Data preprocessing(city_by_day).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#                     Air Quality Index Prediction using ML                   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step1 : Importing all Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =pd.read_csv('cities_by_day.csv',parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "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>Date</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</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City       Date  PM2.5  PM10     NO    NO2    NOx  NH3     CO    SO2  \\\n",
       "0  Ahmedabad 2015-01-01    NaN   NaN   0.92  18.22  17.15  NaN   0.92  27.64   \n",
       "1  Ahmedabad 2015-01-02    NaN   NaN   0.97  15.69  16.46  NaN   0.97  24.55   \n",
       "2  Ahmedabad 2015-01-03    NaN   NaN  17.40  19.30  29.70  NaN  17.40  29.07   \n",
       "3  Ahmedabad 2015-01-04    NaN   NaN   1.70  18.48  17.97  NaN   1.70  18.59   \n",
       "4  Ahmedabad 2015-01-05    NaN   NaN  22.10  21.42  37.76  NaN  22.10  39.33   \n",
       "\n",
       "       O3  C6H6   C7H8  C8H10  AQI AQI_Bucket  \n",
       "0  133.36  0.00   0.02   0.00  NaN        NaN  \n",
       "1   34.06  3.68   5.50   3.77  NaN        NaN  \n",
       "2   30.70  6.80  16.40   2.25  NaN        NaN  \n",
       "3   36.08  4.43  10.14   1.00  NaN        NaN  \n",
       "4   39.31  7.01  18.89   2.78  NaN        NaN  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step2 : Preprocessing in City_day Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "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>Date</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</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        City       Date  PM2.5  PM10     NO    NO2    NOx  NH3     CO    SO2  \\\n",
       "0  Ahmedabad 2015-01-01    NaN   NaN   0.92  18.22  17.15  NaN   0.92  27.64   \n",
       "1  Ahmedabad 2015-01-02    NaN   NaN   0.97  15.69  16.46  NaN   0.97  24.55   \n",
       "2  Ahmedabad 2015-01-03    NaN   NaN  17.40  19.30  29.70  NaN  17.40  29.07   \n",
       "3  Ahmedabad 2015-01-04    NaN   NaN   1.70  18.48  17.97  NaN   1.70  18.59   \n",
       "4  Ahmedabad 2015-01-05    NaN   NaN  22.10  21.42  37.76  NaN  22.10  39.33   \n",
       "\n",
       "       O3  C6H6   C7H8  C8H10  AQI AQI_Bucket  \n",
       "0  133.36  0.00   0.02   0.00  NaN        NaN  \n",
       "1   34.06  3.68   5.50   3.77  NaN        NaN  \n",
       "2   30.70  6.80  16.40   2.25  NaN        NaN  \n",
       "3   36.08  4.43  10.14   1.00  NaN        NaN  \n",
       "4   39.31  7.01  18.89   2.78  NaN        NaN  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['City', 'Date', 'PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
       "       'O3', 'C6H6', 'C7H8', 'C8H10', 'AQI', 'AQI_Bucket'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29531, 16)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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>24933.000000</td>\n",
       "      <td>18391.000000</td>\n",
       "      <td>25949.000000</td>\n",
       "      <td>25946.000000</td>\n",
       "      <td>25346.000000</td>\n",
       "      <td>19203.000000</td>\n",
       "      <td>27472.000000</td>\n",
       "      <td>25677.000000</td>\n",
       "      <td>25509.000000</td>\n",
       "      <td>23908.000000</td>\n",
       "      <td>21490.000000</td>\n",
       "      <td>11422.000000</td>\n",
       "      <td>24850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>67.450578</td>\n",
       "      <td>118.127103</td>\n",
       "      <td>17.574730</td>\n",
       "      <td>28.560659</td>\n",
       "      <td>32.309123</td>\n",
       "      <td>23.483476</td>\n",
       "      <td>2.248598</td>\n",
       "      <td>14.531977</td>\n",
       "      <td>34.491430</td>\n",
       "      <td>3.280840</td>\n",
       "      <td>8.700972</td>\n",
       "      <td>3.070128</td>\n",
       "      <td>166.463581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>64.661449</td>\n",
       "      <td>90.605110</td>\n",
       "      <td>22.785846</td>\n",
       "      <td>24.474746</td>\n",
       "      <td>31.646011</td>\n",
       "      <td>25.684275</td>\n",
       "      <td>6.962884</td>\n",
       "      <td>18.133775</td>\n",
       "      <td>21.694928</td>\n",
       "      <td>15.811136</td>\n",
       "      <td>19.969164</td>\n",
       "      <td>6.323247</td>\n",
       "      <td>140.696585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</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>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.820000</td>\n",
       "      <td>56.255000</td>\n",
       "      <td>5.630000</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>12.820000</td>\n",
       "      <td>8.580000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>5.670000</td>\n",
       "      <td>18.860000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>48.570000</td>\n",
       "      <td>95.680000</td>\n",
       "      <td>9.890000</td>\n",
       "      <td>21.690000</td>\n",
       "      <td>23.520000</td>\n",
       "      <td>15.850000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>9.160000</td>\n",
       "      <td>30.840000</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>2.970000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>80.590000</td>\n",
       "      <td>149.745000</td>\n",
       "      <td>19.950000</td>\n",
       "      <td>37.620000</td>\n",
       "      <td>40.127500</td>\n",
       "      <td>30.020000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>15.220000</td>\n",
       "      <td>45.570000</td>\n",
       "      <td>3.080000</td>\n",
       "      <td>9.150000</td>\n",
       "      <td>3.350000</td>\n",
       "      <td>208.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>949.990000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>390.680000</td>\n",
       "      <td>362.210000</td>\n",
       "      <td>467.630000</td>\n",
       "      <td>352.890000</td>\n",
       "      <td>175.810000</td>\n",
       "      <td>193.860000</td>\n",
       "      <td>257.730000</td>\n",
       "      <td>455.030000</td>\n",
       "      <td>454.850000</td>\n",
       "      <td>170.370000</td>\n",
       "      <td>2049.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PM2.5          PM10            NO           NO2           NOx  \\\n",
       "count  24933.000000  18391.000000  25949.000000  25946.000000  25346.000000   \n",
       "mean      67.450578    118.127103     17.574730     28.560659     32.309123   \n",
       "std       64.661449     90.605110     22.785846     24.474746     31.646011   \n",
       "min        0.040000      0.010000      0.020000      0.010000      0.000000   \n",
       "25%       28.820000     56.255000      5.630000     11.750000     12.820000   \n",
       "50%       48.570000     95.680000      9.890000     21.690000     23.520000   \n",
       "75%       80.590000    149.745000     19.950000     37.620000     40.127500   \n",
       "max      949.990000   1000.000000    390.680000    362.210000    467.630000   \n",
       "\n",
       "                NH3            CO           SO2            O3          C6H6  \\\n",
       "count  19203.000000  27472.000000  25677.000000  25509.000000  23908.000000   \n",
       "mean      23.483476      2.248598     14.531977     34.491430      3.280840   \n",
       "std       25.684275      6.962884     18.133775     21.694928     15.811136   \n",
       "min        0.010000      0.000000      0.010000      0.010000      0.000000   \n",
       "25%        8.580000      0.510000      5.670000     18.860000      0.120000   \n",
       "50%       15.850000      0.890000      9.160000     30.840000      1.070000   \n",
       "75%       30.020000      1.450000     15.220000     45.570000      3.080000   \n",
       "max      352.890000    175.810000    193.860000    257.730000    455.030000   \n",
       "\n",
       "               C7H8         C8H10           AQI  \n",
       "count  21490.000000  11422.000000  24850.000000  \n",
       "mean       8.700972      3.070128    166.463581  \n",
       "std       19.969164      6.323247    140.696585  \n",
       "min        0.000000      0.000000     13.000000  \n",
       "25%        0.600000      0.140000     81.000000  \n",
       "50%        2.970000      0.980000    118.000000  \n",
       "75%        9.150000      3.350000    208.000000  \n",
       "max      454.850000    170.370000   2049.000000  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                  object\n",
       "Date          datetime64[ns]\n",
       "PM2.5                float64\n",
       "PM10                 float64\n",
       "NO                   float64\n",
       "NO2                  float64\n",
       "NOx                  float64\n",
       "NH3                  float64\n",
       "CO                   float64\n",
       "SO2                  float64\n",
       "O3                   float64\n",
       "C6H6                 float64\n",
       "C7H8                 float64\n",
       "C8H10                float64\n",
       "AQI                  float64\n",
       "AQI_Bucket            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AQI', ylabel='City'>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Grouping the AQI by city and calculating the average AQI per city\n",
    "x=pd.DataFrame(df.groupby(['City'])[['AQI']].mean().sort_values(by='AQI',ascending=False).head(10))\n",
    "x=x.reset_index('City')\n",
    "\n",
    "#plotting the average AQI per city\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.barplot(data=x,x='AQI',y='City',orient='h',palette='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahmedabad has highest AQI Index Value.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "#segregating dates into months and years\n",
    "\n",
    "df['Month']=df.Date.dt.month.astype(str)\n",
    "df['Year']=df.Date.dt.year.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the average AQI over the years for top 5 cities which have AQI greater than 500\n",
    "\n",
    "df['City'][df['AQI']>=200].value_counts()\n",
    "\n",
    "cols=['Ahmedabad','Delhi','Patna','Gurugram','Lucknow']\n",
    "\n",
    "for col in cols:\n",
    "    plt.figure(figsize=(5,5))\n",
    "    x=df[df['City']==col]\n",
    "    sns.barplot(x='Year',y='AQI',data=x)\n",
    "    plt.title(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['City', 'Date', 'PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
       "       'O3', 'C6H6', 'C7H8', 'C8H10', 'AQI', 'AQI_Bucket', 'Month', 'Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_value(df):\n",
    "        # Total missing values\n",
    "        mis_val = df.isnull().sum()\n",
    "        \n",
    "        # Percentage of missing values\n",
    "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "        \n",
    "        # Make a table with the results\n",
    "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "        \n",
    "        # Rename the columns\n",
    "        mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "        \n",
    "        # Sort the table by percentage of missing descending\n",
    "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "        '% of Total Values', ascending=False).round(1)\n",
    "        \n",
    "        # Print some summary information\n",
    "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "              \" columns that have missing values.\")\n",
    "        \n",
    "        # Return the dataframe with missing information\n",
    "        return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 18 columns.\n",
      "There are 14 columns that have missing values.\n"
     ]
    },
    {
     "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>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C8H10</th>\n",
       "      <td>18109</td>\n",
       "      <td>61.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>11140</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>10328</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C7H8</th>\n",
       "      <td>8041</td>\n",
       "      <td>27.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C6H6</th>\n",
       "      <td>5623</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>4681</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI_Bucket</th>\n",
       "      <td>4681</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>4598</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>4185</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>4022</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>3854</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>3585</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>3582</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>2059</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Missing Values  % of Total Values\n",
       "C8H10                18109               61.3\n",
       "PM10                 11140               37.7\n",
       "NH3                  10328               35.0\n",
       "C7H8                  8041               27.2\n",
       "C6H6                  5623               19.0\n",
       "AQI                   4681               15.9\n",
       "AQI_Bucket            4681               15.9\n",
       "PM2.5                 4598               15.6\n",
       "NOx                   4185               14.2\n",
       "O3                    4022               13.6\n",
       "SO2                   3854               13.1\n",
       "NO2                   3585               12.1\n",
       "NO                    3582               12.1\n",
       "CO                    2059                7.0"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_value(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C8H10 have more than 50% missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see except City and Date all columns have missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.heatmap(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ubnrmmWf0xBNPaPDgwc4pY5HR9ceYJIWGhioqKkq1atVyttu/mVbl+nuK5cuXa/jw4Tp16tRdrj5zMGI8Gzhy5Ijef/99xcTEaOLEiSpYsKA8PT1ltVrVtm1b5cyZU0uXLpWXl5fGjh2rr7/+WpJuGDluJomJiRo+fLief/55VapUSdK10aiPPfaYqlevLjc3N9ntds2aNUuRkZHOkeM2m00eHh4qX768ypcv7+K9yHpWq1Xe3t6SJC8vL3l7eyshIcH5C+DChQvl7++vokWLqmjRonrvvfd07tw5zZ0719Q3/LfbR7du3er8lbVo0aKuLt8l/vjjD23cuPFft9mXX34pwzA0YMAANWzY0NXlu9Rf+xqPkf6960dSrlu3TvHx8cqbN69atmypli1bymq1auTIkcqdO7eioqKcj57SrhlHdWzbtk1nzpzRvHnzVK5cOW3ZskXLly/XwIEDNX36dD344IMaNmyYJk+erG+//Vbnz59Xx44d5ePj4+K9cB3DMBQaGqoGDRroww8/VJUqVZxrweB/1q1bp5iYGPXp00f+/v5KTExUrly5ZBiGkpKS5O3trZiYGLVv3161atXS8OHDtXnzZk2ZMkXVq1dX0aJFTdlXHd9Rjv999dVX9dFHH6lYsWKqWbOmDh8+LE9PT6WkpMjNze2G0fV8x+HfuP44qV+/vr766islJSVluLf08vK64YcXM95/Xu+v/RP/Dsfb7XFkHpGRkapcubLatWunXbt26ezZsypRooRzO29v75suvOnm5qZHHnlEW7ZsUcGCBbO6fJdy3B9Uq1ZNnp6eSk9P1+DBg50joPE/Nwu9K1eurJ49e8owjAztdquR49e/tmrVKk2cOFGjR4/O8ERIduZmmO1no2zIZrNp9+7deumll1S0aFEtXLhQPj4+atWqlfz8/DRjxgwVKFBAbm5uSk5O1rhx47Rz505Vr15dI0aMkK+vr6t3IUs55iJ7//339dBDD2nMmDGqWLGi0tLSdOXKFQUHBzu3/f777zVz5kydPn06Qzie3Sf/vxusVqt69+4tPz8/zZw5U5K0a9cuVapUST4+Pjp//rw+/fRTPfbYY84T7RdffKF+/fpp1qxZevLJJ11YvWulp6dr165d9NF/ITk5WYcPH1ZoaKh27NihIUOGqEiRIv+6zR5++GGNHDnSVG0mXVuA6IsvvtDOnTt15swZFStWTA899JDatm3r6tKytetvlPr376/t27crPT1dvr6+qlGjhiZOnCgvLy+tW7dOo0aNUsuWLdWrV68MNxO4Nn3K8ePHdfHiRc2dO9d5jty0aZMWL14sq9XqDMcvXbqkK1euKFeuXBnOt/c7wzCUnp6e4frBcRPw9ttva968eVq3bp2KFy9u2hv4W9m8ebP69++vbt26ORd1HTVqlFavXq1JkyZp0KBBmjdvnmrWrKmxY8fK399fCxcu1DvvvKOPP/5Y/v7+rt6FLOU4rk6fPq08efLIMAz5+fnpu+++0/HjxxUdHa3Y2Fjt3btXqampqlixopKTk1WvXj2FhISoSJEizms74Fb+epzZbDYFBATIYrE4+5wjJujZs6fi4+O1fv16SZLFYtHChQtVu3Zt083b+9d2k6ScOXMSkP8Djrfb99fMY+TIkQoNDdWFCxeUP39+53Y2m00dO3ZUUFCQ5s+fL+lamy1ZskStWrVSsWLFXLUL2Up0dLTmz5+vn376iXD8OsePH9eOHTv07bffKjU1VaVLl9ZDDz2kFi1aSJJ+/fVXzZkzJ0O7Ofrq9YG6459XrFihCRMmaMyYMffUfSzBuIs4Dp7rR7rt2LFDgwcPVtGiRRUfH6/cuXM7wyPpf4s8paSkaPjw4Tp69KgWL15sqhtT6Vo7rFixQnPmzFFwcLC8vb01evRoVatWLcO8647O6QjHz507p3nz5pl24YmEhARNnjxZP/74o2rUqKHx48dLyjja0mKxyM/Pz9l258+fV7NmzTR48GC1b9/eZbW7gs1m09WrV2UYhoKCgiTRR/9JYmKiOnfurJw5c2rZsmWSpN27d9Nm/8Bisah79+5KTU2V3W5XsWLFnMHHo48+qkGDBqlChQr3xMIlrjJy5Ej98MMPeuONN1S6dGm99dZb+vzzz/XYY49pwoQJ8vb21vr16zVq1Cg9/vjjGjJkiIoXL+7qsrON5557Tjt37lRISIg2btyYIfz97LPPtHjxYtlsNk2aNMl0UxtdunRJefPmdf57UlKS3n33XdWsWVMPPvig8/V27dqpYMGCzh+ekdEHH3ygYcOGydfXV2+99Zbq1asnSerVq5e2bdumGjVqaNasWQoICNDly5c1duxYXb58WTNnzjRVMJ6UlKR58+Zpz549OnLkiHx9fVWqVCl17tw5w9NUaWlpOn78uCIiIlS+fHk99NBD+uqrr3T8+HHlzZtX77//vvM8e79LTU3V7t27lZKSosDAQFWrVs3VJWV7tzrOIiIiVK9evRuePujatavS09O1cuVKJSQkaMqUKVq3bp0+/vjjDN+D97u/65916tRhTYRb4Hi7MzfLPN544w2FhoZK+t/gELvdrtatW6tkyZKaPn26EhISNHXqVK1du1affPKJypQp49odyUKpqanavn27fv75Z8XHx6t48eJ68MEHnefPffv2ae7cuYTj/2/Pnj0aOnSoAgIC5OnpqTx58mjPnj26cuWKGjZsqDFjxihfvnz69ddfNXfuXP344483hOPS/wLylStXavz48fdcKC4RjLtEYmKipkyZ4lw8ISoqSjVq1JCXl5d27NihN954Q0eOHNG6detuWG3e8QWYmpqqK1euZPi10EwuXLigdu3aKU+ePCpQoIBOnz6tcePGqXLlyjd9tO3HH3/UmDFjZBiGPv74Y3l5eZnqV31HW8THx2vBggXatGmT6tSpo3HjxknKOMXK9X7//Xf169dPL730kqlOHBaLRQMGDND58+d16tQpdevWTT169JCPj4927txJH70Ji8Wili1b6uzZs8qfP78+/PBDBQQEyDAM/fzzz7TZLSQnJ6t9+/bKly+f+vXrpwoVKsjT01Pnzp3Tli1bNH/+fOXNm1ejR49W1apVXV1utnTy5En17dtXL7zwgho1auQMcHfu3KnLly/r4Ycf1sSJE+Xt7a1Vq1Zp+vTp2rRpk2lCo7+61WjmoUOH6pNPPlGPHj0UGRmpXLlyOd9zTGsRFBSkFStWmOYcmpqaqlGjRqlUqVKKioqSJE2fPl3Lli2Tt7e3unbtqlq1aqlatWpav3691q5dq8mTJ6tMmTKMHvwLx6KaktSpUydFRUWpQIECiomJ0fjx47V9+3Z16tRJhmHo2LFj+vnnn7V69WrnAupmYLFY1KlTJ+XIkUNly5ZV5cqVtX//fm3fvl0nTpxQ165d1alTJxUpUkR2u11Wq1WRkZGqXr26Bg0apEuXLiktLU3p6emmWeTbYrGoc+fOSkpK0sWLF5Wamqq+ffs6+ytu9E/HWbdu3dS5c2fntAtpaWl6/vnn5e3trXnz5mnkyJH67LPPtGbNGj300EMu3pusc7vthms43v6bf8o87Ha7kpOT1aFDB4WGhmrMmDEaPny4PvvsM73zzjummjrWYrGod+/eio+Pl8ViUb58+XTw4EHZ7XY9/vjjGj16tPLkyaP9+/drzpw5GUJeMzp48KC6du2qFi1aqH379s71gmJiYvTee+9p+fLlqlixot566y0FBQUpOjpaCxYs0E8//aQpU6boiSeeyDDA8l4OxSVJBrJUUlKS0bRpUyM8PNwYOHCg0blzZ6NixYrGli1bDMMwDKvVamzfvt2oV6+e0alTJ+PMmTM3fEZ6enpWl52tOPb/o48+Mjp16mRMnz7daNeundG0aVNj7969hmEYht1uz/C/hmEYP/30kxETE5P1BWcTjraIjY01pk+fbtSrV88YNWpUhm1OnTplbN++3Th27Jjx559/GkOHDjUaNGhgnDt3zgUVu0ZycrLRrFkzo1OnTsbKlSuNGTNmOPunYRhGamqqsX37dqNu3br00f+XkJBgNGzY0OjcubOxadMmo3bt2sbRo0ed79tsNmP79u1G/fr1abO/WLBggdG2bVvjjz/+cPbRtLQ0wzAMIyUlxfj222+NunXrGuHh4aZto39y8OBBo0aNGsaXX35pGMa1c0PTpk2NDz/80Bg3bpwREhJiDB061Lh06ZJhGIZx9epVV5brUjabzfnPBw8eNH755Rdj586dztcGDRpk1K5d25gzZ46RkJCQ4W+//PJL4+TJk1lWa3aQnJxsjBs3zqhbt64xd+5cIyIiwvjtt9+MPXv2GLNnzzZq1KhhPPXUU8aYMWOMAwcOGI8++qgxY8YMV5edLVx/rBmGYSQmJhrnzp0zVq9ebYSEhBivv/66ceHCBcMwrp1Xx48fb4SHhxvNmjUzBg8ebPz++++uKNtlUlNTjY4dOxpdunQxjh07luG9Q4cOGW+88YZRrlw5Y8KECUZSUpLzvUGDBhnNmjUzDCPjNa8ZpKWlGZGRkUaXLl2MgwcPGnv37jUmTpxoVK1a1Thw4ICry8uW7vQ4e/755402bdoY48aNMypVqmT89ttvWVy5a91pu5kdx9t/828yD8O49l3YqlUrIyoqyhg/frwp2ywpKclo1qyZERkZaezYscN5L3X06FFj4sSJRuXKlY2IiAhnpvHLL78YvXr1MipXruy8fzALu91upKamGsOGDTMGDBhgxMXFOd+zWq2GYRjGlStXjLVr1xpVqlQx+vbt63x/7969xosvvmiEhIRkuH9Yvny5Ub58eWP9+vVZtyOZjGA8i82fP99o2rSpce7cOedNQ8eOHY2IiIgM223fvt2oXbu2ER4ebpw+fdoVpWZL1wdDBw8eNCIjI43PP//c+Oabb4wWLVr8YzhuJjfb79TUVMMwDOPcuXNGjRo1jCpVqhjDhw93vv/ll18aISEhRmhoqPHMM88Yjz32mHHw4MEsqzk72Lx5s/HMM88YR44ccbZhXFyccejQIePUqVNGcnKyYRj0UQdHKN6lSxfj0qVLRkJCglG9enVj2bJlGbaz2+3OHxTM3mbX69Gjh/HCCy84L0T+ymq1Ghs3bjRCQkKMxYsXZ3F12c/NvtfOnz9vtG3b1vj111+NkydPGqGhocbs2bMNw7j2XVeuXDkjJCTEaNasmZGammrac8L1589XXnnFePLJJ40KFSoYFStWNHr37u38rv+7cNws9u7da5w6dcowDMM4efKk0adPH6NChQpGzZo1MwS+v//+u7Fs2TKjbt26Rvv27Y1GjRoZtWvXNt1586+ub6MffvjB+PLLL424uDjn66tWrTJCQkKMUaNGOcNxw7h2jCYmJjqvVczkq6++Mpo0aWJs377d+dr154VLly4ZEydONMqVK2ds2rTJ+frKlSuNsLAw4+LFi1lab3YQFxdnNG3a1Ni4caPztR9++MF4+OGHjb179xqnT592hiO45k6OM5vNZowYMcIICQkxwsLCjP3792d53a52p/3T7Dje7tztZB6pqalGVFSUERISYlSvXt2UbTZr1iyjY8eOxp9//ulsO8exdvnyZeOdd94xQkNDjV69ejn/Zu/evUafPn2MkJAQ4+uvv3ZF2S6TmppqPPnkk8bcuXNvuU18fLwxceJEIyQkJMP32p49e4zJkyc7z6+nTp0yatasaaxbt+6u1303sTJQFrtw4YI8PT3l7+/vfOygTJkyKlCggLZu3arvvvtOR48eVY0aNTR16lSdOHFCQ4cOVUxMjIsrd43ExETNmjVLX331lWw2W4bHv8uVK6cqVapo0qRJqlGjhgYOHCgvLy8NHz5c+/btM/VK4cnJyRo+fLj279/vfC0tLU3e3t5KSUlRjx49VLhwYTVv3lzff/+9XnvtNUnSE088odmzZ2vUqFEaNGiQ1qxZY7r5ZK9evar4+HgFBATIzc1NX3zxhZo3b67w8HA1atRI/fr10w8//EAf1bUpeHr06KEHHnhAkydPVu7cueXt7a2KFSs6j73rV0gPCwvTm2++qZiYGNO2mYPj0ccjR46oQoUK8vLykt1uv2E7Ly8v1a9fXw8++KD27t3rgkqzD5vN5vw+v3z5suLj4xUfH6/8+fNr7ty5qlixotasWaPSpUvrxRdflHRtwZiSJUtq7ty5mjdvnry9vU11TjD+f7Y8wzCc589Ro0bp559/1oABA7RgwQKNGzdOu3fv1iuvvKLffvtNU6dO1cMPP6x3331XCxculMViceUuZLmEhASNGDFC7777riSpaNGiunr1qgICAuTu7q4VK1Y4t33wwQfVtWtXbdmyxdlPL168qJ07d0rSTfv0/c5utzuvbwcPHqwBAwaoT58+ioiI0BdffCGr1apOnTppxIgRWr9+vebPn6/o6GitWrVKFy9eVM6cOW86tdv9bu/evUpJSckwZdb18+7myZNHHTp0UKVKlbRo0SLFx8dLunZ8Ssow16dZpKWl6Y8//lBaWprztYCAANlsNr366qt67LHH1LlzZ23cuNGFVWYvt3ucXb58WR4eHmrRooVKlCihlStXmnKtpjvtn2bH8fbv3WnmER0dLW9vbzVr1kxFihTRmjVrTNNm19u9e7eKFSumokWLOtvOcawFBgaqcePG6tatm77++mtt2LBBklS5cmU9//zzatKkifNcahbnz5/XuXPnnNPV/fUawm63K3fu3Hr22WeVI0cOnThxQpJ05coVVa1aVUOGDJGnp6dsNpsKFy6s9957T+3atcvy/chMBONZxHFzFBAQoBMnTujs2bNKSUlRWlqafvrpJ33zzTcaOXKk+vfvrwEDBjgXIpo2bZqio6P1xhtvyGazuXgvslZaWppat26tOXPmaOLEierevbsOHjyoq1evOrfp0aOHihcvrpUrV6p+/frq3r27fHx89Prrr2vPnj2mCkCud/r0ab333nuaNGmSDh06pLS0NHl5eSk1NdX5Bbd8+XINHjxYzzzzjL777jsNGzZM0rVwvGXLlnriiSdMNQevo4/myJFDSUlJkqSzZ8/qlVdeUfPmzTVjxgwNHz5ccXFxmjBhgnbs2GH6PupYBX369OnKnz+/3N3d5e3trSeeeEKfffaZjh49mmHBSEc4PmXKFNO2mfS/kNLLy0uBgYHav3+/kpOTbzrvs81mU1BQkGrUqKGTJ0/KarWaKmxLSUnRmTNnJMm5IOSYMWPUs2dPNWvWTF26dNGmTZvk4+Mj6X8/PktSXFycvvvuOxUqVEjVq1dX4cKFXbMTLmKxWNStWzft3r1bbm5ustvtio2N1YEDB9SzZ089/fTTql27tpo3b661a9cqNjbWOQf0jBkzVKJECW3dulVWq9XFe5K1cubMqTlz5qh///5KTEzUpUuX9NJLL+mtt97SI488omXLljkXF5b+t0ZH7969NWPGDPXq1Uvz5s1TXFzcTfv0/c6xz0OGDNEvv/yioUOHauPGjbJarZo3b562bt2qtLQ0Zzi+evVqvfDCC3rzzTeVnJzs4upd58qVK8qRI8ff/ihQvHhx1a1bVydPnnSGweXKldNHH31kqgWrHYKDg1WnTh1duXJFycnJMgxDgwcPVrFixdSpUyfNmjVLycnJWr58uXbt2uXqcrOFq1ev3tZx5rhGq1ixojZs2KCyZctmVanZyp32T7O73XYz6/H2XzKPMWPGKDo6Wo0bN9b69etNtTipdO2e6vLlyzp48KAqVaokT0/PG0JewzAUEBCgVq1aKTAwUAcOHJB0bSBhaGioJkyY4Jxf2wwMw5CXl5d8fHx08OBBSTcO5HBcy/n7+6tChQrasmWLXn31Vb355pu6dOmSczvHff79sLaC+a7Ys5jdbpfNZtPZs2dlGIbzV9G2bdsqMjJS9evXV1BQkJYsWaLvv/9e06ZNU44cObRkyRJZLBZVr15dS5cu1bBhw5w3+2bh5eWlpk2bOv/58uXLioyM1IQJE/T9999LuhZi1qhRQ1999ZUkqXHjxoqMjJTFYtGbb76p1NRUU42iSUpK0ooVK1SiRAm9++67+vPPPzVmzBidP39eqampatOmjfz8/DRjxgzlypVLuXPnVlRUlBo3bqwdO3ZowIABrt6FLOfoo+fOnZNhGHrsscdUrlw5jRgxQr/88osaNGigPn36qE6dOurYsaP69+8vwzD0wQcfSJKqVatmuj7qWPQrJSVFTz/9tPLkyeN8XZIeffRRFSpUSF9++aXsdvsNq1Y/8sgjpmszKeOxZrPZ5Onpqdq1a+unn37S0aNHJf1vhL2Do31OnTqlvHnzytvb2zRhm81mU3h4uObNm+d8bciQIdq6dasaN26srl276pFHHtGgQYM0d+5cpaSkqF69etq7d686dOigqKgoffbZZ3r55ZcVEBDgwj1xjTfffFPbt29X//79FR0dLXd3d12+fFn79+9Xrly5nMeRzWZTyZIl9eabb+qnn37SJ598IklaunSpli5dqrx587pyN7KMxWJRXFycjhw5Ij8/P6WkpGjEiBF68sknlTdvXoWFhalnz56qVq1ahnDc29vbeTPv7u6uli1bKmfOnM6bDTPatWuXfv31V40dO1atW7dW6dKlVb16dZ08eVLTpk3TV199JavVqo4dO2rp0qXq0qWLPvzwQxUvXtzVpWeZpKQkrVy5UleuXJF0beTk+fPndfjwYUk3jt5yBG1hYWFKT0/XhQsXJEkFChQwzSCG9PR0WSwW7dmzR7///ruSk5P1xhtvKCoqSjly5JCbm5tGjBihN998Ux07dtSTTz6pWbNm6dy5c9qxY4ery88WAgMD7+g48/HxybAg8/3uv/bP2NjYrC04m8is7zWzHW//JfNISEjQpEmTZLPZTHO99lfe3t7y8/PT77//LunGkNcxULJo0aIKCwvTrl27tHjxYk2aNEmxsbGmekrNMZtCwYIF9eijj+rdd99VQkKCPDw8MtyDpqamasSIEerYsaN27dqltLQ0nTt3TqVKlcqw3f00CNUcd9cukpiYqHHjxqlTp05q3bq1mjdvrl9//VVTpkzRSy+9pKZNm8rNzU3dunVThQoV5ObmpgYNGqh169b65ZdfdPnyZbm7u6t69eoqUaKEq3cnyzgejTxy5Ijat2+v0aNH6+jRo2revLkiIiJksVjUq1cvDR8+XHv27FGvXr0UHx+v6dOnS7p2ohgyZIgmT54sHx+f+6rD/pMvv/xS48eP16xZs1SuXDnNnz9fx44d05AhQ9S8eXP5+/tr1qxZzl/17Ha78uTJo549e6pOnTo6evSo86LEDK7vo61atVKzZs30/fffq2nTpjp79qymTZumhIQE5cyZ0xl81KtXT02bNtWXX36pK1euyNPT01R91NFmzz33nJ555hm1adNG33zzTYbH/kqXLq1KlSrp3XffVWpqqnO0qoObm5up2kzKeKy1bNlSzZs317Zt29S0aVMFBQXp5Zdfdl6YGP+/yrx07QLm5MmTSkpKUr169ZyvmUFycrLS09OdT3AcOnRI+/bt09ChQxUREaHnn39e4eHhkqTcuXPrypUrqlu3rsaMGaNcuXKpfPnyWrNmjfMxQbNp3LixihcvrqtXryoyMlI7d+5UcHCwAgMDdfjwYaWmpkr632iPsmXLKk+ePDp79qzzM8wSuP366696+eWX9eyzz6pt27bq2LGjtm/frhYtWqhIkSLq3LmzTp06pXLlyqlXr156+OGHtWLFCi1ZskSXLl3Stm3bnFPOnDhxQqdOndLFixddvFd3n91uV0JCgs6dO5dhBNGFCxd0+fJl5+PcH3/8sU6fPq233npLXl5emjZtmrZu3SqLxaJatWqpR48epjofSNeu18aNG6elS5dKkpo0aaK0tDTnVD1/PW86Hgn/5ZdflD9/ftO1V2Jiol577TV16tRJ4eHhatGihRo1aqQ1a9Zo3759zu1q166tkJAQZ/sVKVJEJUqU0OnTp11YvWtYrVbt2rVL77//vvM6rWnTpkpLS9PKlSslcZzdyn/tn2b6ke96fK/dnszIPF5++WVNnjzZVOHu9dzc3OTn56dHHnlEW7du1aVLl24Iee12u1avXq1nn31WX331la5evar169fL3d09w5PN96vz588rISFB0rX2cvwg1aJFC8XHx6tfv35KSUmRh4eHc0Cbj4+PHnjgATVu3FiFChVSv379tGzZMkVGRipfvnyu3J27xjxD9bJYYmKi2rdvr4CAAFWrVk2tW7fW7t27ZbPZlC9fPnXq1El79+5VXFycSpUqJQ8PD+d0F76+vipUqJD8/f1dvRtZzmKx6KWXXtIff/yhCxcuqGTJkho2bJhee+01TZgwQUOGDNHgwYPVpk0bTZs2Tbt27VKlSpX09NNP65dfftHJkydVrFgxPfnkk67eFZdo3ry5Ll++rMmTJys9PV0DBgzQwoULNWDAAJ0+fVojR45U/vz5Jf1vOgfDMBQYGKhBgwY5p20wg1v1US8vL7Vp00bHjh3TRx99pJiYGFksFvn7+8tut8vd3V158+ZVYGCgqX50kW7eZnv27FFaWppzZLOjjV566SV16tRJkyZN0uuvv+481szWZtKt281qtap06dLq2rWrpkyZoo4dO2rWrFkqWrSosz0vXLigJUuW6PTp03r88ccl3V+/zv+dXLlyqW7duvrkk09ktVp1+fJlnThxQvnz55enp6eOHTumDh06qEmTJmrUqJFz5GDbtm3VqlUrSTLVEwl/VapUKRUsWFClSpWSn5+foqKitH79erVs2VIrV65U9erV9eijjzpvTB3zaBcqVMjFlWet6Oho9ejRQ4899pgiIyNVvHhxbd++XUFBQapUqZJy5MihN954Q507d9aKFStUrlw59ezZU4sWLdLcuXP15ptv6pFHHtHjjz+uixcvav/+/QoKCsowp+r9KDExURMmTNCBAwd04sQJ5ciRQ23atNGTTz6psmXLKnfu3EpNTdWBAwf0xhtv6MUXX1SDBg2UlJSkQYMG6c0339TOnTv12muvmeY77Xp/vV6LjIxUeHi4lixZovz586t///43PB106dIl/fbbb6pWrVqGeXrvdxaLRe3atVNQUJCaNGmiyZMn69ChQ9q9e7cWLVqkH374QX379lXDhg3l5uamlJQU+fr6yt3dXefOnXOeayWZ5jrEYrHoxRdf1IULF5zTdw4ePFht27ZVhw4dtGzZMuXPn1/9+vXjOLsJ+uedod3+PTKPO5OWlia73e6cQtExnV2LFi20ZcsWZ/bh6+ur9PR0ubu7y93dXRcvXlSpUqV07tw5RUVFqUWLFqbI2g4fPqwWLVqofv36Klq0qPr37+9su1q1aqldu3Zau3atevToobfeesuZA6Wlpempp57ShAkTVLJkSTVs2FDS/+7z70fmvWO8i6xWqwYNGqR8+fJp9OjRKlasmCSpffv2zm3sdrvy5s2r4OBgLVu2TGPHjpWXl5euXLmiH3/8UQULFjTdL38Wi0UtW7ZUgQIF1LlzZ8XFxenDDz9U7969tXLlSvXt21eTJk1SUlKSXnjhBVWuXFk//fSTli5dqs8++0zp6enat2+fs73NyM3NTV26dJFhGJo8ebIkqV+/fpo5c6aioqI0b948BQUFOUfTOP7GMAzlzp3blaVnqb/ro46bJseUKWvWrFFkZKRmz56twMBAWSwW/fzzzwoODjbFr8wO/+Z7LT093dkmefPmVcuWLbV161Zt2rRJjRs3NsXN6F/9m3Zr1aqV0tPTtWTJErVu3VpPPvmkqlSpoiNHjuj06dM6ePCgFi1aZLqFYSSpWLFizoU2g4ODlTt3bsXHx+vo0aPq2LGjatWqpbFjx+rq1avatWuXKlasqMqVK5s2EL8+9MmXL5+6deumgQMHasiQIUpOTlZ4eLgmTJig+Ph4DRo0SP369VPNmjWVkpKiVatWyWq16uGHH3bxXmSds2fPatSoUWrWrJn69eunwMBASXI+nSFJoaGhGjVqlEaPHq2IiAitXLlS5cqV0wsvvOCcF/WFF16Qm5ubgoOD1bx5c7Vr1+6+HVEjXbtea9u2rQICAvToo4/q6aef1v79+7V48WJ9/vnneu655zRr1izlz59fQ4cOVYMGDdSzZ09J0pkzZ1S6dGnVrVtX4eHh9+0N1j/56/Vajhw59NRTTykmJkbz5s3T1atX1bVrVxUpUkRubm46dOiQVq9erT179mjFihWmuUdIS0vT4MGDlS9fPo0dO1aFCxeWu7u7QkJC1KJFC1WrVk1jxozRzJkzFRgYqBIlSmjTpk0qUqSIvLy89Omnn+rixYvO8MgM1yGJiYlq27at8uXLp+HDh6t48eI6efKk0tPTFRgYqDZt2igmJkZz585VfHy8unXrZvrj7K/on3eGdvt3yDzuTHJyslq2bKkyZco4Byk4jpnQ0FC1bdtWa9asUa9evTRz5swM0yg+99xzWrRokfz8/PT444+bIhSXrk3FKV27P9i7d68ee+wxNWnSRM8884xzIdccOXLo7bffVrNmzVSvXj2VKVNGBw4cUExMjOx2u9auXSt3d/f7OhSXCMbviqNHj+r8+fN68cUXVaRIEUkZAyPHQVW0aFHVqFFDBw4cUEREhMqVK6djx47pt99+06pVq+Tn5+fK3chSFotFzZs3V5EiRTR16lTnDeVDDz2k4cOH64MPPtBrr70mi8WimTNnym63KzIyUk2aNFGTJk309ttv6/vvv1f58uVdvCdZzxGEOI4xNzc3de7cWZI0efJkZ8g7f/589ezZU2+88YZGjBihcuXKOT/DDDcK1/u7Pup4xCggIEAjR46Uj4+PfvjhB+ejRF5eXjpz5oyWL19uqj76T99rhmE4/9kx8rRNmzbavn275s2bpwIFCpgqcHP4p3az2Wzy8/NTly5dVKFCBX3//ffavHmzvv/+exUsWFA1a9bUsGHDTPd4qaONHn30Ufn6+io6OlpPPfWUihYtqnHjxunq1auqW7euJk2aJG9vb506dUo5c+ZUyZIlXV26SziuK64/FxiGoVq1aqlp06Y6ffq0oqKilJiYqKFDhzrbbfz48fLw8HA+STRv3jxTjBh3nDd///132e12tWjRwhmKX98/09PT5e3trSpVqujFF1/UihUr1KVLF61YsUKlS5fOsFiTY9TS/f4DlsViUYsWLVS4cGGNGzcuw/7+9NNPGjhwoBYvXqycOXMqJCREFotFpUqVknRtZOChQ4dUs2ZNDRo0yDQhiMOtrtccIZKnp6e6deum/Pnza926ddq8ebMeeOABSdfOFcnJyVqyZImpFgk7ceKEzpw5o169ejlDcbvdLjc3N7m5ually5by8PDQkCFD9Omnn6p58+bauHGjDhw4oMKFC8vPz09LliwxTYBkt9s1adIk5c2bN0P/LFq0qHMatkKFCqlXr14qWrSo1q9fr88++8y5nVmPM4n+eadot9tD5nHn9u/frxMnTiggIEDnz5/XmjVr1K5dOz322GOqUqWK+vXrJ09PT7399ttq2rSpWrZsqfLly+vQoUM6cuSI9uzZo6VLl94XC0X+W48//riaN2+uuLg4vfXWW3rnnXf0zTffaP369XrmmWf09NNPq3///qpZs6a2bNmib775Rt9//70qVKigJ554Qs8//7w8PT2da2Pdz9wMs0xWmoXWrVuniRMn6vPPP7/l/JxJSUlatGiRfv31V1WrVk0///yzkpOTVapUKT3//PMqU6ZMFlftOomJiWrZsqVKliypN954QwUKFMgw6q1du3bKlSuXlixZIkmaPn26FixYoH79+qlDhw7OhSZSU1Odj4aYQVJSkr777jsVLlxY5cqVU3p6eob9NwxDixcv1rRp0xQZGamBAwfq4MGD6tmzp8qUKaNXXnnFOf+n2fybPpqYmKiFCxfqjz/+0AsvvKCvvvpKFy9eVOHChfX000+bbv7Af9NmqampevPNNxUbG6uJEycqR44c2rdvnwYOHKgVK1aocOHCWVy16/3b88GMGTN08eJFTZ06VRaLRXa7XQEBAff9r/MOiYmJ+vjjj1W4cGHnDyg5c+ZUamqqmjVrpieeeEIvv/yyjh49qiFDhuiPP/7QwoULFRoaqri4OM2dO1c7duzQqlWrTBHsXi8pKUmTJk1S4cKFFRUV5XzdcR5ds2aNVq1apZUrVyopKUmvvfaafvvtN61Zs0ZWq1VHjhxRnjx5VK5cOdPMKe4wefJkffHFF9q8efMtnwC6ePGihg4dqnz58qlZs2aaMGGCYmJi9OWXX97Xo8Jv5mbXa9K1Y80xPduePXvUt29f5cyZUyNHjtR7772nQ4cOqXbt2oqNjdXPP/+sFStW6MEHH3Tx3mSdf3O9tmjRIk2bNk19+vRRu3btdOXKFW3cuNG5MFjNmjUVFhZmqht6Sfrggw80YsQIffHFFzd8t19/rzB58mRt2LBBmzdvlt1u14ULF+Tr66s8efI4Fwg3g5SUFEVGRqpBgwYZzgd2u12LFy/Wjh079PPPPyssLExVq1ZVixYttG7dOp09e9a0xxn9887QbrePzOO/GzlypA4dOqQxY8bo3Xff1XfffaezZ8+qVatWevrpp1WzZk1988032rRpk77++mtZrVbnvUXXrl2dP9SbyZo1a7RkyRJNmzZNVapU0R9//KE///xTAwcOlN1u16OPPqonn3xSrVq1Us6cOZ3TkTlcP2DkfnZ/x/4uYrVa5evrq5w5c0q6+Xx2OXPm1L59+xQbG6vnn39eL7zwgnORADMceA6GYeill15STEyMhg4d6rzJcvwqZRiGrFar/Pz8nKOxBg4cKOnayLbU1FR17dpVefLkMdUJwjAM9e3bVz/88IM8PT1VunRpBQcH69FHH1WRIkUUFhamgIAA9ejRQwEBAXr99dfl6empF154QQsXLlS7du00Y8YMzZ4923QjtqR/10f9/Pz022+/KSYmRuXKlVPFihVdUWq28W/azMfHRzExMfrzzz+dYW7lypW1adMmU/XP6/3b88GxY8d05MgRWa1W+fv7O0d2meF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      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.dendrogram(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</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>Month</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29501</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-02</td>\n",
       "      <td>9.91</td>\n",
       "      <td>43.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>19.91</td>\n",
       "      <td>12.60</td>\n",
       "      <td>9.16</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.78</td>\n",
       "      <td>19.68</td>\n",
       "      <td>1.37</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.73</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29502</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-03</td>\n",
       "      <td>17.94</td>\n",
       "      <td>102.58</td>\n",
       "      <td>5.60</td>\n",
       "      <td>30.32</td>\n",
       "      <td>20.59</td>\n",
       "      <td>9.49</td>\n",
       "      <td>0.58</td>\n",
       "      <td>6.48</td>\n",
       "      <td>23.82</td>\n",
       "      <td>2.65</td>\n",
       "      <td>7.59</td>\n",
       "      <td>1.23</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29503</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-04</td>\n",
       "      <td>19.14</td>\n",
       "      <td>77.06</td>\n",
       "      <td>4.12</td>\n",
       "      <td>35.57</td>\n",
       "      <td>22.18</td>\n",
       "      <td>8.82</td>\n",
       "      <td>0.68</td>\n",
       "      <td>7.19</td>\n",
       "      <td>25.56</td>\n",
       "      <td>3.06</td>\n",
       "      <td>8.18</td>\n",
       "      <td>1.61</td>\n",
       "      <td>71.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29504</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-05</td>\n",
       "      <td>29.71</td>\n",
       "      <td>112.53</td>\n",
       "      <td>2.98</td>\n",
       "      <td>44.77</td>\n",
       "      <td>24.69</td>\n",
       "      <td>10.51</td>\n",
       "      <td>0.85</td>\n",
       "      <td>10.64</td>\n",
       "      <td>21.94</td>\n",
       "      <td>4.50</td>\n",
       "      <td>18.10</td>\n",
       "      <td>1.62</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29505</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>21.66</td>\n",
       "      <td>74.97</td>\n",
       "      <td>3.38</td>\n",
       "      <td>38.42</td>\n",
       "      <td>22.56</td>\n",
       "      <td>8.63</td>\n",
       "      <td>0.64</td>\n",
       "      <td>7.02</td>\n",
       "      <td>28.86</td>\n",
       "      <td>2.57</td>\n",
       "      <td>11.54</td>\n",
       "      <td>1.40</td>\n",
       "      <td>101.0</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29506</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-07</td>\n",
       "      <td>29.69</td>\n",
       "      <td>101.97</td>\n",
       "      <td>2.84</td>\n",
       "      <td>35.51</td>\n",
       "      <td>20.66</td>\n",
       "      <td>9.85</td>\n",
       "      <td>0.57</td>\n",
       "      <td>5.32</td>\n",
       "      <td>31.71</td>\n",
       "      <td>3.07</td>\n",
       "      <td>10.65</td>\n",
       "      <td>1.17</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29507</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>31.14</td>\n",
       "      <td>125.15</td>\n",
       "      <td>6.55</td>\n",
       "      <td>41.56</td>\n",
       "      <td>27.26</td>\n",
       "      <td>9.71</td>\n",
       "      <td>0.64</td>\n",
       "      <td>6.61</td>\n",
       "      <td>36.65</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.76</td>\n",
       "      <td>1.78</td>\n",
       "      <td>115.0</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29508</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>31.84</td>\n",
       "      <td>85.41</td>\n",
       "      <td>4.69</td>\n",
       "      <td>50.11</td>\n",
       "      <td>30.35</td>\n",
       "      <td>10.01</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5.06</td>\n",
       "      <td>33.86</td>\n",
       "      <td>4.07</td>\n",
       "      <td>9.24</td>\n",
       "      <td>2.52</td>\n",
       "      <td>96.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29509</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-10</td>\n",
       "      <td>32.55</td>\n",
       "      <td>79.28</td>\n",
       "      <td>4.78</td>\n",
       "      <td>44.59</td>\n",
       "      <td>27.38</td>\n",
       "      <td>9.34</td>\n",
       "      <td>0.69</td>\n",
       "      <td>5.90</td>\n",
       "      <td>35.06</td>\n",
       "      <td>2.96</td>\n",
       "      <td>7.89</td>\n",
       "      <td>2.08</td>\n",
       "      <td>92.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29510</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-11</td>\n",
       "      <td>37.71</td>\n",
       "      <td>98.53</td>\n",
       "      <td>9.97</td>\n",
       "      <td>52.10</td>\n",
       "      <td>35.79</td>\n",
       "      <td>9.91</td>\n",
       "      <td>0.70</td>\n",
       "      <td>5.59</td>\n",
       "      <td>26.59</td>\n",
       "      <td>5.29</td>\n",
       "      <td>10.38</td>\n",
       "      <td>3.38</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29511</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-12</td>\n",
       "      <td>37.67</td>\n",
       "      <td>76.27</td>\n",
       "      <td>12.17</td>\n",
       "      <td>53.59</td>\n",
       "      <td>35.52</td>\n",
       "      <td>9.85</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.74</td>\n",
       "      <td>17.35</td>\n",
       "      <td>4.77</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.85</td>\n",
       "      <td>84.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29512</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>22.75</td>\n",
       "      <td>86.77</td>\n",
       "      <td>3.85</td>\n",
       "      <td>38.23</td>\n",
       "      <td>22.60</td>\n",
       "      <td>9.24</td>\n",
       "      <td>0.67</td>\n",
       "      <td>10.70</td>\n",
       "      <td>27.24</td>\n",
       "      <td>3.06</td>\n",
       "      <td>8.49</td>\n",
       "      <td>1.26</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29513</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-14</td>\n",
       "      <td>21.33</td>\n",
       "      <td>83.23</td>\n",
       "      <td>2.81</td>\n",
       "      <td>34.56</td>\n",
       "      <td>20.43</td>\n",
       "      <td>9.78</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.37</td>\n",
       "      <td>29.44</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.91</td>\n",
       "      <td>0.97</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29514</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>24.66</td>\n",
       "      <td>100.53</td>\n",
       "      <td>3.66</td>\n",
       "      <td>37.93</td>\n",
       "      <td>22.97</td>\n",
       "      <td>9.22</td>\n",
       "      <td>0.71</td>\n",
       "      <td>7.02</td>\n",
       "      <td>28.63</td>\n",
       "      <td>3.13</td>\n",
       "      <td>6.12</td>\n",
       "      <td>1.51</td>\n",
       "      <td>92.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29515</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>21.06</td>\n",
       "      <td>80.51</td>\n",
       "      <td>6.67</td>\n",
       "      <td>40.33</td>\n",
       "      <td>26.81</td>\n",
       "      <td>8.81</td>\n",
       "      <td>0.63</td>\n",
       "      <td>11.09</td>\n",
       "      <td>21.69</td>\n",
       "      <td>3.77</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1.69</td>\n",
       "      <td>83.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29516</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>17.05</td>\n",
       "      <td>73.22</td>\n",
       "      <td>6.08</td>\n",
       "      <td>36.31</td>\n",
       "      <td>24.23</td>\n",
       "      <td>6.69</td>\n",
       "      <td>0.54</td>\n",
       "      <td>11.08</td>\n",
       "      <td>26.09</td>\n",
       "      <td>2.96</td>\n",
       "      <td>6.96</td>\n",
       "      <td>1.39</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29517</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>21.16</td>\n",
       "      <td>88.80</td>\n",
       "      <td>4.94</td>\n",
       "      <td>42.96</td>\n",
       "      <td>26.79</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.68</td>\n",
       "      <td>11.05</td>\n",
       "      <td>27.65</td>\n",
       "      <td>4.38</td>\n",
       "      <td>7.65</td>\n",
       "      <td>1.69</td>\n",
       "      <td>71.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29518</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>14.41</td>\n",
       "      <td>66.81</td>\n",
       "      <td>6.22</td>\n",
       "      <td>40.06</td>\n",
       "      <td>26.10</td>\n",
       "      <td>7.51</td>\n",
       "      <td>1.07</td>\n",
       "      <td>13.23</td>\n",
       "      <td>27.34</td>\n",
       "      <td>2.55</td>\n",
       "      <td>8.98</td>\n",
       "      <td>1.45</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29519</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>26.67</td>\n",
       "      <td>86.29</td>\n",
       "      <td>3.78</td>\n",
       "      <td>37.36</td>\n",
       "      <td>22.46</td>\n",
       "      <td>15.05</td>\n",
       "      <td>0.74</td>\n",
       "      <td>11.76</td>\n",
       "      <td>41.97</td>\n",
       "      <td>3.14</td>\n",
       "      <td>11.89</td>\n",
       "      <td>1.87</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29520</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>25.96</td>\n",
       "      <td>91.75</td>\n",
       "      <td>5.65</td>\n",
       "      <td>40.22</td>\n",
       "      <td>25.78</td>\n",
       "      <td>13.32</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.10</td>\n",
       "      <td>38.27</td>\n",
       "      <td>3.10</td>\n",
       "      <td>10.26</td>\n",
       "      <td>1.57</td>\n",
       "      <td>92.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29521</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>33.17</td>\n",
       "      <td>108.22</td>\n",
       "      <td>5.58</td>\n",
       "      <td>42.45</td>\n",
       "      <td>27.06</td>\n",
       "      <td>13.70</td>\n",
       "      <td>0.73</td>\n",
       "      <td>13.65</td>\n",
       "      <td>34.85</td>\n",
       "      <td>3.99</td>\n",
       "      <td>10.24</td>\n",
       "      <td>2.32</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29522</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>25.40</td>\n",
       "      <td>83.38</td>\n",
       "      <td>2.76</td>\n",
       "      <td>34.09</td>\n",
       "      <td>19.92</td>\n",
       "      <td>13.13</td>\n",
       "      <td>0.54</td>\n",
       "      <td>10.40</td>\n",
       "      <td>43.27</td>\n",
       "      <td>2.88</td>\n",
       "      <td>12.03</td>\n",
       "      <td>1.33</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29523</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>34.36</td>\n",
       "      <td>90.90</td>\n",
       "      <td>1.22</td>\n",
       "      <td>23.38</td>\n",
       "      <td>13.12</td>\n",
       "      <td>14.45</td>\n",
       "      <td>0.56</td>\n",
       "      <td>10.92</td>\n",
       "      <td>35.12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1.60</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29524</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>13.45</td>\n",
       "      <td>58.54</td>\n",
       "      <td>2.30</td>\n",
       "      <td>21.60</td>\n",
       "      <td>13.09</td>\n",
       "      <td>12.27</td>\n",
       "      <td>0.41</td>\n",
       "      <td>8.19</td>\n",
       "      <td>29.38</td>\n",
       "      <td>1.28</td>\n",
       "      <td>5.64</td>\n",
       "      <td>0.92</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29525</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>7.63</td>\n",
       "      <td>32.27</td>\n",
       "      <td>5.91</td>\n",
       "      <td>23.27</td>\n",
       "      <td>17.19</td>\n",
       "      <td>11.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>6.87</td>\n",
       "      <td>19.90</td>\n",
       "      <td>1.45</td>\n",
       "      <td>5.37</td>\n",
       "      <td>1.45</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29526</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>15.02</td>\n",
       "      <td>50.94</td>\n",
       "      <td>7.68</td>\n",
       "      <td>25.06</td>\n",
       "      <td>19.54</td>\n",
       "      <td>12.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.55</td>\n",
       "      <td>23.30</td>\n",
       "      <td>2.24</td>\n",
       "      <td>12.07</td>\n",
       "      <td>0.73</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29527</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>24.38</td>\n",
       "      <td>74.09</td>\n",
       "      <td>3.42</td>\n",
       "      <td>26.06</td>\n",
       "      <td>16.53</td>\n",
       "      <td>11.99</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.72</td>\n",
       "      <td>30.14</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29528</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>22.91</td>\n",
       "      <td>65.73</td>\n",
       "      <td>3.45</td>\n",
       "      <td>29.53</td>\n",
       "      <td>18.33</td>\n",
       "      <td>10.71</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.42</td>\n",
       "      <td>30.96</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29529</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>16.64</td>\n",
       "      <td>49.97</td>\n",
       "      <td>4.05</td>\n",
       "      <td>29.26</td>\n",
       "      <td>18.80</td>\n",
       "      <td>10.03</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.84</td>\n",
       "      <td>28.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29530</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2020-07-01</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",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City       Date  PM2.5    PM10     NO    NO2    NOx    NH3  \\\n",
       "29501  Visakhapatnam 2020-06-02   9.91   43.30   2.81  19.91  12.60   9.16   \n",
       "29502  Visakhapatnam 2020-06-03  17.94  102.58   5.60  30.32  20.59   9.49   \n",
       "29503  Visakhapatnam 2020-06-04  19.14   77.06   4.12  35.57  22.18   8.82   \n",
       "29504  Visakhapatnam 2020-06-05  29.71  112.53   2.98  44.77  24.69  10.51   \n",
       "29505  Visakhapatnam 2020-06-06  21.66   74.97   3.38  38.42  22.56   8.63   \n",
       "29506  Visakhapatnam 2020-06-07  29.69  101.97   2.84  35.51  20.66   9.85   \n",
       "29507  Visakhapatnam 2020-06-08  31.14  125.15   6.55  41.56  27.26   9.71   \n",
       "29508  Visakhapatnam 2020-06-09  31.84   85.41   4.69  50.11  30.35  10.01   \n",
       "29509  Visakhapatnam 2020-06-10  32.55   79.28   4.78  44.59  27.38   9.34   \n",
       "29510  Visakhapatnam 2020-06-11  37.71   98.53   9.97  52.10  35.79   9.91   \n",
       "29511  Visakhapatnam 2020-06-12  37.67   76.27  12.17  53.59  35.52   9.85   \n",
       "29512  Visakhapatnam 2020-06-13  22.75   86.77   3.85  38.23  22.60   9.24   \n",
       "29513  Visakhapatnam 2020-06-14  21.33   83.23   2.81  34.56  20.43   9.78   \n",
       "29514  Visakhapatnam 2020-06-15  24.66  100.53   3.66  37.93  22.97   9.22   \n",
       "29515  Visakhapatnam 2020-06-16  21.06   80.51   6.67  40.33  26.81   8.81   \n",
       "29516  Visakhapatnam 2020-06-17  17.05   73.22   6.08  36.31  24.23   6.69   \n",
       "29517  Visakhapatnam 2020-06-18  21.16   88.80   4.94  42.96  26.79   3.22   \n",
       "29518  Visakhapatnam 2020-06-19  14.41   66.81   6.22  40.06  26.10   7.51   \n",
       "29519  Visakhapatnam 2020-06-20  26.67   86.29   3.78  37.36  22.46  15.05   \n",
       "29520  Visakhapatnam 2020-06-21  25.96   91.75   5.65  40.22  25.78  13.32   \n",
       "29521  Visakhapatnam 2020-06-22  33.17  108.22   5.58  42.45  27.06  13.70   \n",
       "29522  Visakhapatnam 2020-06-23  25.40   83.38   2.76  34.09  19.92  13.13   \n",
       "29523  Visakhapatnam 2020-06-24  34.36   90.90   1.22  23.38  13.12  14.45   \n",
       "29524  Visakhapatnam 2020-06-25  13.45   58.54   2.30  21.60  13.09  12.27   \n",
       "29525  Visakhapatnam 2020-06-26   7.63   32.27   5.91  23.27  17.19  11.15   \n",
       "29526  Visakhapatnam 2020-06-27  15.02   50.94   7.68  25.06  19.54  12.47   \n",
       "29527  Visakhapatnam 2020-06-28  24.38   74.09   3.42  26.06  16.53  11.99   \n",
       "29528  Visakhapatnam 2020-06-29  22.91   65.73   3.45  29.53  18.33  10.71   \n",
       "29529  Visakhapatnam 2020-06-30  16.64   49.97   4.05  29.26  18.80  10.03   \n",
       "29530  Visakhapatnam 2020-07-01  15.00   66.00   0.40  26.85  14.05   5.20   \n",
       "\n",
       "         CO    SO2     O3  C6H6   C7H8  C8H10    AQI    AQI_Bucket Month  Year  \n",
       "29501  0.47   8.78  19.68  1.37   3.44   0.73   50.0          Good     6  2020  \n",
       "29502  0.58   6.48  23.82  2.65   7.59   1.23   88.0  Satisfactory     6  2020  \n",
       "29503  0.68   7.19  25.56  3.06   8.18   1.61   71.0  Satisfactory     6  2020  \n",
       "29504  0.85  10.64  21.94  4.50  18.10   1.62   93.0  Satisfactory     6  2020  \n",
       "29505  0.64   7.02  28.86  2.57  11.54   1.40  101.0      Moderate     6  2020  \n",
       "29506  0.57   5.32  31.71  3.07  10.65   1.17   82.0  Satisfactory     6  2020  \n",
       "29507  0.64   6.61  36.65  3.47   9.76   1.78  115.0      Moderate     6  2020  \n",
       "29508  0.88   5.06  33.86  4.07   9.24   2.52   96.0  Satisfactory     6  2020  \n",
       "29509  0.69   5.90  35.06  2.96   7.89   2.08   92.0  Satisfactory     6  2020  \n",
       "29510  0.70   5.59  26.59  5.29  10.38   3.38   90.0  Satisfactory     6  2020  \n",
       "29511  0.97   5.74  17.35  4.77  18.89   2.85   84.0  Satisfactory     6  2020  \n",
       "29512  0.67  10.70  27.24  3.06   8.49   1.26   81.0  Satisfactory     6  2020  \n",
       "29513  0.58   9.37  29.44  2.29   5.91   0.97   88.0  Satisfactory     6  2020  \n",
       "29514  0.71   7.02  28.63  3.13   6.12   1.51   92.0  Satisfactory     6  2020  \n",
       "29515  0.63  11.09  21.69  3.77   6.55   1.69   83.0  Satisfactory     6  2020  \n",
       "29516  0.54  11.08  26.09  2.96   6.96   1.39   90.0  Satisfactory     6  2020  \n",
       "29517  0.68  11.05  27.65  4.38   7.65   1.69   71.0  Satisfactory     6  2020  \n",
       "29518  1.07  13.23  27.34  2.55   8.98   1.45  110.0      Moderate     6  2020  \n",
       "29519  0.74  11.76  41.97  3.14  11.89   1.87   74.0  Satisfactory     6  2020  \n",
       "29520  0.71  10.10  38.27  3.10  10.26   1.57   92.0  Satisfactory     6  2020  \n",
       "29521  0.73  13.65  34.85  3.99  10.24   2.32   95.0  Satisfactory     6  2020  \n",
       "29522  0.54  10.40  43.27  2.88  12.03   1.33  100.0  Satisfactory     6  2020  \n",
       "29523  0.56  10.92  35.12  2.99   3.15   1.60   86.0  Satisfactory     6  2020  \n",
       "29524  0.41   8.19  29.38  1.28   5.64   0.92   77.0  Satisfactory     6  2020  \n",
       "29525  0.46   6.87  19.90  1.45   5.37   1.45   47.0          Good     6  2020  \n",
       "29526  0.47   8.55  23.30  2.24  12.07   0.73   41.0          Good     6  2020  \n",
       "29527  0.52  12.72  30.14  0.74   2.21   0.38   70.0  Satisfactory     6  2020  \n",
       "29528  0.48   8.42  30.96  0.01   0.01   0.00   68.0  Satisfactory     6  2020  \n",
       "29529  0.52   9.84  28.30  0.00   0.00   0.00   54.0  Satisfactory     6  2020  \n",
       "29530  0.59   2.10  17.05   NaN    NaN    NaN   50.0          Good     7  2020  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"from sklearn import preprocessing\\nlabel_encoder=preprocessing.LabelEncoder()\\ncity_day['City']=label_encoder.fit_transform(city_day['City'])\\ncity_day['City'].unique()\""
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''from sklearn import preprocessing\n",
    "label_encoder=preprocessing.LabelEncoder()\n",
    "city_day['City']=label_encoder.fit_transform(city_day['City'])\n",
    "city_day['City'].unique()'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Poor', 'Very Poor', 'Severe', 'Moderate', 'Satisfactory',\n",
       "       'Good'], dtype=object)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AQI_Bucket'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#city_day['AQI_Bucket']=city_day['AQI_Bucket'].map({'Good':0,'Satisfactory':1,'Moderate':2,'Poor':3,'Very Poor':4,'Severe':5})\n",
    "#city_day.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                  object\n",
       "Date          datetime64[ns]\n",
       "PM2.5                float64\n",
       "PM10                 float64\n",
       "NO                   float64\n",
       "NO2                  float64\n",
       "NOx                  float64\n",
       "NH3                  float64\n",
       "CO                   float64\n",
       "SO2                  float64\n",
       "O3                   float64\n",
       "C6H6                 float64\n",
       "C7H8                 float64\n",
       "C8H10                float64\n",
       "AQI                  float64\n",
       "AQI_Bucket            object\n",
       "Month                 object\n",
       "Year                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[:,1:14]=df.groupby(\"City\").transform(lambda x:x.fillna(x.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</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>Month</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City       Date      PM2.5        PM10     NO    NO2    NOx  NH3  \\\n",
       "0  Ahmedabad 2015-01-01  67.854497  114.584029   0.92  18.22  17.15  NaN   \n",
       "1  Ahmedabad 2015-01-02  67.854497  114.584029   0.97  15.69  16.46  NaN   \n",
       "2  Ahmedabad 2015-01-03  67.854497  114.584029  17.40  19.30  29.70  NaN   \n",
       "3  Ahmedabad 2015-01-04  67.854497  114.584029   1.70  18.48  17.97  NaN   \n",
       "4  Ahmedabad 2015-01-05  67.854497  114.584029  22.10  21.42  37.76  NaN   \n",
       "\n",
       "      CO    SO2      O3  C6H6   C7H8  C8H10  AQI AQI_Bucket Month  Year  \n",
       "0   0.92  27.64  133.36  0.00   0.02   0.00  NaN        NaN     1  2015  \n",
       "1   0.97  24.55   34.06  3.68   5.50   3.77  NaN        NaN     1  2015  \n",
       "2  17.40  29.07   30.70  6.80  16.40   2.25  NaN        NaN     1  2015  \n",
       "3   1.70  18.59   36.08  4.43  10.14   1.00  NaN        NaN     1  2015  \n",
       "4  22.10  39.33   39.31  7.01  18.89   2.78  NaN        NaN     1  2015  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City              0\n",
       "Date              0\n",
       "PM2.5             0\n",
       "PM10           2009\n",
       "NO                0\n",
       "NO2               0\n",
       "NOx            1169\n",
       "NH3            2009\n",
       "CO                0\n",
       "SO2               0\n",
       "O3              162\n",
       "C6H6           2732\n",
       "C7H8           4010\n",
       "C8H10         13047\n",
       "AQI            4681\n",
       "AQI_Bucket     4681\n",
       "Month             0\n",
       "Year              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</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>Month</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City       Date      PM2.5        PM10     NO    NO2    NOx  NH3  \\\n",
       "0  Ahmedabad 2015-01-01  67.854497  114.584029   0.92  18.22  17.15  NaN   \n",
       "1  Ahmedabad 2015-01-02  67.854497  114.584029   0.97  15.69  16.46  NaN   \n",
       "2  Ahmedabad 2015-01-03  67.854497  114.584029  17.40  19.30  29.70  NaN   \n",
       "3  Ahmedabad 2015-01-04  67.854497  114.584029   1.70  18.48  17.97  NaN   \n",
       "4  Ahmedabad 2015-01-05  67.854497  114.584029  22.10  21.42  37.76  NaN   \n",
       "\n",
       "      CO    SO2      O3  C6H6   C7H8  C8H10  AQI AQI_Bucket Month  Year  \n",
       "0   0.92  27.64  133.36  0.00   0.02   0.00  NaN        NaN     1  2015  \n",
       "1   0.97  24.55   34.06  3.68   5.50   3.77  NaN        NaN     1  2015  \n",
       "2  17.40  29.07   30.70  6.80  16.40   2.25  NaN        NaN     1  2015  \n",
       "3   1.70  18.59   36.08  4.43  10.14   1.00  NaN        NaN     1  2015  \n",
       "4  22.10  39.33   39.31  7.01  18.89   2.78  NaN        NaN     1  2015  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City              0\n",
       "Date              0\n",
       "PM2.5             0\n",
       "PM10           2009\n",
       "NO                0\n",
       "NO2               0\n",
       "NOx            1169\n",
       "NH3            2009\n",
       "CO                0\n",
       "SO2               0\n",
       "O3              162\n",
       "C6H6           2732\n",
       "C7H8           4010\n",
       "C8H10         13047\n",
       "AQI            4681\n",
       "AQI_Bucket     4681\n",
       "Month             0\n",
       "Year              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</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>Month</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City       Date      PM2.5        PM10     NO    NO2    NOx  NH3  \\\n",
       "0  Ahmedabad 2015-01-01  67.854497  114.584029   0.92  18.22  17.15  NaN   \n",
       "1  Ahmedabad 2015-01-02  67.854497  114.584029   0.97  15.69  16.46  NaN   \n",
       "2  Ahmedabad 2015-01-03  67.854497  114.584029  17.40  19.30  29.70  NaN   \n",
       "3  Ahmedabad 2015-01-04  67.854497  114.584029   1.70  18.48  17.97  NaN   \n",
       "4  Ahmedabad 2015-01-05  67.854497  114.584029  22.10  21.42  37.76  NaN   \n",
       "\n",
       "      CO    SO2      O3  C6H6   C7H8  C8H10  AQI AQI_Bucket Month  Year  \n",
       "0   0.92  27.64  133.36  0.00   0.02   0.00  NaN        NaN     1  2015  \n",
       "1   0.97  24.55   34.06  3.68   5.50   3.77  NaN        NaN     1  2015  \n",
       "2  17.40  29.07   30.70  6.80  16.40   2.25  NaN        NaN     1  2015  \n",
       "3   1.70  18.59   36.08  4.43  10.14   1.00  NaN        NaN     1  2015  \n",
       "4  22.10  39.33   39.31  7.01  18.89   2.78  NaN        NaN     1  2015  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.iloc[:,:3:14]=df.fillna(df.iloc[:,3:14].mean())\n",
    "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()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</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>Month</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City       Date      PM2.5        PM10     NO    NO2    NOx  \\\n",
       "0  Ahmedabad 2015-01-01  67.854497  114.584029   0.92  18.22  17.15   \n",
       "1  Ahmedabad 2015-01-02  67.854497  114.584029   0.97  15.69  16.46   \n",
       "2  Ahmedabad 2015-01-03  67.854497  114.584029  17.40  19.30  29.70   \n",
       "3  Ahmedabad 2015-01-04  67.854497  114.584029   1.70  18.48  17.97   \n",
       "4  Ahmedabad 2015-01-05  67.854497  114.584029  22.10  21.42  37.76   \n",
       "\n",
       "         NH3     CO    SO2      O3  C6H6   C7H8  C8H10  AQI AQI_Bucket Month  \\\n",
       "0  23.024137   0.92  27.64  133.36  0.00   0.02   0.00  NaN        NaN     1   \n",
       "1  23.024137   0.97  24.55   34.06  3.68   5.50   3.77  NaN        NaN     1   \n",
       "2  23.024137  17.40  29.07   30.70  6.80  16.40   2.25  NaN        NaN     1   \n",
       "3  23.024137   1.70  18.59   36.08  4.43  10.14   1.00  NaN        NaN     1   \n",
       "4  23.024137  22.10  39.33   39.31  7.01  18.89   2.78  NaN        NaN     1   \n",
       "\n",
       "   Year  \n",
       "0  2015  \n",
       "1  2015  \n",
       "2  2015  \n",
       "3  2015  \n",
       "4  2015  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City             0\n",
       "Date             0\n",
       "PM2.5            0\n",
       "PM10             0\n",
       "NO               0\n",
       "NO2              0\n",
       "NOx              0\n",
       "NH3              0\n",
       "CO               0\n",
       "SO2              0\n",
       "O3               0\n",
       "C6H6             0\n",
       "C7H8             0\n",
       "C8H10            0\n",
       "AQI           4681\n",
       "AQI_Bucket    4681\n",
       "Month            0\n",
       "Year             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imputation of AQI Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City             0\n",
       "Date             0\n",
       "PM2.5            0\n",
       "PM10             0\n",
       "NO               0\n",
       "NO2              0\n",
       "NOx              0\n",
       "NH3              0\n",
       "CO               0\n",
       "SO2              0\n",
       "O3               0\n",
       "C6H6             0\n",
       "C7H8             0\n",
       "C8H10            0\n",
       "AQI           4681\n",
       "AQI_Bucket    4681\n",
       "Month            0\n",
       "Year             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def aqi_calc(df):\n",
    "  #PM2.5 (Particulate Matter 2.5-micrometer)\n",
    "  #PM2.5 is measured in ug / m3 (micrograms per cubic meter of air). The predefined groups are defined in the function below:\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",
    "  df[\"PM2.5_SubIndex\"] = df[\"PM2.5\"].apply(lambda x: get_PM25_subindex(x))\n",
    "\n",
    "\n",
    "  #PM10 (Particulate Matter 10-micrometer)\n",
    "  #PM10 is measured in ug / m3 (micrograms per cubic meter of air). The predefined groups are defined in the function below:\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\"].apply(lambda x: get_PM10_subindex(x))\n",
    "\n",
    "\n",
    "  #SO2 (Sulphur Dioxide)\n",
    "  #SO2 is measured in ug / m3 (micrograms per cubic meter of air). The predefined groups are defined in the function below:\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\"].apply(lambda x: get_SO2_subindex(x))\n",
    "\n",
    "\n",
    "  #NOx (Any Nitric x-oxide)\n",
    "  #NOx is measured in ppb (parts per billion). The predefined groups are defined in the function below:\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\"].apply(lambda x: get_NOx_subindex(x))\n",
    "\n",
    "\n",
    "  #NH3 (Ammonia)\n",
    "  #NH3 is measured in ug / m3 (micrograms per cubic meter of air). The predefined groups are defined in the function below: \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\"].apply(lambda x: get_NH3_subindex(x))\n",
    "\n",
    "\n",
    "  #CO (Carbon Monoxide)\n",
    "  #CO is measured in mg / m3 (milligrams per cubic meter of air). The predefined groups are defined in the function below:\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\"].apply(lambda x: get_CO_subindex(x))\n",
    "\n",
    "\n",
    "  #O3 (Ozone or Trioxygen)\n",
    "  #O3 is measured in ug / m3 (micrograms per cubic meter of air). The predefined groups are defined in the function below:\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\"].apply(lambda x: get_O3_subindex(x))\n",
    "  \n",
    "\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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "aqi_calc(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29531, 28)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                        0\n",
       "Date                        0\n",
       "PM2.5                       0\n",
       "PM10                        0\n",
       "NO                          0\n",
       "NO2                         0\n",
       "NOx                         0\n",
       "NH3                         0\n",
       "CO                          0\n",
       "SO2                         0\n",
       "O3                          0\n",
       "C6H6                        0\n",
       "C7H8                        0\n",
       "C8H10                       0\n",
       "AQI                      4681\n",
       "AQI_Bucket               4681\n",
       "Month                       0\n",
       "Year                        0\n",
       "PM2.5_SubIndex              0\n",
       "PM10_SubIndex               0\n",
       "SO2_SubIndex                0\n",
       "NOx_SubIndex                0\n",
       "NH3_SubIndex                0\n",
       "CO_SubIndex                 0\n",
       "O3_SubIndex                 0\n",
       "Checks                      0\n",
       "AQI_calculated              0\n",
       "AQI_bucket_calculated       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29531, 28)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24850, 28)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25])"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "label_encoder=preprocessing.LabelEncoder()\n",
    "df['City']=label_encoder.fit_transform(df['City'])\n",
    "df['City'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['AQI_bucket_calculated']=df['AQI_calculated'].map({'Good':0,'Satisfactory':1,'Moderate':2,'Poor':3,'Very Poor':4,'Severe':5})\n",
    "#city_day.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_csv('Neha City_day_after_AQI_Imputation.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</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>...</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>28</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>83.13</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>6.93</td>\n",
       "      <td>28.71</td>\n",
       "      <td>33.72</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>6.93</td>\n",
       "      <td>49.52</td>\n",
       "      <td>...</td>\n",
       "      <td>177.100000</td>\n",
       "      <td>109.722686</td>\n",
       "      <td>61.9000</td>\n",
       "      <td>42.1500</td>\n",
       "      <td>5.756034</td>\n",
       "      <td>161.625000</td>\n",
       "      <td>59.760000</td>\n",
       "      <td>7</td>\n",
       "      <td>177.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>79.84</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>13.85</td>\n",
       "      <td>28.68</td>\n",
       "      <td>41.08</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>13.85</td>\n",
       "      <td>48.49</td>\n",
       "      <td>...</td>\n",
       "      <td>166.133333</td>\n",
       "      <td>109.722686</td>\n",
       "      <td>60.6125</td>\n",
       "      <td>51.3500</td>\n",
       "      <td>5.756034</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>97.070000</td>\n",
       "      <td>7</td>\n",
       "      <td>255.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>94.52</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>24.39</td>\n",
       "      <td>32.66</td>\n",
       "      <td>52.61</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>24.39</td>\n",
       "      <td>67.39</td>\n",
       "      <td>...</td>\n",
       "      <td>215.066667</td>\n",
       "      <td>109.722686</td>\n",
       "      <td>84.2375</td>\n",
       "      <td>65.7625</td>\n",
       "      <td>5.756034</td>\n",
       "      <td>343.470588</td>\n",
       "      <td>116.661765</td>\n",
       "      <td>7</td>\n",
       "      <td>343.0</td>\n",
       "      <td>Very Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>135.99</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>43.48</td>\n",
       "      <td>42.08</td>\n",
       "      <td>84.57</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>43.48</td>\n",
       "      <td>75.23</td>\n",
       "      <td>...</td>\n",
       "      <td>312.300000</td>\n",
       "      <td>109.722686</td>\n",
       "      <td>94.0375</td>\n",
       "      <td>104.5700</td>\n",
       "      <td>5.756034</td>\n",
       "      <td>455.764706</td>\n",
       "      <td>103.970588</td>\n",
       "      <td>7</td>\n",
       "      <td>456.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-02</td>\n",
       "      <td>178.33</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>54.56</td>\n",
       "      <td>35.31</td>\n",
       "      <td>72.80</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>54.56</td>\n",
       "      <td>55.04</td>\n",
       "      <td>...</td>\n",
       "      <td>344.869231</td>\n",
       "      <td>109.722686</td>\n",
       "      <td>68.8000</td>\n",
       "      <td>91.0000</td>\n",
       "      <td>5.756034</td>\n",
       "      <td>520.941176</td>\n",
       "      <td>110.852941</td>\n",
       "      <td>7</td>\n",
       "      <td>521.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-03</td>\n",
       "      <td>139.70</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>30.61</td>\n",
       "      <td>28.40</td>\n",
       "      <td>56.73</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>30.61</td>\n",
       "      <td>33.79</td>\n",
       "      <td>...</td>\n",
       "      <td>315.153846</td>\n",
       "      <td>109.722686</td>\n",
       "      <td>42.2375</td>\n",
       "      <td>70.9125</td>\n",
       "      <td>5.756034</td>\n",
       "      <td>380.058824</td>\n",
       "      <td>73.600000</td>\n",
       "      <td>7</td>\n",
       "      <td>380.0</td>\n",
       "      <td>Very Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-04</td>\n",
       "      <td>80.65</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>2.37</td>\n",
       "      <td>22.83</td>\n",
       "      <td>24.00</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>2.37</td>\n",
       "      <td>25.73</td>\n",
       "      <td>...</td>\n",
       "      <td>168.833333</td>\n",
       "      <td>109.722686</td>\n",
       "      <td>32.1625</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>5.756034</td>\n",
       "      <td>104.625000</td>\n",
       "      <td>47.300000</td>\n",
       "      <td>7</td>\n",
       "      <td>169.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-05</td>\n",
       "      <td>58.36</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>2.60</td>\n",
       "      <td>21.39</td>\n",
       "      <td>23.31</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>2.60</td>\n",
       "      <td>32.66</td>\n",
       "      <td>...</td>\n",
       "      <td>97.266667</td>\n",
       "      <td>109.722686</td>\n",
       "      <td>40.8250</td>\n",
       "      <td>29.1375</td>\n",
       "      <td>5.756034</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>53.540000</td>\n",
       "      <td>7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-06</td>\n",
       "      <td>79.29</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>1.16</td>\n",
       "      <td>26.94</td>\n",
       "      <td>26.83</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>1.16</td>\n",
       "      <td>67.41</td>\n",
       "      <td>...</td>\n",
       "      <td>164.300000</td>\n",
       "      <td>109.722686</td>\n",
       "      <td>84.2625</td>\n",
       "      <td>33.5375</td>\n",
       "      <td>5.756034</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>59.300000</td>\n",
       "      <td>7</td>\n",
       "      <td>164.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-07</td>\n",
       "      <td>88.70</td>\n",
       "      <td>114.584029</td>\n",
       "      <td>7.29</td>\n",
       "      <td>31.32</td>\n",
       "      <td>37.73</td>\n",
       "      <td>23.024137</td>\n",
       "      <td>7.29</td>\n",
       "      <td>80.09</td>\n",
       "      <td>...</td>\n",
       "      <td>195.666667</td>\n",
       "      <td>109.722686</td>\n",
       "      <td>100.0300</td>\n",
       "      <td>47.1625</td>\n",
       "      <td>5.756034</td>\n",
       "      <td>166.125000</td>\n",
       "      <td>44.760000</td>\n",
       "      <td>7</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    City       Date   PM2.5        PM10     NO    NO2    NOx        NH3  \\\n",
       "28     0 2015-01-29   83.13  114.584029   6.93  28.71  33.72  23.024137   \n",
       "29     0 2015-01-30   79.84  114.584029  13.85  28.68  41.08  23.024137   \n",
       "30     0 2015-01-31   94.52  114.584029  24.39  32.66  52.61  23.024137   \n",
       "31     0 2015-02-01  135.99  114.584029  43.48  42.08  84.57  23.024137   \n",
       "32     0 2015-02-02  178.33  114.584029  54.56  35.31  72.80  23.024137   \n",
       "33     0 2015-02-03  139.70  114.584029  30.61  28.40  56.73  23.024137   \n",
       "34     0 2015-02-04   80.65  114.584029   2.37  22.83  24.00  23.024137   \n",
       "35     0 2015-02-05   58.36  114.584029   2.60  21.39  23.31  23.024137   \n",
       "36     0 2015-02-06   79.29  114.584029   1.16  26.94  26.83  23.024137   \n",
       "37     0 2015-02-07   88.70  114.584029   7.29  31.32  37.73  23.024137   \n",
       "\n",
       "       CO    SO2  ...  PM2.5_SubIndex  PM10_SubIndex  SO2_SubIndex  \\\n",
       "28   6.93  49.52  ...      177.100000     109.722686       61.9000   \n",
       "29  13.85  48.49  ...      166.133333     109.722686       60.6125   \n",
       "30  24.39  67.39  ...      215.066667     109.722686       84.2375   \n",
       "31  43.48  75.23  ...      312.300000     109.722686       94.0375   \n",
       "32  54.56  55.04  ...      344.869231     109.722686       68.8000   \n",
       "33  30.61  33.79  ...      315.153846     109.722686       42.2375   \n",
       "34   2.37  25.73  ...      168.833333     109.722686       32.1625   \n",
       "35   2.60  32.66  ...       97.266667     109.722686       40.8250   \n",
       "36   1.16  67.41  ...      164.300000     109.722686       84.2625   \n",
       "37   7.29  80.09  ...      195.666667     109.722686      100.0300   \n",
       "\n",
       "    NOx_SubIndex  NH3_SubIndex CO_SubIndex O3_SubIndex Checks  AQI_calculated  \\\n",
       "28       42.1500      5.756034  161.625000   59.760000      7           177.0   \n",
       "29       51.3500      5.756034  255.000000   97.070000      7           255.0   \n",
       "30       65.7625      5.756034  343.470588  116.661765      7           343.0   \n",
       "31      104.5700      5.756034  455.764706  103.970588      7           456.0   \n",
       "32       91.0000      5.756034  520.941176  110.852941      7           521.0   \n",
       "33       70.9125      5.756034  380.058824   73.600000      7           380.0   \n",
       "34       30.0000      5.756034  104.625000   47.300000      7           169.0   \n",
       "35       29.1375      5.756034  107.500000   53.540000      7           110.0   \n",
       "36       33.5375      5.756034   58.000000   59.300000      7           164.0   \n",
       "37       47.1625      5.756034  166.125000   44.760000      7           196.0   \n",
       "\n",
       "    AQI_bucket_calculated  \n",
       "28               Moderate  \n",
       "29                   Poor  \n",
       "30              Very Poor  \n",
       "31                 Severe  \n",
       "32                 Severe  \n",
       "33              Very Poor  \n",
       "34               Moderate  \n",
       "35               Moderate  \n",
       "36               Moderate  \n",
       "37               Moderate  \n",
       "\n",
       "[10 rows x 28 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24850, 28)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['AQI_bucket_calculated']=df['AQI_calculated'].map({'Good':0,'Satisfactory':1,'Moderate':2,'Poor':3,'Very Poor':4,'Severe':5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Neha_City_day_after_AQI2_Imputation.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Neha_City_day_after_AQI2_Imputation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</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>...</th>\n",
       "      <th>Year</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>6.0</td>\n",
       "      <td>29.5225</td>\n",
       "      <td>62.834404</td>\n",
       "      <td>5.71</td>\n",
       "      <td>11.9800</td>\n",
       "      <td>14.0225</td>\n",
       "      <td>10.832500</td>\n",
       "      <td>0.59</td>\n",
       "      <td>5.79</td>\n",
       "      <td>19.44</td>\n",
       "      <td>...</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>49.204167</td>\n",
       "      <td>62.834404</td>\n",
       "      <td>7.2375</td>\n",
       "      <td>17.528125</td>\n",
       "      <td>2.708125</td>\n",
       "      <td>29.5</td>\n",
       "      <td>19.44</td>\n",
       "      <td>7.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>12.0</td>\n",
       "      <td>49.6850</td>\n",
       "      <td>108.625000</td>\n",
       "      <td>10.02</td>\n",
       "      <td>21.9800</td>\n",
       "      <td>24.9100</td>\n",
       "      <td>18.780000</td>\n",
       "      <td>0.93</td>\n",
       "      <td>9.31</td>\n",
       "      <td>31.68</td>\n",
       "      <td>...</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>82.808333</td>\n",
       "      <td>105.750000</td>\n",
       "      <td>11.6375</td>\n",
       "      <td>31.137500</td>\n",
       "      <td>4.695000</td>\n",
       "      <td>46.5</td>\n",
       "      <td>31.68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>19.0</td>\n",
       "      <td>79.5800</td>\n",
       "      <td>134.460000</td>\n",
       "      <td>20.04</td>\n",
       "      <td>37.9375</td>\n",
       "      <td>38.6375</td>\n",
       "      <td>29.322199</td>\n",
       "      <td>1.47</td>\n",
       "      <td>15.40</td>\n",
       "      <td>45.65</td>\n",
       "      <td>...</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>165.266667</td>\n",
       "      <td>122.973333</td>\n",
       "      <td>19.2500</td>\n",
       "      <td>48.296875</td>\n",
       "      <td>7.330550</td>\n",
       "      <td>73.5</td>\n",
       "      <td>45.65</td>\n",
       "      <td>7.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      City    PM2.5        PM10     NO      NO2      NOx        NH3    CO  \\\n",
       "0.25   6.0  29.5225   62.834404   5.71  11.9800  14.0225  10.832500  0.59   \n",
       "0.50  12.0  49.6850  108.625000  10.02  21.9800  24.9100  18.780000  0.93   \n",
       "0.75  19.0  79.5800  134.460000  20.04  37.9375  38.6375  29.322199  1.47   \n",
       "\n",
       "        SO2     O3  ...    Year  PM2.5_SubIndex  PM10_SubIndex  SO2_SubIndex  \\\n",
       "0.25   5.79  19.44  ...  2017.0       49.204167      62.834404        7.2375   \n",
       "0.50   9.31  31.68  ...  2018.0       82.808333     105.750000       11.6375   \n",
       "0.75  15.40  45.65  ...  2019.0      165.266667     122.973333       19.2500   \n",
       "\n",
       "      NOx_SubIndex  NH3_SubIndex  CO_SubIndex  O3_SubIndex  Checks  \\\n",
       "0.25     17.528125      2.708125         29.5        19.44     7.0   \n",
       "0.50     31.137500      4.695000         46.5        31.68     7.0   \n",
       "0.75     48.296875      7.330550         73.5        45.65     7.0   \n",
       "\n",
       "      AQI_calculated  \n",
       "0.25            76.0  \n",
       "0.50           113.0  \n",
       "0.75           196.0  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile([.25,.5,.75],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1_AQI 25 percentile of given data is  76.0\n",
      "Q3_AQI 25 percentile of given data is  196.0\n",
      "interceptile Range of given data is  120.0\n",
      "low_lim_AQI is  -104.0\n",
      "up_lim_AQI is  256.0\n",
      "outlier in the dataset [343.0, 456.0, 521.0, 380.0, 280.0, 260.0, 307.0, 421.0, 279.0, 317.0, 285.0, 289.0, 260.0, 399.0, 564.0, 467.0, 657.0, 296.0, 307.0, 485.0, 299.0, 295.0, 349.0, 472.0, 330.0, 331.0, 349.0, 468.0, 361.0, 340.0, 336.0, 321.0, 329.0, 391.0, 310.0, 310.0, 301.0, 314.0, 319.0, 273.0, 306.0, 381.0, 317.0, 265.0, 305.0, 296.0, 258.0, 314.0, 307.0, 305.0, 320.0, 307.0, 325.0, 308.0, 273.0, 322.0, 273.0, 324.0, 302.0, 301.0, 301.0, 301.0, 300.0, 301.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 299.0, 299.0, 300.0, 290.0, 306.0, 396.0, 306.0, 319.0, 283.0, 307.0, 360.0, 323.0, 296.0, 319.0, 307.0, 300.0, 304.0, 321.0, 508.0, 339.0, 364.0, 390.0, 404.0, 1055.0, 394.0, 367.0, 433.0, 356.0, 306.0, 474.0, 793.0, 310.0, 356.0, 710.0, 695.0, 627.0, 668.0, 692.0, 729.0, 339.0, 331.0, 331.0, 268.0, 264.0, 304.0, 315.0, 327.0, 322.0, 316.0, 264.0, 311.0, 373.0, 501.0, 353.0, 417.0, 385.0, 364.0, 325.0, 343.0, 331.0, 331.0, 331.0, 331.0, 331.0, 1234.0, 817.0, 617.0, 619.0, 525.0, 346.0, 294.0, 305.0, 416.0, 453.0, 570.0, 428.0, 486.0, 504.0, 477.0, 778.0, 894.0, 470.0, 318.0, 316.0, 300.0, 274.0, 272.0, 341.0, 469.0, 423.0, 528.0, 610.0, 524.0, 266.0, 437.0, 379.0, 355.0, 365.0, 285.0, 322.0, 305.0, 352.0, 317.0, 374.0, 274.0, 359.0, 456.0, 380.0, 270.0, 314.0, 353.0, 386.0, 336.0, 290.0, 462.0, 379.0, 321.0, 333.0, 262.0, 459.0, 374.0, 391.0, 583.0, 381.0, 373.0, 510.0, 727.0, 511.0, 274.0, 408.0, 584.0, 693.0, 450.0, 753.0, 273.0, 630.0, 620.0, 493.0, 350.0, 359.0, 483.0, 841.0, 929.0, 320.0, 370.0, 390.0, 416.0, 479.0, 840.0, 979.0, 638.0, 594.0, 746.0, 993.0, 377.0, 406.0, 310.0, 351.0, 330.0, 507.0, 476.0, 443.0, 508.0, 385.0, 502.0, 612.0, 301.0, 422.0, 675.0, 519.0, 466.0, 301.0, 331.0, 276.0, 377.0, 432.0, 380.0, 449.0, 572.0, 314.0, 560.0, 505.0, 301.0, 461.0, 334.0, 324.0, 437.0, 275.0, 331.0, 318.0, 382.0, 307.0, 291.0, 332.0, 357.0, 336.0, 373.0, 388.0, 487.0, 319.0, 349.0, 479.0, 452.0, 349.0, 299.0, 260.0, 280.0, 357.0, 318.0, 334.0, 307.0, 348.0, 307.0, 283.0, 262.0, 257.0, 290.0, 344.0, 409.0, 433.0, 458.0, 455.0, 424.0, 443.0, 430.0, 373.0, 408.0, 396.0, 429.0, 409.0, 587.0, 464.0, 439.0, 422.0, 351.0, 356.0, 329.0, 312.0, 324.0, 350.0, 440.0, 402.0, 374.0, 302.0, 300.0, 317.0, 345.0, 371.0, 350.0, 380.0, 376.0, 322.0, 305.0, 381.0, 363.0, 385.0, 382.0, 406.0, 350.0, 405.0, 454.0, 431.0, 375.0, 375.0, 341.0, 363.0, 376.0, 369.0, 356.0, 350.0, 351.0, 332.0, 308.0, 370.0, 348.0, 372.0, 348.0, 362.0, 325.0, 349.0, 372.0, 381.0, 364.0, 308.0, 355.0, 378.0, 341.0, 372.0, 382.0, 380.0, 402.0, 413.0, 383.0, 395.0, 357.0, 355.0, 396.0, 441.0, 407.0, 367.0, 367.0, 343.0, 403.0, 437.0, 458.0, 389.0, 357.0, 372.0, 386.0, 399.0, 389.0, 404.0, 371.0, 334.0, 429.0, 336.0, 337.0, 332.0, 353.0, 361.0, 345.0, 320.0, 437.0, 452.0, 452.0, 548.0, 357.0, 283.0, 381.0, 393.0, 405.0, 404.0, 471.0, 400.0, 462.0, 348.0, 316.0, 336.0, 366.0, 372.0, 357.0, 307.0, 365.0, 755.0, 863.0, 677.0, 404.0, 504.0, 379.0, 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491.0, 572.0, 287.0, 262.0, 274.0, 388.0, 397.0, 368.0, 823.0, 324.0, 337.0, 277.0, 353.0, 503.0, 479.0, 325.0, 279.0, 261.0, 365.0, 344.0, 338.0, 345.0, 313.0, 275.0, 268.0, 326.0, 351.0, 349.0, 326.0, 276.0, 308.0, 385.0, 314.0, 292.0, 308.0, 312.0, 342.0, 341.0, 343.0, 324.0, 317.0, 313.0, 327.0, 311.0, 303.0, 385.0, 356.0, 480.0, 373.0, 356.0, 270.0, 308.0, 302.0, 313.0, 294.0, 317.0, 320.0, 306.0, 325.0, 330.0, 266.0, 262.0, 277.0, 291.0, 285.0, 303.0, 276.0, 303.0, 306.0, 291.0, 361.0, 280.0, 303.0, 262.0, 319.0, 260.0, 271.0, 360.0, 283.0, 294.0, 271.0, 309.0, 310.0, 322.0, 315.0, 289.0, 309.0, 297.0, 313.0, 313.0, 312.0, 286.0, 300.0, 364.0, 305.0, 304.0, 304.0, 318.0, 307.0, 295.0, 285.0, 270.0, 278.0, 295.0, 289.0, 319.0, 265.0, 281.0, 289.0, 344.0]\n"
     ]
    }
   ],
   "source": [
    "#Calculate Q1,Q2,Q3 and IQR\n",
    "Q1_AQI,Q3_AQI=df['AQI_calculated'].quantile([0.25,0.75])\n",
    "print('Q1_AQI 25 percentile of given data is ',Q1_AQI)\n",
    "print('Q3_AQI 25 percentile of given data is ',Q3_AQI)\n",
    "IQR_AQI=Q3_AQI-Q1_AQI\n",
    "print('interceptile Range of given data is ',IQR_AQI)\n",
    "low_lim_AQI=Q1_AQI-1.5*IQR_AQI\n",
    "up_lim_AQI=Q1_AQI+1.5*IQR_AQI\n",
    "print('low_lim_AQI is ',low_lim_AQI)\n",
    "print('up_lim_AQI is ',up_lim_AQI)\n",
    "Outlier_AQI=[]\n",
    "for x in df['AQI_calculated']:\n",
    "    if ((x>up_lim_AQI)or (x<low_lim_AQI)):\n",
    "        Outlier_AQI.append(x)\n",
    "        \n",
    "print('outlier in the dataset', Outlier_AQI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['AQI_calculated']< up_lim_AQI]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot('AQI_calculated')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AQI_calculated'>"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ykuZhS+oBVTHgVHUtOtlMwUULOFVdC4OqRG0PnN/JZgpr14SIOF9VELWPOLMhalscDGFt0cYOiDMbLqnvwHNaO7e5elXlYh+B81tal9B+mlvbpmvX3NhN16Sl8Ztb66ZjhdbS2jxVBRFtTdc32j40tzeh106gr8CP7JfSR6CtufmKxo/7mo5naCwklj6jXauha93c83QRva6W9iQwnh7yWmppXoH96WQztTjWpVyTze114Pq/1LVp6fWmC9Hi+KHtlzNmnNkA0bhmfl1ccg62JOZ38unp6SgrKwMAHDhwAKmpqcFjTqcTLpcLdXV1qKmpwbFjx5Camor09HTs2rULAFBWVoaMjIyofVutVqSlpV3Sf3abFboAkjvbcK62Hsmdw/9HbnJnGzRdRG0PnH+utj6s3aAoEecHxmjaxwWfFrXtXG09Lvg0XPBpzY4d0Nw5gecHxgj02do8mqtXFwiO29rcmq5bc2vbdO2aGzuwJqE1Nzd+c2vddKzQWlqbpy4Q0dZ076LNp7U1Cvx7wafBoCpR62+p3+bmqyhK2OPQPYm1z2jXauhaN/c8VYleV0t7EhhPDXkttTQvXSB4fbY01qVck83tdaD/S12bc7X1La5JS+OHtl/OmBd8GpTGNTOqyiXnYFpaGpoTc8gPGzYMZrMZOTk5eO655zB9+nSsW7cOb7/9Nq699lqMHTsWubm5+N3vfoepU6fCYrFg0qRJeOONN5CTk4NPPvkEY8aMiXX4oCS7GRajguLR6djmOolFI5zBBQ18zrW27DiWjAxvXzTCiW2uk1g1JgPbXCfDzldVgVWj08PONxoQ0bYssy8S7aaobdtcJ9HZbkLXeHPE2KubjNkzsaGt6by7xpvRM9GGLg5zw/N2HkOi3RTR34uj+gfPAYCtH3+BVU36Kx6dDk3XsHrnMSR3tiHRbkIXR+TclmX2bViX0enY+vEXwfVcvfNYxLnR1m5t2XEsz+obUUvPRFvYucmJNiR3tkb0uTyrL/y6FtFHtLFCawmsX3PzFEJvde+SO1sjxu2Z2LC+0a6dwL9LRjqRnGgDILD14y9Q3OQ6ua6zFc9n9wtr62w3YXlW36jX7KoxGXjn8FcR4xWPTsfasuNI7mxDt2ssEddMot0UXKemfS7P6ovOTepdMtIJTdewLLNvi2vn17WodbW0Jw39WPHO4a+C1+LqnceiXhvJna0wGxUYDcDqnceiXr+BvkOPRZ3v6HQAInKMRBs0XYu6Ns9n98N1na1Ra2tuTQyqiNrXi6P6w2RUwp4T2Lum+xH6mg19nQTWLMlmRltQROjPhT8BLpcLcXFxLX5nairq3TWi4f+EX+rdNfXaxTs12vPumnpNXLzLp8ndNboQsITcXaM33nkSeneNv7GO5u6u8Wk6DIG7a4SACiV4F4Cx8U4DPeTuGkBcvLPnCt5do4f0oaNhXlf07hoVMKDh7hq/dnFOLd5d03hnTODuDEUBtMu8u0Zvcp3oAlAUAU2/eIdGS3fX1Db+xNDa3TWGwF0fQsCoqo2PRfBOkKh31zTevXW5d9doIXt3OXfXhN5R0pZ31/g1HYAS9poOvbsmdG0C+2n8Ee+uMaoqjI3rLgTgD9kPQ+N1HMvdNS6XK+qnI21zj047U1UFneIs6BTXxh3b27i/EM3NtbKy8rK+wbXkmrZejyvoUuru3Np+XKH9uuYK/ypHtNov+Vq+gtdoW44fbe9a2/MW9zuGuu3Wyzu/1eutBde0cKzVa72N95S/8UpEJDGGPBGRxBjyREQSY8gTEUmMIU9EJDGGPBGRxBjyREQSY8gTEUmMIU9EJDGGPBGRxBjyREQSY8gTEUmMIU9EJDGGPBGRxBjyREQSY8gTEUmMIU9EJDGGPBGRxBjyREQSY8gTEUmMIU9EJDGGPBGRxBjyREQSY8gTEUmMIU9EJDGGPBGRxBjyREQSY8gTEUmMIU9EJDGGPBGRxBjyREQSM8byJK/Xi2nTpuG7776D3W7HokWLkJiYGHbOihUrsHPnThiNRsyYMQNOpxOVlZWYN28eDAYDzGYzFi1ahC5durRJIUREFCmmd/IbN25EamoqXnvtNTz00EMoLi4OO37o0CHs27cPpaWlKCoqwpw5cwAACxYswKxZs1BSUoJhw4Zh7dq1P7wCIiJqVkwh73K5MHjwYADAkCFDsHfv3ojjgwYNgqIo6NGjBzRNQ1VVFYqKipCWlgYA0DQNFovlB06fiIha0urHNaWlpXj11VfD2pKSkhAfHw8AsNvtqKmpCTvudrvRqVOn4OPAOb169QIA7N+/H+vXr8eGDRuijun1elFZWXlZhciio9beUesGOm7trPvH0WrIZ2ZmIjMzM6xt8uTJ8Hg8AACPx4OEhISw4w6HI3g8cE7gm8I///lPrFq1CmvWrIn4HD/AarUG3/F3NJWVlR2y9o5aN9Bxa2fdbcvlckVtj+njmvT0dOzatQsAUFZWhoyMjIjju3fvhq7r+PLLL6HrOhITE/GPf/wD69evR0lJCXr27BnL0EREdBliurtm1KhRyM/Px6hRo2AymbBs2TIAwOLFi3HPPffA6XRiwIAByM7Ohq7rmD17NjRNw4IFC9C9e3fk5eUBAG677TZMmTKl7aohIqIwMYW8zWbDCy+8ENH+9NNPB7/Oy8sLhnnAvn37YhmOiIhixF+GIiKSGEOeiEhiDHkiIokx5ImIJMaQJyKSGEOeiEhiDHkiIokx5ImIJMaQJyKSGEOeiEhiDHkiIokx5ImIJMaQJyKSGEOeiEhiDHkiIokx5ImIJMaQJyKSGEOeiEhiDHkiIokx5ImIJMaQJyKSGEOeiEhiDHkiIokx5ImIJMaQJyKSGEOeiEhiDHkiIokx5ImIJMaQJyKSGEOeiEhiDHkiIonFFPJerxd5eXnIzc3F+PHjUVVVFXHOihUrMHLkSOTk5KC8vDzs2Ouvv47s7OzYZkxERJcsppDfuHEjUlNT8dprr+Ghhx5CcXFx2PFDhw5h3759KC0tRVFREebMmRM8dvjwYWzduhVCiB82cyIialVMIe9yuTB48GAAwJAhQ7B3796I44MGDYKiKOjRowc0TUNVVRWqq6tRVFSEGTNm/PCZExFRq4ytnVBaWopXX301rC0pKQnx8fEAALvdjpqamrDjbrcbnTp1Cj622+04d+4cli5diunTp8NisbTB1ImIqDWthnxmZiYyMzPD2iZPngyPxwMA8Hg8SEhICDvucDiCxwPnuN1unDhxAgUFBairq8Nnn32GBQsWYObMmRFjer1eVFZWxlTQ1a6j1t5R6wY6bu2s+8fRashHk56ejl27dsHpdKKsrAwZGRkRx5csWYLHHnsMX3/9NXRdh9PpxBtvvAEAOHXqFJ544omoAQ8AVqsVaWlpsUztqldZWdkha++odQMdt3bW3bZcLlfU9phCftSoUcjPz8eoUaNgMpmwbNkyAMDixYtxzz33wOl0YsCAAcjOzoau65g9e3bsMyciopjFFPI2mw0vvPBCRPvTTz8d/DovLw95eXlRn5+cnIwtW7bEMjQREV0G/jIUEZHEGPJERBJjyBMRSYwhT0QkMYY8EZHEGPJERBJjyBMRSYwhT0QkMYY8EZHEGPJERBJjyBMRSYwhT0QkMYY8EZHEGPJERBJjyBMRSYwhT0QkMYY8EZHEGPJERBJjyBMRSYwhT0QkMYY8EZHEGPJERBJjyBMRSYwhT0QkMYY8EZHEFCGEaO9JhHK5XO09BSKiq1JGRkZE208u5ImIqO3w4xoiIokx5ImIJGZs7wmE0nUdBQUFOHr0KMxmM+bPn49evXq197SuqN/+9rdwOBwAgOTkZGRnZ2PBggUwGAwYNGgQJk+e3M4zbFsHDx7E0qVLUVJSghMnTuCZZ56Boii46aab8Oyzz0JVVaxYsQI7d+6E0WjEjBkz4HQ623vaP1ho3YcPH8aECRNw/fXXAwBGjRqFe++9V7q66+vrMWPGDJw+fRo+nw+TJk3CjTfeKP2eR6u7e/fu7bfn4ifkzTffFPn5+UIIIT755BMxceLEdp7RleX1esWDDz4Y1vbAAw+IEydOCF3Xxe9//3tx6NCh9pncFbBmzRpx3333iczMTCGEEBMmTBAffPCBEEKIWbNmibfeektUVFSIsWPHCl3XxenTp8Xw4cPbc8ptomndW7ZsES+//HLYOTLWvXXrVjF//nwhhBDV1dXi9ttv7xB7Hq3u9tzzn9THNS6XC4MHDwYA9OvXDxUVFe08oyvryJEjqK2txbhx4/Dwww/jo48+gs/nQ0pKChRFwaBBg/D++++39zTbTEpKCl588cXg40OHDuGXv/wlAGDIkCF4//334XK5MGjQICiKgh49ekDTNFRVVbXXlNtE07orKiqwc+dOjB49GjNmzIDb7Zay7nvuuQd/+tOfAABCCBgMhg6x59Hqbs89/0mFvNvtDn50AQAGgwF+v78dZ3RlWa1WPPbYY3j55ZcxZ84cTJ8+HTabLXjcbrejpqamHWfYtu6++24YjRc/IRRCQFEUABdrbXoNyLAGTet2Op14+umnsWHDBvTs2RMrV66Usm673Q6HwwG3240pU6bg8ccf7xB7Hq3u9tzzn1TIOxwOeDye4GNd18NeHLLp3bs3HnjgASiKgt69eyM+Ph7nzp0LHvd4PEhISGi/CV5hqnrx8gvU2vQa8Hg8iI+Pb4/pXTHDhg3DrbfeGvz68OHD0tb91Vdf4eGHH8aDDz6I+++/v8PsedO623PPf1Ihn56ejrKyMgDAgQMHkJqa2s4zurK2bt2KhQsXAgC++eYb1NbWIi4uDl988QWEENi9ezcGDBjQzrO8cm6++WZ8+OGHAICysjIMGDAA6enp2L17N3Rdx5dffgld15GYmNjOM21bjz32GMrLywEAe/fuxS233CJl3d9++y3GjRuHadOmYeTIkQA6xp5Hq7s99/wn9TZ52LBh2LNnD3JyciCEQGFhYXtP6YoaOXIkpk+fjlGjRkFRFBQWFkJVVTz11FPQNA2DBg1C375923uaV0x+fj5mzZqFoqIi9OnTB3fffTcMBgMGDBiA7Oxs6LqO2bNnt/c021xBQQHmzZsHk8mELl26YN68eXA4HNLVvXr1apw/fx7FxcUoLi4GAMycORPz58+Xes+j1f3MM8+gsLCwXfacv/FKRCSxn9THNURE1LYY8kREEmPIExFJjCFPRCQxhjwRkcQY8kREEmPI01Vl7dq1GDRoEOrq6oJtf/vb35CZmYkxY8Zg7Nix2LVrV/DY0KFDw86N1YcffoipU6de1nP+/ve/Y+nSpc0eP3fuHF5//fVL7i8rKwunTp26rDkQ/aR+GYqoNdu3b8e9996LN954A8OHD0dJSQkOHjyIDRs2wGw2o7q6Gn/4wx9wzTXXoF+/fu093RYdPXoU77zzDu6///72ngpJjCFPV40PP/wQKSkpyMnJwbRp0zB8+HCsX78eGzduhNlsBgB07twZeXl52Lhx4yWF/LvvvosVK1ZACIFbbrkFc+bMwVtvvYUNGzbA7/dDURSsWLEi7DmlpaXYuHEjdF3H0KFDMWXKFAwcOBB79uwBAEydOhU5OTlhz1m2bBkqKipw7tw5/PznP8dzzz2H1atX48iRI9i8eTOGDBmCWbNmoa6uDhaLBfPmzUP37t2xfPlyvPfee/jZz36G6urqtllI6lAY8nTVKC0tRWZmJvr06QOz2YyDBw/i/PnzEX/v47rrrsPp06db7c/v92PevHkoLS1FUlIS1q5di6+//hqff/451qxZA5vNhtmzZ2P37t3o1q0bAOC7777D2rVrsX37dlgsFixbtizsj0xF43a7kZCQgHXr1kHXdfzmN7/BN998g4kTJ2LTpk3Izs7G448/jrFjx+L222/H3r17sXTpUjzyyCP46KOPsHXrVly4cAF33XVX7ItHHRZDnq4K33//PcrKylBVVYWSkhK43W6sX78eCQkJqKqqCgv6zz//PBjKLamurkZCQgKSkpIAAOPHjwcAJCUlIT8/H3a7HcePHw/7ieDkyZO46aabYLVaAQBPPfVURL9N/1KIxWJBVVUVnnjiCcTFxeHChQuor68PO+fTTz/FX//6V7z00ksQQsBoNOLzzz/HrbfeClVV4XA4pP+DfXRlMOTpqrB9+3aMGDEC+fn5AIDa2lrceeedyM3NRWFhIQoLC7Fjxw689957qKysxJ///OdW+0xKSsL58+dx7tw5dOrUCfPnz8ddd92FF154ATt37gQAPProo2GhnZKSguPHj8Pn88FsNmPKlCmYOXMm/H4/PB4PTCYTPvvss7BxysrK8NVXX+H5559HVVUV/vWvf0EIAVVVoes6AKBPnz4YN24c0tPTcezYMXz00Ue48cYbsWHDBui6Dq/XG9Ev0aVgyNNVobS0FIsXLw4+ttlsuOuuu2A0GuF0OjFmzBioqgpN0+BwOHD8+PFW/0yzqqp49tlnMWHCBKiqiptvvhm33XYb0tPTkZ2dDaPRiISEBJw5cwbJyckAgMTERIwfPx5jxoyBoii444470K1bNzz88MPIzs5GcnIyevToETaO0+lEcXExRo8eDUVR0LNnT5w5cwYpKSn49NNP8corryA/Px8FBQWoq6uD1+vFzJkzkZaWhiFDhmDkyJHo2rVr8CcOosvBv0JJ0tE0DRUVFVL/mWaiS8WQJ+mVl5djyZIlEe2//vWvkZub2w4zIvrxMOSJiCTG33glIpIYQ56ISGIMeSIiiTHkiYgkxpAnIpLY/wfKgdKDCSLe3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='AQI_calculated',y=np.zeros_like(df.AQI_calculated),data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AQI_calculated'>"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vpmYnT/13P69tOYJOq2HuVfGMTojs0uG0ybGhlNY0ctps67J9CuFOEvLdzOP/2sehCjMZl/clyvDj/d8Reh1v3XUFl/UL54GsXN7YfvSS9nusqoHpf9/OC5sOkdY3nHvHJ9Dn2z7yrjQ8JgyAb2QopegmJOS7EVNxFRtMx7l77AASolzP6mgI8OP1X1/O1Um9WPjeXl747CCKorRrn4qi8F5uKdf/7QsOlpt5bmYqt6TFouugK1nba1hMCID0y4tuQ/rku4lmp8LC9/YSHRrAfRMSeC/3xDnb/NgImZduT+OhDXk89fEBjlQ28OQvhl7QyJvyOiv/+24+n+wrJ9UYxnMzUomLCLro+Wc6giHAjwE99XLlq+g2JOS7iTVflbD3RB0vZKSib+dJTj+NmuXTRhAXEcTznx0k73gNf5uRypA+IW1ub21qZlvRaZZ+VIDd4eSR6wdzx+j+aDWe8cUxOSaULw9XubsMIbqEhHw3UG9tYvnH+/nJgB5MHn5xFxap1Sp+PymRy/tF8EDWbq5/7gvGDoxkxmVGYsMDqay3cbLOyuFKM3uO1dLY1MzVSVE8OnkI/TtgquCONDw2jHdzT3Cq3nre8xJC+AIJ+W7gje3F1DQ08cfrB7d7JEtbXSufzLuKNV+VkLm9mP9Z83WrdX4aFQOjDPxsUE8eum7wJdXdWYbHhAKQX1rLhMES8sK3Scj7uHprE698cZiJg6NIjg3rkOcM1+v4n/EJ/GbcAPKO11JtsfOfvSfpodcREx6IVu0Z3TI/ZmifEFQqyDtey4TBHXsNgBCeRkLex313FP+7qy/tFnpt8dOoGdk3HIBT9d4z7lzvryWhZ7CMsBHdgoS8D+uMo/jO0tUjbobHhvLFwUoURZF7Gwif5tnfq8UleWtHSacdxXu75JhQKuptlNd5zzcQIS6GhLyPsjmaeW3LEcYkRHr8Ubw7DP/230RuByh8nYS8j3pv9wlO1duYc9UAd5fikYZEh6BRq2R6A+HzpE/eBzmdCn/PKWJIdAhjEtw3b7o7r2x1JVCnYWBUMHly8lX4ODmS90GfFp6iqMLCnKsGyEnF80iODeWb0tp2z8cjhDeRI3kftDKniJiwwIu+utUVTz5Cb4/hsWFk7zpOaU0jseFB7i5HiE4hR/I+5pvjtew8Ws2vR/fzmLliPFXyt1e+ynh54cvkSN7HrN56BL1OQ/plcYDvHHV3hsHRBvw0Z06+/ryTvvUI4W5yqOdDqhod/CvvBLeOjCUkwM/d5Xg8f62GxF4GGWEjfJqEvA/59/46mpoVfvnTfu4uxWskx4aSd1xOvgrfJd01XuqH3TCOZicf7q9n/KCeDOjp+q5P4ozhMWGs/eoYx6oaMfaQk6/C98iRvI/IO15LjbWZX4/u7+5SvEpy7JmTr3mlNe4tRIhOIiHvAxRFYVtRJXGhfowd6L6Ln7xRYi8DOo1aRtgIn+Wyu8bpdLJo0SL279+PTqdj8eLF9O3bt2V9dnY269atQ6vVMnfuXMaPH09VVRUPPvggVquVqKgoli5dSmBgIJs3b+bFF19EURSGDh3KY489JhfrdICjpxs4UWvlvisj5d+znXRaNUnRBrnyVfgsl0fyGzduxG63k5WVxfz581m2bFnLuoqKCjIzM1m3bh2rVq3i6aefxm63s2LFCqZMmcKaNWsYMmQIWVlZmM1m/vrXv/Lyyy+zfv16YmJiqK6u7tTGdRfbiioJ9NMwIV764i/G8NhQ8ktrcTrl5KvwPS5D3mQyMXbsWABSUlLIz89vWZeXl0dqaio6nQ6DwYDRaKSwsLDVY8aNG8e2bdvYvXs3iYmJ/PnPfyYjI4PIyEgiIiI6qVndR3WDnX0n6risXzgBWul9uxjJMWHU2xwcPW1xdylCdDiX3TVms5ng4LNHiBqNBofDgVarxWw2YzAYWtbp9XrMZnOr5Xq9nvr6eqqrq9mxYwfvvvsuQUFB3HbbbaSkpNC//7knCgsKCjqibedltVq7ZD+dpexkHQBbi88E0wBDc5tt+m47b+VoaqLsZFmHPV9BwblBHmw/M6f8R1/tY8IAwznrO5q3v/faIm3yXC5DPjg4GIvl7AfD6XSi1WrbXGexWDAYDC3LAwICsFgshISEEBYWxvDhw+nZsycAo0aNoqCgoM2QT0pKuuSGuVJQUNAl++ksu+tKcDid7DcVkhQdQmLfWAICLOe0aXedd1/xWnayjOjeHXc1alKS8ZxlA5udBP33JKccQfLeu0jSJvczmUxtLnf5/T4tLY2cnBwAcnNzSUxMbFmXnJyMyWTCZrNRX19PUVERiYmJpKWlsXnzZgBycnIYOXIkQ4cO5cCBA1RVVeFwONizZw8JCQkd0bZuq6CsHou9mcv6SbfXpdB+e6/aHUeq3F2KEB3O5ZH8pEmT2Lp1KzNmzEBRFJYsWcLq1asxGo1MnDiRWbNmkZGRgaIozJs3D39/f+bOncuCBQvIzs4mPDyc5cuXExQUxPz587nrrrsAuO6661r9wRDt99WR04QF+jGwl5xwvVSX94vg6Y0HqGmwExakc3c5QnQYlyGvVqt54oknWi2Lj49v+Tk9PZ309PRW6yMjI1m1atU5zzV58mQmT558sbWK7zlttlFUYeHqpF6oZdjkJbu8fwSKAruOVnP1kF7uLkeIDiPDMbzUzqPVqFUwqm+4u0vxCSPiwtBp1Ow4ctrdpQjRoSTkvZDd4cRUUs3g3iGEBMpskx0hwE9DSlwYX0m/vPAxMkGZF/pkXzkWm+OcE67/PlDn9aNpOltb8+tnXHFmxM3l/SN4aXMRZpuDYH/5aAjfIEfyXmjtVyVywrUTXN4/gmanwtfFciW28B0S8l7maKWFLYcqGdUvQk64drC0vuFo1CrpshE+RULey6zbeQyNWiUnXDtBsL+WYTGhcvJV+BQJeS9idzjZYDrGxMFRcsK1k/w0vge7S2qobWxydylCdAg5u+RFPi0op9JsZ+blRspqre4uxyddnRTFS58XsflABTeO6OOWGs53cliI9pKQ9wLffegztx/FEKCltKZR+uM7SUpcOBF6HZ8WlLst5IXoSNJd4yUsNgf7y+sZERsmAd+JNGoV4wdFsanwFE3NTneXI8QlkyN5L5FXWotTgVRjmLtL8Tk/7B7x16qpszrYdbSan8T3cFNVQnQMOZL3Erkl1fQOCSA6NNDdpfi8gb2C0WnUfFpQ7u5ShLhkEvJeoLLexrHqRlLiwtxdSrfgr9VwZXwPPi085e5ShLhkEvJeYPexGlScmURLdI2rk6I4Umnh0Cmzu0sR4pJIyHs4p1Mh91g18VHBhMrY+C5z7dDeqFXwztfH3V2KEJdEQt7D7SquprqhiVQ5iu9SvUICmDA4ivWm4zLKRng1CXkP98/dx/HTqBjSJ8TdpXQ70y8zUlFvY5P0zQsvJiHvwaxNzXyQV8bQPqH4azXuLqfbGT+oJ1EGf7J2HnN3KUJcNAl5D/ZZ4SnqrQ7pqnETrUbNtFGxbNp/ipMyjYTwUhLyHuydr0uJMvgTHyXzxrtL+qg4nApk75KjeeGdJOQ9VLXFzuf7T3FTSh+ZxsCN+vbQc1ViT1ZvPUK9VWamFN5HQt5D/WfvSRxOhZtSYtxdSrf3+0mJVDc08dqWo+4uRYh2k5D3MGt2lLBmRwmvfnGYHnode47VuLukbm9EXBjXDu3Fq18cptpid3c5QrSLTFDmgeqtTRyusHDVoJ6opKvGI8y/ZhAf783hd+t2c92w6JblMs+78HQuj+SdTicLFy5k+vTpzJo1i+Li4lbrs7OzmTp1Kunp6WzatAmAqqoq7rjjDjIyMnjggQdobGxs9Xx33XUXa9eu7eCm+I69J+pQgOSYMHeXIr6V2MvAiLgwth8+zWmzzd3lCHHBXIb8xo0bsdvtZGVlMX/+fJYtW9ayrqKigszMTNatW8eqVat4+umnsdvtrFixgilTprBmzRqGDBlCVlZWy2OeffZZ6urqOqc1PiLveC09Df70CvF3dynie64d2huNWsUG03GciuLucoS4IC5D3mQyMXbsWABSUlLIz89vWZeXl0dqaio6nQ6DwYDRaKSwsLDVY8aNG8e2bdsA+M9//oNKpWpZJ85V19hE8WkLyTGh0lXjYUID/bghuQ/FVQ1sOVjp7nKEuCAu++TNZjPBwWfHaWs0GhwOB1qtFrPZjMFgaFmn1+sxm82tluv1eurr6zlw4AAffPABzz33HC+++OJ591lQUHCx7blgVqu1S/bTXlsLy1GA3gF2yk6Wteuxjqamdj/G07mrTQUFlnOWlZ2so5efwoAIHR/vO0m4xkpByLnbueLqvVd28txvum3V40k89fN0KXylTS5DPjg4GIvl7BvM6XSi1WrbXGexWDAYDC3LAwICsFgshISE8O6771JeXs4vf/lLSktL8fPzIyYmhnHjxp2zz6SkpI5o23kVFBR0yX7aq/hfJ+kdEsDQ/nHtfmzZyTKie0e73tCLuKtNSUnnnlDdXXfmDlIzIhw8/9lBPjpk4fc3jKKnoX3daq7ee9/tx1U9nsRTP0+XwtvaZDKZ2lzusrsmLS2NnJwcAHJzc0lMTGxZl5ycjMlkwmazUV9fT1FREYmJiaSlpbF582YAcnJyGDlyJA899BDr168nMzOTm2++mV/96ldtBnx3VlrTSElVA8mxoe4uRZxHsL+WWVf2xWJzcPcbu7A2Nbu7JCF+lMsj+UmTJrF161ZmzJiBoigsWbKE1atXYzQamThxIrNmzSIjIwNFUZg3bx7+/v7MnTuXBQsWkJ2dTXh4OMuXL++Ktni9D/NOADA8RkLe08WGBzF9VBxvfVXC79bt5oWMNPw0ctmJ8DwuQ16tVvPEE0+0WhYfH9/yc3p6Ounp6a3WR0ZGsmrVqh99zvvuu6+9dXYLH+SVERMWSI9gGVXjDYb0CWXhlCE8/q993L92N8/NTJWgFx5H3pEeovi0hbzjtXIU72X8tRomD4/mo/yT3PTCVt7YdtTdJQnRioS8h/gg78wIkuHSH+91RidEckNyNPvK6li15YhcLCU8ioS8h/gwr4xUYxjhQTp3lyIuwk/iI5lxWRylNY3c9OJWCtsYBimEO8jcNR7gcIWZfWV1/GnKEHeXIi5BcuyZP9JvflnMlOe2MGlIL0YnRLZMFS3z3Ah3kJD3AB/klaFSweTh0Xwm9xP1anERQdw7IYH3ck/wUf5J8ktrmZjUi4HnufGL3eFkV3EVmw9UkFtSw/7yeuobHUQE6+gbEcRQub+vuAQS8m60ZseZi17e/LKYvhFBEvA+whDgx21XGMk9VsN/957k9W1HiQ4NoLzOytCYUMyVVkqVck7UNvLFwUq2HarEYm/GT6NieEwo/XroCfbXUlFvY++JOnYVV1NptrPoxqHtvvBKCAl5Nyuvs3Kq3sYNI/q4uxTRgVQqFanGcIbHhrLnWA3bD5/mhU2HcLbMa3bmmoiYsEB+kRrDzwZF8ZP4HgT7a1v++AM0OxW+OFjBJ/vK2VZUyepfX06K3PNXtIOEvJvlHa9FBQyTr+Q+SatWM7JvBCP7RnBzagwFJ+vI33+YEYPjiTT40yc04LwT0WnUKn42KIqHrhvEnf/YxaxVO1h795UMk6G24gLJ6Bo3UhSFb0pr6N9TjyHAz93liE4WqNOQZgznstggRsSFERMWeMEzjSZEGVhz95WEBvpx26s7KCiT0TviwkjIu1FZrZVKs11uDiIuSExYIGvvvpIgnYY7X99JpYzHFxdAQt6N8o7XoFYhoyfEBYuLCOKV2aM4bbHzP299TVOz090lCQ8nffJu4nQq7Dley8AoA3p/eRk8zfdPfnqaYTGh/PmWZB7IyuX/Pixg0Y1D3V2S8GCSLm5iKqmmtrGJa4f2cncpoot894ej7GRdy5zxF3uB1C9SY8gvreXVLUcY2ieEaaPaf/8B0T1IyLvJe7ml+GlUJEVLV013dinfGB7++WAKTtbx6Lv5LTcaF+KHpE/eDZqanXyYV0ZSdAj+Wo27yxFeSqtR8/zMNHoG+zMn08Spequ7SxIeSI7k3WDLwUqqG5qYkhzm7lKEl2jriD/jCiMReh0rZ4/k1pe2c+fru1j7mysJlnM84nvkSN4N3sstJTTQj4G9fnw+EyEu1NA+obx4Wyr7yuqY+6YJu0NG3IizJOS7WKO9mY/3lXP98N5o1fLPLzrGhMG9WHrzcL44WMm87FwJetFCvtd1sY0F5TTYm7lxRAxHKi3uLkf4kPTL4qhusLP0o0LqGpt46faR0nUjJOS72nu5J+gdEsDl/SMk5MUlaauffs5V8UTodTz8zjekv7ydF29Lo3+k/pKeU+bB927SX9CFahrsbD5wihtGRKNRX9icJUK017RRcbz6y1Ecq27g53/L4dUvDtN8dvpL0c1IyHehj/JP0tSscFNKjLtLET5u/KAoNv7+KsYkRLL4wwKuezaHf+4+jkOmQeh2JOS70Pu5JxjQUy9z1Ygu0SskgFdmj+LFjDRUKpiXtYdxf9nEso8K2XuiFkWRo/vuwGWfvNPpZNGiRezfvx+dTsfixYvp27dvy/rs7GzWrVuHVqtl7ty5jB8/nqqqKh588EGsVitRUVEsXbqUwMBAXn/9dT788EMArrrqKu69997Oa5mHOVHTyJdHTvO7iQMveHpZIS6VSqVicnI01Q12Csvq+eroaVbmFPHy5iLie+q5cUQMN6X0oV87+u2Fd3EZ8hs3bsRut5OVlUVubi7Lli3jpZdeAqCiooLMzEzefvttbDYbGRkZjB49mhUrVjBlyhSmTp3KypUrycrKYuLEibz//vusX78etVrNzJkzufrqqxk8eHCnN9ITbDAdR1Fgamqsu0sR3ZBapWJInxCG9AnBYnOQf6KWPcdqeWbjAZ7ZeABjRBApcWEkx4YSpJPxGL7EZXeNyWRi7NixAKSkpJCfn9+yLi8vj9TUVHQ6HQaDAaPRSGFhYavHjBs3jm3bttG7d29effVVNBoNKpUKh8OBv3/3uF+l06mQvesYP43vgbFHkLvLEd2c3l/LFf178JtxA3jo2kFcN7Q31qZm3t9zgqX/LuTNL4s5UF4v3Tk+wuWfbLPZTHDw2SszNRoNDocDrVaL2WzGYDC0rNPr9ZjN5lbL9Xo99fX1+Pn5ERERgaIo/OUvf2HIkCH079+/E5rkebYfPs3x6kb+cO0gd5ciRCthQTrGJfZk7MBIymqt7C6pZs/xWvaV1dE7JICxAyOZNioWP42cvvNWLkM+ODgYi+XseG6n04lWq21zncViwWAwtCwPCAjAYrEQEnLmRKPNZuORRx5Br9fz2GOP/eg+CwoKLrpBF8pqtXbJfgAe/7AUf42KwsPHOFR8vNP242hqouxkWac9vztIm9qnoKDtay/KTrq+XaAKSItSMSIylAOVNr4+0ch603E+LyzjF0mh/DwxhEC/tsO+Kz9PXcVX2uQy5NPS0ti0aRPXX389ubm5JCYmtqxLTk7m2WefxWazYbfbKSoqIjExkbS0NDZv3szUqVPJyclh5MiRKIrCb3/7W6644gp+85vfnHefSUlJl94yFwoKCrpkP7UNTRyuOsKofuHExfTp1H2VnSwjund0p+6jq0mb2icpqe0Ll76bv/5CxfaB8cMVDpTXU3iynld2VbF+Xz2//ml/fvnTvoQF6Vpt31Wfp67kbW0ymUxtLncZ8pMmTWLr1q3MmDEDRVFYsmQJq1evxmg0MnHiRGbNmkVGRgaKojBv3jz8/f2ZO3cuCxYsIDs7m/DwcJYvX87GjRv56quvsNvtfPHFFwD8/ve/JzU1tWNb6mHezS3F4VQY1TfC3aUI0S4qlYpBvUN4/KZh7C6p5sVNRTyz8QArc4oY2Tec0QmRLTegLztZhxflYbfiMuTVajVPPPFEq2Xx8fEtP6enp5Oent5qfWRkJKtWrWq1bNKkSXzzzTeXUqvXURSFN7YfJTY8kD5hge4uR4iLlmoM59VfjqKgrI4VnxfxwZ4TbCs6TXJsKFf074FGTtJ6LBkr1Ym2HKqkqMLCtJEybFJ0jc6+N21SdAjPz0xlYFQwWw9VsvtYDV+X1BCl12KMOcYNI/oQqJMb4XgSCflO9I9tR4kM1jE8JtTdpQjRoSKD/bkpJYZrh/Ym91gNWw6U89DbeTzxwT6uTori+uHRjEvsSYCfBL67Sch3kpLTDXxaeIp7xyegleFnwkcF+Gm4ckAPjIE2xiYP5J2vS/nvvpO8m3uCYH8tE5OimDSkF6PjIwnX61w/oehwEvKdJPPLo6hVKm67oi+fFZ5ydzlCXLQL6QJSqVQUVVgYERfGsJhQDleY+aa0lk/2lfNe7glUQHRYAAk9DSREBfPgtYlyZW0XkX/lTlBnbWLdzmNcN7Q3vUMD3F2OEF1Ko1YxsJeBgb0M3ORUKK1p5NCpeg6dsrD1UCU5Byv4x/ajDO5tICUujFRjOClxYQyI1KOWKbg7nIR8J8jcXky91cE9V8W73lgIH6ZRqzBGBGGMCGLCYLA5mjla2UBIoJbcYzW8n3uCt779phCk05AUHcKQ6DNz7CRFh5BbUoNO27q7U25i0j4S8h2swe5g1ZYj/GxQT4bHyglXIb7PX6thUG9DS1A7nQpFFWZ2H6vhn7tLKauxkr3rGLZv71GrAiIN/kSHBtAnNJDo0AAqzTYigy9s3iu505WEfIdb+9Uxqix27h2f4O5ShPB46u917Tiaz4y1VxSF6oYmymobKau1UlZrpaSqgbzjtQCs3naUngZ/EnoG07+nngGRevp/+19cRJDMs/MDEvIdyOZoZmVOEVcOiGBUP7nCVYiLoVKpiNDriNDrGNrn7LfhRntzq+AvrWlkz/EaGuzNLdt81z0U31PPwF4GKuttRBkC6GnwP6fbp7uQkO9Aa3aUUF5nY/m0FHeXIoTPCdRpGNAzmAE9g1stb7A5SOsXzpEKC0cqLRyuNHPolJnNBypo+vbbgQroHRpAvx56ogz+jBkY2W3G8EvId5Dahib+9ulBEnoGU3zaQklVg7tLEqJbCPLXUlhWD0CfsDNTiIxJ6EmzU+G02capehsn66wcPW1hV3EV2w+fJtBPw88G9ST9sjjGDeyJpo1RPf8+UHfOxG7e2J8vId9BXth0kNrGJmZd2Vdu7yeEC509/QKc6bqJCgkgKiSAYd9ede5wOjlSaWHfiTpyDlTwUf5JwgL9uHdCArdd0dcnp2SQkO8AJacb+Me2YqaNjCU6VCYiE8JTadVqBkYZGBhlYHJyNAVl9Xx5+DSLPyzg5c1FzP1ZArN/0tenTt5KyF8iRVFY/OE+NGoV868ZxKcFcnWrEN5Aq1YzPCaU4TGhHK208FnhKZ78YB9/31zEDSP64Cs36vSdP1du8kFeGR/vK+d3Vw+kV4hc3SqEN+oXqeeOMf2ZdWVfmpqdrNpyhM+KzNi/Ha/vzeRI/hKszDnMsxsPEBseiF6n7ZJ+RiFE50mKDiEhKpiNBeV8cbCSk58dZPplccSGe+9xvRzJXyRFUfjXnhPYHE5uSYtt8+y8EML7+GnU/HxYNFOHhNLsVPh7zmF2HDmN4qU3RpGQv0hv7ijhm9JaJg6Okm4aIXxQTOiZUTfxPfW8l3uCt78uxdrU7PqBHkZC/iLsOHyax9/fy6BeBsYl9nR3OUKIThKk0zL7J/2YMDiKr0uqufXlbRzzsmtgJOTb6VhVA79962uMPYKYflkcahkTL4RPU6tUXJ3Ui9lX9qX4dAM3vLCFzQcq3F3WBZOQb4eS0w3MfOVL7M1OXpk9qttcFi2EgMHRIfzr3jH0DgngV6u/4vlPD+J0en4/vYT8BSqqMJP+9+2YbQ7W3HUl8T+YP0MI4fv6Rer5529Hc9OIPiz/5AB3/mMnp+qt7i7rvCTkL8B/8k9yy0vbaGp2svbuK2WeeCG6sUCdhmemp/DETUPZWnSaa57J4YO8Ex47+kZC/jyqLXYefjuPe940ERcexNtzf0pSdIi7yxJCuJlKpWL2T/rx7/vH0DciiHvX7Ob2VTvIL611d2nncHkxlNPpZNGiRezfvx+dTsfixYvp27dvy/rs7GzWrVuHVqtl7ty5jB8/nqqqKh588EGsVitRUVEsXbqUwMDANrf1RBX1Nv6x7SivbzuKxe5g7s/imXd1Yredj1oI0baEKANvz/0pb2wv5vnPDjLl+S1cO7QXt13RlzEJkR5xz1qXIb9x40bsdjtZWVnk5uaybNkyXnrpJQAqKirIzMzk7bffxmazkZGRwejRo1mxYgVTpkxh6tSprFy5kqysLCZPntzmtjqdrtMb6UqD3UFBWR27S2r4ZF85O49WoQDXD4/m/gkDGdTb4O4ShRAeSqtRc8eY/twyMpZXcg6z5qsS/ru3nJiwQMYl9mTswEiGx4QSExboltB3GfImk4mxY8cCkJKSQn5+fsu6vLw8UlNT0el06HQ6jEYjhYWFmEwm5syZA8C4ceN4+umniYuLa3Pb5OTkDmnI1yXVbC86DZy5GlVRwKmAwpmfm5qdNNibabQ309DUzKnTNdg+q+JkrZXyeivfdacNjArm3gkDuXFEHxKizp5clSkLhBDnExrox4PXDuK+iQn8d2857+ee4F97TrD2qzPZEeinITY8kNBAvzP/BZ35f6CfBj+NmkG9DVw/PLrD63IZ8mazmeDgs2Gn0WhwOBxotVrMZjMGw9mjXL1ej9lsbrVcr9dTX1//o9u2xWQyXVRjrmxXd/l3G7d1tWo9tcf2Yzp2dskgL5jlZ1CsFvCe8bsXQtrkHbpLm0ymC2tjDDB3mIq5wyLPs1Xzt/99y16PyXSivWW65DK6goODsVgsLb87nU60Wm2b6ywWCwaDoWV5QEAAFouFkJCQH932h0aOHHlJDRJCCHGWyzOJaWlp5OTkAJCbm0tiYmLLuuTkZEwmEzabjfr6eoqKikhMTCQtLY3NmzcDkJOTw8iRI390WyGEEJ1HpbgY3Pnd6JoDBw6gKApLliwhJycHo9HIxIkTyc7OJisrC0VRmDNnDtdeey2VlZUsWLAAi8VCeHg4y5cvJygoqM1thRBCdB6XIe9rXA0J9SY333xzy/mS2NhYpk+fzv/93/+h0WgYM2YM9957r5srvHB79uzhqaeeIjMzk+LiYh5++GFUKhUDBw7kscceQ61W88ILL/D555+j1Wp55JFHOuykfWf5fpv27dvHnDlz6NevHwAzZ87k+uuv95o2NTU18cgjj1BaWordbmfu3LkkJCR49evUVpuio6O9+nVqk9LN/Pe//1UWLFigKIqi7N69W7nnnnvcXNHFsVqtyk033dRq2Y033qgUFxcrTqdTueuuu5S9e/e6p7h2WrlypTJlyhRl2rRpiqIoypw5c5Qvv/xSURRF+dOf/qR8/PHHSn5+vjJr1izF6XQqpaWlytSpU91Zsks/bFN2drayatWqVtt4U5s2bNigLF68WFEURamurlauuuoqr3+d2mqTt79Obel2V/ecb0ioNyksLKSxsZE77riD2bNns3PnTux2O0ajEZVKxZgxY9i2bZu7y7wgRqOR559/vuX3vXv3cvnllwNnhuBu27YNk8nEmDFjUKlU9OnTh+bmZqqqqtxVsks/bFN+fj6ff/45t912G4888ghms9mr2nTdddfxu9/9DjgzRFmj0Xj969RWm7z9dWpLtwv5HxsS6m0CAgK48847WbVqFY8//jh//OMfCQwMbFn/3dBVb3Dttde2jNiCMx841bdTOH9/CO73XzdPb98P25ScnMxDDz3EW2+9RVxcHC+++KJXtUmv1xMcHIzZbOb+++/ngQce8PrXqa02efvr1JZuF/LnGxLqTfr378+NN96ISqWif//+GAwGampqWtZ/N3TVG6nVZ9+W7R2C66kmTZrEsGHDWn7et2+f17WprKyM2bNnc9NNN3HDDTf4xOv0wzb5wuv0Q90u5M83JNSbbNiwgWXLlgFQXl5OY2MjQUFBlJSUoCgKW7ZsYdSoUW6u8uIMGTKEHTt2AGeG4I4aNYq0tDS2bNmC0+nkxIkTOJ1OIiIi3FzphbvzzjvJy8sDYPv27QwdOtSr2lRZWckdd9zBH/7wB2699VbA+1+nttrk7a9TW7zvEPYSTZo0ia1btzJjxoyWIaHe6NZbb+WPf/wjM2fORKVSsWTJEtRqNQ8++CDNzc2MGTOGESNGuLvMi7JgwQL+9Kc/8fTTTzNgwACuvfZaNBoNo0aNYvr06TidThYuXOjuMttl0aJFPPnkk/j5+REZGcmTTz5JcHCw17Tp5Zdfpq6ujhUrVrBixQoAHn30URYvXuy1r1NbbXr44YdZsmSJ175Obel2QyiFEKI76XbdNUII0Z1IyAshhA+TkBdCCB8mIS+EED5MQl4IIXyYhLwQQvgwCXnhVV555RXGjBmDzWZrWfbGG28wbdo0br/9dmbNmtVyLwOACRMmtNr2Yu3YsYN58+a16zHvvPMOTz311I+ur6mp4V//+tcFP196ejrHjx9vVw1CdLuLoYR3e//997n++uv58MMPmTp1KpmZmezZs4e33noLnU5HdXU1v/nNbwgNDSUlJcXd5Z7X/v37+eyzz7jhhhvcXYrwYRLywmvs2LEDo9HIjBkz+MMf/sDUqVN58803Wbt2LTqdDoDw8HDuu+8+1q5de0Ehv2nTJl544QUURWHo0KE8/vjjfPzxx7z11ls4HA5UKhUvvPBCq8esX7+etWvX4nQ6mTBhAvfffz+jR49m69atAMybN48ZM2a0eszy5cvJz8+npqaGwYMHs3TpUl5++WUKCwvJyspi3Lhx/OlPf8Jms+Hv78+TTz5JdHQ0zzzzDF988QW9e/emurq6Y/4hRbciIS+8xvr165k2bRoDBgxAp9OxZ88e6urqzplHJCYmhtLSUpfP53A4ePLJJ1m/fj09evTglVde4eTJkxw9epSVK1cSGBjIwoUL2bJlC7169QLg9OnTvPLKK7z//vv4+/uzfPnyVpNXtcVsNhMSEsLq1atxOp1MnjyZ8vJy7rnnHtatW8f06dN54IEHmDVrFldddRXbt2/nqaee4le/+hU7d+5kw4YNNDQ0cM0111z8P57otiTkhVeora0lJyeHqqoqMjMzMZvNvPnmm4SEhFBVVdUq6I8ePdoSyudTXV1NSEgIPXr0AODuu+8GoEePHixYsAC9Xs/hw4dbfSM4duwYAwcOJCAgAIAHH3zwnOf94Uwh/v7+VFVV8fvf/56goCAaGhpoampqtc2BAwf4+9//zquvvoqiKGi1Wo4ePcqwYcNQq9UEBwd77WR6wr0k5IVXeP/997nllltYsGABAI2NjUycOJGMjAyWLFnCkiVL2LhxI1988QUFBQX87//+r8vn7NGjB3V1ddTU1BAWFsbixYu55ppreO655/j8888B+PWvf90qtI1GI4cPH8Zut6PT6bj//vt59NFHcTgcWCwW/Pz8OHToUKv95OTkUFZWxrPPPktVVRWffPIJiqKgVqtxOp0ADBgwgDvuuIO0tDSKiorYuXMnCQkJvPXWWzidTqxW6znPK8SFkJAXXmH9+vX85S9/afk9MDCQa665Bq1WS3JyMrfffjtqtZrm5maCg4M5fPiwy6mW1Wo1jz32GHPmzEGtVjNkyBAuu+wy0tLSmD59OlqtlpCQEE6dOkVsbCwAERER3H333dx+++2oVCrGjx9Pr169mD17NtOnTyc2NpY+ffq02k9ycjIrVqzgtttuQ6VSERcXx6lTpzAajRw4cIDXX3+dBQsWsGjRImw2G1arlUcffZSkpCTGjRvHrbfeSlRUVMs3DiHaQ2ahFD6nubmZ/Px8r51qWYiOJCEvfF5eXh5//etfz1n+85//nIyMDDdUJETXkZAXQggfJle8CiGED5OQF0IIHyYhL4QQPkxCXgghfJiEvBBC+LD/B9pvOA+nMdsAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['AQI_calculated'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Data Preprocessing(city_by_day).csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}