From 6987b4129ab36b55d8b69e46a0be4f924d51b57e Mon Sep 17 00:00:00 2001 From: jrasero Date: Tue, 29 Oct 2024 11:12:51 -0400 Subject: [PATCH] Update documentation --- 06_numpy_intro.html | 4 +- .../module-4/044-PandasII-exploration.ipynb | 296 +++++++++++++----- .../module-4/045-PandasIII-manipulation.ipynb | 20 +- chapters/module-4/041-numpyI.html | 34 +- chapters/module-4/042-numpyII.html | 62 ++-- .../module-4/043-PandasI-Introduction.html | 2 +- ...II-Exploration_and_Manipulation-Copy1.html | 2 +- .../module-4/044-PandasII-exploration.html | 117 ++++++- .../module-4/045-PandasIII-manipulation.html | 7 + ...III-Merging_Concatenating_Aggregating.html | 120 +++---- genindex.html | 4 +- index.html | 6 +- search.html | 4 +- searchindex.js | 2 +- 14 files changed, 450 insertions(+), 230 deletions(-) diff --git a/06_numpy_intro.html b/06_numpy_intro.html index 48700ba..e4a21c3 100644 --- a/06_numpy_intro.html +++ b/06_numpy_intro.html @@ -636,9 +636,7 @@

Images are Numerical Data 
Requirement already satisfied: scikit-image in /home/javi/anaconda3/lib/python3.11/site-packages (0.20.0)
 Requirement already satisfied: matplotlib in /home/javi/anaconda3/lib/python3.11/site-packages (3.7.1)
-
-
-
Requirement already satisfied: numpy>=1.21.1 in /home/javi/anaconda3/lib/python3.11/site-packages (from scikit-image) (1.24.3)
+Requirement already satisfied: numpy>=1.21.1 in /home/javi/anaconda3/lib/python3.11/site-packages (from scikit-image) (1.24.3)
 Requirement already satisfied: scipy>=1.8 in /home/javi/anaconda3/lib/python3.11/site-packages (from scikit-image) (1.13.1)
 Requirement already satisfied: networkx>=2.8 in /home/javi/anaconda3/lib/python3.11/site-packages (from scikit-image) (3.3)
 Requirement already satisfied: pillow>=9.0.1 in /home/javi/anaconda3/lib/python3.11/site-packages (from scikit-image) (10.4.0)
diff --git a/_sources/chapters/module-4/044-PandasII-exploration.ipynb b/_sources/chapters/module-4/044-PandasII-exploration.ipynb
index c7c55e1..6610953 100644
--- a/_sources/chapters/module-4/044-PandasII-exploration.ipynb
+++ b/_sources/chapters/module-4/044-PandasII-exploration.ipynb
@@ -8,7 +8,7 @@
    "source": [
     "# Pandas: Data Exploration\n",
     "\n",
-    "Pandas is extremely useful for all the steps from data acquisition to analysis. Between these stages, an essential process involves exploring and processing the data to ensure it’s ready for meaningful insights.\n",
+    "Pandas is extremely useful for all the steps from data acquisition to analysis. Between these stages, an essential process involves exploring and processing the data to first learn about your data, and later to ensure they are ready for analysis.\n",
     "\n",
     "![](https://ds1002-resources.s3.amazonaws.com/images/workflow.png)\n",
     "\n",
@@ -1047,7 +1047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "30896c3d",
    "metadata": {},
    "outputs": [
@@ -1139,7 +1139,7 @@
        "petal_width   150.0  1.199333  0.762238  0.1  0.3  1.30  1.8  2.5"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1161,7 +1161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "294d2ade",
    "metadata": {},
    "outputs": [
@@ -1218,7 +1218,7 @@
        "freq        50"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1229,7 +1229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "03ed92bf",
    "metadata": {},
    "outputs": [
@@ -1247,7 +1247,7 @@
        "Name: sepal_length, dtype: float64"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1266,7 +1266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "8979b360",
    "metadata": {},
    "outputs": [
@@ -1279,7 +1279,7 @@
        "Name: species, dtype: int64"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1298,7 +1298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "1345fc52",
    "metadata": {},
    "outputs": [
@@ -1311,7 +1311,7 @@
        "Name: species, dtype: float64"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1330,7 +1330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "8fee461e",
    "metadata": {},
    "outputs": [
@@ -1338,7 +1338,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_15501/1934569051.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "/tmp/ipykernel_13281/1934569051.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
       "  iris_df.corr()\n"
      ]
     },
@@ -1410,7 +1410,7 @@
        "petal_width       0.817941    -0.366126      0.962865     1.000000"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1429,7 +1429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "02d1c9ee",
    "metadata": {},
    "outputs": [
@@ -1479,7 +1479,7 @@
        "petal_length      0.871754      1.000000"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1490,7 +1490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "f433fe95",
    "metadata": {},
    "outputs": [
@@ -1550,7 +1550,7 @@
        "sepal_width      -0.117570     -0.428440      1.00000"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1574,14 +1574,14 @@
    "source": [
     "### Visualizing data\n",
     "\n",
-    "Pandas dataframes have a `plot` method, which enables quick data visualization. \n",
+    "Pandas dataframe objects have a `plot` method, which enables quick data visualization. \n",
     "\n",
-    "This method is built on Matplotlib, the primary library for creating visualizations in Python. Another important library for this purpose is Seaborn, which works seamlessly with DataFrames and produces professional, visually appealing results."
+    "This method is built on [Matplotlib](https://matplotlib.org), the primary library for creating visualizations in Python. Another important library for this purpose is [Seaborn](https://seaborn.pydata.org), which works seamlessly with DataFrames and produces professional, visually appealing results. We will come to both at the end of the course, if time permits."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 34,
    "id": "8f29d5f7-2454-4ce0-adad-e4bb020e8ee2",
    "metadata": {},
    "outputs": [
@@ -1591,7 +1591,7 @@
        ""
       ]
      },
-     "execution_count": 30,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1613,7 +1613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 35,
    "id": "80af1a62-0ffb-438c-b20f-b1a0d31be6c7",
    "metadata": {},
    "outputs": [
@@ -1623,7 +1623,7 @@
        ""
       ]
      },
-     "execution_count": 31,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1645,7 +1645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "id": "72063e59-daab-4efb-9f13-13913ac0195b",
    "metadata": {},
    "outputs": [
@@ -1655,7 +1655,7 @@
        ""
       ]
      },
-     "execution_count": 32,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1677,7 +1677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "id": "c1d226c8-bc09-4f3e-82fd-7ba398a6298e",
    "metadata": {},
    "outputs": [
@@ -1687,7 +1687,7 @@
        ""
       ]
      },
-     "execution_count": 33,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1717,7 +1717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "id": "e66af9f1-353c-4b2d-8b5e-7f598336f08e",
    "metadata": {},
    "outputs": [
@@ -1727,7 +1727,7 @@
        ""
       ]
      },
-     "execution_count": 36,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1751,12 +1751,12 @@
    "id": "a1eaa8bf-4132-4e0a-899f-228e0b0f1735",
    "metadata": {},
    "source": [
-    "But this does not work as expected... We have to use `boxplot`:"
+    "But this does not work as expected... We have to use the `boxplot` method instead to get the desired behavior:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 40,
    "id": "b611f816-8c48-4a33-8c70-aa923d6a04d5",
    "metadata": {},
    "outputs": [
@@ -1766,7 +1766,7 @@
        ""
       ]
      },
-     "execution_count": 37,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1813,7 +1813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 41,
    "id": "28365a2f-af89-40f4-8db9-d57456586599",
    "metadata": {},
    "outputs": [
@@ -1835,7 +1835,7 @@
        " pandas.core.series.Series)"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1847,7 +1847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 42,
    "id": "1497be19-6561-4adf-bb35-ae71bf3f419f",
    "metadata": {},
    "outputs": [
@@ -1871,13 +1871,13 @@
        " pandas.core.frame.DataFrame)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# double bracket gives you the selected column as new dataframe\n",
+    "# double bracket also gives you the selected column, but as dataframe\n",
     "iris_df[['sepal_length']], type(iris_df[['sepal_length']])"
    ]
   },
@@ -1886,12 +1886,12 @@
    "id": "a003b66b-5881-4423-9f2a-3f64943c10d7",
    "metadata": {},
    "source": [
-    "This notation allows you to select multiple columns. To do this, use double brackets `[[.., ..]]`, ensuring that the returned object is a dataframe:"
+    "This notation allows you to select multiple columns. To do this, use double brackets `[[.., ..]]`. This will return a dataframe:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 43,
    "id": "b250fd93-8c83-4389-bff1-bf87e89ea368",
    "metadata": {},
    "outputs": [
@@ -1998,7 +1998,7 @@
        "[150 rows x 2 columns]"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2017,7 +2017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 44,
    "id": "0ab28157-28dd-4a10-8714-c83cab905d8c",
    "metadata": {},
    "outputs": [
@@ -2039,7 +2039,7 @@
        " pandas.core.series.Series)"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2079,7 +2079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 45,
    "id": "98a9ae6e",
    "metadata": {},
    "outputs": [
@@ -2094,7 +2094,7 @@
        "Name: 2, dtype: object"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2106,7 +2106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 46,
    "id": "014d872a-dfbd-403f-a5a6-a22db9482075",
    "metadata": {},
    "outputs": [
@@ -2121,7 +2121,7 @@
        "Name: 2, dtype: object"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2141,7 +2141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 47,
    "id": "c5c45d06",
    "metadata": {},
    "outputs": [
@@ -2200,7 +2200,7 @@
        "2           4.7          3.2           1.3          0.2  setosa"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2219,7 +2219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 48,
    "id": "408ba901",
    "metadata": {},
    "outputs": [
@@ -2272,7 +2272,7 @@
        "2           4.7          3.2           1.3"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2291,7 +2291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 49,
    "id": "5056b057",
    "metadata": {},
    "outputs": [
@@ -2301,7 +2301,7 @@
        "Index(['sepal_length', 'sepal_width', 'petal_length'], dtype='object')"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2328,7 +2328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 50,
    "id": "cd1a1a13",
    "metadata": {},
    "outputs": [
@@ -2396,7 +2396,7 @@
        "3           4.6          3.1           1.5          0.2  setosa"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2416,7 +2416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 51,
    "id": "e951d7b7-88ae-4d8b-adc2-8092a4af37c2",
    "metadata": {},
    "outputs": [
@@ -2475,7 +2475,7 @@
        "2           4.7          3.2           1.3          0.2  setosa"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2494,7 +2494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 52,
    "id": "79d4b175-c76c-4be5-aebe-89e926dc531b",
    "metadata": {},
    "outputs": [
@@ -2580,7 +2580,7 @@
        "obs4           5.0          3.6           1.4          0.2  setosa"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2594,7 +2594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 53,
    "id": "f54afba0-3b6e-4fac-8b2f-971eb75dafcf",
    "metadata": {},
    "outputs": [
@@ -2653,7 +2653,7 @@
        "obs2           4.7          3.2           1.3          0.2  setosa"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2665,7 +2665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 54,
    "id": "12bb3a2b-a11b-40fb-bb6d-4b45abe39861",
    "metadata": {},
    "outputs": [
@@ -2676,7 +2676,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[56], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# But this will give an error\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m iris_copy\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m3\u001b[39m,:]\n",
+      "Cell \u001b[0;32mIn[54], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# But this will give an error\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m iris_copy\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m3\u001b[39m,:]\n",
       "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1067\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1065\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[1;32m   1066\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[0;32m-> 1067\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_tuple(key)\n\u001b[1;32m   1068\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1069\u001b[0m     \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[1;32m   1070\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
       "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1256\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take_opportunity(tup):\n\u001b[1;32m   1254\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take(tup)\n\u001b[0;32m-> 1256\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_tuple_same_dim(tup)\n",
       "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:924\u001b[0m, in \u001b[0;36m_LocationIndexer._getitem_tuple_same_dim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    921\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_null_slice(key):\n\u001b[1;32m    922\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 924\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(retval, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\u001b[38;5;241m.\u001b[39m_getitem_axis(key, axis\u001b[38;5;241m=\u001b[39mi)\n\u001b[1;32m    925\u001b[0m \u001b[38;5;66;03m# We should never have retval.ndim < self.ndim, as that should\u001b[39;00m\n\u001b[1;32m    926\u001b[0m \u001b[38;5;66;03m#  be handled by the _getitem_lowerdim call above.\u001b[39;00m\n\u001b[1;32m    927\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m retval\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim\n",
@@ -2697,7 +2697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 55,
    "id": "8c3b45de-117d-4081-8a5a-cdc529df1292",
    "metadata": {},
    "outputs": [
@@ -2765,7 +2765,7 @@
        "obs3           4.6          3.1           1.5          0.2  setosa"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2785,7 +2785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 56,
    "id": "332696cc",
    "metadata": {},
    "outputs": [
@@ -2802,7 +2802,7 @@
        " obs3           4.6          0.2)"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2821,7 +2821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 57,
    "id": "e322dddf",
    "metadata": {},
    "outputs": [
@@ -2928,7 +2928,7 @@
        "[150 rows x 2 columns]"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2957,7 +2957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 93,
    "id": "2ae69551",
    "metadata": {},
    "outputs": [],
@@ -2967,21 +2967,140 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 94,
    "id": "88d0b1c6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "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",
+       "
xy
02.0NaN
1NaNNaN
21.06.0
31.06.0
\n",
+       "
"
+      ],
+      "text/plain": [
+       "     x    y\n",
+       "0  2.0  NaN\n",
+       "1  NaN  NaN\n",
+       "2  1.0  6.0\n",
+       "3  1.0  6.0"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df_miss = pd.DataFrame({\n",
-    "    'x':[2, np.nan, 1], \n",
-    "    'y':[np.nan, np.nan, 6]}\n",
-    ")"
+    "df_miss_dupl = pd.DataFrame({\n",
+    "    'x':[2, np.nan, 1, 1], \n",
+    "    'y':[np.nan, np.nan, 6, 6]}\n",
+    ")\n",
+    "\n",
+    "df_miss_dupl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed9f6746-251d-4f2c-a6b3-fbc32cded933",
+   "metadata": {},
+   "source": [
+    "### Drop duplicated data\n",
+    "\n",
+    "Dataframe have a `duplicated` method, which can identify duplicate rows and duplicate columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "id": "8404fdeb",
+   "execution_count": 95,
+   "id": "b8d7395e-b2aa-446b-bece-c262372b9da9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1    False\n",
+       "2    False\n",
+       "3     True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_miss_dupl.duplicated()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "6c83e366-a73a-47b5-8d05-b292b59d40b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can see calculate how many duplicates we have -- useful for large datasets\n",
+    "print(df_miss_dupl.duplicated().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "40e2b32d-7b22-49ba-8b0e-cb0d15da42a8",
    "metadata": {},
    "outputs": [
     {
@@ -3036,12 +3155,15 @@
        "2  1.0  6.0"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# remove the duplicate rows - but be sure to keep one copy!\n",
+    "df_miss = df_miss_dupl.drop_duplicates()\n",
+    "\n",
     "df_miss"
    ]
   },
@@ -3057,7 +3179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 98,
    "id": "0f90aff6",
    "metadata": {},
    "outputs": [
@@ -3101,7 +3223,7 @@
        "2  1.0  6.0"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3121,7 +3243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 99,
    "id": "ba5ad471",
    "metadata": {},
    "outputs": [
@@ -3171,7 +3293,7 @@
        "2  1.0  6.0"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3188,14 +3310,14 @@
    "source": [
     "### Impute missing values \n",
     "\n",
-    "We can use `fillna()` to replace missing data to whatever value you like, e.g. $0$s. Look at its [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html) for further details.\n",
+    "We can use `fillna()` to replace missing data to whatever value you like, e.g. zeros. Look at its [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html) for further details.\n",
     "\n",
     "We can pass the results of an operation -- for example to peform simple imputation, we can replace missing values in each column with the median value of the respective column:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 100,
    "id": "c697c8f4",
    "metadata": {},
    "outputs": [],
@@ -3205,7 +3327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 101,
    "id": "cc10a2b7",
    "metadata": {},
    "outputs": [
@@ -3261,7 +3383,7 @@
        "2  1.0  6.0"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/_sources/chapters/module-4/045-PandasIII-manipulation.ipynb b/_sources/chapters/module-4/045-PandasIII-manipulation.ipynb
index 726e55b..6d7cc44 100644
--- a/_sources/chapters/module-4/045-PandasIII-manipulation.ipynb
+++ b/_sources/chapters/module-4/045-PandasIII-manipulation.ipynb
@@ -3151,12 +3151,22 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13003483-345a-4c58-9efb-c9babc5e3491",
+   "cell_type": "markdown",
+   "id": "5746aedd-e014-4292-ab0f-ebce0329b472",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "1) Use `apply()` to append a new column that is the minimum of (petal_length, petal_width)\n",
+    "\n",
+    "Print the head, tail of the new dataframe to check things look correct."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b06eb244-0fa2-46ed-af5b-e9ed2e60bf63",
+   "metadata": {},
+   "source": [
+    "2. Create a categorical Create a new column that categorizes age (e.g., <25 as 'Young', >=25 as 'Adult'). Set and reset an index on a non-unique column, exploring how indexing can help in data analysis."
+   ]
   }
  ],
  "metadata": {
diff --git a/chapters/module-4/041-numpyI.html b/chapters/module-4/041-numpyI.html
index b620b27..f7a8955 100644
--- a/chapters/module-4/041-numpyI.html
+++ b/chapters/module-4/041-numpyI.html
@@ -483,7 +483,7 @@ 
The ndarray object
-
-0.24326299558377007 <class 'float'>
+
0.9983151051472091 <class 'float'>
 

 

 

@@ -497,8 +497,8 @@ 
The ndarray object
-
[[-0.54691547  0.73067779  0.02729858]
- [-0.28328225  1.11208023  0.74172046]] <class 'numpy.ndarray'>
+
[[-0.16765433  0.29513178  0.46948154]
+ [ 0.37200198 -1.24104694  0.01450445]] <class 'numpy.ndarray'>
 

 

 

@@ -511,8 +511,8 @@ 
The ndarray object
-
array([[-5.46915473,  7.30677793,  0.27298579],
-       [-2.83282247, 11.1208023 ,  7.41720455]])
+
array([[ -1.67654327,   2.95131775,   4.69481541],
+       [  3.7200198 , -12.41046939,   0.14504455]])
 

 

 

@@ -527,10 +527,10 @@ 
The ndarray object
-
[[-1.09383095  1.46135559  0.05459716]
- [-0.56656449  2.22416046  1.48344091]]
-[[-1.09383095  1.46135559  0.05459716]
- [-0.56656449  2.22416046  1.48344091]]
+
[[-0.33530865  0.59026355  0.93896308]
+ [ 0.74400396 -2.48209388  0.02900891]]
+[[-0.33530865  0.59026355  0.93896308]
+ [ 0.74400396 -2.48209388  0.02900891]]
 

 

 

@@ -558,9 +558,9 @@ 
The ndarray object
-
[[-0.54691547  0.73067779]
- [ 0.02729858 -0.28328225]
- [ 1.11208023  0.74172046]]
+
[[-0.16765433  0.29513178]
+ [ 0.46948154  0.37200198]
+ [-1.24104694  0.01450445]]
 (3, 2)
 

 

@@ -847,11 +847,11 @@ 
Creating ndarrays
-
array([[-0.07873659, -0.94355901],
-       [ 0.74936841, -1.44108587],
-       [ 0.94781372, -1.71985611],
-       [-2.74408967,  0.63226768],
-       [ 0.76942668,  1.57904328]])
+
array([[ 9.00243578e-04,  1.84055731e-02],
+       [ 3.60407289e-01, -4.39962457e-01],
+       [-4.46618444e-01, -1.63880396e+00],
+       [-4.95315855e-01, -5.70438779e-01],
+       [ 2.57633731e-01, -1.21895535e+00]])
 

 

 

diff --git a/chapters/module-4/042-numpyII.html b/chapters/module-4/042-numpyII.html
index 52806d6..2d875a2 100644
--- a/chapters/module-4/042-numpyII.html
+++ b/chapters/module-4/042-numpyII.html
@@ -819,13 +819,13 @@ 
Boolean slicing
 
['Bob' 'Joe' 'Will' 'Bob' 'Will' 'Joe' 'Joe']
-[[ 0.92955365  0.66158729 -0.39141742 -1.20167438]
- [-0.7099215  -1.47968728  2.44189276  0.29293041]
- [ 1.64752804 -0.42051231 -0.33339607  0.11959951]
- [ 0.91276971 -0.75328513  1.43879725  0.21027777]
- [ 2.36320733 -0.03742102  0.07586576  0.38709055]
- [ 1.85886751 -0.86425065  1.12457198  0.39906275]
- [-0.94769757  0.35331011  2.08108815  0.2433657 ]]
+[[-0.36748832  0.41549724 -0.07892885  1.03054864]
+ [ 2.1515256   0.0252963  -0.50860192 -0.81510486]
+ [-1.50438063  1.34625813  0.19001997 -2.75474839]
+ [-2.02413974 -0.40117379 -1.11674174 -0.04430263]
+ [-1.68668132  0.4919675   0.46076492  1.68898003]
+ [ 2.05100384  2.15532653  0.63420939 -0.05512468]
+ [ 0.16083873 -2.08108768  0.47970436 -1.7483949 ]]
 

 

 

@@ -864,8 +864,8 @@ 
Boolean slicing
-
array([[ 0.92955365,  0.66158729, -0.39141742, -1.20167438],
-       [ 0.91276971, -0.75328513,  1.43879725,  0.21027777]])
+
array([[-0.36748832,  0.41549724, -0.07892885,  1.03054864],
+       [-2.02413974, -0.40117379, -1.11674174, -0.04430263]])
 

 

 

@@ -878,8 +878,8 @@ 
Boolean slicing
-
array([[-0.39141742, -1.20167438],
-       [ 1.43879725,  0.21027777]])
+
array([[-0.07892885,  1.03054864],
+       [-1.11674174, -0.04430263]])
 

 

 

@@ -894,11 +894,11 @@ 
Boolean slicing
-
array([[-0.7099215 , -1.47968728,  2.44189276,  0.29293041],
-       [ 1.64752804, -0.42051231, -0.33339607,  0.11959951],
-       [ 2.36320733, -0.03742102,  0.07586576,  0.38709055],
-       [ 1.85886751, -0.86425065,  1.12457198,  0.39906275],
-       [-0.94769757,  0.35331011,  2.08108815,  0.2433657 ]])
+
array([[ 2.1515256 ,  0.0252963 , -0.50860192, -0.81510486],
+       [-1.50438063,  1.34625813,  0.19001997, -2.75474839],
+       [-1.68668132,  0.4919675 ,  0.46076492,  1.68898003],
+       [ 2.05100384,  2.15532653,  0.63420939, -0.05512468],
+       [ 0.16083873, -2.08108768,  0.47970436, -1.7483949 ]])
 

 

 

@@ -911,11 +911,11 @@ 
Boolean slicing
-
array([[-0.7099215 , -1.47968728,  2.44189276,  0.29293041],
-       [ 1.64752804, -0.42051231, -0.33339607,  0.11959951],
-       [ 2.36320733, -0.03742102,  0.07586576,  0.38709055],
-       [ 1.85886751, -0.86425065,  1.12457198,  0.39906275],
-       [-0.94769757,  0.35331011,  2.08108815,  0.2433657 ]])
+
array([[ 2.1515256 ,  0.0252963 , -0.50860192, -0.81510486],
+       [-1.50438063,  1.34625813,  0.19001997, -2.75474839],
+       [-1.68668132,  0.4919675 ,  0.46076492,  1.68898003],
+       [ 2.05100384,  2.15532653,  0.63420939, -0.05512468],
+       [ 0.16083873, -2.08108768,  0.47970436, -1.7483949 ]])
 

 

 

@@ -931,10 +931,10 @@ 
Boolean slicing
 
[ True False  True  True  True False False]
-[[ 0.92955365  0.66158729 -0.39141742 -1.20167438]
- [ 1.64752804 -0.42051231 -0.33339607  0.11959951]
- [ 0.91276971 -0.75328513  1.43879725  0.21027777]
- [ 2.36320733 -0.03742102  0.07586576  0.38709055]]
+[[-0.36748832  0.41549724 -0.07892885  1.03054864]
+ [-1.50438063  1.34625813  0.19001997 -2.75474839]
+ [-2.02413974 -0.40117379 -1.11674174 -0.04430263]
+ [-1.68668132  0.4919675   0.46076492  1.68898003]]
 

 

 

@@ -949,12 +949,12 @@ 
Boolean slicing
 
array([[ 7.        ,  7.        ,  7.        ,  7.        ],
-       [-0.7099215 , -1.47968728,  2.44189276,  0.29293041],
+       [ 2.1515256 ,  0.0252963 , -0.50860192, -0.81510486],
        [ 7.        ,  7.        ,  7.        ,  7.        ],
        [ 7.        ,  7.        ,  7.        ,  7.        ],
        [ 7.        ,  7.        ,  7.        ,  7.        ],
-       [ 1.85886751, -0.86425065,  1.12457198,  0.39906275],
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+       [ 2.05100384,  2.15532653,  0.63420939, -0.05512468],
+       [ 0.16083873, -2.08108768,  0.47970436, -1.7483949 ]])
 

 

 

@@ -1306,8 +1306,8 @@ 
More useful calculations
-
0x7fc546428810
-0x7fc546428d50
+
0x7f6150364bd0
+0x7f6150365110
 

 

 

@@ -1586,7 +1586,7 @@ 
More useful calculations
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/tmp/ipykernel_14437/1198364157.py:1: RuntimeWarning: invalid value encountered in sqrt
+
/tmp/ipykernel_40024/1198364157.py:1: RuntimeWarning: invalid value encountered in sqrt
   np.sqrt(np.array([4, -3, 16, 9, -5]))
 

 

diff --git a/chapters/module-4/043-PandasI-Introduction.html b/chapters/module-4/043-PandasI-Introduction.html
index 219a546..53421e2 100644
--- a/chapters/module-4/043-PandasI-Introduction.html
+++ b/chapters/module-4/043-PandasI-Introduction.html
@@ -1463,7 +1463,7 @@ 
An introduction to some attributes and methods
-
0x7f995b833ed0 0x7f995b817510 0x7f995b833ed0
+
0x7fcb0093b910 0x7fcb0090ddd0 0x7fcb0093b910
 

 

 

diff --git a/chapters/module-4/044-PandasII-Exploration_and_Manipulation-Copy1.html b/chapters/module-4/044-PandasII-Exploration_and_Manipulation-Copy1.html
index e419cde..d053100 100644
--- a/chapters/module-4/044-PandasII-Exploration_and_Manipulation-Copy1.html
+++ b/chapters/module-4/044-PandasII-Exploration_and_Manipulation-Copy1.html
@@ -1340,7 +1340,7 @@ 
Summarizing data
-
/tmp/ipykernel_15133/1934569051.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
+
/tmp/ipykernel_40070/1934569051.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
   iris_df.corr()
 

 

diff --git a/chapters/module-4/044-PandasII-exploration.html b/chapters/module-4/044-PandasII-exploration.html
index a0435d9..e6a7f57 100644
--- a/chapters/module-4/044-PandasII-exploration.html
+++ b/chapters/module-4/044-PandasII-exploration.html
@@ -32,9 +32,9 @@
     
     
     
-    
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-    
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@@ -58,8 +58,6 @@
     
     
     
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@@ -372,6 +370,7 @@ 
 Contents 

 
 
 
  • Basic data cleaning
@@ -390,7 +389,7 @@ 
     Contents 
    
                   
   
    
 
    Pandas: Data Exploration#
    
-
    Pandas is extremely useful for all the steps from data acquisition to analysis. Between these stages, an essential process involves exploring and processing the data to ensure it’s ready for meaningful insights.
    
+
    Pandas is extremely useful for all the steps from data acquisition to analysis. Between these stages, an essential process involves exploring and processing the data to first learn about your data, and later to ensure they are ready for analysis.
    
 
    
 
    In this lesson, you will learn how to use Pandas to get a first idea about your data. Specifically, we will cover:
    
 
    
 
diff --git a/chapters/module-4/046-PandasIII-Merging_Concatenating_Aggregating.html b/chapters/module-4/046-PandasIII-Merging_Concatenating_Aggregating.html
index 63592e1..2b3612a 100644
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+++ b/chapters/module-4/046-PandasIII-Merging_Concatenating_Aggregating.html
@@ -541,22 +541,22 @@ 
    pd.concat()
 
    
 
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+2  0.611838  0.641097  1.345133 -0.617344
 
    
 
    
 
    
@@ -608,36 +608,36 @@ 
    pd.concat()
     
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@@ -677,24 +677,24 @@ 
    pd.concat()
     
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@@ -734,24 +734,24 @@ 
    pd.concat()
     
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       1
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diff --git a/genindex.html b/genindex.html
index dc75e9f..c109319 100644
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index 3484bcb..bb0b747 100644
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