Vbr755nD77beH0aNHh+WWWy78+Mc/DqeddlpYeumliz48SZIkSVIDMUArSVLBPvvss/CPf/wjTJ06Nfz617/+1te//vpr+85KkiRJUpVqUvQBSJJU65myP/vZz8IXX3wRdtxxx7rPjx07Nlx99dXx6z/5yU/ClltuWehxSpIkSZIWDwO0kiQV6Prrrw+tW7cOAwcOrMuSJTi77777hg4dOoQZM2aEww8/PPz5z38OO+20k78rSZIkSaoytjiQJKlAPXv2jMHXTp061X3umGOOCV27dg1HH310/DuZtHfeeWcYPHhwgUcqSZIkSVocGi+Wf1WSJM3TlClTwoQJE8J6661X97m33norvPHGG+GII46o+xwtDsaMGeMZlSRJkqQqZIBWkqSCzJo1K24ANn78+LrPkS17yCGHhKZNm84WyF1ppZUKOkpJkiRJ0uJkD1pJkgrSvHnzsMkmm4Rjjz02ZszSe/bJJ58M55577mzf9+ijj4ZNN93U35MkSZIkVSEzaCVJKlDv3r3DyiuvHE499dRw9913hyuvvDIst9xydV//+OOPw1VXXRX23ntvf0+SJEmSVIXcJEySpAx88803oXHjxrO1PzjrrLPC0KFDQ7du3eJGYpIkSZKk6mOLA0mSMlAanMUnn3wS3n///XDggQeGI488srDjkiRJkiQtXmbQSpIkSZIkSVJB7EErSZIkSZIkSQUxQCtJkiRJkiRJBTFAK0lSBmbMmBFmzpxZ9GFIkiRJkpYwA7SSJBVo/Pjx4fDDDw9dunQJG220Ubjvvvvi56dMmeLvRZIkSZJqgAFaSZIK8vXXX4djjz02bLPNNuGOO+4ISy+9dOjcuXP82nHHHRdeeeUVfzeSJEmSVOUM0EqSVJDhw4eHTTbZJBx22GGhUaNGoWXLlqFt27bxa61atQp33XWXvxtJkiRJqnIGaCVJKsioUaNCu3bt4p+HDRsWevToUfc12h18+eWX/m4kSZIkqcoZoJUkqSCtW7cOd999d9wg7IknnpgtQPvMM8+EDh06+LuRJEmSpCpngFaSpIJst912oUmTJuEnP/lJGDFiRGxr8MYbb4Q+ffqERx99NOyyyy7+biRJkiSpyjWaNWvWrKIPQpKkWjV16tRw2WWXhdtvvz189dVX8XMrrrhi6N27dwzgSpIkSZKqmwFaSZIK8tFHH4Vp06aFNm3ahClTpoRXX301sG7auXPnsOyyy/p7kSRJkqQa0KToA5AkqRZdddVV4fLLL48BWTJmN99887DFFluELbfc0uCsJEmSJNUQM2glSVrC3n///bD77ruHCy+8MLRt2za8+OKLcZOwp59+Onz55ZfxcwMHDoyBW0mSJElSdTODVpKkJYyA7NZbbx122223+PeuXbuGQw89NMycOTO88MIL8WVwVpIkSZJqgwFaSZKWgL/97W+hXbt2sZUBPWfJlP3WQ7lJk7DxxhvHlyRJkiSpNtjiQJKkxezrr78Ohx12WHj22Wfjnzt16hTeeuutcNxxx4VDDjkkLLfccv4OJEmSJKlGGaCVJGkx++ijj+o2/qLP7LBhw+KLIO3SSy8dunfvHnr06BFf66+/fmjcuLG/E0mSJEmqEQZoJUlazM4999yw9tprxyzaUhMmTKgL1j755JMxkLvZZpuFG264wd+JJEmSJNUIA7SSJC1mPXv2jD1oO3ToMNfve/3118OkSZPC97//fX8nkiRJklQj3CRMkqTFaOzYsWHq1KnzDM6iY8eO/i4kSZIkqcbY5E6SpMXoiSeeiP1n77nnnvDxxx97riVJkiRJs7HFgSRJi9EJJ5wQnnnmmTBx4sTQqFGjmCXLZmBbbrllbGWwzDLLeP4lSZIkqYYZoJUkaTH55ptvwhZbbBHuuuuuumzatCEYAdtmzZqF7t27x2AtQdv1118/NG5scYskSZIk1RIDtJIkLSYvvfRSOPPMM2N7g1KzZs2KG4IRrOU1YsSIMGXKlDBo0KCw3nrr+fuQJEmSpBpigFaSpMXk6quvDh999FE4++yz5/p906dPD88//3zYZJNNYhsESZIkSVLtMEArSdJiMmPGjJgZu8IKK3iOJUmSJEn1MkArSZIkSZIkSQVxJxJJkiRJkiRJKogBWkmSJEmSJEkqiAFaSZIkSZIkSSqIAVpJkiRJkiRJKogBWkmSJEmSJEkqiAFaSZIkSZIkSSqIAVpJkiRJkiRJKkiTov7HkiRJWnTTp08PV1111Ry/vssuu4R11llnts+98MILoWvXrt/63qlTp4ahQ4eG7bbbLjRp8v+GiWPHjg3vvfde2HzzzUOjRo0W+Pj4bz/55JPQpUuXBfrvXn/99fDhhx+Grbfeeo7fM2zYsLD66quH9u3bL/BxSZIkSbkwQCtJklTBZsyYEa688srw4x//OLRp02a2r/3tb38LHTp0mC1Ae80114R//OMf4ZFHHglNmzad7fv//e9/hyuuuCIMGTKkLkB71113heuvvz48/fTTCxyg/eqrr8Kxxx4bZs2aFe68886w1FJLzfF7X3zxxTBw4MDw/vvvh1dffTU0btw4/OhHPwpbbrll/DMmTJgQXn755RiY5RiXXnrpcMQRRxiglSRJUkUzQCtJklQjbr311nD55ZfHwG15cJZgKsFbMlL5mNxzzz1hpZVWCn/5y19m+34yYn/wgx/M8f/16aefhl69esVM2L322iv89a9/ne3r3bp1C1tttVXd3zt16hS+973vhU022SS89NJL4cQTT4x/P+2002LQdsyYMWHatGlhgw02CD169IiBZI75O9/5znz//ASAO3fuHDp27Fj3Of5NsobJ9CXYvfbaa8es3WbNmoWGwM/y6KOPhl/84hdh2WWXDYvLxx9/HG6++eZ5ft8WW2wRA9vzOqaFOe4BAwaEjTfe2IC5JEnSArIHrSRJUpWbOXNm6N27d/jtb38bzjvvvHoDq3379g3jx4+PrQySL774IgZGt9lmmwX6/40YMSLst99+MfOVIO13v/vduq+RHUvG78iRI2f7bwgYH3bYYWG33XarC44StN1www3DWmutFYPK1113Xfy+Qw45JH7ukksuCUcddVRszTAvjz32WPjzn/8c2rVrV/e5Bx98MGy//fYx2EsQmAzdyy67LOywww7xZ2gI/Jz8vFOmTAlL0kcffRT/v88///x8HdNrr70W3nnnnbl+z7wQ+P3Vr34V225IkiRp/plBK0mSVMUI1BH0JMOSQOSOO+4Yg4/f//73677nv//9b+jfv/98/5v8t2RilnvjjTdiP9z//Oc/4YADDoiBXQKgpVmZN954Y9hoo43Cz3/+89n+W4KjZGBi8uTJMXtz4sSJ4aCDDgp//OMfw9577x2zMz/77LNw7bXXxkDg3//+9/DDH/4w/ptHHnnkHI+XLNlzzz03nHrqqWGZZZaJn6MvLn/nvyd4nZBFS1uGY445Jtx///1h5ZVXDouCjF+C1IszexatWrUKxx9//GyB8H/9618xG5jAd3kgtfSY3nrrrZjlTJC+NIC9oPbcc894HZGBfdxxxy3CTyNJklRbzKCVJEmqYGSUEmxL/WfJNCU4B4KMBFLPOeecGGwkODt48OCYdUrgFvSWJVBJ1mj69xYG2bl77LFHbGlAkLZ169Yx0EmW6zfffBODqD/72c9iewECeAQJ68P3lmZt9uvXL7Rs2TK2VKANAdm1O+20U/warRf+9Kc/xWDw3PD//vrrr+OGaQnHyf+HoGL5+SS4SPYwGbalRo8eHf75z3/G46cHLv9m8uyzz8YM3y+//DLcdNNN4bbbbovB5DmZ27+VgtSDBg0KV199dWxd8Oabb4bFgXPK+cG9994bbrnllrl+/9yOmx7FBNTpcczxS5Ikaf4YoJUkSapQBFkJhs4NQbcVV1wxBjnJSj3zzDPDRRddFFZdddX4dbJS6S9L0HZ+kWVajl6xBEsJKtJCgX+PvxNg3XnnnWPrALJc2XBshRVW+NZ/T+9bMkDpS0uQdtttt41/J9Dbvn37+HPSouC5556LwWb+zIt/a/nll5/r8RLgJEM0bXwGNk8j2FveaiG1VuDn4WPCOdp1113D448/HrN9f/Ob38TA8Oeffx6/znHxs/LfEcQkY3m55Zart1XAvP4tMlp79uwZM2AnTZoU2xSwCVx5H98l3XZhXseN3XffPfYGvvvuuxvkWCVJkmqBLQ4kSZKqCFmz5WbNmhUzHilhJ6hWmkm61FJLxYBq6lVaGsRMyJKkj23pv1euefPm8d/la6+88kp4+OGHY5COz9PqgM2m1lhjjXkeP9mZIHuV46IHLr1R2VSsHMFe/k02/poTjoX+sqUtHbDKKqvE7F4yf1944YXQvXv3WN5PMJiNwjjeZNiwYbE9xCmnnFIXyCbLluxbevoSiAYtGbbccstw8MEHz/F45uffIhBKwJpMXPr4gsAoWb0NrW3btjHrlcxZfn8E0xf2uEHbBPoGP/TQQ+HAAw9s8OOVJEmqRgZoJUmSKhRZsGSZUlpP1mJ9CLidfvrpYezYsTGIRmbmu+++G7M76Vtarr7gK0Hc0sDtiy++GP89NusC/x59bPk8/W0//fTT+HkCnfvvv38M0lLyX4pAJkHRUvSoJbDH99MjluBfeWB1QRGgxbrrrvutrxGQpEcu7QlGjRoVhg8fHltEcPy0Z9h3333j95HJSqZuad9cetOSPUowtbScv74N2ErNz79F1iztG1JwFmQUF2l+jrtFixbx82Qem0ErSZI0/wzQSpIkVThK68laLM0kpXydwC2bRJ199tnhu9/9bszKJBuWnrNsXsXGWeVKe4pSCk9v23ltlEUbAr6HgCebd9Hm4M4774xZn/UhM5QgbGmAlgDzGWecEYOitDDYb7/9YjbrAw88sEj9V1OvXQKe9aEXLn16Szc9e+yxx2J26NSpU8Ohhx4ag7dkhrIpWSmOi/NFhm9CK4m5mZ9/i8A3/3+CxPze2NiM7F6C7Zy3IszPcafrj3NAti/X4OLeHE2SJKkaGKCVJEmqcqVZqGSIkv1aGmijrUDKNJ1fZJmyIRaZuGTXpmzTtFkUrQfI7q3Pf/7zn2997te//nUM6J100kkxQItNN900BmgJUvJvUWZP8JfAJYHV8qzc+qSM4PLMYDJ12cRr7733/tZ/QxYsvXn59wnQEoCsLzBKH1tepQFsso3nZn7+LQKdBInZyGzatGkxO5XfGxuGEfgmYLukLcg5KM38lSRJ0rwZoJUkSapwhx9+eOwhS8BxwIABsRyejMZf/vKXs/V9peycjaYoSS/P9OT7evXqFf/8xhtvxCApm28RmGNDqc0333yR2w3MDZtrkUWbyuRBMJb+sGy4xYZgBJbBz0BQmMAlgVaCg7RuqA9Zw6BlQum/TeB38ODBYbfddqs34MlGZdOnT49//t73vhd74ZYHnDnnnKt5Zc2Wmt9/i/YTPXr0qPs6x8jvh/Ox2WabhYZEQL2hjhtch5xrs2clSZLmj8vbkiRJFW6ttdYK//vf/2KZPpmltDsgi5Egawoy3nHHHeHMM8+M7Q2222672f57NrUi8MZrq622Ck8//XTYYYcdYpsBWiGQRUp/WTJa0/fxInu2oay55pphvfXWm+v3kElLkLK+jczmpEuXLvHja6+9Ntvn2RiLjc/OOeecunOUPPnkk7EvLcFb7LPPPmH06NHhrrvuqvseMltpEXHjjTcuUEbr/Pxb//73v2MGbam33347fqTlRENr2rRp/DhjxoxFOu6EgO3666/f4McpSZJUrcyglSRJqlAEZa+66qoYvOvbt29dZiVBPIJ8v/3tb2OQlWArf6c/LNm29WFjqmuvvTb2rSUge8opp8RNxXDWWWfFHq5HHHFE3CTqF7/4RVhxxRXnemzjx4+PWa/1SZuIzctee+0V++DS57S+dgm0IOBnm1P2LCi/J7DLBmA77bTTbIHbP/7xj+G8886L/z29evkZ33rrrfDEE0+EAw88MBxzzDHxe/n60UcfHXvkknnbtm3bMGTIkHjOrr/++rAg5uff4txxXBwvgev33nsv3HfffeHkk0+O39/Q6CHMz37DDTfEbOXDDjtsoY4bBLvZ7I1WFZIkSZo/BmglSZIqFMG7H/3oRzGQR4YrPUop+6fcnCzT3/zmN7Hlwb333ht69+4dv7ccmaU333xzDADSwuBf//rXbJuNgWxcMlf5/xAQ5iNZuHvuuedsZfhLSv/+/cP7778fPvzww/lqL0CGMOeGPrfNmjWr+zybmvFzPPXUUzE7lOAiWbNk1ZZnqhLcJmBMdi29dwkY89+SsYxu3brFc1Teg5YMZD5fWu4/r3+L7+fYRowYEdsFkLnMpmFrr732fJ+jVVZZJf47G2200be+Vn5MnBOugUceeWSRjhtcR7RM2GOPPeb7WCVJkmpdo1nlOyZIkiSp4owbNy5mydJLlo2v0kZNbDTF5k5kkdbn888/Dw8//HDcGKs82HnZZZfFnqeUsJciCEyQjgBxx44dv/Vv8jWyPks3Dit10003xRL47t271/t1MjIJFtOqgSxN+seWBpf59wlerrrqqjFDeF6bUk2dOjVmgNLeob4gtRoG0wp+H9tvv31d9rEkSZLmzQCtJEmS6jVs2LBYbv+Tn/yk4s/Qo48+Gi644IK4MdiC9IzV/GNTN1pksFBQmqksSZKkubPFgSRJkupVRPuCxWXbbbeN/VXfeeederN+tejYNOzyyy83OCtJkrSAzKCVJEmSJEmSpILMvWGXJEmSJEmSJGmxMUArSZIkSZIkSQUxQCtJkiRJkiRJBTFAK0mSJEmSJEkFMUArSZIkSZIkSQUxQCtJkiRJkiRJBTFAK0mSJEmSJEkFMUArSZIkSZIkSQUxQCtJkiRJkiRJoRj/H9rCAY1TVWF/AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" ] }, "metadata": {}, @@ -8027,95 +14561,167 @@ } ], "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", - "# 1. 讀取資料集\n", - "df = pd.read_csv(\"netflix/data/netflix.titles.composite.csv\")\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", - "# 2. 排除關鍵 NaN 值以計算正確的 P50 門檻 (以 TMDb 為基準)\n", - "df_valid_tmdb = df.dropna(subset=[\"tmdb_vote_average\", \"tmdb_vote_count\"])\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", - "tmdb_rating_p50 = df_valid_tmdb[\"tmdb_vote_average\"].median()\n", - "tmdb_votes_p50 = df_valid_tmdb[\"tmdb_vote_count\"].median()\n", - "netflix_hours_mean = df[\"netflix_viewing_hours\"].mean()\n", + "print(\"--- 【前 10 筆資料預覽】 ---\")\n", + "display(df_brief.head(10))\n", "\n", - "# 3. 篩選出 71 部 The Hit 影集\n", - "hit_condition = (\n", - " (df[\"tmdb_vote_average\"] >= tmdb_rating_p50) &\n", - " (df[\"tmdb_vote_count\"] >= tmdb_votes_p50) &\n", - " (df[\"netflix_viewing_hours\"] > netflix_hours_mean)\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", - "df_hits = df[hit_condition].copy().reset_index(drop=True)\n", - "\n", - "# 4. 對 4 個關鍵量化屬性進行 Min-Max 標準化\n", - "target_cols = [\"netflix_viewing_hours\", \"netflix_weeks\", \"tmdb_vote_average\", \"tmdb_vote_count\"]\n", - "for col in target_cols:\n", - " min_val = df_hits[col].min()\n", - " max_val = df_hits[col].max()\n", - " if max_val - min_val != 0:\n", - " df_hits[f\"{col}_norm\"] = ((df_hits[col] - min_val) / (max_val - min_val))\n", - " else:\n", - " df_hits[f\"{col}_norm\"] = 1.0000\n", - "\n", - "# 5. 計算【綜合 The Hit 指數】 (採用無 IMDb 的優化版權重)\n", - "df_hits['綜合 The Hit 指數'] = (\n", - " df_hits['netflix_viewing_hours_norm'] * 0.35 +\n", - " df_hits['tmdb_vote_average_norm'] * 0.30 +\n", - " df_hits['netflix_weeks_norm'] * 0.20 +\n", - " df_hits['tmdb_vote_count_norm'] * 0.15\n", - ").round(4)\n", - "\n", - "# ─── 步驟 6：動態識別「語言」、「成人限制」、「類型」欄位 ───\n", - "\n", - "# 定義可能的欄位候選清單 (相容不同的欄位命名)\n", - "language_candidates = [\"original_language\", \"tmdb_original_language\", \"language\", \"netflix_language\"]\n", - "adult_candidates = [\"adult\", \"is_adult\", \"rating\", \"age_certification\"]\n", - "genre_candidates = [\"genres\", \"tmdb_genres\", \"listed_in\", \"genre\"]\n", - "\n", - "# 自動尋找資料表中實際存在的欄位\n", - "lang_col = next((c for c in language_candidates if c in df_hits.columns), None)\n", - "adult_col = next((c for c in adult_candidates if c in df_hits.columns), None)\n", - "genre_col = next((c for c in genre_candidates if c in df_hits.columns), None)\n", - "\n", - "# ─── 步驟 7：整理並無省略輸出 ───\n", - "\n", - "# 設定 Pandas 完整顯示不省略\n", - "pd.set_option('display.max_rows', None)\n", - "pd.set_option('display.max_columns', None)\n", - "\n", - "# 建立顯示欄位清單\n", - "display_cols = [\"netflix_title\", \"綜合 The Hit 指數\"]\n", - "rename_mapping = {\n", - " \"netflix_title\": \"影劇名稱\",\n", - " \"綜合 The Hit 指數\": \"綜合 The Hit 指數\"\n", - "}\n", "\n", - "# 動態加入找到的屬性\n", - "if lang_col:\n", - " display_cols.append(lang_col)\n", - " rename_mapping[lang_col] = f\"主要語言 ({lang_col})\"\n", - "if adult_col:\n", - " display_cols.append(adult_col)\n", - " rename_mapping[adult_col] = f\"成人限制 ({adult_col})\"\n", - "if genre_col:\n", - " display_cols.append(genre_col)\n", - " rename_mapping[genre_col] = f\"類型/流派 ({genre_col})\"\n", - "\n", - "# 依綜合指數從高到低（降冪）排序\n", - "df_hits_sorted = df_hits.sort_values(by=\"綜合 The Hit 指數\", ascending=False).reset_index(drop=True)\n", - "\n", - "print(\"=\" * 120)\n", - "print(\"🏆 【The Hit 影集】綜合指數與「語言、成人與否、類型」特徵總表\")\n", - "print(\" (已依『綜合 The Hit 指數』由高至低排序，幫助您快速識別頂級爆款的共通基因)\")\n", - "print(\"=\" * 120)\n", - "\n", - "# 印出結果\n", - "display(df_hits_sorted[display_cols].rename(columns=rename_mapping))" + "# 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": "markdown", - "id": "f4ce3f65", + "id": "a19abdef", "metadata": {}, "source": [ "## our analysis\n", @@ -8124,7 +14730,7 @@ }, { "cell_type": "markdown", - "id": "58a83a66", + "id": "1b3a1b99", "metadata": {}, "source": [ "## our analysis\n", @@ -8133,7 +14739,7 @@ }, { "cell_type": "markdown", - "id": "c7ceb048", + "id": "45753096", "metadata": {}, "source": [ "## our analysis\n", @@ -8143,7 +14749,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b21b87a2", + "id": "013dc149", "metadata": {}, "outputs": [], "source": [] @@ -8151,7 +14757,7 @@ { "cell_type": "code", "execution_count": null, - "id": "02788717", + "id": "af945a0c", "metadata": {}, "outputs": [], "source": [] @@ -8159,7 +14765,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0d64c4aa", + "id": "10301c99", "metadata": {}, "outputs": [], "source": [] @@ -8175,7 +14781,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "venv (3.13.9)", "language": "python", "name": "python3" }, diff --git a/netflix/data/seasons_episodes_dual_axis.png b/netflix/data/seasons_episodes_dual_axis.png new file 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release analyze \ - check-python python-init python-lint python-clean python-requirements python-fetch-data python-build-dataset help - -# Default target executed when no arguments are given to make. -all: help - -# initialize local development environment. -# takes a few minutes to complete -init: - @echo "===============================================================================" - @echo "Initializing local development environment. This will verify and set up your" - @echo "Python virtual environment, install all 3rd-party package requirements." - @echo "This may take a few minutes..." - @echo "===============================================================================" - @echo "" - @echo "" - @echo "===============================================================================" - @echo "Initialization complete!" - @echo "===============================================================================" - make python-init - -activate: - ./scripts/activate.sh - -# run the web application from Docker -# takes around 30 seconds to complete -run: - @echo "===============================================================================" - @echo "Running solution ..." - @echo "===============================================================================" - make python-fetch-data - make python-build-dataset - -# destroy all Docker build and local artifacts -# takes around 1 minute to complete -tear-down: - @echo "===============================================================================" - @echo "Tearing down solution ..." - @echo "===============================================================================" - make python-clean - -pre-commit-init: - @echo "===============================================================================" - @echo "Installing and configuring pre-commit ..." - @echo "===============================================================================" - pre-commit install - pre-commit autoupdate - -pre-commit-run: - @echo "===============================================================================" - @echo "Running pre-commit hooks on all files ..." - @echo "===============================================================================" - pre-commit run --all-files - -release: - @echo "===============================================================================" - @echo "Forcing a new semantic release on GitHub by creating an empty commit and pushing to the repository ..." - @echo "===============================================================================" - git commit -m "fix: force a new release" --allow-empty && git push - -analyze: - @echo "===============================================================================" - @echo "Generating code analysis report using cloc ..." - @echo "===============================================================================" - cloc . --exclude-ext=svg,zip --fullpath --not-match-d=smarter/smarter/static/assets/ --vcs=git - -# --------------------------------------------------------- -# Python -# --------------------------------------------------------- -check-python: - @echo "" - @echo "===============================================================================" - @echo "Verifying that Python $(PYTHON) is installed ..." - @echo "===============================================================================" - @echo "" - @command -v $(PYTHON) >/dev/null 2>&1 || { \ - echo >&2 "This project requires $(PYTHON) but it's not installed. Aborting."; \ - echo >&2 "python --version output:"; \ - python --version 2>&1; \ - exit 1; \ -} - -python-init: - @echo "===============================================================================" - @echo "Initializing Python virtual environment and installing dependencies. This may take a few minutes..." - @echo "===============================================================================" - rm -r -f venv && \ - mkdir -p .pypi_cache && \ - make check-python - make python-clean && \ - npm install && \ - $(PYTHON) -m venv venv && \ - $(ACTIVATE_VENV) && \ - $(PIP) install pip==25.3 setuptools wheel pip-tools && \ - PIP_CACHE_DIR=.pypi_cache $(PIP) install -r requirements/local.txt - $(ACTIVATE_VENV) && $(PYTHON) -m ipykernel install --user --name py311 --display-name "Python 3.13" - -python-lint: - @echo "" - @echo "===============================================================================" - @echo "Running Python linting using pre-commit ..." - @echo "===============================================================================" - @echo "" - make check-python - make pre-commit-run - pylint netflix - -python-clean: - @echo "" - @echo "===============================================================================" - @echo "Cleaning Python virtual environment and __pycache__ directories ..." - @echo "===============================================================================" - @echo "" - rm -rf venv - find ./netflix/ -name __pycache__ -type d -exec rm -rf {} + - -# FIX NOTE: mcdaniel. the pip version pin temporarily resolves a pip-compile issue. -# AttributeError: 'PackageFinder' object has no attribute 'allow_all_prereleases' -python-requirements: - @echo "===============================================================================" - @echo "Compiling and updating Python dependency files using pip-compile ..." - @echo "===============================================================================" - pip install pip==25.3 setuptools wheel pip-tools - pip-compile requirements/in/base.in -o requirements/base.txt - pip-compile requirements/in/local.in -o requirements/local.txt - -python-fetch-data: - @echo "===============================================================================" - @echo "Fetching data from external APIs and saving to local files ..." - @echo "===============================================================================" - $(ACTIVATE_VENV) && python -m netflix.fetch - -python-build-dataset: - @echo "===============================================================================" - @echo "Building composite Netflix dataset from fetched data ..." - @echo "===============================================================================" - $(ACTIVATE_VENV) && python -m netflix.build - -python-build-stories: - @echo "===============================================================================" - @echo "Building ..." - @echo "===============================================================================" - $(ACTIVATE_VENV) && python -m netflix.build.story_codes - - -###################### -# HELP -###################### - -help: - @echo '====================================================================' - @echo 'init - Initialize local and Docker environments' - @echo 'activate - Activate Python virtual environment' - @echo 'build - Build Docker containers' - @echo 'run - Run web application from Docker' - @echo 'test - Run Python-Django unit tests in Docker' - @echo 'clean - Delete all local artifacts, virtual environment, node_modules, and Docker containers' - @echo 'tear-down - Destroy all Docker build and local artifacts' - @echo 'pre-commit-init - Install and configure pre-commit hooks' - @echo 'pre-commit-run - Run pre-commit hooks on all files' - @echo 'release - Force a new semantic release on GitHub by creating an empty commit and pushing to the repository' - @echo 'analyze - Generate code analysis report using cloc' - @echo '<************************** Python **************************>' - @echo 'check-python - Verify Python 3.13 is installed' - @echo 'python-init - Create a Python virtual environment and install dependencies' - @echo 'python-lint - Run Python linting using pre-commit and pylint' - @echo 'python-clean - Destroy the Python virtual environment and remove __pycache__ directories' - @echo 'python-requirements - Compile and update Python dependency files' - @echo 'python-fetch-data - Fetch data from external APIs and save to local files' - @echo 'python-build-dataset - Build composite Netflix dataset from fetched data' - @echo '====================================================================' +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! + From 2b81788b2ca60baa33d70dc5b163a0ba7f5d58f6 Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Thu, 16 Jul 2026 08:08:19 -0700 Subject: [PATCH 12/14] check ver 2 --- README.md | 18 ++++++++- netflix-writer.ipynb | 78 ++++++------------------------------ team5 final project (2).pdf | Bin 0 -> 2808948 bytes 3 files changed, 29 insertions(+), 67 deletions(-) create mode 100644 team5 final project (2).pdf diff --git a/README.md b/README.md index f1fc877..57920b9 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,21 @@ -# Netflix AI Greenlight Challenge +# group member ! +Vincent kuo +Iris +Sean + + + + + + + + + + + + +# 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/) diff --git a/netflix-writer.ipynb b/netflix-writer.ipynb index a0820ec..f7ba3f2 100644 --- a/netflix-writer.ipynb +++ b/netflix-writer.ipynb @@ -2620,22 +2620,13 @@ "outputs": [], "source": [] }, - { - "cell_type": "markdown", - "id": "aa6e69bf", - "metadata": {}, - "source": [ - "## our analysis\n", - "口碑分數以及觀看時數關係" - ] - }, { "cell_type": "markdown", "id": "8532b959", "metadata": {}, "source": [ "## our analysisy\n", - "資料表屬性總表" + "Data table attribute summary table" ] }, { @@ -3425,7 +3416,7 @@ "metadata": {}, "source": [ "## our analysis\n", - "？？" + "Data table attribute summary table; Threshold settings; Comparison with other data." ] }, { @@ -5314,7 +5305,11 @@ "metadata": {}, "source": [ "## our analysis\n", - "？？" + "Skewness Correction \n", + "\n", + "Standardization & PCA \n", + "\n", + "Score Scaling & Loadings" ] }, { @@ -6603,7 +6598,7 @@ "metadata": {}, "source": [ "## our analysis\n", - "？？" + "final check and exper" ] }, { @@ -13227,7 +13222,9 @@ "metadata": {}, "source": [ "## our analysis\n", - "？？" + "IN the 71 \"the hit\"\n", + "\n", + "compared with the attitudes" ] }, { @@ -14276,7 +14273,7 @@ "metadata": {}, "source": [ "## our analysis\n", - "？？" + "Tabele analysis\n" ] }, { @@ -14719,57 +14716,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "markdown", - "id": "a19abdef", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, - { - "cell_type": "markdown", - "id": "1b3a1b99", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, - { - "cell_type": "markdown", - "id": "45753096", - "metadata": {}, - "source": [ - "## our analysis\n", - "？？" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "013dc149", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "af945a0c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10301c99", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, diff --git a/team5 final project (2).pdf b/team5 final project (2).pdf new file mode 100644 index 0000000000000000000000000000000000000000..f5259cf51bc903f7729a0fcfec8f672503cfe8db GIT binary patch literal 2808948 zcmeEu2UJwcvi6X(WF!cTeUgQ7%g%qmt{!e9H4R~ihy*G3#}{9ol0v{@ zU|%N>(i100MPXrnaImPVBht~!*Ns%v93Bve@bv+UqFRd@Il93E!BC*N38|<)9O>u+ zv;&J{%BIMGAZH{j01h|t^+kduu&o>eJhgpXeZi8LvYw-l8<-E_BCM^(50FQ|L*M~M z0dQA%0NlqJATEVzW*CI@Lihk>>F;H~Af&br67GWx1l!1kEVq(0&f54mizApc$i7q@W#5cet5Fn$hWd!$t zx%x}!3#llp8TptSI%;bu17wtgknX4snIixO_@Md-3vl%FgS&u5H5|PH;iRI@zCK7t zgioL{LKE%-4*D%Ilu@P1r7^IeKCDMRk%AsD?9*h8g2k{ z4)=7Hb>UD_Bs~$};wopNrU4H1_VNjo14;@U?ntDcoTz9>NQg*?xQK6nnU{q;gPJEGWt>LTVja3gn!HU$ne{(d|-M2Z5(-$BZcrdW6=aG>FVsRu?Z zhlMuG*VpS8^537qQE*k@K;cSq0mw$UhW*p6u@m7x z(;mxeIX}k$fK3pz;sB}xunviuwNQG5`a|oIvKR8OjIHrPpfm{0eFctCVHdcoV~`h; 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Vincent kuo -Iris -Sean - - - +Iris +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. From 7364c29afcd3aaa35b781be57594df54c344a92d Mon Sep 17 00:00:00 2001 From: vincent-copy Date: Fri, 17 Jul 2026 09:01:03 -0700 Subject: [PATCH 14/14] README modified --- README.md | 2 +- netflix-writer copy.ipynb | 263 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 264 insertions(+), 1 deletion(-) create mode 100644 netflix-writer copy.ipynb diff --git a/README.md b/README.md index 1007032..018d17a 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # group member ! Vincent kuo -Iris +Iris Su Sean Shen Jeannie LAN # The changes include: 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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", 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    " + ] + }, + "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": [ + "
    " + ] + }, + "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 +}

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