5.   sds_mean-2.ipynb

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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>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>Xylene</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",
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       "    <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29531 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City        Date  PM2.5   PM10     NO    NO2    NOx    NH3  \\\n",
       "0          Ahmedabad  2015-01-01    NaN    NaN   0.92  18.22  17.15    NaN   \n",
       "1          Ahmedabad  2015-01-02    NaN    NaN   0.97  15.69  16.46    NaN   \n",
       "2          Ahmedabad  2015-01-03    NaN    NaN  17.40  19.30  29.70    NaN   \n",
       "3          Ahmedabad  2015-01-04    NaN    NaN   1.70  18.48  17.97    NaN   \n",
       "4          Ahmedabad  2015-01-05    NaN    NaN  22.10  21.42  37.76    NaN   \n",
       "...              ...         ...    ...    ...    ...    ...    ...    ...   \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  Benzene  Toluene  Xylene   AQI    AQI_Bucket  \n",
       "0       0.92  27.64  133.36     0.00     0.02    0.00   NaN           NaN  \n",
       "1       0.97  24.55   34.06     3.68     5.50    3.77   NaN           NaN  \n",
       "2      17.40  29.07   30.70     6.80    16.40    2.25   NaN           NaN  \n",
       "3       1.70  18.59   36.08     4.43    10.14    1.00   NaN           NaN  \n",
       "4      22.10  39.33   39.31     7.01    18.89    2.78   NaN           NaN  \n",
       "...      ...    ...     ...      ...      ...     ...   ...           ...  \n",
       "29526   0.47   8.55   23.30     2.24    12.07    0.73  41.0          Good  \n",
       "29527   0.52  12.72   30.14     0.74     2.21    0.38  70.0  Satisfactory  \n",
       "29528   0.48   8.42   30.96     0.01     0.01    0.00  68.0  Satisfactory  \n",
       "29529   0.52   9.84   28.30     0.00     0.00    0.00  54.0  Satisfactory  \n",
       "29530   0.59   2.10   17.05      NaN      NaN     NaN  50.0          Good  \n",
       "\n",
       "[29531 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('/Users/manaswini/Desktop/city_day.csv')\n",
    "data"
   ]
  },
  {
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       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</th>\n",
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       "      <th>PM10</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>16.46</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
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       "      <td>6.80</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>62.12</td>\n",
       "      <td>118.67</td>\n",
       "      <td>9.18</td>\n",
       "      <td>56.35</td>\n",
       "      <td>19.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.49</td>\n",
       "      <td>12.44</td>\n",
       "      <td>68.05</td>\n",
       "      <td>1.32</td>\n",
       "      <td>37.76</td>\n",
       "      <td>1.62</td>\n",
       "      <td>92.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>31.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.37</td>\n",
       "      <td>23.99</td>\n",
       "      <td>16.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.52</td>\n",
       "      <td>11.01</td>\n",
       "      <td>26.34</td>\n",
       "      <td>1.37</td>\n",
       "      <td>49.58</td>\n",
       "      <td>1.34</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>29.75</td>\n",
       "      <td>127.98</td>\n",
       "      <td>9.06</td>\n",
       "      <td>25.15</td>\n",
       "      <td>18.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.67</td>\n",
       "      <td>12.10</td>\n",
       "      <td>34.99</td>\n",
       "      <td>1.39</td>\n",
       "      <td>60.21</td>\n",
       "      <td>0.79</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>40.02</td>\n",
       "      <td>121.10</td>\n",
       "      <td>7.09</td>\n",
       "      <td>58.92</td>\n",
       "      <td>33.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.73</td>\n",
       "      <td>16.39</td>\n",
       "      <td>41.64</td>\n",
       "      <td>1.21</td>\n",
       "      <td>44.10</td>\n",
       "      <td>1.35</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>37.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.42</td>\n",
       "      <td>35.04</td>\n",
       "      <td>20.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>14.40</td>\n",
       "      <td>9.69</td>\n",
       "      <td>1.73</td>\n",
       "      <td>47.05</td>\n",
       "      <td>1.87</td>\n",
       "      <td>119.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City        Date  PM2.5    PM10     NO    NO2    NOx  NH3     CO  \\\n",
       "0     Ahmedabad  2015-01-01    NaN     NaN   0.92  18.22  17.15  NaN   0.92   \n",
       "1     Ahmedabad  2015-01-02    NaN     NaN   0.97  15.69  16.46  NaN   0.97   \n",
       "2     Ahmedabad  2015-01-03    NaN     NaN  17.40  19.30  29.70  NaN  17.40   \n",
       "3     Ahmedabad  2015-01-04    NaN     NaN   1.70  18.48  17.97  NaN   1.70   \n",
       "4     Ahmedabad  2015-01-05    NaN     NaN  22.10  21.42  37.76  NaN  22.10   \n",
       "...         ...         ...    ...     ...    ...    ...    ...  ...    ...   \n",
       "2004  Ahmedabad  2020-06-27  62.12  118.67   9.18  56.35  19.86  NaN   0.49   \n",
       "2005  Ahmedabad  2020-06-28  31.57     NaN   6.37  23.99  16.40  NaN   0.52   \n",
       "2006  Ahmedabad  2020-06-29  29.75  127.98   9.06  25.15  18.92  NaN   0.67   \n",
       "2007  Ahmedabad  2020-06-30  40.02  121.10   7.09  58.92  33.41  NaN   0.73   \n",
       "2008  Ahmedabad  2020-07-01  37.63     NaN   4.42  35.04  20.17  NaN   0.28   \n",
       "\n",
       "        SO2      O3  Benzene  Toluene  Xylene    AQI    AQI_Bucket  \n",
       "0     27.64  133.36     0.00     0.02    0.00    NaN           NaN  \n",
       "1     24.55   34.06     3.68     5.50    3.77    NaN           NaN  \n",
       "2     29.07   30.70     6.80    16.40    2.25    NaN           NaN  \n",
       "3     18.59   36.08     4.43    10.14    1.00    NaN           NaN  \n",
       "4     39.33   39.31     7.01    18.89    2.78    NaN           NaN  \n",
       "...     ...     ...      ...      ...     ...    ...           ...  \n",
       "2004  12.44   68.05     1.32    37.76    1.62   92.0  Satisfactory  \n",
       "2005  11.01   26.34     1.37    49.58    1.34   82.0  Satisfactory  \n",
       "2006  12.10   34.99     1.39    60.21    0.79   74.0  Satisfactory  \n",
       "2007  16.39   41.64     1.21    44.10    1.35   98.0  Satisfactory  \n",
       "2008  14.40    9.69     1.73    47.05    1.87  119.0      Moderate  \n",
       "\n",
       "[2009 rows x 16 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "City=data.groupby(data.City)\n",
    "Ahmedabad=City.get_group(\"Ahmedabad\")\n",
    "Ahmedabad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "2.131486949733564\n",
      "NO\n",
      "2.066999287241625\n",
      "NO2\n",
      "2.063605199742049\n",
      "NOx\n",
      "2.016087974747989\n",
      "NH3\n",
      "6.818721786647659\n",
      "CO\n",
      "2.066999287241625\n",
      "SO2\n",
      "2.172215999728473\n",
      "O3\n",
      "2.3249499372093814\n",
      "Benzene\n",
      "1.639344262295082\n",
      "Toluene\n",
      "1.639344262295082\n",
      "Xylene\n",
      "1.6427383497946577\n",
      "PM2.5 67.85449674149172\n",
      "NO 22.42802142857145\n",
      "NO2 59.0254960742327\n",
      "NOx 47.36689752650181\n",
      "CO 22.193407142857158\n",
      "SO2 55.253732651570424\n",
      "O3 39.155407854984865\n",
      "Benzene 5.413807339449538\n",
      "Toluene 27.740524246395903\n",
      "452.12293853073464\n",
      "2020-07-01 00:00:00 2015-01-01 00:00:00\n"
     ]
    },
    {
     "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",
       "\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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</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>67.854497</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</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>166.0</td>\n",
       "      <td>moderate</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>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</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>166.0</td>\n",
       "      <td>moderate</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>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</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>166.0</td>\n",
       "      <td>moderate</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>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</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>166.0</td>\n",
       "      <td>moderate</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>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</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>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>62.120000</td>\n",
       "      <td>9.18</td>\n",
       "      <td>56.35</td>\n",
       "      <td>19.86</td>\n",
       "      <td>0.49</td>\n",
       "      <td>12.44</td>\n",
       "      <td>68.05</td>\n",
       "      <td>1.32</td>\n",
       "      <td>37.76</td>\n",
       "      <td>92.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>31.570000</td>\n",
       "      <td>6.37</td>\n",
       "      <td>23.99</td>\n",
       "      <td>16.40</td>\n",
       "      <td>0.52</td>\n",
       "      <td>11.01</td>\n",
       "      <td>26.34</td>\n",
       "      <td>1.37</td>\n",
       "      <td>49.58</td>\n",
       "      <td>82.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>29.750000</td>\n",
       "      <td>9.06</td>\n",
       "      <td>25.15</td>\n",
       "      <td>18.92</td>\n",
       "      <td>0.67</td>\n",
       "      <td>12.10</td>\n",
       "      <td>34.99</td>\n",
       "      <td>1.39</td>\n",
       "      <td>60.21</td>\n",
       "      <td>74.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>40.020000</td>\n",
       "      <td>7.09</td>\n",
       "      <td>58.92</td>\n",
       "      <td>33.41</td>\n",
       "      <td>0.73</td>\n",
       "      <td>16.39</td>\n",
       "      <td>41.64</td>\n",
       "      <td>1.21</td>\n",
       "      <td>44.10</td>\n",
       "      <td>98.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>37.630000</td>\n",
       "      <td>4.42</td>\n",
       "      <td>35.04</td>\n",
       "      <td>20.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>14.40</td>\n",
       "      <td>9.69</td>\n",
       "      <td>1.73</td>\n",
       "      <td>47.05</td>\n",
       "      <td>119.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City       Date      PM2.5     NO    NO2    NOx     CO    SO2  \\\n",
       "0     ahmedabad 2015-01-01  67.854497   0.92  18.22  17.15   0.92  27.64   \n",
       "1     ahmedabad 2015-01-02  67.854497   0.97  15.69  16.46   0.97  24.55   \n",
       "2     ahmedabad 2015-01-03  67.854497  17.40  19.30  29.70  17.40  29.07   \n",
       "3     ahmedabad 2015-01-04  67.854497   1.70  18.48  17.97   1.70  18.59   \n",
       "4     ahmedabad 2015-01-05  67.854497  22.10  21.42  37.76  22.10  39.33   \n",
       "...         ...        ...        ...    ...    ...    ...    ...    ...   \n",
       "2004  ahmedabad 2020-06-27  62.120000   9.18  56.35  19.86   0.49  12.44   \n",
       "2005  ahmedabad 2020-06-28  31.570000   6.37  23.99  16.40   0.52  11.01   \n",
       "2006  ahmedabad 2020-06-29  29.750000   9.06  25.15  18.92   0.67  12.10   \n",
       "2007  ahmedabad 2020-06-30  40.020000   7.09  58.92  33.41   0.73  16.39   \n",
       "2008  ahmedabad 2020-07-01  37.630000   4.42  35.04  20.17   0.28  14.40   \n",
       "\n",
       "          O3  Benzene  Toluene    AQI    AQI_Bucket  \n",
       "0     133.36     0.00     0.02  166.0      moderate  \n",
       "1      34.06     3.68     5.50  166.0      moderate  \n",
       "2      30.70     6.80    16.40  166.0      moderate  \n",
       "3      36.08     4.43    10.14  166.0      moderate  \n",
       "4      39.31     7.01    18.89  166.0      moderate  \n",
       "...      ...      ...      ...    ...           ...  \n",
       "2004   68.05     1.32    37.76   92.0  satisfactory  \n",
       "2005   26.34     1.37    49.58   82.0  satisfactory  \n",
       "2006   34.99     1.39    60.21   74.0  satisfactory  \n",
       "2007   41.64     1.21    44.10   98.0  satisfactory  \n",
       "2008    9.69     1.73    47.05  119.0      moderate  \n",
       "\n",
       "[2009 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ahmedabad = Ahmedabad.drop(columns=['PM10'])\n",
    "Ahmedabad\n",
    "\n",
    "citynull = pd.isnull(Ahmedabad[\"City\"])\n",
    "Ahmedabad[citynull]\n",
    "\n",
    "datenull = pd.isnull(Ahmedabad[\"Date\"])\n",
    "Ahmedabad[datenull]\n",
    "\n",
    "for column in Ahmedabad[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Ahmedabad[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Ahmedabad = Ahmedabad.drop(columns = ['Xylene', 'NH3'])\n",
    "Ahmedabad\n",
    "\n",
    "for column in Ahmedabad[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Ahmedabad[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Ahmedabad[column].fillna(m, inplace = True)\n",
    "\n",
    "Ahmedabad\n",
    "\n",
    "print(Ahmedabad['AQI'].mean(skipna = True))\n",
    "Ahmedabad['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Ahmedabad\n",
    "\n",
    "Ahmedabad['AQI_Bucket'].value_counts()\n",
    "\n",
    "Ahmedabad['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Ahmedabad\n",
    "\n",
    "Ahmedabad['Date'] = pd.to_datetime(Ahmedabad['Date'])\n",
    "\n",
    "print(max(Ahmedabad['Date']),min(Ahmedabad['Date']))\n",
    "\n",
    "temp=Ahmedabad.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Ahmedabad['City'] = Ahmedabad['City'].str.lower()\n",
    "Ahmedabad['AQI_Bucket'] = Ahmedabad['AQI_Bucket'].str.lower()\n",
    "Ahmedabad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.20025116247496863\n",
      "NO\n",
      "0.18667481247666565\n",
      "NO2\n",
      "0.18667481247666565\n",
      "NOx\n",
      "0.18667481247666565\n",
      "NH3\n",
      "0.18667481247666565\n",
      "CO\n",
      "0.3292264874588467\n",
      "SO2\n",
      "0.22061568747242305\n",
      "O3\n",
      "0.18667481247666565\n",
      "Benzene\n",
      "0.2545565624681804\n",
      "Toluene\n",
      "0.2545565624681804\n",
      "Xylene\n",
      "0.9130095373858739\n",
      "PM2.5 37.77321748878925\n",
      "NO 4.436071428571422\n",
      "NO2 22.175747767857157\n",
      "NOx 15.380412946428581\n",
      "CO 0.6279039812646355\n",
      "SO2 14.258340857787822\n",
      "O3 38.11510044642858\n",
      "Benzene 0.546415525114154\n",
      "Toluene 1.877636986301371\n",
      "95.29964328180738\n",
      "2020-07-01 00:00:00 2017-11-24 00:00:00\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2122</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>71.36</td>\n",
       "      <td>1.75</td>\n",
       "      <td>20.65</td>\n",
       "      <td>12.40</td>\n",
       "      <td>0.10</td>\n",
       "      <td>10.76</td>\n",
       "      <td>109.26</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.92</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
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       "    <tr>\n",
       "      <th>2123</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>81.40</td>\n",
       "      <td>1.44</td>\n",
       "      <td>20.50</td>\n",
       "      <td>12.08</td>\n",
       "      <td>0.12</td>\n",
       "      <td>15.24</td>\n",
       "      <td>127.09</td>\n",
       "      <td>0.20</td>\n",
       "      <td>6.50</td>\n",
       "      <td>184.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2124</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-26</td>\n",
       "      <td>78.32</td>\n",
       "      <td>1.26</td>\n",
       "      <td>26.00</td>\n",
       "      <td>14.85</td>\n",
       "      <td>0.14</td>\n",
       "      <td>26.96</td>\n",
       "      <td>117.44</td>\n",
       "      <td>0.22</td>\n",
       "      <td>7.95</td>\n",
       "      <td>197.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>88.76</td>\n",
       "      <td>6.60</td>\n",
       "      <td>30.85</td>\n",
       "      <td>21.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>33.59</td>\n",
       "      <td>111.81</td>\n",
       "      <td>0.29</td>\n",
       "      <td>7.63</td>\n",
       "      <td>198.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>64.18</td>\n",
       "      <td>2.56</td>\n",
       "      <td>28.07</td>\n",
       "      <td>17.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>19.00</td>\n",
       "      <td>138.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.02</td>\n",
       "      <td>188.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>14.50</td>\n",
       "      <td>1.53</td>\n",
       "      <td>6.53</td>\n",
       "      <td>4.72</td>\n",
       "      <td>0.55</td>\n",
       "      <td>13.75</td>\n",
       "      <td>33.76</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.27</td>\n",
       "      <td>42.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>16.65</td>\n",
       "      <td>1.43</td>\n",
       "      <td>8.32</td>\n",
       "      <td>5.59</td>\n",
       "      <td>0.66</td>\n",
       "      <td>10.86</td>\n",
       "      <td>37.34</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.43</td>\n",
       "      <td>49.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3070</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>20.96</td>\n",
       "      <td>1.65</td>\n",
       "      <td>9.55</td>\n",
       "      <td>6.43</td>\n",
       "      <td>0.66</td>\n",
       "      <td>14.79</td>\n",
       "      <td>43.29</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.69</td>\n",
       "      <td>56.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>21.34</td>\n",
       "      <td>1.74</td>\n",
       "      <td>10.69</td>\n",
       "      <td>7.10</td>\n",
       "      <td>0.66</td>\n",
       "      <td>14.58</td>\n",
       "      <td>45.32</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.42</td>\n",
       "      <td>61.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3072</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>9.68</td>\n",
       "      <td>6.40</td>\n",
       "      <td>0.59</td>\n",
       "      <td>10.88</td>\n",
       "      <td>29.15</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.50</td>\n",
       "      <td>54.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>951 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City       Date  PM2.5    NO    NO2    NOx    CO    SO2      O3  \\\n",
       "2122  amaravati 2017-11-24  71.36  1.75  20.65  12.40  0.10  10.76  109.26   \n",
       "2123  amaravati 2017-11-25  81.40  1.44  20.50  12.08  0.12  15.24  127.09   \n",
       "2124  amaravati 2017-11-26  78.32  1.26  26.00  14.85  0.14  26.96  117.44   \n",
       "2125  amaravati 2017-11-27  88.76  6.60  30.85  21.77  0.11  33.59  111.81   \n",
       "2126  amaravati 2017-11-28  64.18  2.56  28.07  17.01  0.09  19.00  138.18   \n",
       "...         ...        ...    ...   ...    ...    ...   ...    ...     ...   \n",
       "3068  amaravati 2020-06-27  14.50  1.53   6.53   4.72  0.55  13.75   33.76   \n",
       "3069  amaravati 2020-06-28  16.65  1.43   8.32   5.59  0.66  10.86   37.34   \n",
       "3070  amaravati 2020-06-29  20.96  1.65   9.55   6.43  0.66  14.79   43.29   \n",
       "3071  amaravati 2020-06-30  21.34  1.74  10.69   7.10  0.66  14.58   45.32   \n",
       "3072  amaravati 2020-07-01  22.00  1.50   9.68   6.40  0.59  10.88   29.15   \n",
       "\n",
       "      Benzene  Toluene    AQI    AQI_Bucket  \n",
       "2122     0.17     5.92  166.0      moderate  \n",
       "2123     0.20     6.50  184.0      moderate  \n",
       "2124     0.22     7.95  197.0      moderate  \n",
       "2125     0.29     7.63  198.0      moderate  \n",
       "2126     0.17     5.02  188.0      moderate  \n",
       "...       ...      ...    ...           ...  \n",
       "3068     0.06     0.27   42.0          good  \n",
       "3069     0.10     0.43   49.0          good  \n",
       "3070     0.12     0.69   56.0  satisfactory  \n",
       "3071     0.14     1.42   61.0  satisfactory  \n",
       "3072     0.10     0.50   54.0  satisfactory  \n",
       "\n",
       "[951 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Amaravati=City.get_group(\"Amaravati\")\n",
    "Amaravati\n",
    "\n",
    "Amaravati = Amaravati.drop(columns=['PM10'])\n",
    "Amaravati\n",
    "\n",
    "citynull = pd.isnull(Amaravati[\"City\"])\n",
    "Amaravati[citynull]\n",
    "\n",
    "datenull = pd.isnull(Amaravati[\"Date\"])\n",
    "Amaravati[datenull]\n",
    "\n",
    "for column in Amaravati[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Amaravati[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Amaravati = Amaravati.drop(columns = ['Xylene', 'NH3'])\n",
    "Amaravati\n",
    "\n",
    "for column in Amaravati[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Amaravati[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Amaravati[column].fillna(m, inplace = True)\n",
    "\n",
    "Amaravati\n",
    "\n",
    "print(Amaravati['AQI'].mean(skipna = True))\n",
    "Amaravati['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Amaravati\n",
    "\n",
    "Amaravati['AQI_Bucket'].value_counts()\n",
    "\n",
    "Amaravati['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Amaravati\n",
    "\n",
    "Amaravati['Date'] = pd.to_datetime(Amaravati['Date'])\n",
    "\n",
    "print(max(Amaravati['Date']),min(Amaravati['Date']))\n",
    "\n",
    "temp=Amaravati.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Amaravati['City'] = Amaravati['City'].str.lower()\n",
    "Amaravati['AQI_Bucket'] = Amaravati['AQI_Bucket'].str.lower()\n",
    "Amaravati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.43444319994569464\n",
      "NO\n",
      "0.3156501374605437\n",
      "NO2\n",
      "0.1493398499813325\n",
      "NOx\n",
      "0.9808912873773886\n",
      "NH3\n",
      "0.14255167498218105\n",
      "CO\n",
      "0.25795064996775613\n",
      "SO2\n",
      "0.5702066999287242\n",
      "O3\n",
      "0.39032006245121\n",
      "Benzene\n",
      "0.6618470624172691\n",
      "Toluene\n",
      "0.7195465499100567\n",
      "Xylene\n",
      "0.7229406374096324\n",
      "PM2.5 55.468334858188456\n",
      "NO 18.727925531914867\n",
      "NO2 18.5221155480034\n",
      "NOx 35.64829399141628\n",
      "CO 0.5513013100436684\n",
      "SO2 8.148917378917375\n",
      "O3 22.41614828209768\n",
      "Benzene 4.182290448343083\n",
      "Toluene 3.036263627353808\n",
      "119.92095914742451\n",
      "2020-07-01 00:00:00 2017-02-27 00:00:00\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>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3073</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2017-02-27</td>\n",
       "      <td>13.42</td>\n",
       "      <td>15.06</td>\n",
       "      <td>8.86</td>\n",
       "      <td>35.648294</td>\n",
       "      <td>0.81</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.96</td>\n",
       "      <td>4.18229</td>\n",
       "      <td>3.036264</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>12.45</td>\n",
       "      <td>17.44</td>\n",
       "      <td>8.05</td>\n",
       "      <td>35.648294</td>\n",
       "      <td>0.67</td>\n",
       "      <td>16.06</td>\n",
       "      <td>11.88</td>\n",
       "      <td>4.18229</td>\n",
       "      <td>3.036264</td>\n",
       "      <td>52.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3075</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>19.52</td>\n",
       "      <td>14.77</td>\n",
       "      <td>6.02</td>\n",
       "      <td>35.648294</td>\n",
       "      <td>0.43</td>\n",
       "      <td>26.24</td>\n",
       "      <td>11.53</td>\n",
       "      <td>4.18229</td>\n",
       "      <td>3.036264</td>\n",
       "      <td>31.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3076</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>77.71</td>\n",
       "      <td>10.49</td>\n",
       "      <td>3.88</td>\n",
       "      <td>35.648294</td>\n",
       "      <td>0.36</td>\n",
       "      <td>22.35</td>\n",
       "      <td>18.68</td>\n",
       "      <td>4.18229</td>\n",
       "      <td>3.036264</td>\n",
       "      <td>106.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3077</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>19.70</td>\n",
       "      <td>9.91</td>\n",
       "      <td>3.13</td>\n",
       "      <td>35.648294</td>\n",
       "      <td>0.37</td>\n",
       "      <td>14.29</td>\n",
       "      <td>22.22</td>\n",
       "      <td>4.18229</td>\n",
       "      <td>3.036264</td>\n",
       "      <td>98.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4289</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>51.10</td>\n",
       "      <td>25.25</td>\n",
       "      <td>20.80</td>\n",
       "      <td>35.750000</td>\n",
       "      <td>0.66</td>\n",
       "      <td>4.55</td>\n",
       "      <td>16.48</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>74.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4290</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>45.24</td>\n",
       "      <td>23.11</td>\n",
       "      <td>17.90</td>\n",
       "      <td>27.470000</td>\n",
       "      <td>0.63</td>\n",
       "      <td>5.22</td>\n",
       "      <td>16.48</td>\n",
       "      <td>1.16000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4291</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>26.77</td>\n",
       "      <td>26.99</td>\n",
       "      <td>19.83</td>\n",
       "      <td>34.030000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>4.89</td>\n",
       "      <td>16.48</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>49.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4292</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>41.64</td>\n",
       "      <td>22.03</td>\n",
       "      <td>15.97</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>0.69</td>\n",
       "      <td>4.67</td>\n",
       "      <td>16.48</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4293</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>57.67</td>\n",
       "      <td>32.81</td>\n",
       "      <td>15.11</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>3.48</td>\n",
       "      <td>16.48</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>78.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1221 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          City       Date  PM2.5     NO    NO2        NOx    CO    SO2     O3  \\\n",
       "3073  amritsar 2017-02-27  13.42  15.06   8.86  35.648294  0.81  11.00  12.96   \n",
       "3074  amritsar 2017-02-28  12.45  17.44   8.05  35.648294  0.67  16.06  11.88   \n",
       "3075  amritsar 2017-03-01  19.52  14.77   6.02  35.648294  0.43  26.24  11.53   \n",
       "3076  amritsar 2017-03-02  77.71  10.49   3.88  35.648294  0.36  22.35  18.68   \n",
       "3077  amritsar 2017-03-03  19.70   9.91   3.13  35.648294  0.37  14.29  22.22   \n",
       "...        ...        ...    ...    ...    ...        ...   ...    ...    ...   \n",
       "4289  amritsar 2020-06-27  51.10  25.25  20.80  35.750000  0.66   4.55  16.48   \n",
       "4290  amritsar 2020-06-28  45.24  23.11  17.90  27.470000  0.63   5.22  16.48   \n",
       "4291  amritsar 2020-06-29  26.77  26.99  19.83  34.030000  0.67   4.89  16.48   \n",
       "4292  amritsar 2020-06-30  41.64  22.03  15.97  30.600000  0.69   4.67  16.48   \n",
       "4293  amritsar 2020-07-01  57.67  32.81  15.11  30.200000  0.59   3.48  16.48   \n",
       "\n",
       "      Benzene   Toluene    AQI    AQI_Bucket  \n",
       "3073  4.18229  3.036264  166.0      moderate  \n",
       "3074  4.18229  3.036264   52.0  satisfactory  \n",
       "3075  4.18229  3.036264   31.0          good  \n",
       "3076  4.18229  3.036264  106.0      moderate  \n",
       "3077  4.18229  3.036264   98.0  satisfactory  \n",
       "...       ...       ...    ...           ...  \n",
       "4289  1.30000  1.100000   74.0  satisfactory  \n",
       "4290  1.16000  0.980000   85.0  satisfactory  \n",
       "4291  1.30000  1.100000   49.0          good  \n",
       "4292  1.30000  1.100000   66.0  satisfactory  \n",
       "4293  1.30000  1.100000   78.0  satisfactory  \n",
       "\n",
       "[1221 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Amritsar=City.get_group(\"Amritsar\")\n",
    "Amritsar\n",
    "\n",
    "Amritsar = Amritsar.drop(columns=['PM10'])\n",
    "Amritsar\n",
    "\n",
    "citynull = pd.isnull(Amritsar[\"City\"])\n",
    "Amritsar[citynull]\n",
    "\n",
    "datenull = pd.isnull(Amritsar[\"Date\"])\n",
    "Amritsar[datenull]\n",
    "\n",
    "for column in Amritsar[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Amritsar[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Amritsar = Amritsar.drop(columns = ['Xylene', 'NH3'])\n",
    "Amritsar\n",
    "\n",
    "for column in Amritsar[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Amritsar[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Amritsar[column].fillna(m, inplace = True)\n",
    "\n",
    "Amritsar\n",
    "\n",
    "print(Amritsar['AQI'].mean(skipna = True))\n",
    "Amritsar['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Amritsar\n",
    "\n",
    "Amritsar['AQI_Bucket'].value_counts()\n",
    "\n",
    "Amritsar['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Amritsar\n",
    "\n",
    "Amritsar['Date'] = pd.to_datetime(Amritsar['Date'])\n",
    "\n",
    "print(max(Amritsar['Date']),min(Amritsar['Date']))\n",
    "\n",
    "temp=Amritsar.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Amritsar['City'] = Amritsar['City'].str.lower()\n",
    "Amritsar['AQI_Bucket'] = Amritsar['AQI_Bucket'].str.lower()\n",
    "Amritsar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.49553677493805787\n",
      "NO\n",
      "0.020364524997454433\n",
      "NO2\n",
      "0.020364524997454433\n",
      "NOx\n",
      "0.013576349998302957\n",
      "NH3\n",
      "0.6889997624138751\n",
      "CO\n",
      "0.03733496249533313\n",
      "SO2\n",
      "0.020364524997454433\n",
      "O3\n",
      "0.4887485999389064\n",
      "Benzene\n",
      "0.9028272748871465\n",
      "Toluene\n",
      "0.3156501374605437\n",
      "Xylene\n",
      "6.818721786647659\n",
      "PM2.5 35.81982823403109\n",
      "NO 9.409136295556674\n",
      "NO2 27.995042436345486\n",
      "NOx 19.717092269326685\n",
      "CO 1.8386336336336362\n",
      "SO2 5.5190214677983\n",
      "O3 32.86808579088473\n",
      "Benzene 3.2225530694205324\n",
      "Toluene 4.753194154488517\n",
      "94.31832460732984\n",
      "2020-07-01 00:00:00 2015-01-01 00:00:00\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4294</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>35.819828</td>\n",
       "      <td>3.26</td>\n",
       "      <td>17.33</td>\n",
       "      <td>10.88</td>\n",
       "      <td>0.33</td>\n",
       "      <td>3.54</td>\n",
       "      <td>10.73</td>\n",
       "      <td>0.56</td>\n",
       "      <td>4.64</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4295</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>35.819828</td>\n",
       "      <td>6.05</td>\n",
       "      <td>19.73</td>\n",
       "      <td>14.14</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.97</td>\n",
       "      <td>22.77</td>\n",
       "      <td>0.65</td>\n",
       "      <td>5.31</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4296</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>35.819828</td>\n",
       "      <td>11.91</td>\n",
       "      <td>19.88</td>\n",
       "      <td>20.72</td>\n",
       "      <td>17.40</td>\n",
       "      <td>13.61</td>\n",
       "      <td>12.03</td>\n",
       "      <td>0.53</td>\n",
       "      <td>19.25</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4297</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>35.819828</td>\n",
       "      <td>7.45</td>\n",
       "      <td>21.61</td>\n",
       "      <td>16.88</td>\n",
       "      <td>5.05</td>\n",
       "      <td>6.52</td>\n",
       "      <td>17.70</td>\n",
       "      <td>0.55</td>\n",
       "      <td>7.47</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4298</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>35.819828</td>\n",
       "      <td>9.52</td>\n",
       "      <td>22.17</td>\n",
       "      <td>21.76</td>\n",
       "      <td>1.83</td>\n",
       "      <td>4.71</td>\n",
       "      <td>12.72</td>\n",
       "      <td>0.40</td>\n",
       "      <td>4.36</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6298</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>3.06</td>\n",
       "      <td>13.68</td>\n",
       "      <td>13.07</td>\n",
       "      <td>0.67</td>\n",
       "      <td>7.29</td>\n",
       "      <td>15.69</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.18</td>\n",
       "      <td>51.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6299</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>20.440000</td>\n",
       "      <td>2.69</td>\n",
       "      <td>10.33</td>\n",
       "      <td>10.58</td>\n",
       "      <td>0.66</td>\n",
       "      <td>6.60</td>\n",
       "      <td>17.59</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.94</td>\n",
       "      <td>61.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6300</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>28.680000</td>\n",
       "      <td>3.62</td>\n",
       "      <td>12.12</td>\n",
       "      <td>12.94</td>\n",
       "      <td>0.56</td>\n",
       "      <td>6.33</td>\n",
       "      <td>16.99</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.17</td>\n",
       "      <td>65.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6301</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>14.470000</td>\n",
       "      <td>4.61</td>\n",
       "      <td>12.69</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>6.45</td>\n",
       "      <td>16.08</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.86</td>\n",
       "      <td>63.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6302</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>3.95</td>\n",
       "      <td>13.25</td>\n",
       "      <td>14.83</td>\n",
       "      <td>0.54</td>\n",
       "      <td>6.66</td>\n",
       "      <td>15.40</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.65</td>\n",
       "      <td>43.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City       Date      PM2.5     NO    NO2    NOx     CO    SO2  \\\n",
       "4294  bengaluru 2015-01-01  35.819828   3.26  17.33  10.88   0.33   3.54   \n",
       "4295  bengaluru 2015-01-02  35.819828   6.05  19.73  14.14   1.35   3.97   \n",
       "4296  bengaluru 2015-01-03  35.819828  11.91  19.88  20.72  17.40  13.61   \n",
       "4297  bengaluru 2015-01-04  35.819828   7.45  21.61  16.88   5.05   6.52   \n",
       "4298  bengaluru 2015-01-05  35.819828   9.52  22.17  21.76   1.83   4.71   \n",
       "...         ...        ...        ...    ...    ...    ...    ...    ...   \n",
       "6298  bengaluru 2020-06-27  16.600000   3.06  13.68  13.07   0.67   7.29   \n",
       "6299  bengaluru 2020-06-28  20.440000   2.69  10.33  10.58   0.66   6.60   \n",
       "6300  bengaluru 2020-06-29  28.680000   3.62  12.12  12.94   0.56   6.33   \n",
       "6301  bengaluru 2020-06-30  14.470000   4.61  12.69  15.00   0.56   6.45   \n",
       "6302  bengaluru 2020-07-01  17.500000   3.95  13.25  14.83   0.54   6.66   \n",
       "\n",
       "         O3  Benzene  Toluene    AQI    AQI_Bucket  \n",
       "4294  10.73     0.56     4.64  166.0      moderate  \n",
       "4295  22.77     0.65     5.31  166.0      moderate  \n",
       "4296  12.03     0.53    19.25  166.0      moderate  \n",
       "4297  17.70     0.55     7.47  166.0      moderate  \n",
       "4298  12.72     0.40     4.36  166.0      moderate  \n",
       "...     ...      ...      ...    ...           ...  \n",
       "6298  15.69     0.21     1.18   51.0  satisfactory  \n",
       "6299  17.59     0.12     0.94   61.0  satisfactory  \n",
       "6300  16.99     0.17     1.17   65.0  satisfactory  \n",
       "6301  16.08     0.18     0.86   63.0  satisfactory  \n",
       "6302  15.40     0.27     0.65   43.0          good  \n",
       "\n",
       "[2009 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bengaluru=City.get_group(\"Bengaluru\")\n",
    "Bengaluru\n",
    "\n",
    "Bengaluru = Bengaluru.drop(columns=['PM10'])\n",
    "Bengaluru\n",
    "\n",
    "citynull = pd.isnull(Bengaluru[\"City\"])\n",
    "Bengaluru[citynull]\n",
    "\n",
    "datenull = pd.isnull(Bengaluru[\"Date\"])\n",
    "Bengaluru[datenull]\n",
    "\n",
    "for column in Bengaluru[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Bengaluru[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Bengaluru = Bengaluru.drop(columns = ['Xylene', 'NH3'])\n",
    "Bengaluru\n",
    "\n",
    "for column in Bengaluru[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Bengaluru[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Bengaluru[column].fillna(m, inplace = True)\n",
    "\n",
    "Bengaluru\n",
    "\n",
    "print(Bengaluru['AQI'].mean(skipna = True))\n",
    "Bengaluru['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Bengaluru\n",
    "\n",
    "Bengaluru['AQI_Bucket'].value_counts()\n",
    "\n",
    "Bengaluru['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Bengaluru\n",
    "\n",
    "Bengaluru['Date'] = pd.to_datetime(Bengaluru['Date'])\n",
    "\n",
    "print(max(Bengaluru['Date']),min(Bengaluru['Date']))\n",
    "\n",
    "temp=Bengaluru.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Bengaluru['City'] = Bengaluru['City'].str.lower()\n",
    "Bengaluru['AQI_Bucket'] = Bengaluru['AQI_Bucket'].str.lower()\n",
    "Bengaluru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.6279061874215117\n",
      "NO\n",
      "0.8247632623969046\n",
      "NO2\n",
      "0.7942164749007229\n",
      "NOx\n",
      "0.5939653124257543\n",
      "NH3\n",
      "0.767063774904117\n",
      "CO\n",
      "0.5091131249363609\n",
      "SO2\n",
      "0.6550588874181177\n",
      "O3\n",
      "0.6889997624138751\n",
      "Benzene\n",
      "0.5871771374266028\n",
      "Toluene\n",
      "3.183654074602043\n",
      "Xylene\n",
      "3.183654074602043\n",
      "PM2.5 64.05760956175307\n",
      "NO 17.301812949640276\n",
      "NO2 16.52053977272729\n",
      "NOx 24.542647444298808\n",
      "CO 1.7982741116751257\n",
      "SO2 9.783221476510072\n",
      "O3 16.59965986394557\n",
      "Benzene 4.842562091503271\n",
      "Toluene nan\n",
      "150.2805049088359\n",
      "2020-07-01 00:00:00 2017-12-07 00:00:00\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6592</th>\n",
       "      <td>brajrajnagar</td>\n",
       "      <td>2017-12-07</td>\n",
       "      <td>171.38000</td>\n",
       "      <td>17.301813</td>\n",
       "      <td>41.07000</td>\n",
       "      <td>24.542647</td>\n",
       "      <td>3.260000</td>\n",
       "      <td>5.830000</td>\n",
       "      <td>4.69000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
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       "    <tr>\n",
       "      <th>6593</th>\n",
       "      <td>brajrajnagar</td>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>126.67000</td>\n",
       "      <td>17.301813</td>\n",
       "      <td>20.16000</td>\n",
       "      <td>24.542647</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>10.51000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>304.0</td>\n",
       "      <td>very poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6594</th>\n",
       "      <td>brajrajnagar</td>\n",
       "      <td>2017-12-09</td>\n",
       "      <td>105.11000</td>\n",
       "      <td>17.301813</td>\n",
       "      <td>18.40000</td>\n",
       "      <td>24.542647</td>\n",
       "      <td>2.830000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>10.33000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>291.0</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6595</th>\n",
       "      <td>brajrajnagar</td>\n",
       "      <td>2017-12-10</td>\n",
       "      <td>65.25000</td>\n",
       "      <td>17.301813</td>\n",
       "      <td>15.21000</td>\n",
       "      <td>24.542647</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>8.37000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6596</th>\n",
       "      <td>brajrajnagar</td>\n",
       "      <td>2017-12-11</td>\n",
       "      <td>64.05761</td>\n",
       "      <td>17.301813</td>\n",
       "      <td>16.52054</td>\n",
       "      <td>24.542647</td>\n",
       "      <td>1.798274</td>\n",
       "      <td>9.783221</td>\n",
       "      <td>16.59966</td>\n",
       "      <td>4.842562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7525</th>\n",
       "      <td>brajrajnagar</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>64.05761</td>\n",
       "      <td>17.301813</td>\n",
       "      <td>16.52054</td>\n",
       "      <td>24.542647</td>\n",
       "      <td>1.798274</td>\n",
       "      <td>9.783221</td>\n",
       "      <td>16.59966</td>\n",
       "      <td>4.842562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7526</th>\n",
       "      <td>brajrajnagar</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>64.05761</td>\n",
       "      <td>17.301813</td>\n",
       "      <td>16.52054</td>\n",
       "      <td>24.542647</td>\n",
       "      <td>1.798274</td>\n",
       "      <td>9.783221</td>\n",
       "      <td>16.59966</td>\n",
       "      <td>4.842562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7527</th>\n",
       "      <td>brajrajnagar</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>64.05761</td>\n",
       "      <td>17.301813</td>\n",
       "      <td>16.52054</td>\n",
       "      <td>24.542647</td>\n",
       "      <td>1.798274</td>\n",
       "      <td>9.783221</td>\n",
       "      <td>16.59966</td>\n",
       "      <td>4.842562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7528</th>\n",
       "      <td>brajrajnagar</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>64.05761</td>\n",
       "      <td>17.301813</td>\n",
       "      <td>16.52054</td>\n",
       "      <td>24.542647</td>\n",
       "      <td>1.798274</td>\n",
       "      <td>9.783221</td>\n",
       "      <td>16.59966</td>\n",
       "      <td>4.842562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7529</th>\n",
       "      <td>brajrajnagar</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>64.05761</td>\n",
       "      <td>17.301813</td>\n",
       "      <td>16.52054</td>\n",
       "      <td>24.542647</td>\n",
       "      <td>1.798274</td>\n",
       "      <td>9.783221</td>\n",
       "      <td>16.59966</td>\n",
       "      <td>4.842562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>938 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              City       Date      PM2.5         NO       NO2        NOx  \\\n",
       "6592  brajrajnagar 2017-12-07  171.38000  17.301813  41.07000  24.542647   \n",
       "6593  brajrajnagar 2017-12-08  126.67000  17.301813  20.16000  24.542647   \n",
       "6594  brajrajnagar 2017-12-09  105.11000  17.301813  18.40000  24.542647   \n",
       "6595  brajrajnagar 2017-12-10   65.25000  17.301813  15.21000  24.542647   \n",
       "6596  brajrajnagar 2017-12-11   64.05761  17.301813  16.52054  24.542647   \n",
       "...            ...        ...        ...        ...       ...        ...   \n",
       "7525  brajrajnagar 2020-06-27   64.05761  17.301813  16.52054  24.542647   \n",
       "7526  brajrajnagar 2020-06-28   64.05761  17.301813  16.52054  24.542647   \n",
       "7527  brajrajnagar 2020-06-29   64.05761  17.301813  16.52054  24.542647   \n",
       "7528  brajrajnagar 2020-06-30   64.05761  17.301813  16.52054  24.542647   \n",
       "7529  brajrajnagar 2020-07-01   64.05761  17.301813  16.52054  24.542647   \n",
       "\n",
       "            CO       SO2        O3   Benzene  Toluene    AQI AQI_Bucket  \n",
       "6592  3.260000  5.830000   4.69000  0.000000      NaN  166.0   moderate  \n",
       "6593  2.940000  5.430000  10.51000  0.000000      NaN  304.0  very poor  \n",
       "6594  2.830000  5.100000  10.33000  0.000000      NaN  291.0       poor  \n",
       "6595  2.740000  4.750000   8.37000  0.000000      NaN  189.0   moderate  \n",
       "6596  1.798274  9.783221  16.59966  4.842562      NaN  166.0   moderate  \n",
       "...        ...       ...       ...       ...      ...    ...        ...  \n",
       "7525  1.798274  9.783221  16.59966  4.842562      NaN  166.0   moderate  \n",
       "7526  1.798274  9.783221  16.59966  4.842562      NaN  166.0   moderate  \n",
       "7527  1.798274  9.783221  16.59966  4.842562      NaN  166.0   moderate  \n",
       "7528  1.798274  9.783221  16.59966  4.842562      NaN  166.0   moderate  \n",
       "7529  1.798274  9.783221  16.59966  4.842562      NaN  166.0   moderate  \n",
       "\n",
       "[938 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Brajrajnagar=City.get_group(\"Brajrajnagar\")\n",
    "Brajrajnagar\n",
    "\n",
    "Brajrajnagar = Brajrajnagar.drop(columns=['PM10'])\n",
    "Brajrajnagar\n",
    "\n",
    "citynull = pd.isnull(Brajrajnagar[\"City\"])\n",
    "Brajrajnagar[citynull]\n",
    "\n",
    "datenull = pd.isnull(Brajrajnagar[\"Date\"])\n",
    "Brajrajnagar[datenull]\n",
    "\n",
    "for column in Brajrajnagar[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Brajrajnagar[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Brajrajnagar = Brajrajnagar.drop(columns = ['Xylene', 'NH3'])\n",
    "Brajrajnagar\n",
    "\n",
    "for column in Brajrajnagar[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Brajrajnagar[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Brajrajnagar[column].fillna(m, inplace = True)\n",
    "\n",
    "Brajrajnagar\n",
    "\n",
    "print(Brajrajnagar['AQI'].mean(skipna = True))\n",
    "Brajrajnagar['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Brajrajnagar\n",
    "\n",
    "Brajrajnagar['AQI_Bucket'].value_counts()\n",
    "\n",
    "Brajrajnagar['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Brajrajnagar\n",
    "\n",
    "Brajrajnagar['Date'] = pd.to_datetime(Brajrajnagar['Date'])\n",
    "\n",
    "print(max(Brajrajnagar['Date']),min(Brajrajnagar['Date']))\n",
    "\n",
    "temp=Brajrajnagar.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Brajrajnagar['City'] = Brajrajnagar['City'].str.lower()\n",
    "Brajrajnagar['AQI_Bucket'] = Brajrajnagar['AQI_Bucket'].str.lower()\n",
    "Brajrajnagar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.39710823745036145\n",
      "NO\n",
      "0.1221871499847266\n",
      "NO2\n",
      "0.1221871499847266\n",
      "NOx\n",
      "0.1221871499847266\n",
      "NH3\n",
      "1.5680684248039916\n",
      "CO\n",
      "0.08485218748939347\n",
      "SO2\n",
      "0.1221871499847266\n",
      "O3\n",
      "0.1663102874792112\n",
      "Benzene\n",
      "0.9673149373790857\n",
      "Toluene\n",
      "0.858704137392662\n",
      "Xylene\n",
      "6.818721786647659\n",
      "PM2.5 50.425613107822414\n",
      "NO 9.190562595032926\n",
      "NO2 16.857622909275207\n",
      "NOx 17.667942219969564\n",
      "CO 1.0673487903225722\n",
      "SO2 7.87718195641157\n",
      "O3 32.443816326530595\n",
      "Benzene 1.7092053364269306\n",
      "Toluene 2.595666287015953\n",
      "114.50265392781316\n",
      "2020-07-01 00:00:00 2015-01-01 00:00:00\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>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7834</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>50.425613</td>\n",
       "      <td>16.30</td>\n",
       "      <td>15.39</td>\n",
       "      <td>22.68</td>\n",
       "      <td>1.17</td>\n",
       "      <td>9.20</td>\n",
       "      <td>11.35</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>2.595666</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7835</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>50.425613</td>\n",
       "      <td>16.49</td>\n",
       "      <td>13.42</td>\n",
       "      <td>23.09</td>\n",
       "      <td>1.23</td>\n",
       "      <td>8.61</td>\n",
       "      <td>9.16</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>2.595666</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7836</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>50.425613</td>\n",
       "      <td>9.72</td>\n",
       "      <td>19.56</td>\n",
       "      <td>9.99</td>\n",
       "      <td>0.77</td>\n",
       "      <td>48.23</td>\n",
       "      <td>13.45</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>2.595666</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7837</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>50.425613</td>\n",
       "      <td>9.60</td>\n",
       "      <td>16.20</td>\n",
       "      <td>11.71</td>\n",
       "      <td>1.00</td>\n",
       "      <td>27.96</td>\n",
       "      <td>10.33</td>\n",
       "      <td>1.709205</td>\n",
       "      <td>2.595666</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7838</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>50.425613</td>\n",
       "      <td>9.16</td>\n",
       "      <td>16.30</td>\n",
       "      <td>12.94</td>\n",
       "      <td>0.90</td>\n",
       "      <td>16.60</td>\n",
       "      <td>9.36</td>\n",
       "      <td>1.709205</td>\n",
       "      <td>2.595666</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9838</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>26.420000</td>\n",
       "      <td>7.25</td>\n",
       "      <td>12.96</td>\n",
       "      <td>19.59</td>\n",
       "      <td>1.10</td>\n",
       "      <td>7.29</td>\n",
       "      <td>68.51</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>95.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9839</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>25.930000</td>\n",
       "      <td>7.81</td>\n",
       "      <td>10.00</td>\n",
       "      <td>16.39</td>\n",
       "      <td>0.76</td>\n",
       "      <td>6.48</td>\n",
       "      <td>77.45</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9840</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>7.65</td>\n",
       "      <td>9.69</td>\n",
       "      <td>16.74</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6.62</td>\n",
       "      <td>62.57</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>104.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9841</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>24.140000</td>\n",
       "      <td>8.42</td>\n",
       "      <td>12.38</td>\n",
       "      <td>20.29</td>\n",
       "      <td>1.05</td>\n",
       "      <td>7.50</td>\n",
       "      <td>68.75</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9842</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>15.950000</td>\n",
       "      <td>6.22</td>\n",
       "      <td>10.72</td>\n",
       "      <td>16.44</td>\n",
       "      <td>1.02</td>\n",
       "      <td>9.23</td>\n",
       "      <td>48.37</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         City       Date      PM2.5     NO    NO2    NOx    CO    SO2     O3  \\\n",
       "7834  chennai 2015-01-01  50.425613  16.30  15.39  22.68  1.17   9.20  11.35   \n",
       "7835  chennai 2015-01-02  50.425613  16.49  13.42  23.09  1.23   8.61   9.16   \n",
       "7836  chennai 2015-01-03  50.425613   9.72  19.56   9.99  0.77  48.23  13.45   \n",
       "7837  chennai 2015-01-04  50.425613   9.60  16.20  11.71  1.00  27.96  10.33   \n",
       "7838  chennai 2015-01-05  50.425613   9.16  16.30  12.94  0.90  16.60   9.36   \n",
       "...       ...        ...        ...    ...    ...    ...   ...    ...    ...   \n",
       "9838  chennai 2020-06-27  26.420000   7.25  12.96  19.59  1.10   7.29  68.51   \n",
       "9839  chennai 2020-06-28  25.930000   7.81  10.00  16.39  0.76   6.48  77.45   \n",
       "9840  chennai 2020-06-29  21.300000   7.65   9.69  16.74  0.96   6.62  62.57   \n",
       "9841  chennai 2020-06-30  24.140000   8.42  12.38  20.29  1.05   7.50  68.75   \n",
       "9842  chennai 2020-07-01  15.950000   6.22  10.72  16.44  1.02   9.23  48.37   \n",
       "\n",
       "       Benzene   Toluene    AQI    AQI_Bucket  \n",
       "7834  0.170000  2.595666  166.0      moderate  \n",
       "7835  0.130000  2.595666  166.0      moderate  \n",
       "7836  0.030000  2.595666  166.0      moderate  \n",
       "7837  1.709205  2.595666  166.0      moderate  \n",
       "7838  1.709205  2.595666  166.0      moderate  \n",
       "...        ...       ...    ...           ...  \n",
       "9838  0.100000  0.070000   95.0  satisfactory  \n",
       "9839  0.090000  0.000000   98.0  satisfactory  \n",
       "9840  0.090000  0.010000  104.0      moderate  \n",
       "9841  0.170000  0.160000  110.0      moderate  \n",
       "9842  0.090000  0.000000   92.0  satisfactory  \n",
       "\n",
       "[2009 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chennai=City.get_group(\"Chennai\")\n",
    "Chennai\n",
    "\n",
    "Chennai = Chennai.drop(columns=['PM10'])\n",
    "Chennai\n",
    "\n",
    "citynull = pd.isnull(Chennai[\"City\"])\n",
    "Chennai[citynull]\n",
    "\n",
    "datenull = pd.isnull(Chennai[\"Date\"])\n",
    "Chennai[datenull]\n",
    "\n",
    "for column in Chennai[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Chennai[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Chennai = Chennai.drop(columns = ['Xylene', 'NH3'])\n",
    "Chennai\n",
    "\n",
    "for column in Chennai[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Chennai[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Chennai[column].fillna(m, inplace = True)\n",
    "\n",
    "Chennai\n",
    "\n",
    "print(Chennai['AQI'].mean(skipna = True))\n",
    "Chennai['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Chennai\n",
    "\n",
    "Chennai['AQI_Bucket'].value_counts()\n",
    "\n",
    "Chennai['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Chennai\n",
    "\n",
    "Chennai['Date'] = pd.to_datetime(Chennai['Date'])\n",
    "\n",
    "print(max(Chennai['Date']),min(Chennai['Date']))\n",
    "\n",
    "temp=Chennai.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Chennai['City'] = Chennai['City'].str.lower()\n",
    "Chennai['AQI_Bucket'] = Chennai['AQI_Bucket'].str.lower()\n",
    "Chennai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.006788174999151479\n",
      "NO\n",
      "0.006788174999151479\n",
      "NO2\n",
      "0.006788174999151479\n",
      "NOx\n",
      "0.0\n",
      "NH3\n",
      "0.03054678749618165\n",
      "CO\n",
      "0.0\n",
      "SO2\n",
      "0.3733496249533313\n",
      "O3\n",
      "0.2851033499643621\n",
      "Benzene\n",
      "0.0\n",
      "Toluene\n",
      "0.0\n",
      "Xylene\n",
      "2.650782337168652\n",
      "PM2.5 117.19615346287999\n",
      "NO 38.98559541604395\n",
      "NO2 50.785181863477845\n",
      "NOx 58.56702339472371\n",
      "CO 1.9760527625684374\n",
      "SO2 15.901253291205885\n",
      "O3 51.32361038961032\n",
      "Benzene 3.544479840716772\n",
      "Toluene 17.185042309606786\n",
      "259.48774387193595\n",
      "2020-07-01 00:00:00 2015-01-01 00:00:00\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>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10229</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>313.22</td>\n",
       "      <td>69.16</td>\n",
       "      <td>36.39</td>\n",
       "      <td>110.59</td>\n",
       "      <td>15.20</td>\n",
       "      <td>9.25</td>\n",
       "      <td>41.68</td>\n",
       "      <td>14.36</td>\n",
       "      <td>24.86</td>\n",
       "      <td>472.0</td>\n",
       "      <td>severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10230</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>186.18</td>\n",
       "      <td>62.09</td>\n",
       "      <td>32.87</td>\n",
       "      <td>88.14</td>\n",
       "      <td>9.54</td>\n",
       "      <td>6.65</td>\n",
       "      <td>29.97</td>\n",
       "      <td>10.55</td>\n",
       "      <td>20.09</td>\n",
       "      <td>454.0</td>\n",
       "      <td>severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10231</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>87.18</td>\n",
       "      <td>25.73</td>\n",
       "      <td>30.31</td>\n",
       "      <td>47.95</td>\n",
       "      <td>10.61</td>\n",
       "      <td>2.65</td>\n",
       "      <td>19.71</td>\n",
       "      <td>3.91</td>\n",
       "      <td>10.23</td>\n",
       "      <td>143.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10232</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>151.84</td>\n",
       "      <td>25.01</td>\n",
       "      <td>36.91</td>\n",
       "      <td>48.62</td>\n",
       "      <td>11.54</td>\n",
       "      <td>4.63</td>\n",
       "      <td>25.36</td>\n",
       "      <td>4.26</td>\n",
       "      <td>9.71</td>\n",
       "      <td>319.0</td>\n",
       "      <td>very poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10233</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>146.60</td>\n",
       "      <td>14.01</td>\n",
       "      <td>34.92</td>\n",
       "      <td>38.25</td>\n",
       "      <td>9.20</td>\n",
       "      <td>3.33</td>\n",
       "      <td>23.20</td>\n",
       "      <td>2.80</td>\n",
       "      <td>6.21</td>\n",
       "      <td>325.0</td>\n",
       "      <td>very poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12233</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>39.80</td>\n",
       "      <td>10.88</td>\n",
       "      <td>21.46</td>\n",
       "      <td>22.47</td>\n",
       "      <td>0.87</td>\n",
       "      <td>10.38</td>\n",
       "      <td>18.88</td>\n",
       "      <td>1.69</td>\n",
       "      <td>19.99</td>\n",
       "      <td>112.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12234</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>59.52</td>\n",
       "      <td>12.67</td>\n",
       "      <td>21.60</td>\n",
       "      <td>23.86</td>\n",
       "      <td>0.94</td>\n",
       "      <td>10.70</td>\n",
       "      <td>18.05</td>\n",
       "      <td>1.71</td>\n",
       "      <td>25.13</td>\n",
       "      <td>196.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12235</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>44.86</td>\n",
       "      <td>10.50</td>\n",
       "      <td>21.57</td>\n",
       "      <td>21.94</td>\n",
       "      <td>0.88</td>\n",
       "      <td>11.58</td>\n",
       "      <td>26.61</td>\n",
       "      <td>2.13</td>\n",
       "      <td>23.80</td>\n",
       "      <td>233.0</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12236</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>39.80</td>\n",
       "      <td>5.99</td>\n",
       "      <td>17.96</td>\n",
       "      <td>15.44</td>\n",
       "      <td>0.84</td>\n",
       "      <td>10.51</td>\n",
       "      <td>37.29</td>\n",
       "      <td>1.57</td>\n",
       "      <td>16.37</td>\n",
       "      <td>114.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12237</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>54.01</td>\n",
       "      <td>6.33</td>\n",
       "      <td>21.05</td>\n",
       "      <td>16.81</td>\n",
       "      <td>0.97</td>\n",
       "      <td>11.15</td>\n",
       "      <td>29.73</td>\n",
       "      <td>2.03</td>\n",
       "      <td>23.57</td>\n",
       "      <td>101.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        City       Date   PM2.5     NO    NO2     NOx     CO    SO2     O3  \\\n",
       "10229  delhi 2015-01-01  313.22  69.16  36.39  110.59  15.20   9.25  41.68   \n",
       "10230  delhi 2015-01-02  186.18  62.09  32.87   88.14   9.54   6.65  29.97   \n",
       "10231  delhi 2015-01-03   87.18  25.73  30.31   47.95  10.61   2.65  19.71   \n",
       "10232  delhi 2015-01-04  151.84  25.01  36.91   48.62  11.54   4.63  25.36   \n",
       "10233  delhi 2015-01-05  146.60  14.01  34.92   38.25   9.20   3.33  23.20   \n",
       "...      ...        ...     ...    ...    ...     ...    ...    ...    ...   \n",
       "12233  delhi 2020-06-27   39.80  10.88  21.46   22.47   0.87  10.38  18.88   \n",
       "12234  delhi 2020-06-28   59.52  12.67  21.60   23.86   0.94  10.70  18.05   \n",
       "12235  delhi 2020-06-29   44.86  10.50  21.57   21.94   0.88  11.58  26.61   \n",
       "12236  delhi 2020-06-30   39.80   5.99  17.96   15.44   0.84  10.51  37.29   \n",
       "12237  delhi 2020-07-01   54.01   6.33  21.05   16.81   0.97  11.15  29.73   \n",
       "\n",
       "       Benzene  Toluene    AQI AQI_Bucket  \n",
       "10229    14.36    24.86  472.0     severe  \n",
       "10230    10.55    20.09  454.0     severe  \n",
       "10231     3.91    10.23  143.0   moderate  \n",
       "10232     4.26     9.71  319.0  very poor  \n",
       "10233     2.80     6.21  325.0  very poor  \n",
       "...        ...      ...    ...        ...  \n",
       "12233     1.69    19.99  112.0   moderate  \n",
       "12234     1.71    25.13  196.0   moderate  \n",
       "12235     2.13    23.80  233.0       poor  \n",
       "12236     1.57    16.37  114.0   moderate  \n",
       "12237     2.03    23.57  101.0   moderate  \n",
       "\n",
       "[2009 rows x 13 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delhi=City.get_group(\"Delhi\")\n",
    "Delhi\n",
    "\n",
    "Delhi = Delhi.drop(columns=['PM10'])\n",
    "Delhi\n",
    "\n",
    "citynull = pd.isnull(Delhi[\"City\"])\n",
    "Delhi[citynull]\n",
    "\n",
    "datenull = pd.isnull(Delhi[\"Date\"])\n",
    "Delhi[datenull]\n",
    "\n",
    "for column in Delhi[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Delhi[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Delhi = Delhi.drop(columns = ['Xylene', 'NH3'])\n",
    "Delhi\n",
    "\n",
    "for column in Delhi[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Delhi[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Delhi[column].fillna(m, inplace = True)\n",
    "\n",
    "Delhi\n",
    "\n",
    "print(Delhi['AQI'].mean(skipna = True))\n",
    "Delhi['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Delhi\n",
    "\n",
    "Delhi['AQI_Bucket'].value_counts()\n",
    "\n",
    "Delhi['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Delhi\n",
    "\n",
    "Delhi['Date'] = pd.to_datetime(Delhi['Date'])\n",
    "\n",
    "print(max(Delhi['Date']),min(Delhi['Date']))\n",
    "\n",
    "temp=Delhi.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Delhi['City'] = Delhi['City'].str.lower()\n",
    "Delhi['AQI_Bucket'] = Delhi['AQI_Bucket'].str.lower()\n",
    "Delhi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.5226894749346638\n",
      "NO\n",
      "0.468384074941452\n",
      "NO2\n",
      "0.4615958999423005\n",
      "NOx\n",
      "1.534127549808234\n",
      "NH3\n",
      "5.294776499338153\n",
      "CO\n",
      "0.3699555374537556\n",
      "SO2\n",
      "0.5939653124257543\n",
      "O3\n",
      "0.3937141499507858\n",
      "Benzene\n",
      "1.5001866748124766\n",
      "Toluene\n",
      "2.070393374741201\n",
      "Xylene\n",
      "5.294776499338153\n",
      "PM2.5 117.10400655737696\n",
      "NO 17.534282933160284\n",
      "NO2 23.378165910563816\n",
      "NOx 29.3773105134474\n",
      "CO 1.2575159235668785\n",
      "SO2 9.41970079787235\n",
      "O3 34.40937300063977\n",
      "Benzene 1.7241713823767162\n",
      "Toluene 5.7807109448082326\n",
      "225.12388162422573\n",
      "2020-07-01 00:00:00 2015-11-27 00:00:00\n"
     ]
    },
    {
     "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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12400</th>\n",
       "      <td>gurugram</td>\n",
       "      <td>2015-11-27</td>\n",
       "      <td>117.104007</td>\n",
       "      <td>5.28</td>\n",
       "      <td>8.37</td>\n",
       "      <td>9.57</td>\n",
       "      <td>2.88</td>\n",
       "      <td>15.92</td>\n",
       "      <td>5.96</td>\n",
       "      <td>1.724171</td>\n",
       "      <td>5.780711</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12401</th>\n",
       "      <td>gurugram</td>\n",
       "      <td>2015-11-28</td>\n",
       "      <td>117.104007</td>\n",
       "      <td>4.71</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.74</td>\n",
       "      <td>0.47</td>\n",
       "      <td>5.78</td>\n",
       "      <td>8.13</td>\n",
       "      <td>1.724171</td>\n",
       "      <td>5.780711</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12402</th>\n",
       "      <td>gurugram</td>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>117.104007</td>\n",
       "      <td>3.10</td>\n",
       "      <td>8.04</td>\n",
       "      <td>8.69</td>\n",
       "      <td>1.67</td>\n",
       "      <td>5.26</td>\n",
       "      <td>16.52</td>\n",
       "      <td>1.724171</td>\n",
       "      <td>5.780711</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12403</th>\n",
       "      <td>gurugram</td>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>117.104007</td>\n",
       "      <td>3.90</td>\n",
       "      <td>8.21</td>\n",
       "      <td>9.54</td>\n",
       "      <td>1.45</td>\n",
       "      <td>7.51</td>\n",
       "      <td>6.29</td>\n",
       "      <td>1.724171</td>\n",
       "      <td>5.780711</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12404</th>\n",
       "      <td>gurugram</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>117.104007</td>\n",
       "      <td>3.36</td>\n",
       "      <td>8.13</td>\n",
       "      <td>8.86</td>\n",
       "      <td>1.45</td>\n",
       "      <td>3.72</td>\n",
       "      <td>10.50</td>\n",
       "      <td>1.724171</td>\n",
       "      <td>5.780711</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14074</th>\n",
       "      <td>gurugram</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>47.820000</td>\n",
       "      <td>3.12</td>\n",
       "      <td>8.05</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9.18</td>\n",
       "      <td>19.54</td>\n",
       "      <td>2.160000</td>\n",
       "      <td>4.780000</td>\n",
       "      <td>121.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14075</th>\n",
       "      <td>gurugram</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>64.720000</td>\n",
       "      <td>5.62</td>\n",
       "      <td>9.65</td>\n",
       "      <td>8.97</td>\n",
       "      <td>0.67</td>\n",
       "      <td>7.82</td>\n",
       "      <td>17.28</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>3.590000</td>\n",
       "      <td>153.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14076</th>\n",
       "      <td>gurugram</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>46.890000</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.97</td>\n",
       "      <td>9.65</td>\n",
       "      <td>0.57</td>\n",
       "      <td>7.86</td>\n",
       "      <td>36.50</td>\n",
       "      <td>2.340000</td>\n",
       "      <td>3.840000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14077</th>\n",
       "      <td>gurugram</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>47.270000</td>\n",
       "      <td>6.29</td>\n",
       "      <td>10.38</td>\n",
       "      <td>12.08</td>\n",
       "      <td>0.78</td>\n",
       "      <td>7.36</td>\n",
       "      <td>58.07</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>131.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14078</th>\n",
       "      <td>gurugram</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>61.640000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>10.58</td>\n",
       "      <td>12.37</td>\n",
       "      <td>1.11</td>\n",
       "      <td>7.57</td>\n",
       "      <td>44.14</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>4.460000</td>\n",
       "      <td>157.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1679 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City       Date       PM2.5    NO    NO2    NOx    CO    SO2  \\\n",
       "12400  gurugram 2015-11-27  117.104007  5.28   8.37   9.57  2.88  15.92   \n",
       "12401  gurugram 2015-11-28  117.104007  4.71   8.00   9.74  0.47   5.78   \n",
       "12402  gurugram 2015-11-29  117.104007  3.10   8.04   8.69  1.67   5.26   \n",
       "12403  gurugram 2015-11-30  117.104007  3.90   8.21   9.54  1.45   7.51   \n",
       "12404  gurugram 2015-12-01  117.104007  3.36   8.13   8.86  1.45   3.72   \n",
       "...         ...        ...         ...   ...    ...    ...   ...    ...   \n",
       "14074  gurugram 2020-06-27   47.820000  3.12   8.05   7.24  0.66   9.18   \n",
       "14075  gurugram 2020-06-28   64.720000  5.62   9.65   8.97  0.67   7.82   \n",
       "14076  gurugram 2020-06-29   46.890000  4.00   8.97   9.65  0.57   7.86   \n",
       "14077  gurugram 2020-06-30   47.270000  6.29  10.38  12.08  0.78   7.36   \n",
       "14078  gurugram 2020-07-01   61.640000  5.99  10.58  12.37  1.11   7.57   \n",
       "\n",
       "          O3   Benzene   Toluene    AQI AQI_Bucket  \n",
       "12400   5.96  1.724171  5.780711  166.0   moderate  \n",
       "12401   8.13  1.724171  5.780711  166.0   moderate  \n",
       "12402  16.52  1.724171  5.780711  166.0   moderate  \n",
       "12403   6.29  1.724171  5.780711  166.0   moderate  \n",
       "12404  10.50  1.724171  5.780711  166.0   moderate  \n",
       "...      ...       ...       ...    ...        ...  \n",
       "14074  19.54  2.160000  4.780000  121.0   moderate  \n",
       "14075  17.28  2.650000  3.590000  153.0   moderate  \n",
       "14076  36.50  2.340000  3.840000  140.0   moderate  \n",
       "14077  58.07  2.400000  4.300000  131.0   moderate  \n",
       "14078  44.14  2.670000  4.460000  157.0   moderate  \n",
       "\n",
       "[1679 rows x 13 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gurugram=City.get_group(\"Gurugram\")\n",
    "Gurugram\n",
    "\n",
    "Gurugram = Gurugram.drop(columns=['PM10'])\n",
    "Gurugram\n",
    "\n",
    "citynull = pd.isnull(Gurugram[\"City\"])\n",
    "Gurugram[citynull]\n",
    "\n",
    "datenull = pd.isnull(Gurugram[\"Date\"])\n",
    "Gurugram[datenull]\n",
    "\n",
    "for column in Gurugram[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Gurugram[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Gurugram = Gurugram.drop(columns = ['Xylene', 'NH3'])\n",
    "Gurugram\n",
    "\n",
    "for column in Gurugram[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Gurugram[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Gurugram[column].fillna(m, inplace = True)\n",
    "\n",
    "Gurugram\n",
    "\n",
    "print(Gurugram['AQI'].mean(skipna = True))\n",
    "Gurugram['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Gurugram\n",
    "\n",
    "Gurugram['AQI_Bucket'].value_counts()\n",
    "\n",
    "Gurugram['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Gurugram\n",
    "\n",
    "Gurugram['Date'] = pd.to_datetime(Gurugram['Date'])\n",
    "\n",
    "print(max(Gurugram['Date']),min(Gurugram['Date']))\n",
    "\n",
    "temp=Gurugram.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Gurugram['City'] = Gurugram['City'].str.lower()\n",
    "Gurugram['AQI_Bucket'] = Gurugram['AQI_Bucket'].str.lower()\n",
    "Gurugram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.39032006245121\n",
      "NO\n",
      "0.09503444998812069\n",
      "NO2\n",
      "0.09503444998812069\n",
      "NOx\n",
      "0.023758612497030172\n",
      "NH3\n",
      "1.2218714998472662\n",
      "CO\n",
      "0.016970437497878695\n",
      "SO2\n",
      "0.08824627498896923\n",
      "O3\n",
      "0.08824627498896923\n",
      "Benzene\n",
      "0.4310491124461188\n",
      "Toluene\n",
      "0.4310491124461188\n",
      "Xylene\n",
      "0.4310491124461188\n",
      "PM2.5 47.03535695399267\n",
      "NO 7.817194135490368\n",
      "NO2 28.38675429726997\n",
      "NOx 19.438754377188616\n",
      "CO 0.5907796101949023\n",
      "SO2 9.173712121212121\n",
      "O3 33.611005050505085\n",
      "Benzene 1.1733528472591814\n",
      "Toluene 7.874550292708891\n",
      "109.20744680851064\n",
      "2020-07-01 00:00:00 2015-01-04 00:00:00\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>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14581</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>47.035357</td>\n",
       "      <td>3.70</td>\n",
       "      <td>17.19</td>\n",
       "      <td>20.89</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.58</td>\n",
       "      <td>30.34</td>\n",
       "      <td>0.41</td>\n",
       "      <td>6.15</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14582</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>47.035357</td>\n",
       "      <td>3.61</td>\n",
       "      <td>9.69</td>\n",
       "      <td>13.30</td>\n",
       "      <td>0.23</td>\n",
       "      <td>5.19</td>\n",
       "      <td>31.28</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.22</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14583</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>47.035357</td>\n",
       "      <td>4.21</td>\n",
       "      <td>22.02</td>\n",
       "      <td>26.23</td>\n",
       "      <td>0.31</td>\n",
       "      <td>9.09</td>\n",
       "      <td>28.56</td>\n",
       "      <td>0.52</td>\n",
       "      <td>5.14</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14584</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>47.035357</td>\n",
       "      <td>26.08</td>\n",
       "      <td>40.08</td>\n",
       "      <td>66.15</td>\n",
       "      <td>0.53</td>\n",
       "      <td>9.03</td>\n",
       "      <td>31.08</td>\n",
       "      <td>1.92</td>\n",
       "      <td>16.35</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14585</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>47.035357</td>\n",
       "      <td>10.31</td>\n",
       "      <td>33.02</td>\n",
       "      <td>43.33</td>\n",
       "      <td>0.44</td>\n",
       "      <td>8.47</td>\n",
       "      <td>35.72</td>\n",
       "      <td>1.89</td>\n",
       "      <td>14.27</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16582</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>23.290000</td>\n",
       "      <td>6.38</td>\n",
       "      <td>22.64</td>\n",
       "      <td>15.39</td>\n",
       "      <td>0.36</td>\n",
       "      <td>5.39</td>\n",
       "      <td>24.22</td>\n",
       "      <td>0.88</td>\n",
       "      <td>6.72</td>\n",
       "      <td>58.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16583</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>15.800000</td>\n",
       "      <td>5.79</td>\n",
       "      <td>22.73</td>\n",
       "      <td>14.89</td>\n",
       "      <td>0.36</td>\n",
       "      <td>6.36</td>\n",
       "      <td>22.25</td>\n",
       "      <td>0.82</td>\n",
       "      <td>6.48</td>\n",
       "      <td>49.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16584</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>20.240000</td>\n",
       "      <td>3.96</td>\n",
       "      <td>27.59</td>\n",
       "      <td>17.09</td>\n",
       "      <td>0.38</td>\n",
       "      <td>6.29</td>\n",
       "      <td>19.98</td>\n",
       "      <td>1.31</td>\n",
       "      <td>10.83</td>\n",
       "      <td>47.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16585</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>19.380000</td>\n",
       "      <td>2.63</td>\n",
       "      <td>24.58</td>\n",
       "      <td>14.59</td>\n",
       "      <td>0.39</td>\n",
       "      <td>5.30</td>\n",
       "      <td>17.40</td>\n",
       "      <td>1.45</td>\n",
       "      <td>13.07</td>\n",
       "      <td>46.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16586</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>26.830000</td>\n",
       "      <td>2.94</td>\n",
       "      <td>27.18</td>\n",
       "      <td>16.13</td>\n",
       "      <td>0.43</td>\n",
       "      <td>6.07</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.31</td>\n",
       "      <td>16.51</td>\n",
       "      <td>48.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2006 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            City       Date      PM2.5     NO    NO2    NOx    CO   SO2  \\\n",
       "14581  hyderabad 2015-01-04  47.035357   3.70  17.19  20.89  0.30  2.58   \n",
       "14582  hyderabad 2015-01-05  47.035357   3.61   9.69  13.30  0.23  5.19   \n",
       "14583  hyderabad 2015-01-06  47.035357   4.21  22.02  26.23  0.31  9.09   \n",
       "14584  hyderabad 2015-01-07  47.035357  26.08  40.08  66.15  0.53  9.03   \n",
       "14585  hyderabad 2015-01-08  47.035357  10.31  33.02  43.33  0.44  8.47   \n",
       "...          ...        ...        ...    ...    ...    ...   ...   ...   \n",
       "16582  hyderabad 2020-06-27  23.290000   6.38  22.64  15.39  0.36  5.39   \n",
       "16583  hyderabad 2020-06-28  15.800000   5.79  22.73  14.89  0.36  6.36   \n",
       "16584  hyderabad 2020-06-29  20.240000   3.96  27.59  17.09  0.38  6.29   \n",
       "16585  hyderabad 2020-06-30  19.380000   2.63  24.58  14.59  0.39  5.30   \n",
       "16586  hyderabad 2020-07-01  26.830000   2.94  27.18  16.13  0.43  6.07   \n",
       "\n",
       "          O3  Benzene  Toluene    AQI    AQI_Bucket  \n",
       "14581  30.34     0.41     6.15  166.0      moderate  \n",
       "14582  31.28     0.39     7.22  166.0      moderate  \n",
       "14583  28.56     0.52     5.14  166.0      moderate  \n",
       "14584  31.08     1.92    16.35  166.0      moderate  \n",
       "14585  35.72     1.89    14.27  166.0      moderate  \n",
       "...      ...      ...      ...    ...           ...  \n",
       "16582  24.22     0.88     6.72   58.0  satisfactory  \n",
       "16583  22.25     0.82     6.48   49.0          good  \n",
       "16584  19.98     1.31    10.83   47.0          good  \n",
       "16585  17.40     1.45    13.07   46.0          good  \n",
       "16586  13.20     1.31    16.51   48.0          good  \n",
       "\n",
       "[2006 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hyderabad=City.get_group(\"Hyderabad\")\n",
    "Hyderabad\n",
    "\n",
    "Hyderabad = Hyderabad.drop(columns=['PM10'])\n",
    "Hyderabad\n",
    "\n",
    "citynull = pd.isnull(Hyderabad[\"City\"])\n",
    "Hyderabad[citynull]\n",
    "\n",
    "datenull = pd.isnull(Hyderabad[\"Date\"])\n",
    "Hyderabad[datenull]\n",
    "\n",
    "for column in Hyderabad[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Hyderabad[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Hyderabad = Hyderabad.drop(columns = ['Xylene', 'NH3'])\n",
    "Hyderabad\n",
    "\n",
    "for column in Hyderabad[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Hyderabad[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Hyderabad[column].fillna(m, inplace = True)\n",
    "\n",
    "Hyderabad\n",
    "\n",
    "print(Hyderabad['AQI'].mean(skipna = True))\n",
    "Hyderabad['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Hyderabad\n",
    "\n",
    "Hyderabad['AQI_Bucket'].value_counts()\n",
    "\n",
    "Hyderabad['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Hyderabad\n",
    "\n",
    "Hyderabad['Date'] = pd.to_datetime(Hyderabad['Date'])\n",
    "\n",
    "print(max(Hyderabad['Date']),min(Hyderabad['Date']))\n",
    "\n",
    "temp=Hyderabad.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Hyderabad['City'] = Hyderabad['City'].str.lower()\n",
    "Hyderabad['AQI_Bucket'] = Hyderabad['AQI_Bucket'].str.lower()\n",
    "Hyderabad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.04072904999490887\n",
      "NO\n",
      "0.05430539999321183\n",
      "NO2\n",
      "0.047517224994060345\n",
      "NOx\n",
      "0.38013779995248276\n",
      "NH3\n",
      "0.05091131249363609\n",
      "CO\n",
      "0.023758612497030172\n",
      "SO2\n",
      "0.04072904999490887\n",
      "O3\n",
      "0.05091131249363609\n",
      "Benzene\n",
      "0.07466992499066626\n",
      "Toluene\n",
      "0.078064012490242\n",
      "Xylene\n",
      "3.7810134745273736\n",
      "PM2.5 54.5007078039928\n",
      "NO 14.632987249544634\n",
      "NO2 32.418627272727264\n",
      "NOx 39.63717564870251\n",
      "CO 0.800894308943089\n",
      "SO2 11.0710617059891\n",
      "O3 46.71546860782531\n",
      "Benzene 1.5711721611721605\n",
      "Toluene 6.685572868927594\n",
      "133.67915904936015\n",
      "2020-07-01 00:00:00 2017-06-14 00:00:00\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16587</th>\n",
       "      <td>jaipur</td>\n",
       "      <td>2017-06-14</td>\n",
       "      <td>54.500708</td>\n",
       "      <td>14.632987</td>\n",
       "      <td>32.418627</td>\n",
       "      <td>39.637176</td>\n",
       "      <td>0.800894</td>\n",
       "      <td>11.071062</td>\n",
       "      <td>46.715469</td>\n",
       "      <td>1.571172</td>\n",
       "      <td>6.685573</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16588</th>\n",
       "      <td>jaipur</td>\n",
       "      <td>2017-06-15</td>\n",
       "      <td>54.500708</td>\n",
       "      <td>14.632987</td>\n",
       "      <td>32.418627</td>\n",
       "      <td>39.637176</td>\n",
       "      <td>0.800894</td>\n",
       "      <td>11.071062</td>\n",
       "      <td>46.715469</td>\n",
       "      <td>1.571172</td>\n",
       "      <td>6.685573</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16589</th>\n",
       "      <td>jaipur</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>54.500708</td>\n",
       "      <td>14.632987</td>\n",
       "      <td>32.418627</td>\n",
       "      <td>39.637176</td>\n",
       "      <td>0.800894</td>\n",
       "      <td>11.071062</td>\n",
       "      <td>46.715469</td>\n",
       "      <td>1.571172</td>\n",
       "      <td>6.685573</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16590</th>\n",
       "      <td>jaipur</td>\n",
       "      <td>2017-06-17</td>\n",
       "      <td>54.500708</td>\n",
       "      <td>14.632987</td>\n",
       "      <td>32.418627</td>\n",
       "      <td>39.637176</td>\n",
       "      <td>0.800894</td>\n",
       "      <td>11.071062</td>\n",
       "      <td>46.715469</td>\n",
       "      <td>1.571172</td>\n",
       "      <td>6.685573</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16591</th>\n",
       "      <td>jaipur</td>\n",
       "      <td>2017-06-18</td>\n",
       "      <td>54.500708</td>\n",
       "      <td>14.632987</td>\n",
       "      <td>32.418627</td>\n",
       "      <td>39.637176</td>\n",
       "      <td>0.800894</td>\n",
       "      <td>11.071062</td>\n",
       "      <td>46.715469</td>\n",
       "      <td>1.571172</td>\n",
       "      <td>6.685573</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17696</th>\n",
       "      <td>jaipur</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>28.330000</td>\n",
       "      <td>6.150000</td>\n",
       "      <td>14.720000</td>\n",
       "      <td>18.140000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>9.960000</td>\n",
       "      <td>39.120000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>89.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17697</th>\n",
       "      <td>jaipur</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>23.760000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>14.050000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.930000</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17698</th>\n",
       "      <td>jaipur</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>30.580000</td>\n",
       "      <td>6.210000</td>\n",
       "      <td>16.630000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>10.590000</td>\n",
       "      <td>39.210000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17699</th>\n",
       "      <td>jaipur</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>30.030000</td>\n",
       "      <td>5.480000</td>\n",
       "      <td>13.770000</td>\n",
       "      <td>16.940000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>10.360000</td>\n",
       "      <td>50.710000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>1.480000</td>\n",
       "      <td>79.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17700</th>\n",
       "      <td>jaipur</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>58.790000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>10.880000</td>\n",
       "      <td>14.160000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>53.680000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1114 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         City       Date      PM2.5         NO        NO2        NOx  \\\n",
       "16587  jaipur 2017-06-14  54.500708  14.632987  32.418627  39.637176   \n",
       "16588  jaipur 2017-06-15  54.500708  14.632987  32.418627  39.637176   \n",
       "16589  jaipur 2017-06-16  54.500708  14.632987  32.418627  39.637176   \n",
       "16590  jaipur 2017-06-17  54.500708  14.632987  32.418627  39.637176   \n",
       "16591  jaipur 2017-06-18  54.500708  14.632987  32.418627  39.637176   \n",
       "...       ...        ...        ...        ...        ...        ...   \n",
       "17696  jaipur 2020-06-27  28.330000   6.150000  14.720000  18.140000   \n",
       "17697  jaipur 2020-06-28  23.760000   5.600000  14.050000  17.200000   \n",
       "17698  jaipur 2020-06-29  30.580000   6.210000  16.630000  19.500000   \n",
       "17699  jaipur 2020-06-30  30.030000   5.480000  13.770000  16.940000   \n",
       "17700  jaipur 2020-07-01  58.790000   5.190000  10.880000  14.160000   \n",
       "\n",
       "             CO        SO2         O3   Benzene   Toluene    AQI    AQI_Bucket  \n",
       "16587  0.800894  11.071062  46.715469  1.571172  6.685573  166.0      moderate  \n",
       "16588  0.800894  11.071062  46.715469  1.571172  6.685573  166.0      moderate  \n",
       "16589  0.800894  11.071062  46.715469  1.571172  6.685573  166.0      moderate  \n",
       "16590  0.800894  11.071062  46.715469  1.571172  6.685573  166.0      moderate  \n",
       "16591  0.800894  11.071062  46.715469  1.571172  6.685573  166.0      moderate  \n",
       "...         ...        ...        ...       ...       ...    ...           ...  \n",
       "17696  0.590000   9.960000  39.120000  0.230000  0.590000   89.0  satisfactory  \n",
       "17697  0.500000   8.930000  34.400000  0.160000  0.390000   75.0  satisfactory  \n",
       "17698  0.550000  10.590000  39.210000  0.220000  0.470000   69.0  satisfactory  \n",
       "17699  0.670000  10.360000  50.710000  0.480000  1.480000   79.0  satisfactory  \n",
       "17700  0.540000   5.190000  53.680000  0.220000  0.460000   70.0  satisfactory  \n",
       "\n",
       "[1114 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jaipur=City.get_group(\"Jaipur\")\n",
    "Jaipur\n",
    "\n",
    "Jaipur = Jaipur.drop(columns=['PM10'])\n",
    "Jaipur\n",
    "\n",
    "citynull = pd.isnull(Jaipur[\"City\"])\n",
    "Jaipur[citynull]\n",
    "\n",
    "datenull = pd.isnull(Jaipur[\"Date\"])\n",
    "Jaipur[datenull]\n",
    "\n",
    "for column in Jaipur[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Jaipur[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Jaipur = Jaipur.drop(columns = ['Xylene', 'NH3'])\n",
    "Jaipur\n",
    "\n",
    "for column in Jaipur[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Jaipur[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Jaipur[column].fillna(m, inplace = True)\n",
    "\n",
    "Jaipur\n",
    "\n",
    "print(Jaipur['AQI'].mean(skipna = True))\n",
    "Jaipur['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Jaipur\n",
    "\n",
    "Jaipur['AQI_Bucket'].value_counts()\n",
    "\n",
    "Jaipur['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Jaipur\n",
    "\n",
    "Jaipur['Date'] = pd.to_datetime(Jaipur['Date'])\n",
    "\n",
    "print(max(Jaipur['Date']),min(Jaipur['Date']))\n",
    "\n",
    "temp=Jaipur.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Jaipur['City'] = Jaipur['City'].str.lower()\n",
    "Jaipur['AQI_Bucket'] = Jaipur['AQI_Bucket'].str.lower()\n",
    "Jaipur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "2.681329124664834\n",
      "NO\n",
      "0.9164036248854495\n",
      "NO2\n",
      "0.9164036248854495\n",
      "NOx\n",
      "3.967688287004039\n",
      "NH3\n",
      "1.249024199843872\n",
      "CO\n",
      "1.741166887282354\n",
      "SO2\n",
      "1.1913247123510844\n",
      "O3\n",
      "0.9978617248752674\n",
      "Benzene\n",
      "3.967688287004039\n",
      "Toluene\n",
      "3.967688287004039\n",
      "Xylene\n",
      "3.967688287004039\n",
      "PM2.5 64.22891820580477\n",
      "NO 11.015183537263617\n",
      "NO2 9.342947719688539\n",
      "NOx nan\n",
      "CO 0.6630487804878046\n",
      "SO2 33.64518337408316\n",
      "O3 32.42305142857144\n",
      "Benzene nan\n",
      "Toluene nan\n",
      "159.25162127107652\n",
      "2020-07-01 00:00:00 2017-04-20 00:00:00\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17701</th>\n",
       "      <td>jorapokhar</td>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>64.228918</td>\n",
       "      <td>7.75</td>\n",
       "      <td>9.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.32</td>\n",
       "      <td>28.43</td>\n",
       "      <td>18.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17702</th>\n",
       "      <td>jorapokhar</td>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>64.228918</td>\n",
       "      <td>8.00</td>\n",
       "      <td>10.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27</td>\n",
       "      <td>29.35</td>\n",
       "      <td>15.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17703</th>\n",
       "      <td>jorapokhar</td>\n",
       "      <td>2017-04-22</td>\n",
       "      <td>64.228918</td>\n",
       "      <td>7.92</td>\n",
       "      <td>9.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.29</td>\n",
       "      <td>33.34</td>\n",
       "      <td>17.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17704</th>\n",
       "      <td>jorapokhar</td>\n",
       "      <td>2017-04-23</td>\n",
       "      <td>64.228918</td>\n",
       "      <td>7.74</td>\n",
       "      <td>9.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.31</td>\n",
       "      <td>34.10</td>\n",
       "      <td>21.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17705</th>\n",
       "      <td>jorapokhar</td>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>64.228918</td>\n",
       "      <td>7.49</td>\n",
       "      <td>10.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33</td>\n",
       "      <td>38.16</td>\n",
       "      <td>17.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18865</th>\n",
       "      <td>jorapokhar</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>64.228918</td>\n",
       "      <td>23.88</td>\n",
       "      <td>11.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.73</td>\n",
       "      <td>25.84</td>\n",
       "      <td>87.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18866</th>\n",
       "      <td>jorapokhar</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>64.228918</td>\n",
       "      <td>25.44</td>\n",
       "      <td>10.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.87</td>\n",
       "      <td>23.11</td>\n",
       "      <td>69.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18867</th>\n",
       "      <td>jorapokhar</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>64.228918</td>\n",
       "      <td>24.73</td>\n",
       "      <td>11.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.65</td>\n",
       "      <td>24.05</td>\n",
       "      <td>57.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18868</th>\n",
       "      <td>jorapokhar</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>64.228918</td>\n",
       "      <td>25.10</td>\n",
       "      <td>10.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.68</td>\n",
       "      <td>28.08</td>\n",
       "      <td>30.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18869</th>\n",
       "      <td>jorapokhar</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>64.228918</td>\n",
       "      <td>20.90</td>\n",
       "      <td>14.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.67</td>\n",
       "      <td>10.57</td>\n",
       "      <td>18.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1169 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             City       Date      PM2.5     NO    NO2  NOx    CO    SO2  \\\n",
       "17701  jorapokhar 2017-04-20  64.228918   7.75   9.26  NaN  0.32  28.43   \n",
       "17702  jorapokhar 2017-04-21  64.228918   8.00  10.20  NaN  0.27  29.35   \n",
       "17703  jorapokhar 2017-04-22  64.228918   7.92   9.45  NaN  0.29  33.34   \n",
       "17704  jorapokhar 2017-04-23  64.228918   7.74   9.39  NaN  0.31  34.10   \n",
       "17705  jorapokhar 2017-04-24  64.228918   7.49  10.72  NaN  0.33  38.16   \n",
       "...           ...        ...        ...    ...    ...  ...   ...    ...   \n",
       "18865  jorapokhar 2020-06-27  64.228918  23.88  11.32  NaN  1.73  25.84   \n",
       "18866  jorapokhar 2020-06-28  64.228918  25.44  10.63  NaN  1.87  23.11   \n",
       "18867  jorapokhar 2020-06-29  64.228918  24.73  11.96  NaN  1.65  24.05   \n",
       "18868  jorapokhar 2020-06-30  64.228918  25.10  10.80  NaN  1.68  28.08   \n",
       "18869  jorapokhar 2020-07-01  64.228918  20.90  14.11  NaN  1.67  10.57   \n",
       "\n",
       "          O3  Benzene  Toluene    AQI    AQI_Bucket  \n",
       "17701  18.88      NaN      NaN  166.0      moderate  \n",
       "17702  15.85      NaN      NaN  148.0      moderate  \n",
       "17703  17.76      NaN      NaN  135.0      moderate  \n",
       "17704  21.71      NaN      NaN  107.0      moderate  \n",
       "17705  17.94      NaN      NaN  124.0      moderate  \n",
       "...      ...      ...      ...    ...           ...  \n",
       "18865  87.92      NaN      NaN  203.0          poor  \n",
       "18866  69.95      NaN      NaN  156.0      moderate  \n",
       "18867  57.76      NaN      NaN  135.0      moderate  \n",
       "18868  30.12      NaN      NaN  107.0      moderate  \n",
       "18869  18.23      NaN      NaN   86.0  satisfactory  \n",
       "\n",
       "[1169 rows x 13 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jorapokhar=City.get_group(\"Jorapokhar\")\n",
    "Jorapokhar\n",
    "\n",
    "Jorapokhar = Jorapokhar.drop(columns=['PM10'])\n",
    "Jorapokhar\n",
    "\n",
    "citynull = pd.isnull(Jorapokhar[\"City\"])\n",
    "Jorapokhar[citynull]\n",
    "\n",
    "datenull = pd.isnull(Jorapokhar[\"Date\"])\n",
    "Jorapokhar[datenull]\n",
    "\n",
    "for column in Jorapokhar[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Jorapokhar[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Jorapokhar = Jorapokhar.drop(columns = ['Xylene', 'NH3'])\n",
    "Jorapokhar\n",
    "\n",
    "for column in Jorapokhar[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Jorapokhar[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Jorapokhar[column].fillna(m, inplace = True)\n",
    "\n",
    "Jorapokhar\n",
    "\n",
    "print(Jorapokhar['AQI'].mean(skipna = True))\n",
    "Jorapokhar['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Jorapokhar\n",
    "\n",
    "Jorapokhar['AQI_Bucket'].value_counts()\n",
    "\n",
    "Jorapokhar['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Jorapokhar\n",
    "\n",
    "Jorapokhar['Date'] = pd.to_datetime(Jorapokhar['Date'])\n",
    "\n",
    "print(max(Jorapokhar['Date']),min(Jorapokhar['Date']))\n",
    "\n",
    "temp=Jorapokhar.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Jorapokhar['City'] = Jorapokhar['City'].str.lower()\n",
    "Jorapokhar['AQI_Bucket'] = Jorapokhar['AQI_Bucket'].str.lower()\n",
    "Jorapokhar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.3461969249567254\n",
      "NO\n",
      "0.08145809998981773\n",
      "NO2\n",
      "0.08145809998981773\n",
      "NOx\n",
      "1.0996843498625395\n",
      "NH3\n",
      "3.407663849574042\n",
      "CO\n",
      "0.09503444998812069\n",
      "SO2\n",
      "0.21722159997284732\n",
      "O3\n",
      "0.09164036248854496\n",
      "Benzene\n",
      "0.33601466245799816\n",
      "Toluene\n",
      "0.4615958999423005\n",
      "Xylene\n",
      "6.818721786647659\n",
      "PM2.5 109.71405873099121\n",
      "NO 15.233329974811062\n",
      "NO2 33.24440302266999\n",
      "NOx 22.525299703264093\n",
      "CO 2.130328117112569\n",
      "SO2 9.949676092544987\n",
      "O3 36.91749747729571\n",
      "Benzene 2.8874188481675382\n",
      "Toluene 4.950368392952479\n",
      "217.973058637084\n",
      "2020-07-01 00:00:00 2015-01-01 00:00:00\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "    }\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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19846</th>\n",
       "      <td>lucknow</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>109.714059</td>\n",
       "      <td>2.11</td>\n",
       "      <td>13.46</td>\n",
       "      <td>4.57</td>\n",
       "      <td>12.15</td>\n",
       "      <td>169.57</td>\n",
       "      <td>25.92</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.93</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19847</th>\n",
       "      <td>lucknow</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>109.714059</td>\n",
       "      <td>2.37</td>\n",
       "      <td>10.76</td>\n",
       "      <td>3.80</td>\n",
       "      <td>14.00</td>\n",
       "      <td>169.46</td>\n",
       "      <td>21.66</td>\n",
       "      <td>1.97</td>\n",
       "      <td>5.74</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19848</th>\n",
       "      <td>lucknow</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>109.714059</td>\n",
       "      <td>2.27</td>\n",
       "      <td>12.43</td>\n",
       "      <td>4.22</td>\n",
       "      <td>12.78</td>\n",
       "      <td>169.77</td>\n",
       "      <td>22.40</td>\n",
       "      <td>1.83</td>\n",
       "      <td>4.76</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19849</th>\n",
       "      <td>lucknow</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>109.714059</td>\n",
       "      <td>2.24</td>\n",
       "      <td>12.46</td>\n",
       "      <td>4.22</td>\n",
       "      <td>11.85</td>\n",
       "      <td>169.56</td>\n",
       "      <td>21.41</td>\n",
       "      <td>2.27</td>\n",
       "      <td>4.77</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19850</th>\n",
       "      <td>lucknow</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>109.714059</td>\n",
       "      <td>2.32</td>\n",
       "      <td>12.27</td>\n",
       "      <td>4.22</td>\n",
       "      <td>13.34</td>\n",
       "      <td>169.48</td>\n",
       "      <td>20.60</td>\n",
       "      <td>2.49</td>\n",
       "      <td>4.78</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21850</th>\n",
       "      <td>lucknow</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>32.230000</td>\n",
       "      <td>9.47</td>\n",
       "      <td>19.22</td>\n",
       "      <td>17.78</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3.77</td>\n",
       "      <td>56.36</td>\n",
       "      <td>10.16</td>\n",
       "      <td>24.19</td>\n",
       "      <td>74.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21851</th>\n",
       "      <td>lucknow</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>34.580000</td>\n",
       "      <td>10.39</td>\n",
       "      <td>17.67</td>\n",
       "      <td>17.60</td>\n",
       "      <td>0.81</td>\n",
       "      <td>2.85</td>\n",
       "      <td>56.40</td>\n",
       "      <td>10.98</td>\n",
       "      <td>13.78</td>\n",
       "      <td>77.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21852</th>\n",
       "      <td>lucknow</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>30.190000</td>\n",
       "      <td>12.14</td>\n",
       "      <td>18.47</td>\n",
       "      <td>18.87</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3.36</td>\n",
       "      <td>43.61</td>\n",
       "      <td>15.05</td>\n",
       "      <td>11.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21853</th>\n",
       "      <td>lucknow</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>38.270000</td>\n",
       "      <td>6.14</td>\n",
       "      <td>11.94</td>\n",
       "      <td>11.95</td>\n",
       "      <td>0.72</td>\n",
       "      <td>4.31</td>\n",
       "      <td>41.17</td>\n",
       "      <td>21.78</td>\n",
       "      <td>7.49</td>\n",
       "      <td>69.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21854</th>\n",
       "      <td>lucknow</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>34.970000</td>\n",
       "      <td>8.50</td>\n",
       "      <td>14.44</td>\n",
       "      <td>6.73</td>\n",
       "      <td>0.73</td>\n",
       "      <td>6.02</td>\n",
       "      <td>36.07</td>\n",
       "      <td>7.79</td>\n",
       "      <td>2.62</td>\n",
       "      <td>76.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          City       Date       PM2.5     NO    NO2    NOx     CO     SO2  \\\n",
       "19846  lucknow 2015-01-01  109.714059   2.11  13.46   4.57  12.15  169.57   \n",
       "19847  lucknow 2015-01-02  109.714059   2.37  10.76   3.80  14.00  169.46   \n",
       "19848  lucknow 2015-01-03  109.714059   2.27  12.43   4.22  12.78  169.77   \n",
       "19849  lucknow 2015-01-04  109.714059   2.24  12.46   4.22  11.85  169.56   \n",
       "19850  lucknow 2015-01-05  109.714059   2.32  12.27   4.22  13.34  169.48   \n",
       "...        ...        ...         ...    ...    ...    ...    ...     ...   \n",
       "21850  lucknow 2020-06-27   32.230000   9.47  19.22  17.78   0.80    3.77   \n",
       "21851  lucknow 2020-06-28   34.580000  10.39  17.67  17.60   0.81    2.85   \n",
       "21852  lucknow 2020-06-29   30.190000  12.14  18.47  18.87   0.80    3.36   \n",
       "21853  lucknow 2020-06-30   38.270000   6.14  11.94  11.95   0.72    4.31   \n",
       "21854  lucknow 2020-07-01   34.970000   8.50  14.44   6.73   0.73    6.02   \n",
       "\n",
       "          O3  Benzene  Toluene    AQI    AQI_Bucket  \n",
       "19846  25.92     1.35     3.93  166.0      moderate  \n",
       "19847  21.66     1.97     5.74  166.0      moderate  \n",
       "19848  22.40     1.83     4.76  166.0      moderate  \n",
       "19849  21.41     2.27     4.77  166.0      moderate  \n",
       "19850  20.60     2.49     4.78  166.0      moderate  \n",
       "...      ...      ...      ...    ...           ...  \n",
       "21850  56.36    10.16    24.19   74.0  satisfactory  \n",
       "21851  56.40    10.98    13.78   77.0  satisfactory  \n",
       "21852  43.61    15.05    11.58   65.0  satisfactory  \n",
       "21853  41.17    21.78     7.49   69.0  satisfactory  \n",
       "21854  36.07     7.79     2.62   76.0  satisfactory  \n",
       "\n",
       "[2009 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lucknow=City.get_group(\"Lucknow\")\n",
    "Lucknow\n",
    "\n",
    "Lucknow = Lucknow.drop(columns=['PM10'])\n",
    "Lucknow\n",
    "\n",
    "citynull = pd.isnull(Lucknow[\"City\"])\n",
    "Lucknow[citynull]\n",
    "\n",
    "datenull = pd.isnull(Lucknow[\"Date\"])\n",
    "Lucknow[datenull]\n",
    "\n",
    "for column in Lucknow[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Lucknow[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Lucknow = Lucknow.drop(columns = ['Xylene', 'NH3'])\n",
    "Lucknow\n",
    "\n",
    "for column in Lucknow[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Lucknow[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Lucknow[column].fillna(m, inplace = True)\n",
    "\n",
    "Lucknow\n",
    "\n",
    "print(Lucknow['AQI'].mean(skipna = True))\n",
    "Lucknow['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Lucknow\n",
    "\n",
    "Lucknow['AQI_Bucket'].value_counts()\n",
    "\n",
    "Lucknow['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Lucknow\n",
    "\n",
    "Lucknow['Date'] = pd.to_datetime(Lucknow['Date'])\n",
    "\n",
    "print(max(Lucknow['Date']),min(Lucknow['Date']))\n",
    "\n",
    "temp=Lucknow.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Lucknow['City'] = Lucknow['City'].str.lower()\n",
    "Lucknow['AQI_Bucket'] = Lucknow['AQI_Bucket'].str.lower()\n",
    "Lucknow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "1.0895020873638122\n",
      "NO\n",
      "0.6889997624138751\n",
      "NO2\n",
      "0.6923938499134508\n",
      "NOx\n",
      "0.6889997624138751\n",
      "NH3\n",
      "5.6579438617927575\n",
      "CO\n",
      "0.5125072124359367\n",
      "SO2\n",
      "0.6957879374130265\n",
      "O3\n",
      "0.9435563248820555\n",
      "Benzene\n",
      "0.5837830499270271\n",
      "Toluene\n",
      "0.5871771374266028\n",
      "Xylene\n",
      "0.5837830499270271\n",
      "PM2.5 123.49856213402742\n",
      "NO 31.842132930513593\n",
      "NO2 37.487636033857285\n",
      "NOx 45.952471299093695\n",
      "CO 1.5335910954891605\n",
      "SO2 22.132504537205094\n",
      "O3 37.249145569620225\n",
      "Benzene 1.65774021352313\n",
      "Toluene 10.370937685459952\n",
      "240.7820424948595\n",
      "2020-07-01 00:00:00 2015-06-01 00:00:00\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23864</th>\n",
       "      <td>patna</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>123.498562</td>\n",
       "      <td>14.41</td>\n",
       "      <td>25.06</td>\n",
       "      <td>39.32</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.80</td>\n",
       "      <td>8.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23865</th>\n",
       "      <td>patna</td>\n",
       "      <td>2015-06-02</td>\n",
       "      <td>123.498562</td>\n",
       "      <td>25.00</td>\n",
       "      <td>22.48</td>\n",
       "      <td>47.50</td>\n",
       "      <td>2.35</td>\n",
       "      <td>9.69</td>\n",
       "      <td>9.90</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.83</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23866</th>\n",
       "      <td>patna</td>\n",
       "      <td>2015-06-03</td>\n",
       "      <td>123.498562</td>\n",
       "      <td>14.29</td>\n",
       "      <td>17.16</td>\n",
       "      <td>29.81</td>\n",
       "      <td>1.69</td>\n",
       "      <td>20.61</td>\n",
       "      <td>12.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23867</th>\n",
       "      <td>patna</td>\n",
       "      <td>2015-06-04</td>\n",
       "      <td>123.498562</td>\n",
       "      <td>13.03</td>\n",
       "      <td>15.62</td>\n",
       "      <td>28.63</td>\n",
       "      <td>1.20</td>\n",
       "      <td>4.35</td>\n",
       "      <td>9.77</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.28</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23868</th>\n",
       "      <td>patna</td>\n",
       "      <td>2015-06-05</td>\n",
       "      <td>123.498562</td>\n",
       "      <td>10.40</td>\n",
       "      <td>10.36</td>\n",
       "      <td>20.14</td>\n",
       "      <td>1.29</td>\n",
       "      <td>7.22</td>\n",
       "      <td>11.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25717</th>\n",
       "      <td>patna</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>17.710000</td>\n",
       "      <td>9.47</td>\n",
       "      <td>23.01</td>\n",
       "      <td>22.28</td>\n",
       "      <td>0.87</td>\n",
       "      <td>3.63</td>\n",
       "      <td>23.39</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.07</td>\n",
       "      <td>65.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25718</th>\n",
       "      <td>patna</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>19.270000</td>\n",
       "      <td>30.19</td>\n",
       "      <td>18.13</td>\n",
       "      <td>36.76</td>\n",
       "      <td>0.72</td>\n",
       "      <td>3.92</td>\n",
       "      <td>17.37</td>\n",
       "      <td>1.18</td>\n",
       "      <td>2.90</td>\n",
       "      <td>82.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25719</th>\n",
       "      <td>patna</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>17.240000</td>\n",
       "      <td>42.40</td>\n",
       "      <td>20.51</td>\n",
       "      <td>47.69</td>\n",
       "      <td>0.88</td>\n",
       "      <td>3.60</td>\n",
       "      <td>17.50</td>\n",
       "      <td>1.51</td>\n",
       "      <td>4.91</td>\n",
       "      <td>88.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25720</th>\n",
       "      <td>patna</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>29.760000</td>\n",
       "      <td>42.12</td>\n",
       "      <td>27.50</td>\n",
       "      <td>52.04</td>\n",
       "      <td>0.83</td>\n",
       "      <td>3.91</td>\n",
       "      <td>21.70</td>\n",
       "      <td>1.58</td>\n",
       "      <td>8.59</td>\n",
       "      <td>93.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25721</th>\n",
       "      <td>patna</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>35.420000</td>\n",
       "      <td>44.50</td>\n",
       "      <td>31.15</td>\n",
       "      <td>57.72</td>\n",
       "      <td>0.82</td>\n",
       "      <td>3.99</td>\n",
       "      <td>25.76</td>\n",
       "      <td>1.73</td>\n",
       "      <td>5.50</td>\n",
       "      <td>98.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1858 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        City       Date       PM2.5     NO    NO2    NOx    CO    SO2     O3  \\\n",
       "23864  patna 2015-06-01  123.498562  14.41  25.06  39.32  1.56   1.80   8.89   \n",
       "23865  patna 2015-06-02  123.498562  25.00  22.48  47.50  2.35   9.69   9.90   \n",
       "23866  patna 2015-06-03  123.498562  14.29  17.16  29.81  1.69  20.61  12.63   \n",
       "23867  patna 2015-06-04  123.498562  13.03  15.62  28.63  1.20   4.35   9.77   \n",
       "23868  patna 2015-06-05  123.498562  10.40  10.36  20.14  1.29   7.22  11.90   \n",
       "...      ...        ...         ...    ...    ...    ...   ...    ...    ...   \n",
       "25717  patna 2020-06-27   17.710000   9.47  23.01  22.28  0.87   3.63  23.39   \n",
       "25718  patna 2020-06-28   19.270000  30.19  18.13  36.76  0.72   3.92  17.37   \n",
       "25719  patna 2020-06-29   17.240000  42.40  20.51  47.69  0.88   3.60  17.50   \n",
       "25720  patna 2020-06-30   29.760000  42.12  27.50  52.04  0.83   3.91  21.70   \n",
       "25721  patna 2020-07-01   35.420000  44.50  31.15  57.72  0.82   3.99  25.76   \n",
       "\n",
       "       Benzene  Toluene    AQI    AQI_Bucket  \n",
       "23864     0.00     0.29  166.0      moderate  \n",
       "23865     0.08     0.83  166.0      moderate  \n",
       "23866     0.00     0.33  166.0      moderate  \n",
       "23867     0.01     0.28  166.0      moderate  \n",
       "23868     0.00     0.15  166.0      moderate  \n",
       "...        ...      ...    ...           ...  \n",
       "25717     1.09     3.07   65.0  satisfactory  \n",
       "25718     1.18     2.90   82.0  satisfactory  \n",
       "25719     1.51     4.91   88.0  satisfactory  \n",
       "25720     1.58     8.59   93.0  satisfactory  \n",
       "25721     1.73     5.50   98.0  satisfactory  \n",
       "\n",
       "[1858 rows x 13 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Patna=City.get_group(\"Patna\")\n",
    "Patna\n",
    "\n",
    "Patna = Patna.drop(columns=['PM10'])\n",
    "Patna\n",
    "\n",
    "citynull = pd.isnull(Patna[\"City\"])\n",
    "Patna[citynull]\n",
    "\n",
    "datenull = pd.isnull(Patna[\"Date\"])\n",
    "Patna[datenull]\n",
    "\n",
    "for column in Patna[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Patna[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Patna = Patna.drop(columns = ['Xylene', 'NH3'])\n",
    "Patna\n",
    "\n",
    "for column in Patna[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Patna[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Patna[column].fillna(m, inplace = True)\n",
    "\n",
    "Patna\n",
    "\n",
    "print(Patna['AQI'].mean(skipna = True))\n",
    "Patna['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Patna\n",
    "\n",
    "Patna['AQI_Bucket'].value_counts()\n",
    "\n",
    "Patna['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Patna\n",
    "\n",
    "Patna['Date'] = pd.to_datetime(Patna['Date'])\n",
    "\n",
    "print(max(Patna['Date']),min(Patna['Date']))\n",
    "\n",
    "temp=Patna.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Patna['City'] = Patna['City'].str.lower()\n",
    "Patna['AQI_Bucket'] = Patna['AQI_Bucket'].str.lower()\n",
    "Patna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.14255167498218105\n",
      "NO\n",
      "0.10521671248684791\n",
      "NO2\n",
      "0.10521671248684791\n",
      "NOx\n",
      "0.05430539999321183\n",
      "NH3\n",
      "0.3835318874520585\n",
      "CO\n",
      "0.05430539999321183\n",
      "SO2\n",
      "0.10521671248684791\n",
      "O3\n",
      "0.10521671248684791\n",
      "Benzene\n",
      "3.774225299528222\n",
      "Toluene\n",
      "3.774225299528222\n",
      "Xylene\n",
      "3.774225299528222\n",
      "PM2.5 28.03661682242993\n",
      "NO 3.4549768732654957\n",
      "NO2 9.380101757631818\n",
      "NOx 8.181496350364963\n",
      "CO 0.9513777372262769\n",
      "SO2 5.656558741905638\n",
      "O3 34.68134135060126\n",
      "Benzene nan\n",
      "Toluene nan\n",
      "75.87832699619771\n",
      "2020-07-01 00:00:00 2017-06-16 00:00:00\n"
     ]
    },
    {
     "data": {
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       "  <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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26957</th>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26958</th>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>2017-06-17</td>\n",
       "      <td>28.036617</td>\n",
       "      <td>3.454977</td>\n",
       "      <td>9.380102</td>\n",
       "      <td>8.181496</td>\n",
       "      <td>0.951378</td>\n",
       "      <td>5.656559</td>\n",
       "      <td>34.681341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26959</th>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>2017-06-18</td>\n",
       "      <td>28.036617</td>\n",
       "      <td>3.454977</td>\n",
       "      <td>9.380102</td>\n",
       "      <td>8.181496</td>\n",
       "      <td>0.951378</td>\n",
       "      <td>5.656559</td>\n",
       "      <td>34.681341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26960</th>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>2017-06-19</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26961</th>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>2017-06-20</td>\n",
       "      <td>28.036617</td>\n",
       "      <td>3.454977</td>\n",
       "      <td>9.380102</td>\n",
       "      <td>8.181496</td>\n",
       "      <td>0.951378</td>\n",
       "      <td>5.656559</td>\n",
       "      <td>34.681341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28064</th>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>18.060000</td>\n",
       "      <td>8.190000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>12.460000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>8.380000</td>\n",
       "      <td>29.890000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28065</th>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>22.510000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.380000</td>\n",
       "      <td>9.570000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>8.320000</td>\n",
       "      <td>27.730000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28066</th>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>23.170000</td>\n",
       "      <td>8.870000</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>12.880000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>8.070000</td>\n",
       "      <td>25.610000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28067</th>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>20.660000</td>\n",
       "      <td>8.650000</td>\n",
       "      <td>9.930000</td>\n",
       "      <td>12.410000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28068</th>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>6.150000</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1112 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     City       Date      PM2.5        NO        NO2  \\\n",
       "26957  thiruvananthapuram 2017-06-16  38.000000  2.700000   7.100000   \n",
       "26958  thiruvananthapuram 2017-06-17  28.036617  3.454977   9.380102   \n",
       "26959  thiruvananthapuram 2017-06-18  28.036617  3.454977   9.380102   \n",
       "26960  thiruvananthapuram 2017-06-19  24.000000  0.700000   5.200000   \n",
       "26961  thiruvananthapuram 2017-06-20  28.036617  3.454977   9.380102   \n",
       "...                   ...        ...        ...       ...        ...   \n",
       "28064  thiruvananthapuram 2020-06-27  18.060000  8.190000   9.500000   \n",
       "28065  thiruvananthapuram 2020-06-28  22.510000  7.000000   7.380000   \n",
       "28066  thiruvananthapuram 2020-06-29  23.170000  8.870000  10.750000   \n",
       "28067  thiruvananthapuram 2020-06-30  20.660000  8.650000   9.930000   \n",
       "28068  thiruvananthapuram 2020-07-01   2.000000  1.680000   2.300000   \n",
       "\n",
       "             NOx        CO       SO2         O3  Benzene  Toluene    AQI  \\\n",
       "26957   6.200000  0.710000  4.100000  18.300000      NaN      NaN  166.0   \n",
       "26958   8.181496  0.951378  5.656559  34.681341      NaN      NaN  166.0   \n",
       "26959   8.181496  0.951378  5.656559  34.681341      NaN      NaN  166.0   \n",
       "26960   3.300000  0.730000  4.500000  19.100000      NaN      NaN  166.0   \n",
       "26961   8.181496  0.951378  5.656559  34.681341      NaN      NaN  166.0   \n",
       "...          ...       ...       ...        ...      ...      ...    ...   \n",
       "28064  12.460000  0.610000  8.380000  29.890000      NaN      NaN   42.0   \n",
       "28065   9.570000  0.610000  8.320000  27.730000      NaN      NaN   43.0   \n",
       "28066  12.880000  0.680000  8.070000  25.610000      NaN      NaN   49.0   \n",
       "28067  12.410000  0.530000  8.400000  26.000000      NaN      NaN   40.0   \n",
       "28068   2.580000  0.420000  6.150000  22.100000      NaN      NaN   41.0   \n",
       "\n",
       "      AQI_Bucket  \n",
       "26957   moderate  \n",
       "26958   moderate  \n",
       "26959   moderate  \n",
       "26960   moderate  \n",
       "26961   moderate  \n",
       "...          ...  \n",
       "28064       good  \n",
       "28065       good  \n",
       "28066       good  \n",
       "28067       good  \n",
       "28068       good  \n",
       "\n",
       "[1112 rows x 13 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Thiruvananthapuram=City.get_group(\"Thiruvananthapuram\")\n",
    "Thiruvananthapuram\n",
    "\n",
    "Thiruvananthapuram = Thiruvananthapuram.drop(columns=['PM10'])\n",
    "Thiruvananthapuram\n",
    "\n",
    "citynull = pd.isnull(Thiruvananthapuram[\"City\"])\n",
    "Thiruvananthapuram[citynull]\n",
    "\n",
    "datenull = pd.isnull(Thiruvananthapuram[\"Date\"])\n",
    "Thiruvananthapuram[datenull]\n",
    "\n",
    "for column in Thiruvananthapuram[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Thiruvananthapuram[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Thiruvananthapuram = Thiruvananthapuram.drop(columns = ['Xylene', 'NH3'])\n",
    "Thiruvananthapuram\n",
    "\n",
    "for column in Thiruvananthapuram[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Thiruvananthapuram[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Thiruvananthapuram[column].fillna(m, inplace = True)\n",
    "\n",
    "Thiruvananthapuram\n",
    "\n",
    "print(Thiruvananthapuram['AQI'].mean(skipna = True))\n",
    "Thiruvananthapuram['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Thiruvananthapuram\n",
    "\n",
    "Thiruvananthapuram['AQI_Bucket'].value_counts()\n",
    "\n",
    "Thiruvananthapuram['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Thiruvananthapuram\n",
    "\n",
    "Thiruvananthapuram['Date'] = pd.to_datetime(Thiruvananthapuram['Date'])\n",
    "\n",
    "print(max(Thiruvananthapuram['Date']),min(Thiruvananthapuram['Date']))\n",
    "\n",
    "temp=Thiruvananthapuram.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Thiruvananthapuram['City'] = Thiruvananthapuram['City'].str.lower()\n",
    "Thiruvananthapuram['AQI_Bucket'] = Thiruvananthapuram['AQI_Bucket'].str.lower()\n",
    "Thiruvananthapuram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n",
      "0.78064012490242\n",
      "NO\n",
      "0.767063774904117\n",
      "NO2\n",
      "0.8417336998947833\n",
      "NOx\n",
      "0.12897532498387806\n",
      "NH3\n",
      "0.7229406374096324\n",
      "CO\n",
      "0.13236941248345382\n",
      "SO2\n",
      "0.7704578624036927\n",
      "O3\n",
      "0.7772460374028443\n",
      "Benzene\n",
      "0.12897532498387806\n",
      "Toluene\n",
      "0.12897532498387806\n",
      "Xylene\n",
      "0.12897532498387806\n",
      "PM2.5 46.9696103896104\n",
      "NO 12.73216828478964\n",
      "NO2 37.19318780889617\n",
      "NOx 25.23582865168539\n",
      "CO 0.7367392832044974\n",
      "SO2 12.660153846153843\n",
      "O3 37.52374695863746\n",
      "Benzene 3.8913483146067387\n",
      "Toluene 8.350983146067408\n",
      "117.26985482493595\n",
      "2020-07-01 00:00:00 2016-07-01 00:00:00\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>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28069</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>44.92000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>56.960000</td>\n",
       "      <td>40.240000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.670000</td>\n",
       "      <td>10.450000</td>\n",
       "      <td>6.430000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28070</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2016-07-02</td>\n",
       "      <td>46.96961</td>\n",
       "      <td>12.732168</td>\n",
       "      <td>37.193188</td>\n",
       "      <td>25.235829</td>\n",
       "      <td>0.736739</td>\n",
       "      <td>12.660154</td>\n",
       "      <td>37.523747</td>\n",
       "      <td>3.891348</td>\n",
       "      <td>8.350983</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28071</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2016-07-03</td>\n",
       "      <td>46.96961</td>\n",
       "      <td>12.732168</td>\n",
       "      <td>37.193188</td>\n",
       "      <td>25.235829</td>\n",
       "      <td>0.736739</td>\n",
       "      <td>12.660154</td>\n",
       "      <td>37.523747</td>\n",
       "      <td>3.891348</td>\n",
       "      <td>8.350983</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28072</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2016-07-04</td>\n",
       "      <td>46.96961</td>\n",
       "      <td>12.732168</td>\n",
       "      <td>37.193188</td>\n",
       "      <td>25.235829</td>\n",
       "      <td>0.736739</td>\n",
       "      <td>12.660154</td>\n",
       "      <td>37.523747</td>\n",
       "      <td>3.891348</td>\n",
       "      <td>8.350983</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28073</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2016-07-05</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>7.320000</td>\n",
       "      <td>25.080000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>20.080000</td>\n",
       "      <td>30.540000</td>\n",
       "      <td>2.960000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29526</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>15.02000</td>\n",
       "      <td>7.680000</td>\n",
       "      <td>25.060000</td>\n",
       "      <td>19.540000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>8.550000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>12.070000</td>\n",
       "      <td>41.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29527</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>24.38000</td>\n",
       "      <td>3.420000</td>\n",
       "      <td>26.060000</td>\n",
       "      <td>16.530000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>12.720000</td>\n",
       "      <td>30.140000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29528</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>22.91000</td>\n",
       "      <td>3.450000</td>\n",
       "      <td>29.530000</td>\n",
       "      <td>18.330000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>8.420000</td>\n",
       "      <td>30.960000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29529</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>16.64000</td>\n",
       "      <td>4.050000</td>\n",
       "      <td>29.260000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>9.840000</td>\n",
       "      <td>28.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29530</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>26.850000</td>\n",
       "      <td>14.050000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>17.050000</td>\n",
       "      <td>3.891348</td>\n",
       "      <td>8.350983</td>\n",
       "      <td>50.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1462 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City       Date     PM2.5         NO        NO2        NOx  \\\n",
       "28069  visakhapatnam 2016-07-01  44.92000  16.750000  56.960000  40.240000   \n",
       "28070  visakhapatnam 2016-07-02  46.96961  12.732168  37.193188  25.235829   \n",
       "28071  visakhapatnam 2016-07-03  46.96961  12.732168  37.193188  25.235829   \n",
       "28072  visakhapatnam 2016-07-04  46.96961  12.732168  37.193188  25.235829   \n",
       "28073  visakhapatnam 2016-07-05  17.00000   7.320000  25.080000  17.660000   \n",
       "...              ...        ...       ...        ...        ...        ...   \n",
       "29526  visakhapatnam 2020-06-27  15.02000   7.680000  25.060000  19.540000   \n",
       "29527  visakhapatnam 2020-06-28  24.38000   3.420000  26.060000  16.530000   \n",
       "29528  visakhapatnam 2020-06-29  22.91000   3.450000  29.530000  18.330000   \n",
       "29529  visakhapatnam 2020-06-30  16.64000   4.050000  29.260000  18.800000   \n",
       "29530  visakhapatnam 2020-07-01  15.00000   0.400000  26.850000  14.050000   \n",
       "\n",
       "             CO        SO2         O3   Benzene    Toluene    AQI  \\\n",
       "28069  1.000000  38.670000  10.450000  6.430000  12.500000  166.0   \n",
       "28070  0.736739  12.660154  37.523747  3.891348   8.350983  166.0   \n",
       "28071  0.736739  12.660154  37.523747  3.891348   8.350983  166.0   \n",
       "28072  0.736739  12.660154  37.523747  3.891348   8.350983  166.0   \n",
       "28073  0.660000  20.080000  30.540000  2.960000   7.800000  166.0   \n",
       "...         ...        ...        ...       ...        ...    ...   \n",
       "29526  0.470000   8.550000  23.300000  2.240000  12.070000   41.0   \n",
       "29527  0.520000  12.720000  30.140000  0.740000   2.210000   70.0   \n",
       "29528  0.480000   8.420000  30.960000  0.010000   0.010000   68.0   \n",
       "29529  0.520000   9.840000  28.300000  0.000000   0.000000   54.0   \n",
       "29530  0.590000   2.100000  17.050000  3.891348   8.350983   50.0   \n",
       "\n",
       "         AQI_Bucket  \n",
       "28069      moderate  \n",
       "28070      moderate  \n",
       "28071      moderate  \n",
       "28072      moderate  \n",
       "28073      moderate  \n",
       "...             ...  \n",
       "29526          good  \n",
       "29527  satisfactory  \n",
       "29528  satisfactory  \n",
       "29529  satisfactory  \n",
       "29530          good  \n",
       "\n",
       "[1462 rows x 13 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Visakhapatnam=City.get_group(\"Visakhapatnam\")\n",
    "Visakhapatnam\n",
    "\n",
    "Visakhapatnam = Visakhapatnam.drop(columns=['PM10'])\n",
    "Visakhapatnam\n",
    "\n",
    "citynull = pd.isnull(Visakhapatnam[\"City\"])\n",
    "Visakhapatnam[citynull]\n",
    "\n",
    "datenull = pd.isnull(Visakhapatnam[\"Date\"])\n",
    "Visakhapatnam[datenull]\n",
    "\n",
    "for column in Visakhapatnam[['PM2.5', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene', 'Xylene']]:\n",
    "    print(column)\n",
    "    count = Visakhapatnam[column].isna().sum()\n",
    "    print((count/29463)*100)\n",
    "\n",
    "Visakhapatnam = Visakhapatnam.drop(columns = ['Xylene', 'NH3'])\n",
    "Visakhapatnam\n",
    "\n",
    "for column in Visakhapatnam[['PM2.5', 'NO', 'NO2', 'NOx', 'CO', 'SO2', 'O3', 'Benzene', 'Toluene']]:\n",
    "    m = Visakhapatnam[column].mean(skipna = True)\n",
    "    print(column, m)\n",
    "    Visakhapatnam[column].fillna(m, inplace = True)\n",
    "\n",
    "Visakhapatnam\n",
    "\n",
    "print(Visakhapatnam['AQI'].mean(skipna = True))\n",
    "Visakhapatnam['AQI'].fillna(166, inplace = True)\n",
    "\n",
    "\n",
    "Visakhapatnam\n",
    "\n",
    "Visakhapatnam['AQI_Bucket'].value_counts()\n",
    "\n",
    "Visakhapatnam['AQI_Bucket'].fillna('Moderate', inplace = True)\n",
    "Visakhapatnam\n",
    "\n",
    "Visakhapatnam['Date'] = pd.to_datetime(Visakhapatnam['Date'])\n",
    "\n",
    "print(max(Visakhapatnam['Date']),min(Visakhapatnam['Date']))\n",
    "\n",
    "temp=Visakhapatnam.groupby(['AQI_Bucket']).mean()\n",
    "\n",
    "temp = temp.sort_values(by=['AQI'])\n",
    "temp\n",
    "\n",
    "Visakhapatnam['City'] = Visakhapatnam['City'].str.lower()\n",
    "Visakhapatnam['AQI_Bucket'] = Visakhapatnam['AQI_Bucket'].str.lower()\n",
    "Visakhapatnam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</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>67.854497</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</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>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>3.680000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</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>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>16.400000</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</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>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>4.430000</td>\n",
       "      <td>10.140000</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</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>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>7.010000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29526</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>15.020000</td>\n",
       "      <td>7.68</td>\n",
       "      <td>25.06</td>\n",
       "      <td>19.54</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.55</td>\n",
       "      <td>23.30</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>12.070000</td>\n",
       "      <td>41.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29527</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>24.380000</td>\n",
       "      <td>3.42</td>\n",
       "      <td>26.06</td>\n",
       "      <td>16.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.72</td>\n",
       "      <td>30.14</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29528</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>3.45</td>\n",
       "      <td>29.53</td>\n",
       "      <td>18.33</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.42</td>\n",
       "      <td>30.96</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29529</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>16.640000</td>\n",
       "      <td>4.05</td>\n",
       "      <td>29.26</td>\n",
       "      <td>18.80</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.84</td>\n",
       "      <td>28.30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29530</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>26.85</td>\n",
       "      <td>14.05</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17.05</td>\n",
       "      <td>3.891348</td>\n",
       "      <td>8.350983</td>\n",
       "      <td>50.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23555 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City       Date      PM2.5     NO    NO2    NOx     CO    SO2  \\\n",
       "0          ahmedabad 2015-01-01  67.854497   0.92  18.22  17.15   0.92  27.64   \n",
       "1          ahmedabad 2015-01-02  67.854497   0.97  15.69  16.46   0.97  24.55   \n",
       "2          ahmedabad 2015-01-03  67.854497  17.40  19.30  29.70  17.40  29.07   \n",
       "3          ahmedabad 2015-01-04  67.854497   1.70  18.48  17.97   1.70  18.59   \n",
       "4          ahmedabad 2015-01-05  67.854497  22.10  21.42  37.76  22.10  39.33   \n",
       "...              ...        ...        ...    ...    ...    ...    ...    ...   \n",
       "29526  visakhapatnam 2020-06-27  15.020000   7.68  25.06  19.54   0.47   8.55   \n",
       "29527  visakhapatnam 2020-06-28  24.380000   3.42  26.06  16.53   0.52  12.72   \n",
       "29528  visakhapatnam 2020-06-29  22.910000   3.45  29.53  18.33   0.48   8.42   \n",
       "29529  visakhapatnam 2020-06-30  16.640000   4.05  29.26  18.80   0.52   9.84   \n",
       "29530  visakhapatnam 2020-07-01  15.000000   0.40  26.85  14.05   0.59   2.10   \n",
       "\n",
       "           O3   Benzene    Toluene    AQI    AQI_Bucket  \n",
       "0      133.36  0.000000   0.020000  166.0      moderate  \n",
       "1       34.06  3.680000   5.500000  166.0      moderate  \n",
       "2       30.70  6.800000  16.400000  166.0      moderate  \n",
       "3       36.08  4.430000  10.140000  166.0      moderate  \n",
       "4       39.31  7.010000  18.890000  166.0      moderate  \n",
       "...       ...       ...        ...    ...           ...  \n",
       "29526   23.30  2.240000  12.070000   41.0          good  \n",
       "29527   30.14  0.740000   2.210000   70.0  satisfactory  \n",
       "29528   30.96  0.010000   0.010000   68.0  satisfactory  \n",
       "29529   28.30  0.000000   0.000000   54.0  satisfactory  \n",
       "29530   17.05  3.891348   8.350983   50.0          good  \n",
       "\n",
       "[23555 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=[Ahmedabad,Amaravati,Amritsar,Bengaluru,Brajrajnagar,Chennai,Delhi,Gurugram,Hyderabad,Jaipur,Jorapokhar,Lucknow,Patna,Thiruvananthapuram,Visakhapatnam]\n",
    "data_clean=pd.concat(data1)\n",
    "data_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</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>67.854497</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</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>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</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>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</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>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</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>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29526</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>15.020000</td>\n",
       "      <td>7.68</td>\n",
       "      <td>25.06</td>\n",
       "      <td>19.54</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.55</td>\n",
       "      <td>23.30</td>\n",
       "      <td>41.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29527</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>24.380000</td>\n",
       "      <td>3.42</td>\n",
       "      <td>26.06</td>\n",
       "      <td>16.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.72</td>\n",
       "      <td>30.14</td>\n",
       "      <td>70.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29528</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>3.45</td>\n",
       "      <td>29.53</td>\n",
       "      <td>18.33</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.42</td>\n",
       "      <td>30.96</td>\n",
       "      <td>68.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29529</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>16.640000</td>\n",
       "      <td>4.05</td>\n",
       "      <td>29.26</td>\n",
       "      <td>18.80</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.84</td>\n",
       "      <td>28.30</td>\n",
       "      <td>54.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29530</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>26.85</td>\n",
       "      <td>14.05</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17.05</td>\n",
       "      <td>50.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23555 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City       Date      PM2.5     NO    NO2    NOx     CO    SO2  \\\n",
       "0          ahmedabad 2015-01-01  67.854497   0.92  18.22  17.15   0.92  27.64   \n",
       "1          ahmedabad 2015-01-02  67.854497   0.97  15.69  16.46   0.97  24.55   \n",
       "2          ahmedabad 2015-01-03  67.854497  17.40  19.30  29.70  17.40  29.07   \n",
       "3          ahmedabad 2015-01-04  67.854497   1.70  18.48  17.97   1.70  18.59   \n",
       "4          ahmedabad 2015-01-05  67.854497  22.10  21.42  37.76  22.10  39.33   \n",
       "...              ...        ...        ...    ...    ...    ...    ...    ...   \n",
       "29526  visakhapatnam 2020-06-27  15.020000   7.68  25.06  19.54   0.47   8.55   \n",
       "29527  visakhapatnam 2020-06-28  24.380000   3.42  26.06  16.53   0.52  12.72   \n",
       "29528  visakhapatnam 2020-06-29  22.910000   3.45  29.53  18.33   0.48   8.42   \n",
       "29529  visakhapatnam 2020-06-30  16.640000   4.05  29.26  18.80   0.52   9.84   \n",
       "29530  visakhapatnam 2020-07-01  15.000000   0.40  26.85  14.05   0.59   2.10   \n",
       "\n",
       "           O3    AQI    AQI_Bucket  \n",
       "0      133.36  166.0      moderate  \n",
       "1       34.06  166.0      moderate  \n",
       "2       30.70  166.0      moderate  \n",
       "3       36.08  166.0      moderate  \n",
       "4       39.31  166.0      moderate  \n",
       "...       ...    ...           ...  \n",
       "29526   23.30   41.0          good  \n",
       "29527   30.14   70.0  satisfactory  \n",
       "29528   30.96   68.0  satisfactory  \n",
       "29529   28.30   54.0  satisfactory  \n",
       "29530   17.05   50.0          good  \n",
       "\n",
       "[23555 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean = data_clean.drop(columns = ['Benzene', 'Toluene'])\n",
    "data_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
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       "  </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>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
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       "      <th>1</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2015</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>29526</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>15.020000</td>\n",
       "      <td>7.68</td>\n",
       "      <td>25.06</td>\n",
       "      <td>19.54</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.55</td>\n",
       "      <td>23.30</td>\n",
       "      <td>41.0</td>\n",
       "      <td>good</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>24.380000</td>\n",
       "      <td>3.42</td>\n",
       "      <td>26.06</td>\n",
       "      <td>16.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.72</td>\n",
       "      <td>30.14</td>\n",
       "      <td>70.0</td>\n",
       "      <td>satisfactory</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>22.910000</td>\n",
       "      <td>3.45</td>\n",
       "      <td>29.53</td>\n",
       "      <td>18.33</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.42</td>\n",
       "      <td>30.96</td>\n",
       "      <td>68.0</td>\n",
       "      <td>satisfactory</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>16.640000</td>\n",
       "      <td>4.05</td>\n",
       "      <td>29.26</td>\n",
       "      <td>18.80</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.84</td>\n",
       "      <td>28.30</td>\n",
       "      <td>54.0</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
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       "    <tr>\n",
       "      <th>29530</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>26.85</td>\n",
       "      <td>14.05</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17.05</td>\n",
       "      <td>50.0</td>\n",
       "      <td>good</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>23555 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City       Date      PM2.5     NO    NO2    NOx     CO    SO2  \\\n",
       "0          ahmedabad 2015-01-01  67.854497   0.92  18.22  17.15   0.92  27.64   \n",
       "1          ahmedabad 2015-01-02  67.854497   0.97  15.69  16.46   0.97  24.55   \n",
       "2          ahmedabad 2015-01-03  67.854497  17.40  19.30  29.70  17.40  29.07   \n",
       "3          ahmedabad 2015-01-04  67.854497   1.70  18.48  17.97   1.70  18.59   \n",
       "4          ahmedabad 2015-01-05  67.854497  22.10  21.42  37.76  22.10  39.33   \n",
       "...              ...        ...        ...    ...    ...    ...    ...    ...   \n",
       "29526  visakhapatnam 2020-06-27  15.020000   7.68  25.06  19.54   0.47   8.55   \n",
       "29527  visakhapatnam 2020-06-28  24.380000   3.42  26.06  16.53   0.52  12.72   \n",
       "29528  visakhapatnam 2020-06-29  22.910000   3.45  29.53  18.33   0.48   8.42   \n",
       "29529  visakhapatnam 2020-06-30  16.640000   4.05  29.26  18.80   0.52   9.84   \n",
       "29530  visakhapatnam 2020-07-01  15.000000   0.40  26.85  14.05   0.59   2.10   \n",
       "\n",
       "           O3    AQI    AQI_Bucket  year  \n",
       "0      133.36  166.0      moderate  2015  \n",
       "1       34.06  166.0      moderate  2015  \n",
       "2       30.70  166.0      moderate  2015  \n",
       "3       36.08  166.0      moderate  2015  \n",
       "4       39.31  166.0      moderate  2015  \n",
       "...       ...    ...           ...   ...  \n",
       "29526   23.30   41.0          good  2020  \n",
       "29527   30.14   70.0  satisfactory  2020  \n",
       "29528   30.96   68.0  satisfactory  2020  \n",
       "29529   28.30   54.0  satisfactory  2020  \n",
       "29530   17.05   50.0          good  2020  \n",
       "\n",
       "[23555 rows x 12 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean['year'] = pd.DatetimeIndex(data_clean['Date']).year\n",
    "data_clean.head()\n",
    "data_clean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>ahmedabad</td>\n",
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       "      <td>67.854497</td>\n",
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       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0.97</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</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>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>29526</th>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>15.020000</td>\n",
       "      <td>7.68</td>\n",
       "      <td>25.06</td>\n",
       "      <td>19.54</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.55</td>\n",
       "      <td>23.30</td>\n",
       "      <td>41.0</td>\n",
       "      <td>good</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>24.380000</td>\n",
       "      <td>3.42</td>\n",
       "      <td>26.06</td>\n",
       "      <td>16.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.72</td>\n",
       "      <td>30.14</td>\n",
       "      <td>70.0</td>\n",
       "      <td>satisfactory</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>22.910000</td>\n",
       "      <td>3.45</td>\n",
       "      <td>29.53</td>\n",
       "      <td>18.33</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.42</td>\n",
       "      <td>30.96</td>\n",
       "      <td>68.0</td>\n",
       "      <td>satisfactory</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>16.640000</td>\n",
       "      <td>4.05</td>\n",
       "      <td>29.26</td>\n",
       "      <td>18.80</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.84</td>\n",
       "      <td>28.30</td>\n",
       "      <td>54.0</td>\n",
       "      <td>satisfactory</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>15.000000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>26.85</td>\n",
       "      <td>14.05</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17.05</td>\n",
       "      <td>50.0</td>\n",
       "      <td>good</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23555 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City       Date      PM2.5     NO    NO2    NOx     CO    SO2  \\\n",
       "0          ahmedabad 2015-01-01  67.854497   0.92  18.22  17.15   0.92  27.64   \n",
       "1          ahmedabad 2015-01-02  67.854497   0.97  15.69  16.46   0.97  24.55   \n",
       "2          ahmedabad 2015-01-03  67.854497  17.40  19.30  29.70  17.40  29.07   \n",
       "3          ahmedabad 2015-01-04  67.854497   1.70  18.48  17.97   1.70  18.59   \n",
       "4          ahmedabad 2015-01-05  67.854497  22.10  21.42  37.76  22.10  39.33   \n",
       "...              ...        ...        ...    ...    ...    ...    ...    ...   \n",
       "29526  visakhapatnam 2020-06-27  15.020000   7.68  25.06  19.54   0.47   8.55   \n",
       "29527  visakhapatnam 2020-06-28  24.380000   3.42  26.06  16.53   0.52  12.72   \n",
       "29528  visakhapatnam 2020-06-29  22.910000   3.45  29.53  18.33   0.48   8.42   \n",
       "29529  visakhapatnam 2020-06-30  16.640000   4.05  29.26  18.80   0.52   9.84   \n",
       "29530  visakhapatnam 2020-07-01  15.000000   0.40  26.85  14.05   0.59   2.10   \n",
       "\n",
       "           O3    AQI    AQI_Bucket  year  \n",
       "0      133.36  166.0      moderate  2015  \n",
       "1       34.06  166.0      moderate  2015  \n",
       "2       30.70  166.0      moderate  2015  \n",
       "3       36.08  166.0      moderate  2015  \n",
       "4       39.31  166.0      moderate  2015  \n",
       "...       ...    ...           ...   ...  \n",
       "29526   23.30   41.0          good  2020  \n",
       "29527   30.14   70.0  satisfactory  2020  \n",
       "29528   30.96   68.0  satisfactory  2020  \n",
       "29529   28.30   54.0  satisfactory  2020  \n",
       "29530   17.05   50.0          good  2020  \n",
       "\n",
       "[23555 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
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       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>AQI</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>23550</th>\n",
       "      <td>29526</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>15.020000</td>\n",
       "      <td>7.68</td>\n",
       "      <td>25.06</td>\n",
       "      <td>19.54</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.55</td>\n",
       "      <td>23.30</td>\n",
       "      <td>41.0</td>\n",
       "      <td>good</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23551</th>\n",
       "      <td>29527</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>24.380000</td>\n",
       "      <td>3.42</td>\n",
       "      <td>26.06</td>\n",
       "      <td>16.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.72</td>\n",
       "      <td>30.14</td>\n",
       "      <td>70.0</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23552</th>\n",
       "      <td>29528</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>3.45</td>\n",
       "      <td>29.53</td>\n",
       "      <td>18.33</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.42</td>\n",
       "      <td>30.96</td>\n",
       "      <td>68.0</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23553</th>\n",
       "      <td>29529</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>16.640000</td>\n",
       "      <td>4.05</td>\n",
       "      <td>29.26</td>\n",
       "      <td>18.80</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.84</td>\n",
       "      <td>28.30</td>\n",
       "      <td>54.0</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23554</th>\n",
       "      <td>29530</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>26.85</td>\n",
       "      <td>14.05</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17.05</td>\n",
       "      <td>50.0</td>\n",
       "      <td>good</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23555 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index           City       Date      PM2.5     NO    NO2    NOx     CO  \\\n",
       "0          0      ahmedabad 2015-01-01  67.854497   0.92  18.22  17.15   0.92   \n",
       "1          1      ahmedabad 2015-01-02  67.854497   0.97  15.69  16.46   0.97   \n",
       "2          2      ahmedabad 2015-01-03  67.854497  17.40  19.30  29.70  17.40   \n",
       "3          3      ahmedabad 2015-01-04  67.854497   1.70  18.48  17.97   1.70   \n",
       "4          4      ahmedabad 2015-01-05  67.854497  22.10  21.42  37.76  22.10   \n",
       "...      ...            ...        ...        ...    ...    ...    ...    ...   \n",
       "23550  29526  visakhapatnam 2020-06-27  15.020000   7.68  25.06  19.54   0.47   \n",
       "23551  29527  visakhapatnam 2020-06-28  24.380000   3.42  26.06  16.53   0.52   \n",
       "23552  29528  visakhapatnam 2020-06-29  22.910000   3.45  29.53  18.33   0.48   \n",
       "23553  29529  visakhapatnam 2020-06-30  16.640000   4.05  29.26  18.80   0.52   \n",
       "23554  29530  visakhapatnam 2020-07-01  15.000000   0.40  26.85  14.05   0.59   \n",
       "\n",
       "         SO2      O3    AQI    AQI_Bucket  year  \n",
       "0      27.64  133.36  166.0      moderate  2015  \n",
       "1      24.55   34.06  166.0      moderate  2015  \n",
       "2      29.07   30.70  166.0      moderate  2015  \n",
       "3      18.59   36.08  166.0      moderate  2015  \n",
       "4      39.33   39.31  166.0      moderate  2015  \n",
       "...      ...     ...    ...           ...   ...  \n",
       "23550   8.55   23.30   41.0          good  2020  \n",
       "23551  12.72   30.14   70.0  satisfactory  2020  \n",
       "23552   8.42   30.96   68.0  satisfactory  2020  \n",
       "23553   9.84   28.30   54.0  satisfactory  2020  \n",
       "23554   2.10   17.05   50.0          good  2020  \n",
       "\n",
       "[23555 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ahmedabad = Ahmedabad.drop(columns=['Benzene','Toluene'])\n",
    "Amaravati = Amaravati.drop(columns=['Benzene','Toluene'])\n",
    "Amritsar = Amritsar.drop(columns=['Benzene','Toluene'])\n",
    "Bengaluru = Bengaluru.drop(columns=['Benzene','Toluene'])\n",
    "Brajrajnagar = Brajrajnagar.drop(columns=['Benzene','Toluene'])\n",
    "Chennai = Chennai.drop(columns=['Benzene','Toluene'])\n",
    "Delhi = Delhi.drop(columns=['Benzene','Toluene'])\n",
    "Gurugram = Gurugram.drop(columns=['Benzene','Toluene'])\n",
    "Hyderabad = Hyderabad.drop(columns=['Benzene','Toluene'])\n",
    "Jaipur = Jaipur.drop(columns=['Benzene','Toluene'])\n",
    "Jorapokhar = Jorapokhar.drop(columns=['Benzene','Toluene'])\n",
    "Lucknow = Lucknow.drop(columns=['Benzene','Toluene'])\n",
    "Patna = Patna.drop(columns=['Benzene','Toluene'])\n",
    "Thiruvananthapuram = Thiruvananthapuram.drop(columns=['Benzene','Toluene'])\n",
    "Visakhapatnam = Visakhapatnam.drop(columns=['Benzene','Toluene'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  </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>0.92</td>\n",
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       "      <td>24.55</td>\n",
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       "      <td>30.70</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
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       "      <th>3</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
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       "      <th>4</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>62.120000</td>\n",
       "      <td>9.18</td>\n",
       "      <td>56.35</td>\n",
       "      <td>19.86</td>\n",
       "      <td>0.49</td>\n",
       "      <td>12.44</td>\n",
       "      <td>68.05</td>\n",
       "      <td>92.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>31.570000</td>\n",
       "      <td>6.37</td>\n",
       "      <td>23.99</td>\n",
       "      <td>16.40</td>\n",
       "      <td>0.52</td>\n",
       "      <td>11.01</td>\n",
       "      <td>26.34</td>\n",
       "      <td>82.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>29.750000</td>\n",
       "      <td>9.06</td>\n",
       "      <td>25.15</td>\n",
       "      <td>18.92</td>\n",
       "      <td>0.67</td>\n",
       "      <td>12.10</td>\n",
       "      <td>34.99</td>\n",
       "      <td>74.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>40.020000</td>\n",
       "      <td>7.09</td>\n",
       "      <td>58.92</td>\n",
       "      <td>33.41</td>\n",
       "      <td>0.73</td>\n",
       "      <td>16.39</td>\n",
       "      <td>41.64</td>\n",
       "      <td>98.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>37.630000</td>\n",
       "      <td>4.42</td>\n",
       "      <td>35.04</td>\n",
       "      <td>20.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>14.40</td>\n",
       "      <td>9.69</td>\n",
       "      <td>119.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City       Date      PM2.5     NO    NO2    NOx     CO    SO2  \\\n",
       "0     ahmedabad 2015-01-01  67.854497   0.92  18.22  17.15   0.92  27.64   \n",
       "1     ahmedabad 2015-01-02  67.854497   0.97  15.69  16.46   0.97  24.55   \n",
       "2     ahmedabad 2015-01-03  67.854497  17.40  19.30  29.70  17.40  29.07   \n",
       "3     ahmedabad 2015-01-04  67.854497   1.70  18.48  17.97   1.70  18.59   \n",
       "4     ahmedabad 2015-01-05  67.854497  22.10  21.42  37.76  22.10  39.33   \n",
       "...         ...        ...        ...    ...    ...    ...    ...    ...   \n",
       "2004  ahmedabad 2020-06-27  62.120000   9.18  56.35  19.86   0.49  12.44   \n",
       "2005  ahmedabad 2020-06-28  31.570000   6.37  23.99  16.40   0.52  11.01   \n",
       "2006  ahmedabad 2020-06-29  29.750000   9.06  25.15  18.92   0.67  12.10   \n",
       "2007  ahmedabad 2020-06-30  40.020000   7.09  58.92  33.41   0.73  16.39   \n",
       "2008  ahmedabad 2020-07-01  37.630000   4.42  35.04  20.17   0.28  14.40   \n",
       "\n",
       "          O3    AQI    AQI_Bucket  \n",
       "0     133.36  166.0      moderate  \n",
       "1      34.06  166.0      moderate  \n",
       "2      30.70  166.0      moderate  \n",
       "3      36.08  166.0      moderate  \n",
       "4      39.31  166.0      moderate  \n",
       "...      ...    ...           ...  \n",
       "2004   68.05   92.0  satisfactory  \n",
       "2005   26.34   82.0  satisfactory  \n",
       "2006   34.99   74.0  satisfactory  \n",
       "2007   41.64   98.0  satisfactory  \n",
       "2008    9.69  119.0      moderate  \n",
       "\n",
       "[2009 rows x 11 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ahmedabad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE MEAN AND VARIANCE FOR AHMEDABAD FOR EACH COLUMN:\n",
      "\n",
      "Mean of column PM2.5: 67.85449674149201\n",
      "Mean of column NO: 22.428021428571515\n",
      "Mean of column NO2: 59.02549607423244\n",
      "Mean of column NOX: 47.36689752650188\n",
      "Mean of column CO: 22.19340714285712\n",
      "Mean of column SO2: 55.25373265157089\n",
      "Mean of column O3: 39.15540785498473\n",
      "Mean of column AQI: 355.9890492782479\n",
      "Variance of column PM2.5: 1071.8191860434451\n",
      "Variance of column NO: 320.13866863513846\n",
      "Variance of column NO2: 1558.7719543228204\n",
      "Variance of column NOx: 795.1500046215848\n",
      "Variance of column CO: 326.3171849840282\n",
      "Variance of column SO2: 1078.2867893556543\n",
      "Variance of column O3: 336.0376998405167\n",
      "Variance of column AQI: 82782.79171269134\n"
     ]
    }
   ],
   "source": [
    "a_mean1 = Ahmedabad['PM2.5'].mean()\n",
    "a_mean2 = Ahmedabad['NO'].mean()\n",
    "a_mean3 = Ahmedabad['NO2'].mean()\n",
    "a_mean4 = Ahmedabad['NOx'].mean()\n",
    "a_mean5 = Ahmedabad['CO'].mean()\n",
    "a_mean6 = Ahmedabad['SO2'].mean()\n",
    "a_mean7 = Ahmedabad['O3'].mean()\n",
    "a_mean10 = Ahmedabad['AQI'].mean()\n",
    "print(\"THE MEAN AND VARIANCE FOR AHMEDABAD FOR EACH COLUMN:\\n\")\n",
    "print ('Mean of column PM2.5: ' + str(a_mean1))\n",
    "print ('Mean of column NO: ' + str(a_mean2))\n",
    "print ('Mean of column NO2: ' + str(a_mean3))\n",
    "print ('Mean of column NOX: ' + str(a_mean4))\n",
    "print ('Mean of column CO: ' + str(a_mean5))\n",
    "print ('Mean of column SO2: ' + str(a_mean6))\n",
    "print ('Mean of column O3: ' + str(a_mean7))\n",
    "print ('Mean of column AQI: ' + str(a_mean10))\n",
    "a_var1 = Ahmedabad['PM2.5'].var()  \n",
    "a_var2 = Ahmedabad['NO'].var()  \n",
    "a_var3 = Ahmedabad['NO2'].var()  \n",
    "a_var4 = Ahmedabad['NOx'].var()  \n",
    "a_var5 = Ahmedabad['CO'].var()  \n",
    "a_var6 = Ahmedabad['SO2'].var()  \n",
    "a_var7 = Ahmedabad['O3'].var()  \n",
    "a_var10 = Ahmedabad['AQI'].var()\n",
    "print ('Variance of column PM2.5: ' + str(a_var1))\n",
    "print ('Variance of column NO: ' + str(a_var2))\n",
    "print ('Variance of column NO2: ' + str(a_var3))\n",
    "print ('Variance of column NOx: ' + str(a_var4))\n",
    "print ('Variance of column CO: ' + str(a_var5))\n",
    "print ('Variance of column SO2: ' + str(a_var6))\n",
    "print ('Variance of column O3: ' + str(a_var7))\n",
    "print ('Variance of column AQI: ' + str(a_var10)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2122</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>71.36</td>\n",
       "      <td>1.75</td>\n",
       "      <td>20.65</td>\n",
       "      <td>12.40</td>\n",
       "      <td>0.10</td>\n",
       "      <td>10.76</td>\n",
       "      <td>109.26</td>\n",
       "      <td>166.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2123</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>81.40</td>\n",
       "      <td>1.44</td>\n",
       "      <td>20.50</td>\n",
       "      <td>12.08</td>\n",
       "      <td>0.12</td>\n",
       "      <td>15.24</td>\n",
       "      <td>127.09</td>\n",
       "      <td>184.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2124</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-26</td>\n",
       "      <td>78.32</td>\n",
       "      <td>1.26</td>\n",
       "      <td>26.00</td>\n",
       "      <td>14.85</td>\n",
       "      <td>0.14</td>\n",
       "      <td>26.96</td>\n",
       "      <td>117.44</td>\n",
       "      <td>197.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>88.76</td>\n",
       "      <td>6.60</td>\n",
       "      <td>30.85</td>\n",
       "      <td>21.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>33.59</td>\n",
       "      <td>111.81</td>\n",
       "      <td>198.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>64.18</td>\n",
       "      <td>2.56</td>\n",
       "      <td>28.07</td>\n",
       "      <td>17.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>19.00</td>\n",
       "      <td>138.18</td>\n",
       "      <td>188.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>14.50</td>\n",
       "      <td>1.53</td>\n",
       "      <td>6.53</td>\n",
       "      <td>4.72</td>\n",
       "      <td>0.55</td>\n",
       "      <td>13.75</td>\n",
       "      <td>33.76</td>\n",
       "      <td>42.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>16.65</td>\n",
       "      <td>1.43</td>\n",
       "      <td>8.32</td>\n",
       "      <td>5.59</td>\n",
       "      <td>0.66</td>\n",
       "      <td>10.86</td>\n",
       "      <td>37.34</td>\n",
       "      <td>49.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3070</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>20.96</td>\n",
       "      <td>1.65</td>\n",
       "      <td>9.55</td>\n",
       "      <td>6.43</td>\n",
       "      <td>0.66</td>\n",
       "      <td>14.79</td>\n",
       "      <td>43.29</td>\n",
       "      <td>56.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>21.34</td>\n",
       "      <td>1.74</td>\n",
       "      <td>10.69</td>\n",
       "      <td>7.10</td>\n",
       "      <td>0.66</td>\n",
       "      <td>14.58</td>\n",
       "      <td>45.32</td>\n",
       "      <td>61.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3072</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>9.68</td>\n",
       "      <td>6.40</td>\n",
       "      <td>0.59</td>\n",
       "      <td>10.88</td>\n",
       "      <td>29.15</td>\n",
       "      <td>54.0</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>951 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City       Date  PM2.5    NO    NO2    NOx    CO    SO2      O3  \\\n",
       "2122  amaravati 2017-11-24  71.36  1.75  20.65  12.40  0.10  10.76  109.26   \n",
       "2123  amaravati 2017-11-25  81.40  1.44  20.50  12.08  0.12  15.24  127.09   \n",
       "2124  amaravati 2017-11-26  78.32  1.26  26.00  14.85  0.14  26.96  117.44   \n",
       "2125  amaravati 2017-11-27  88.76  6.60  30.85  21.77  0.11  33.59  111.81   \n",
       "2126  amaravati 2017-11-28  64.18  2.56  28.07  17.01  0.09  19.00  138.18   \n",
       "...         ...        ...    ...   ...    ...    ...   ...    ...     ...   \n",
       "3068  amaravati 2020-06-27  14.50  1.53   6.53   4.72  0.55  13.75   33.76   \n",
       "3069  amaravati 2020-06-28  16.65  1.43   8.32   5.59  0.66  10.86   37.34   \n",
       "3070  amaravati 2020-06-29  20.96  1.65   9.55   6.43  0.66  14.79   43.29   \n",
       "3071  amaravati 2020-06-30  21.34  1.74  10.69   7.10  0.66  14.58   45.32   \n",
       "3072  amaravati 2020-07-01  22.00  1.50   9.68   6.40  0.59  10.88   29.15   \n",
       "\n",
       "        AQI    AQI_Bucket  \n",
       "2122  166.0      moderate  \n",
       "2123  184.0      moderate  \n",
       "2124  197.0      moderate  \n",
       "2125  198.0      moderate  \n",
       "2126  188.0      moderate  \n",
       "...     ...           ...  \n",
       "3068   42.0          good  \n",
       "3069   49.0          good  \n",
       "3070   56.0  satisfactory  \n",
       "3071   61.0  satisfactory  \n",
       "3072   54.0  satisfactory  \n",
       "\n",
       "[951 rows x 11 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Amaravati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "scaler.fit(Ahmedabad[[\"PM2.5\",\"NO\",\"NO2\",\"NOx\",\"CO\",\"SO2\",\"O3\",\"AQI\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29526</th>\n",
       "      <td>-0.434257</td>\n",
       "      <td>-0.143034</td>\n",
       "      <td>-0.403507</td>\n",
       "      <td>-0.255445</td>\n",
       "      <td>-0.329887</td>\n",
       "      <td>-0.515865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29527</th>\n",
       "      <td>-0.621219</td>\n",
       "      <td>-0.102175</td>\n",
       "      <td>-0.498623</td>\n",
       "      <td>-0.248263</td>\n",
       "      <td>-0.099925</td>\n",
       "      <td>-0.200578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29528</th>\n",
       "      <td>-0.619902</td>\n",
       "      <td>0.039607</td>\n",
       "      <td>-0.441743</td>\n",
       "      <td>-0.254008</td>\n",
       "      <td>-0.337056</td>\n",
       "      <td>-0.162780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29529</th>\n",
       "      <td>-0.593570</td>\n",
       "      <td>0.028575</td>\n",
       "      <td>-0.426891</td>\n",
       "      <td>-0.248263</td>\n",
       "      <td>-0.258747</td>\n",
       "      <td>-0.285392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29530</th>\n",
       "      <td>-0.753760</td>\n",
       "      <td>-0.069896</td>\n",
       "      <td>-0.576992</td>\n",
       "      <td>-0.238210</td>\n",
       "      <td>-0.685584</td>\n",
       "      <td>-0.803956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29531 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1         2         3         4         5\n",
       "0     -0.730938 -0.422511 -0.479031 -0.190815  0.722865  4.557310\n",
       "1     -0.728744 -0.525885 -0.500835 -0.183634  0.552462 -0.019887\n",
       "2     -0.007668 -0.378383 -0.082449  2.176063  0.801725 -0.174765\n",
       "3     -0.696706 -0.411888 -0.453119 -0.078790  0.223787  0.073225\n",
       "4      0.198604 -0.291762  0.172249  2.851083  1.367531  0.222110\n",
       "...         ...       ...       ...       ...       ...       ...\n",
       "29526 -0.434257 -0.143034 -0.403507 -0.255445 -0.329887 -0.515865\n",
       "29527 -0.621219 -0.102175 -0.498623 -0.248263 -0.099925 -0.200578\n",
       "29528 -0.619902  0.039607 -0.441743 -0.254008 -0.337056 -0.162780\n",
       "29529 -0.593570  0.028575 -0.426891 -0.248263 -0.258747 -0.285392\n",
       "29530 -0.753760 -0.069896 -0.576992 -0.238210 -0.685584 -0.803956\n",
       "\n",
       "[29531 rows x 6 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "standard=Ahmedabad.loc[:,[\"NO\",\"NO2\",\"NOx\",\"CO\",\"SO2\",\"O3\"]]\n",
    "x=standard.values.astype(float)\n",
    "x_scaled=preprocessing.scale(data[[\"NO\",\"NO2\",\"NOx\",\"CO\",\"SO2\",\"O3\"]])\n",
    "standardized=pd.DataFrame(x_scaled)\n",
    "standardized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.66049061],\n",
       "       [-0.66049061],\n",
       "       [-0.66049061],\n",
       "       ...,\n",
       "       [-0.98032555],\n",
       "       [-0.89689035],\n",
       "       [-0.82388455]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "AQI=Ahmedabad.loc[:,[\"AQI\"]]\n",
    "x=AQI.values.astype(float)\n",
    "x_scaled_AQI=scaler.fit_transform(x)\n",
    "x_scaled_AQI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scaled_AQI.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scaled_AQI.var(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE MEAN AND VARIANCE FOR GURUGRAM FOR EACH COLUMN:\n",
      "\n",
      "Mean of column PM2.5: 117.10400655737692\n",
      "Mean of column NO: 17.534282933160288\n",
      "Mean of column NO2: 23.378165910563784\n",
      "Mean of column NOX: 29.377310513447444\n",
      "Mean of column CO: 1.257515923566879\n",
      "Mean of column SO2: 9.41970079787238\n",
      "Mean of column O3: 34.409373000639775\n",
      "Mean of column AQI: 217.16557474687315\n",
      "Variance of column PM2.5: 8911.637942085794\n",
      "Variance of column NO: 494.26393857725856\n",
      "Variance of column NO2: 417.3279318888716\n",
      "Variance of column NOx: 576.6930293949316\n",
      "Variance of column CO: 7.241283141896222\n",
      "Variance of column SO2: 65.32612596266922\n",
      "Variance of column O3: 315.29998735729447\n",
      "Variance of column AQI: 12856.195453051469\n"
     ]
    }
   ],
   "source": [
    "g_mean1 = Gurugram['PM2.5'].mean()\n",
    "g_mean2 = Gurugram['NO'].mean()\n",
    "g_mean3 = Gurugram['NO2'].mean()\n",
    "g_mean4 = Gurugram['NOx'].mean()\n",
    "g_mean5 = Gurugram['CO'].mean()\n",
    "g_mean6 = Gurugram['SO2'].mean()\n",
    "g_mean7 = Gurugram['O3'].mean()\n",
    "g_mean10 = Gurugram['AQI'].mean()\n",
    "print(\"THE MEAN AND VARIANCE FOR GURUGRAM FOR EACH COLUMN:\\n\")\n",
    "print ('Mean of column PM2.5: ' + str(g_mean1))\n",
    "print ('Mean of column NO: ' + str(g_mean2))\n",
    "print ('Mean of column NO2: ' + str(g_mean3))\n",
    "print ('Mean of column NOX: ' + str(g_mean4))\n",
    "print ('Mean of column CO: ' + str(g_mean5))\n",
    "print ('Mean of column SO2: ' + str(g_mean6))\n",
    "print ('Mean of column O3: ' + str(g_mean7))\n",
    "print ('Mean of column AQI: ' + str(g_mean10))\n",
    "g_var1 = Gurugram['PM2.5'].var()  \n",
    "g_var2 = Gurugram['NO'].var()  \n",
    "g_var3 = Gurugram['NO2'].var()  \n",
    "g_var4 = Gurugram['NOx'].var()  \n",
    "g_var5 = Gurugram['CO'].var()  \n",
    "g_var6 = Gurugram['SO2'].var()  \n",
    "g_var7 = Gurugram['O3'].var()  \n",
    "g_var10 = Gurugram['AQI'].var()\n",
    "print ('Variance of column PM2.5: ' + str(g_var1))\n",
    "print ('Variance of column NO: ' + str(g_var2))\n",
    "print ('Variance of column NO2: ' + str(g_var3))\n",
    "print ('Variance of column NOx: ' + str(g_var4))\n",
    "print ('Variance of column CO: ' + str(g_var5))\n",
    "print ('Variance of column SO2: ' + str(g_var6))\n",
    "print ('Variance of column O3: ' + str(g_var7))\n",
    "print ('Variance of column AQI: ' + str(g_var10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE MEAN AND VARIANCE FOR HYDERABAD FOR EACH COLUMN:\n",
      "\n",
      "Mean of column PM2.5: 47.03535695399269\n",
      "Mean of column NO: 7.817194135490365\n",
      "Mean of column NO2: 28.386754297269967\n",
      "Mean of column NOX: 19.438754377188623\n",
      "Mean of column CO: 0.5907796101949024\n",
      "Mean of column SO2: 9.173712121212123\n",
      "Mean of column O3: 33.6110050505051\n",
      "Mean of column AQI: 112.77467597208374\n",
      "Variance of column PM2.5: 1398.8732645556377\n",
      "Variance of column NO: 23.29298500122299\n",
      "Variance of column NO2: 250.04654481924646\n",
      "Variance of column NOx: 115.00516628349082\n",
      "Variance of column CO: 0.2646720118743635\n",
      "Variance of column SO2: 52.60236070618898\n",
      "Variance of column O3: 220.21446428925185\n",
      "Variance of column AQI: 2836.63149802463\n"
     ]
    }
   ],
   "source": [
    "h_mean1 = Hyderabad['PM2.5'].mean()\n",
    "h_mean2 = Hyderabad['NO'].mean()\n",
    "h_mean3 = Hyderabad['NO2'].mean()\n",
    "h_mean4 = Hyderabad['NOx'].mean()\n",
    "h_mean5 = Hyderabad['CO'].mean()\n",
    "h_mean6 = Hyderabad['SO2'].mean()\n",
    "h_mean7 = Hyderabad['O3'].mean()\n",
    "h_mean10 = Hyderabad['AQI'].mean()\n",
    "print(\"THE MEAN AND VARIANCE FOR HYDERABAD FOR EACH COLUMN:\\n\")\n",
    "print ('Mean of column PM2.5: ' + str(h_mean1))\n",
    "print ('Mean of column NO: ' + str(h_mean2))\n",
    "print ('Mean of column NO2: ' + str(h_mean3))\n",
    "print ('Mean of column NOX: ' + str(h_mean4))\n",
    "print ('Mean of column CO: ' + str(h_mean5))\n",
    "print ('Mean of column SO2: ' + str(h_mean6))\n",
    "print ('Mean of column O3: ' + str(h_mean7))\n",
    "print ('Mean of column AQI: ' + str(h_mean10))\n",
    "h_var1 = Hyderabad['PM2.5'].var()  \n",
    "h_var2 = Hyderabad['NO'].var()  \n",
    "h_var3 = Hyderabad['NO2'].var()  \n",
    "h_var4 = Hyderabad['NOx'].var()  \n",
    "h_var5 = Hyderabad['CO'].var()  \n",
    "h_var6 = Hyderabad['SO2'].var()  \n",
    "h_var7 = Hyderabad['O3'].var()  \n",
    "h_var10 = Hyderabad['AQI'].var()\n",
    "print ('Variance of column PM2.5: ' + str(h_var1))\n",
    "print ('Variance of column NO: ' + str(h_var2))\n",
    "print ('Variance of column NO2: ' + str(h_var3))\n",
    "print ('Variance of column NOx: ' + str(h_var4))\n",
    "print ('Variance of column CO: ' + str(h_var5))\n",
    "print ('Variance of column SO2: ' + str(h_var6))\n",
    "print ('Variance of column O3: ' + str(h_var7))\n",
    "print ('Variance of column AQI: ' + str(h_var10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE MEAN AND VARIANCE FOR JAIPUR FOR EACH COLUMN:\n",
      "\n",
      "Mean of column PM2.5: 54.50070780399279\n",
      "Mean of column NO: 14.632987249544637\n",
      "Mean of column NO2: 32.41862727272727\n",
      "Mean of column NOX: 39.63717564870254\n",
      "Mean of column CO: 0.8008943089430891\n",
      "Mean of column SO2: 11.071061705989102\n",
      "Mean of column O3: 46.71546860782532\n",
      "Mean of column AQI: 134.25942549371635\n",
      "Variance of column PM2.5: 697.398415676472\n",
      "Variance of column NO: 178.41923037001027\n",
      "Variance of column NO2: 241.5332550109451\n",
      "Variance of column NOx: 382.5490000962137\n",
      "Variance of column CO: 0.1526235531304107\n",
      "Variance of column SO2: 8.4293359009919\n",
      "Variance of column O3: 440.0960799943096\n",
      "Variance of column AQI: 2914.9380287801623\n"
     ]
    }
   ],
   "source": [
    "j_mean1 = Jaipur['PM2.5'].mean()\n",
    "j_mean2 = Jaipur['NO'].mean()\n",
    "j_mean3 = Jaipur['NO2'].mean()\n",
    "j_mean4 = Jaipur['NOx'].mean()\n",
    "j_mean5 = Jaipur['CO'].mean()\n",
    "j_mean6 = Jaipur['SO2'].mean()\n",
    "j_mean7 = Jaipur['O3'].mean()\n",
    "j_mean10 = Jaipur['AQI'].mean()\n",
    "print(\"THE MEAN AND VARIANCE FOR JAIPUR FOR EACH COLUMN:\\n\")\n",
    "print ('Mean of column PM2.5: ' + str(j_mean1))\n",
    "print ('Mean of column NO: ' + str(j_mean2))\n",
    "print ('Mean of column NO2: ' + str(j_mean3))\n",
    "print ('Mean of column NOX: ' + str(j_mean4))\n",
    "print ('Mean of column CO: ' + str(j_mean5))\n",
    "print ('Mean of column SO2: ' + str(j_mean6))\n",
    "print ('Mean of column O3: ' + str(j_mean7))\n",
    "print ('Mean of column AQI: ' + str(j_mean10))\n",
    "j_var1 = Jaipur['PM2.5'].var()  \n",
    "j_var2 = Jaipur['NO'].var()  \n",
    "j_var3 = Jaipur['NO2'].var()  \n",
    "j_var4 = Jaipur['NOx'].var()  \n",
    "j_var5 = Jaipur['CO'].var()  \n",
    "j_var6 = Jaipur['SO2'].var()  \n",
    "j_var7 = Jaipur['O3'].var()  \n",
    "j_var10 = Jaipur['AQI'].var()\n",
    "print ('Variance of column PM2.5: ' + str(j_var1))\n",
    "print ('Variance of column NO: ' + str(j_var2))\n",
    "print ('Variance of column NO2: ' + str(j_var3))\n",
    "print ('Variance of column NOx: ' + str(j_var4))\n",
    "print ('Variance of column CO: ' + str(j_var5))\n",
    "print ('Variance of column SO2: ' + str(j_var6))\n",
    "print ('Variance of column O3: ' + str(j_var7))\n",
    "print ('Variance of column AQI: ' + str(j_var10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE MEAN AND VARIANCE FOR THIRUVANANTHAPURAM FOR EACH COLUMN:\n",
      "\n",
      "Mean of column PM2.5: 28.03661682242991\n",
      "Mean of column NO: 3.4549768732654953\n",
      "Mean of column NO2: 9.380101757631817\n",
      "Mean of column NOX: 8.181496350364961\n",
      "Mean of column CO: 0.9513777372262766\n",
      "Mean of column SO2: 5.656558741905633\n",
      "Mean of column O3: 34.681341350601286\n",
      "Mean of column AQI: 80.7410071942446\n",
      "Variance of column PM2.5: 305.45649086669437\n",
      "Variance of column NO: 12.97809291127077\n",
      "Variance of column NO2: 34.97933851377739\n",
      "Variance of column NOx: 41.017087440130894\n",
      "Variance of column CO: 0.2779280104561558\n",
      "Variance of column SO2: 15.288530511719106\n",
      "Variance of column O3: 314.73004496403314\n",
      "Variance of column AQI: 1177.7564447092195\n"
     ]
    }
   ],
   "source": [
    "t_mean1 = Thiruvananthapuram['PM2.5'].mean()\n",
    "t_mean2 = Thiruvananthapuram['NO'].mean()\n",
    "t_mean3 = Thiruvananthapuram['NO2'].mean()\n",
    "t_mean4 = Thiruvananthapuram['NOx'].mean()\n",
    "t_mean5 = Thiruvananthapuram['CO'].mean()\n",
    "t_mean6 = Thiruvananthapuram['SO2'].mean()\n",
    "t_mean7 = Thiruvananthapuram['O3'].mean()\n",
    "t_mean8 = Thiruvananthapuram['AQI'].mean()\n",
    "print(\"THE MEAN AND VARIANCE FOR THIRUVANANTHAPURAM FOR EACH COLUMN:\\n\")\n",
    "print ('Mean of column PM2.5: ' + str(t_mean1))\n",
    "print ('Mean of column NO: ' + str(t_mean2))\n",
    "print ('Mean of column NO2: ' + str(t_mean3))\n",
    "print ('Mean of column NOX: ' + str(t_mean4))\n",
    "print ('Mean of column CO: ' + str(t_mean5))\n",
    "print ('Mean of column SO2: ' + str(t_mean6))\n",
    "print ('Mean of column O3: ' + str(t_mean7))\n",
    "print ('Mean of column AQI: ' + str(t_mean8))\n",
    "t_var1 = Thiruvananthapuram['PM2.5'].var()  \n",
    "t_var2 = Thiruvananthapuram['NO'].var()  \n",
    "t_var3 = Thiruvananthapuram['NO2'].var()  \n",
    "t_var4 = Thiruvananthapuram['NOx'].var()  \n",
    "t_var5 = Thiruvananthapuram['CO'].var()  \n",
    "t_var6 = Thiruvananthapuram['SO2'].var()  \n",
    "t_var7 = Thiruvananthapuram['O3'].var()  \n",
    "t_var8 = Thiruvananthapuram['AQI'].var()\n",
    "print ('Variance of column PM2.5: ' + str(t_var1))\n",
    "print ('Variance of column NO: ' + str(t_var2))\n",
    "print ('Variance of column NO2: ' + str(t_var3))\n",
    "print ('Variance of column NOx: ' + str(t_var4))\n",
    "print ('Variance of column CO: ' + str(t_var5))\n",
    "print ('Variance of column SO2: ' + str(t_var6))\n",
    "print ('Variance of column O3: ' + str(t_var7))\n",
    "print ('Variance of column AQI: ' + str(t_var8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ahmedabad[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Ahmedabad[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</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>8.683555e-16</td>\n",
       "      <td>-1.202374</td>\n",
       "      <td>-1.033798</td>\n",
       "      <td>-1.071849</td>\n",
       "      <td>-1.177945</td>\n",
       "      <td>-0.841136</td>\n",
       "      <td>5.140273</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.660491</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>8.683555e-16</td>\n",
       "      <td>-1.199579</td>\n",
       "      <td>-1.097894</td>\n",
       "      <td>-1.096324</td>\n",
       "      <td>-1.175177</td>\n",
       "      <td>-0.935260</td>\n",
       "      <td>-0.278031</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>-0.660491</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>8.683555e-16</td>\n",
       "      <td>-0.281084</td>\n",
       "      <td>-1.006436</td>\n",
       "      <td>-0.626677</td>\n",
       "      <td>-0.265419</td>\n",
       "      <td>-0.797577</td>\n",
       "      <td>-0.461369</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>-0.660491</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>8.683555e-16</td>\n",
       "      <td>-1.158769</td>\n",
       "      <td>-1.027210</td>\n",
       "      <td>-1.042762</td>\n",
       "      <td>-1.134756</td>\n",
       "      <td>-1.116806</td>\n",
       "      <td>-0.167810</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>-0.660491</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>8.683555e-16</td>\n",
       "      <td>-0.018338</td>\n",
       "      <td>-0.952726</td>\n",
       "      <td>-0.340774</td>\n",
       "      <td>-0.005172</td>\n",
       "      <td>-0.485049</td>\n",
       "      <td>0.008435</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>-0.660491</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>-1.752035e-01</td>\n",
       "      <td>-0.740611</td>\n",
       "      <td>-0.067783</td>\n",
       "      <td>-0.975720</td>\n",
       "      <td>-1.201755</td>\n",
       "      <td>-1.304140</td>\n",
       "      <td>1.576633</td>\n",
       "      <td>1.32</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-0.917749</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>-1.108584e+00</td>\n",
       "      <td>-0.897700</td>\n",
       "      <td>-0.887616</td>\n",
       "      <td>-1.098452</td>\n",
       "      <td>-1.200094</td>\n",
       "      <td>-1.347698</td>\n",
       "      <td>-0.699273</td>\n",
       "      <td>1.37</td>\n",
       "      <td>49.58</td>\n",
       "      <td>-0.952514</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>-1.164189e+00</td>\n",
       "      <td>-0.747319</td>\n",
       "      <td>-0.858228</td>\n",
       "      <td>-1.009063</td>\n",
       "      <td>-1.191788</td>\n",
       "      <td>-1.314496</td>\n",
       "      <td>-0.227285</td>\n",
       "      <td>1.39</td>\n",
       "      <td>60.21</td>\n",
       "      <td>-0.980326</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>-8.504146e-01</td>\n",
       "      <td>-0.857449</td>\n",
       "      <td>-0.002673</td>\n",
       "      <td>-0.495077</td>\n",
       "      <td>-1.188466</td>\n",
       "      <td>-1.183819</td>\n",
       "      <td>0.135572</td>\n",
       "      <td>1.21</td>\n",
       "      <td>44.10</td>\n",
       "      <td>-0.896890</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>-9.234352e-01</td>\n",
       "      <td>-1.006712</td>\n",
       "      <td>-0.607667</td>\n",
       "      <td>-0.964724</td>\n",
       "      <td>-1.213383</td>\n",
       "      <td>-1.244436</td>\n",
       "      <td>-1.607780</td>\n",
       "      <td>1.73</td>\n",
       "      <td>47.05</td>\n",
       "      <td>-0.823885</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City       Date         PM2.5        NO       NO2       NOx  \\\n",
       "0     ahmedabad 2015-01-01  8.683555e-16 -1.202374 -1.033798 -1.071849   \n",
       "1     ahmedabad 2015-01-02  8.683555e-16 -1.199579 -1.097894 -1.096324   \n",
       "2     ahmedabad 2015-01-03  8.683555e-16 -0.281084 -1.006436 -0.626677   \n",
       "3     ahmedabad 2015-01-04  8.683555e-16 -1.158769 -1.027210 -1.042762   \n",
       "4     ahmedabad 2015-01-05  8.683555e-16 -0.018338 -0.952726 -0.340774   \n",
       "...         ...        ...           ...       ...       ...       ...   \n",
       "2004  ahmedabad 2020-06-27 -1.752035e-01 -0.740611 -0.067783 -0.975720   \n",
       "2005  ahmedabad 2020-06-28 -1.108584e+00 -0.897700 -0.887616 -1.098452   \n",
       "2006  ahmedabad 2020-06-29 -1.164189e+00 -0.747319 -0.858228 -1.009063   \n",
       "2007  ahmedabad 2020-06-30 -8.504146e-01 -0.857449 -0.002673 -0.495077   \n",
       "2008  ahmedabad 2020-07-01 -9.234352e-01 -1.006712 -0.607667 -0.964724   \n",
       "\n",
       "            CO       SO2        O3  Benzene  Toluene       AQI    AQI_Bucket  \n",
       "0    -1.177945 -0.841136  5.140273     0.00     0.02 -0.660491      moderate  \n",
       "1    -1.175177 -0.935260 -0.278031     3.68     5.50 -0.660491      moderate  \n",
       "2    -0.265419 -0.797577 -0.461369     6.80    16.40 -0.660491      moderate  \n",
       "3    -1.134756 -1.116806 -0.167810     4.43    10.14 -0.660491      moderate  \n",
       "4    -0.005172 -0.485049  0.008435     7.01    18.89 -0.660491      moderate  \n",
       "...        ...       ...       ...      ...      ...       ...           ...  \n",
       "2004 -1.201755 -1.304140  1.576633     1.32    37.76 -0.917749  satisfactory  \n",
       "2005 -1.200094 -1.347698 -0.699273     1.37    49.58 -0.952514  satisfactory  \n",
       "2006 -1.191788 -1.314496 -0.227285     1.39    60.21 -0.980326  satisfactory  \n",
       "2007 -1.188466 -1.183819  0.135572     1.21    44.10 -0.896890  satisfactory  \n",
       "2008 -1.213383 -1.244436 -1.607780     1.73    47.05 -0.823885      moderate  \n",
       "\n",
       "[2009 rows x 13 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ahmedabad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Amaravati[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Amaravati[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Amritsar[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Amritsar[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bengaluru[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Bengaluru[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "Brajrajnagar[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Brajrajnagar[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chennai[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Chennai[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Delhi[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Delhi[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gurugram[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Gurugram[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hyderabad[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Hyderabad[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "Jaipur[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Jaipur[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "Jorapokhar[['PM2.5','NO','NO2','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Jorapokhar[['PM2.5','NO','NO2','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lucknow[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Lucknow[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "Patna[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Patna[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "Thiruvananthapuram[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Thiruvananthapuram[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "Visakhapatnam[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(Visakhapatnam[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2122</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>1.273098</td>\n",
       "      <td>-0.665506</td>\n",
       "      <td>-0.076484</td>\n",
       "      <td>-0.234899</td>\n",
       "      <td>-1.702158</td>\n",
       "      <td>-0.532693</td>\n",
       "      <td>3.803166</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.92</td>\n",
       "      <td>1.090472</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2123</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>1.653662</td>\n",
       "      <td>-0.742313</td>\n",
       "      <td>-0.084003</td>\n",
       "      <td>-0.260120</td>\n",
       "      <td>-1.637670</td>\n",
       "      <td>0.149477</td>\n",
       "      <td>4.756298</td>\n",
       "      <td>0.20</td>\n",
       "      <td>6.50</td>\n",
       "      <td>1.404415</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2124</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-26</td>\n",
       "      <td>1.536915</td>\n",
       "      <td>-0.786910</td>\n",
       "      <td>0.191705</td>\n",
       "      <td>-0.041804</td>\n",
       "      <td>-1.573183</td>\n",
       "      <td>1.934083</td>\n",
       "      <td>4.240441</td>\n",
       "      <td>0.22</td>\n",
       "      <td>7.95</td>\n",
       "      <td>1.631151</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>1.932641</td>\n",
       "      <td>0.536139</td>\n",
       "      <td>0.434829</td>\n",
       "      <td>0.503591</td>\n",
       "      <td>-1.669914</td>\n",
       "      <td>2.943633</td>\n",
       "      <td>3.939480</td>\n",
       "      <td>0.29</td>\n",
       "      <td>7.63</td>\n",
       "      <td>1.648592</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>1.000942</td>\n",
       "      <td>-0.464819</td>\n",
       "      <td>0.295471</td>\n",
       "      <td>0.128435</td>\n",
       "      <td>-1.734401</td>\n",
       "      <td>0.722013</td>\n",
       "      <td>5.349131</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.02</td>\n",
       "      <td>1.474180</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>-0.882165</td>\n",
       "      <td>-0.720014</td>\n",
       "      <td>-0.784301</td>\n",
       "      <td>-0.840194</td>\n",
       "      <td>-0.251191</td>\n",
       "      <td>-0.077405</td>\n",
       "      <td>-0.232809</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.27</td>\n",
       "      <td>-1.072243</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>-0.800670</td>\n",
       "      <td>-0.744790</td>\n",
       "      <td>-0.694571</td>\n",
       "      <td>-0.771625</td>\n",
       "      <td>0.103489</td>\n",
       "      <td>-0.517466</td>\n",
       "      <td>-0.041434</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.43</td>\n",
       "      <td>-0.950154</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3070</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>-0.637301</td>\n",
       "      <td>-0.690283</td>\n",
       "      <td>-0.632912</td>\n",
       "      <td>-0.705421</td>\n",
       "      <td>0.103489</td>\n",
       "      <td>0.080956</td>\n",
       "      <td>0.276633</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.69</td>\n",
       "      <td>-0.828065</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>-0.622897</td>\n",
       "      <td>-0.667984</td>\n",
       "      <td>-0.575766</td>\n",
       "      <td>-0.652616</td>\n",
       "      <td>0.103489</td>\n",
       "      <td>0.048979</td>\n",
       "      <td>0.385150</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.42</td>\n",
       "      <td>-0.740859</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3072</th>\n",
       "      <td>amaravati</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>-0.597880</td>\n",
       "      <td>-0.727447</td>\n",
       "      <td>-0.626396</td>\n",
       "      <td>-0.707786</td>\n",
       "      <td>-0.122216</td>\n",
       "      <td>-0.514420</td>\n",
       "      <td>-0.479244</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.862948</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>951 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City       Date     PM2.5        NO       NO2       NOx        CO  \\\n",
       "2122  amaravati 2017-11-24  1.273098 -0.665506 -0.076484 -0.234899 -1.702158   \n",
       "2123  amaravati 2017-11-25  1.653662 -0.742313 -0.084003 -0.260120 -1.637670   \n",
       "2124  amaravati 2017-11-26  1.536915 -0.786910  0.191705 -0.041804 -1.573183   \n",
       "2125  amaravati 2017-11-27  1.932641  0.536139  0.434829  0.503591 -1.669914   \n",
       "2126  amaravati 2017-11-28  1.000942 -0.464819  0.295471  0.128435 -1.734401   \n",
       "...         ...        ...       ...       ...       ...       ...       ...   \n",
       "3068  amaravati 2020-06-27 -0.882165 -0.720014 -0.784301 -0.840194 -0.251191   \n",
       "3069  amaravati 2020-06-28 -0.800670 -0.744790 -0.694571 -0.771625  0.103489   \n",
       "3070  amaravati 2020-06-29 -0.637301 -0.690283 -0.632912 -0.705421  0.103489   \n",
       "3071  amaravati 2020-06-30 -0.622897 -0.667984 -0.575766 -0.652616  0.103489   \n",
       "3072  amaravati 2020-07-01 -0.597880 -0.727447 -0.626396 -0.707786 -0.122216   \n",
       "\n",
       "           SO2        O3  Benzene  Toluene       AQI    AQI_Bucket  \n",
       "2122 -0.532693  3.803166     0.17     5.92  1.090472      moderate  \n",
       "2123  0.149477  4.756298     0.20     6.50  1.404415      moderate  \n",
       "2124  1.934083  4.240441     0.22     7.95  1.631151      moderate  \n",
       "2125  2.943633  3.939480     0.29     7.63  1.648592      moderate  \n",
       "2126  0.722013  5.349131     0.17     5.02  1.474180      moderate  \n",
       "...        ...       ...      ...      ...       ...           ...  \n",
       "3068 -0.077405 -0.232809     0.06     0.27 -1.072243          good  \n",
       "3069 -0.517466 -0.041434     0.10     0.43 -0.950154          good  \n",
       "3070  0.080956  0.276633     0.12     0.69 -0.828065  satisfactory  \n",
       "3071  0.048979  0.385150     0.14     1.42 -0.740859  satisfactory  \n",
       "3072 -0.514420 -0.479244     0.10     0.50 -0.862948  satisfactory  \n",
       "\n",
       "[951 rows x 13 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Amaravati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>3073</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2017-02-27</td>\n",
       "      <td>-0.865132</td>\n",
       "      <td>-0.286745</td>\n",
       "      <td>-0.642377</td>\n",
       "      <td>-1.082469e-15</td>\n",
       "      <td>0.512037</td>\n",
       "      <td>0.476609</td>\n",
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       "      <td>4.18229</td>\n",
       "      <td>3.036264</td>\n",
       "      <td>0.567311</td>\n",
       "      <td>moderate</td>\n",
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       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>-0.885089</td>\n",
       "      <td>-0.100685</td>\n",
       "      <td>-0.696229</td>\n",
       "      <td>-1.082469e-15</td>\n",
       "      <td>0.234938</td>\n",
       "      <td>1.322477</td>\n",
       "      <td>-1.058177</td>\n",
       "      <td>4.18229</td>\n",
       "      <td>3.036264</td>\n",
       "      <td>-0.954638</td>\n",
       "      <td>satisfactory</td>\n",
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       "    <tr>\n",
       "      <th>3075</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>-0.739626</td>\n",
       "      <td>-0.309416</td>\n",
       "      <td>-0.831191</td>\n",
       "      <td>-1.082469e-15</td>\n",
       "      <td>-0.240089</td>\n",
       "      <td>3.024244</td>\n",
       "      <td>-1.093328</td>\n",
       "      <td>4.18229</td>\n",
       "      <td>3.036264</td>\n",
       "      <td>-1.234997</td>\n",
       "      <td>good</td>\n",
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       "    <tr>\n",
       "      <th>3076</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>0.457616</td>\n",
       "      <td>-0.644011</td>\n",
       "      <td>-0.973467</td>\n",
       "      <td>-1.082469e-15</td>\n",
       "      <td>-0.378639</td>\n",
       "      <td>2.373962</td>\n",
       "      <td>-0.375233</td>\n",
       "      <td>4.18229</td>\n",
       "      <td>3.036264</td>\n",
       "      <td>-0.233715</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3077</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>-0.735923</td>\n",
       "      <td>-0.689353</td>\n",
       "      <td>-1.023330</td>\n",
       "      <td>-1.082469e-15</td>\n",
       "      <td>-0.358846</td>\n",
       "      <td>1.026590</td>\n",
       "      <td>-0.019700</td>\n",
       "      <td>4.18229</td>\n",
       "      <td>3.036264</td>\n",
       "      <td>-0.340518</td>\n",
       "      <td>satisfactory</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>4289</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>-0.089877</td>\n",
       "      <td>0.509872</td>\n",
       "      <td>0.151443</td>\n",
       "      <td>5.164767e-03</td>\n",
       "      <td>0.215145</td>\n",
       "      <td>-0.601623</td>\n",
       "      <td>-0.596185</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>-0.660928</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4290</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>-0.210445</td>\n",
       "      <td>0.342574</td>\n",
       "      <td>-0.041361</td>\n",
       "      <td>-4.153047e-01</td>\n",
       "      <td>0.155767</td>\n",
       "      <td>-0.489620</td>\n",
       "      <td>-0.596185</td>\n",
       "      <td>1.16000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>-0.514074</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4291</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>-0.590460</td>\n",
       "      <td>0.645899</td>\n",
       "      <td>0.086953</td>\n",
       "      <td>-8.217912e-02</td>\n",
       "      <td>0.234938</td>\n",
       "      <td>-0.544786</td>\n",
       "      <td>-0.596185</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>-0.994689</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4292</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>-0.284514</td>\n",
       "      <td>0.258144</td>\n",
       "      <td>-0.169675</td>\n",
       "      <td>-2.563591e-01</td>\n",
       "      <td>0.274523</td>\n",
       "      <td>-0.581562</td>\n",
       "      <td>-0.596185</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>-0.767732</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4293</th>\n",
       "      <td>amritsar</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>0.045299</td>\n",
       "      <td>1.100885</td>\n",
       "      <td>-0.226851</td>\n",
       "      <td>-2.766716e-01</td>\n",
       "      <td>0.076595</td>\n",
       "      <td>-0.780492</td>\n",
       "      <td>-0.596185</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>-0.607527</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1221 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          City       Date     PM2.5        NO       NO2           NOx  \\\n",
       "3073  amritsar 2017-02-27 -0.865132 -0.286745 -0.642377 -1.082469e-15   \n",
       "3074  amritsar 2017-02-28 -0.885089 -0.100685 -0.696229 -1.082469e-15   \n",
       "3075  amritsar 2017-03-01 -0.739626 -0.309416 -0.831191 -1.082469e-15   \n",
       "3076  amritsar 2017-03-02  0.457616 -0.644011 -0.973467 -1.082469e-15   \n",
       "3077  amritsar 2017-03-03 -0.735923 -0.689353 -1.023330 -1.082469e-15   \n",
       "...        ...        ...       ...       ...       ...           ...   \n",
       "4289  amritsar 2020-06-27 -0.089877  0.509872  0.151443  5.164767e-03   \n",
       "4290  amritsar 2020-06-28 -0.210445  0.342574 -0.041361 -4.153047e-01   \n",
       "4291  amritsar 2020-06-29 -0.590460  0.645899  0.086953 -8.217912e-02   \n",
       "4292  amritsar 2020-06-30 -0.284514  0.258144 -0.169675 -2.563591e-01   \n",
       "4293  amritsar 2020-07-01  0.045299  1.100885 -0.226851 -2.766716e-01   \n",
       "\n",
       "            CO       SO2        O3  Benzene   Toluene       AQI    AQI_Bucket  \n",
       "3073  0.512037  0.476609 -0.949709  4.18229  3.036264  0.567311      moderate  \n",
       "3074  0.234938  1.322477 -1.058177  4.18229  3.036264 -0.954638  satisfactory  \n",
       "3075 -0.240089  3.024244 -1.093328  4.18229  3.036264 -1.234997          good  \n",
       "3076 -0.378639  2.373962 -0.375233  4.18229  3.036264 -0.233715      moderate  \n",
       "3077 -0.358846  1.026590 -0.019700  4.18229  3.036264 -0.340518  satisfactory  \n",
       "...        ...       ...       ...      ...       ...       ...           ...  \n",
       "4289  0.215145 -0.601623 -0.596185  1.30000  1.100000 -0.660928  satisfactory  \n",
       "4290  0.155767 -0.489620 -0.596185  1.16000  0.980000 -0.514074  satisfactory  \n",
       "4291  0.234938 -0.544786 -0.596185  1.30000  1.100000 -0.994689          good  \n",
       "4292  0.274523 -0.581562 -0.596185  1.30000  1.100000 -0.767732  satisfactory  \n",
       "4293  0.076595 -0.780492 -0.596185  1.30000  1.100000 -0.607527  satisfactory  \n",
       "\n",
       "[1221 rows x 13 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Amritsar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>City</th>\n",
       "      <th>Date</th>\n",
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       "      <th>4294</th>\n",
       "      <td>bengaluru</td>\n",
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       "      <td>-1.671837e-15</td>\n",
       "      <td>-0.824555</td>\n",
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       "      <th>4296</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>-1.671837e-15</td>\n",
       "      <td>0.335348</td>\n",
       "      <td>-0.611851</td>\n",
       "      <td>0.066576</td>\n",
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       "      <td>0.53</td>\n",
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       "      <th>4297</th>\n",
       "      <td>bengaluru</td>\n",
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       "      <th>4298</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2015-01-05</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6298</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>-9.044474e-01</td>\n",
       "      <td>-0.851374</td>\n",
       "      <td>-1.079313</td>\n",
       "      <td>-0.441256</td>\n",
       "      <td>-0.295249</td>\n",
       "      <td>0.423835</td>\n",
       "      <td>-0.968992</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.18</td>\n",
       "      <td>-1.118468</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6299</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>-7.237445e-01</td>\n",
       "      <td>-0.900988</td>\n",
       "      <td>-1.331893</td>\n",
       "      <td>-0.606551</td>\n",
       "      <td>-0.297776</td>\n",
       "      <td>0.258703</td>\n",
       "      <td>-0.861815</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.879738</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6300</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>-3.359863e-01</td>\n",
       "      <td>-0.776282</td>\n",
       "      <td>-1.196932</td>\n",
       "      <td>-0.449886</td>\n",
       "      <td>-0.323040</td>\n",
       "      <td>0.194085</td>\n",
       "      <td>-0.895660</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.17</td>\n",
       "      <td>-0.784245</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6301</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>-1.004681e+00</td>\n",
       "      <td>-0.643530</td>\n",
       "      <td>-1.153956</td>\n",
       "      <td>-0.313136</td>\n",
       "      <td>-0.323040</td>\n",
       "      <td>0.222804</td>\n",
       "      <td>-0.946992</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.831992</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6302</th>\n",
       "      <td>bengaluru</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>-8.620951e-01</td>\n",
       "      <td>-0.732031</td>\n",
       "      <td>-1.111733</td>\n",
       "      <td>-0.324422</td>\n",
       "      <td>-0.328093</td>\n",
       "      <td>0.273062</td>\n",
       "      <td>-0.985350</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-1.309452</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City       Date         PM2.5        NO       NO2       NOx  \\\n",
       "4294  bengaluru 2015-01-01 -1.671837e-15 -0.824555 -0.804113 -0.586636   \n",
       "4295  bengaluru 2015-01-02 -1.671837e-15 -0.450436 -0.623160 -0.370226   \n",
       "4296  bengaluru 2015-01-03 -1.671837e-15  0.335348 -0.611851  0.066576   \n",
       "4297  bengaluru 2015-01-04 -1.671837e-15 -0.262706 -0.481414 -0.188336   \n",
       "4298  bengaluru 2015-01-05 -1.671837e-15  0.014866 -0.439191  0.135615   \n",
       "...         ...        ...           ...       ...       ...       ...   \n",
       "6298  bengaluru 2020-06-27 -9.044474e-01 -0.851374 -1.079313 -0.441256   \n",
       "6299  bengaluru 2020-06-28 -7.237445e-01 -0.900988 -1.331893 -0.606551   \n",
       "6300  bengaluru 2020-06-29 -3.359863e-01 -0.776282 -1.196932 -0.449886   \n",
       "6301  bengaluru 2020-06-30 -1.004681e+00 -0.643530 -1.153956 -0.313136   \n",
       "6302  bengaluru 2020-07-01 -8.620951e-01 -0.732031 -1.111733 -0.324422   \n",
       "\n",
       "            CO       SO2        O3  Benzene  Toluene       AQI    AQI_Bucket  \n",
       "4294 -0.381148 -0.473625 -1.248778     0.56     4.64  1.626932      moderate  \n",
       "4295 -0.123451 -0.370716 -0.569619     0.65     5.31  1.626932      moderate  \n",
       "4296  3.931497  1.936354 -1.175447     0.53    19.25  1.626932      moderate  \n",
       "4297  0.811335  0.239557 -0.855610     0.55     7.47  1.626932      moderate  \n",
       "4298 -0.002181 -0.193617 -1.136525     0.40     4.36  1.626932      moderate  \n",
       "...        ...       ...       ...      ...      ...       ...           ...  \n",
       "6298 -0.295249  0.423835 -0.968992     0.21     1.18 -1.118468  satisfactory  \n",
       "6299 -0.297776  0.258703 -0.861815     0.12     0.94 -0.879738  satisfactory  \n",
       "6300 -0.323040  0.194085 -0.895660     0.17     1.17 -0.784245  satisfactory  \n",
       "6301 -0.323040  0.222804 -0.946992     0.18     0.86 -0.831992  satisfactory  \n",
       "6302 -0.328093  0.273062 -0.985350     0.27     0.65 -1.309452          good  \n",
       "\n",
       "[2009 rows x 13 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bengaluru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "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>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</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>8.683555e-16</td>\n",
       "      <td>-1.202374</td>\n",
       "      <td>-1.033798</td>\n",
       "      <td>-1.071849</td>\n",
       "      <td>-1.177945</td>\n",
       "      <td>-0.841136</td>\n",
       "      <td>5.140273</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.660491</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>8.683555e-16</td>\n",
       "      <td>-1.199579</td>\n",
       "      <td>-1.097894</td>\n",
       "      <td>-1.096324</td>\n",
       "      <td>-1.175177</td>\n",
       "      <td>-0.935260</td>\n",
       "      <td>-0.278031</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>-0.660491</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>8.683555e-16</td>\n",
       "      <td>-0.281084</td>\n",
       "      <td>-1.006436</td>\n",
       "      <td>-0.626677</td>\n",
       "      <td>-0.265419</td>\n",
       "      <td>-0.797577</td>\n",
       "      <td>-0.461369</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>-0.660491</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>8.683555e-16</td>\n",
       "      <td>-1.158769</td>\n",
       "      <td>-1.027210</td>\n",
       "      <td>-1.042762</td>\n",
       "      <td>-1.134756</td>\n",
       "      <td>-1.116806</td>\n",
       "      <td>-0.167810</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>-0.660491</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>8.683555e-16</td>\n",
       "      <td>-0.018338</td>\n",
       "      <td>-0.952726</td>\n",
       "      <td>-0.340774</td>\n",
       "      <td>-0.005172</td>\n",
       "      <td>-0.485049</td>\n",
       "      <td>0.008435</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>-0.660491</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>-1.752035e-01</td>\n",
       "      <td>-0.740611</td>\n",
       "      <td>-0.067783</td>\n",
       "      <td>-0.975720</td>\n",
       "      <td>-1.201755</td>\n",
       "      <td>-1.304140</td>\n",
       "      <td>1.576633</td>\n",
       "      <td>1.32</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-0.917749</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>-1.108584e+00</td>\n",
       "      <td>-0.897700</td>\n",
       "      <td>-0.887616</td>\n",
       "      <td>-1.098452</td>\n",
       "      <td>-1.200094</td>\n",
       "      <td>-1.347698</td>\n",
       "      <td>-0.699273</td>\n",
       "      <td>1.37</td>\n",
       "      <td>49.58</td>\n",
       "      <td>-0.952514</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>-1.164189e+00</td>\n",
       "      <td>-0.747319</td>\n",
       "      <td>-0.858228</td>\n",
       "      <td>-1.009063</td>\n",
       "      <td>-1.191788</td>\n",
       "      <td>-1.314496</td>\n",
       "      <td>-0.227285</td>\n",
       "      <td>1.39</td>\n",
       "      <td>60.21</td>\n",
       "      <td>-0.980326</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>-8.504146e-01</td>\n",
       "      <td>-0.857449</td>\n",
       "      <td>-0.002673</td>\n",
       "      <td>-0.495077</td>\n",
       "      <td>-1.188466</td>\n",
       "      <td>-1.183819</td>\n",
       "      <td>0.135572</td>\n",
       "      <td>1.21</td>\n",
       "      <td>44.10</td>\n",
       "      <td>-0.896890</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>-9.234352e-01</td>\n",
       "      <td>-1.006712</td>\n",
       "      <td>-0.607667</td>\n",
       "      <td>-0.964724</td>\n",
       "      <td>-1.213383</td>\n",
       "      <td>-1.244436</td>\n",
       "      <td>-1.607780</td>\n",
       "      <td>1.73</td>\n",
       "      <td>47.05</td>\n",
       "      <td>-0.823885</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City       Date         PM2.5        NO       NO2       NOx  \\\n",
       "0     ahmedabad 2015-01-01  8.683555e-16 -1.202374 -1.033798 -1.071849   \n",
       "1     ahmedabad 2015-01-02  8.683555e-16 -1.199579 -1.097894 -1.096324   \n",
       "2     ahmedabad 2015-01-03  8.683555e-16 -0.281084 -1.006436 -0.626677   \n",
       "3     ahmedabad 2015-01-04  8.683555e-16 -1.158769 -1.027210 -1.042762   \n",
       "4     ahmedabad 2015-01-05  8.683555e-16 -0.018338 -0.952726 -0.340774   \n",
       "...         ...        ...           ...       ...       ...       ...   \n",
       "2004  ahmedabad 2020-06-27 -1.752035e-01 -0.740611 -0.067783 -0.975720   \n",
       "2005  ahmedabad 2020-06-28 -1.108584e+00 -0.897700 -0.887616 -1.098452   \n",
       "2006  ahmedabad 2020-06-29 -1.164189e+00 -0.747319 -0.858228 -1.009063   \n",
       "2007  ahmedabad 2020-06-30 -8.504146e-01 -0.857449 -0.002673 -0.495077   \n",
       "2008  ahmedabad 2020-07-01 -9.234352e-01 -1.006712 -0.607667 -0.964724   \n",
       "\n",
       "            CO       SO2        O3  Benzene  Toluene       AQI    AQI_Bucket  \n",
       "0    -1.177945 -0.841136  5.140273     0.00     0.02 -0.660491      moderate  \n",
       "1    -1.175177 -0.935260 -0.278031     3.68     5.50 -0.660491      moderate  \n",
       "2    -0.265419 -0.797577 -0.461369     6.80    16.40 -0.660491      moderate  \n",
       "3    -1.134756 -1.116806 -0.167810     4.43    10.14 -0.660491      moderate  \n",
       "4    -0.005172 -0.485049  0.008435     7.01    18.89 -0.660491      moderate  \n",
       "...        ...       ...       ...      ...      ...       ...           ...  \n",
       "2004 -1.201755 -1.304140  1.576633     1.32    37.76 -0.917749  satisfactory  \n",
       "2005 -1.200094 -1.347698 -0.699273     1.37    49.58 -0.952514  satisfactory  \n",
       "2006 -1.191788 -1.314496 -0.227285     1.39    60.21 -0.980326  satisfactory  \n",
       "2007 -1.188466 -1.183819  0.135572     1.21    44.10 -0.896890  satisfactory  \n",
       "2008 -1.213383 -1.244436 -1.607780     1.73    47.05 -0.823885      moderate  \n",
       "\n",
       "[2009 rows x 13 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ahmedabad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clean[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']]=StandardScaler().fit_transform(data_clean[['PM2.5','NO','NO2','NOx','CO','SO2','O3','AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'AQI OF ALL CITIES')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_clean.AQI)\n",
    "plt.xlabel(\"AQI\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"AQI OF ALL CITIES\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NO LEVELS OF ALL CITIES')"
      ]
     },
     "execution_count": 68,
     "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": [
    "plt.style.use({'figure.facecolor':'white'})\n",
    "sns.distplot(data_clean.NO)\n",
    "plt.xlabel(\"NO levels\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"NO LEVELS OF ALL CITIES\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NO2 LEVELS OF ALL CITIES')"
      ]
     },
     "execution_count": 69,
     "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": [
    "plt.style.use({'figure.facecolor':'white'})\n",
    "sns.distplot(data_clean.NO2)\n",
    "plt.xlabel(\"NO2 levels\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"NO2 LEVELS OF ALL CITIES\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NOx LEVELS OF ALL CITIES')"
      ]
     },
     "execution_count": 70,
     "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": [
    "plt.style.use({'figure.facecolor':'white'})\n",
    "sns.distplot(data_clean.NOx)\n",
    "plt.xlabel(\"NOx levels\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"NOx LEVELS OF ALL CITIES\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SO2 LEVELS OF ALL CITIES')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I0a5IENTDrdbuGgJfEMgYgRCiPZEgqIdbrX3VEPiC4GSJE3fVPAMhhAh3EgT18AVBHS2C2Ai8GpwskVaBEKJ9kCCoR71B0MkGwLFiCQIhRPsgQVAPTwNjBADHz8mAsRCifZAgqIdL9WKpc4wgApBlJoQQ7YcEQT18l4/WPj2RFhOdIs1yCakQot2QIKiDW/Xi1aizawggOdYmYwRCiHZDgqAOTrdvd7K6Lh8FmVQmhGhfJAjq4HTX3ougum6dIiQIhBDthm5BMGfOHJKSkujfv3+dj7/55psMHDiQgQMHcsMNN7B37169Smm0Cy2CerqGOtkocXoodcoGNUKI8KdbEMyePZsNGzbU+3ivXr345JNP2LdvH48++iiZmZl6ldJol+oa6hq4hFTGCYQQ4U+3IBg+fDjx8fH1Pn7DDTcQFxcHwNChQ8nPz9erlEarqAqCi1cf9esml5AKIdqRNjFG8Morr3DHHXeEuoyACpcvCOq6fBQgKdoXBKfLXK1WkxBC6MUU6gI2b97MK6+8wmeffVbvMVlZWWRlZQFQWFioe01Oj2+wuK4JZQAxVXsXF5dLEAghwl9IWwT79u1j7ty5rFmzhs6dO9d7XGZmJtnZ2WRnZ5OYmKh7XZdqEURbTSgKlFTIYLEQIvyFLAiOHDnChAkTeP311+nTp0+oyqhTpafhMQKDQSHWZqZYgkAI0Q7o1jU0ffp0tmzZQlFREQ6HgyeeeAK32/fFOW/ePP7whz9w+vRp5s+f7yvEZCI7O1uvchrF3yIwm+rPyVibmXMSBEKIdkC3IFi5cmWDj7/88su8/PLLer19swQuHzXUPUYA0MlmprhcgkAIEf7axFVDbU2Ff2ZxAy2CGGkRCCHaCQmCOvjnEZgaahFEWiQIhBDtggRBHSrdKmajgqLUHwSxNpMEgRCiXZAgqEOFW8VkaPjUdLL5WgSaprVSVUIIoQ8Jgjo43SqWBsYHwHfVkOrVKKv0tFJVQgihj5DPLG6LKtzeehecW7HtCAAHT5QAsPyLH4mzW7jnupRWq08IIVqStAjqUOFS612C2s9mNvqOrRpYFkKIcCVBUIdKTxBBYPE1pspdEgRCiPAmQVCHCpeKqZ6uIT9pEQgh2gsJgjo4PWq96wz52SxVQSAtAiFEmJMgqIOvRSBjBEKIjkGCoA5Ot7fevQj8zEYFo0GhwiWXjwohwpsEQR2c7ksPFiuKQqTZKC0CIUTYkyCoQ0UQQQAQYTHKVUNCiLAnQXARTdOqWgQNdw0B0iIQQrQLEgQXcalevBpBtQhsFqNcNSSECHsSBBdx+vciCCYIpEUghGgHJAguEtidTFoEQogOQrcgmDNnDklJSfTv37/OxzVN4xe/+AWpqakMHDiQXbt26VVKo1wIgkuPEdgsRio9XlSvLEUthAhfugXB7Nmz2bBhQ72Pr1+/ntzcXHJzc8nKyuLnP/+5XqU0SkVjWgQyqUwI0Q7oFgTDhw8nPj6+3sfXrFnDrFmzUBSFoUOHUlxczPHjx/UqJ2j+rp6grhqqWmbCKd1DQogwFrIxgoKCArp37x647XA4KCgoqPPYrKwsMjIyyMjIoLCwUNe6GjtYDFAuLQIhRBgLWRDUtcVjfXsEZ2Zmkp2dTXZ2NomJibrW1bjBYt9S1DJgLIQIZ0EFwf79+1v8jR0OB0ePHg3czs/PJzk5ucXfp7EaFQSBMQJZb0gIEb6CCoJ58+Zx7bXX8re//Y3i4uIWeeNx48axfPlyNE3jq6++IjY2lq5du7bIazdHRSOvGgJpEQghwltQexZ/9tln5Obm8o9//IOMjAyuvfZa7r//fm677bZ6nzN9+nS2bNlCUVERDoeDJ554ArfbDfiCZcyYMaxbt47U1FQiIyNZtmxZy3yiZpIxAiFERxP05vVXXHEFTz75JBkZGfziF79g9+7daJrG008/zYQJE2odv3LlygZfT1EUXnzxxcZXrLPGXD5qNChYTAa5akgIEdaC6hrat28fixYtIi0tjU2bNvHBBx/wzTffsGnTJhYtWqR3ja2qMRPKwLfwnKxAKoQIZ0G1CB5++GEefPBBnn76aWw2W+D+5ORknnzySd2KCwWnW8Wg+H7bD4bNIusNCSHCW1BBsG7dOmw2G0ajr0/c6/XidDqJjIxk5syZuhbY2ipcKhFmY72Xsl4s0mLkfKVcNSSECF9BdQ2NHDmSioqKwO3y8nJGjhypW1Gh5PSogUHgYERHmCmTIBBChLGggsDpdBIVFRW4HRUVRXl5uW5FhVKFy0tEI4IgymqirNJT5wQ5IYQIB0EFgd1ur7E66M6dO2uMFbQnTo9KhDn4CdfRESbcqiatAiFE2ApqjODPf/4zkydPDsz8PX78OG+99ZauhYWK06UGJooFI8rqO4WFpZVER5j1KksIIXQTVBAMGTKEgwcPcujQITRN48orr8Rsbp9fehVulQhT48YIwBcEvROjLnG0EEK0PUFPKNuxYweHDx/G4/Gwe/duAGbNmqVbYaHidKvYrUGfFqIiqloEZZV6lSSEELoK6htv5syZfP/99wwaNChwCamiKO0yCCrcXjpHNaJFUBUaRaUSBEKI8BRUEGRnZ5OTkxP0tfXhrNKtNuqqIZvFiEGRFoEQInwFdXlM//79OXHihN61tAkVbhVbI64aMigKUVYThdIiEEKEqaBaBEVFRfTr149rr70Wq9UauH/t2rW6FRYqFY1sEYBvnECCQAgRroIKgscff1znMtoOp7txM4sBoq1m6RoSQoStoIJgxIgR/Pjjj+Tm5jJy5EjKy8tR1fa30JqmaTjdXqxNaBHkn22fM62FEO1fUJ3hL730EpMmTeJnP/sZ4Nt4fvz48boWFgqVHt+mNI1vEZgoKnPh9coyE0KI8BNUELz44ot8/vnnxMTEAL5Nak6dOqVrYaHg33KyMYPF4GsRqF6Ns+UuPcoSQghdBfWNZ7VasVgsgdsej6ddXkrq9PiCoLGDxYHZxTJOIIQIQ0EFwYgRI3j66aepqKjgo48+YvLkyYwdO/aSz9uwYQN9+/YlNTWVxYsX13r83LlzjB07lquuuor09PSQ71scaBE0Yq0huLDeUFGptAiEEOEnqCBYvHgxiYmJDBgwgL///e+MGTPmkjuTqarKQw89xPr168nJyWHlypXk5OTUOObFF1+kX79+7N27ly1btvDLX/4Slyt0X6b+ncasjVhrCHwrkAIUljlbvCYhhNBbUFcNGQwGHnzwQR588MGgX3j79u2kpqbSu3dvAKZNm8aaNWvo169f4BhFUSgtLUXTNMrKyoiPj8dkCn6dn5bmdFcNFluMcD7451VfgVQIIcJNUN+6vXr1qnNM4Icffqj3OQUFBXTv3j1w2+FwsG3bthrHPPzww4wbN47k5GRKS0t56623MBhqN1KysrLIysoCoLCwMJiSm8S/cX2EqXGDxVaTgQizQYJACBGWgl5ryM/pdPLOO+9w5syZBp9T145dF4fJv/71LwYNGsSmTZv4/vvvue2227jxxhsDVyf5ZWZmkpmZCUBGRkYwJTeJPwgaO0agKAqJ0VYJAiFEWArqV9/OnTsHfrp168bChQvZtGlTg89xOBwcPXo0cDs/Pz+wsY3fsmXLmDBhAoqikJqaSq9evTh48GATPkbL8I8RNHYeAUBilFWuGhJChKWgWgTVt6n0er1kZ2dTWlra4HOGDBlCbm4ueXl5dOvWjVWrVrFixYoax6SkpLBx40ZuvPFGTp48yaFDhwJjCqHgHyNo7OWjAInRVvKKGjGwIIQQbURQQfDLX/7ywhNMJnr27Mnbb7/d8AubTCxZsoRRo0ahqipz5swhPT2dpUuXAjBv3jweffRRZs+ezYABA9A0jWeffZaEhIRmfJzm8bcImhoE2/Ma7i4TQoi2KKgg2Lx5c5NefMyYMYwZM6bGffPmzQv8OTk5mX//+99Nem09OF3+IGjcYDFAYlQEZ8vduFUvZmPjny+EEKESVBD87//+b4OPP/LIIy1STKg5m9kiACgqq6RrrK1F6xJCCD0FfdXQjh07GDduHAAffPABw4cPr3F5aHtQ4VYxG5Um/UafVBUEJ0skCIQQ4SXojWl27dpFdHQ04NufYPLkybz88su6FtfanG4vEY2cVezXJTYCgJMlMrtYCBFegvrV98iRIzUWnbNYLBw+fFivmkKmwq0S0cg5BH5JMb4WwSkJAiFEmAmqRTBz5kyuvfZafvrTn6IoCqtXr2bWrFl619bqfBvXN22gt7PditGgcEKCQAgRZoIKgt/97nfccccdbN26FfBNBBs8eLCuhYVCRRO2qfQzGhSSoq2cLJFJZUKI8BL0r7/l5eXExMSwYMECHA4HeXl5etYVEk3ZuL66pJgIGSMQQoSdoILgiSee4Nlnn+WZZ54BwO12c++99+paWCg4mxkEXWKsEgRCiLATVBCsXr2atWvXYrfbAQKrhbY3FW5vk7uGAC6LieDEOQkCIUR4CSoILBYLiqIEVg89f759rqnTnMFi8AVBidMT2OlMCCHCQVDfelOmTOFnP/sZxcXFvPTSS4wcObJRm9SEi+YMFoMvCEDmEgghwsslrxrSNI2pU6dy8OBBYmJiOHToEH/4wx+47bbbWqO+VtX8MYILQdAzwd5SZQkhhK4uGQSKojB+/Hh27tzZLr/8q6twNS8ILquaVCZzCYQQ4SSorqGhQ4eyY8cOvWsJOafb27wgqFpm4pTMJRBChJGgl6FeunQpPXv2xG63o2kaiqKwb98+vetrNapXw6U276qhaKsJm9koLQIhRFhpMAiOHDlCSkoK69evb616QubCfsVNv2pIURQuk7kEQogw02AQjB8/nl27dtGjRw8mTpzIe++911p1tbrm7EVQ3WUyu1gIEWYa/PVX07TAn3/44YdGv/iGDRvo27cvqampLF68uM5jtmzZwqBBg0hPT2fEiBGNfo+W0pxtKqvzBYGMEQghwkeDLQL/BLKL/xwMVVV56KGH+Oijj3A4HAwZMoRx48bRr1+/wDHFxcXMnz+fDRs2kJKSwqlTpxpZfstpbotgxbYjAJw97+JYcQVvfvUjiqJwz3UpLVajEELoocEg2Lt3LzExMWiaRkVFBTExMQCBweKSkpJ6n7t9+3ZSU1Pp3bs3ANOmTWPNmjU1gmDFihVMmDCBlBTfl2VSUlKzP1BTOd1egGYNFgNE28x4vBoVbpVIS1Bj8UIIEVINdg2pqkpJSQmlpaV4PB5KSkoCtxsKAYCCgoIaW1k6HA4KCgpqHPPtt99y9uxZbrrpJq655hqWL1/ejI/SPP6uoeYGQUyE78u/pMLT7JqEEKI16PYra/XxBb+Lu5c8Hg87d+5k48aNVFRUcP311zN06FD69OlT47isrCyysrIAKCws1KXeC11DTb9qCCAmwgxAidMd2L5SCCHasuZ96zXA4XBw9OjRwO38/HySk5NrHTN69GjsdjsJCQkMHz6cvXv31nqtzMxMsrOzyc7OJjExUZd6/QvFNXewOMZWFQQV7mbXJIQQrUG3IBgyZAi5ubnk5eXhcrlYtWoV48aNq3HM3XffzdatW/F4PJSXl7Nt2zbS0tL0KqlBTo9vjKC5QRDt7xpyShAIIcKDbl1DJpOJJUuWMGrUKFRVZc6cOaSnp7N06VIA5s2bR1paGqNHj2bgwIEYDAbmzp1L//799SqpQU6Xf0JZ84LAbDRgtxg5Jy0CIUSYULS6OvPbsIyMDLKzs1v0NVdsO8KX3xfxwb7j/HZMGlHW5uXj37Z8R4TZyJxhveTyUSFEm9DQd6duXUPhxq368tBibP4piYu0cPa8q9mvI4QQrUGCoIpb9Y0RmIyNmzhXl7hIM8Xlbrzh1dgSQnRQEgRV3KqGyaBgaOQM6rrE2S2omkapU+YSCCHaPgmCKm7Vi7kFuoXA1zUESPeQECIsSBBU8QVB81sDUC0IyiUIhBBtnwRBlZZsEXSK9E0qkyAQQoQDCYIqblVrsSAwGw1EW02cLZe5BEKItk+CoEpLdg2Bb8BYWgRCiHAgQVClJbuGwNc9JIPFQohwIEFQpSW7hgDiIy2cq3DjqZqfIIQQbZUEQZUW7xqKtODV4ITsXyyEaOMkCKq0eNeQ3XflUP7ZihZ7TSGE0IMEQRU9umBMRdEAABiKSURBVIZAgkAI0fZJEFRp6a6hWJsZBTh6przFXlMIIfQgQVClpbuGTEYDMTaztAiEEG2eBAGgejW8GphNLXs6OkWayT8rLQIhRNsmQcCFJajNhpbrGgLfOIG0CIQQbZ0EAdWCoIVbBPF2C8fOVeB0qy36ukII0ZJ0DYINGzbQt29fUlNTWbx4cb3H7dixA6PRyLvvvqtnOfXy705mNrTs6UiMtqJpkFd0vkVfVwghWpJuQaCqKg899BDr168nJyeHlStXkpOTU+dxv/71rxk1apRepVxSpcf3G7ulhVsEidFWAL4vLGvR1xVCiJakWxBs376d1NRUevfujcViYdq0aaxZs6bWcS+88AITJ04kKSlJr1IuqdLt6xqKMBtb9HU7231B8EOhtAiEEG2XbkFQUFBA9+7dA7cdDgcFBQW1jlm9ejXz5s1r8LWysrLIyMggIyODwsLCFq/VWdUisLZwi8BiMtCtk01aBEKINk23INDq2LhduWg/4IULF/Lss89iNDb8m3hmZibZ2dlkZ2eTmJjYonWCfi0CgMuToqRFIIRo00x6vbDD4eDo0aOB2/n5+SQnJ9c4Jjs7m2nTpgFQVFTEunXrMJlMjB8/Xq+y6hRoEZhbPhd7J9h5+/AZNE2rFYRCCNEW6BYEQ4YMITc3l7y8PLp168aqVatYsWJFjWPy8vICf549ezZ33XVXq4cAVGsRmPRpEZS7VE6UOOkaa2vx1xdCiObSLQhMJhNLlixh1KhRqKrKnDlzSE9PZ+nSpQCXHBdoTU6PikGhRdca8rs8wQ74BowlCIQQbZFuQQAwZswYxowZU+O++gLg1Vdf1bOUBlW6vVhNRl26bi5PigJ8l5AOS01o8dcXQojmkpnFgNOt6jI+AJAUbSXKauL7U3LlkBCibZIgACo9Xl3GB8B3pVTvRDs/yOxiIUQbJUGAb4xArxYBwOWJUdIiEEK0WRIE+MYI9GoRAFyeaOfYOSflLo9u7yGEEE0lQYC+YwQAvRN9A8YysUwI0RZJEKDvGAFAatWVQ9+eLNXtPYQQoqkkCPCtPqr3GEGkxci+/HO6vYcQQjSVrvMIwoFb9eJWNaw6tQhWbDsCwGUxEXz8zUn6XBYNwD3XpejyfkII0VgdvkVQ5vQN4Ebo2CIAcMTZOH7OiadqNzQhhGgrJAgqq4JAxzECAEdcJKpX40SJU9f3EUKIxurwQVDidAP6rDxaXfc43zpDR2UzeyFEG9Phg8DfNaTXGIFfrM1MtNVE/plyXd9HCCEaS4KgsnXGCBRFwRFnI19aBEKINqbDB0Gps3XGCAAc8ZEUllXidKu6v5cQQgRLgqCqRaD3GAH4rhwCpFUghGhTOnwQtNYYAYCjUyQA+WdlnEAI0XZ0+CAodbp1253sYjaLkYQoK9/JSqRCiDakwwdBWaVHt93J6pLRI44fis7z+XdFrfJ+QghxKboGwYYNG+jbty+pqaksXry41uNvvvkmAwcOZODAgdxwww3s3btXz3LqVOb06H7FUHXXX96ZuEgz//nOXt746kdWbDsS+K9/OQohhGhNun0DqqrKQw89xPr168nJyWHlypXk5OTUOKZXr1588skn7Nu3j0cffZTMzEy9yqlXaaWHCLP+4wN+ZqOBUeldOH7Oyfa8M2w5dIr//jCH9fuPt1oNenO6Vf7+yfdUeuTqKCHCgW5BsH37dlJTU+nduzcWi4Vp06axZs2aGsfccMMNxMXFATB06FDy8/P1KqdepU43VlPr9pAN6BZL9zgba/ce4985J4mymtiaW8T+gvaxOukn3xbyzPqDfJxzKtSlCCGCoNs3YEFBAd27dw/cdjgcFBQU1Hv8K6+8wh133FHnY1lZWWRkZJCRkUFhYWGL1ukfI2hNiqIwfnA30pNjmHtjLxaMvAJHnI33duXz4+nw37zGf3nszh/PhrgSIUQwdAsCTdNq3VffgOzmzZt55ZVXePbZZ+t8PDMzk+zsbLKzs0lMTGzROsucnlaZQ3CxrrE2ZlzXg94JUZgMBqZfm4JBUVj41p5Wr6WlFQSC4EyIKxFCBEO3b0CHw8HRo0cDt/Pz80lOTq513L59+5g7dy5r1qyhc+fOepVTr1Jn644R1Ccu0sLNVyax+0hxs1oFmqbVGcKtyT9P4sCxEipcMk4gRFunWxAMGTKE3Nxc8vLycLlcrFq1inHjxtU45siRI0yYMIHXX3+dPn366FVKg0orPUS08hhBfdK6+Dat2XSw6X3rs/6xnd+9v7+lSmqS/LMVRFqMeLwae44Wh7QWIcSl6fYNaDKZWLJkCaNGjSItLY0pU6aQnp7O0qVLWbp0KQB/+MMfOH36NPPnz2fQoEFkZGToVU6dKj0qLo8XaxtoEQB0jrKSmhTV5CCo9Kh89cNpNn0T2kHa/LPljEy7DJDuISHCga5bVY4ZM4YxY8bUuG/evHmBP7/88su8/PLLepbQoMDuZG2kRQBw65VJ/OPzPMoqPURZG/fX8+2JMtyqb/ObY8UVJHey6VRl3VZsO0KFS6XE6cGtekmKtrJ27zEevuWKVq1DCNE4becbMATKAgvOtY0WAcAtVybhVjU+y2381VFfV7v8dNeR0FyxU1zhAqBTpIWU+EiOnCnH6w3tmIUQomEdOghK22CL4JoeccREmNjYhO6drwuKiY4wYTUZ2PVjaPrmz5737fgWF2mmR2c7TreX7wplbSUh2jJdu4baOn8QtKUWgcloYETfJDYfOoXXq2EwBL8G0tcF5xjoiMXl8baJFoH/aqzsw2fpc1l0SOoRQlxa2/lVOARaa+P6xrr1yiSKylxkBzEhy79G0fIvDvPNsVKMisLVKXEcOHYuJBvgnD3vwmxUsFuMdLZbsFuMMrFMiDaugwdB62xc31i3piWREGXhqXXfoAbZv36ypBJV00juZGNwShxuVePAsdZfsuJsuZu4SAuKoqAoCt3ibCGpQwgRvLb1DdjKLmxK07ZOQ3SEmd/dmcbeo8Ws3B7ciqQFxb7ZvI64SK7u0QkgJOMExeUu4iItgdvJsTZyT5XJ9pxCtGEdeoygxD9Y3IbGCPxLUWuaRu8EO0/+M4fzlR5+NuLyBp9XUFyOzWwkLtJMUnQEjjhbSMYJzpa76R4fGbjdtZMN1avx7clSBjo6tXo9QohLa1u/CreyEqcbs1HB1IgB2daiKArjBiXj9mi8uzM/MJ5Rn4LiCpI7RQTWc7o6JY5dR8626nITTrdKhVut0SLoVjWX4cCxklarQwjROB06CHJPltErwd5qu5M1VlJ0BOOuSub7wjIm/u0Ljp6pe69jj+rl5LnKwJcuwJBe8ZwsqWR/Qet9AZ8t918xZA7cFxdpJjrC1G6W2BaiPeqwQaBpGvvyzzGgW9vurhjSK57ZN/TiRImTcUs+48vvT9c6Zv+xElRNo1vchS6ZuwclEx1h4sXN37VarcXl/jkEF1oEiqLQr2uMtAiEaMM6bBCcLKmkqKySAd1iQl3KJaUmRfH+Q8OIt1uY+co23vjqx8Bj+WfLWb07n+5xtsCidQAxEWZm39CTDQdOkHuytFXq9LcI4uyWGvenJ8dy8EQJHtXbKnUIIRqnwwbBvnzfFTUDwmQA88vvTzPjuh5cnhjF79/fz7DFm3jg1R28/tWP2K0m7h3aA5Ox5l/n/cN6YTMb+duW71ulxuJyd2AOQXX9u8XgdHv5oSj8N90Roj3qsEGwv+AcBgX6dW37LQK/CLORmdf34M4BXTEovuWqXR4vs67vSXSEudbx8XYL9w5NYe3eY3zybSGnyypxulWOnC5nf8E53NV+Qz9f6eHQidKg5y1czK16+fZkKQlR1lpjLunJsQAyn0CINqrDXj66r+AcfS6LxmZpO5eOBsOgKAxLTWBYagKlTjdeDWJttUPA78Ebe7Nq+1Hu+8f2Wo/F2szc3u8yco6XcOhEKR6vRqTFSN/LoumVYOfB4b25IimqVkujLq9+fphTpZXce11KrccuT7RjNRk4UFDCTwc37vMKIfTXIYNA0zT2F5zjpr5JoS6lWepqBcCFuQh+D9+SSkFxBafLXFR6vMTaTJiMBg6dKGXt3mOYjQYyesaRHGvjh6LzHDxRyu6jxfzf7gLi7RbuHpTM3YO60bmq7/9chZtjxRWoXo2b+iZxptzF8x9/y5Vdokmro4VlMhq4sku0DBgL0UZ1yCA4fs5JUZmLgY7YUJfSKqIjzFzZpXZoXOXohLdqnoGhqjsno2c8Xk3jTJmL7p1tfJxzije/OsKyzw/X+doxESaSYiLQNBh7VXK9l+Kmd4vlg73HcLrVNjWBTwjRQYPAv25//24dIwgaYqjji9ugKCREW6lweRmWmsDglE7knirDo2qARoTZSKzNTKXHy47DZzhQUMLo/l1qXDZ6sTv6d2Hl9iP8YuVu/t+912Bsg5P4hOioOmYQ5J/DaFDCaqA4lCItJq6q5+qqyxOjUK/RLvnFfuMViTx2Vz8e/yCH37//Nb8Zk4bXq2E1GcNunEaI9kbXINiwYQMLFixAVVXmzp3Lf/3Xf9V4XNM0FixYwLp164iMjOTVV1/l6quv1rMkwNciuCIpSrooWsilQsA/ZmExGbmpTyIrtx9l5fajgcftFiNxdgua5rv6SPVquFUvJqOBrrERdI210a1TBMmdbNitJpxulUqPlyiriRibiZgIMzE2MwlRVnrERwa1h4OmaXg1MCgENbPc6VYpq/Rgt5iIMBuaNRtd0zQ8Xg2TQWmzs9pFx6JbEKiqykMPPcRHH32Ew+FgyJAhjBs3jn79+gWOWb9+Pbm5ueTm5rJt2zZ+/vOfs23bNr1K4uiZcv7xeR5ffn+anw7uptv7iPrd1u8yLouJoNTpRlEUPKqXskoP510qCr5QMRgUjIqCx6tRUuFmf8E5PvuuEKf70hPSIi1G+lwWjcVkwK16cateXB7fT6XHi9Ot4nR7qfSo+K+UNSi+91UU37pTJoOC2WjAZFQwGQyUON2BTYxqHI9ChNlAvN1CjM1MpdtLhVvFYjIQazNjNRkod6lUuFTK3R4qAn9W0TTfa0SYDNgsRiLMRqKsJhKjrXS2W3CrGuddvuDp0TmSLrERVWGkcr7Sw/lKj++8VXqocKtYTUbsViOdIi10iYkgzm6hsiq8yl2+5/jD1WxQfP81GqrW2jJgNIBb9QVwVISJhCgrEWZj4L0URalxnoxVtw0GBcMlHjMaqHa/gsFA1f1KIIjre8ygKNitJqKsJiwmA6pXQ/X6glSt/qNpqKqGx+vFWxW0HlXDWxX4NrORSIuv9Wm3mDAbff++fP9GtMC/lYv/7HT7/s48Xg271Ui01YzdaiQqwoTVaAQF/FmucOGXCt+f/fcrVM/76vebqv69h5puQbB9+3ZSU1Pp3bs3ANOmTWPNmjU1gmDNmjXMmjULRVEYOnQoxcXFHD9+nK5du7Z4PWv2FLDorT0YFIWxVyXzyO19Wvw9xKUpisJV3Zs2ic/pVnF5vFhMBowGpdoXu2+xu3Plbo6XODlZ4kTTwGTwfaGYjQYizEbMBt+Xu/8L0GBQ0DTwahqadqGV4P9i8VZ9yaTERwa2AHV5vFSq3qrjwaV6KXd5cLpVzEYD8RYLbtVLcbkLj6phMfm+cDvbrZhjDFiMSqB+j1fD7bnw5VPhVskrOs/+gnOYDAYsJgNOt8r6/cepPr3DYjJgrfrxv77q1ah0eznv8n3xV2c2KliMvve88Nl8n1v1alSfOWJQoIlTSUQT+cNY1bQa/waVqiA0Kr4gMRoU5v6kF4/c3rfFa9AtCAoKCujevXvgtsPhqPXbfl3HFBQU1AqCrKwssrKyADh48CAZGRlNqim+6r9b/w1b/6dJL9EkhYWFJCYmtt4bthHyuaEpc6mNgAZU4vvNMr6BY11VP36RVT+h0BH/vlv7M69YByt+27TnHj58uN7HdAuCupY/vrg/NJhjADIzM8nMzGy54lpZRkYG2dnZoS6j1cnn7lg64uduL59ZtyUmHA4HR49eGBDMz88nOTm50ccIIYTQl25BMGTIEHJzc8nLy8PlcrFq1SrGjRtX45hx48axfPlyNE3jq6++IjY2VpfxASGEEPXTrWvIZDKxZMkSRo0ahaqqzJkzh/T0dJYuXQrAvHnzGDNmDOvWrSM1NZXIyEiWLVumVzkhFc7dWs0hn7tj6Yifu718ZkVrzb0MhRBCtDkddhlqIYQQPhIEQgjRwUkQ6GzDhg307duX1NRUFi9eHOpyWsXRo0e5+eabSUtLIz09nb/85S+hLqnVqKrK4MGDueuuu0JdSqspLi5m0qRJXHnllaSlpfHll1+GuqRW8fzzz5Oenk7//v2ZPn06Tqcz1CU1mQSBjvzLbKxfv56cnBxWrlxJTk5OqMvSnclk4rnnnuObb77hq6++4sUXX+wQnxvgL3/5C2lpaaEuo1UtWLCA0aNHc/DgQfbu3dshPn9BQQF//etfyc7OZv/+/aiqyqpVq0JdVpNJEOio+jIbFoslsMxGe9e1a9fA4oHR0dGkpaVRUFAQ4qr0l5+fzz//+U/mzp0b6lJaTUlJCZ9++ikPPPAAABaLhU6dwmMf8ObyeDxUVFTg8XgoLy8P6zlQEgQ6qm8JjY7k8OHD7N69m+uuuy7Upehu4cKF/PGPf8Rg6Dj/W/3www8kJiZy//33M3jwYObOncv5801ZWCO8dOvWjf/8z/8kJSWFrl27Ehsby+233x7qspqs4/yLDYFgl9Bor8rKypg4cSJ//vOfiYlp33s/fPjhhyQlJXHNNdeEupRW5fF42LVrFz//+c/ZvXs3dru9Q4yFnT17ljVr1pCXl8exY8c4f/48b7zxRqjLajIJAh115CU03G43EydOZMaMGUyYMCHU5eju888/Z+3atfTs2ZNp06axadMm7r333lCXpTuHw4HD4Qi0+CZNmsSuXbtCXJX+Pv74Y3r16kViYiJms5kJEybwxRdfhLqsJpMg0FEwy2y0R5qm8cADD5CWlsYjjzwS6nJaxTPPPEN+fj6HDx9m1apV3HLLLWH9G2KwunTpQvfu3Tl06BAAGzdurLHUfHuVkpLCV199RXl5OZqmsXHjxrAeJO+QW1W2lvqW2WjvPv/8c15//XUGDBjAoEGDAHj66acZM2ZMiCsTenjhhReYMWMGLpeL3r17t9ulYqq77rrrmDRpEldffTUmk4nBgweH9XITssSEEEJ0cNI1JIQQHZwEgRBCdHASBEII0cFJEAghRAcnQSCEEB2cBIHoEJ566inS09MZOHAggwYNYtu2bQC4XC4WLlzI5ZdfzhVXXMHdd99Nfn4+EPwqqo8//jh/+tOfWrRePV5TiPrIPALR7n355Zd8+OGH7Nq1C6vVSlFRES6XC4Df/va3lJaW8u2332I0Glm2bBkTJkxg27ZtgVVUr776akpLS7nmmmu47bbbOsSEKdGxSItAtHvHjx8nISEBq9UKQEJCAsnJyZSXl7Ns2TKef/55jEYjAPfffz9Wq5VNmzY1aRXV77//ntGjR3PNNddw4403cvDgQc6dO0fPnj3xer0AlJeX0717d9xud53HX+yvf/0r/fr1Y+DAgUybNq0lT40QgASB6ABuv/12jh49Sp8+fZg/fz6ffPIJAN999x0pKSm1FsTLyMjgwIEDNe4LdhXVzMxMXnjhBXbu3Mmf/vQn5s+fT2xsLFdddVXgfT/44ANGjRqF2Wyu8/iLLV68mN27d7Nv3z6WLl3anFMhRJ2ka0i0e1FRUezcuZOtW7eyefNmpk6dyuLFixk8eHCdq8Fqmlbj/mBXUS0rK+OLL75g8uTJgfsqKysBmDp1Km+99RY333wzq1atYv78+Q0eX93AgQOZMWMG48ePZ/z48U06B0I0RIJAdAhGo5GbbrqJm266iQEDBvDaa68xefJkfvzxR0pLS4mOjg4cu2vXLsaOHQs0bhVVr9dLp06d2LNnT63Hxo0bx29+8xvOnDnDzp07ueWWWzh//ny9x1f3z3/+k08//ZS1a9fy3//93xw4cACTSf7XFS1HuoZEu3fo0CFyc3MDt/fs2UOPHj2w2+3cd999PPLII6iqCsDy5cspLy/nlltuafQqqjExMfTq1Yt33nkH8LUs9u7dC/haJddeey0LFizgrrvuwmg0Nni8n9frDVy99Mc//pHi4mLKyspa5LwI4SdBINq9srIy7rvvvsCAa05ODo8//jjgWz46IiKCPn36cMUVV/DOO++wevVqFEUJrKK6adMmBg0axKBBg1i3bl2D7/Xmm2/yyiuvcNVVV5Genl5ja9KpU6fyxhtvMHXq1KCOB9++1/feey8DBgxg8ODBLFq0qMNsBSlaj6w+KoQQHZy0CIQQooOTIBBCiA5OgkAIITo4CQIhhOjgJAiEEKKDkyAQQogOToJACCE6uP8PZYjCeOSQ4yAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use({'figure.facecolor':'white'})\n",
    "sns.distplot(data_clean.SO2)\n",
    "plt.xlabel(\"SO2 levels\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"SO2 LEVELS OF ALL CITIES\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'O3 LEVELS OF ALL CITIES')"
      ]
     },
     "execution_count": 74,
     "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": [
    "plt.style.use({'figure.facecolor':'white'})\n",
    "sns.distplot(data_clean.O3)\n",
    "plt.xlabel(\"O3 levels\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"O3 LEVELS OF ALL CITIES\")"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}