Update Pandas_walkthrough.ipynb

Author: marcoalopez

notebooks/Pandas_walkthrough.ipynb

@@ -982,7 +982,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -993,7 +992,17 @@
     ">new_df = df.loc[:, ['CO_eclogite', 'CO_granulite', 'CO_pyroxenite']] ❌  \n",
     ">new_df = df.loc[:, ['CO_eclogite', 'CO_granulite', 'CO_pyroxenite']].copy() ✅  \n",
     ">```\n",
-    "> ⚠️ Beware! Contrary to regular Python or Numpy, the Pandas ``.loc`` method **includes** the  index stop point so that ``df.loc[0:2]`` is equivalent to ``array[0:3]``"
+    "> ⚠️ Beware! Contrary to regular Python or Numpy, the Pandas ``.loc`` method **includes** the  index stop point so that ``df.loc[0:2]`` is equivalent to ``array[0:3]``\n",
+    "\n",
+    "\n",
+    "If you want to extract a single value from a Pandas DataFrame as a plain Python data type (e.g. a float), the best practice is to use the ``.at[]`` or ``.iat[]``  method. The difference between the two is that ``.at[]`` is optimised for scalar lookups by label (as in the ``.loc`` method) while ``.iat[]`` is optimised for scalar lookup by integer position (as in the ``.iloc`` method).\n",
+    "\n",
+    "| method         | what for                                                     |\n",
+    "| -------------- | ------------------------------------------------------------ |\n",
+    "| DataFrame.at   | Access a single value for a row/column pair by label.        |\n",
+    "| DataFrame.iat  | Access a single value for a row/column pair by integer  position. |\n",
+    "| DataFrame.loc  | Access a group of rows and columns by label(s).              |\n",
+    "| DataFrame.iloc | Access a group of rows and columns by integer position(s).   |"
    ]
   },
   {