Visualization_on_cities_by_hour_after_imputation.ipynb

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  "metadata": {
    "colab": {
      "name": "Visualization on cities_by_hour after imputation",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "gXZZocR06kws"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from matplotlib import rcParams\n",
        "import seaborn as sns\n",
        "from scipy.stats import skew\n",
        "import missingno as msno"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "QxQlJA4u9xOQ",
        "outputId": "a44431f5-ab96-49f5-d344-1981107654e1"
      },
      "source": [
        "df=pd.read_csv('/content/cities_by_hours.csv',parse_dates=['Datetime'])\n",
        "df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City</th>\n",
              "      <th>Datetime</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>PM10</th>\n",
              "      <th>NO</th>\n",
              "      <th>NO2</th>\n",
              "      <th>NOx</th>\n",
              "      <th>NH3</th>\n",
              "      <th>CO</th>\n",
              "      <th>SO2</th>\n",
              "      <th>O3</th>\n",
              "      <th>C6H6</th>\n",
              "      <th>C7H8</th>\n",
              "      <th>C8H10</th>\n",
              "      <th>AQI</th>\n",
              "      <th>AQI_Bucket</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 01:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>40.01</td>\n",
              "      <td>36.37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>122.07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 02:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.02</td>\n",
              "      <td>27.75</td>\n",
              "      <td>19.73</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.02</td>\n",
              "      <td>85.90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 03:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.08</td>\n",
              "      <td>19.32</td>\n",
              "      <td>11.08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.08</td>\n",
              "      <td>52.83</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 04:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.30</td>\n",
              "      <td>16.45</td>\n",
              "      <td>9.20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.30</td>\n",
              "      <td>39.53</td>\n",
              "      <td>153.58</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 05:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.12</td>\n",
              "      <td>14.90</td>\n",
              "      <td>7.85</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.12</td>\n",
              "      <td>32.63</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>...</th>\n",
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              "      <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>707870</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 20:00:00</td>\n",
              "      <td>9.50</td>\n",
              "      <td>36.00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>25.57</td>\n",
              "      <td>15.85</td>\n",
              "      <td>4.57</td>\n",
              "      <td>0.62</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>51.0</td>\n",
              "      <td>Satisfactory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707871</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 21:00:00</td>\n",
              "      <td>17.25</td>\n",
              "      <td>49.25</td>\n",
              "      <td>3.62</td>\n",
              "      <td>33.20</td>\n",
              "      <td>20.62</td>\n",
              "      <td>3.78</td>\n",
              "      <td>0.76</td>\n",
              "      <td>2.02</td>\n",
              "      <td>25.58</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>51.0</td>\n",
              "      <td>Satisfactory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707872</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 22:00:00</td>\n",
              "      <td>36.00</td>\n",
              "      <td>71.00</td>\n",
              "      <td>2.20</td>\n",
              "      <td>30.80</td>\n",
              "      <td>18.20</td>\n",
              "      <td>3.67</td>\n",
              "      <td>0.58</td>\n",
              "      <td>1.77</td>\n",
              "      <td>26.15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707873</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 23:00:00</td>\n",
              "      <td>15.75</td>\n",
              "      <td>63.00</td>\n",
              "      <td>1.02</td>\n",
              "      <td>28.90</td>\n",
              "      <td>16.00</td>\n",
              "      <td>3.80</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.75</td>\n",
              "      <td>15.82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707874</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-07-01 00:00:00</td>\n",
              "      <td>15.00</td>\n",
              "      <td>66.00</td>\n",
              "      <td>0.40</td>\n",
              "      <td>26.85</td>\n",
              "      <td>14.05</td>\n",
              "      <td>5.20</td>\n",
              "      <td>0.59</td>\n",
              "      <td>2.10</td>\n",
              "      <td>17.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>707875 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 City            Datetime  PM2.5  ...  C8H10   AQI    AQI_Bucket\n",
              "0           Ahmedabad 2015-01-01 01:00:00    NaN  ...    0.0   NaN           NaN\n",
              "1           Ahmedabad 2015-01-01 02:00:00    NaN  ...    0.0   NaN           NaN\n",
              "2           Ahmedabad 2015-01-01 03:00:00    NaN  ...    0.0   NaN           NaN\n",
              "3           Ahmedabad 2015-01-01 04:00:00    NaN  ...    0.0   NaN           NaN\n",
              "4           Ahmedabad 2015-01-01 05:00:00    NaN  ...    0.0   NaN           NaN\n",
              "...               ...                 ...    ...  ...    ...   ...           ...\n",
              "707870  Visakhapatnam 2020-06-30 20:00:00   9.50  ...    NaN  51.0  Satisfactory\n",
              "707871  Visakhapatnam 2020-06-30 21:00:00  17.25  ...    NaN  51.0  Satisfactory\n",
              "707872  Visakhapatnam 2020-06-30 22:00:00  36.00  ...    NaN  50.0          Good\n",
              "707873  Visakhapatnam 2020-06-30 23:00:00  15.75  ...    NaN  50.0          Good\n",
              "707874  Visakhapatnam 2020-07-01 00:00:00  15.00  ...    NaN  50.0          Good\n",
              "\n",
              "[707875 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "QneQVFF-92nM",
        "outputId": "1b6c7bc4-c9f2-40b8-a7df-8588b0d6876b"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>PM10</th>\n",
              "      <th>NO</th>\n",
              "      <th>NO2</th>\n",
              "      <th>NOx</th>\n",
              "      <th>NH3</th>\n",
              "      <th>CO</th>\n",
              "      <th>SO2</th>\n",
              "      <th>O3</th>\n",
              "      <th>C6H6</th>\n",
              "      <th>C7H8</th>\n",
              "      <th>C8H10</th>\n",
              "      <th>AQI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>562787.000000</td>\n",
              "      <td>411138.000000</td>\n",
              "      <td>591243.000000</td>\n",
              "      <td>590753.000000</td>\n",
              "      <td>584651.000000</td>\n",
              "      <td>435333.000000</td>\n",
              "      <td>621358.000000</td>\n",
              "      <td>577502.000000</td>\n",
              "      <td>578667.000000</td>\n",
              "      <td>544229.000000</td>\n",
              "      <td>487268.000000</td>\n",
              "      <td>252046.000000</td>\n",
              "      <td>578795.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>67.622994</td>\n",
              "      <td>119.075804</td>\n",
              "      <td>17.421755</td>\n",
              "      <td>28.885157</td>\n",
              "      <td>32.287565</td>\n",
              "      <td>23.607959</td>\n",
              "      <td>2.183539</td>\n",
              "      <td>14.038307</td>\n",
              "      <td>34.798979</td>\n",
              "      <td>3.087595</td>\n",
              "      <td>8.660927</td>\n",
              "      <td>3.130537</td>\n",
              "      <td>166.413500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>74.730496</td>\n",
              "      <td>104.224752</td>\n",
              "      <td>32.095211</td>\n",
              "      <td>29.162194</td>\n",
              "      <td>39.756669</td>\n",
              "      <td>28.831900</td>\n",
              "      <td>10.970514</td>\n",
              "      <td>19.305540</td>\n",
              "      <td>29.806379</td>\n",
              "      <td>16.456599</td>\n",
              "      <td>21.741023</td>\n",
              "      <td>7.834832</td>\n",
              "      <td>162.112729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>26.200000</td>\n",
              "      <td>52.380000</td>\n",
              "      <td>3.840000</td>\n",
              "      <td>10.810000</td>\n",
              "      <td>10.660000</td>\n",
              "      <td>8.120000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>4.880000</td>\n",
              "      <td>13.420000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.370000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>79.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>46.420000</td>\n",
              "      <td>91.500000</td>\n",
              "      <td>7.960000</td>\n",
              "      <td>20.320000</td>\n",
              "      <td>20.790000</td>\n",
              "      <td>15.380000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>8.370000</td>\n",
              "      <td>26.240000</td>\n",
              "      <td>0.860000</td>\n",
              "      <td>2.590000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>116.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>79.490000</td>\n",
              "      <td>147.520000</td>\n",
              "      <td>16.150000</td>\n",
              "      <td>36.350000</td>\n",
              "      <td>37.150000</td>\n",
              "      <td>29.230000</td>\n",
              "      <td>1.370000</td>\n",
              "      <td>14.780000</td>\n",
              "      <td>47.620000</td>\n",
              "      <td>2.750000</td>\n",
              "      <td>8.410000</td>\n",
              "      <td>3.120000</td>\n",
              "      <td>208.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>999.990000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>499.990000</td>\n",
              "      <td>499.510000</td>\n",
              "      <td>498.610000</td>\n",
              "      <td>499.970000</td>\n",
              "      <td>498.570000</td>\n",
              "      <td>199.960000</td>\n",
              "      <td>497.620000</td>\n",
              "      <td>498.070000</td>\n",
              "      <td>499.400000</td>\n",
              "      <td>499.990000</td>\n",
              "      <td>3133.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               PM2.5           PM10  ...          C8H10            AQI\n",
              "count  562787.000000  411138.000000  ...  252046.000000  578795.000000\n",
              "mean       67.622994     119.075804  ...       3.130537     166.413500\n",
              "std        74.730496     104.224752  ...       7.834832     162.112729\n",
              "min         0.010000       0.010000  ...       0.000000       8.000000\n",
              "25%        26.200000      52.380000  ...       0.100000      79.000000\n",
              "50%        46.420000      91.500000  ...       0.790000     116.000000\n",
              "75%        79.490000     147.520000  ...       3.120000     208.000000\n",
              "max       999.990000    1000.000000  ...     499.990000    3133.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "zHe3hV3F-UJP",
        "outputId": "5a60848f-f9a6-42c8-aa40-a1848d7eb7f8"
      },
      "source": [
        "missing_values=pd.DataFrame(df.isna().sum()/len(df))\n",
        "missing_values.columns=['Proportion']\n",
        "missing_values=missing_values.sort_values(by='Proportion',ascending=False)\n",
        "missing_values.style.background_gradient(cmap='Blues')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            background-color:  #3b8bc2;\n",
              "            color:  #000000;\n",
              "        }#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row2_col0{\n",
              "            background-color:  #4a98c9;\n",
              "            color:  #000000;\n",
              "        }#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row3_col0{\n",
              "            background-color:  #72b2d8;\n",
              "            color:  #000000;\n",
              "        }#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row4_col0{\n",
              "            background-color:  #a4cce3;\n",
              "            color:  #000000;\n",
              "        }#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row5_col0{\n",
              "            background-color:  #b0d2e7;\n",
              "            color:  #000000;\n",
              "        }#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row6_col0{\n",
              "            background-color:  #bad6eb;\n",
              "            color:  #000000;\n",
              "        }#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row7_col0,#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row8_col0,#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row9_col0{\n",
              "            background-color:  #bcd7eb;\n",
              "            color:  #000000;\n",
              "        }#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row10_col0{\n",
              "            background-color:  #bfd8ed;\n",
              "            color:  #000000;\n",
              "        }#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row11_col0,#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row12_col0{\n",
              "            background-color:  #c4daee;\n",
              "            color:  #000000;\n",
              "        }#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row13_col0{\n",
              "            background-color:  #d2e3f3;\n",
              "            color:  #000000;\n",
              "        }#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row14_col0,#T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row15_col0{\n",
              "            background-color:  #f7fbff;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Proportion</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >C8H10</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.643940</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >PM10</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.419194</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >NH3</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.385014</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >C7H8</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.311647</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >C6H6</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.231179</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >PM2.5</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.204963</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >SO2</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.184175</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >O3</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.182529</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >AQI</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row8_col0\" class=\"data row8 col0\" >0.182349</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >AQI_Bucket</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row9_col0\" class=\"data row9 col0\" >0.182349</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >NOx</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row10_col0\" class=\"data row10 col0\" >0.174076</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >NO2</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row11_col0\" class=\"data row11 col0\" >0.165456</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >NO</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row12_col0\" class=\"data row12 col0\" >0.164764</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >CO</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row13_col0\" class=\"data row13 col0\" >0.122221</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >City</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row14_col0\" class=\"data row14 col0\" >0.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >Datetime</th>\n",
              "                        <td id=\"T_8c03e4a8_cf67_11eb_9b3a_0242ac1c0002row15_col0\" class=\"data row15 col0\" >0.000000</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fb439e30890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0z0As7_7-umP",
        "outputId": "c9af812d-b2d4-4ef7-c2b1-79badef81860"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['City', 'Datetime', 'PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO',\n",
              "       'SO2', 'O3', 'C6H6', 'C7H8', 'C8H10', 'AQI', 'AQI_Bucket'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTND1KmF-zDl",
        "outputId": "935491b9-56b5-440e-ae4d-0fb8fa092468"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(707875, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "jmV_wnAh_YfZ",
        "outputId": "d3a99f49-93d4-4d2f-fd52-4de07b8d0980"
      },
      "source": [
        "sns.heatmap(df.isnull(),cmap=\"YlGnBu\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb43ad44310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1jA2ZND_ehc"
      },
      "source": [
        "df.iloc[:,1:14]=df.groupby(\"City\").transform(lambda x:x.fillna(x.mean()))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "IboTgCfn_hO0",
        "outputId": "5840fa04-96a4-4795-9d84-48aca896e14b"
      },
      "source": [
        "sns.heatmap(df.isnull(),cmap=\"YlGnBu\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb439801f50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/A/gWA1YvzeTUOMH26HzM4tILHE+KPx8EQVAtKm4n33Ilb7uWro9sGTOVFMR+R9JmLMA5wO9JGaH+XOg+kWQSOZicewa7fk448iDJwSoIgqBa9MFKfiY5tME7gZuB5fLEDfA4SQ1Tzz40WIHXyamxf05Ke6akJXK7N5FUPxV4JwuCIGjAiDaOHg2vFHnC/SVwkO1ZYtFks0bXtd+KNMkfWkLOKcDbgNGk1f73c/l4khrnpRZjC4/XIAh6w8gR5Y8eUNZOfj7SxHy+7V/l4ickrWD7sZy+78lC+w1I4YY/aPvpFnJqiUNqbX4KXJ5PNwV2kXQssDjwhqRXsmklhf7h8RoEQU/wcHeGyvFozgCm2j6+UDUB2BM4Ov/8dW6/CvAr4FO2/1pCDrWHRT7dCbgbwPYWhTbjgZfqJ/ggCIKeUm0z+VIr+c2BTwF3SZqcy75GmtwvlrQPKcfrrrnuCGAp4MfZu7WWHKShnGxJc6yk0SSVz0M0SBMYBEFQSSq+8VrGuuaPNHfc3aZB+88An2lHju1PlRjH+FZtgiAI5jrDXV0TBEEQDELFk4bEJB8EQdABrri6puWWgaSVJV0v6R5JUyQdmMu/le3aJ0u6RtJbcvmWkp7P5ZMlHVGQdaCku7Ocg+quc4Cke3PdsblsTEHOHZJ26u7tB0EQdMgIlT96QJmVfC0cwW2S3gzcKula4Djb3wCQ9AXShuvncp/Zkm9LWp8Un2YMKTTxVZIutz1tkKTgd5Niy8/IZpp3SPqN7Rmd3XYQBEGXqLhOvuVK3vZjtm/Ln18EpgIr1jlELUKdM1QD3g7cbPvlPEn/AfhormuYFLzQFmDBEtcIgiCYu/SLxyvMHo5A0nckPQJ8gllztDZKvn03KfH3Ujmv6/bAyrmuWVJwJG2aE5DcBXyu0So+PF6DIOgZwz1AWY1G4QhsHw4cLumrwP6kjFG15NsvSdqelCZwTdtTJR0DXEMKNjaZlPC7No5aUvB3kezv3+rEzcB6kt4OnCPpStuvFMcWHq9BMPzol3jyfZE0pFk4ggLnA1cARxbVOPXJt22fQfJ6RdJ3ScnAoXlS8H8WZE2V9BKwPjCp3RsNgqBa9Es8+b4NayBpTdv359MdgXtzedPk25KWtf1kDn3wUdLKHZokBZe0OvBI3nhdFViH5BEbBEGPqcIEWwmqvZDvKKzBPpLWBt4ghTWoWdYMlnz7l5KWAv4D7Gf7uVzeLCn4e4DDJP0nX+fztp/q5IaDIAi6ynBfyQ8SjqA+e1OtfdPk28WAY3Xlr9E4Kfh5wHmtxhgEQdAzKu4MFR6vQRAEnVDxSX7IHq+5rpGX6vySzpJ0Vzaj3LLQfn5Jp0n6a+63cy4/oeDZ+ldJz+Xy0ZJuyvLvlLRb138DQRAEHeCRKn30gk48XpejsZfqZwFsvyOXXSnpXbbfAA4HnrS9lqQRJLNJbB9cu5ikA0i2+AAvA3vYvj+HTbhV0tUFXX4QBEFv6QOdfLME3J+lgZcqsC7wu1pZXpVvAvwF+DTJQoY86TfaRN2dZG9PMemI7X9IehJYBohJPgiCajDc1TVF6jxem3mp3gHsIGlUNoHcGFhZ0uK5/luSbpP0C0nL1clfFVid/JCoqxtDMq38WztjDoIgmKOojaMHdJLIu+il+mWSl6pI5pDTSQ5LPwD+TPJsHQWsBPzZ9kbATcD36i4zFrjE9uvFwhyc7Dxg7/wGUD+2CGsQBEFPGDGi/FEGSdtJuk/SNEmHNWmza2Gf9OeDyevE47Whl6rtfwJFHfufSXEGnibp2Gv9fwHsU3epscB+dddeFPgtcLjtiY3GF2ENgiDoFWUn7zJIGgmcDHyANMfeImmC7XsKbdYEvgpsbvvZwn5o4/GVuGizBNw1L1XqvFQXlrRILv8AKcfrPflh8Btgy9x/G6A48HWAJUgr/FrZ/MClwLm2L2k11iAIgrmNpNJHCcYA02w/kP2HLiQZuBT5LHCy7Wdhlv3QhnTi8drMS3VZ4Oq8sn80961xKHCepB+Q4tLsXagbC1xY8I6FlBz8vcBSkvbKZXvZnkwQBEEF6LJxzYrAI4Xz6cCmdW3WStfVn4CRwHjbVzUT2Gki70Zeqg8BazeR9XfSpN2obnyDsp8BP2s1xiAIgl7RziQvaRwwrlB0WlY3t8MoYE2SVmQl4AZJ72hmWh4er0EQBB2gNnTydfuHjXiUgTwbkCbxR+vaTCclYPoP8KCkv5Im/VsaCSy78bogcAOwQO5zie0js4nkhcBSwK3Ap2y/JulzpA3U14GXgHG278lmkLUbFOk149J8jYOBz5CyP91FsqR5RdL+wEHA24BlIkBZMJypRPzzoKt0WV1zC7BmnlsfJamxP17X5jKSP9FZkpYmqW8eaCaw7DPoVWBr2xsCo4HtJL0bOAY4wfYawLMMWMv83PY7bI8GjgVqG7a1nK2jge2AU7M9/YrAF3Ld+iQ909jc50/A+0mRLoMgCCrFyBHlj1bkzHf7A1eTUq1ebHuKpKMk7ZCbXQ08Leke4Hrgy7afbiaz1Eo+b4a+lE/ny4eBrRl4ypwDjAdOaZb/1fbLhfL6nK2jgIVyWOGFgX/kPrcDZXemg6DSRAz2AfolM1S3pybbV1AX5df2EYXPBg7JR0vacYYama1rngSuJXmePlfIuTqdtDNca7+fpL+RVvJfKJTPlrPV9qMkx6iHSSEUnrd9TdmxBUEQ9Ioum1B2ndIbr9kLdXQOT3ApOQbNIO1PBk6W9HHg68CeuXy2nK3AQiRb0NVJcWl+IemT2bqmJcUd61NPPZVx47Yse1tBEPSIfkn/187Gay9o27rG9nOSrgc2AxaXNCqv5hvtAkPamD2lgZxiztbVgQeztyySfgX8FyXNJ8PjNQiCXlF1TXKpZ5CkZWoBxiQtRHK5nUpS+u+Sm+0J/Dq3WbPQ/UPA/bl8dUmj8udiztaHgXdnb1mRvGGndnRnQRAEc4Fux67pNmVX8iuQVCsjSQ+Gi21fnnd3L5T0beB2UvgDgP0lvZ+Uy/VZsqoGaJaz9SlJlwC3keLX305emUv6AvAVYHngTklX2P5MR3cdBEHQJSoeabi0dc2dDCTyKJY/QIq1UF9+YH1ZLm+as9X2keQ48nXlPwR+WGacQRAEc5uqq2vC4zUIgqADYpIPgiDoY1RxfU2ZUMMLSvqLUlLuKZK+mcvPyGV3SrokJxVpmpQ71+0p6f587Fko3y3LmSLpmEL5ApIuUgqef7NSZqogCILKIJU/ekGZlXwtpMFLSslD/pht2w+uebZKOp7kint0s6TckpYk6dw3IXm63ippAulBcxywse1/SjpH0ja2ryOFSXjW9hqSxpLCKOzWnVsPgiDonF5ZzZSl5fCcmC2kQWGCF8mZyQ267w7U/Ib/H3Ct7WdysPtrSfFr3grcX7ORB/4P2Dl/3pEULgHgEmAbRXyDIAgqxAiVP3pB2SiUI0lRJtcgZSS5OZefBWxPyvD0xbo+9Um5GwXDXxG4Clg7q2KmAx8hZZmapY/tGZKeJ0W8nCUSZXi8BkF79DpuTD9F06z6srOsCeUsIQ0krW/7btt75wfAj0hqlLMK3Rom5W4g+1lJ+wIXkWzn/0wKK1ya8HgNgqBXVD2sQVvDy5lHriepWWplr5NCF+xc13wsA6oaGCQYvu3f2N7U9mbAfQzM0jP7ZE/ZxUgJwYMgCCpB1Tdey1jXNAppcJ+kNXKZgB2Aewt9ZkvKTYqBvK2kJSQtAWyby1DONp7LPw+cnvtMYMBbdhfgd3U5YIMgCHpKP0ShnC2kAfBb4EZJi5IyPN0B7FvoM1tSbtvPSPoWAymqjrL9TP58oqQNC+W1lfwZpMTf04BnGEgkEgRBB/RLBMgqUHXrmjKJvBuGNAA2H6TP+CblZwJnNijfvUn7V4CPtRpjEARBr+iLjdcgCIKgMRV3eO3I4/XGgmfrPyRdlssl6YfZS/VOSRvl8q0K7SdLekXSR3Ld1pJuk3R3doaqhSNeQtKlWc5fJK0/534VQRAE7dMPdvINPV5tb1FrIOmX5FjywAeBNfOxKSlhyKa2ryclAa95v04DrpE0guTwtI3tv0o6irTZegbwNWCy7Z3yZu7JpFjzQRAElWCEqm0LMmSP11p93nzdGrgsF+0InJv7TSRlj1qhTuwuwJU5sfdSwGuFzdZrGTDHXJfsTGX7XmA1Scu1eY9BEARzjFEqf/SCspmhZkniXfN4zXwEuK4W5oDmnq1Fijb0TwGjJG2Sz3dhwJ7+DuCjeQxjgFVJ9vVBEASVYIRc+ujJ+Mo0sv267dGkCXZMnW68GJ+mJXlV/w6yjXw2sxwLnCDpL8CLQM1L9mjSm8Bk4ABSxqjZPGgljZM0SdKk0047rb46CIJgjtEPOvmZFJJ4bwfcLWlpUmaonQrNmnq2ZnYFLrX9n4Lcm4AtACRtC6yVy18A9s7lAh4EHmgwrghrEARBT6i4mfyQPV5r3q27AJdne/YaE4A9spXNu4HnbT9WqJ9t5V/weF0AOBT4ST5fXFItWNlngBsKaqEgCIKe0w8r+YZJvHPdWJJKpcgVpMiU04CXyStxgBxpcmXgD3V9vizpw1n+KbZrkSvfnq9tYAopvnwQBEFlUMWtazrxeMX2lg3KDOzXpP1DzL4Ji+0vA19uUH4TWXUTBEFQRXplNVOW8HgNgiDogGFvJ18jm1HeLunyfL66Ut7VaUp5WOeva7+zJNdMIyWtJunfBY/XnxTabizprizrh3mTFUmjJU3M7SdlM8ogCILK0A86+RoHAlOBRfP5McAJti/ME/Y+JO9WJL05t7+5TsbfsilmPacAn83tryBZ71wJHAt80/aVkrbP51u2MeYgCOYQnWaG6nV2qpqMTqm6dU3Z9H8rAR8CvgMcklfaWwMfz03OAcaTJ3ngW6SHwGx69gayVwAWzd6xSDqX5GB1JcmztvZQWQz4R5nxBkFQffol3PGwD1CW+QHwFVJ6PkihCJ6zPSOfz/RqzQHJVrb92wZyVs8qnz9IqsW+WTH3r1H0kD0IOE7SI8D3gK+WHG8QBMFcYdh7vGbTxidt31qi7QjgeOqSemceA1ax/U7gEODnOe7NYOwLHGx7ZeBgUtCyRtcNj9cgCHpC1WPXlFHXbA7skHXiC5LUJyeSwg2Myqv5mlfrm4H1gd/nvdPlgQmSdrA9iRTREtu3SvobyTzyUWaNR1P0kN2TpNsH+AUDaQFnITxegyDoFcPeusb2V22vZHs1kvPT72x/gpTQe5fcbE/g17aft7207dVy+4nADrYnZc/ZkQCS3koKRfxA9oZ9QdK7s65/DwbCFv8DeF/+vDVwfxfuOQiCoGtU3bqmk43hQ0mbsNNIOvqGqpQC7wXuzMHGLgE+V8jxWkvePQ34G2nTFZLFzfcl3QF8FxjXwXiDIAi6TrcneUnbSbovm5QfNki7WczUm9FugLLfA7/Pnx8gBScbrP2Whc+/BH7ZpN0kkpqnvvyPwMbtjDEIgmBu0k0TyqztOJkUI2w6cIukCbbvqWvXzEx9jo4vCIJgnmPUCJc+SjAGmGb7AduvAReSEjHVUzNTf6VB3azja+dmgmAoVMFhpZ/oFyeifrmPdlbKksYxq9r5tGw4UqNR0qVN62TMNFOX1NIXqfQkn18jJgGP2v6wpDOATQCRzFn2ynlgVyE5Ry0OjAQOs32FpA+QIlbOD7wGfLkWbVLSd0gbrkvYflPhmicAW+XThYFlbS9edsxBEFSXedEZqs4SsG0KZup7le3TSViDg2ux3SUdD+xPmsS/TgpHfIqkdUlhClYjpfn7b9v/yJmlrmbA6ek3wEnUWc/YPrhwcwfQJBpmEATDj35ZyXc51HCrpEutzNRno2yO11pYg5l26oUJXsBCDCT3bhiKwPbttmthCaYAC+UkIdieWJdYpBFtpRkMgiCYG3TZuuYWYM0cAHJ+ktn6hFrlYGbqzQSWXcnXwhq8uVgo6SxSgpB7GPByHQ9ck1feiwDvbyBvZ+A226+WubikVYHVgd81qZ+p5zr11FMZN27LMmKDIOghfaOu6aIs2zMk7U/SdIwEzrQ9RdJRwCTbEwaXMDstJ/liWANJW9YNaO+sq/8RsBtwFmnFfbbt70vaDDhP0vq238jy1iPtCm/bxtDg/6gAACAASURBVDjHApfYni2Jdx5HeLwGwTCjX9Q1Ja1mSmP7CpKau1h2RJO2W7aSN6SwBpJ+ZvuT+SKvS7qQtNI/ixRyeLtcd5OkBYGlgSez2udSYA/bfytx7RpjaZJtKgiC4UnfrOQrHoWyTPq/r5KjP+aV/JeAT0law/a0rJPfgYHk3g8D2wBnS3o76cHwT6Vk4L8lWdv8qewAJa0DLAHcVPqugiCoPP2ykh/ZsYQ5y1Dt5EVKsL1o/nwHKWIkJN38TyUdTNqE3cu2s55pDeAISbVXj21tPynpWFJs+oUlTQdOtz0+txkLXJhzxwbDkCqstoJZqcLfpH9W8tWemoYc1oCkxmnU5p5Gdba/DXy7SZ+vkNQ9jerGtzPGIOh3qjCxQXXG0WuGvbomCIIgaE7VJ/lOEnnfWEjK/Q9Jl+XydSTdJOlVSV+qk3GgpLslTZF0UKH8OEn3SrpT0qVZf1+r2yDLm6KU7HvBzm87CIKgO8w3ovzRC9q5bM3jFQDbW9genRNz3wT8Klc9A3yBlK5vJtnL9bOkADwbAh+WtEauvhZY3/YGJPvH2kbvKOBnpLDE65GSeP+nnRsMgiCYkwz79H/Q2OO1ULcoKaHHZQC2n7R9C7NPxm8Hbrb9cs4m9Qfgo7nPNYV8sRMZyBS1LXCn7Ttyu6eb2coHQRD0gn5JGlKfyLvIR4DramEOBuFuYAtJS0lamOQpu3KDdp9mIGnIWoAlXS3pNkkNN2eDIAh6xcg2jl7QkcdrZnea5F4tYnuqpGOAa4B/AZOBWVblkg4HZgDnF8b3HuBdwMvAdZJutX1dXb++DGtQBRvgIGhGJ9/Pbtm4V+F/pOobrx15vEpamqRj36nMxWyfQU4TKOm7pFjJ5PO9gA8D2xRs4qcDN9h+Kre5AtgImGWSj7AGQTD86Bc7+fm6HNag2ww1kfcnc/UuwOW2W2YnAZC0bP65Ckkf//N8vh1JHbSD7ZcLXa4G3iFp4bwJ+z5SMLQgCIJKUHWdfKd28mNJMeRnIml5UnKRRYE3sqnkulln/0tJS5E2Zfez/VzudhKwAHBtjpE80fbnbD+bY9XfQvKevcL2bzsccxAEQdfoB3XNTOo8XhtGQLP9OAPWMfV1WzQpX6NRea77GcmMMgiCoHL01SQfBEEQzMrIfopdEwRBEMxKjxxZS1PWGeqhHFJgsqRJuWxJSddKuj//XCKXbynp+ULIgyMKchaXdEkOYTA1JxVB0rdySIPJkq6R9JZWsoIgCKrAqBHlj56Mr422W9VMGTOHkZygjpZ0WD4/NNfdaPvDDWScCFxle5ecv3DhXH6c7W8ASPoCcATwuRaygiAIek7V1TWdPFt2BM7Jn88heb42RdJiwHvJdvK2X6tZ19R5yy7CQFLwIAiCSlN1E8qyk7xJyblvzd6lAMvZfix/fhxYrtB+M0l3SLoy53SFlIj7n8BZOZrl6ZIWqXWQ9B1JjwCfIK3kB5M1C5LGSZokadJpp53WqEkQBMEcoeqTfFl1zXtsP5qdma6VdG+xMmd+qq2+bwNWtf1S9pK9DFgzX2sj4ADbN0s6kaTi+UaWcThwuKSvAvsDRw4iaxbC4zUIgl5RdRPKUit524/mn0+SEnGPAZ6QtAJA/vlkbvOC7Zfy5yuA+XL4g+nAdNs3Z7GXkCb9es4Hdm4hKwiCoBLMN8Klj17QcpKXtIikN9c+k8L/3g1MAPbMzfYEfp3bLK/stippTL7G09lJ6hFJa+c+25BDFEgqrs53JCcFbyZryHcbBEHQZUa0cfSCMuqa5YBL81w7Cvi57ask3QJcLGkf4O/Arrn9LsC+kmYA/wbGFgKOHQCcny1rHgD2zuVH58n/jSzrcyVkBUEQ9Jyqq2taTvK2HyBlcqovf5q0Gq8vP4kUi6aRrMnAJg3Kd27SvqmseYEqRNgLgmZ0+v3sl1DDI4f7JB8EQRA0p1dp/crSTiLvRl6vzTxVl1BKyH2npL/k/K5IWjCf36GUmHu2pYCkH0p6qUH5zpIsabY3gSAIgl5RdRPKdvcCtsrJu2sT7XG2N8jJvC9nwL79a8DknJh7D5KnK8CrwNa2NwRGA9tJendNeJ7Al6i/aN74PRC4ub4uCIKgl4xS+aMXdLThO4in6rrA73Kbe4HVJC3nRG2VPl8+DCBpJHAcKXlIPd8CjgFKJScJgiCYW0jlj17QziTfyOu1mafqHaTMTzXTx1XJMeYljZQ0mWRXf23Bbn5/YELBi7YmfyNg5UgWEgRBFVEbRy9oZ5J/j+2NgA8C+0l6LyRPVdsrk5yY9s9tjwYWz5P5AcDt5KTdtl/P6p2VgDGS1s+6/I8BPypeUNII4Hjgi4MNLMIaBEHQK6q+ki9tXVP0epVU83q9odDkfOAK4MisxtkbIDszPUiyiy/Ke07S9cB2wFRgDWBatsdfWNI0YGNgfeD3uXx5YIKkHWxPKsiKsAZBEPSEfokn39DrdRBP1cWzwxPAZ4AbbL8gaRlJi+c2CwEfAO61/Vvby9teLScMf9n2Graft710oXwiKdn3JIIgCCqA5NJHLyi7km/m9frLJp6qbwfOyUHLpgD75PIVcvlI0gPmYtuXd+dWgiAYTnTD2a8KDoPD3uMVBvV6beapehOwVoPyO4F3lrjem5qUb9mqbxAEwdyk23O8pO1IZucjgdNtH11XfwhJQzKDFL7907b/3kxe1dVJQRAElaabzlBZy3EyycBlXWB3SevWNbsd2CT7IV0CHDvo+MrcRBNv14s0kHv1oWxJg6QxhfI7JO1UkLOdpPskTVNKGVgrP1vSg4V+o3P5jgWP2kmS3lNmvEEQBHOLLptQjgGm2X7A9mvAhaT9zpnYvt72y/l0Itk8vRlDzvFqe7faZ0nfB57Pp3eTnjIzcpz5OyT9hmRnfzJps3U6cIukCbbvyf2+bPuSumteR7Kdt6QNgIuBddoYcxAEwRyly6aRKwKPFM6nA5sO0n4f4MrBBHYcoCybSO4KbA1QeMIALMiAF+zMJ1TuV3tC3UMTCt6xELlfgyCoIO3ovLMj6bhC0WnZBLxtJH2SFNX3fYO1KzvJ17xdDZxaN6gtgCds31+4+KbAmSRP10/lVX2rJ9R3JB1BWr0fZvvVLGsn4H+BZYEPlRxvEAQVp19CDbdjXVPn09OIR4GVC+cr5bJZkPR+4HDgfbW5shlDzvFqu+YItTswy28qhypYT1LNlHLQ1wngq6Rk4POTfgGHAkdlWZeSzDffS4ph8/76zsWn46mnnsq4cVuWvK0gCHpFv5hQdtm65hZgTUmrkyb3scDHZ7me9E7gVGC7nJJ1UMqaUDb0dpU0ihSjZuMm/abmsMHrM8gTqhCv5lVJZwFfaiDrBklvlbR0cW8g14XHaxAMM/plJd9NJ6es9dgfuJpkQnmm7SmSjgIm2Z5ACuT4JuAX2XfpYds7NJPZcpLPHq4jbL9Y8HY9Kle/n+SxOr3QfnXgkTzYVUkbpQ8Bz9HkCSVpBduPZf3+R0ibt0haA/hb3njdCFiAyPEaBH1BrOQbY/sKUoiYYtkRhc+zaTMGY8g5XnPdWOpUNcB7gMMk/YfkCfv52sq70RMq9zlf0jKk39dkBjxndwb2yLL+DewWOV6DIKgSvQo8VpYh53jNdXs1KDsPOK9J+9meULl86ybtjyHFkQ+CoM/oF3VN5HgNgiDoYyo+x8ckHwRBb+gbnXzFZ/myYQ0Wl3SJpHslTZW0maQlJV0r6f78c4lC+y1zKIIpkv6Qy9YuhC2YLOkFSQcV+hyQ5U+RdGwu+4BSJqq78s+Gap0gCIJeUfXMUGVX8icCV9neJceJX5iUrPs620fnODSHAYfmePE/JtlwPpxt67F9Hyl5dy0Iz6PApfl8K5L364a2X631AZ4C/tv2PyStT9q0XbHz2w6CIOgOVQ813HIlL2kx4L3AGQC2X7P9HGlSPic3O4dk+gjJLPJXth/O7RsZ629DMo2shcfcFzi65rlV62P7dtv/yG2mAAtJWqC9WwyCIJhzVH0lX0ZdszopZvFZkm6XdHq2l1+u4MT0OMnUElIc+SUk/T6rWPZoILPe9HItYAtJN0v6g6R3NeizM3BbIxdeRY7XIAh6xAi59NELyqhrRgEbAQfYvlnSiSTVzEyys5IL7TcmrdYXAm6SNNH2XwGyumcHUiiD4jWWBN4NvAu4WNJbazbxktYjmVJu22iA4fEaBEGv6IeN1+nA9ByPBlKQ+o2AJ3IoYfLPJwvtr7b9r+wEdQOz2tl/kLQif6LuGr9y4i8kJ6qls+yVSLr7PWz/bSg3GQRBMKeourqmjDPU45IekbR23jzdhhQe+B5gT+Do/PPXucuvgZNyXJv5SZEmTyiInC2gGXAZsBVwvaS1cr+n8ibub0lRKf80xHsMgmAO0IkjUrccmargDFX19HplrWsOIIUemB94ANibnIhb0j6kJN67wsygZFcBd5JW5KfbrsWiWYSUNOR/6uSfCZwp6W7gNWDPrALaH1gDOCKHIQbYtkzktSAIgrlB1dU1ZaNQTiYFp69nmybtjyNFSqsv/xewVIPy14BPNij/NvDtMmMMgmB40TfOUBVfy4fHaxAEQQdI1Z7kh+zxWqj7oiRLqm2UStIPlZJ135lDBBdlLSppuqST8vmb6zxhn5L0g1z3Xkm3SZohaZfu3XYQBEG3qPbWaycer0hamWTW+HCh7QeBNfOxKXAKs6b5+xbJ4gYA2y+SPWGzzFuBX+XTh4G9aJBEJAiCoAqo4iHKyiQNqXm87gUz9eev5eoTgK8wYFkDyRP23GzjPjG/BdSSgmxMcpq6igY6/mxZsyxwY77WQ7n8jaHcXBAE1aVfrGuqHoeyzEq+6PG6IXArcCApK9Sjtu/QrNvLjRJ2ryjpCeD7pA3WZplNxgIXRWKQIOh/+mbjtQ908jWP11NsvxP4FzCeFKDsiEH61fN54IpiqsAGNMo01ZIIaxAEQa8QI0ofvaDMSr6Rx+t40gq/topfCbhN0hiaJ+zejBSf5vOkJLTzS3rJ9mEA+S1hlO1b272JCGsQzEv0j5qjPxj2OvkmHq+32Z5pIy/pIWAT209JmgDsL+lC0obr8zmQ2ScK7ffK7YsxcBp5wgZBEFScaqtrOvF4bcYVwPbANODlFm2L7Jr7zSRHo7wUWAL4b0nftL1eSXlBEARzHFXc5bVTj9da/WqFzwb2ayHvbODsurK3Nmh3C0ndEwRBUFH6YJIPgiAIGlN1nXwnOV4vKnipPiRpcl2fVSS9JOlLhbIzJT2ZA5EV246WNDHLmpQ3cFt6zwZBEPQaMbL00QuG7PFqe7dapaTvA8/X9TkeuLKu7GzgJODcuvJjgW/avlLS9vl8S1p7zwZBEPSUYa+Tb+HxitId7gpsXSj7CPAgyaZ+JrZvkLRag8sYWDR/Xgyo5XVt6j1b4t6CIAjmAsN8kqeJx2sOGwywBfCE7fsBJL0JOJQUN75szJmDgKslfY+kQvqvXN7QexaIST6YZ6mKp2gVvE2rQNVDDQ/V43Uw+/bxwAm2X2pjHPsCB9teGTgYOKONvuHxGgRBDxn+USgbebzWvFRHAR8lJe6usSmwi6RjgcWBNyS9YvukQa6xJykeDsAvgNPz52bes7MQHq9BMPzoF8/dYR+7xvbjwCOS1s5FtRyvkAKN3VuMR2N7C9urZdv5HwDfbTHBQ9LBvy9/3hq4P3+eAOyRrWzezYD3bBAEQSXoh9g10Nzjta2AYpIuIFnNLC1pOnCk7TOAzwIn5jeDV4BxuctQvWeDIKg4/bMvMPw3Xpt6vNreq0W/8XXnuzdp90dmVfnUylt6zwZBMDzpG3VNP0zyQRAEQWOGvZ18EATBnKB/1DXDfOM1CIIgaE63N14lbSfpvhzO5bAG9QvksDLTJN3cxMF0JjHJB0EQdICk0kcJWSOBk0khXdYFdpe0bl2zfYBnba9ByrN9zGAyY5IPgiDoiBFtHC0ZA0yz/UAOIXMhKbxLkR2Bc/LnS4BtNNgTxPY8dwDjei2jCmOoiowqjCHuI34Xc+MgmYdPKhzj6up3AU4vnH8KOKmuzd3ASoXzvwFLN7vmvLqSH9e6yRyXUYUxVEVGFcbQDRlVGENVZFRhDN2S0TVsn2Z7k8Ixx+OwzKuTfBAEQRUpE8plZpvsQLoY8HQzgTHJB0EQVIdbgDUlrZ4jDIwlhXcpMoEU7wuSeud3znqbRsyrdvLdeEXqVEYVxlAVGVUYQzdkVGEMVZFRhTF0S8Zcw/YMSfsDVwMjgTNtT5F0FDDJ9gRSlN7zJE0DniE9CJqiQR4AQRAEwTAn1DVBEAR9TEzyQRAEfUxM8kEQBH3MPDHJS1qq12MIqomkN+W8xEEfIGnzMmXzEvPExquk+4HJwFnAlYOZG7WQsxzwXeAttj+YY0ps5pT4ZLB+G9i+M3+ej5TofAzJc+3btl8uef11SC7NK+aiR4EJtqe2cQ/LkmL0r5eLpgA/tv1Eyf4jgc+Q7Hevsv2nQt3XbX+7pJx9ir+3LPfrtkuFFZQ0hpRy4Jb8d9iOlKXsipL9P09KY7kIKevDi8Axtn9cpn+WsQ7pb3GzCzmNJW1n+6qSMkaQbuSNbDK3PvCQ7WdK9J0f+E/t+yxpK1I+5ntsX9nGfawCvGD7uRzsahPS7/LuNmQM+bsp6ZDB6m0f38Y4brO9UauyeYl5YiUPrEUypfoUcL+k70paawhyziaZNr0ln/8VOKhkvxpHA2sA3wcWAn5S5sKSDiXFsRDwl3wIuKBRpLomMjYn2eECnJsPgJvbWO2cSkrV+DTwQ0nFf8CPlpQBKd7GFZJWkLQeMBF4c5mOko4EfgicIul/gZNIk/Vhkg4v0f/rwIeBLW0vZXtJYCvgg7muzBi+APyalDXtbknF+CLfLSnjI8BjwKO5/43AccCdkv67hIhbSHmUkfRl4Duk79Qh+fdSZgyHAX8AJkr6DHAVKTjWRa0m34KMTr+bb25xlBnDZpK+CCwj6ZDCMZ5kijjv0utYDj2IHbEVaZXxHOnLvVkbfW/JP28vlE0u0W+W9sB8tcUXcGfJa/+11q+ufH7g/pIyJgLvbFA+mrQaLSPjzsLnUaSH56+ABYr3WVLWbsBTwN+BzdvodxfpH3dh4AVg0Vy+UJnfJ3AfsGCD8oWAv7Yxhjflz6uR4pAcWP/3bvW9AJYHVs/3sXYuX5VkE92q/92Fz5OAhQp/l7Lfqyn5vpcivc0sk8sXKcqf09/NTg/SwuNI0kPzyMJxCLDm3BhDVY95whkq6+Q/SVrJP0FafU0gTW6/IP2TleFfWVbt9fjdwPMl+i0maSfSm9MCtv8DSdcgqazq6A3SG8Tf68pXyHVlWNT27fWFtidLKrViIv3j1vrNAMZJOgL4HVBaty1pTeBA4JfA24FPSbrd5VRXM2y/Drws6W+2X8jj+bekMr8L236lQWHZ/gAjnFU0th+StCVwiaRVaSPpp+3HASQ9bPu+XPb3mhqnBS9IWt9JrfIUsCDwb9IkX/Yt/fV836/lvk/nMfyrjYxHHX8385vMV0jfBUgPraNs/1HSYrYH/T+z/QfgD5LOzr+/hUt+l/qeeWKSB24CzgM+Ynt6oXySpFLqkswhpIfD2yT9CViG5Fbcij8AO+TPEyUtZ/sJScuT/jnLcBBwXd5feCSXrUJS/exfUoYkLWH72brCJSk/KUyq1znbPkrSP4BTSsoA+A2wv+3/y2FSDyGpH9YbvBsArxX+iWfmBpa0GOUmlUclbWP7umKhpG1IK8EyPCFptFP+Y2y/JOnDwJnAO0rKQNII228Any6UjaTwMB2EzwHnS7oDeJL0t7khX7+Uygi4TdLPSSv364BzJF0FbA3cU1JGR99NSfuSYqR/hTS5Q9oXOFbSicDXgA1LjuUtkq4kLThWkbQh8D+2P1+yf98xr2y87mr74rqyj9n+xRBkjQLWJq3W7qutyucGeXU3hlk3t27Jq9oy/ccBnwW+BNyWizcmJR040/ap3R3xoGNZtLYCL5StZfuvJfouYPvVBuVLAyvYvqtF//VI+vQ/Arfm4k2AzYEdbU8pMYaVSG8Ujzeo29yFDelBZLyLpPaBNCECTAOWA7aw/bMSMkYC25L2nUYB04GrbT/Xqm/uPwr4GOnt9BJgU2B34GHgZNv/KilnyN9NSVNJ6rpn6sqXyvdzsO2ye1c3kxZeE2y/M5fdbXv9Mv37kXllku/Kjnv+h/oQSQc78y3Ibez+N5C5fKOJok0Zb3LBuqNF2w+TVkxF65rjbP+mjet1aqHTaf8lB6uvnywa9F+DpAtfqzCGe0i6+sds/63MOLKsrQoypti+vo2+85E2Sz9NUnWIFF3wbOBrc3MBMaco892UNNX225vU3Wt7nTaud7PtTbPqrzbJ32G77JtA39HX6hpJHwS2B1aU9MNC1aLAjCGI/A3wCmn1VVZ324ozSA+OTriH9HrcEtuXA5cP9ULZCufnpImoZp2zMclC5xOtVrCd9s/cSlp5iqT3/QcDenADb23R/wfAV22fWTe2d+S6lpYtklYkbTi/wsDbwMckHQPsZLs+PGwjjiVZj6xu+8Usd1HgeyQrm0EttyTdRd4fqq8i7TtsUOI+msmAJKSljBaU+W6+IGlD23fUjW1Dyu15FXlE0n8Bzg/RA4HSJsb9SF+v5POXZDRwFHBEoepF4Pp63XQJeXd24Us/JAYxZxNwuJMZYCsZRwxSbdvfKiFjIrBv/QaupNHAqbY3nZP9G8ibuWJro88ttt/VpO4u2y116pIuBX5t++y68j2AnW3Xp2xrJON+YC3X/RPmN8Z7ba/Zov+qtY/Ab0kLmpnYrt8InVMyOvpuSnoPcD7Jj6WoPtsT+KTtP7YaQ0HW0sCJwPvz9a8BvtDq7a6f6euVfF4Z3CHp/GwJ0ilXStrW9jVdkNUu3yWt7hrdR9lN00b61UVIm15LAS0neTq30OmGhc8sXYfQZ/FB6hYqKWNd2zvNNhj7XJWw1R9oPvsqy/brKmF1VZyAJb1aZkKeEzLo8LuZLWg2BT4P7EX6m04F9iDtD5Se5IF32f5EsUDS5yjpj9KP9PUkL+li27sCtzf6pxnCqnwicGneZPoPA6/Fi7YYxzuAn5I2pa4EDq29RUj6i+0xJa59G3CZ7VvrK5ScWFpi+/uFPm8mvcruTXJk+X6zfrNfriMLnW5Y+HTKJEmftf3TujF8hoGVZCsajjV/N8o639wjaQ/b5xYLJX0SuLekjCrQje/m48ARkjYiTeyfAt5DMrFth2/kh9Xv8vW/TLIUikm+T/lR1gF/uK58ZWAom53HA5sBdzVagQ3CKcB40kPiM8AfJe2QN/jmKyljb1KCgEZsUnYgeTI9BPgEKeP7Rm2qrU4ArpHUyELnhLnQv149sGy9uqDERvhBpIf1J5hVPTA/MNvqvAm/lfRT4KCaBYqkRUj3UCq0Amnz+VeSPl03joXKjCNPiDUWkvROCjb6tm+bvVf3ZdDhd1PJ+3z3fDwFXERSJW9V4tr17ABcnif37YBauIV5ln7XyV9O2mC7q678HcB3bZdxHS/2u4HkCt/Wpmv97n62yKiFWfhxu1Y+Q0XScaTQA6eRzONKWeQ0kNORhU4X+h85WL3Lx7/ZihQrBpJlzO/K9Mt95yOpKfZmwAloFdKD82u2X2tD1tYUrHxcZ78/SL/BLHlse+u5IaNTlBzQbgT2sT0tlz1gu9UGejN5ywL/R3pwfrrNBVnf0e+TfMcbbHV9ziZZblwJzLTTbrVyVHJWea8LXnuSNiC9ii5pu2WUTEn1eR5nwfYOg9VnGW/kcc9gVl12KbVTMICSjft0UniMNYAtSVY59wLj56WNPkm/YXALnUG/m0oxfMaS/BSuIqkPT7dd1hMdSS8yYHFl0ltZ7Xs+T3+3+11d040NtiIP5mN+ynkk1jiG5K49sVZg+04lD8tvlJSxGcmb8ALgZijvOl+4Zsc6704tdLpk4dOxjC5wKvB+p5AASwBfJYXLGE16UyrjCd0RStEjn7T9iiSRNi03Ipkt/rSMsYGkHUjOU7M5l7XB92riSHtPpfTwNWxfBlyW1V07ktRpy0o6Bbi0jKGD7aFs2s8T9PtK/gJSJvNGG2wfsL1bb0bWPtms7gMkveUGJHO3C1zCO7PL4/hig+KZFjq2B41f02n/bsnolKIKTtLJwD9tj8/nk22PngtjuBsYY/vlbJ//NuAy0kYjtj89WP8s498kq6srSQuIq13Sg7qJvLZNWpvIWYLkibub7W3a6LcT6X/++Xy+OEnFelmnYxqu9PskvxxwKfAaDTbYXNLTVNIPbB/U7LW0xOtox6qWOnkLkCb744Bv2j6pnf7domChsw9wMfB920/Orf7dkjEU8gQ72vYMSfcC42zfUKvzXHCjl3SP7XXz51tJ5oNv5PNSXp6Sbic9FHYhqUzWJ/3PXOAU9KvdMfU0dnujB2y3HjzDlb5W1zi5yf9X3Qbbb9vZYMucl39+b9BWzelY1QIzJ/cPkSb41Ugx1S8d4piGTKcWOl2w8OmKjA65gBT18ClS9MYb87jWoH0vzaHyiKSt8/f5IZLV2N/VXiY059/bT4GfKgXN2xU4WtJKtlduJUCzhpkYmVfhRQudubk/0Ugl2dfzXCv6eiXfbSQdaPvEVmUN+nWsapF0LulBdQVwodvI2tNNOrXQ6YaFT7eshDpFKdT0CsA1BTPKtUhx5suYHnZ6/ZVJoSFGkh4s7yHlK1gc+FIZK53BVrmSVnU5j9cHGdj0rMdDtZIZCpLOJG2Gn5yL9iMZN+w1t8ZQNWKSb4NGr6LtvgoOVdWSLWOKHqu1P9xctYzp1EKnGxY+YSWU0ECgtaeZNQrlAsCjLhFoTSky6RTXxQzK/iWPl5FRJfLm7TdIYQ0AriWl2CwVTbMfmadfY8oiaXfg48Dqdfr1N9PcCaReRkeqlm5YxnSDTsfRjfuoyu+iAtQCrU2lEIRLbQRaIzkP3dSg/IU2E8NzVAAABSRJREFUZCDpvcATtu/LD4jNSDb/ZR3DukKezEulw5xXiEm+HH8mJZNYmlnd/18E7mzVuU7V8s2hqFokLUhKErFGvuaZZUzkgr5mOTeInW/7LqWE3HNFhqQfkGLJj5J0NbANyVrnkLxn8KWSY+mY7NzVyDhijjt1VZVQ17SJUtS+NZ0yGi0EjHIOEztIn45VLZIuIsXLuZGUaPnvtg8cyj0E/YGk+90kUqWkabbXaFQ3B2RMIS1iFiIlC1kxm3XOR8p3O9cSdkjauHC6ILAzKbnLV+bWGKpGrOTbQNJngXHAkiSb5JVIgY8GtePtknphXWcPXUlnAH/pgsxgeNONQGvdkGHb1kB+3Noi5g3mXtC52kDqx/wnSfP0/0pM8u2xH+m19GYA2/crxckYlC6pWmZmCcq22W12D/qQbgRa61awthtJK+fTgYuV8ga8j5TfeK5RZ845ghT8brG5OYaqEeqaNlBdajGl/Ji3uUXI4m6oWiS9zoDKR6RX45eZxyxKgtlRB4HWuiFD0pokU9LXbE+U9DbSA2Jhkqnw/e2OZ6jUmXPOIIUhOcptJB7pN2KSbwNJx5JscPcgxSn5PMmCYNAkESoEQ8sPhr/00iswCLqJuhztNeguYYrWHocB/yTleP0f4IpWE3xmFlXLHBpbEPSKphY6JHPhuYKkVZXS/yHp3ZK+pBThcp4mVvJt0IHHa6hagr6lGxY6XRjDNxhIHXghyRnq98CmwB22B02K3s/EJN8G3fB4DYJ+QxWI9irpHlKY54WBh4HlsxnnKGDy3DTjrBphXVOCbni8BkEf0w0LnU55xSkb12uS/mb7ZZhpiVY6S1c/EpN8OTryeA2CfqaL0V47YXFJHyWpQBfNn8nnYUIZBEEwnJF01mD1tveeW2OpGjHJt0EOLfsjUiq/+UkhXv8VG6dBMDyQtKftc3o9jrlJmFC2x0mkKJL3kyxkPsNA3OogCKrPPBfvKSb5NrE9DRhp+3XbZwHb9XpMQRCUZp6LBxIbr+3xsqT5gcnZ+/Ux4kEZBMOJeU4/HRNUe3yK9Dvbn+TctDIplGkQBMODWMkHzbH9d0nL5M/f7PV4giBomz+1btJfhHVNCZTi+h5JWsGPYCDC3Y9sH9XLsQVBAJIOGaze9vFzayxVI9Q15TgY2Bx4l+0lbS9BiomxuaSDezu0IAhI3ueDHfMssZIvgaTbSTE4nqorXwa4JmLXBEFQVWIlX4756id4ANv/BObrwXiCIKhD0o6S/iTpmXxcI+k9uW6eDW0Qk3w5BgtwNE8HPwqCKiBpX+Ab+VgtH0cDx0raDbihZ4PrMaGuKUFdPPhZqoAFbcdqPgh6iKSpwOa2n6krXwqYDhxs+yc9GVyPiUk+CIJhj6Sptt/epO5e2+vM7TFVhVDXBEHQD7wgacP6wlz2fA/GUxnCGSoIgn7gi8CEHHK4mLhkT+CTPRtVBQh1TRAEfYGk5YHPA+uRYtRMBa4FdrO9Xy/H1ktikg+CoK+QtBEpJPjHgAeBX9o+qbej6h2hrgmCYNgjaS3SxL478BRwEWkRu1VPB1YBYiX//9u7YxsEYiCIon9iIlqgFK4AJBIKoCwyGiAnuYA+KIUlMCG5xfJfBRONrLXllfTzkryAB3D+7HwgybOqdnOTzefrGkkdHBn7HdYklyQLf/it8Dee5CW1kWQDHBhjmz1wBW5VdZ8abCJLXlJLSbaMy9dTVS2z88xiyUtSY87kJakxS16SGrPkJakxS16SGrPkJamxN7miyplrTC7AAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l15zObKO_tRQ",
        "outputId": "62ce96cf-d1b4-4681-9745-5fc5dfcb2f67"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "City               0\n",
              "Datetime           0\n",
              "PM2.5              0\n",
              "PM10           48192\n",
              "NO                 0\n",
              "NO2                0\n",
              "NOx            28025\n",
              "NH3            48192\n",
              "CO                 0\n",
              "SO2                0\n",
              "O3              3852\n",
              "C6H6           65433\n",
              "C7H8           96049\n",
              "C8H10         312656\n",
              "AQI           129080\n",
              "AQI_Bucket    129080\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLJp3CiE_17-"
      },
      "source": [
        "df['PM10']=df['PM10'].fillna((df['PM10'].mean()))\n",
        "df['NOx']=df['NOx'].fillna((df['NOx'].mean()))\n",
        "df['NH3']=df['NH3'].fillna((df['NH3'].mean()))\n",
        "df['O3']=df['O3'].fillna((df['O3'].mean()))\n",
        "df['C6H6']=df['C6H6'].fillna((df['C6H6'].mean()))\n",
        "df['C7H8']=df['C7H8'].fillna((df['C7H8'].mean()))\n",
        "df['C8H10']=df['C8H10'].fillna((df['C8H10'].mean()))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "g6AP_RjkASWS",
        "outputId": "394d600d-8b25-4dd0-ffbf-135379c0ec7c"
      },
      "source": [
        "missing_values=pd.DataFrame(df.isnull().sum())\n",
        "missing_values.style.background_gradient(cmap=\"Blues\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
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              "            background-color:  #f7fbff;\n",
              "            color:  #000000;\n",
              "        }#T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row14_col0,#T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row15_col0{\n",
              "            background-color:  #08306b;\n",
              "            color:  #f1f1f1;\n",
              "        }</style><table id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >City</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >Datetime</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >PM2.5</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >PM10</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >NO</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >NO2</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >NOx</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >NH3</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >CO</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row8_col0\" class=\"data row8 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >SO2</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row9_col0\" class=\"data row9 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >O3</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row10_col0\" class=\"data row10 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >C6H6</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row11_col0\" class=\"data row11 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >C7H8</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row12_col0\" class=\"data row12 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >C8H10</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row13_col0\" class=\"data row13 col0\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >AQI</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row14_col0\" class=\"data row14 col0\" >129080</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >AQI_Bucket</th>\n",
              "                        <td id=\"T_9c2e7c1c_cf67_11eb_9b3a_0242ac1c0002row15_col0\" class=\"data row15 col0\" >129080</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fb43a02fc90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UZKaMDyBJ_h"
      },
      "source": [
        "def aqi_calc(df):\n",
        "    df[\"PM10_24hr_avg\"] = df.groupby(\"City\")[\"PM10\"].rolling(window = 24, min_periods = 16).mean().values\n",
        "    df[\"PM2.5_24hr_avg\"] = df.groupby(\"City\")[\"PM2.5\"].rolling(window = 24, min_periods = 16).mean().values\n",
        "    df[\"SO2_24hr_avg\"] = df.groupby(\"City\")[\"SO2\"].rolling(window = 24, min_periods = 16).mean().values\n",
        "    df[\"NOx_24hr_avg\"] = df.groupby(\"City\")[\"NOx\"].rolling(window = 24, min_periods = 16).mean().values\n",
        "    df[\"NH3_24hr_avg\"] = df.groupby(\"City\")[\"NH3\"].rolling(window = 24, min_periods = 16).mean().values\n",
        "    df[\"CO_8hr_max\"] = df.groupby(\"City\")[\"CO\"].rolling(window = 8, min_periods = 1).max().values\n",
        "    df[\"O3_8hr_max\"] = df.groupby(\"City\")[\"O3\"].rolling(window = 8, min_periods = 1).max().values"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WW-QloqGDWBM"
      },
      "source": [
        "aqi_calc(df)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xCzctp4kCUfo"
      },
      "source": [
        "## PM2.5 Sub-Index calculation\n",
        "def get_PM25_subindex(x):\n",
        "        if x <= 30:\n",
        "            return x * 50 / 30\n",
        "        elif x <= 60:\n",
        "            return 50 + (x - 30) * 50 / 30\n",
        "        elif x <= 90:\n",
        "            return 100 + (x - 60) * 100 / 30\n",
        "        elif x <= 120:\n",
        "            return 200 + (x - 90) * 100 / 30\n",
        "        elif x <= 250:\n",
        "            return 300 + (x - 120) * 100 / 130\n",
        "        elif x > 250:\n",
        "            return 400 + (x - 250) * 100 / 130\n",
        "        else:\n",
        "            return 0\n",
        "\n",
        "\n",
        "df[\"PM2.5_SubIndex\"] = df[\"PM2.5_24hr_avg\"].apply(lambda x: get_PM25_subindex(x))\n",
        "\n",
        "    ## PM10 Sub-Index calculation\n",
        "def get_PM10_subindex(x):\n",
        "        if x <= 50:\n",
        "            return x\n",
        "        elif x <= 100:\n",
        "            return x\n",
        "        elif x <= 250:\n",
        "            return 100 + (x - 100) * 100 / 150\n",
        "        elif x <= 350:\n",
        "            return 200 + (x - 250)\n",
        "        elif x <= 430:\n",
        "            return 300 + (x - 350) * 100 / 80\n",
        "        elif x > 430:\n",
        "            return 400 + (x - 430) * 100 / 80\n",
        "        else:\n",
        "            return 0\n",
        "\n",
        "df[\"PM10_SubIndex\"] = df[\"PM10_24hr_avg\"].apply(lambda x: get_PM10_subindex(x))\n",
        "\n",
        "\n",
        "\n",
        "    ## SO2 Sub-Index calculation\n",
        "def get_SO2_subindex(x):\n",
        "        if x <= 40:\n",
        "            return x * 50 / 40\n",
        "        elif x <= 80:\n",
        "            return 50 + (x - 40) * 50 / 40\n",
        "        elif x <= 380:\n",
        "            return 100 + (x - 80) * 100 / 300\n",
        "        elif x <= 800:\n",
        "            return 200 + (x - 380) * 100 / 420\n",
        "        elif x <= 1600:\n",
        "            return 300 + (x - 800) * 100 / 800\n",
        "        elif x > 1600:\n",
        "            return 400 + (x - 1600) * 100 / 800\n",
        "        else:\n",
        "            return 0\n",
        "\n",
        "df[\"SO2_SubIndex\"] = df[\"SO2_24hr_avg\"].apply(lambda x: get_SO2_subindex(x))\n",
        "    ## NOx Sub-Index calculation\n",
        "def get_NOx_subindex(x):\n",
        "        if x <= 40:\n",
        "            return x * 50 / 40\n",
        "        elif x <= 80:\n",
        "            return 50 + (x - 40) * 50 / 40\n",
        "        elif x <= 180:\n",
        "            return 100 + (x - 80) * 100 / 100\n",
        "        elif x <= 280:\n",
        "            return 200 + (x - 180) * 100 / 100\n",
        "        elif x <= 400:\n",
        "            return 300 + (x - 280) * 100 / 120\n",
        "        elif x > 400:\n",
        "            return 400 + (x - 400) * 100 / 120\n",
        "        else:\n",
        "            return 0\n",
        "\n",
        "df[\"NOx_SubIndex\"] = df[\"NOx_24hr_avg\"].apply(lambda x: get_NOx_subindex(x))\n",
        "\n",
        "    ## NH3 Sub-Index calculation\n",
        "def get_NH3_subindex(x):\n",
        "        if x <= 200:\n",
        "            return x * 50 / 200\n",
        "        elif x <= 400:\n",
        "            return 50 + (x - 200) * 50 / 200\n",
        "        elif x <= 800:\n",
        "            return 100 + (x - 400) * 100 / 400\n",
        "        elif x <= 1200:\n",
        "            return 200 + (x - 800) * 100 / 400\n",
        "        elif x <= 1800:\n",
        "            return 300 + (x - 1200) * 100 / 600\n",
        "        elif x > 1800:\n",
        "            return 400 + (x - 1800) * 100 / 600\n",
        "        else:\n",
        "            return 0\n",
        "\n",
        "df[\"NH3_SubIndex\"] = df[\"NH3_24hr_avg\"].apply(lambda x: get_NH3_subindex(x))\n",
        "\n",
        "    ## CO Sub-Index calculation\n",
        "def get_CO_subindex(x):\n",
        "        if x <= 1:\n",
        "            return x * 50 / 1\n",
        "        elif x <= 2:\n",
        "            return 50 + (x - 1) * 50 / 1\n",
        "        elif x <= 10:\n",
        "            return 100 + (x - 2) * 100 / 8\n",
        "        elif x <= 17:\n",
        "            return 200 + (x - 10) * 100 / 7\n",
        "        elif x <= 34:\n",
        "            return 300 + (x - 17) * 100 / 17\n",
        "        elif x > 34:\n",
        "            return 400 + (x - 34) * 100 / 17\n",
        "        else:\n",
        "            return 0\n",
        "\n",
        "df[\"CO_SubIndex\"] = df[\"CO_8hr_max\"].apply(lambda x: get_CO_subindex(x))\n",
        "\n",
        "    ## O3 Sub-Index calculation\n",
        "def get_O3_subindex(x):\n",
        "        if x <= 50:\n",
        "            return x * 50 / 50\n",
        "        elif x <= 100:\n",
        "            return 50 + (x - 50) * 50 / 50\n",
        "        elif x <= 168:\n",
        "            return 100 + (x - 100) * 100 / 68\n",
        "        elif x <= 208:\n",
        "            return 200 + (x - 168) * 100 / 40\n",
        "        elif x <= 748:\n",
        "            return 300 + (x - 208) * 100 / 539\n",
        "        elif x > 748:\n",
        "            return 400 + (x - 400) * 100 / 539\n",
        "        else:\n",
        "            return 0\n",
        "\n",
        "df[\"O3_SubIndex\"] = df[\"O3_8hr_max\"].apply(lambda x: get_O3_subindex(x))\n",
        "    ## AQI bucketing\n",
        "def get_AQI_bucket(x):\n",
        "        if x <= 50:\n",
        "            return \"Good\"\n",
        "        elif x <= 100:\n",
        "            return \"Satisfactory\"\n",
        "        elif x <= 200:\n",
        "          return \"Moderate\"\n",
        "        elif x <= 300:\n",
        "            return \"Poor\"\n",
        "        elif x <= 400:\n",
        "            return \"Very Poor\"\n",
        "        elif x > 400:\n",
        "            return \"Severe\"\n",
        "        else:\n",
        "            return np.NaN\n",
        "\n",
        "df[\"Checks\"] = (df[\"PM2.5_SubIndex\"] > 0).astype(int) + \\\n",
        "                    (df[\"PM10_SubIndex\"] > 0).astype(int) + \\\n",
        "                    (df[\"SO2_SubIndex\"] > 0).astype(int) + \\\n",
        "                    (df[\"NOx_SubIndex\"] > 0).astype(int) + \\\n",
        "                    (df[\"NH3_SubIndex\"] > 0).astype(int) + \\\n",
        "                    (df[\"CO_SubIndex\"] > 0).astype(int) + \\\n",
        "                    (df[\"O3_SubIndex\"] > 0).astype(int)\n",
        "\n",
        "df[\"AQI_calculated\"] = round(df[[\"PM2.5_SubIndex\", \"PM10_SubIndex\", \"SO2_SubIndex\", \"NOx_SubIndex\",\n",
        "                                     \"NH3_SubIndex\", \"CO_SubIndex\", \"O3_SubIndex\"]].max(axis = 1))\n",
        "df.loc[df[\"PM2.5_SubIndex\"] + df[\"PM10_SubIndex\"] <= 0, \"AQI_calculated\"] = np.NaN\n",
        "df.loc[df.Checks < 3, \"AQI_calculated\"] = np.NaN\n",
        "\n",
        "df[\"AQI_bucket_calculated\"] = df[\"AQI_calculated\"].apply(lambda x: get_AQI_bucket(x))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 773
        },
        "id": "193Ldy8UL9Y5",
        "outputId": "833f6a99-8209-4565-b145-dae28c3a41ae"
      },
      "source": [
        "df"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City</th>\n",
              "      <th>Datetime</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>PM10</th>\n",
              "      <th>NO</th>\n",
              "      <th>NO2</th>\n",
              "      <th>NOx</th>\n",
              "      <th>NH3</th>\n",
              "      <th>CO</th>\n",
              "      <th>SO2</th>\n",
              "      <th>O3</th>\n",
              "      <th>C6H6</th>\n",
              "      <th>C7H8</th>\n",
              "      <th>C8H10</th>\n",
              "      <th>AQI</th>\n",
              "      <th>AQI_Bucket</th>\n",
              "      <th>PM10_24hr_avg</th>\n",
              "      <th>PM2.5_24hr_avg</th>\n",
              "      <th>SO2_24hr_avg</th>\n",
              "      <th>NOx_24hr_avg</th>\n",
              "      <th>NH3_24hr_avg</th>\n",
              "      <th>CO_8hr_max</th>\n",
              "      <th>O3_8hr_max</th>\n",
              "      <th>PM2.5_SubIndex</th>\n",
              "      <th>PM10_SubIndex</th>\n",
              "      <th>SO2_SubIndex</th>\n",
              "      <th>NOx_SubIndex</th>\n",
              "      <th>NH3_SubIndex</th>\n",
              "      <th>CO_SubIndex</th>\n",
              "      <th>O3_SubIndex</th>\n",
              "      <th>Checks</th>\n",
              "      <th>AQI_calculated</th>\n",
              "      <th>AQI_bucket_calculated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 01:00:00</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>1.00</td>\n",
              "      <td>40.01</td>\n",
              "      <td>36.37</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>1.00</td>\n",
              "      <td>122.070000</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.0</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 02:00:00</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>0.02</td>\n",
              "      <td>27.75</td>\n",
              "      <td>19.73</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>0.02</td>\n",
              "      <td>85.900000</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.0</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 03:00:00</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>0.08</td>\n",
              "      <td>19.32</td>\n",
              "      <td>11.08</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>0.08</td>\n",
              "      <td>52.830000</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.0</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 04:00:00</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>0.30</td>\n",
              "      <td>16.45</td>\n",
              "      <td>9.20</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>0.30</td>\n",
              "      <td>39.530000</td>\n",
              "      <td>153.580000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>153.580000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.0</td>\n",
              "      <td>178.794118</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 05:00:00</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>0.12</td>\n",
              "      <td>14.90</td>\n",
              "      <td>7.85</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>0.12</td>\n",
              "      <td>32.630000</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>153.580000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.0</td>\n",
              "      <td>178.794118</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <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",
              "      <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>707870</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 20:00:00</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>2.75</td>\n",
              "      <td>25.57</td>\n",
              "      <td>15.85</td>\n",
              "      <td>4.570000</td>\n",
              "      <td>0.62</td>\n",
              "      <td>12.750689</td>\n",
              "      <td>27.750000</td>\n",
              "      <td>3.842899</td>\n",
              "      <td>8.179757</td>\n",
              "      <td>2.725523</td>\n",
              "      <td>51.0</td>\n",
              "      <td>Satisfactory</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>16.156250</td>\n",
              "      <td>10.231307</td>\n",
              "      <td>17.695833</td>\n",
              "      <td>10.135000</td>\n",
              "      <td>0.66</td>\n",
              "      <td>50.730000</td>\n",
              "      <td>26.927083</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>12.789134</td>\n",
              "      <td>22.119792</td>\n",
              "      <td>2.533750</td>\n",
              "      <td>33.0</td>\n",
              "      <td>50.730000</td>\n",
              "      <td>7</td>\n",
              "      <td>51.0</td>\n",
              "      <td>Satisfactory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707871</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 21:00:00</td>\n",
              "      <td>17.250000</td>\n",
              "      <td>49.250000</td>\n",
              "      <td>3.62</td>\n",
              "      <td>33.20</td>\n",
              "      <td>20.62</td>\n",
              "      <td>3.780000</td>\n",
              "      <td>0.76</td>\n",
              "      <td>2.020000</td>\n",
              "      <td>25.580000</td>\n",
              "      <td>3.842899</td>\n",
              "      <td>8.179757</td>\n",
              "      <td>2.725523</td>\n",
              "      <td>51.0</td>\n",
              "      <td>Satisfactory</td>\n",
              "      <td>48.447917</td>\n",
              "      <td>16.218750</td>\n",
              "      <td>10.085057</td>\n",
              "      <td>18.109167</td>\n",
              "      <td>10.036250</td>\n",
              "      <td>0.76</td>\n",
              "      <td>50.730000</td>\n",
              "      <td>27.031250</td>\n",
              "      <td>48.447917</td>\n",
              "      <td>12.606322</td>\n",
              "      <td>22.636458</td>\n",
              "      <td>2.509062</td>\n",
              "      <td>38.0</td>\n",
              "      <td>50.730000</td>\n",
              "      <td>7</td>\n",
              "      <td>51.0</td>\n",
              "      <td>Satisfactory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707872</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 22:00:00</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>2.20</td>\n",
              "      <td>30.80</td>\n",
              "      <td>18.20</td>\n",
              "      <td>3.670000</td>\n",
              "      <td>0.58</td>\n",
              "      <td>1.770000</td>\n",
              "      <td>26.150000</td>\n",
              "      <td>3.842899</td>\n",
              "      <td>8.179757</td>\n",
              "      <td>2.725523</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>49.770833</td>\n",
              "      <td>16.895833</td>\n",
              "      <td>10.069224</td>\n",
              "      <td>18.496667</td>\n",
              "      <td>10.007917</td>\n",
              "      <td>0.76</td>\n",
              "      <td>41.550000</td>\n",
              "      <td>28.159722</td>\n",
              "      <td>49.770833</td>\n",
              "      <td>12.586530</td>\n",
              "      <td>23.120833</td>\n",
              "      <td>2.501979</td>\n",
              "      <td>38.0</td>\n",
              "      <td>41.550000</td>\n",
              "      <td>7</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707873</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 23:00:00</td>\n",
              "      <td>15.750000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>1.02</td>\n",
              "      <td>28.90</td>\n",
              "      <td>16.00</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>15.820000</td>\n",
              "      <td>3.842899</td>\n",
              "      <td>8.179757</td>\n",
              "      <td>2.725523</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>49.968750</td>\n",
              "      <td>16.635417</td>\n",
              "      <td>10.081724</td>\n",
              "      <td>18.799583</td>\n",
              "      <td>10.031667</td>\n",
              "      <td>0.76</td>\n",
              "      <td>41.380000</td>\n",
              "      <td>27.725694</td>\n",
              "      <td>49.968750</td>\n",
              "      <td>12.602155</td>\n",
              "      <td>23.499479</td>\n",
              "      <td>2.507917</td>\n",
              "      <td>38.0</td>\n",
              "      <td>41.380000</td>\n",
              "      <td>7</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707874</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-07-01 00:00:00</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>0.40</td>\n",
              "      <td>26.85</td>\n",
              "      <td>14.05</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>0.59</td>\n",
              "      <td>2.100000</td>\n",
              "      <td>17.050000</td>\n",
              "      <td>3.842899</td>\n",
              "      <td>8.179757</td>\n",
              "      <td>2.725523</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>49.885417</td>\n",
              "      <td>15.593750</td>\n",
              "      <td>9.637945</td>\n",
              "      <td>19.039167</td>\n",
              "      <td>10.092083</td>\n",
              "      <td>0.76</td>\n",
              "      <td>41.380000</td>\n",
              "      <td>25.989583</td>\n",
              "      <td>49.885417</td>\n",
              "      <td>12.047432</td>\n",
              "      <td>23.798958</td>\n",
              "      <td>2.523021</td>\n",
              "      <td>38.0</td>\n",
              "      <td>41.380000</td>\n",
              "      <td>7</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>707875 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 City            Datetime  ...  AQI_calculated  AQI_bucket_calculated\n",
              "0           Ahmedabad 2015-01-01 01:00:00  ...             NaN                    NaN\n",
              "1           Ahmedabad 2015-01-01 02:00:00  ...             NaN                    NaN\n",
              "2           Ahmedabad 2015-01-01 03:00:00  ...             NaN                    NaN\n",
              "3           Ahmedabad 2015-01-01 04:00:00  ...             NaN                    NaN\n",
              "4           Ahmedabad 2015-01-01 05:00:00  ...             NaN                    NaN\n",
              "...               ...                 ...  ...             ...                    ...\n",
              "707870  Visakhapatnam 2020-06-30 20:00:00  ...            51.0           Satisfactory\n",
              "707871  Visakhapatnam 2020-06-30 21:00:00  ...            51.0           Satisfactory\n",
              "707872  Visakhapatnam 2020-06-30 22:00:00  ...            50.0                   Good\n",
              "707873  Visakhapatnam 2020-06-30 23:00:00  ...            50.0                   Good\n",
              "707874  Visakhapatnam 2020-07-01 00:00:00  ...            50.0                   Good\n",
              "\n",
              "[707875 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQRZIbYkN7L0",
        "outputId": "2599bf8b-9b28-4631-f091-62b1d9f65c17"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(707875, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWaipBSnNg8C"
      },
      "source": [
        "df.drop(['AQI','AQI_Bucket'],axis=1,inplace=True)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXYPDcF6N1TE"
      },
      "source": [
        "df.dropna(inplace=True,axis=0)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVrflV1jG34c",
        "outputId": "298f2d89-0a84-4562-8ba4-eead638648f7"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(707485, 31)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aj0ursrjLW12",
        "outputId": "59ee0ccb-8a10-4fbe-b612-54e99880f0c2"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['City', 'Datetime', 'PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO',\n",
              "       'SO2', 'O3', 'C6H6', 'C7H8', 'C8H10', 'PM10_24hr_avg', 'PM2.5_24hr_avg',\n",
              "       'SO2_24hr_avg', 'NOx_24hr_avg', 'NH3_24hr_avg', 'CO_8hr_max',\n",
              "       'O3_8hr_max', 'PM2.5_SubIndex', 'PM10_SubIndex', 'SO2_SubIndex',\n",
              "       'NOx_SubIndex', 'NH3_SubIndex', 'CO_SubIndex', 'O3_SubIndex', 'Checks',\n",
              "       'AQI_calculated', 'AQI_bucket_calculated'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 773
        },
        "id": "v0CppNC3OEo0",
        "outputId": "5a39fe5b-df58-41ed-f0ec-357e8928e66b"
      },
      "source": [
        "df"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City</th>\n",
              "      <th>Datetime</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>PM10</th>\n",
              "      <th>NO</th>\n",
              "      <th>NO2</th>\n",
              "      <th>NOx</th>\n",
              "      <th>NH3</th>\n",
              "      <th>CO</th>\n",
              "      <th>SO2</th>\n",
              "      <th>O3</th>\n",
              "      <th>C6H6</th>\n",
              "      <th>C7H8</th>\n",
              "      <th>C8H10</th>\n",
              "      <th>PM10_24hr_avg</th>\n",
              "      <th>PM2.5_24hr_avg</th>\n",
              "      <th>SO2_24hr_avg</th>\n",
              "      <th>NOx_24hr_avg</th>\n",
              "      <th>NH3_24hr_avg</th>\n",
              "      <th>CO_8hr_max</th>\n",
              "      <th>O3_8hr_max</th>\n",
              "      <th>PM2.5_SubIndex</th>\n",
              "      <th>PM10_SubIndex</th>\n",
              "      <th>SO2_SubIndex</th>\n",
              "      <th>NOx_SubIndex</th>\n",
              "      <th>NH3_SubIndex</th>\n",
              "      <th>CO_SubIndex</th>\n",
              "      <th>O3_SubIndex</th>\n",
              "      <th>Checks</th>\n",
              "      <th>AQI_calculated</th>\n",
              "      <th>AQI_bucket_calculated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 16:00:00</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>0.87</td>\n",
              "      <td>17.28</td>\n",
              "      <td>18.32</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>0.87</td>\n",
              "      <td>17.830000</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>33.943750</td>\n",
              "      <td>16.070625</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>2.27</td>\n",
              "      <td>122.08</td>\n",
              "      <td>124.244236</td>\n",
              "      <td>107.662005</td>\n",
              "      <td>42.429688</td>\n",
              "      <td>20.088281</td>\n",
              "      <td>5.753233</td>\n",
              "      <td>103.375</td>\n",
              "      <td>132.470588</td>\n",
              "      <td>7</td>\n",
              "      <td>132.0</td>\n",
              "      <td>Moderate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 17:00:00</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>0.80</td>\n",
              "      <td>19.04</td>\n",
              "      <td>20.00</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>0.80</td>\n",
              "      <td>16.140000</td>\n",
              "      <td>187.620000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>32.896471</td>\n",
              "      <td>16.301765</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>2.27</td>\n",
              "      <td>187.62</td>\n",
              "      <td>124.244236</td>\n",
              "      <td>107.662005</td>\n",
              "      <td>41.120588</td>\n",
              "      <td>20.377206</td>\n",
              "      <td>5.753233</td>\n",
              "      <td>103.375</td>\n",
              "      <td>249.050000</td>\n",
              "      <td>7</td>\n",
              "      <td>249.0</td>\n",
              "      <td>Poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 18:00:00</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>0.47</td>\n",
              "      <td>21.24</td>\n",
              "      <td>22.70</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>0.47</td>\n",
              "      <td>11.930000</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>31.731667</td>\n",
              "      <td>16.657222</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>2.27</td>\n",
              "      <td>187.62</td>\n",
              "      <td>124.244236</td>\n",
              "      <td>107.662005</td>\n",
              "      <td>39.664583</td>\n",
              "      <td>20.821528</td>\n",
              "      <td>5.753233</td>\n",
              "      <td>103.375</td>\n",
              "      <td>249.050000</td>\n",
              "      <td>7</td>\n",
              "      <td>249.0</td>\n",
              "      <td>Poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 19:00:00</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>0.53</td>\n",
              "      <td>25.63</td>\n",
              "      <td>27.42</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>0.53</td>\n",
              "      <td>14.990000</td>\n",
              "      <td>39.067821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>30.850526</td>\n",
              "      <td>17.223684</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>1.85</td>\n",
              "      <td>187.62</td>\n",
              "      <td>124.244236</td>\n",
              "      <td>107.662005</td>\n",
              "      <td>38.563158</td>\n",
              "      <td>21.529605</td>\n",
              "      <td>5.753233</td>\n",
              "      <td>92.500</td>\n",
              "      <td>249.050000</td>\n",
              "      <td>7</td>\n",
              "      <td>249.0</td>\n",
              "      <td>Poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-01-01 20:00:00</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>0.47</td>\n",
              "      <td>16.22</td>\n",
              "      <td>16.00</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>0.47</td>\n",
              "      <td>13.660000</td>\n",
              "      <td>187.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.230000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>111.493008</td>\n",
              "      <td>67.273271</td>\n",
              "      <td>29.991000</td>\n",
              "      <td>17.162500</td>\n",
              "      <td>23.012932</td>\n",
              "      <td>1.85</td>\n",
              "      <td>187.62</td>\n",
              "      <td>124.244236</td>\n",
              "      <td>107.662005</td>\n",
              "      <td>37.488750</td>\n",
              "      <td>21.453125</td>\n",
              "      <td>5.753233</td>\n",
              "      <td>92.500</td>\n",
              "      <td>249.050000</td>\n",
              "      <td>7</td>\n",
              "      <td>249.0</td>\n",
              "      <td>Poor</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",
              "      <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",
              "      <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>707870</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 20:00:00</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>2.75</td>\n",
              "      <td>25.57</td>\n",
              "      <td>15.85</td>\n",
              "      <td>4.570000</td>\n",
              "      <td>0.62</td>\n",
              "      <td>12.750689</td>\n",
              "      <td>27.750000</td>\n",
              "      <td>3.842899</td>\n",
              "      <td>8.179757</td>\n",
              "      <td>2.725523</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>16.156250</td>\n",
              "      <td>10.231307</td>\n",
              "      <td>17.695833</td>\n",
              "      <td>10.135000</td>\n",
              "      <td>0.66</td>\n",
              "      <td>50.73</td>\n",
              "      <td>26.927083</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>12.789134</td>\n",
              "      <td>22.119792</td>\n",
              "      <td>2.533750</td>\n",
              "      <td>33.000</td>\n",
              "      <td>50.730000</td>\n",
              "      <td>7</td>\n",
              "      <td>51.0</td>\n",
              "      <td>Satisfactory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707871</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 21:00:00</td>\n",
              "      <td>17.250000</td>\n",
              "      <td>49.250000</td>\n",
              "      <td>3.62</td>\n",
              "      <td>33.20</td>\n",
              "      <td>20.62</td>\n",
              "      <td>3.780000</td>\n",
              "      <td>0.76</td>\n",
              "      <td>2.020000</td>\n",
              "      <td>25.580000</td>\n",
              "      <td>3.842899</td>\n",
              "      <td>8.179757</td>\n",
              "      <td>2.725523</td>\n",
              "      <td>48.447917</td>\n",
              "      <td>16.218750</td>\n",
              "      <td>10.085057</td>\n",
              "      <td>18.109167</td>\n",
              "      <td>10.036250</td>\n",
              "      <td>0.76</td>\n",
              "      <td>50.73</td>\n",
              "      <td>27.031250</td>\n",
              "      <td>48.447917</td>\n",
              "      <td>12.606322</td>\n",
              "      <td>22.636458</td>\n",
              "      <td>2.509062</td>\n",
              "      <td>38.000</td>\n",
              "      <td>50.730000</td>\n",
              "      <td>7</td>\n",
              "      <td>51.0</td>\n",
              "      <td>Satisfactory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707872</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 22:00:00</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>2.20</td>\n",
              "      <td>30.80</td>\n",
              "      <td>18.20</td>\n",
              "      <td>3.670000</td>\n",
              "      <td>0.58</td>\n",
              "      <td>1.770000</td>\n",
              "      <td>26.150000</td>\n",
              "      <td>3.842899</td>\n",
              "      <td>8.179757</td>\n",
              "      <td>2.725523</td>\n",
              "      <td>49.770833</td>\n",
              "      <td>16.895833</td>\n",
              "      <td>10.069224</td>\n",
              "      <td>18.496667</td>\n",
              "      <td>10.007917</td>\n",
              "      <td>0.76</td>\n",
              "      <td>41.55</td>\n",
              "      <td>28.159722</td>\n",
              "      <td>49.770833</td>\n",
              "      <td>12.586530</td>\n",
              "      <td>23.120833</td>\n",
              "      <td>2.501979</td>\n",
              "      <td>38.000</td>\n",
              "      <td>41.550000</td>\n",
              "      <td>7</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707873</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-06-30 23:00:00</td>\n",
              "      <td>15.750000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>1.02</td>\n",
              "      <td>28.90</td>\n",
              "      <td>16.00</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>15.820000</td>\n",
              "      <td>3.842899</td>\n",
              "      <td>8.179757</td>\n",
              "      <td>2.725523</td>\n",
              "      <td>49.968750</td>\n",
              "      <td>16.635417</td>\n",
              "      <td>10.081724</td>\n",
              "      <td>18.799583</td>\n",
              "      <td>10.031667</td>\n",
              "      <td>0.76</td>\n",
              "      <td>41.38</td>\n",
              "      <td>27.725694</td>\n",
              "      <td>49.968750</td>\n",
              "      <td>12.602155</td>\n",
              "      <td>23.499479</td>\n",
              "      <td>2.507917</td>\n",
              "      <td>38.000</td>\n",
              "      <td>41.380000</td>\n",
              "      <td>7</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707874</th>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>2020-07-01 00:00:00</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>0.40</td>\n",
              "      <td>26.85</td>\n",
              "      <td>14.05</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>0.59</td>\n",
              "      <td>2.100000</td>\n",
              "      <td>17.050000</td>\n",
              "      <td>3.842899</td>\n",
              "      <td>8.179757</td>\n",
              "      <td>2.725523</td>\n",
              "      <td>49.885417</td>\n",
              "      <td>15.593750</td>\n",
              "      <td>9.637945</td>\n",
              "      <td>19.039167</td>\n",
              "      <td>10.092083</td>\n",
              "      <td>0.76</td>\n",
              "      <td>41.38</td>\n",
              "      <td>25.989583</td>\n",
              "      <td>49.885417</td>\n",
              "      <td>12.047432</td>\n",
              "      <td>23.798958</td>\n",
              "      <td>2.523021</td>\n",
              "      <td>38.000</td>\n",
              "      <td>41.380000</td>\n",
              "      <td>7</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>707485 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 City            Datetime  ...  AQI_calculated  AQI_bucket_calculated\n",
              "15          Ahmedabad 2015-01-01 16:00:00  ...           132.0               Moderate\n",
              "16          Ahmedabad 2015-01-01 17:00:00  ...           249.0                   Poor\n",
              "17          Ahmedabad 2015-01-01 18:00:00  ...           249.0                   Poor\n",
              "18          Ahmedabad 2015-01-01 19:00:00  ...           249.0                   Poor\n",
              "19          Ahmedabad 2015-01-01 20:00:00  ...           249.0                   Poor\n",
              "...               ...                 ...  ...             ...                    ...\n",
              "707870  Visakhapatnam 2020-06-30 20:00:00  ...            51.0           Satisfactory\n",
              "707871  Visakhapatnam 2020-06-30 21:00:00  ...            51.0           Satisfactory\n",
              "707872  Visakhapatnam 2020-06-30 22:00:00  ...            50.0                   Good\n",
              "707873  Visakhapatnam 2020-06-30 23:00:00  ...            50.0                   Good\n",
              "707874  Visakhapatnam 2020-07-01 00:00:00  ...            50.0                   Good\n",
              "\n",
              "[707485 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "G52_haYuMatz",
        "outputId": "e43bf16c-8854-4800-bbe5-3e9375f13655"
      },
      "source": [
        "#Correlation analysis\n",
        "plt.figure(figsize=(14,10))\n",
        "\n",
        "mask=np.triu(df.corr(method='spearman'))\n",
        "sns.heatmap(df.corr(method='spearman'),\n",
        "            annot=True,fmt='0.1f',\n",
        "            mask=mask,\n",
        "            robust=True,\n",
        "            cmap='inferno')\n",
        "plt.title('Correlation Analysis')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation Analysis')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oASuZT_FSWun",
        "outputId": "a5c9fa06-de71-44e3-97c0-a817f665f5bc"
      },
      "source": [
        "df['City'][df['AQI_calculated']>=500].value_counts()\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ahmedabad     8129\n",
              "Delhi         1088\n",
              "Gurugram       643\n",
              "Patna          491\n",
              "Lucknow        393\n",
              "Hyderabad      155\n",
              "Amritsar       121\n",
              "Jorapokhar     107\n",
              "Talcher         97\n",
              "Guwahati        62\n",
              "Jaipur          22\n",
              "Name: City, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bb3lWrYTl5xK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "9b2686f0-e001-4a15-d923-0259cf5d9309"
      },
      "source": [
        "#piechart for the top cities with higher vakues of AQI\n",
        "cols=['AQI_calculated']\n",
        "\n",
        "cmap=plt.get_cmap('Spectral')\n",
        "color=[cmap(i) for i in np.linspace(0,1,8)]\n",
        "\n",
        "for col in cols:\n",
        "    plt.figure(figsize=(8,6))   \n",
        "    x=df.groupby('City')[col].sum().sort_values(ascending=False)\n",
        "    x.reset_index('City')\n",
        "    x[:8].plot.pie(shadow=True,autopct='%1.1f%%', colors=color, wedgeprops={'edgecolor':'black','linewidth':0.3})"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}