4G_Evaluation of house prices(filling_erasing gaps in datasets and interpolation in time series).ipynb

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    {
      "id": "understood-drama",
      "cell_type": "markdown",
      "source": "# Evaluation of house prices(filling/erasing gaps in datasets and interpolation in time series)",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "framed-relay",
      "cell_type": "markdown",
      "source": "* We will use a dataset of California home values.\n\n* Data taken from academic paper by Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, Statistics and Probability Letters, 33 (1997) 291-297.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "sharing-public",
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "id": "rapid-description",
      "cell_type": "code",
      "source": "cal_housing = pd.read_csv('cal_housing.csv')\ncal_housing",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
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              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
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              "      <th>totalBedrooms</th>\n",
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              "<p>20640 rows × 9 columns</p>\n",
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              "       longitude  latitude  housingMedianAge  totalRooms  totalBedrooms  \\\n",
              "0        -122.23     37.88              41.0       880.0          129.0   \n",
              "1        -122.22     37.86              21.0      7099.0         1106.0   \n",
              "2        -122.24     37.85              52.0      1467.0          190.0   \n",
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              "4        -122.25     37.85              52.0      1627.0          280.0   \n",
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              "20637    -121.22     39.43              17.0      2254.0          485.0   \n",
              "20638    -121.32     39.43              18.0      1860.0          409.0   \n",
              "20639    -121.24     39.37              16.0      2785.0          616.0   \n",
              "\n",
              "       population  households  medianIncome  medianHouseValue  \n",
              "0           322.0       126.0        8.3252          452600.0  \n",
              "1          2401.0      1138.0        8.3014          358500.0  \n",
              "2           496.0       177.0        7.2574          352100.0  \n",
              "3           558.0       219.0        5.6431          341300.0  \n",
              "4           565.0       259.0        3.8462          342200.0  \n",
              "...           ...         ...           ...               ...  \n",
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              "20636       356.0       114.0        2.5568           77100.0  \n",
              "20637      1007.0       433.0        1.7000           92300.0  \n",
              "20638       741.0       349.0        1.8672           84700.0  \n",
              "20639      1387.0       530.0        2.3886           89400.0  \n",
              "\n",
              "[20640 rows x 9 columns]"
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          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 2
    },
    {
      "id": "grateful-korean",
      "cell_type": "markdown",
      "source": "* This data is complete, there are no gaps.\n\n* Unfortunately this is not the rule.\n\n* Most of the time there are gaps in the data available to us.\n\n* When a record contains a space instead of a real number, the space is represented as `NaN` (Not a Number).",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "durable-detector",
      "cell_type": "markdown",
      "source": "* So we here degrade the quality of our data by introducing gaps in it.\n\n* We will impute blanks in 75% of the rows of the `DataFrame` (method comes from the [scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/impute/plot_missing_values.html)).",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "urban-wells",
      "cell_type": "code",
      "source": "n_samples, n_features = cal_housing.shape\n\n# Add missing values in 75% of the lines\nmissing_rate = 0.75\nn_missing_samples = int(n_samples * missing_rate)\nn_missing_samples",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "15480"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 3
    },
    {
      "id": "sticky-announcement",
      "cell_type": "markdown",
      "source": "* We make an array with as many elements as the rows of `cal_housing` `DataFrame`, with `False` values.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "mathematical-scheme",
      "cell_type": "code",
      "source": "missing_samples = np.zeros(n_samples, dtype=bool)\nprint(missing_samples.shape, missing_samples)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(20640,) [False False False ... False False False]\n"
          ]
        }
      ],
      "execution_count": 4
    },
    {
      "id": "returning-narrative",
      "cell_type": "markdown",
      "source": "* We'll make the first 75% of its elements equal to `True'.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "violent-pillow",
      "cell_type": "code",
      "source": "missing_samples[:n_missing_samples] = True",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [],
      "execution_count": 5
    },
    {
      "id": "worth-obligation",
      "cell_type": "markdown",
      "source": "* Shuffle the elements of the table so that `True` and `False` are scattered.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "considerable-montgomery",
      "cell_type": "code",
      "source": "rng = np.random.default_rng(42)\nrng.shuffle(missing_samples)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [],
      "execution_count": 6
    },
    {
      "id": "blessed-excerpt",
      "cell_type": "markdown",
      "source": "* For each of the lines in which we will insert a space, we randomly select a column whose contents we will erase.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "coastal-constant",
      "cell_type": "code",
      "source": "missing_features = rng.integers(0, n_features, n_missing_samples)\nprint(missing_features.shape, missing_features)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(15480,) [4 3 1 ... 7 2 1]\n"
          ]
        }
      ],
      "execution_count": 7
    },
    {
      "id": "superb-pathology",
      "cell_type": "markdown",
      "source": "* We change the values ​​of `cal_housing`, putting `np.nan` in the selected rows and columns.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "maritime-blowing",
      "cell_type": "code",
      "source": "cal_housing.values[missing_samples, missing_features] = np.nan\ncal_housing",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
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              "      <th>longitude</th>\n",
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            "text/plain": [
              "       longitude  latitude  housingMedianAge  totalRooms  totalBedrooms  \\\n",
              "0        -122.23     37.88              41.0       880.0            NaN   \n",
              "1        -122.22     37.86              21.0         NaN         1106.0   \n",
              "2        -122.24       NaN              52.0      1467.0          190.0   \n",
              "3        -122.25     37.85              52.0      1274.0          235.0   \n",
              "4            NaN     37.85              52.0      1627.0          280.0   \n",
              "...          ...       ...               ...         ...            ...   \n",
              "20635        NaN     39.48              25.0      1665.0          374.0   \n",
              "20636    -121.21     39.49              18.0         NaN          150.0   \n",
              "20637    -121.22     39.43              17.0      2254.0          485.0   \n",
              "20638    -121.32     39.43               NaN      1860.0          409.0   \n",
              "20639    -121.24       NaN              16.0      2785.0          616.0   \n",
              "\n",
              "       population  households  medianIncome  medianHouseValue  \n",
              "0           322.0       126.0        8.3252          452600.0  \n",
              "1          2401.0      1138.0        8.3014          358500.0  \n",
              "2           496.0       177.0        7.2574          352100.0  \n",
              "3           558.0         NaN        5.6431          341300.0  \n",
              "4           565.0       259.0        3.8462          342200.0  \n",
              "...           ...         ...           ...               ...  \n",
              "20635       845.0       330.0        1.5603           78100.0  \n",
              "20636       356.0       114.0        2.5568           77100.0  \n",
              "20637      1007.0       433.0           NaN           92300.0  \n",
              "20638       741.0       349.0        1.8672           84700.0  \n",
              "20639      1387.0       530.0        2.3886           89400.0  \n",
              "\n",
              "[20640 rows x 9 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 8
    },
    {
      "id": "clinical-resource",
      "cell_type": "markdown",
      "source": "* The main thing we can do when we have gaps is to establish the existence of non-existence.\n\n* If we want pandas to check if a value does not exist, we can use the `isna()` method.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "moral-guitar",
      "cell_type": "code",
      "source": "cal_housing['totalRooms'].isna()",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        False\n",
              "1         True\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "20635    False\n",
              "20636     True\n",
              "20637    False\n",
              "20638    False\n",
              "20639    False\n",
              "Name: totalRooms, Length: 20640, dtype: bool"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 9
    },
    {
      "id": "japanese-coordinate",
      "cell_type": "markdown",
      "source": "* A synonym of the `isna()` method is the `isnull()` method.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "statistical-operation",
      "cell_type": "code",
      "source": "cal_housing['totalRooms'].isnull()",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        False\n",
              "1         True\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "20635    False\n",
              "20636     True\n",
              "20637    False\n",
              "20638    False\n",
              "20639    False\n",
              "Name: totalRooms, Length: 20640, dtype: bool"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 10
    },
    {
      "id": "distinguished-engagement",
      "cell_type": "markdown",
      "source": "* Conversely, if we want to check that a value exists, we can use the `notna()` method.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "occasional-surface",
      "cell_type": "code",
      "source": "cal_housing['latitude'].notna()",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         True\n",
              "1         True\n",
              "2        False\n",
              "3         True\n",
              "4         True\n",
              "         ...  \n",
              "20635     True\n",
              "20636     True\n",
              "20637     True\n",
              "20638     True\n",
              "20639    False\n",
              "Name: latitude, Length: 20640, dtype: bool"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 11
    },
    {
      "id": "happy-couple",
      "cell_type": "markdown",
      "source": "* Synonymous with `notna()` is the `notnull()` method.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "multiple-graham",
      "cell_type": "code",
      "source": "cal_housing['latitude'].notnull()",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         True\n",
              "1         True\n",
              "2        False\n",
              "3         True\n",
              "4         True\n",
              "         ...  \n",
              "20635     True\n",
              "20636     True\n",
              "20637     True\n",
              "20638     True\n",
              "20639    False\n",
              "Name: latitude, Length: 20640, dtype: bool"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 12
    },
    {
      "id": "anonymous-infection",
      "cell_type": "markdown",
      "source": "* If we want to count how many spaces there are, we can sum the result of `isna()`, since in the sum `True` values ​​become 1 and `False` values ​​become 0.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "touched-artist",
      "cell_type": "code",
      "source": "cal_housing['latitude'].isna().sum()",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1680"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 13
    },
    {
      "id": "negative-request",
      "cell_type": "markdown",
      "source": "* Based on these methods we can filter our data so that we don't have gaps.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "pressed-artist",
      "cell_type": "code",
      "source": "cal_housing[cal_housing['medianHouseValue'].notna()]",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
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              "      <td>1.5603</td>\n",
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              "    <tr>\n",
              "      <th>20636</th>\n",
              "      <td>-121.21</td>\n",
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              "<p>18890 rows × 9 columns</p>\n",
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              "       longitude  latitude  housingMedianAge  totalRooms  totalBedrooms  \\\n",
              "0        -122.23     37.88              41.0       880.0            NaN   \n",
              "1        -122.22     37.86              21.0         NaN         1106.0   \n",
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              "...          ...       ...               ...         ...            ...   \n",
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              "20638    -121.32     39.43               NaN      1860.0          409.0   \n",
              "20639    -121.24       NaN              16.0      2785.0          616.0   \n",
              "\n",
              "       population  households  medianIncome  medianHouseValue  \n",
              "0           322.0       126.0        8.3252          452600.0  \n",
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              "2           496.0       177.0        7.2574          352100.0  \n",
              "3           558.0         NaN        5.6431          341300.0  \n",
              "4           565.0       259.0        3.8462          342200.0  \n",
              "...           ...         ...           ...               ...  \n",
              "20635       845.0       330.0        1.5603           78100.0  \n",
              "20636       356.0       114.0        2.5568           77100.0  \n",
              "20637      1007.0       433.0           NaN           92300.0  \n",
              "20638       741.0       349.0        1.8672           84700.0  \n",
              "20639      1387.0       530.0        2.3886           89400.0  \n",
              "\n",
              "[18890 rows x 9 columns]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 14
    },
    {
      "id": "painted-inventory",
      "cell_type": "markdown",
      "source": "* Another solution is to drop the lines (entries) where there are spaces using the `dropna()` method.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "whole-variable",
      "cell_type": "code",
      "source": "cal_housing.dropna()",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
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              "      <td>59.0</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>162500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20632</th>\n",
              "      <td>-121.45</td>\n",
              "      <td>39.26</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2319.0</td>\n",
              "      <td>416.0</td>\n",
              "      <td>1047.0</td>\n",
              "      <td>385.0</td>\n",
              "      <td>3.1250</td>\n",
              "      <td>115600.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5160 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  housingMedianAge  totalRooms  totalBedrooms  \\\n",
              "7        -122.25     37.84              52.0      3104.0          687.0   \n",
              "11       -122.26     37.85              52.0      3503.0          752.0   \n",
              "17       -122.27     37.85              52.0      1228.0          293.0   \n",
              "20       -122.27     37.85              40.0       751.0          184.0   \n",
              "21       -122.27     37.85              42.0      1639.0          367.0   \n",
              "...          ...       ...               ...         ...            ...   \n",
              "20617    -121.53     39.06              20.0       561.0          109.0   \n",
              "20620    -121.48     39.05              40.0       198.0           41.0   \n",
              "20623    -121.37     39.03              32.0      1158.0          244.0   \n",
              "20627    -121.32     39.13               5.0       358.0           65.0   \n",
              "20632    -121.45     39.26              15.0      2319.0          416.0   \n",
              "\n",
              "       population  households  medianIncome  medianHouseValue  \n",
              "7          1157.0       647.0        3.1200          241400.0  \n",
              "11         1504.0       734.0        3.2705          241800.0  \n",
              "17          648.0       303.0        2.1202          155500.0  \n",
              "20          409.0       166.0        1.3578          147500.0  \n",
              "21          929.0       366.0        1.7135          159800.0  \n",
              "...           ...         ...           ...               ...  \n",
              "20617       308.0       114.0        3.3021           70800.0  \n",
              "20620       151.0        48.0        4.5625          100000.0  \n",
              "20623       598.0       227.0        2.8235           65500.0  \n",
              "20627       169.0        59.0        3.0000          162500.0  \n",
              "20632      1047.0       385.0        3.1250          115600.0  \n",
              "\n",
              "[5160 rows x 9 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 15
    },
    {
      "id": "national-disclaimer",
      "cell_type": "markdown",
      "source": "* The problem is that such an approach can be overwhelming because we lose a large amount of our data.\n\n* We may eventually wish to remove lines where there are spaces only in certain columns.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "endless-customer",
      "cell_type": "code",
      "source": "cal_housing.dropna(subset=['medianHouseValue', 'medianIncome'])",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
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              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
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              "      <td>322.0</td>\n",
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              "      <td>8.3014</td>\n",
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              "    <tr>\n",
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              "      <td>-122.24</td>\n",
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              "      <td>52.0</td>\n",
              "      <td>1467.0</td>\n",
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              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558.0</td>\n",
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              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
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              "      <td>1627.0</td>\n",
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              "      <th>20633</th>\n",
              "      <td>-121.53</td>\n",
              "      <td>39.19</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2080.0</td>\n",
              "      <td>412.0</td>\n",
              "      <td>1082.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.5495</td>\n",
              "      <td>98300.0</td>\n",
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              "    <tr>\n",
              "      <th>20635</th>\n",
              "      <td>NaN</td>\n",
              "      <td>39.48</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1665.0</td>\n",
              "      <td>374.0</td>\n",
              "      <td>845.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>1.5603</td>\n",
              "      <td>78100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20636</th>\n",
              "      <td>-121.21</td>\n",
              "      <td>39.49</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>356.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>2.5568</td>\n",
              "      <td>77100.0</td>\n",
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              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>-121.32</td>\n",
              "      <td>39.43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1860.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>741.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>1.8672</td>\n",
              "      <td>84700.0</td>\n",
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              "    <tr>\n",
              "      <th>20639</th>\n",
              "      <td>-121.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2785.0</td>\n",
              "      <td>616.0</td>\n",
              "      <td>1387.0</td>\n",
              "      <td>530.0</td>\n",
              "      <td>2.3886</td>\n",
              "      <td>89400.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>17092 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  housingMedianAge  totalRooms  totalBedrooms  \\\n",
              "0        -122.23     37.88              41.0       880.0            NaN   \n",
              "1        -122.22     37.86              21.0         NaN         1106.0   \n",
              "2        -122.24       NaN              52.0      1467.0          190.0   \n",
              "3        -122.25     37.85              52.0      1274.0          235.0   \n",
              "4            NaN     37.85              52.0      1627.0          280.0   \n",
              "...          ...       ...               ...         ...            ...   \n",
              "20633    -121.53     39.19              27.0      2080.0          412.0   \n",
              "20635        NaN     39.48              25.0      1665.0          374.0   \n",
              "20636    -121.21     39.49              18.0         NaN          150.0   \n",
              "20638    -121.32     39.43               NaN      1860.0          409.0   \n",
              "20639    -121.24       NaN              16.0      2785.0          616.0   \n",
              "\n",
              "       population  households  medianIncome  medianHouseValue  \n",
              "0           322.0       126.0        8.3252          452600.0  \n",
              "1          2401.0      1138.0        8.3014          358500.0  \n",
              "2           496.0       177.0        7.2574          352100.0  \n",
              "3           558.0         NaN        5.6431          341300.0  \n",
              "4           565.0       259.0        3.8462          342200.0  \n",
              "...           ...         ...           ...               ...  \n",
              "20633      1082.0         NaN        2.5495           98300.0  \n",
              "20635       845.0       330.0        1.5603           78100.0  \n",
              "20636       356.0       114.0        2.5568           77100.0  \n",
              "20638       741.0       349.0        1.8672           84700.0  \n",
              "20639      1387.0       530.0        2.3886           89400.0  \n",
              "\n",
              "[17092 rows x 9 columns]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 16
    },
    {
      "id": "organic-bikini",
      "cell_type": "markdown",
      "source": "* If we don't want to throw away data, we can mutate our data, giving a default value where there are spaces, with the `fillna()` method.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "critical-classification",
      "cell_type": "code",
      "source": "cal_housing.fillna(0)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
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              "      <td>8.3014</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>52.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
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              "      <td>1627.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>259.0</td>\n",
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              "      <td>342200.0</td>\n",
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              "      <td>39.48</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1665.0</td>\n",
              "      <td>374.0</td>\n",
              "      <td>845.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>1.5603</td>\n",
              "      <td>78100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20636</th>\n",
              "      <td>-121.21</td>\n",
              "      <td>39.49</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>356.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>2.5568</td>\n",
              "      <td>77100.0</td>\n",
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              "    <tr>\n",
              "      <th>20637</th>\n",
              "      <td>-121.22</td>\n",
              "      <td>39.43</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2254.0</td>\n",
              "      <td>485.0</td>\n",
              "      <td>1007.0</td>\n",
              "      <td>433.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>92300.0</td>\n",
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              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>-121.32</td>\n",
              "      <td>39.43</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1860.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>741.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>1.8672</td>\n",
              "      <td>84700.0</td>\n",
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              "    <tr>\n",
              "      <th>20639</th>\n",
              "      <td>-121.24</td>\n",
              "      <td>0.00</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2785.0</td>\n",
              "      <td>616.0</td>\n",
              "      <td>1387.0</td>\n",
              "      <td>530.0</td>\n",
              "      <td>2.3886</td>\n",
              "      <td>89400.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20640 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  housingMedianAge  totalRooms  totalBedrooms  \\\n",
              "0        -122.23     37.88              41.0       880.0            0.0   \n",
              "1        -122.22     37.86              21.0         0.0         1106.0   \n",
              "2        -122.24      0.00              52.0      1467.0          190.0   \n",
              "3        -122.25     37.85              52.0      1274.0          235.0   \n",
              "4           0.00     37.85              52.0      1627.0          280.0   \n",
              "...          ...       ...               ...         ...            ...   \n",
              "20635       0.00     39.48              25.0      1665.0          374.0   \n",
              "20636    -121.21     39.49              18.0         0.0          150.0   \n",
              "20637    -121.22     39.43              17.0      2254.0          485.0   \n",
              "20638    -121.32     39.43               0.0      1860.0          409.0   \n",
              "20639    -121.24      0.00              16.0      2785.0          616.0   \n",
              "\n",
              "       population  households  medianIncome  medianHouseValue  \n",
              "0           322.0       126.0        8.3252          452600.0  \n",
              "1          2401.0      1138.0        8.3014          358500.0  \n",
              "2           496.0       177.0        7.2574          352100.0  \n",
              "3           558.0         0.0        5.6431          341300.0  \n",
              "4           565.0       259.0        3.8462          342200.0  \n",
              "...           ...         ...           ...               ...  \n",
              "20635       845.0       330.0        1.5603           78100.0  \n",
              "20636       356.0       114.0        2.5568           77100.0  \n",
              "20637      1007.0       433.0        0.0000           92300.0  \n",
              "20638       741.0       349.0        1.8672           84700.0  \n",
              "20639      1387.0       530.0        2.3886           89400.0  \n",
              "\n",
              "[20640 rows x 9 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 17
    },
    {
      "id": "acquired-elements",
      "cell_type": "markdown",
      "source": "* If we still don't want to insert the default value everywhere, we can select the subset of columns in which to use it.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "following-culture",
      "cell_type": "code",
      "source": "cal_housing[['medianIncome', 'medianHouseValue']].fillna(0)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>medianIncome</th>\n",
              "      <th>medianHouseValue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>452600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>358500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>342200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20635</th>\n",
              "      <td>1.5603</td>\n",
              "      <td>78100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20636</th>\n",
              "      <td>2.5568</td>\n",
              "      <td>77100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20637</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>92300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>1.8672</td>\n",
              "      <td>84700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20639</th>\n",
              "      <td>2.3886</td>\n",
              "      <td>89400.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20640 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       medianIncome  medianHouseValue\n",
              "0            8.3252          452600.0\n",
              "1            8.3014          358500.0\n",
              "2            7.2574          352100.0\n",
              "3            5.6431          341300.0\n",
              "4            3.8462          342200.0\n",
              "...             ...               ...\n",
              "20635        1.5603           78100.0\n",
              "20636        2.5568           77100.0\n",
              "20637        0.0000           92300.0\n",
              "20638        1.8672           84700.0\n",
              "20639        2.3886           89400.0\n",
              "\n",
              "[20640 rows x 2 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 18
    },
    {
      "id": "infinite-dominant",
      "cell_type": "markdown",
      "source": "* Maybe filling in the blanks can be done a bit more intelligently.\n\n* For example, instead of a predetermined value to use the average, or more generally values ​​that will result from calculations.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "abstract-american",
      "cell_type": "code",
      "source": "cal_housing.fillna(cal_housing.mean())",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housingMedianAge</th>\n",
              "      <th>totalRooms</th>\n",
              "      <th>totalBedrooms</th>\n",
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              "      <td>35.625127</td>\n",
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              "      <td>352100.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.250000</td>\n",
              "      <td>37.850000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>1274.000000</td>\n",
              "      <td>235.000000</td>\n",
              "      <td>558.0</td>\n",
              "      <td>499.158</td>\n",
              "      <td>5.643100</td>\n",
              "      <td>341300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-119.574345</td>\n",
              "      <td>37.850000</td>\n",
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              "      <td>1627.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>565.0</td>\n",
              "      <td>259.000</td>\n",
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              "      <td>39.480000</td>\n",
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              "      <td>1665.000000</td>\n",
              "      <td>374.000000</td>\n",
              "      <td>845.0</td>\n",
              "      <td>330.000</td>\n",
              "      <td>1.560300</td>\n",
              "      <td>78100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20636</th>\n",
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              "      <td>18.000000</td>\n",
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              "      <td>150.000000</td>\n",
              "      <td>356.0</td>\n",
              "      <td>114.000</td>\n",
              "      <td>2.556800</td>\n",
              "      <td>77100.0</td>\n",
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              "      <td>485.000000</td>\n",
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              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>-121.320000</td>\n",
              "      <td>39.430000</td>\n",
              "      <td>28.662049</td>\n",
              "      <td>1860.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>741.0</td>\n",
              "      <td>349.000</td>\n",
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              "      <td>1387.0</td>\n",
              "      <td>530.000</td>\n",
              "      <td>2.388600</td>\n",
              "      <td>89400.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>20640 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        longitude   latitude  housingMedianAge   totalRooms  totalBedrooms  \\\n",
              "0     -122.230000  37.880000         41.000000   880.000000     539.232767   \n",
              "1     -122.220000  37.860000         21.000000  2641.457729    1106.000000   \n",
              "2     -122.240000  35.625127         52.000000  1467.000000     190.000000   \n",
              "3     -122.250000  37.850000         52.000000  1274.000000     235.000000   \n",
              "4     -119.574345  37.850000         52.000000  1627.000000     280.000000   \n",
              "...           ...        ...               ...          ...            ...   \n",
              "20635 -119.574345  39.480000         25.000000  1665.000000     374.000000   \n",
              "20636 -121.210000  39.490000         18.000000  2641.457729     150.000000   \n",
              "20637 -121.220000  39.430000         17.000000  2254.000000     485.000000   \n",
              "20638 -121.320000  39.430000         28.662049  1860.000000     409.000000   \n",
              "20639 -121.240000  35.625127         16.000000  2785.000000     616.000000   \n",
              "\n",
              "       population  households  medianIncome  medianHouseValue  \n",
              "0           322.0     126.000      8.325200          452600.0  \n",
              "1          2401.0    1138.000      8.301400          358500.0  \n",
              "2           496.0     177.000      7.257400          352100.0  \n",
              "3           558.0     499.158      5.643100          341300.0  \n",
              "4           565.0     259.000      3.846200          342200.0  \n",
              "...           ...         ...           ...               ...  \n",
              "20635       845.0     330.000      1.560300           78100.0  \n",
              "20636       356.0     114.000      2.556800           77100.0  \n",
              "20637      1007.0     433.000      3.870723           92300.0  \n",
              "20638       741.0     349.000      1.867200           84700.0  \n",
              "20639      1387.0     530.000      2.388600           89400.0  \n",
              "\n",
              "[20640 rows x 9 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 19
    },
    {
      "id": "changed-reference",
      "cell_type": "markdown",
      "source": "* If we don't want to apply it to the whole `DataFrame`, we can still select subset of columns.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "minimal-spell",
      "cell_type": "code",
      "source": "cal_housing.fillna(cal_housing.mean()[\"medianIncome\":\"medianHouseValue\"])",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housingMedianAge</th>\n",
              "      <th>totalRooms</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>235.0</td>\n",
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              "      <td>52.0</td>\n",
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              "      <td>1665.0</td>\n",
              "      <td>374.0</td>\n",
              "      <td>845.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>1.560300</td>\n",
              "      <td>78100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20636</th>\n",
              "      <td>-121.21</td>\n",
              "      <td>39.49</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>356.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>2.556800</td>\n",
              "      <td>77100.0</td>\n",
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              "    <tr>\n",
              "      <th>20637</th>\n",
              "      <td>-121.22</td>\n",
              "      <td>39.43</td>\n",
              "      <td>17.0</td>\n",
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              "      <td>485.0</td>\n",
              "      <td>1007.0</td>\n",
              "      <td>433.0</td>\n",
              "      <td>3.870723</td>\n",
              "      <td>92300.0</td>\n",
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              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>-121.32</td>\n",
              "      <td>39.43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1860.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>741.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>1.867200</td>\n",
              "      <td>84700.0</td>\n",
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              "    <tr>\n",
              "      <th>20639</th>\n",
              "      <td>-121.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2785.0</td>\n",
              "      <td>616.0</td>\n",
              "      <td>1387.0</td>\n",
              "      <td>530.0</td>\n",
              "      <td>2.388600</td>\n",
              "      <td>89400.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20640 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  housingMedianAge  totalRooms  totalBedrooms  \\\n",
              "0        -122.23     37.88              41.0       880.0            NaN   \n",
              "1        -122.22     37.86              21.0         NaN         1106.0   \n",
              "2        -122.24       NaN              52.0      1467.0          190.0   \n",
              "3        -122.25     37.85              52.0      1274.0          235.0   \n",
              "4            NaN     37.85              52.0      1627.0          280.0   \n",
              "...          ...       ...               ...         ...            ...   \n",
              "20635        NaN     39.48              25.0      1665.0          374.0   \n",
              "20636    -121.21     39.49              18.0         NaN          150.0   \n",
              "20637    -121.22     39.43              17.0      2254.0          485.0   \n",
              "20638    -121.32     39.43               NaN      1860.0          409.0   \n",
              "20639    -121.24       NaN              16.0      2785.0          616.0   \n",
              "\n",
              "       population  households  medianIncome  medianHouseValue  \n",
              "0           322.0       126.0      8.325200          452600.0  \n",
              "1          2401.0      1138.0      8.301400          358500.0  \n",
              "2           496.0       177.0      7.257400          352100.0  \n",
              "3           558.0         NaN      5.643100          341300.0  \n",
              "4           565.0       259.0      3.846200          342200.0  \n",
              "...           ...         ...           ...               ...  \n",
              "20635       845.0       330.0      1.560300           78100.0  \n",
              "20636       356.0       114.0      2.556800           77100.0  \n",
              "20637      1007.0       433.0      3.870723           92300.0  \n",
              "20638       741.0       349.0      1.867200           84700.0  \n",
              "20639      1387.0       530.0      2.388600           89400.0  \n",
              "\n",
              "[20640 rows x 9 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 20
    },
    {
      "id": "proprietary-indicator",
      "cell_type": "markdown",
      "source": "* With the `fillna()` method we can use other strategies for handling gaps.\n\n* It is more convenient to see them with another, smaller and artificial `DataFrame`.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "institutional-regard",
      "cell_type": "code",
      "source": "df = pd.DataFrame(\n    np.random.randn(5, 3),\n    columns=[\"a\", \"b\", \"c\"],\n)\ndf",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.098835</td>\n",
              "      <td>0.225479</td>\n",
              "      <td>0.859195</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.246654</td>\n",
              "      <td>-0.156775</td>\n",
              "      <td>-0.244600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.348405</td>\n",
              "      <td>-1.791612</td>\n",
              "      <td>-0.500346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.260509</td>\n",
              "      <td>0.851701</td>\n",
              "      <td>1.177573</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          a         b         c\n",
              "0  0.664319 -1.323232  0.585482\n",
              "1  0.098835  0.225479  0.859195\n",
              "2 -0.246654 -0.156775 -0.244600\n",
              "3 -0.348405 -1.791612 -0.500346\n",
              "4 -1.260509  0.851701  1.177573"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 21
    },
    {
      "id": "concerned-representative",
      "cell_type": "markdown",
      "source": "* We will insert spaces by making a mask of `NaN' values.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "pacific-canal",
      "cell_type": "code",
      "source": "np.random.seed(0)\nnan_mask = np.where(np.random.randint(low=0, high=1+1, size=(5,3)) == 1, 0, np.nan)\nnan_mask",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[nan,  0.,  0.],\n",
              "       [nan,  0.,  0.],\n",
              "       [ 0.,  0.,  0.],\n",
              "       [ 0.,  0., nan],\n",
              "       [nan,  0., nan]])"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 22
    },
    {
      "id": "apart-belize",
      "cell_type": "markdown",
      "source": "* We add the mask to our `DataFrame`.\n\n* By chance we also see a basic characteristic of non-existence: non-existence spreads.\n\n* Operations with non-existent data give non-existent results (unless the documentation says otherwise).",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "damaged-equation",
      "cell_type": "code",
      "source": "df",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
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              "          a         b         c\n",
              "0  0.664319 -1.323232  0.585482\n",
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              "4 -1.260509  0.851701  1.177573"
            ]
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          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 23
    },
    {
      "id": "alpha-engineer",
      "cell_type": "code",
      "source": "df = df + nan_mask\ndf",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
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              "      <th>3</th>\n",
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            "text/plain": [
              "          a         b         c\n",
              "0       NaN -1.323232  0.585482\n",
              "1       NaN  0.225479  0.859195\n",
              "2 -0.246654 -0.156775 -0.244600\n",
              "3 -0.348405 -1.791612       NaN\n",
              "4       NaN  0.851701       NaN"
            ]
          },
          "execution_count": 24,
          "metadata": {},
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        }
      ],
      "execution_count": 24
    },
    {
      "id": "bored-lotus",
      "cell_type": "markdown",
      "source": "* Let us confirm again what we said before, that non-existence is contagious.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "constant-victor",
      "cell_type": "code",
      "source": "df",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
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              "      <th>3</th>\n",
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            ],
            "text/plain": [
              "          a         b         c\n",
              "0       NaN -1.323232  0.585482\n",
              "1       NaN  0.225479  0.859195\n",
              "2 -0.246654 -0.156775 -0.244600\n",
              "3 -0.348405 -1.791612       NaN\n",
              "4       NaN  0.851701       NaN"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 25
    },
    {
      "id": "rolled-holocaust",
      "cell_type": "code",
      "source": "df.a + df.b",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         NaN\n",
              "1         NaN\n",
              "2   -0.403429\n",
              "3   -2.140017\n",
              "4         NaN\n",
              "dtype: float64"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 26
    },
    {
      "id": "foster-contractor",
      "cell_type": "markdown",
      "source": "* Giving `fillna(method='pad')` the gap is filled with the value on the previous line.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "extreme-donor",
      "cell_type": "code",
      "source": "df",
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "          a         b         c\n",
              "0       NaN -1.323232  0.585482\n",
              "1       NaN  0.225479  0.859195\n",
              "2 -0.246654 -0.156775 -0.244600\n",
              "3 -0.348405 -1.791612       NaN\n",
              "4       NaN  0.851701       NaN"
            ]
          },
          "execution_count": 27,
          "metadata": {},
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        }
      ],
      "execution_count": 27
    },
    {
      "id": "united-consensus",
      "cell_type": "code",
      "source": "df.fillna(method='pad')",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "</table>\n",
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            ],
            "text/plain": [
              "          a         b         c\n",
              "0       NaN -1.323232  0.585482\n",
              "1       NaN  0.225479  0.859195\n",
              "2 -0.246654 -0.156775 -0.244600\n",
              "3 -0.348405 -1.791612 -0.244600\n",
              "4 -0.348405  0.851701 -0.244600"
            ]
          },
          "execution_count": 28,
          "metadata": {},
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        }
      ],
      "execution_count": 28
    },
    {
      "id": "controlled-holiday",
      "cell_type": "markdown",
      "source": "* We can do the same by giving `fillna(method='ffill')` (forward fill).",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "obvious-stylus",
      "cell_type": "code",
      "source": "df",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
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              "          a         b         c\n",
              "0       NaN -1.323232  0.585482\n",
              "1       NaN  0.225479  0.859195\n",
              "2 -0.246654 -0.156775 -0.244600\n",
              "3 -0.348405 -1.791612       NaN\n",
              "4       NaN  0.851701       NaN"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 29
    },
    {
      "id": "comic-student",
      "cell_type": "code",
      "source": "df.fillna(method='ffill')",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          a         b         c\n",
              "0       NaN -1.323232  0.585482\n",
              "1       NaN  0.225479  0.859195\n",
              "2 -0.246654 -0.156775 -0.244600\n",
              "3 -0.348405 -1.791612 -0.244600\n",
              "4 -0.348405  0.851701 -0.244600"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 30
    },
    {
      "id": "final-mercury",
      "cell_type": "markdown",
      "source": "* If we want to fill in reverse, i.e. the space is filled by the value of the next line, we use `fillna(method='bfill')` (backfill).",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "artificial-publisher",
      "cell_type": "code",
      "source": "df",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
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              "          a         b         c\n",
              "0       NaN -1.323232  0.585482\n",
              "1       NaN  0.225479  0.859195\n",
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              "3 -0.348405 -1.791612       NaN\n",
              "4       NaN  0.851701       NaN"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 31
    },
    {
      "id": "informational-curve",
      "cell_type": "code",
      "source": "df.fillna(method='bfill')",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
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              "      <th>3</th>\n",
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              "      <td>NaN</td>\n",
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              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          a         b         c\n",
              "0 -0.246654 -1.323232  0.585482\n",
              "1 -0.246654  0.225479  0.859195\n",
              "2 -0.246654 -0.156775 -0.244600\n",
              "3 -0.348405 -1.791612       NaN\n",
              "4       NaN  0.851701       NaN"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 32
    },
    {
      "id": "historic-turkey",
      "cell_type": "markdown",
      "source": "* We can do the same by giving `fillna(method='backfill')`.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "retained-casting",
      "cell_type": "code",
      "source": "df",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>a</th>\n",
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              "      <th>2</th>\n",
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              "      <td>-0.156775</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.348405</td>\n",
              "      <td>-1.791612</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.851701</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          a         b         c\n",
              "0       NaN -1.323232  0.585482\n",
              "1       NaN  0.225479  0.859195\n",
              "2 -0.246654 -0.156775 -0.244600\n",
              "3 -0.348405 -1.791612       NaN\n",
              "4       NaN  0.851701       NaN"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 33
    },
    {
      "id": "nominated-vermont",
      "cell_type": "code",
      "source": "df.fillna(method='backfill')",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.246654</td>\n",
              "      <td>-1.323232</td>\n",
              "      <td>0.585482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.246654</td>\n",
              "      <td>0.225479</td>\n",
              "      <td>0.859195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.246654</td>\n",
              "      <td>-0.156775</td>\n",
              "      <td>-0.244600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.348405</td>\n",
              "      <td>-1.791612</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.851701</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          a         b         c\n",
              "0 -0.246654 -1.323232  0.585482\n",
              "1 -0.246654  0.225479  0.859195\n",
              "2 -0.246654 -0.156775 -0.244600\n",
              "3 -0.348405 -1.791612       NaN\n",
              "4       NaN  0.851701       NaN"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 34
    },
    {
      "id": "historical-faith",
      "cell_type": "markdown",
      "source": "* Another way to handle missing values ​​is with *interpolation*.\n\n* This can be particularly useful in cases where the values ​​have a continuum, such as in *time series*.\n\n* pandas supports various interpolation methods. Here we will see the simplest, the *linear interpolation* (linear interpolation).",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "radical-fruit",
      "cell_type": "markdown",
      "source": "* To see interference in action, we will generate some synthetic data that will simulate a *random walk*.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "promising-married",
      "cell_type": "code",
      "source": "steps = np.random.randint(-1, 1+1, size=365)\ndata = steps.cumsum()\n\nts = pd.Series(data=data,\n               index=pd.date_range(start='2021-01-01', end='2021-12-31', freq='D'))\nts",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2021-01-01    -1\n",
              "2021-01-02    -2\n",
              "2021-01-03    -3\n",
              "2021-01-04    -2\n",
              "2021-01-05    -2\n",
              "              ..\n",
              "2021-12-27   -15\n",
              "2021-12-28   -14\n",
              "2021-12-29   -15\n",
              "2021-12-30   -14\n",
              "2021-12-31   -15\n",
              "Freq: D, Length: 365, dtype: int64"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 35
    },
    {
      "id": "fuzzy-peoples",
      "cell_type": "code",
      "source": "_ = ts.plot(figsize=(12, 10))",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "execution_count": 36
    },
    {
      "id": "capital-appendix",
      "cell_type": "markdown",
      "source": "* We will again randomly delete some of the data (about half).",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "occupational-advocacy",
      "cell_type": "code",
      "source": "nan_mask = np.where(np.random.randint(low=0, high=1+1, size=(365)) == 1, 0, np.nan)\nts = ts + nan_mask\nprint(ts.isna().sum())\nts",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "179\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "2021-01-01    -1.0\n",
              "2021-01-02     NaN\n",
              "2021-01-03    -3.0\n",
              "2021-01-04     NaN\n",
              "2021-01-05    -2.0\n",
              "              ... \n",
              "2021-12-27     NaN\n",
              "2021-12-28     NaN\n",
              "2021-12-29   -15.0\n",
              "2021-12-30     NaN\n",
              "2021-12-31     NaN\n",
              "Freq: D, Length: 365, dtype: float64"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 37
    },
    {
      "id": "alien-romance",
      "cell_type": "code",
      "source": "_ = ts.plot(figsize=(12, 10))",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "execution_count": 38
    },
    {
      "id": "loose-great",
      "cell_type": "markdown",
      "source": "* This is what we get if we use linear interpolation with the `interpolate()` method.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "numerous-estate",
      "cell_type": "code",
      "source": "ts = ts.interpolate()\nts",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2021-01-01    -1.000000\n",
              "2021-01-02    -2.000000\n",
              "2021-01-03    -3.000000\n",
              "2021-01-04    -2.500000\n",
              "2021-01-05    -2.000000\n",
              "                ...    \n",
              "2021-12-27   -14.333333\n",
              "2021-12-28   -14.666667\n",
              "2021-12-29   -15.000000\n",
              "2021-12-30   -15.000000\n",
              "2021-12-31   -15.000000\n",
              "Freq: D, Length: 365, dtype: float64"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 39
    },
    {
      "id": "executive-window",
      "cell_type": "code",
      "source": "_ = ts.plot(figsize=(12, 10))",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "execution_count": 40
    },
    {
      "id": "pursuant-solid",
      "cell_type": "markdown",
      "source": "* Additional strategies for handling data with gaps are provided by the [scikit-learn](https://scikit-learn.org/stable/) library, via imputation mechanisms.\n\n* The simplest of these are given by the `SimpleImputer` class, which offers the alternatives:\n\n * `mean`\n\n * `median`\n\n * `most_frequent'\n\n * `constant'",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "democratic-giant",
      "cell_type": "code",
      "source": "from sklearn.impute import SimpleImputer\n\nimputer = SimpleImputer(missing_values=np.nan, strategy=\"constant\", fill_value=0)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [],
      "execution_count": 41
    },
    {
      "id": "trying-flooring",
      "cell_type": "markdown",
      "source": "* Back to the data from California:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "distinguished-executive",
      "cell_type": "code",
      "source": "cal_housing",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housingMedianAge</th>\n",
              "      <th>totalRooms</th>\n",
              "      <th>totalBedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>medianIncome</th>\n",
              "      <th>medianHouseValue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.23</td>\n",
              "      <td>37.88</td>\n",
              "      <td>41.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>322.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>8.3252</td>\n",
              "      <td>452600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-122.22</td>\n",
              "      <td>37.86</td>\n",
              "      <td>21.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>8.3014</td>\n",
              "      <td>358500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-122.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1467.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>496.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1627.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>3.8462</td>\n",
              "      <td>342200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20635</th>\n",
              "      <td>NaN</td>\n",
              "      <td>39.48</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1665.0</td>\n",
              "      <td>374.0</td>\n",
              "      <td>845.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>1.5603</td>\n",
              "      <td>78100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20636</th>\n",
              "      <td>-121.21</td>\n",
              "      <td>39.49</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>356.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>2.5568</td>\n",
              "      <td>77100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20637</th>\n",
              "      <td>-121.22</td>\n",
              "      <td>39.43</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2254.0</td>\n",
              "      <td>485.0</td>\n",
              "      <td>1007.0</td>\n",
              "      <td>433.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>92300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>-121.32</td>\n",
              "      <td>39.43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1860.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>741.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>1.8672</td>\n",
              "      <td>84700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20639</th>\n",
              "      <td>-121.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2785.0</td>\n",
              "      <td>616.0</td>\n",
              "      <td>1387.0</td>\n",
              "      <td>530.0</td>\n",
              "      <td>2.3886</td>\n",
              "      <td>89400.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20640 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  housingMedianAge  totalRooms  totalBedrooms  \\\n",
              "0        -122.23     37.88              41.0       880.0            NaN   \n",
              "1        -122.22     37.86              21.0         NaN         1106.0   \n",
              "2        -122.24       NaN              52.0      1467.0          190.0   \n",
              "3        -122.25     37.85              52.0      1274.0          235.0   \n",
              "4            NaN     37.85              52.0      1627.0          280.0   \n",
              "...          ...       ...               ...         ...            ...   \n",
              "20635        NaN     39.48              25.0      1665.0          374.0   \n",
              "20636    -121.21     39.49              18.0         NaN          150.0   \n",
              "20637    -121.22     39.43              17.0      2254.0          485.0   \n",
              "20638    -121.32     39.43               NaN      1860.0          409.0   \n",
              "20639    -121.24       NaN              16.0      2785.0          616.0   \n",
              "\n",
              "       population  households  medianIncome  medianHouseValue  \n",
              "0           322.0       126.0        8.3252          452600.0  \n",
              "1          2401.0      1138.0        8.3014          358500.0  \n",
              "2           496.0       177.0        7.2574          352100.0  \n",
              "3           558.0         NaN        5.6431          341300.0  \n",
              "4           565.0       259.0        3.8462          342200.0  \n",
              "...           ...         ...           ...               ...  \n",
              "20635       845.0       330.0        1.5603           78100.0  \n",
              "20636       356.0       114.0        2.5568           77100.0  \n",
              "20637      1007.0       433.0           NaN           92300.0  \n",
              "20638       741.0       349.0        1.8672           84700.0  \n",
              "20639      1387.0       530.0        2.3886           89400.0  \n",
              "\n",
              "[20640 rows x 9 columns]"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 42
    },
    {
      "id": "proprietary-struggle",
      "cell_type": "markdown",
      "source": "* To use a scikit-learn enumerator our data must be in the form of a 2D array.\n\n* We can do this by calling `reshape(-1, 1)` which means \"reshape the data into one column and as many rows as needed\".",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "verified-comfort",
      "cell_type": "code",
      "source": "cal_housing['totalBedrooms'].values.reshape(-1, 1)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[  nan],\n",
              "       [1106.],\n",
              "       [ 190.],\n",
              "       ...,\n",
              "       [ 485.],\n",
              "       [ 409.],\n",
              "       [ 616.]])"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 43
    },
    {
      "id": "modern-arkansas",
      "cell_type": "markdown",
      "source": "* We call the `fit_transform()` method of our enumerator:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "guilty-father",
      "cell_type": "code",
      "source": "imputer.fit_transform(cal_housing['totalBedrooms'].values.reshape(-1, 1))",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[   0.],\n",
              "       [1106.],\n",
              "       [ 190.],\n",
              "       ...,\n",
              "       [ 485.],\n",
              "       [ 409.],\n",
              "       [ 616.]])"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 44
    },
    {
      "id": "micro-arena",
      "cell_type": "markdown",
      "source": "* Instead of entering a fixed value, let's put the median.\n\n* We will also ask, by giving `add_indicator=True', to return an additional column with values ​​0 and 1, where the value 1 means that the enumerator entered the value in this line.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "automatic-fabric",
      "cell_type": "code",
      "source": "imputer = SimpleImputer(missing_values=np.nan, strategy=\"median\", add_indicator=True)\nimputer.fit_transform(cal_housing['totalBedrooms'].values.reshape(-1, 1))",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[4.350e+02, 1.000e+00],\n",
              "       [1.106e+03, 0.000e+00],\n",
              "       [1.900e+02, 0.000e+00],\n",
              "       ...,\n",
              "       [4.850e+02, 0.000e+00],\n",
              "       [4.090e+02, 0.000e+00],\n",
              "       [6.160e+02, 0.000e+00]])"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 45
    },
    {
      "id": "demonstrated-amsterdam",
      "cell_type": "markdown",
      "source": "* scikit-learn also offers other enumerators:\n\n * `KNNComputer`\n\n * `IterativeImputer`",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "arctic-communications",
      "cell_type": "markdown",
      "source": "* The most difficult point in managing gaps is not the technical part.\n\n* We see that there are tools with which we can handle them.\n\n* The most difficult point is the operational one. Do we want to delete data, or fill in the blanks? If we want to fill in the blanks, what values ​​does it make sense to fill them in?\n\n* If reality has gaps, we must be sure that by filling them we do not falsify reality.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    }
  ]
}