6.    hypo_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</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",
       "      <th>year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>731</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
       "      <td>22.208613</td>\n",
       "      <td>55.273918</td>\n",
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       "      <td>moderate</td>\n",
       "      <td>2017</td>\n",
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       "      <td>1</td>\n",
       "      <td>732</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
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       "      <td>2</td>\n",
       "      <td>733</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
       "      <td>22.208613</td>\n",
       "      <td>55.273918</td>\n",
       "      <td>39.084203</td>\n",
       "      <td>166</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>734</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
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       "      <td>4</td>\n",
       "      <td>735</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
       "      <td>22.208613</td>\n",
       "      <td>55.273918</td>\n",
       "      <td>39.084203</td>\n",
       "      <td>166</td>\n",
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       "      <td>18002</td>\n",
       "      <td>29526</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>15.020000</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>41</td>\n",
       "      <td>good</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18003</td>\n",
       "      <td>29527</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>24.380000</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>70</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18004</td>\n",
       "      <td>29528</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>3.450000</td>\n",
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       "      <td>30.960000</td>\n",
       "      <td>68</td>\n",
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       "      <td>2020</td>\n",
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       "    <tr>\n",
       "      <td>18005</td>\n",
       "      <td>29529</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>16.640000</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>54</td>\n",
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       "      <td>18006</td>\n",
       "      <td>29530</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>15.000000</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>50</td>\n",
       "      <td>good</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18007 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "       Unnamed: 0           City       Date      PM2.5         NO        NO2  \\\n",
       "0             731      ahmedabad 2017-01-01  67.854497  22.443395  59.054643   \n",
       "1             732      ahmedabad 2017-02-01  67.854497  22.443395  59.054643   \n",
       "2             733      ahmedabad 2017-03-01  67.854497  22.443395  59.054643   \n",
       "3             734      ahmedabad 2017-04-01  67.854497  22.443395  59.054643   \n",
       "4             735      ahmedabad 2017-05-01  67.854497  22.443395  59.054643   \n",
       "...           ...            ...        ...        ...        ...        ...   \n",
       "18002       29526  visakhapatnam 2020-06-27  15.020000   7.680000  25.060000   \n",
       "18003       29527  visakhapatnam 2020-06-28  24.380000   3.420000  26.060000   \n",
       "18004       29528  visakhapatnam 2020-06-29  22.910000   3.450000  29.530000   \n",
       "18005       29529  visakhapatnam 2020-06-30  16.640000   4.050000  29.260000   \n",
       "18006       29530  visakhapatnam 2020-01-07  15.000000   0.400000  26.850000   \n",
       "\n",
       "             NOx         CO        SO2         O3  AQI    AQI_Bucket  year  \n",
       "0      47.388267  22.208613  55.273918  39.084203  166      moderate  2017  \n",
       "1      47.388267  22.208613  55.273918  39.084203  166      moderate  2017  \n",
       "2      47.388267  22.208613  55.273918  39.084203  166      moderate  2017  \n",
       "3      47.388267  22.208613  55.273918  39.084203  166      moderate  2017  \n",
       "4      47.388267  22.208613  55.273918  39.084203  166      moderate  2017  \n",
       "...          ...        ...        ...        ...  ...           ...   ...  \n",
       "18002  19.540000   0.470000   8.550000  23.300000   41          good  2020  \n",
       "18003  16.530000   0.520000  12.720000  30.140000   70  satisfactory  2020  \n",
       "18004  18.330000   0.480000   8.420000  30.960000   68  satisfactory  2020  \n",
       "18005  18.800000   0.520000   9.840000  28.300000   54  satisfactory  2020  \n",
       "18006  14.050000   0.590000   2.100000  17.050000   50          good  2020  \n",
       "\n",
       "[18007 rows x 13 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_excel('clean_ver.xlsx')\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>City</th>\n",
       "      <th>Date</th>\n",
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       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
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       "      <th>AQI</th>\n",
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       "      <td>0</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-01-01</td>\n",
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       "      <td>1</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
       "      <td>22.208613</td>\n",
       "      <td>55.273918</td>\n",
       "      <td>39.084203</td>\n",
       "      <td>166</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
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       "      <td>166</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
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       "      <td>166</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
       "      <td>22.208613</td>\n",
       "      <td>55.273918</td>\n",
       "      <td>39.084203</td>\n",
       "      <td>166</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2017</td>\n",
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       "      <td>18002</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>15.020000</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>41</td>\n",
       "      <td>good</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18003</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>24.380000</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>70</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18004</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>22.910000</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>68</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18005</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>16.640000</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>54</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18006</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>15.000000</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>50</td>\n",
       "      <td>good</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18007 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City       Date      PM2.5         NO        NO2        NOx  \\\n",
       "0          ahmedabad 2017-01-01  67.854497  22.443395  59.054643  47.388267   \n",
       "1          ahmedabad 2017-02-01  67.854497  22.443395  59.054643  47.388267   \n",
       "2          ahmedabad 2017-03-01  67.854497  22.443395  59.054643  47.388267   \n",
       "3          ahmedabad 2017-04-01  67.854497  22.443395  59.054643  47.388267   \n",
       "4          ahmedabad 2017-05-01  67.854497  22.443395  59.054643  47.388267   \n",
       "...              ...        ...        ...        ...        ...        ...   \n",
       "18002  visakhapatnam 2020-06-27  15.020000   7.680000  25.060000  19.540000   \n",
       "18003  visakhapatnam 2020-06-28  24.380000   3.420000  26.060000  16.530000   \n",
       "18004  visakhapatnam 2020-06-29  22.910000   3.450000  29.530000  18.330000   \n",
       "18005  visakhapatnam 2020-06-30  16.640000   4.050000  29.260000  18.800000   \n",
       "18006  visakhapatnam 2020-01-07  15.000000   0.400000  26.850000  14.050000   \n",
       "\n",
       "              CO        SO2         O3  AQI    AQI_Bucket  year  \n",
       "0      22.208613  55.273918  39.084203  166      moderate  2017  \n",
       "1      22.208613  55.273918  39.084203  166      moderate  2017  \n",
       "2      22.208613  55.273918  39.084203  166      moderate  2017  \n",
       "3      22.208613  55.273918  39.084203  166      moderate  2017  \n",
       "4      22.208613  55.273918  39.084203  166      moderate  2017  \n",
       "...          ...        ...        ...  ...           ...   ...  \n",
       "18002   0.470000   8.550000  23.300000   41          good  2020  \n",
       "18003   0.520000  12.720000  30.140000   70  satisfactory  2020  \n",
       "18004   0.480000   8.420000  30.960000   68  satisfactory  2020  \n",
       "18005   0.520000   9.840000  28.300000   54  satisfactory  2020  \n",
       "18006   0.590000   2.100000  17.050000   50          good  2020  \n",
       "\n",
       "[18007 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.drop(columns=['Unnamed: 0'])\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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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       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
       "      <td>22.208613</td>\n",
       "      <td>55.273918</td>\n",
       "      <td>39.084203</td>\n",
       "      <td>166</td>\n",
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       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
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       "      <td>22.208613</td>\n",
       "      <td>55.273918</td>\n",
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       "      <td>moderate</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
       "      <td>22.208613</td>\n",
       "      <td>55.273918</td>\n",
       "      <td>39.084203</td>\n",
       "      <td>166</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
       "      <td>22.208613</td>\n",
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       "      <td>166</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>67.854497</td>\n",
       "      <td>22.443395</td>\n",
       "      <td>59.054643</td>\n",
       "      <td>47.388267</td>\n",
       "      <td>22.208613</td>\n",
       "      <td>55.273918</td>\n",
       "      <td>39.084203</td>\n",
       "      <td>166</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>18002</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>15.020000</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>41</td>\n",
       "      <td>good</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18003</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>24.380000</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>70</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18004</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>22.910000</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>68</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18005</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>16.640000</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>54</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18006</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>15.000000</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>50</td>\n",
       "      <td>good</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18007 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City       Date      PM2.5         NO        NO2        NOx  \\\n",
       "0          ahmedabad 2017-01-01  67.854497  22.443395  59.054643  47.388267   \n",
       "1          ahmedabad 2017-02-01  67.854497  22.443395  59.054643  47.388267   \n",
       "2          ahmedabad 2017-03-01  67.854497  22.443395  59.054643  47.388267   \n",
       "3          ahmedabad 2017-04-01  67.854497  22.443395  59.054643  47.388267   \n",
       "4          ahmedabad 2017-05-01  67.854497  22.443395  59.054643  47.388267   \n",
       "...              ...        ...        ...        ...        ...        ...   \n",
       "18002  visakhapatnam 2020-06-27  15.020000   7.680000  25.060000  19.540000   \n",
       "18003  visakhapatnam 2020-06-28  24.380000   3.420000  26.060000  16.530000   \n",
       "18004  visakhapatnam 2020-06-29  22.910000   3.450000  29.530000  18.330000   \n",
       "18005  visakhapatnam 2020-06-30  16.640000   4.050000  29.260000  18.800000   \n",
       "18006  visakhapatnam 2020-01-07  15.000000   0.400000  26.850000  14.050000   \n",
       "\n",
       "              CO        SO2         O3  AQI    AQI_Bucket  year  month  \n",
       "0      22.208613  55.273918  39.084203  166      moderate  2017      1  \n",
       "1      22.208613  55.273918  39.084203  166      moderate  2017      2  \n",
       "2      22.208613  55.273918  39.084203  166      moderate  2017      3  \n",
       "3      22.208613  55.273918  39.084203  166      moderate  2017      4  \n",
       "4      22.208613  55.273918  39.084203  166      moderate  2017      5  \n",
       "...          ...        ...        ...  ...           ...   ...    ...  \n",
       "18002   0.470000   8.550000  23.300000   41          good  2020      6  \n",
       "18003   0.520000  12.720000  30.140000   70  satisfactory  2020      6  \n",
       "18004   0.480000   8.420000  30.960000   68  satisfactory  2020      6  \n",
       "18005   0.520000   9.840000  28.300000   54  satisfactory  2020      6  \n",
       "18006   0.590000   2.100000  17.050000   50          good  2020      1  \n",
       "\n",
       "[18007 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['month'] = pd.DatetimeIndex(data['Date']).month\n",
    "data.head()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1097</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>69.54</td>\n",
       "      <td>7.06</td>\n",
       "      <td>15.13</td>\n",
       "      <td>12.65</td>\n",
       "      <td>7.06</td>\n",
       "      <td>35.78</td>\n",
       "      <td>23.66</td>\n",
       "      <td>220</td>\n",
       "      <td>poor</td>\n",
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       "    <tr>\n",
       "      <td>1098</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>66.20</td>\n",
       "      <td>8.97</td>\n",
       "      <td>20.79</td>\n",
       "      <td>16.84</td>\n",
       "      <td>8.97</td>\n",
       "      <td>38.98</td>\n",
       "      <td>23.65</td>\n",
       "      <td>254</td>\n",
       "      <td>poor</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1099</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>55.99</td>\n",
       "      <td>5.41</td>\n",
       "      <td>15.34</td>\n",
       "      <td>11.53</td>\n",
       "      <td>5.41</td>\n",
       "      <td>45.83</td>\n",
       "      <td>23.61</td>\n",
       "      <td>255</td>\n",
       "      <td>poor</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1102</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>61.40</td>\n",
       "      <td>2.38</td>\n",
       "      <td>16.33</td>\n",
       "      <td>9.74</td>\n",
       "      <td>2.38</td>\n",
       "      <td>30.54</td>\n",
       "      <td>23.59</td>\n",
       "      <td>157</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1103</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>38.89</td>\n",
       "      <td>2.41</td>\n",
       "      <td>14.14</td>\n",
       "      <td>8.70</td>\n",
       "      <td>2.41</td>\n",
       "      <td>46.78</td>\n",
       "      <td>23.64</td>\n",
       "      <td>129</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17983</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>31.14</td>\n",
       "      <td>6.55</td>\n",
       "      <td>41.56</td>\n",
       "      <td>27.26</td>\n",
       "      <td>0.64</td>\n",
       "      <td>6.61</td>\n",
       "      <td>36.65</td>\n",
       "      <td>115</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17984</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-09-06</td>\n",
       "      <td>31.84</td>\n",
       "      <td>4.69</td>\n",
       "      <td>50.11</td>\n",
       "      <td>30.35</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5.06</td>\n",
       "      <td>33.86</td>\n",
       "      <td>96</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17985</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-10-06</td>\n",
       "      <td>32.55</td>\n",
       "      <td>4.78</td>\n",
       "      <td>44.59</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.69</td>\n",
       "      <td>5.90</td>\n",
       "      <td>35.06</td>\n",
       "      <td>92</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17986</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>37.71</td>\n",
       "      <td>9.97</td>\n",
       "      <td>52.10</td>\n",
       "      <td>35.79</td>\n",
       "      <td>0.70</td>\n",
       "      <td>5.59</td>\n",
       "      <td>26.59</td>\n",
       "      <td>90</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17987</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-12-06</td>\n",
       "      <td>37.67</td>\n",
       "      <td>12.17</td>\n",
       "      <td>53.59</td>\n",
       "      <td>35.52</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.74</td>\n",
       "      <td>17.35</td>\n",
       "      <td>84</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1560 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City       Date  PM2.5     NO    NO2    NOx    CO    SO2  \\\n",
       "1097       ahmedabad 2020-03-01  69.54   7.06  15.13  12.65  7.06  35.78   \n",
       "1098       ahmedabad 2020-04-01  66.20   8.97  20.79  16.84  8.97  38.98   \n",
       "1099       ahmedabad 2020-05-01  55.99   5.41  15.34  11.53  5.41  45.83   \n",
       "1102       ahmedabad 2020-08-01  61.40   2.38  16.33   9.74  2.38  30.54   \n",
       "1103       ahmedabad 2020-09-01  38.89   2.41  14.14   8.70  2.41  46.78   \n",
       "...              ...        ...    ...    ...    ...    ...   ...    ...   \n",
       "17983  visakhapatnam 2020-08-06  31.14   6.55  41.56  27.26  0.64   6.61   \n",
       "17984  visakhapatnam 2020-09-06  31.84   4.69  50.11  30.35  0.88   5.06   \n",
       "17985  visakhapatnam 2020-10-06  32.55   4.78  44.59  27.38  0.69   5.90   \n",
       "17986  visakhapatnam 2020-11-06  37.71   9.97  52.10  35.79  0.70   5.59   \n",
       "17987  visakhapatnam 2020-12-06  37.67  12.17  53.59  35.52  0.97   5.74   \n",
       "\n",
       "          O3  AQI    AQI_Bucket  year  \n",
       "1097   23.66  220          poor  2020  \n",
       "1098   23.65  254          poor  2020  \n",
       "1099   23.61  255          poor  2020  \n",
       "1102   23.59  157      moderate  2020  \n",
       "1103   23.64  129      moderate  2020  \n",
       "...      ...  ...           ...   ...  \n",
       "17983  36.65  115      moderate  2020  \n",
       "17984  33.86   96  satisfactory  2020  \n",
       "17985  35.06   92  satisfactory  2020  \n",
       "17986  26.59   90  satisfactory  2020  \n",
       "17987  17.35   84  satisfactory  2020  \n",
       "\n",
       "[1560 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1= data[(data['year']==2020)& (data['month']!=1) & (data['month']!=2)&(data['month']!=6)&(data['month']!=7)]\n",
    "df1=df1.drop(columns=['month'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>69.54</td>\n",
       "      <td>7.06</td>\n",
       "      <td>15.13</td>\n",
       "      <td>12.65</td>\n",
       "      <td>7.06</td>\n",
       "      <td>35.78</td>\n",
       "      <td>23.66</td>\n",
       "      <td>220</td>\n",
       "      <td>poor</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>66.20</td>\n",
       "      <td>8.97</td>\n",
       "      <td>20.79</td>\n",
       "      <td>16.84</td>\n",
       "      <td>8.97</td>\n",
       "      <td>38.98</td>\n",
       "      <td>23.65</td>\n",
       "      <td>254</td>\n",
       "      <td>poor</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>55.99</td>\n",
       "      <td>5.41</td>\n",
       "      <td>15.34</td>\n",
       "      <td>11.53</td>\n",
       "      <td>5.41</td>\n",
       "      <td>45.83</td>\n",
       "      <td>23.61</td>\n",
       "      <td>255</td>\n",
       "      <td>poor</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>61.40</td>\n",
       "      <td>2.38</td>\n",
       "      <td>16.33</td>\n",
       "      <td>9.74</td>\n",
       "      <td>2.38</td>\n",
       "      <td>30.54</td>\n",
       "      <td>23.59</td>\n",
       "      <td>157</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>38.89</td>\n",
       "      <td>2.41</td>\n",
       "      <td>14.14</td>\n",
       "      <td>8.70</td>\n",
       "      <td>2.41</td>\n",
       "      <td>46.78</td>\n",
       "      <td>23.64</td>\n",
       "      <td>129</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>1555</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>31.14</td>\n",
       "      <td>6.55</td>\n",
       "      <td>41.56</td>\n",
       "      <td>27.26</td>\n",
       "      <td>0.64</td>\n",
       "      <td>6.61</td>\n",
       "      <td>36.65</td>\n",
       "      <td>115</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1556</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-09-06</td>\n",
       "      <td>31.84</td>\n",
       "      <td>4.69</td>\n",
       "      <td>50.11</td>\n",
       "      <td>30.35</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5.06</td>\n",
       "      <td>33.86</td>\n",
       "      <td>96</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1557</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-10-06</td>\n",
       "      <td>32.55</td>\n",
       "      <td>4.78</td>\n",
       "      <td>44.59</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.69</td>\n",
       "      <td>5.90</td>\n",
       "      <td>35.06</td>\n",
       "      <td>92</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1558</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>37.71</td>\n",
       "      <td>9.97</td>\n",
       "      <td>52.10</td>\n",
       "      <td>35.79</td>\n",
       "      <td>0.70</td>\n",
       "      <td>5.59</td>\n",
       "      <td>26.59</td>\n",
       "      <td>90</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1559</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>2020-12-06</td>\n",
       "      <td>37.67</td>\n",
       "      <td>12.17</td>\n",
       "      <td>53.59</td>\n",
       "      <td>35.52</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.74</td>\n",
       "      <td>17.35</td>\n",
       "      <td>84</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1560 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               City       Date  PM2.5     NO    NO2    NOx    CO    SO2  \\\n",
       "0         ahmedabad 2020-03-01  69.54   7.06  15.13  12.65  7.06  35.78   \n",
       "1         ahmedabad 2020-04-01  66.20   8.97  20.79  16.84  8.97  38.98   \n",
       "2         ahmedabad 2020-05-01  55.99   5.41  15.34  11.53  5.41  45.83   \n",
       "3         ahmedabad 2020-08-01  61.40   2.38  16.33   9.74  2.38  30.54   \n",
       "4         ahmedabad 2020-09-01  38.89   2.41  14.14   8.70  2.41  46.78   \n",
       "...             ...        ...    ...    ...    ...    ...   ...    ...   \n",
       "1555  visakhapatnam 2020-08-06  31.14   6.55  41.56  27.26  0.64   6.61   \n",
       "1556  visakhapatnam 2020-09-06  31.84   4.69  50.11  30.35  0.88   5.06   \n",
       "1557  visakhapatnam 2020-10-06  32.55   4.78  44.59  27.38  0.69   5.90   \n",
       "1558  visakhapatnam 2020-11-06  37.71   9.97  52.10  35.79  0.70   5.59   \n",
       "1559  visakhapatnam 2020-12-06  37.67  12.17  53.59  35.52  0.97   5.74   \n",
       "\n",
       "         O3  AQI    AQI_Bucket  year  \n",
       "0     23.66  220          poor  2020  \n",
       "1     23.65  254          poor  2020  \n",
       "2     23.61  255          poor  2020  \n",
       "3     23.59  157      moderate  2020  \n",
       "4     23.64  129      moderate  2020  \n",
       "...     ...  ...           ...   ...  \n",
       "1555  36.65  115      moderate  2020  \n",
       "1556  33.86   96  satisfactory  2020  \n",
       "1557  35.06   92  satisfactory  2020  \n",
       "1558  26.59   90  satisfactory  2020  \n",
       "1559  17.35   84  satisfactory  2020  \n",
       "\n",
       "[1560 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df1.reset_index(drop=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0 statement: The AQI has reduced by nearly more than 65% during lockdown\n",
      "H1 statement: The AQI has not reduced more than 65% during lockdown\n",
      "Mean of column AQI in 2020 (sample mean):  113.34871794871795\n",
      "Mean of column AQI (population mean):  168.29044260565337\n",
      "65% of population mean:  109.38878769367469\n",
      "Alpha:  0.05\n",
      "Sample size (total observations in 2020):  1559\n",
      "Standard deviation is:  68.2388378983247\n",
      "hypothetical z value:  2.2912842629055534 \n",
      "\n",
      "Failed to reject hypothesis\n",
      "H0 : μ >= 109.38878769367469\n",
      "H1 : μ < 109.38878769367469\n",
      "hypothetical z value:  2.2912842629055534 \n",
      "\n",
      "P value was found to be(in %) 98.9026510906384\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats\n",
    "from scipy.stats import norm\n",
    "import numpy as np\n",
    "import math\n",
    "from math import sqrt\n",
    "a_pop_mean = data['AQI'].mean()\n",
    "pop_mean = 0.65*data['AQI'].mean()\n",
    "pop_var = data['AQI'].var()\n",
    "pop_sd=math.sqrt(pop_var)\n",
    "s_mean = (df1['AQI'].mean())\n",
    "s_var = df1['AQI'].var()\n",
    "s_sd=math.sqrt(s_var)\n",
    "n=1559\n",
    "alpha=0.05\n",
    "print(\"H0 statement: The AQI has reduced by nearly more than 65% during lockdown\")\n",
    "print(\"H1 statement: The AQI has not reduced more than 65% during lockdown\")\n",
    "print('Mean of column AQI in 2020 (sample mean): ' ,s_mean)\n",
    "print('Mean of column AQI (population mean): ' ,a_pop_mean)\n",
    "print('65% of population mean: ' ,pop_mean)\n",
    "print('Alpha: ',alpha)\n",
    "print('Sample size (total observations in 2020): ',n)\n",
    "print('Standard deviation is: ',s_sd)\n",
    "hypo_z=(s_mean-pop_mean)/(s_sd/sqrt(n))\n",
    "print('hypothetical z value: ',hypo_z,'\\n')\n",
    "if ((scipy.stats.norm.sf(abs(hypo_z)))>0.05):\n",
    "    print(\"Reject hypothesis\")\n",
    "else:\n",
    "    print(\"Failed to reject hypothesis\")\n",
    "print('H0 : μ >=', pop_mean)\n",
    "print('H1 : μ <', pop_mean)\n",
    "value=one_sided_hypo(s_mean,pop_mean,s_sd,n,alpha)\n",
    "\n",
    "print(\"P value was found to be(in %)\",100-scipy.stats.norm.sf(abs(hypo_z))*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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