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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data analysis"
      ],
      "metadata": {
        "id": "H3cq8yAWelQg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#load the libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "42qNnMzgesO_"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data= pd.read_csv(\"data_clean.csv\",index_col=0) #index column remove here"
      ],
      "metadata": {
        "id": "NOC4lbIqesfv"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "US-PMTqaesi0",
        "outputId": "59188f49-0d5c-4f4d-91c6-29704d77037f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Ozone  Solar.R  Wind Temp C Month  Day  Year  Temp Weather\n",
              "1   41.0    190.0   7.4     67     5    1  2010    67       S\n",
              "2   36.0    118.0   8.0     72     5    2  2010    72       C\n",
              "3   12.0    149.0  12.6     74     5    3  2010    74      PS\n",
              "4   18.0    313.0  11.5     62     5    4  2010    62       S\n",
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      "execution_count": 52,
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              "     Ozone  Solar.R  Wind Temp C Month  Day  Year  Temp Weather\n",
              "1     41.0    190.0   7.4     67     5    1  2010    67       S\n",
              "2     36.0    118.0   8.0     72     5    2  2010    72       C\n",
              "3     12.0    149.0  12.6     74     5    3  2010    74      PS\n",
              "4     18.0    313.0  11.5     62     5    4  2010    62       S\n",
              "5      NaN      NaN  14.3     56     5    5  2010    56       S\n",
              "..     ...      ...   ...    ...   ...  ...   ...   ...     ...\n",
              "154   41.0    190.0   7.4     67     5    1  2010    67       C\n",
              "155   30.0    193.0   6.9     70     9   26  2010    70      PS\n",
              "156    NaN    145.0  13.2     77     9   27  2010    77       S\n",
              "157   14.0    191.0  14.3     75     9   28  2010    75       S\n",
              "158   18.0    131.0   8.0     76     9   29  2010    76       C\n",
              "\n",
              "[158 rows x 9 columns]"
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "WFFW5Ln4espF",
        "outputId": "28ea7b39-eeb3-4383-e300-f5ac84b1ffde"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Ozone  Solar.R  Wind Temp C Month  Day  Year  Temp Weather\n",
              "149   30.0    193.0   6.9     70     9   26  2010    70       C\n",
              "150    NaN    145.0  13.2     77     9   27  2010    77      PS\n",
              "151   14.0    191.0  14.3     75     9   28  2010    75       S\n",
              "152   18.0    131.0   8.0     76     9   29  2010    76      PS\n",
              "153   20.0    223.0  11.5     68     9   30  2010    68       S\n",
              "154   41.0    190.0   7.4     67     5    1  2010    67       C\n",
              "155   30.0    193.0   6.9     70     9   26  2010    70      PS\n",
              "156    NaN    145.0  13.2     77     9   27  2010    77       S\n",
              "157   14.0    191.0  14.3     75     9   28  2010    75       S\n",
              "158   18.0    131.0   8.0     76     9   29  2010    76       C"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-004ab0fc-a127-43d7-949c-c66ff06fab1d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        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>Ozone</th>\n",
              "      <th>Solar.R</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Temp C</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Year</th>\n",
              "      <th>Temp</th>\n",
              "      <th>Weather</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>30.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>70</td>\n",
              "      <td>9</td>\n",
              "      <td>26</td>\n",
              "      <td>2010</td>\n",
              "      <td>70</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>NaN</td>\n",
              "      <td>145.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>77</td>\n",
              "      <td>9</td>\n",
              "      <td>27</td>\n",
              "      <td>2010</td>\n",
              "      <td>77</td>\n",
              "      <td>PS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>14.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>14.3</td>\n",
              "      <td>75</td>\n",
              "      <td>9</td>\n",
              "      <td>28</td>\n",
              "      <td>2010</td>\n",
              "      <td>75</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>18.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>76</td>\n",
              "      <td>9</td>\n",
              "      <td>29</td>\n",
              "      <td>2010</td>\n",
              "      <td>76</td>\n",
              "      <td>PS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>20.0</td>\n",
              "      <td>223.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>68</td>\n",
              "      <td>9</td>\n",
              "      <td>30</td>\n",
              "      <td>2010</td>\n",
              "      <td>68</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>41.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>67</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>30.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>70</td>\n",
              "      <td>9</td>\n",
              "      <td>26</td>\n",
              "      <td>2010</td>\n",
              "      <td>70</td>\n",
              "      <td>PS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>NaN</td>\n",
              "      <td>145.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>77</td>\n",
              "      <td>9</td>\n",
              "      <td>27</td>\n",
              "      <td>2010</td>\n",
              "      <td>77</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>14.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>14.3</td>\n",
              "      <td>75</td>\n",
              "      <td>9</td>\n",
              "      <td>28</td>\n",
              "      <td>2010</td>\n",
              "      <td>75</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>18.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>76</td>\n",
              "      <td>9</td>\n",
              "      <td>29</td>\n",
              "      <td>2010</td>\n",
              "      <td>76</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-004ab0fc-a127-43d7-949c-c66ff06fab1d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-004ab0fc-a127-43d7-949c-c66ff06fab1d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-004ab0fc-a127-43d7-949c-c66ff06fab1d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data structure\n",
        "print(type(data))\n",
        "print(data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1sJ6KdDVhNC1",
        "outputId": "d17df23c-c417-4cb3-ff13-47187d080e61"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "(158, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data types"
      ],
      "metadata": {
        "id": "AFPbWkglhNGC"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes #Check datatype of all variable"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lz8mVQZNhNJi",
        "outputId": "1cfe229e-5434-44dc-d8b7-abd8956c6c38"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ozone      float64\n",
              "Solar.R    float64\n",
              "Wind       float64\n",
              "Temp C      object\n",
              "Month       object\n",
              "Day          int64\n",
              "Year         int64\n",
              "Temp         int64\n",
              "Weather     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "jM5MNF5QhNM1",
        "outputId": "e975c415-d7fb-4151-e804-fd411665f16a"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Ozone     Solar.R        Wind         Day    Year        Temp\n",
              "count  120.000000  151.000000  158.000000  158.000000   158.0  158.000000\n",
              "mean    41.583333  185.403974    9.957595   16.006329  2010.0   77.727848\n",
              "std     32.620709   88.723103    3.511261    8.997166     0.0    9.377877\n",
              "min      1.000000    7.000000    1.700000    1.000000  2010.0   56.000000\n",
              "25%     18.000000  119.000000    7.400000    8.000000  2010.0   72.000000\n",
              "50%     30.500000  197.000000    9.700000   16.000000  2010.0   78.500000\n",
              "75%     61.500000  257.000000   11.875000   24.000000  2010.0   84.000000\n",
              "max    168.000000  334.000000   20.700000   31.000000  2010.0   97.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c3d01378-d407-432a-abf0-3bc404880910\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ozone</th>\n",
              "      <th>Solar.R</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Day</th>\n",
              "      <th>Year</th>\n",
              "      <th>Temp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>120.000000</td>\n",
              "      <td>151.000000</td>\n",
              "      <td>158.000000</td>\n",
              "      <td>158.000000</td>\n",
              "      <td>158.0</td>\n",
              "      <td>158.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41.583333</td>\n",
              "      <td>185.403974</td>\n",
              "      <td>9.957595</td>\n",
              "      <td>16.006329</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>77.727848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>32.620709</td>\n",
              "      <td>88.723103</td>\n",
              "      <td>3.511261</td>\n",
              "      <td>8.997166</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.377877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>56.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>119.000000</td>\n",
              "      <td>7.400000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>30.500000</td>\n",
              "      <td>197.000000</td>\n",
              "      <td>9.700000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>78.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.500000</td>\n",
              "      <td>257.000000</td>\n",
              "      <td>11.875000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>84.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>168.000000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>20.700000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>97.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3d01378-d407-432a-abf0-3bc404880910')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c3d01378-d407-432a-abf0-3bc404880910 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c3d01378-d407-432a-abf0-3bc404880910');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info() #find missing value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5A16TW3_ifcm",
        "outputId": "0eced14c-154f-4eb9-a9c4-2ecee5f1d308"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 158 entries, 1 to 158\n",
            "Data columns (total 9 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   Ozone    120 non-null    float64\n",
            " 1   Solar.R  151 non-null    float64\n",
            " 2   Wind     158 non-null    float64\n",
            " 3   Temp C   158 non-null    object \n",
            " 4   Month    158 non-null    object \n",
            " 5   Day      158 non-null    int64  \n",
            " 6   Year     158 non-null    int64  \n",
            " 7   Temp     158 non-null    int64  \n",
            " 8   Weather  155 non-null    object \n",
            "dtypes: float64(3), int64(3), object(3)\n",
            "memory usage: 12.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Type Conversion"
      ],
      "metadata": {
        "id": "Z5iPdkosjF2M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Month']=pd.to_numeric(data['Month'],errors='coerce') #replace with NA\n",
        "data['Temp C']=pd.to_numeric(data['Temp C'],errors='coerce')#coerce introduced NA value for num numeri c data in the column\n",
        "data['Weather']=data['Weather'].astype('category')#data=['wind'].astype('int64')"
      ],
      "metadata": {
        "id": "gOPMe3Ywiflm"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qRYiHR7XifpX",
        "outputId": "db2431b2-251f-47a6-a606-76882f4ba149"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 158 entries, 1 to 158\n",
            "Data columns (total 9 columns):\n",
            " #   Column   Non-Null Count  Dtype   \n",
            "---  ------   --------------  -----   \n",
            " 0   Ozone    120 non-null    float64 \n",
            " 1   Solar.R  151 non-null    float64 \n",
            " 2   Wind     158 non-null    float64 \n",
            " 3   Temp C   157 non-null    float64 \n",
            " 4   Month    157 non-null    float64 \n",
            " 5   Day      158 non-null    int64   \n",
            " 6   Year     158 non-null    int64   \n",
            " 7   Temp     158 non-null    int64   \n",
            " 8   Weather  155 non-null    category\n",
            "dtypes: category(1), float64(5), int64(3)\n",
            "memory usage: 11.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Duplicates"
      ],
      "metadata": {
        "id": "aWvRRl-LIVqp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.duplicated() #if any two rows has same value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pdy_hA2WL5l4",
        "outputId": "d7aa96c4-c19a-4027-885c-c633a219b108"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1      False\n",
              "2      False\n",
              "3      False\n",
              "4      False\n",
              "5      False\n",
              "       ...  \n",
              "154    False\n",
              "155    False\n",
              "156    False\n",
              "157     True\n",
              "158    False\n",
              "Length: 158, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#count of duplicated rows\n",
        "data[data.duplicated()].shape #1row having 9 column"
      ],
      "metadata": {
        "id": "i9PEbb8WifsA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb82c521-f3f2-44ca-e598-e8e8f6ade7a0"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "gIvYPj9mifvW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "5972d0e2-9e2d-43e3-f9e4-dca47b2e5ad3"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Ozone  Solar.R  Wind  Temp C  Month  Day  Year  Temp Weather\n",
              "1     41.0    190.0   7.4    67.0    5.0    1  2010    67       S\n",
              "2     36.0    118.0   8.0    72.0    5.0    2  2010    72       C\n",
              "3     12.0    149.0  12.6    74.0    5.0    3  2010    74      PS\n",
              "4     18.0    313.0  11.5    62.0    5.0    4  2010    62       S\n",
              "5      NaN      NaN  14.3    56.0    5.0    5  2010    56       S\n",
              "..     ...      ...   ...     ...    ...  ...   ...   ...     ...\n",
              "154   41.0    190.0   7.4    67.0    5.0    1  2010    67       C\n",
              "155   30.0    193.0   6.9    70.0    9.0   26  2010    70      PS\n",
              "156    NaN    145.0  13.2    77.0    9.0   27  2010    77       S\n",
              "157   14.0    191.0  14.3    75.0    9.0   28  2010    75       S\n",
              "158   18.0    131.0   8.0    76.0    9.0   29  2010    76       C\n",
              "\n",
              "[158 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb440bd9-ce69-4046-a79f-084f8714dafc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ozone</th>\n",
              "      <th>Solar.R</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Temp C</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Year</th>\n",
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              "      <th>Weather</th>\n",
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              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>67.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>72</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>12.6</td>\n",
              "      <td>74.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>74</td>\n",
              "      <td>PS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18.0</td>\n",
              "      <td>313.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>62.0</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>2010</td>\n",
              "      <td>62</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.3</td>\n",
              "      <td>56.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>56</td>\n",
              "      <td>S</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>154</th>\n",
              "      <td>41.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>67.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>30.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>70.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>26</td>\n",
              "      <td>2010</td>\n",
              "      <td>70</td>\n",
              "      <td>PS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>NaN</td>\n",
              "      <td>145.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>77.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27</td>\n",
              "      <td>2010</td>\n",
              "      <td>77</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>14.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>14.3</td>\n",
              "      <td>75.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>28</td>\n",
              "      <td>2010</td>\n",
              "      <td>75</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>18.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>29</td>\n",
              "      <td>2010</td>\n",
              "      <td>76</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>158 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb440bd9-ce69-4046-a79f-084f8714dafc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eb440bd9-ce69-4046-a79f-084f8714dafc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eb440bd9-ce69-4046-a79f-084f8714dafc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print the duplicate row\n",
        "data[data.duplicated()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "FgLE3YUMI7Fc",
        "outputId": "9140002a-9bf4-4bcc-e48c-bed5265afcd5"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Ozone  Solar.R  Wind  Temp C  Month  Day  Year  Temp Weather\n",
              "157   14.0    191.0  14.3    75.0    9.0   28  2010    75       S"
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              "      <td>S</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24f6a782-25bb-4a7d-b67f-23d4d2b00bb5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-24f6a782-25bb-4a7d-b67f-23d4d2b00bb5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-24f6a782-25bb-4a7d-b67f-23d4d2b00bb5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned1=data.drop_duplicates() #kaggle download any dataset for parctice"
      ],
      "metadata": {
        "id": "UjiAtCVXI7JS"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HBqfGhMVI7Mg",
        "outputId": "4934f26f-b3e6-45e8-d663-e09bb9e9eeb3"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(157, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Drop columns"
      ],
      "metadata": {
        "id": "EOFZaK3OO5NA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9UXlxSJgI7P_",
        "outputId": "c344d5a1-3690-4219-dc7a-9fe65ef56936"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Ozone  Solar.R  Wind  Temp C  Month  Day  Year  Temp Weather\n",
              "1     41.0    190.0   7.4    67.0    5.0    1  2010    67       S\n",
              "2     36.0    118.0   8.0    72.0    5.0    2  2010    72       C\n",
              "3     12.0    149.0  12.6    74.0    5.0    3  2010    74      PS\n",
              "4     18.0    313.0  11.5    62.0    5.0    4  2010    62       S\n",
              "5      NaN      NaN  14.3    56.0    5.0    5  2010    56       S\n",
              "..     ...      ...   ...     ...    ...  ...   ...   ...     ...\n",
              "154   41.0    190.0   7.4    67.0    5.0    1  2010    67       C\n",
              "155   30.0    193.0   6.9    70.0    9.0   26  2010    70      PS\n",
              "156    NaN    145.0  13.2    77.0    9.0   27  2010    77       S\n",
              "157   14.0    191.0  14.3    75.0    9.0   28  2010    75       S\n",
              "158   18.0    131.0   8.0    76.0    9.0   29  2010    76       C\n",
              "\n",
              "[158 rows x 9 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ozone</th>\n",
              "      <th>Solar.R</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Temp C</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Year</th>\n",
              "      <th>Temp</th>\n",
              "      <th>Weather</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41.0</td>\n",
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              "      <td>5.0</td>\n",
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              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>72.0</td>\n",
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              "      <td>72</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.0</td>\n",
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              "      <td>12.6</td>\n",
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              "      <td>74</td>\n",
              "      <td>PS</td>\n",
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              "      <th>4</th>\n",
              "      <td>18.0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.3</td>\n",
              "      <td>56.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>56</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>41.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>67.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>30.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>70.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>26</td>\n",
              "      <td>2010</td>\n",
              "      <td>70</td>\n",
              "      <td>PS</td>\n",
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              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>NaN</td>\n",
              "      <td>145.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>77.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27</td>\n",
              "      <td>2010</td>\n",
              "      <td>77</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>14.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>14.3</td>\n",
              "      <td>75.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>28</td>\n",
              "      <td>2010</td>\n",
              "      <td>75</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>158</th>\n",
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              "      <td>8.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>29</td>\n",
              "      <td>2010</td>\n",
              "      <td>76</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>158 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2ac6425-37b9-48d7-bf5b-db3c13457fa0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c2ac6425-37b9-48d7-bf5b-db3c13457fa0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c2ac6425-37b9-48d7-bf5b-db3c13457fa0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned2=data_cleaned1.drop('Temp C',axis=1)"
      ],
      "metadata": {
        "id": "LTfpWEZlI7WP"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "1ABW-YILI7Zq",
        "outputId": "56a50388-959d-4023-e17e-b56c4897db1a"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Ozone  Solar.R  Wind  Month  Day  Year  Temp Weather\n",
              "1     41.0    190.0   7.4    5.0    1  2010    67       S\n",
              "2     36.0    118.0   8.0    5.0    2  2010    72       C\n",
              "3     12.0    149.0  12.6    5.0    3  2010    74      PS\n",
              "4     18.0    313.0  11.5    5.0    4  2010    62       S\n",
              "5      NaN      NaN  14.3    5.0    5  2010    56       S\n",
              "..     ...      ...   ...    ...  ...   ...   ...     ...\n",
              "153   20.0    223.0  11.5    9.0   30  2010    68       S\n",
              "154   41.0    190.0   7.4    5.0    1  2010    67       C\n",
              "155   30.0    193.0   6.9    9.0   26  2010    70      PS\n",
              "156    NaN    145.0  13.2    9.0   27  2010    77       S\n",
              "158   18.0    131.0   8.0    9.0   29  2010    76       C\n",
              "\n",
              "[157 rows x 8 columns]"
            ],
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              "      <th>1</th>\n",
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              "      <td>C</td>\n",
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              "      <th>3</th>\n",
              "      <td>12.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>12.6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>14.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>56</td>\n",
              "      <td>S</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>20.0</td>\n",
              "      <td>223.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30</td>\n",
              "      <td>2010</td>\n",
              "      <td>68</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>41.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>30.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>9.0</td>\n",
              "      <td>26</td>\n",
              "      <td>2010</td>\n",
              "      <td>70</td>\n",
              "      <td>PS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>NaN</td>\n",
              "      <td>145.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27</td>\n",
              "      <td>2010</td>\n",
              "      <td>77</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>18.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>29</td>\n",
              "      <td>2010</td>\n",
              "      <td>76</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>157 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ea4f4fc-2f36-4593-b725-96c3eef116d5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2ea4f4fc-2f36-4593-b725-96c3eef116d5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2ea4f4fc-2f36-4593-b725-96c3eef116d5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rename The columns"
      ],
      "metadata": {
        "id": "A0HK3S_wQQjF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#rename the Solar column\n",
        "data_cleaned3 = data_cleaned2.rename({'Solar.R': 'Solar'}, axis=1)"
      ],
      "metadata": {
        "id": "rIZdAme6P-aa"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "WJDgEiUUP-e1",
        "outputId": "6c169bc7-369d-4778-abbc-29fa60c86abb"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Ozone  Solar  Wind  Month  Day  Year  Temp Weather\n",
              "1     41.0  190.0   7.4    5.0    1  2010    67       S\n",
              "2     36.0  118.0   8.0    5.0    2  2010    72       C\n",
              "3     12.0  149.0  12.6    5.0    3  2010    74      PS\n",
              "4     18.0  313.0  11.5    5.0    4  2010    62       S\n",
              "5      NaN    NaN  14.3    5.0    5  2010    56       S\n",
              "..     ...    ...   ...    ...  ...   ...   ...     ...\n",
              "153   20.0  223.0  11.5    9.0   30  2010    68       S\n",
              "154   41.0  190.0   7.4    5.0    1  2010    67       C\n",
              "155   30.0  193.0   6.9    9.0   26  2010    70      PS\n",
              "156    NaN  145.0  13.2    9.0   27  2010    77       S\n",
              "158   18.0  131.0   8.0    9.0   29  2010    76       C\n",
              "\n",
              "[157 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7ef3ade8-0fb1-48b9-a414-1e1992496781\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\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>Ozone</th>\n",
              "      <th>Solar</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Year</th>\n",
              "      <th>Temp</th>\n",
              "      <th>Weather</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>72</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>12.6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>74</td>\n",
              "      <td>PS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18.0</td>\n",
              "      <td>313.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2010</td>\n",
              "      <td>62</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>56</td>\n",
              "      <td>S</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>20.0</td>\n",
              "      <td>223.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30</td>\n",
              "      <td>2010</td>\n",
              "      <td>68</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>41.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>30.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>9.0</td>\n",
              "      <td>26</td>\n",
              "      <td>2010</td>\n",
              "      <td>70</td>\n",
              "      <td>PS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>NaN</td>\n",
              "      <td>145.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27</td>\n",
              "      <td>2010</td>\n",
              "      <td>77</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>18.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>29</td>\n",
              "      <td>2010</td>\n",
              "      <td>76</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>157 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ef3ade8-0fb1-48b9-a414-1e1992496781')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7ef3ade8-0fb1-48b9-a414-1e1992496781 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7ef3ade8-0fb1-48b9-a414-1e1992496781');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Outliers detection"
      ],
      "metadata": {
        "id": "9PdPAlGpQ5lp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# histogram of Ozone\n",
        "data_cleaned3['Ozone'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "X9h6TfrZP-if",
        "outputId": "947e28c4-41bb-46d7-d944-351c3766b10b"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1af5db6370>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Box plot\n",
        "\n",
        "data_box=data_cleaned3.dropna()#data set is : data box\n",
        "data1_box=data_box.Ozone\n",
        "plt.boxplot(data1_box)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "z3pe6JurP-mE",
        "outputId": "c46e3a47-b2ba-4359-8dd8-3c223037d4c1"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#extracting outliers\n",
        "box=plt.boxplot(data1_box)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "oRBA2478P-p8",
        "outputId": "38df3d13-51ed-4423-fdb4-b9ffb1251df9"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "box"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hcX21BcP-tU",
        "outputId": "534d474b-c4b1-4181-c140-a40dcb4d28d2"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7f1af5c0a7c0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f1af5c0ab20>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f1af5c0ae80>,\n",
              "  <matplotlib.lines.Line2D at 0x7f1af5c18220>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7f1af5c0a460>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f1af5c18580>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f1af5c18880>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[item.get_ydata() for item in box['fliers']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvkHVgRGTv7a",
        "outputId": "5a99ef20-ba94-4beb-b28b-a89cafa8a25d"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([135., 168.])]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "box=plt.boxplot(data['Ozone'].dropna())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "cxBtpVN0Tv-m",
        "outputId": "b8bcd9f2-6e69-4194-8275-7bdba2f1aae8"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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dSQ4n2bvRvUjrZbhLK7sDuHqjm5AGYbhLK6iqB4GjG92HNAjDXZIaZLhLUoMMd0lqkOEuSQ0y3KUVJFkAvgC8Mcn+JHMb3ZO0Vj6hKkkN8sxdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KD/B39Jza5mce47AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned3['Ozone'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icFLjX8HTwBk",
        "outputId": "296a70b9-0b71-403b-c61e-824aff34d2b7"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    119.000000\n",
              "mean      41.815126\n",
              "std       32.659249\n",
              "min        1.000000\n",
              "25%       18.000000\n",
              "50%       31.000000\n",
              "75%       62.000000\n",
              "max      168.000000\n",
              "Name: Ozone, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "09YqkQZtTwEX",
        "outputId": "950ff0cf-643d-49ef-f996-02ca03f97e9b"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Ozone  Solar  Wind  Month  Day  Year  Temp Weather\n",
              "1     41.0  190.0   7.4    5.0    1  2010    67       S\n",
              "2     36.0  118.0   8.0    5.0    2  2010    72       C\n",
              "3     12.0  149.0  12.6    5.0    3  2010    74      PS\n",
              "4     18.0  313.0  11.5    5.0    4  2010    62       S\n",
              "5      NaN    NaN  14.3    5.0    5  2010    56       S\n",
              "..     ...    ...   ...    ...  ...   ...   ...     ...\n",
              "153   20.0  223.0  11.5    9.0   30  2010    68       S\n",
              "154   41.0  190.0   7.4    5.0    1  2010    67       C\n",
              "155   30.0  193.0   6.9    9.0   26  2010    70      PS\n",
              "156    NaN  145.0  13.2    9.0   27  2010    77       S\n",
              "158   18.0  131.0   8.0    9.0   29  2010    76       C\n",
              "\n",
              "[157 rows x 8 columns]"
            ],
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              "\n",
              "  <div id=\"df-9971c867-c6c1-429d-b3d7-cbdf7e3e4853\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ozone</th>\n",
              "      <th>Solar</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Year</th>\n",
              "      <th>Temp</th>\n",
              "      <th>Weather</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>1</th>\n",
              "      <td>41.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36.0</td>\n",
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              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>72</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>12.6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>74</td>\n",
              "      <td>PS</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18.0</td>\n",
              "      <td>313.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2010</td>\n",
              "      <td>62</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>56</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>153</th>\n",
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              "      <td>223.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30</td>\n",
              "      <td>2010</td>\n",
              "      <td>68</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>41.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>30.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>9.0</td>\n",
              "      <td>26</td>\n",
              "      <td>2010</td>\n",
              "      <td>70</td>\n",
              "      <td>PS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>NaN</td>\n",
              "      <td>145.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27</td>\n",
              "      <td>2010</td>\n",
              "      <td>77</td>\n",
              "      <td>S</td>\n",
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              "      <th>158</th>\n",
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              "      <td>131.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>29</td>\n",
              "      <td>2010</td>\n",
              "      <td>76</td>\n",
              "      <td>C</td>\n",
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              "  </tbody>\n",
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              "<p>157 rows × 8 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9971c867-c6c1-429d-b3d7-cbdf7e3e4853')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9971c867-c6c1-429d-b3d7-cbdf7e3e4853 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9971c867-c6c1-429d-b3d7-cbdf7e3e4853');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Weather'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhtMA53vTwHc",
        "outputId": "c53402c4-79cf-4825-b9f4-5d8a0515c407"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S     59\n",
              "C     49\n",
              "PS    47\n",
              "Name: Weather, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Weather'].value_counts().plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "b9_ViJ-3TwLB",
        "outputId": "8bffa491-87c2-41e9-cfa9-f904ffc2c337"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1af5b9d3a0>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD7CAYAAABzGc+QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAMOklEQVR4nO3cb4hl913H8ffHnYaWVk1ixnHJNk4wsSUo+cMYUiKFZq2kRpoVSmwRXWR1EVQiCroqPhB8kDyxrSDi0lRHqE1CatnQltqwSf0Dkna2iabJtiSGhG7IZqc2MW0U041fH8xZdp2d3bk7c+/cfLPvF4R7zu+cm/OFgTdnz9w7qSokSf18z7QHkCRtjAGXpKYMuCQ1ZcAlqSkDLklNGXBJampmKy92ySWX1Pz8/FZeUpLaO3To0Deranb1+pYGfH5+nqWlpa28pCS1l+TZtdZ9hCJJTY0U8CQXJrkvydeSHE7yriQXJ3kgyZPD60WTHlaSdNKod+AfBT5fVe8ErgYOA/uAg1V1JXBw2JckbZF1A57k+4F3A3cBVNWrVfUScCuwOJy2COya1JCSpNONcgd+ObAM/FWSR5J8LMlbgbmqen445ygwN6khJUmnGyXgM8B1wF9U1bXAK6x6XFIrf9JwzT9rmGRvkqUkS8vLy5udV5I0GCXgR4AjVfXwsH8fK0F/Icl2gOH12Fpvrqr9VbVQVQuzs6d9jFGStEHrBryqjgLfSPKOYWkn8ARwP7B7WNsNHJjIhJKkNY36RZ7fBD6R5ALgaeCXWYn/vUn2AM8Ct01mxI2b3/fZaY8wUc/cccu0R5A0RSMFvKoeBRbWOLRzvONIkkblNzElqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNzYxyUpJngG8DrwHHq2ohycXAPcA88AxwW1W9OJkxJUmrncsd+Huq6pqqWhj29wEHq+pK4OCwL0naIpt5hHIrsDhsLwK7Nj+OJGlUowa8gC8kOZRk77A2V1XPD9tHgbmxTydJOqORnoEDP1lVzyX5QeCBJF879WBVVZJa641D8PcCXHbZZZsaVpJ00kh34FX13PB6DPg0cD3wQpLtAMPrsTO8d39VLVTVwuzs7HimliStH/Akb03yvSe2gZ8GvgrcD+weTtsNHJjUkJKk043yCGUO+HSSE+f/bVV9PsmXgXuT7AGeBW6b3JiSpNXWDXhVPQ1cvcb6fwA7JzGUNL/vs9MeYaKeueOWaY+gNwC/iSlJTRlwSWrKgEtSUwZckpoy4JLUlAGXpKZG/Sq9JI3Mj4FuDe/AJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqauSAJ9mW5JEknxn2L0/ycJKnktyT5ILJjSlJWu1c7sBvBw6fsn8n8OGqugJ4EdgzzsEkSWc3UsCT7ABuAT427Ae4CbhvOGUR2DWJASVJaxv1DvwjwO8C/zvs/wDwUlUdH/aPAJeOeTZJ0lmsG/AkPwscq6pDG7lAkr1JlpIsLS8vb+R/IUlawyh34DcC70/yDHA3K49OPgpcmGRmOGcH8Nxab66q/VW1UFULs7OzYxhZkgQjBLyqfr+qdlTVPPBB4MGq+gXgIeADw2m7gQMTm1KSdJrNfA7894DfTvIUK8/E7xrPSJKkUcysf8pJVfVF4IvD9tPA9eMfSZI0Cr+JKUlNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJamrdgCd5c5IvJfnXJI8n+eNh/fIkDyd5Ksk9SS6Y/LiSpBNGuQP/H+CmqroauAa4OckNwJ3Ah6vqCuBFYM/kxpQkrbZuwGvFd4bdNw3/FXATcN+wvgjsmsiEkqQ1jfQMPMm2JI8Cx4AHgH8HXqqq48MpR4BLJzOiJGktIwW8ql6rqmuAHcD1wDtHvUCSvUmWkiwtLy9vcExJ0mrn9CmUqnoJeAh4F3Bhkpnh0A7guTO8Z39VLVTVwuzs7KaGlSSdNMqnUGaTXDhsvwV4L3CYlZB/YDhtN3BgUkNKkk43s/4pbAcWk2xjJfj3VtVnkjwB3J3kT4BHgLsmOKckaZV1A15V/wZcu8b606w8D5ckTYHfxJSkpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDW1bsCTvD3JQ0meSPJ4ktuH9YuTPJDkyeH1osmPK0k6YZQ78OPA71TVVcANwK8nuQrYBxysqiuBg8O+JGmLrBvwqnq+qr4ybH8bOAxcCtwKLA6nLQK7JjWkJOl05/QMPMk8cC3wMDBXVc8Ph44Cc2OdTJJ0ViMHPMnbgE8Bv1VVL596rKoKqDO8b2+SpSRLy8vLmxpWknTSSAFP8iZW4v2Jqvq7YfmFJNuH49uBY2u9t6r2V9VCVS3Mzs6OY2ZJEqN9CiXAXcDhqvrTUw7dD+wetncDB8Y/niTpTGZGOOdG4BeBx5I8Oqz9AXAHcG+SPcCzwG2TGVGStJZ1A15V/wzkDId3jnccSdKo/CamJDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU2tG/AkH09yLMlXT1m7OMkDSZ4cXi+a7JiSpNVGuQP/a+DmVWv7gINVdSVwcNiXJG2hdQNeVf8IfGvV8q3A4rC9COwa81ySpHVs9Bn4XFU9P2wfBebOdGKSvUmWkiwtLy9v8HKSpNU2/UvMqiqgznJ8f1UtVNXC7OzsZi8nSRpsNOAvJNkOMLweG99IkqRRbDTg9wO7h+3dwIHxjCNJGtUoHyP8JPAvwDuSHEmyB7gDeG+SJ4GfGvYlSVtoZr0TqupDZzi0c8yzSJLOgd/ElKSmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNbWpgCe5OcnXkzyVZN+4hpIkrW/DAU+yDfhz4H3AVcCHklw1rsEkSWe3mTvw64GnqurpqnoVuBu4dTxjSZLWs5mAXwp845T9I8OaJGkLzEz6Akn2AnuH3e8k+fqkrzlFlwDf3KqL5c6tutJ5wZ9db2/0n98Pr7W4mYA/B7z9lP0dw9r/U1X7gf2buE4bSZaqamHac+jc+bPr7Xz9+W3mEcqXgSuTXJ7kAuCDwP3jGUuStJ4N34FX1fEkvwH8PbAN+HhVPT62ySRJZ7WpZ+BV9Tngc2Oa5Y3gvHhU9Ablz6638/Lnl6qa9gySpA3wq/SS1JQBl6SmDPgGJPmJJD90yv4vJTmQ5M+SXDzN2bS+JFckuXGN9RuT/Mg0ZpI2woBvzF8CrwIkeTdwB/A3wH9ynv4ypZmPAC+vsf7ycEyvY95AnWTAN2ZbVX1r2P55YH9Vfaqq/gi4YopzaTRzVfXY6sVhbX7rx9E58gZqYMA3ZluSEx/B3Ak8eMqxif95Am3ahWc59pYtm0Ib5Q3UwIBvzCeBf0hyAPhv4J9g5dkqK3cBen1bSvKrqxeT/ApwaArz6Nx4AzXwc+AblOQGYDvwhap6ZVj7UeBtVfWVqQ6ns0oyB3yalX+Gnwj2AnAB8HNVdXRas2l9Sf4Q+BlW/njVZcB1VVXDDdRiVZ32C+o3KgOu81aS9wA/Nuw+XlUPnu18vX54A7XCgEtqJcmbgV9j5Xn3Y8BdVXV8ulNNhwGX1EqSe4DvsvK7p/cBz1bV7dOdajoMuKRWkjxWVT8+bM8AX6qq66Y81lT4KRRJ3Xz3xMb5+ujkBO/AJbWS5DXglRO7rHx2/7+G7aqq75vWbFvNgEtSUz5CkaSmDLgkNWXAJakpAy5JTRlwSWrq/wCNKspx3dOfdAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Missing value imputation"
      ],
      "metadata": {
        "id": "_kJ9uLZXXltR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns #in seaborn documntation colour code is mention\n",
        "cols = data_cleaned3.columns #save column name is cols dataset\n",
        "colours = ['blue', 'yellow'] # specify the colours - yellow is missing. blue is not missing.\n",
        "sns.heatmap(data_cleaned3[cols].isnull(),\n",
        "            cmap=sns.color_palette(colours)) #map colur T = yellow , F = Blue"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "rsNSAaOTW6ht",
        "outputId": "ddf7bca7-c6fb-4bf1-86b2-851a6c4a8ac9"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1af5af0910>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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1DiXZqfyLiOiBpD0kXVf2Ov3ANOe9puyFOm+qcyYkMUdEdEnSHOCLwJ7AtsD+krZtc96jgPcAF3dy3V5Lst9TSq+XSnpvOXaapEXlcZOkRb20ERHNMOzCkoYWmMwHbrT9S9sPAF+nmkY82ceATwD/08lFe5nHvB1wUAnsAeD7kr5j+w0t53yGqvAkIkZc5jG3tTlwa8vrZcCOrSdIeh6wpe3vSjqyk4v2Ml3umcDFtv9QGv8J8Grgk+W1gNcDu/XQRm2a8k3clB/+iMma8m+EmucKSDoYOLjl0MIy3beTzz4C+CxwwJq02UtiXgJ8XNImVPOY92L1raV2AW63fUMPbUREDNXq2+D9meWsvuzEFuXYhEcB2wE/rvqqPBE4W9I+tqfciq/rxGz7GkmfoCq7vh9YxOrl1/sDp3Z7/Yholqb8NtewoYxLgLmStqZKyAuoVtkEwPa9wKYTryX9GHj/dEkZerz5Z/tE28+3/RLgt5Ty67La3KuB06b6bEqyI2LU2V4BHAacC1wDnG57qaRjJO3T7XV7KjBpKb/eiqrnvJPteyTtARxl+6WdXWf4BSZNGT9rSq8koqnGocCk191FvlnGmB8E3mX7nnJ8ASM2jJGEGBFNkZLsiBgp49BjTuVfRETDJDFHRDTMjIlZ0pcl3SFpScuxx0r6gaQbyn8fU47vKunelpLsD3cSxLBLPJty4y8iAjrrMZ8M7DHp2AeAH9qeC/ywvJ7wU9s7lMcx/QkzImJ8zJiYbV8A3D3p8L7AKeX5KcBf9TmuiIix1e10uSfYvq08/zXwhJb3dpa0GPgVVYXL0pkulqlqERGr9DqPGdvWqsx6OfBk2/dJ2gv4FjC31zYiIsZJt7Mybpe0GUD57x0Atn9n+77y/BxgbUmbtrtASrIjItrrNjGfDbylPH8L8G0ASU8sy30iaX65/m/aXcD2QtvzbM9bfUW9iIjxNuNQhqRTgV2BTSUtAz4C/BNwuqQDgZup1l0GeC3wDkkrqJYCXeAOSgubMF0t49wR0RQpyY6IkZKS7IiIqF0Sc0REw3Rbkv26sjP2w5LmtRyf31KOvVjSfoMKPCJituq2JHsJ1Q4lF7Q5Ps/2DuUzx5fdTCIiokMzJk3bF0h6yqRj1wCUmXGtx//Q8nI96t7ONiJiFuj7GLOkHSUtBa4CDi17YkVERIf6nphtX2z7WcALgKMkrdfvNiIiZrOBzcoowx33Adu1ez8l2RER7fX1xpykrYFbba+Q9GTgGcBN7c61vZCSkVNgEhGxSrcl2XcDxwKPA74raZHtvwReDHxA0oPAw8A7bd81UxspyY6IWCUl2RExUlKSHRERtUtijohomG5Lsj8l6VpJV0o6S9LG5fgmks6XdJ+kLwwy8IiI2arbkuwfANvZfg5wPXBUOf4/wN8D71+TIGwN/RER0RRd7ZJt+7yWir6LgC3K8ftt/ydVgo6IiC70Yx7z24DTerlApqpFRKzS080/SR8CVgD/2p9wIiKi68Qs6QBgb+BvOtnXr83nU5IdEdFGV0MZkvYA/g546aSlPjuWkuyIiPZmrPxrLckGbqcqyT4KWBf4TTntItuHlvNvAjYC1gHuAV5p++rp20hijojOjEPlX0qyI2KkNC0xlxGEfwbmACfY/qdJ778PeDvV/bg7gbfZvnm6a6byLyKiS5LmAF8E9gS2BfaXtO2k066g2nLvOcAZwCdnum4Sc0RE9+YDN9r+pe0HgK8D+7aeYPv8lntxK+s+ptPvkux1JJ0k6aqyS/aunf/5IiJGzubArS2vl5VjUzkQ+N5MF+13SfZBALafDewOfEZSeuURMbJap/aWx8FdXueNwDzgUzOd2+0u2ee1vLwIeG15vi3wo3LOHZLuKYH8opPAIyKapnVqbxvLgS1bXm9Rjq1G0iuAD1FNMf7TTG32ozf7NlZ1zRcD+0haq2wz9XxWDzoiYja5BJgraWtJ6wALgLNbT5D0XOB4YB/bd3Ry0Z7WymhTkv1l4JnApcDNwM+Ah3ppIyKiqcr+pocB51JNl/uy7aWSjgEutX021dDFI4FvSAK4xfY+0123o3nMZSjjO7a3azl2AHAI8PKpqv8k/Qx4e7sCkzJOU8Zqjn/+yqcREdNo2jzmQehrSbakDaiS/f2SdgdWTFX1l5LsiIj2ut0le6Ik+welaz5Rkv144FxJD1MNgL9pQHFHRMxaKcmOiJEyDkMZmWMcEdEwScwREQ2TxBwR0TDdrpXxsbJOxiJJ50l6Ujl+ZDm2SNISSQ9Jeuwg/wAREbNNJwvlvwS4D/jKxDxmSRvZ/l15fjiw7cRC+S2fexVwhO3dZgwiN/8iokO5+Ue1VgZw96Rjv2t5uSG0/UPuD5zaU3QREWOo65JsSR8H3gzcC7xs0nsbUK1Id1hP0UVEjKGub/7Z/pDtLanWyZicgF8FXGj77j//ZCW7ZEdEtNf1Whkt720FnDNpHY2zgG/Y/reOgsgYc0R0KGPMU5A0t+XlvsC1Le89Gngp8O3eQouIGE/drpWxl6RtgIeplvdsnZGxH3Ce7fv7H25ExOyXtTIiYqRkKCMiImqXxBwR0TBdlWS3vPe3kixp0/J6V0n3tpRlf3gQQUdEzGadFJicDHwB+ErrQUlbAq8Ebpl0/k9t792X6CIixlBXJdnF56i2l8qNu4iIPup2HvO+wHLbi9u8vbOkxZK+J+lZvYUXETF+1jgxl3UwPgi0Gz++HHiy7e2BY4FvTXOdlGRHRLSxxiXZkp4N/BCY2B17C+BXwHzbv570uZuAebbvmv76GQ6JiM6MwzzmNV5dzvZVVLthA6snX0lPBG63bUnzqXrkv+lXsBER46CT6XKnAj8HtpG0TNKB05z+WmCJpMXA54EFbkJpYUTECElJdkSMlHEYykjlX0REw3S9g0k/2bV/If0ZKZ32iGiGbnfJPlrS8pbS673K8fktxxZL2m+QwUdEzEbd7pJ9NHCf7U9POncD4AHbKyRtBiwGnmR7xfRtZIw5IjqTMWamLclud+4fWpLweqRcOyJijfVy8+8wSVeWoY7HTByUtKOkpcBVwKEz9ZYjImJ13Sbm44CnATsAtwGfmXjD9sW2nwW8ADhK0no9RxkRMUa6Ssy2b7f9kO2HgS8B89uccw3V2PSf7awNWSsjImIqXU2Xk7SZ7dvKy/2AJeX41sCt5ebfk4FnADe1u4bthZSMnJt/ERGrdFuS/UlJV0m6EngZcEQ5/cXAYkmLgLOAd860gFFExCiTtIek6yTdKOkDbd5fV9Jp5f2Ly6Jw01+zCSXZMPzqjhSYRIyGJk2XkzQHuB7YHVgGXALsb/vqlnPeCTzH9qGSFgD72X7DdG02oiRb8tAfERFdmA/caPuXth8Avg7sO+mcfYFTyvMzgJdLmvbLpRGJOSJiRG0O3Nryelk51vacMn34XmCT6S7aiMRso14foEP6cZ3ZEEcTYmhKHImhWXH0I4a6c07rDLLyOLgfMUynEYm5Twb+l9WhJsTRhBigGXEkhlWaEEcTYlgjthfantfyaJ3fuxzYsuX1FuUY7c6RtBbwaGbYQGQ2JeaIiLpdAsyVtLWkdYAFwNmTzjkbeEt5/lrgRzNtINKIZT8jIkZRqdk4DDgXmAN82fZSSccAl9o+GzgR+KqkG6nWHVow03VnU2JuSvlgE+JoQgzQjDgSwypNiKMJMfSV7XOAcyYd+3DL8/8BXrcm11Qz5jFHRMSEjDFHRDRMEnNERMMkMfdA0iMkvX7YcURzSJoj6dphxwEgaT1J75N0pqRvSjoiy/COhpEeY5b0BOAfqLav2lPStsDOtk+sMYZLbc+rq71Jbb96uvdtn1lXLK0kvRB4Ci03l21/pcb2XwV8tyxLWztJ3wbebfuWYbTfEsfpwO+Br5VDfw1sbHuNbkT1GMMcYKntZ9TV5mww6on5e8BJwIdsb18mb19h+9k1xvBPwF3AacD9E8dtd7QdV49tn1SePh54IfCj8vplwM9s7z3oGNrE9FWqTRQWAQ+Vw7Z9eI0xfA3YGfgm1fSlWnuwki4Angv8gtV/JvapOY6rbW8707Ea4mjEF9UoGfXpcpvaPl3SUbByTuFDM32ozyZWiXpXyzEDTx10w7bfCiDpPGDbiTWyy0a4Jw+6/SnMK7EM7Rvf9hslbQTsD5ysapWqk4BTbf++hhD+voY2OnG5pJ1sXwTVtm/ApUOI4zHAUklD/aIaJaOemO+XtAll01dJO1EtEFIb21vX2d4UtmzZuADgdmCrIcWyBHgi1ZZjQ2P7d5LOANYH3ku1ocORkj5v+9gBt/2TQV5/DTwf+JmkiZ7qVsB1kq6i+i3mOTXF0ZQvqpEx6on5fVTljk+TdCHwOKqSx1pJ2g7YlmpncKDeMVXgh5LOBU4tr98A/EeN7SPp36m+IB8FXF16R3+aeL/O3pGkfYC3An8BfAWYb/sOSRsAVwMDTcylg3As8ExgHaqKsPttbzTIdtvYo+b22rL9k7Kj0Vzb/1H+P8wZdlxNNtJjzLByUZBtAAHX2X6w5vY/AuxKlZjPAfYE/tN2rV8Q5UbgLuXlBbbPqrn9l073fp29SEmnACfavqDNey+3/cMBt38pVdntN6iGdt4MPN32UYNsd4pYHkO1gE7rjdjLa47hIKrFix5r+2mS5gL/YvvldcYxSmZDYh72DICrgO2pbjpuX2aKfM327nXF0CSSPmH7f890bDabmKkj6cqJ4QJJV9h+bs1xfAw4APgvWLljh23vVnMci6gWlL944u9A0lV13qQfNSM9lDHVDACqX1/r8kfbD0taUW443cHqywAOXOktf4JqdobKw0P41RmqLXYmJ+E92xwbmAYMJfyhrDS2SNInqcbbh1Ez8HrgaWVnjWH6k+0HJjbtKL/ljnaPcMBGOjHTgBkAwKWSNga+BFwG3Ee1eW2dPgm8yvY1Nbe7kqR3AO8Enlo26Z3wKOBnNYfzBdoMJdTY/puoEvFhVBsVbwm8psb2JywBNqbqLAzTTyR9EFhf0u5UPyf/PuSYGm2khzIkfQM4fNKMhKEpu99uZPvKGU7td7sX2n5RnW22ieHRVNOi/hFo3Sn493XM6Z4Uy9CHEiStD2xl+7q62mwTwzzg21QJeig3YkscjwAOBF5J9dvcucAJQ+5QNdqoJ+bzgR2oJvLX+oMn6XnTvV/nDRZJ/0w1Re1brP73MKzKvznAE1h93L+24oJS4PEK4ATg11RDCQfY3r6m9l8FfBpYx/bWknYAjhlCQlwKHA9cBaysgmzQdL6Ywqgn5rYzAer4wStfClOp9QZLSwXg5BjeVlcMLbEcBhxNNZd6IhnUOWeWMjXrdqrx5SOotvL5f7ZvrKn9y4DdgB8P82aXpEtsv6DONqeI40VUPxNPpvqynrgHMvAirFE10okZVq6XMfHD9wvbwx5PG2tll4YdbU+7p1kNcTwOwPadQ2j7Its7tQ6ftA6r1BjHZ6l+gzqb1X+Tqnu63LVUX5CXseomPcP+GWmykb75V1Z2+xTwY6pv4WMlHWn7jBpjWBt4B/CScujHwPF1zKeW9He2PynpWNrc5a5zfYoWt1Jz9eUEVbf9P0J10+0R5dAK4Fjbx9TQ/jlUpflLJf01MKfM2T2c+m+AQrVeB8BOLcdM1Zuv0722v1dzmyNtpHvMkhYDu0/0kksv6T/qGkssbZ4ArA2cUg69CXjI9ttraPvvqW6kPAd4AFbf2t32Ke0+N+CYTqQq+Pkuq/fSPltD2++jmpp3sO3/LseeChwHfN/25wbc/uuAjwNfpSoFn5jLfi7wMdt/muqzs1HLfZjXU01ZPJMh9txHyagn5tXG7crd38U1ry63ePIXQbtjA2r701Sryj0TuBK4kKpn9rO6Z0K0xPSRdsdtf7SGtq+g+qK+a9LxxwHn1TErQ9IjqdaG2IMqQbcWdgz8y2lSLENdFrdJ92FGzUgPZQDfb7NGRN2/Mj0k6Wm2/wtW9tBqWeHO9vtLm+tQzdd9IdUaEQsl3eOal3csMX20xPTI8vq+Gptfe3JSLjHcWYac6vAA1Qpq6wKPZLiFFCdTlsUtr6+nWp62lsRs+2VQ/Zuw/cvW98q/k5jCSCdm20eWqrcXl0ML614jAjgSOF/SxA/eU6iSY53WBzaimn3waMPrDuoAAASpSURBVOBXVFOkalcWdPoq8Njy+i7gzbaX1tD8dBVuA69+k7QH8Fmqm23Ps/2HQbc5RRxr2V5BM5bFBTgDmDy99BtUq99FGyOdmMsY68mt83UlHWx74FukS3oBcKvtH5YbPIcAfwWcBywedPslhoXAs6h2qbiYahjjs7Z/W0f7U1gIvM/2+QCSdqWqinxhDW1vL+l3bY6LlpX/BuhDwOtq+hKazi+oEuFQl8WV9Ayqn89Ha/Xddjainv8fI2ukEzPwbmCBpMMmEgFwKFVyGLTjqYoYAHakqnZ7N1XBy0LqWX50K6pfmW8AlgPLgHtqaHc6G7b8v8D2jyVtWEfDtoe6lKTtXWY+qxYTN4GHvSzuNsDeVGXhr2o5/nvgoBrjGDmjfvPvCmBfql+LzrD9qbpKb1tv8En6InCn7aPL60W2dxh0DKUtUfVKXlge2wF3Az+33fZG3IDjOQu4nGo4A+CNwPNt71d3LONK0jKqIRWopg2uS5Ws/0Q1Y6jum5A72657/ZiRNuo9ZmzfUioAjytrZ6xfU9NzWsbyXk613uyE2v5ey3oDSyTdQ/Vr6r1UvZT5VHN66/Y24KNUU6MAflqORX3mUN141KTjGwwhFoArJL2LqgPRuplEfi6mMOqJ+bJyswmqIo+3Ud8NhVOpVs26C/gjVQJC0l9Q0ziepMNZ1VN+kDJVDvgyQ7r5V8a3h1HYEqvcVkdBzRr4KnAt8JfAMcDfAENbCXEUjORQRlnP9R+oEvHNVD2DLammB32wjqq7EsdOwGZUc2TvL8eeDjyyjsnzpeT2Qqp5y0NdYU/S2dO9X/cCPuOs7pX0ZjIRz0RZepm6+FPbO8344TE1qj3mT1Gt87u1y67Hqhap/3R57711BOGy+/CkY9fX0XZp6311tdWBnanKsU+lmiEy+dfoqE/Ttmya6CjdU37D/TXVpg4xhVHtMd9AtYeaJx2fA1xre+5wIhtf5e9+d2B/qhLx7wKnNmDqWAyZpLcD36T6uTiJavz7w7b/ZaiBNdioJubrbbfdkWK696IektalStCfAj5q+wtDDilipAxjH7J+uFrSmycflPRGqpsMMQSS1i2FBF+jWmXt80DdlZjRMJKeIOlESd8rr7eVdOCw42qyUe0xb041HeuPVGu8QrVWxPrAfraXDyu2cSXpK1RzqM8Bvm57yZBDioYoCfkk4EOudpJfi2pX+eySPYWRTMwTJO1GNTcS4GrbPxxmPONM0sNUi/fA6gv3DHPH7miAiZ1UJm0cUFsR1iga1VkZANj+EfCjYccRYHtUh8Vi8Ia6ZscoGukec0Q0l6T3smrnls9SDXUtpVqz43W2a1nsaxQlMUfEQLRs5PAMqpvyy4ELqKZR/tm62bFKEnNEDNSkjRx2Lo+hbOQwKkZ6jDkiRkJjNnIYFekxR8RAtNnI4SLgoiFv5DAScic9IgZlYiOHX9OcjRxGQnrMETEwTdvIYVQkMUfEwEnaAngRVXLeG9jE9sbDjaq5kpgjYiCm2cjhZ8BVth8eYniNllkZETEoT6Haj/OIYW/kMGrSY46IaJjMyoiIaJgk5oiIhklijohomCTmiIiGSWKOiGiY/w99lR4tikmZFAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4u9F47gW6lO",
        "outputId": "69e58665-f027-40bc-a81d-91572c7940a9"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Ozone', 'Solar', 'Wind', 'Month', 'Day', 'Year', 'Temp', 'Weather'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned3[data_cleaned3.isnull().any(axis=1)].head()  # find cloumn wise null values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "miZpvFw-W6oq",
        "outputId": "090f9f4f-3326-4dda-8904-d8404da7efe4"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Ozone  Solar  Wind  Month  Day  Year  Temp Weather\n",
              "5     NaN    NaN  14.3    5.0    5  2010    56       S\n",
              "6    28.0    NaN  14.9    5.0    6  2010    66       C\n",
              "10    NaN  194.0   8.6    5.0   10  2010    69       S\n",
              "11    7.0    NaN   6.9    5.0   11  2010    74       C\n",
              "24   32.0   92.0  12.0    NaN   24  2010    61       C"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-32dffe06-dc08-46fc-bc52-3c912c8c1246\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Ozone</th>\n",
              "      <th>Solar</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Year</th>\n",
              "      <th>Temp</th>\n",
              "      <th>Weather</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>56</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>28.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.9</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>66</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>NaN</td>\n",
              "      <td>194.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2010</td>\n",
              "      <td>69</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.9</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11</td>\n",
              "      <td>2010</td>\n",
              "      <td>74</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>32.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24</td>\n",
              "      <td>2010</td>\n",
              "      <td>61</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32dffe06-dc08-46fc-bc52-3c912c8c1246')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-32dffe06-dc08-46fc-bc52-3c912c8c1246 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-32dffe06-dc08-46fc-bc52-3c912c8c1246');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned3.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftVlQK8JW6r7",
        "outputId": "d1fadb98-ae22-4513-8ac8-3f2991e9c494"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ozone      38\n",
              "Solar       7\n",
              "Wind        0\n",
              "Month       1\n",
              "Day         0\n",
              "Year        0\n",
              "Temp        0\n",
              "Weather     3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned3.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBpfKj82bg6M",
        "outputId": "6259baeb-9ca1-4903-b271-8ac88e7d85e5"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 157 entries, 1 to 158\n",
            "Data columns (total 8 columns):\n",
            " #   Column   Non-Null Count  Dtype   \n",
            "---  ------   --------------  -----   \n",
            " 0   Ozone    119 non-null    float64 \n",
            " 1   Solar    150 non-null    float64 \n",
            " 2   Wind     157 non-null    float64 \n",
            " 3   Month    156 non-null    float64 \n",
            " 4   Day      157 non-null    int64   \n",
            " 5   Year     157 non-null    int64   \n",
            " 6   Temp     157 non-null    int64   \n",
            " 7   Weather  154 non-null    category\n",
            "dtypes: category(1), float64(4), int64(3)\n",
            "memory usage: 10.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mean Imputation\n",
        "mean = data_cleaned3['Ozone'].mean()\n",
        "print(mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sq1AkfIwW6vK",
        "outputId": "c1218401-e84a-4af4-a7d4-2901d414c989"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41.81512605042017\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data_cleaned3['Ozone'] = data_cleaned3['Ozone'].fillna(mean)  # use mean of ozone col. to fill na values"
      ],
      "metadata": {
        "id": "HflNzTT2W6yr"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned3.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytae1rP5cJk4",
        "outputId": "041f4381-060a-490e-dd61-a2d195e8caf6"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 157 entries, 1 to 158\n",
            "Data columns (total 8 columns):\n",
            " #   Column   Non-Null Count  Dtype   \n",
            "---  ------   --------------  -----   \n",
            " 0   Ozone    157 non-null    float64 \n",
            " 1   Solar    150 non-null    float64 \n",
            " 2   Wind     157 non-null    float64 \n",
            " 3   Month    156 non-null    float64 \n",
            " 4   Day      157 non-null    int64   \n",
            " 5   Year     157 non-null    int64   \n",
            " 6   Temp     157 non-null    int64   \n",
            " 7   Weather  154 non-null    category\n",
            "dtypes: category(1), float64(4), int64(3)\n",
            "memory usage: 10.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "VtmsCQzTcb4n",
        "outputId": "9967b35b-3649-4150-a467-c413397c9420"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Ozone  Solar  Wind  Month  Day  Year  Temp Weather\n",
              "1    41.000000  190.0   7.4    5.0    1  2010    67       S\n",
              "2    36.000000  118.0   8.0    5.0    2  2010    72       C\n",
              "3    12.000000  149.0  12.6    5.0    3  2010    74      PS\n",
              "4    18.000000  313.0  11.5    5.0    4  2010    62       S\n",
              "5    41.815126    NaN  14.3    5.0    5  2010    56       S\n",
              "..         ...    ...   ...    ...  ...   ...   ...     ...\n",
              "153  20.000000  223.0  11.5    9.0   30  2010    68       S\n",
              "154  41.000000  190.0   7.4    5.0    1  2010    67       C\n",
              "155  30.000000  193.0   6.9    9.0   26  2010    70      PS\n",
              "156  41.815126  145.0  13.2    9.0   27  2010    77       S\n",
              "158  18.000000  131.0   8.0    9.0   29  2010    76       C\n",
              "\n",
              "[157 rows x 8 columns]"
            ],
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              "\n",
              "  <div id=\"df-602be79e-8be7-4c66-8559-7ce697357b19\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ozone</th>\n",
              "      <th>Solar</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
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              "      <th>Temp</th>\n",
              "      <th>Weather</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41.000000</td>\n",
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              "      <td>7.4</td>\n",
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              "      <td>67</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>118.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>72</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>149.0</td>\n",
              "      <td>12.6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
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              "      <td>74</td>\n",
              "      <td>PS</td>\n",
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              "      <td>5.0</td>\n",
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              "      <td>2010</td>\n",
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              "      <th>5</th>\n",
              "      <td>41.815126</td>\n",
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              "      <td>14.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>56</td>\n",
              "      <td>S</td>\n",
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              "      <td>11.5</td>\n",
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              "      <td>30</td>\n",
              "      <td>2010</td>\n",
              "      <td>68</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>41.000000</td>\n",
              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>193.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>9.0</td>\n",
              "      <td>26</td>\n",
              "      <td>2010</td>\n",
              "      <td>70</td>\n",
              "      <td>PS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>41.815126</td>\n",
              "      <td>145.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27</td>\n",
              "      <td>2010</td>\n",
              "      <td>77</td>\n",
              "      <td>S</td>\n",
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              "      <th>158</th>\n",
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              "      <td>76</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>157 rows × 8 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-602be79e-8be7-4c66-8559-7ce697357b19')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-602be79e-8be7-4c66-8559-7ce697357b19 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-602be79e-8be7-4c66-8559-7ce697357b19');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Missing value imputation for categorical vlaue\n",
        "#Get the object columns\n",
        "obj_columns=data_cleaned3[['Weather']]"
      ],
      "metadata": {
        "id": "FB_YilYQcb8L"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "obj_columns.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdfwW1RIccLT",
        "outputId": "94a6b253-4699-4c10-9622-04112de30d0c"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Weather    3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Missing value imputation for categorical vlaue\n",
        "obj_columns=obj_columns.fillna(obj_columns.mode().iloc[0])"
      ],
      "metadata": {
        "id": "eUc6RbyUccOZ"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "obj_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "wNUr3fd4ccRY",
        "outputId": "c6462c15-5ac0-4c4b-b94e-05e9571e1302"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Weather\n",
              "1         S\n",
              "2         C\n",
              "3        PS\n",
              "4         S\n",
              "5         S\n",
              "..      ...\n",
              "153       S\n",
              "154       C\n",
              "155      PS\n",
              "156       S\n",
              "158       C\n",
              "\n",
              "[157 rows x 1 columns]"
            ],
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      "execution_count": 95,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Weather    0\n",
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      "execution_count": 96,
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            "text/plain": [
              "         Ozone  Solar  Wind  Month  Day  Year  Temp Weather\n",
              "1    41.000000  190.0   7.4    5.0    1  2010    67       S\n",
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              "..         ...    ...   ...    ...  ...   ...   ...     ...\n",
              "153  20.000000  223.0  11.5    9.0   30  2010    68       S\n",
              "154  41.000000  190.0   7.4    5.0    1  2010    67       C\n",
              "155  30.000000  193.0   6.9    9.0   26  2010    70      PS\n",
              "156  41.815126  145.0  13.2    9.0   27  2010    77       S\n",
              "158  18.000000  131.0   8.0    9.0   29  2010    76       C\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          "metadata": {},
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        "obj_columns.shape"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "8_bqAaUadydp",
        "outputId": "f2010f21-c559-4255-ae56-7426fa331fb0"
      },
      "execution_count": 97,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "(157, 1)"
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          "metadata": {},
          "execution_count": 97
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    {
      "cell_type": "code",
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        "#data_cleaned4=data_cleaned3.drop(['Weather'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "PfurNsj514qf"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Join the data set with imputed object dataset\n",
        "data_cleaned4=pd.concat([data_cleaned3,obj_columns],axis=1)"
      ],
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        "id": "P0JJJOxndyg1"
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      "outputs": []
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        "data_cleaned4.isnull().sum()"
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      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ozone      0\n",
              "Solar      7\n",
              "Wind       0\n",
              "Month      1\n",
              "Day        0\n",
              "Year       0\n",
              "Temp       0\n",
              "Weather    3\n",
              "Weather    0\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 100
        }
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    {
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        "outputId": "05e37157-c486-4455-8185-2f6e52dbc34b"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Ozone  Solar  Wind  Month  Day  Year  Temp Weather Weather\n",
              "1    41.000000  190.0   7.4    5.0    1  2010    67       S       S\n",
              "2    36.000000  118.0   8.0    5.0    2  2010    72       C       C\n",
              "3    12.000000  149.0  12.6    5.0    3  2010    74      PS      PS\n",
              "4    18.000000  313.0  11.5    5.0    4  2010    62       S       S\n",
              "5    41.815126    NaN  14.3    5.0    5  2010    56       S       S\n",
              "..         ...    ...   ...    ...  ...   ...   ...     ...     ...\n",
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              "154  41.000000  190.0   7.4    5.0    1  2010    67       C       C\n",
              "155  30.000000  193.0   6.9    9.0   26  2010    70      PS      PS\n",
              "156  41.815126  145.0  13.2    9.0   27  2010    77       S       S\n",
              "158  18.000000  131.0   8.0    9.0   29  2010    76       C       C\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Scatter plot and Correlation analysis\n"
      ],
      "metadata": {
        "id": "pz5HGcAodypg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Seaborn visualization library\n",
        "import seaborn as sns\n",
        "# Create the default pairplot\n",
        "sns.pairplot(data_cleaned3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9b0L0iN_dysu",
        "outputId": "d854a406-8ab3-491b-f7db-425822a027f7"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f1af5a38160>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1260x1260 with 56 Axes>"
            ],
            "image/png": 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    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation\n",
        "data_cleaned3.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Q39m0z4ggvEM",
        "outputId": "6c1ed9d8-c1dc-40e6-ef67-d3c130ab3de7"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Ozone     Solar      Wind     Month       Day  Year      Temp\n",
              "Ozone  1.000000  0.308687 -0.520004  0.132860 -0.021916   NaN  0.606500\n",
              "Solar  0.308687  1.000000 -0.057407 -0.094012 -0.155663   NaN  0.273558\n",
              "Wind  -0.520004 -0.057407  1.000000 -0.166216  0.029900   NaN -0.441228\n",
              "Month  0.132860 -0.094012 -0.166216  1.000000  0.050055   NaN  0.398516\n",
              "Day   -0.021916 -0.155663  0.029900  0.050055  1.000000   NaN -0.122787\n",
              "Year        NaN       NaN       NaN       NaN       NaN   NaN       NaN\n",
              "Temp   0.606500  0.273558 -0.441228  0.398516 -0.122787   NaN  1.000000"
            ],
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              "      <th>Ozone</th>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>-0.520004</td>\n",
              "      <td>0.132860</td>\n",
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              "      <td>0.308687</td>\n",
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              "      <td>-0.094012</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.050055</td>\n",
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              "      <th>Day</th>\n",
              "      <td>-0.021916</td>\n",
              "      <td>-0.155663</td>\n",
              "      <td>0.029900</td>\n",
              "      <td>0.050055</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temp</th>\n",
              "      <td>0.606500</td>\n",
              "      <td>0.273558</td>\n",
              "      <td>-0.441228</td>\n",
              "      <td>0.398516</td>\n",
              "      <td>-0.122787</td>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-1163795f-7a68-46bb-9d2f-255c22aad758 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1163795f-7a68-46bb-9d2f-255c22aad758');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tranformation"
      ],
      "metadata": {
        "id": "fG1L_r8Gg0Gm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#dummy variable"
      ],
      "metadata": {
        "id": "QotwOKh1hLI1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned4=pd.get_dummies(data,columns=['Weather'])"
      ],
      "metadata": {
        "id": "tR-mo5pMgvG8"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "KDU91mqdgvKD",
        "outputId": "3c65e1ad-73a9-47ab-9c1d-e36340d0d5d6"
      },
      "execution_count": 106,
      "outputs": [
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              "     Ozone  Solar.R  Wind  Temp C  Month  Day  Year  Temp  Weather_C  \\\n",
              "1     41.0    190.0   7.4    67.0    5.0    1  2010    67          0   \n",
              "2     36.0    118.0   8.0    72.0    5.0    2  2010    72          1   \n",
              "3     12.0    149.0  12.6    74.0    5.0    3  2010    74          0   \n",
              "4     18.0    313.0  11.5    62.0    5.0    4  2010    62          0   \n",
              "5      NaN      NaN  14.3    56.0    5.0    5  2010    56          0   \n",
              "..     ...      ...   ...     ...    ...  ...   ...   ...        ...   \n",
              "154   41.0    190.0   7.4    67.0    5.0    1  2010    67          1   \n",
              "155   30.0    193.0   6.9    70.0    9.0   26  2010    70          0   \n",
              "156    NaN    145.0  13.2    77.0    9.0   27  2010    77          0   \n",
              "157   14.0    191.0  14.3    75.0    9.0   28  2010    75          0   \n",
              "158   18.0    131.0   8.0    76.0    9.0   29  2010    76          1   \n",
              "\n",
              "     Weather_PS  Weather_S  \n",
              "1             0          1  \n",
              "2             0          0  \n",
              "3             1          0  \n",
              "4             0          1  \n",
              "5             0          1  \n",
              "..          ...        ...  \n",
              "154           0          0  \n",
              "155           1          0  \n",
              "156           0          1  \n",
              "157           0          1  \n",
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              "\n",
              "[158 rows x 11 columns]"
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-40861502-0c4b-4601-98bd-0148e6abe5fd');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
          "execution_count": 106
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    {
      "cell_type": "code",
      "source": [
        "data_cleaned4=data_cleaned4.dropna()"
      ],
      "metadata": {
        "id": "ZMIq8rzb2Nob"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "Mib6Delz2Nx1",
        "outputId": "b8830a4b-cfe1-49d8-bbee-91d5c5d10135"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Ozone  Solar.R  Wind  Temp C  Month  Day  Year  Temp  Weather_C  \\\n",
              "1     41.0    190.0   7.4    67.0    5.0    1  2010    67          0   \n",
              "2     36.0    118.0   8.0    72.0    5.0    2  2010    72          1   \n",
              "3     12.0    149.0  12.6    74.0    5.0    3  2010    74          0   \n",
              "4     18.0    313.0  11.5    62.0    5.0    4  2010    62          0   \n",
              "7     23.0    299.0   8.6    65.0    5.0    7  2010    65          0   \n",
              "..     ...      ...   ...     ...    ...  ...   ...   ...        ...   \n",
              "153   20.0    223.0  11.5    68.0    9.0   30  2010    68          0   \n",
              "154   41.0    190.0   7.4    67.0    5.0    1  2010    67          1   \n",
              "155   30.0    193.0   6.9    70.0    9.0   26  2010    70          0   \n",
              "157   14.0    191.0  14.3    75.0    9.0   28  2010    75          0   \n",
              "158   18.0    131.0   8.0    76.0    9.0   29  2010    76          1   \n",
              "\n",
              "     Weather_PS  Weather_S  \n",
              "1             0          1  \n",
              "2             0          0  \n",
              "3             1          0  \n",
              "4             0          1  \n",
              "7             1          0  \n",
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              "      <th>7</th>\n",
              "      <td>23.0</td>\n",
              "      <td>299.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>65.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2010</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>20.0</td>\n",
              "      <td>223.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>68.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30</td>\n",
              "      <td>2010</td>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>41.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>67.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>30.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>70.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>26</td>\n",
              "      <td>2010</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>14.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>14.3</td>\n",
              "      <td>75.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>28</td>\n",
              "      <td>2010</td>\n",
              "      <td>75</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>18.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>29</td>\n",
              "      <td>2010</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>114 rows × 11 columns</p>\n",
              "</div>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bbeef478-d1ed-41d7-b21a-f7b182ff428a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Normalization the data"
      ],
      "metadata": {
        "id": "06l_ezQ0iUEu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalization of the data\n",
        "from numpy import set_printoptions\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "ZxU9WGoTiSrt"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned4.values"
      ],
      "metadata": {
        "id": "qll8-WoogvQ3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68c1fac7-1776-4367-8ecb-3d95e48c4621"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 41. , 190. ,   7.4, ...,   0. ,   0. ,   1. ],\n",
              "       [ 36. , 118. ,   8. , ...,   1. ,   0. ,   0. ],\n",
              "       [ 12. , 149. ,  12.6, ...,   0. ,   1. ,   0. ],\n",
              "       ...,\n",
              "       [ 30. , 193. ,   6.9, ...,   0. ,   1. ,   0. ],\n",
              "       [ 14. , 191. ,  14.3, ...,   0. ,   0. ,   1. ],\n",
              "       [ 18. , 131. ,   8. , ...,   1. ,   0. ,   0. ]])"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "array = data_cleaned4.values\n",
        "\n",
        "scaler = MinMaxScaler(feature_range=(0,1)) # desired range of transformed data 0 to 1\n",
        "rescaledX = scaler.fit_transform(array)# apply normalization on array\n",
        "\n",
        "#transformed data\n",
        "set_printoptions(precision=2)\n",
        "print(rescaledX[0:5,:])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FMM34uap3Y5p",
        "outputId": "71000f08-7bf4-41ec-9506-b56cfac6f0b0"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.24 0.56 0.28 0.25 0.   0.   0.   0.25 0.   0.   1.  ]\n",
            " [0.21 0.34 0.31 0.38 0.   0.03 0.   0.38 1.   0.   0.  ]\n",
            " [0.07 0.43 0.56 0.43 0.   0.07 0.   0.43 0.   1.   0.  ]\n",
            " [0.1  0.94 0.5  0.12 0.   0.1  0.   0.12 0.   0.   1.  ]\n",
            " [0.13 0.89 0.34 0.2  0.   0.2  0.   0.2  0.   1.   0.  ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize data (0 mean, 1 stdev)\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "_5b5cfrc3e4m"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "array = data_cleaned4.values\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(array)\n",
        "rescaledX = scaler.transform(array)\n",
        "\n",
        "# summarize transformed data\n",
        "set_printoptions(precision=2)\n",
        "print(rescaledX[0:5,:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkF7iTZX3e8H",
        "outputId": "438e3909-45b9-4220-dd24-cf0304dcadc9"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.02  0.05 -0.71 -1.15 -1.53 -1.7   0.   -1.15 -0.64 -0.68  1.28]\n",
            " [-0.17 -0.75 -0.54 -0.62 -1.53 -1.59  0.   -0.62  1.57 -0.68 -0.78]\n",
            " [-0.9  -0.41  0.77 -0.4  -1.53 -1.48  0.   -0.4  -0.64  1.47 -0.78]\n",
            " [-0.72  1.43  0.45 -1.69 -1.53 -1.36  0.   -1.69 -0.64 -0.68  1.28]\n",
            " [-0.57  1.27 -0.37 -1.37 -1.53 -1.02  0.   -1.37 -0.64  1.47 -0.78]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Speed up the EDA process"
      ],
      "metadata": {
        "id": "TshvGuy23sWf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas-profiling==3.1.0\n",
        "!pip install sweetviz "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WU2FFwzE3e_z",
        "outputId": "d3740f8c-a324-4e3c-a09e-266705c08c6f"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.24.0->pandas-profiling==3.1.0) (2.10)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib>=3.2.0->pandas-profiling==3.1.0) (1.15.0)\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.8/dist-packages (from imagehash->visions[type_image_path]==0.7.4->pandas-profiling==3.1.0) (1.4.1)\n",
            "Building wheels for collected packages: htmlmin\n",
            "  Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27098 sha256=936370e0c047be606b6efa2870e5f454b7a7e79cda2a30bd3e6f97fd34052cd6\n",
            "  Stored in directory: /root/.cache/pip/wheels/23/14/6e/4be5bfeeb027f4939a01764b48edd5996acf574b0913fe5243\n",
            "Successfully built htmlmin\n",
            "Installing collected packages: htmlmin, tangled-up-in-unicode, multimethod, joblib, imagehash, visions, phik, pandas-profiling\n",
            "  Attempting uninstall: joblib\n",
            "    Found existing installation: joblib 1.2.0\n",
            "    Uninstalling joblib-1.2.0:\n",
            "      Successfully uninstalled joblib-1.2.0\n",
            "  Attempting uninstall: pandas-profiling\n",
            "    Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "Successfully installed htmlmin-0.1.12 imagehash-4.3.1 joblib-1.0.1 multimethod-1.9.1 pandas-profiling-3.1.0 phik-0.12.3 tangled-up-in-unicode-0.1.0 visions-0.7.4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "joblib"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sweetviz\n",
            "  Downloading sweetviz-2.1.4-py3-none-any.whl (15.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.1/15.1 MB\u001b[0m \u001b[31m71.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.43.0 in /usr/local/lib/python3.8/dist-packages (from sweetviz) (4.64.1)\n",
            "Requirement already satisfied: matplotlib>=3.1.3 in /usr/local/lib/python3.8/dist-packages (from sweetviz) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.8/dist-packages (from sweetviz) (1.21.6)\n",
            "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.8/dist-packages (from sweetviz) (2.11.3)\n",
            "Requirement already satisfied: importlib-resources>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from sweetviz) (5.10.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from sweetviz) (1.7.3)\n",
            "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3 in /usr/local/lib/python3.8/dist-packages (from sweetviz) (1.3.5)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=1.2.0->sweetviz) (3.13.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.11.1->sweetviz) (2.0.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.1.3->sweetviz) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.1.3->sweetviz) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.1.3->sweetviz) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.1.3->sweetviz) (0.11.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib>=3.1.3->sweetviz) (1.15.0)\n",
            "Installing collected packages: sweetviz\n",
            "Successfully installed sweetviz-2.1.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas_profiling as pp\n",
        "import sweetviz as sv\n",
        "import joblib"
      ],
      "metadata": {
        "id": "ta1QE5eO3yPm"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EDA_report= pp.ProfileReport(data,vars={\"num\":{\"low_categorical_threshold\":0}})"
      ],
      "metadata": {
        "id": "_s9Q0MtC3yTx"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EDA_report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429,
          "referenced_widgets": [
            "9f291775d80c414097796e6db7b71a22",
            "10f22a01061a46b1ab20b8a2df2f2a89",
            "41a2d5cf8fbf482d83f2f56bc0ea563a",
            "4b35b94706c048e7af3fdc8099eda625",
            "8e5652bee7f5492a8b6ac62100554881",
            "e3442245ebd44041ae82936f47f604db",
            "2e7d34241dbd4cd496a54399c84d15c3",
            "3b1e31a9d3444ac8804acdae5e2e92ce",
            "2967569bf38a490aaf042f93019372d8",
            "1ca757e977dd40a1a85bff535c61bb69",
            "75d26ffedf4445f08826470e70816a35"
          ]
        },
        "id": "6yc5FiI23yXZ",
        "outputId": "6e0f3526-e7d0-4bc5-ed9d-c4f38ef2ff2e"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f291775d80c414097796e6db7b71a22"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    416\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_repr_html_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m         \u001b[0;34m\"\"\"The ipython notebook widgets user interface gets called by the jupyter notebook.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_notebook_iframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mto_notebook_iframe\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    396\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_warnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m             \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_notebook_iframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_widgets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/report/presentation/flavours/widget/notebook.py\u001b[0m in \u001b[0;36mget_notebook_iframe\u001b[0;34m(config, profile)\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_notebook_iframe_src\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprofile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mattribute\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mIframeAttribute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msrcdoc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_notebook_iframe_srcdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprofile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m         raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/report/presentation/flavours/widget/notebook.py\u001b[0m in \u001b[0;36mget_notebook_iframe_srcdoc\u001b[0;34m(config, profile)\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotebook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotebook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m     \u001b[0msrc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mescape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0miframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'<iframe width=\"{width}\" height=\"{height}\" srcdoc=\"{src}\" frameborder=\"0\" allowfullscreen></iframe>'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mto_html\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m         \"\"\"\n\u001b[0;32m--> 368\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhtml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mhtml\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36m_render_html\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpresentation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflavours\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHTMLReport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m         \u001b[0mreport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m         with tqdm(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mreport\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    177\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mRoot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_report_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescription_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mdescription_set\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdescription_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_description_set\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m             self._description_set = describe_df(\n\u001b[0m\u001b[1;32m    162\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/model/describe.py\u001b[0m in \u001b[0;36mdescribe\u001b[0;34m(config, df, summarizer, typeset, sample)\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorrelation_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         correlations = {\n\u001b[0m\u001b[1;32m     96\u001b[0m             correlation_name: progress(\n\u001b[1;32m     97\u001b[0m                 \u001b[0mcalculate_correlation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"Calculate {correlation_name} correlation\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/model/describe.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         correlations = {\n\u001b[0;32m---> 96\u001b[0;31m             correlation_name: progress(\n\u001b[0m\u001b[1;32m     97\u001b[0m                 \u001b[0mcalculate_correlation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"Calculate {correlation_name} correlation\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             )(config, df, correlation_name, series_description)\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/utils/progress_bar.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_postfix_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0mbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/model/correlations.py\u001b[0m in \u001b[0;36mcalculate_correlation\u001b[0;34m(config, df, correlation_name, summary)\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0mcorrelation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m         correlation = correlation_measures[correlation_name].compute(\n\u001b[0m\u001b[1;32m     95\u001b[0m             \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msummary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/multimethod/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype_checkers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    316\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mDispatchError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Function {func.__code__}\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas_profiling/model/pandas/correlations_pandas.py\u001b[0m in \u001b[0;36mpandas_phik_compute\u001b[0;34m(config, df, summary)\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mphik\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mphik_matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m         \u001b[0mcorrelation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mphik_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselected_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintcols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcorrelation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/phik/phik.py\u001b[0m in \u001b[0;36mphik_matrix\u001b[0;34m(df, interval_cols, bins, quantile, noise_correction, dropna, drop_underflow, drop_overflow, verbose, njobs)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m     )\n\u001b[0;32m--> 256\u001b[0;31m     return phik_from_rebinned_df(\n\u001b[0m\u001b[1;32m    257\u001b[0m         \u001b[0mdata_binned\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m         \u001b[0mnoise_correction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/phik/phik.py\u001b[0m in \u001b[0;36mphik_from_rebinned_df\u001b[0;34m(data_binned, noise_correction, dropna, drop_underflow, drop_overflow, njobs)\u001b[0m\n\u001b[1;32m    164\u001b[0m         ]\n\u001b[1;32m    165\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m         phik_list = Parallel(n_jobs=njobs)(\n\u001b[0m\u001b[1;32m    167\u001b[0m             \u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_calc_phik\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mco\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_binned\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mco\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoise_correction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m             for co in itertools.combinations_with_replacement(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1006\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_dispatch\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'all'\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1007\u001b[0m             \u001b[0;31m# prevent further dispatch via multiprocessing callback thread\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1008\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1009\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pre_dispatch_amount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1010\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_initialize_backend\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    773\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    774\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 775\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mconfigure\u001b[0;34m(self, n_jobs, parallel, prefer, require, idle_worker_timeout, **memmappingexecutor_args)\u001b[0m\n\u001b[1;32m    504\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'n_jobs == 0 in Parallel has no meaning'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmp\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m             \u001b[0;31m# multiprocessing is not available or disabled, fallback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    507\u001b[0m             \u001b[0;31m# to sequential mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/executor.py\u001b[0m in \u001b[0;36mget_memmapping_executor\u001b[0;34m(n_jobs, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_memmapping_executor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mMemmappingExecutor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_memmapping_executor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/executor.py\u001b[0m in \u001b[0;36mget_memmapping_executor\u001b[0;34m(cls, n_jobs, timeout, initializer, initargs, env, temp_folder, context_id, **backend_args)\u001b[0m\n\u001b[1;32m     50\u001b[0m             \u001b[0mtemp_folder_resolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolve_temp_folder_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             **backend_args)\n\u001b[0;32m---> 52\u001b[0;31m         _executor, executor_is_reused = super().get_reusable_executor(\n\u001b[0m\u001b[1;32m     53\u001b[0m             \u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_reducers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjob_reducers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_reducers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult_reducers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m             \u001b[0mreuse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreuse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitializer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/externals/loky/reusable_executor.py\u001b[0m in \u001b[0;36mget_reusable_executor\u001b[0;34m(cls, max_workers, context, timeout, kill_workers, reuse, job_reducers, result_reducers, initializer, initargs, env)\u001b[0m\n\u001b[1;32m    138\u001b[0m                 mp.util.debug(\"Create a executor with max_workers={}.\"\n\u001b[1;32m    139\u001b[0m                               .format(max_workers))\n\u001b[0;32m--> 140\u001b[0;31m                 \u001b[0mexecutor_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_next_executor_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m                 \u001b[0m_executor_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m                 _executor = executor = cls(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/externals/loky/reusable_executor.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, submit_resize_lock, max_workers, context, timeout, executor_id, job_reducers, result_reducers, initializer, initargs, env)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0m_ReusablePoolExecutor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mProcessPoolExecutor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     def __init__(self, submit_resize_lock, max_workers=None, context=None,\n\u001b[0;32m---> 95\u001b[0;31m                  \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_reducers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m                  \u001b[0mresult_reducers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitializer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m                  env=None):\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/externals/loky/process_executor.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, max_workers, job_reducers, result_reducers, timeout, context, initializer, initargs, env)\u001b[0m\n\u001b[1;32m   1025\u001b[0m             reducers=job_reducers, ctx=self._context)\n\u001b[1;32m   1026\u001b[0m         \u001b[0;31m# Killed worker processes can produce spurious \"broken pipe\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1027\u001b[0;31m         \u001b[0;31m# tracebacks in the queue's own worker thread. But we detect killed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1028\u001b[0m         \u001b[0;31m# processes anyway, so silence the tracebacks.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1029\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ignore_epipe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/context.py\u001b[0m in \u001b[0;36mLock\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[0mcpu_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcpu_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreducers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    265\u001b[0m         \u001b[0;34m'''Returns a queue object'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mqueues\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mQueue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/synchronize.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0massert_spawning\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mresource_tracker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmultiprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name 'assert_spawning' from 'joblib.externals.loky.backend.context' (/usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/context.py)"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sweet_report = sv.analyze(data)\n",
        "sweet_report.show_html('weather_report.html')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "8923c1d8e0f64165811e248b9c9455cf",
            "3ce553d6835c424b9f7c6a0ff840ef15",
            "3bc98a408e6b4212af304a3b938c95b5",
            "c56bf4400f8c413bbf659d9bc41577ba",
            "4a3ccacb95f94c2c8f0630204ba93f81",
            "f5b8f61bb6454adca3dd7affb9a20a0f",
            "21b9e25ec96a42478e7e352afe69b6d0",
            "26fe6d4b246e4ad7a694739f9a975854",
            "2d85014485e1464186bd91043ef64260",
            "00f082bf232b4159995dbb615536b771",
            "bce6f4d87bb44f9ebe7d03963194d5eb"
          ]
        },
        "id": "9MCmATUm3ya9",
        "outputId": "0e3f1086-1c3c-402d-a207-d8432761a5b5"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                             |          | [  0%]   00:00 -> (? left)"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8923c1d8e0f64165811e248b9c9455cf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Report weather_report.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vDoLWXvF3fDV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}