City_by_hours_imputation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =pd.read_csv('cities_by_hours.csv',parse_dates=['Datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Datetime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>NH3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>C6H6</th>\n",
       "      <th>C7H8</th>\n",
       "      <th>C8H10</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>40.01</td>\n",
       "      <td>36.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>122.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "      <td>27.75</td>\n",
       "      <td>19.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "      <td>85.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08</td>\n",
       "      <td>19.32</td>\n",
       "      <td>11.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08</td>\n",
       "      <td>52.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 04:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.30</td>\n",
       "      <td>16.45</td>\n",
       "      <td>9.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.30</td>\n",
       "      <td>39.53</td>\n",
       "      <td>153.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 05:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>14.90</td>\n",
       "      <td>7.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>32.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City            Datetime  PM2.5  PM10    NO    NO2    NOx  NH3    CO  \\\n",
       "0  Ahmedabad 2015-01-01 01:00:00    NaN   NaN  1.00  40.01  36.37  NaN  1.00   \n",
       "1  Ahmedabad 2015-01-01 02:00:00    NaN   NaN  0.02  27.75  19.73  NaN  0.02   \n",
       "2  Ahmedabad 2015-01-01 03:00:00    NaN   NaN  0.08  19.32  11.08  NaN  0.08   \n",
       "3  Ahmedabad 2015-01-01 04:00:00    NaN   NaN  0.30  16.45   9.20  NaN  0.30   \n",
       "4  Ahmedabad 2015-01-01 05:00:00    NaN   NaN  0.12  14.90   7.85  NaN  0.12   \n",
       "\n",
       "      SO2      O3  C6H6  C7H8  C8H10  AQI AQI_Bucket  \n",
       "0  122.07     NaN   0.0   0.0    0.0  NaN        NaN  \n",
       "1   85.90     NaN   0.0   0.0    0.0  NaN        NaN  \n",
       "2   52.83     NaN   0.0   0.0    0.0  NaN        NaN  \n",
       "3   39.53  153.58   0.0   0.0    0.0  NaN        NaN  \n",
       "4   32.63     NaN   0.0   0.0    0.0  NaN        NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['City', 'Datetime', 'PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO',\n",
       "       'SO2', 'O3', 'C6H6', 'C7H8', 'C8H10', 'AQI', 'AQI_Bucket'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(707875, 16)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                  object\n",
       "Datetime      datetime64[ns]\n",
       "PM2.5                float64\n",
       "PM10                 float64\n",
       "NO                   float64\n",
       "NO2                  float64\n",
       "NOx                  float64\n",
       "NH3                  float64\n",
       "CO                   float64\n",
       "SO2                  float64\n",
       "O3                   float64\n",
       "C6H6                 float64\n",
       "C7H8                 float64\n",
       "C8H10                float64\n",
       "AQI                  float64\n",
       "AQI_Bucket            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AQI', ylabel='City'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Grouping the AQI by city and calculating the average AQI per city\n",
    "x=pd.DataFrame(df.groupby(['City'])[['AQI']].mean().sort_values(by='AQI',ascending=False).head(10))\n",
    "x=x.reset_index('City')\n",
    "\n",
    "#plotting the average AQI per city\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.barplot(data=x,x='AQI',y='City',orient='h',palette='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_value(df):\n",
    "        # Total missing values\n",
    "        mis_val = df.isnull().sum()\n",
    "        \n",
    "        # Percentage of missing values\n",
    "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "        \n",
    "        # Make a table with the results\n",
    "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "        \n",
    "        # Rename the columns\n",
    "        mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "        \n",
    "        # Sort the table by percentage of missing descending\n",
    "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "        '% of Total Values', ascending=False).round(1)\n",
    "        \n",
    "        # Print some summary information\n",
    "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "              \" columns that have missing values.\")\n",
    "        \n",
    "        # Return the dataframe with missing information\n",
    "        return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 16 columns.\n",
      "There are 14 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C8H10</th>\n",
       "      <td>455829</td>\n",
       "      <td>64.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>296737</td>\n",
       "      <td>41.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>272542</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C7H8</th>\n",
       "      <td>220607</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C6H6</th>\n",
       "      <td>163646</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>145088</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>130373</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>129208</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>129080</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI_Bucket</th>\n",
       "      <td>129080</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>123224</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>117122</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>116632</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>86517</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Missing Values  % of Total Values\n",
       "C8H10               455829               64.4\n",
       "PM10                296737               41.9\n",
       "NH3                 272542               38.5\n",
       "C7H8                220607               31.2\n",
       "C6H6                163646               23.1\n",
       "PM2.5               145088               20.5\n",
       "SO2                 130373               18.4\n",
       "O3                  129208               18.3\n",
       "AQI                 129080               18.2\n",
       "AQI_Bucket          129080               18.2\n",
       "NOx                 123224               17.4\n",
       "NO2                 117122               16.5\n",
       "NO                  116632               16.5\n",
       "CO                   86517               12.2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_value(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5cgoODrYWCJMStp30Ox8sPDxcrVq1kp+fnwoXLqxMmTJpwoQJOn36tIYOHao5c+bo3LlzCg8PV/ny5a1pS1KnTq1MmTLp4MGDyXwEKce96mb8/aO/v7/8/f1pNx/SnX34/v37y9PTUxUrVtTQoUP12muv6fz589q0aZPq169v/cAcEhKi9OnT39VXfZyFhYXppZdekr+/vzw9PeV0OrV06VK98sor6tixo5o0aUL+I4mI5T9DwjmFuT05OnXqVIWEhGjy5Mn66aef9Oyzz0qS8ufPr06dOsnT01N//fWXbty4oYIFC+ry5cv65ZdflCNHDhbE+RvxtNfUqVPl5eWlqVOnyt/fX8HBwerYsaMWLlyo0aNHJ9g3e/bsatCggYYPH67y5curZcuWLORyG4fDoQEDBujChQsKDQ3Vk08+qd69e6tgwYIaPHiwvL29NXLkSP35558JOqQZMmRQxowZrc4rEkqVKpWqVKmiffv2afny5bp+/bo++OAD68Y9/v9Xr15VeHi4cufOreDgYP3000/Knj0757rifpVv3bq1/Pz8NH36dGuhi5CQEBlj5O3trbRp01qx/OuvvxQREUEs7yOp8ZTi6md8sjQ+6dy5c2dNmTKFRQQVl0geN26cFZv06dNr//79euONN7R27Vq1b99ekqibDyGp9TMgIEB58+bVjh07VL58eRUrVkx//fWXtm3bpixZslij9h93c+bMUerUqTV9+nRlzJhR7u7uGjdunGbMmKHz58+rY8eOypcvn7W/y+WSl5eXIiIiNGbMGG3evFlr1qyxfmR+3H322WdWDHPmzCk3NzdFRUXpypUr8vX1VZo0aax96Xc+2IwZM+Tt7a2JEycqW7ZskqQJEyZo6dKlatu2rQoVKqRChQpJiltfYP/+/QoMDNS1a9cUHBysJ554gh9C/na/unn58mX5+fkpTZo0ypAhA+3mQ7hXH37UqFHy8PDQs88+qxkzZqhv376aPXu2QkND1apVKx07dkzffPONMmTIYP3o/LgzxmjkyJHWE3SZMmWSl5eXRo8erSVLlig4OFhvvPGGunfvLiluYcabN2+S/7gHYvnPkXBOQaKiotSiRQvlzJlT48ePV/r06RUbG6scOXLo0KFDevbZZxUbGysPDw95enoqPDxcK1eu1CeffCIfHx+lTZtWFy5c0MKFC61RPY8z4mm/CxcuKEuWLMqaNaskKUeOHMqXL58yZMigPXv2yMfHR35+fsqcObMkqWnTpjpy5IhGjhyp7Nmzq3bt2slZ/BQjMjJSbdq0Ufr06dW6dWsdP35c27Zt09y5czVkyBBJcb+OXrlyRVFRUQoNDbU6pdeuXZO3t7d1Q8B8ZbfExyIgIEC5cuVSt27dNHjwYLm7u2v48OEJbjw//vhjrVu3TunSpVPatGl17NgxLV68mHNd0v79+xUTE6Mnn3zSeqRxy5Ytmjp1qoKDg2WM0QsvvKCXXnpJgYGBWrVqFbFMxMGDBx8qnrefz+nSpdPChQt1/fp1+fj4JONRpByXL19W8eLFrUdqCxUqpHTp0ikkJETfffedAgIClDVrVmXKlIm6mQRJqZ/PP/+8evTooUGDBqlr165688035ePjI3d3d504cUJLly69ay2Hx9XVq1eVJUsW5cyZ09o2aNAgBQQEaMmSJXJzc1O3bt2UI0cOGWPkcrkUERGhiRMnauPGjQoKClLx4sWT8QhSlqtXr0qSMmXKJDc3N3399deaMGGCrl+/Lg8PDzVq1EhNmjRR0aJFJdHvfJBz584pZ86cypYtm2JiYuTp6alWrVpp8eLF+v777/XEE0/IGCOn06kvv/xSc+bMkY+Pj9KnT69Dhw5p+fLlJPH/lljddHd3V6NGjdSlSxfazSRKrA8/dOhQpUqVSrVr19aiRYs0YcIEjRw5UmPHjlWGDBkUExOjOXPmEM+/RURE6OTJkwoMDFT27Nmt6ZleffVVbdiwQT///LNmz56tV199VTly5LDyH76+vuQ/7kAs/zkSzilIZGSkGjRooObNm1sJu4CAAFWvXl0ff/yx2rRpk6AhTZs2rapXry4pLhGYP39+PffccwlGTjzOiKc94jukUtwvz/v27bNeM8bo+PHj2rNnj4KCghQbG6tq1arp1VdfVYkSJSRJvXv31o0bN5Q/f/5kKX9K9PPPP8vlcmnw4MEqXLiwpLhHaffs2SNvb2/rEdGWLVuqS5cumjx5spo0aaLY2Fh9+umnOnHihEaOHCmJhXHupVq1alq5cqUKFy6sd955RyNGjJCnp6fef/99LVu2TM8++6wqV66ssLAwXbp0SU888YRGjhzJuf73uf7kk0+qadOmWrx4serVqycPDw+9/vrratq0qYoVK6Zz585p48aNunTpkkaOHGktNkQs761MmTJJjufAgQPvesQ2bdq01gjox52bm5siIiISPNYd/6Pcp59+qvnz50uSXnjhBfXv319PP/00dfMBypQpoxdffFGLFi26b/3csGGDLl26pKlTp2rx4sXavn27zpw5o8KFC+uDDz7g+n4bLy+vBAv/xk/h1rNnT0nSvHnzlCNHDnXo0EHe3t4qW7as5syZozNnzmjlypUkm+/g5eWlkJAQpU6dWkeOHNEbb7yhli1bqmjRojpx4oQ2b96sU6dOqX///tbUbvQ778/lcun06dOSbi18lT17dqVLl043b96UFNfOpkqVSk8++aS6du2q3bt3K2fOnBoyZAgj72/zoLq5ceNGnTp1ShMnTtTSpUu1bds22s1ExN/PJNaHX7JkiRo0aKDJkyfr3Llz2r17t/Lnz6/AwECm0NGtfnzq1KnldDp15coVSbeennN3d9eTTz6pjBkzavXq1cqbN6+6deumatWqyc3NjfzHbYiljQxSlJiYGOvfsbGxxhhjtm/fbqpUqWK++uqrBNvxYMTznwkLCzMdO3Y0X3zxhTHGmN27d5saNWqYatWqmb59+5qqVauali1bmp07d5rr16+bJUuWmJo1a5rBgwebmJgY43K5jDHE+E5r1641xYsXN5cuXbK2LVy40NSqVcsMGDDAtGrVyqxdu9ZcvXrVrF692pQqVcqUKVPGVK9e3bzwwgvm4MGDyVj6lC0mJsYEBwebqlWrmm+//dYYY8yaNWtMyZIlTa1atUyNGjXMyZMnk7eQKVD8uf75559b2/r06WOqVq1q3n33XTNp0iQTHR1tvRYUFGQqVKhgli9fbowx1rmO++vdu/cD47ly5UpjDG3m/bhcLrNkyRIzZ84cExoaaowxZtiwYaZ27drm559/NtevXzfz5883FSpUMDNmzEjm0j5aHnS+ly9f3jrfb+9bPe5CQ0PNsmXLzLVr14wxxuzatcvUrFnTjBs3ztrH6XRa/x49erQpXbq0OXHihDEmrk9atmxZc+TIkf/fgj8izp49a+rUqWPGjRtnli9fbt5++20TFRVlvR4UFGQqVqxo5syZY4y51XbSht7bl19+aTp16mR+/fVXY0xc3YyMjDSVK1c206ZNu+/7uMbf7ezZs6Z27dqJ1s0KFSqY2bNnG2NoNxMTX79cLleiffjq1avTh7+P+H78l19+aYwxZtSoUaZatWpm+fLlVns4adIk89xzzxljjBkyZIh56qmnzLlz55KtzCkVsbSX+4NT0vg3RUZGasuWLVq9erV2796dYHGr+EUwatSooRw5cmjRokXWdsPK1fdEPO0Tv2L9zp07dfbsWUlSYGCgZs6cqYYNGyp37tyKiYnR66+/rgoVKih9+vRq166dXnjhBX311VeKiIiwfq2+fTGsx1l8PcuYMaN8fX114sQJSXEjoGbNmiUfHx/duHFDadOm1cCBA7Vq1Sq99NJL+uyzzzR58mR9+OGHWrp0qfXo6OMsIiJCK1as0OjRo7VixQrt2bNHUtwibJkyZVLFihWtUZDxI/UuXbqkIkWKWL80c97Huf1cP3/+vLV92LBhypUrlz799FPlyZNHnp6eVsxatWqlqlWrau3atZKI5e3Cw8M1e/ZsDRkyRAsXLtShQ4ckPVw8aTPjREVFadeuXdqyZYsOHTokNzc3tWrVSt26dbOmGImPc/x1qHPnzqpVq5Y2btwol8sll8uVzEeRstwZ03hDhw5NtH5Wq1bNqp88WRPH4XDoueee07Zt26wFgooXL64qVapo27ZtWrlypaS4kZBOp1OSNHDgQOXPn1+LFi2SMUY1atTQt99+a02T9Ti7s+08fvy4smbNqho1amjXrl366KOPrAW/o6OjJcXVzVq1amnNmjXWYrYSbah0dz/p0KFDqlu3rgYOHKinnnpKUlzddHd3t+pnvLCwMK1du1bHjx+XxDl/Zyz//PNP5cqVS3Xq1NGPP/5437r5zDPPaM2aNYqNjaVO3iYqKkrff/+9vvrqKx08eDDBOiuJ9eGLFi3KaNF7uL0fH/8EQ5cuXZQvXz7Nnz9fzz77rBo2bKiFCxfqnXfekSR17NhRXl5e+uyzzySJvtLfiKX9mFIjGTkcDrVv3143btxQaGioHA6HunTpotdee02enp5yc3Oz5hh+4403NGDAAC1evFgdOnRIsLAQ4hBP+8QvtpgnTx4VKFBA+/fvlyR5e3urePHiKl68uL755hvNnTtXhQsXloeHh6Kjo+Xl5SV/f38FBARYN1+4NS9ZfP2qUqWKhg4dqrJly0qKm5O0cePGatWqlfLmzStPT09NmjRJM2bMUJ06dVSwYEEevbtNWFiY2rRpI6fTKU9PT33yySfKly+funXrpueff15S3OKKP/74o7p166YBAwbo9OnT6ty5s1asWKG+ffvqww8/5HzX3ed6/JQ5MTExSp8+vVq0aKG8efNaN6eSrClffH19rUdyuZGKExYWpubNm8vlcilt2rTasmWLSpUqJUlWPPPkyXPfeBLHW+Kv6aGhoQoJCVFkZKTeffddtW7d2mpT4687efLkkXTrEUgfHx+lSZOGeN7hXjEdNGiQWrZsKT8/P873h+BwONS4cWPr8fi0adPK5XIpXbp06tu3r/r166dly5ZZ02N5e3tb9dbX11ehoaHWNYj52e/ddpYoUUIFCxZU9+7d9dZbb2nv3r3WehZeXl5Wnz5r1qzWgqpc1+Pcq5+UJ08evfbaa9ai6fH10Rij6OhoK4YOh0OjRo3Sjh079MknnyTnYaQI94vlW2+9pZ49e+rw4cPauXPnfetmqlSpEtwDPO4cDofatm2rkJAQRUREKCIiQi+++KIaNmyo8uXLS5L8/PwS7cNPmjSJ69Df7uzHHzhwQMYYZcmSRVOmTNHWrVu1f/9+eXt7a8qUKSpQoIAkKWfOnEqTJo1u3Lghieu6RCz/LUQjmcTExGjAgAHy9/fXzJkz9emnn2rAgAGaM2eO9u3bZ12U4kfoFi1aVOXKldOWLVv0559/SqJTdTviaR+Hw6EmTZood+7cmjRpkvLmzau9e/dar8ePeEqbNq18fX21fv16SXEdrNDQUB08eFD58uWTy+Vi1KPifsUfMWKEdu/ebcXDw8NDL7zwgrVibc6cOdW7d28VKFDAuqFv0KCBvL29rbkgiWWcmJgYvfvuu8qUKZNmzpypDRs2aMmSJQoPD9fatWutUTrPPPOM3N3d1aNHD3377beaO3euXnvtNfXr10+//vqrgoODk/lIkl9i57qnp6fc3d3VoEEDDR48WPny5dPNmzfl5uYmd3d33bx5UxcvXrSedKB+xp2jkyZNUsaMGTV79mytWbNGO3fuVLly5RQVFSUPDw81btxY77//vvLmzUs8ExEdHa3XX39d/v7+mjRpkhYvXqyWLVtq/PjxOn36tHW9jl+8KjQ0VFJcvb1586bOnTun/PnzE8vb3C+mY8eO1cmTJ+Xp6cn5nkTh4eGqX7++ChYsqHHjxilTpkzWa1FRUcqcObMmT56szJkza9GiRZo6daqkuH7mjRs35O7urowZM9JP+tv92s4KFSooIiJCmTJl0sSJE1W1alXt2rVLgwYNshJ6N2/e1IkTJ6xF8Ijn/ftJkZGRWr16tRWn+GTz9evXFRMTI39/fxljNHbsWH3xxReaO3fuYz8v7v1iGRERoUWLFsnX11djxoxRxYoVqZtJEBsbq3fffVf+/v6aMWOG1q5dq5EjR2rbtm2aOHGi1q1bJyluHQZJ9+3D//XXX8l5GCnG/frx8X2kgIAANW/eXMOGDdO7776r3LlzW++9ceOGMmbMaJ3jj3v9JJb/HkY4J5PIyEidPn1ajRo1shYNq1OnjubPn6+YmBiFhITIx8fHupkKCAhQly5d1LVrV02fPl1Dhw5NsAL244542iM8PFwNGzZUrly5NG7cOAUEBFiPg/3888+qWLGitW+xYsVUtGhRBQUF6ejRo8qbN6/++OMP/fbbb1q5ciUjnP/m5uam1atX69KlS+rRo4dKliyZ4MeN+NFjfn5+Cd4XEhIiX19fa8FLfhCJExUVpZMnT6px48bWY3UlS5bUyy+/rJEjR+rcuXMqUKCA8ubNqx9++EGZMmXSrFmzrFGmzZo1U6NGje6K9+MmKed6bGys9YjolStXtHz5ch07dkyFChXSkSNHtGfPHq1YscL6keRx5+bmphMnTihXrlxW3fzmm2+0ceNGnT59WhUqVNBTTz2l2rVrKyQkRPPnz9fJkyeJ5z2EhobqwoUL6tq1q7UAbWhoqDZs2GAlP9OnT6+0adPq5s2bWrp0qU6dOqXChQtr7969+v3334nlHRKLqcPh0IULF5QhQwb5+Pjo2rVrWrBgAfXzPjZt2qSQkBDVqVPHukZ///33+vzzz3X8+HEFBgbqueee09y5czVixAh99tln+uabb1S0aFFduHBBBw4c0ODBgxkF9bfE2s5Tp06pfPnyevbZZzVz5kwNHTpUX375pX755Rfly5dPDodDR44c0YoVK6wRuo+7B/WTzpw5Y43Mk271L69cuaLhw4dr/fr1CgoKYgFL3T+Wr7zyikaOHKmjR4+qUKFCmjRpkkaPHq2vv/6aupkIY4zOnTun2rVrq2TJkpKkXLlyWX3RxYsXK02aNCpXrpx27dql9OnT04e/j6T04+OnGXJzc1NISIhWrVqlXbt2qVq1atqzZ4/Onz+vZ555RtLjfZ9JLP9d9HSSyfXr13X58mXFxMRY27y9veVwOPTBBx+odu3a6tKli1asWGG9XqJECQ0ZMkR//vmnlThFHOJpj2+++UZVqlTRxIkTlTVrVklxHQGXy6WffvpJkqypSfz8/DRx4kSVL19ef/75p9atWydjjJYvX26tFP64i46Olru7u/z9/bVt2zaNHz9eBw4cSLBP/A3n5cuXdfDgQUVEROjSpUv67LPP5OPjo1y5ciVH0VMkY4xCQ0N1+vRppU+fXm5ubtY5nz9/frm7u8vhcEiKGzU+Y8YMzZ4925q6RJLSpElDR1XS9u3bVbVq1UTP9dvnwPf19ZUxRqdOndK2bdvk4eHBuX6b2NhYRUVFKTY2VunSpZMkbdmyRb169dK1a9eUPXt2ff755xo3bpyWLVumDBkyyNPTUydPniSe9+BwOHTy5EmlTZvW2hb/uOLQoUNVq1Ytvfrqq1q7dq38/PwUFhamvXv3asOGDYqJiSGW9xAWFpZoTJ955hn16NFDn332mQICAqifiShVqpSqVKmi7777TqGhodq+fbu6d++uEydOyN/fX1u3blW/fv30xRdfaMSIERo2bJjy5s2r8+fPK3PmzAoKClLBggWT+zBSBJfLlWjbmSNHDn355ZcaOHCgPv/8c33wwQcaN26cSpUqJTc3NwUGBurjjz+2Bps87lwuV5L7SVJcnz5z5swqUqSIZs6cqc8++0wrV64k2fy3kJCQRGMZEREhKW5tluHDh2vUqFEqXbo0dfMeYmNj5XA4dP36desJT6fTKWOMypUrp/fee0/e3t5asGCBjh07ppkzZ2ru3Ln04e/jm2++eWA/3t3d3Up+pkuXTpkyZdLNmze1fv16hYaGatGiRdaUZI8zYvkv+9eWI8RdHA6HGTlypLXy95gxY0zx4sXNpEmTzLJly0yDBg3Miy++aBYtWmTWrFlj2rVrZ2rUqGE2bdpkjLm1gmv8yuyPO+L574iKirJiE7+i8tixY03dunXN6dOnrf3iX4uOjjYREREmNDQ0werMiBMaGmqefvpp8+GHH5patWqZli1bmn379iXYJyYmxixdutRUrFjRVKlSxTRv3txUrVrVHDx4MJlKnfLcvkJ6v379zMsvv2xu3Lhhbfvzzz9NkSJFzM6dO+/5Hlasv1tYWJj178TO9dvbA5fLZcLCwjjX72POnDmmVKlS5tixY2bixIlm0qRJxuFwGGOMOXjwoOnTp4+pW7eu2b9/v4mNjTWxsbHE828RERHml19+sf7u0KGDmT59unE6nSY2NtY0adLEvPTSS2b58uXmk08+MS1btjT169c3+/btMzExMSY6Oto4HA5ieYfb28GkxnTv3r3Uzzs4HA4zYsQIq8+5fft2U65cOfPBBx+YV1991cydO9e6Jh06dMj07t3bNGrUyBw6dCjB58S3tUho9uzZSWo7//jjj2Qu6aPhYfpJ0dHRpnfv3qZkyZLm6NGjyVHcFOXO+8ukxJI+ZtINHz7cVKhQwZw8edIYc6tvaYwxv/zyi6levbp57733EtyP4t7Cw8OtfyfWj7+Tw+EwkZGR/3r5HiXE8t9Dwvn/SWhoqGnatKkpUqSI2bNnjzHGmCtXrpiJEyea+vXrm6ZNm5qqVauakydPWo3rsWPHTO3atc2oUaOSs+gpEvG0T1RUlPnzzz/NV199ZU6cOGF1muI7WsYYs2XLFlOmTBmzYcMGYww3TEkRH8djx46ZcuXKmYMHD5oDBw6YqlWr3jPpHB0dbRYuXGimTp1qPvroI3PmzJnkKHaKExUVZUJCQowxt+rd4cOHzfbt2xPsF9/5/+GHH6xtERER3Jzexul0mpCQEHPq1KkE228/nznXky4sLMwEBQWZS5cuWdtOnTplWrdubTp27GieffZZ89FHHyV4z/79+0358uXNwoUL/7+Lm6KFhoaaqlWrmvHjx1vJzQsXLiRIdG7YsMEcPHjQuqbv3buXWCbi9rYz/npETP839+pzGmPMsmXLTJEiRUz16tXN77//nuA9e/bsMWXLljWffPLJ/3NpUz6n02muXLliDh8+bG27cOGCadmy5QPbzkWLFiXYTkLq3teiQ4cOJamfFBYWZk6cOGEcDgf9TnPvc/3gwYNJimV4eLj57bffrL+pm7fuMbds2WKOHTtmYmNjzbFjx0yDBg1M586dzfnz540xCZPOGzduNEWLFk0QS8SJbzvvHJB0e11LrB9PnbyFWP7/YUqN/wfxK166ubkpffr01iJgmTNnVr9+/bR27VrVrFlTWbJksRZbk6SCBQsqV65cOnfuXHIWP8UhnvZxOBx6+eWX1b9/f/Xu3Vtdu3bV0KFDFRMTI09PTyt2tWvXVs2aNTVlyhRFRETIw8ODCfHvwel0Jnj0Jl583StWrJjmzZun8+fPa8SIEdq/f7+1j6enpzp27KjevXurZcuWCRYjeFy5XC6NGzdOgwcP1vXr160pHgoXLqwaNWpY+0iyHhH19fW1/h49erSGDh1qrRr8OHM4HOrWrZuaN2+uxo0bq0ePHvr888+txW3iHxPlXE+69evXa9iwYVq3bp0uX74sScqbN6/q16+v4OBgBQcHK3369JLi5oGUpOLFiytfvnwJzv3HncPhUNOmTZU/f3698sor1lyX2bNnl7e3t3WON2jQQEWKFLEeaSxRooSyZ8+u48ePJ1vZU6o7287461F8TOPPaWL6YHf2OU+dOmW91qJFCzVo0ED58+e35iONjY2VFDftRo4cOXTw4MHkKHaKFX8tatu2rRo1aqSuXbvq119/Vfbs2dWwYcMHtp379u1L8HnMlZnwWnTp0iVJUpEiRZLUTxozZoz69OkjY8xj3++881w/ceKEpLiF5pMSyw8++EDvv/++1ed83Ovm7feYvXr1Urdu3fT++++rQIECeuWVV3T27FmNHz9eFy9elIeHh6KjoyVJNWrUUKZMmWg773B729mkSRN169ZNP//8s6SEdS2xfvzjXifjEcv/XySc/2XxF6+cOXPqww8/VI4cOazOanyn1MvLS2FhYbpw4YKuXLkiDw8PuVwuXb58WZGRkQoMDEzGI0hZiKd9nE6nXn31Vfn6+ur999/X119/rZo1a+qbb77R8uXLJcUlTeM7Vy+//LI8PT01ceJEOZ1Oa3Vr3DJ9+nSNGzdO33//vbWtYMGCWrRokXx8fORyuRJNOsfHmrjGcXd317Vr1/TNN99owoQJunbtmqRbcYrfR4pb/EqSfHx85HQ6NXbsWK1du1ajR4+2blwfV06nU126dJHL5VKnTp00YcIEXb58WVOmTNHo0aOtH5icTqckzvWkip/Pdv78+Vq9erWuXLkiSWrbtq3q168vSRoxYoROnDhhLaL6119/KSYmxlp86HHncDisxZjGjh1rzZ13u9uvQ/E/jLhcLl24cEEul0tFihT5fy3zo+BBbeftN0rE9P7u1eeMH+RgjJG3t7def/11jRgxQh4eHnI6ndYN6YULF2SMUf78+ZP5KFIOp9Oprl27ysPDQz179tT06dN1+PBhTZkyRVJc29mgQQO5XC7azodw+7Xok08+UXBwcILXH9RPGjt2rHx8fP5/C53C3OtcP336tKSEfXL6nElzr3vMWrVq6auvvtJHH32kFi1aqGHDhjpw4ICGDx+uM2fOWD82X716VV5eXo99nbzdvdrOQ4cOaebMmQn2i8+F0I+/P2L5/4+lpv9FDodDzz//vAoVKqSxY8cqS5YsKl26tL766it17tw5QUP61FNP6euvv9aQIUPUu3dvhYSE6IsvvtD58+etG9fHHfG01969e3Xx4kWNGjVKFStWlCT17dtXP/30k3766Sd16NBB0q3OValSpVS9enXt3LlTn332mZo1a5ZgUTHEjbI/cOCAFi1apJiYGNWsWVOSrMUx4mNZtGhRzZs3T127dtXo0aP19ttvq1SpUtbr/GoqKwmaIUMGxcTE6JdfftH48eP11ltvKSAgQC6XK8Eo8vgYnzt3TkuWLNH69ev18ccfs/CNpKNHj+rKlSsaM2aMKlSoIEkqV66cpkyZou3bt8vhcGjUqFFWZ59zPWmKFSum0qVLK3Xq1Jo+fbpiY2PVqlUrZc2aVT169FDq1Km1cOFCNW/eXN27d1d0dLROnjzJdehvxhh98MEHOn/+vIKCgpQlSxZJcdemI0eO6OrVqypcuLCqVasmT09PnT9/Xl9//bWefPJJhYaG6ssvv9S1a9esdhZxHqbtvHz5sj7//HNieg9J7XPGjwq9ePGi1qxZo3LlysnhcGjHjh0KCQlR9erVk/MwUpQ9e/bo0qVLGjdunMqXLy8prr6+9dZbunz5srJmzaru3bsrICBAU6ZMoe1Montdi15++WVlzJhRUlxb6+bmRj/pPh7m/jIesUxcYveY27ZtU+vWrfXaa68pXbp0Wrdundq2bas33nhDUVFR+v333xUdHa1y5col81GkHIm1ncHBwcqcObOkW4t904+/P2L5/4+E87/EGKPZs2eraNGiGjVqlHUjVapUKX355Ze6fPmyfHx8rMeZ69atq1OnTmnDhg1q1qyZsmfPrnTp0mnevHn8mi/i+W+4ceOGrl69asXS6XTKx8dHVatW1ZYtW3Tt2jUFBARIihv55OXlpddff107duxQUFCQ6tWrx6/Pf4vvzMffxB85ckTz5s2Tm5ubatSoYf0aensiOT7p3KJFC02ePFmzZ8+2En6Im2JEkk6dOqUWLVrI399fmzZtum/iJHXq1PLy8tL48eN15swZVlm/TXR0tBwOh1W/nE6n/P399eabb8rX11eff/65Ro8erffee08eHh6c60ngcrnkcrnkdDrVrVs31a5dW6NHj5bL5VKHDh2UIUMGdejQQU899ZSCgoKspxzy5MmjpUuXMupRcTGsVKmS9u7dqzVr1qhHjx7aunWr3njjDWXKlEnBwcFKnTq1AgMDNW/ePMXExGj9+vUaM2aMcuTIIV9fXy1YsOCxfwz8Tg/TdgYHB2vjxo3E9B5mzJihIkWKaPTo0fftc95+DQoODtaOHTs0bdo0Zc6cWZkyZdLChQuJ5W0cDodCQkISbEuTJo0yZMigOXPmKDg4WCVLllSXLl1UokQJLVmyhLbzARK7FnXp0kW+vr5W/zNVqlT0k+6Q1PvLew1yIJb3d/369UTvMeMTex07dlRgYKDWr1+vkSNHKl26dMqcObPmzZunnDlzJvNRpByJtZ2zZs3StWvXVKBAAfXo0UPe3t704xNBLP//kXD+l7i5uemll15SxowZrfmdJKlevXqaOHGigoKCNGjQIHl4eFhJ0m7duunFF1/UiRMnlDFjRmXIkMFK+D3uiKf98ubNq+joaP3444/Knz+/lYzy9fVVWFhYgn3jH2lOmzatPvnkE4WEhNDY3sPJkydVv359tW3bVv3799fcuXMlKdGk86effiovLy+SzbeJj1N0dLRu3LihatWqqWPHjoqOjtbXX3+dIHESf77nzZtX/v7+unz5sj766CMVLVo0uQ8jxciePbvc3Ny0bds2lSlTRt7e3oqJiZGPj4+6deum8PBw7dy5Uxs3blTjxo0VExPDuX4ft/+45Ovrq0qVKun777/X0KFDdfXqVc2ZM0dp0qTR77//rqxZs+r9999XyZIldfnyZfn7+ys2NlZp06ZN7sNIETw8PFSzZk398ssv+uGHH5QtWzbNmjVLvXr1Ur169ZQ5c2YtW7ZMH330kYYNG6ZRo0Zp/vz5OnPmjPz8/OTv7881/T6S0nZKcXM2z549W+fOnSOmfwsLC9O1a9fUp08fOZ3OBI/H39nnvD0BVapUKc2dO9eqn+nTp1eGDBmS4xBSlLCwMF28eFFPPPGEChUqJG9vbwUFBSk6OlpZs2bVxIkT5enpqRs3bujmzZtasGCBfv75Z82dO1djxoyh7byPpF6L9uzZI39/f33wwQfKly8f/aQ7uLm5qXnz5goICEj0/vL2c10Sfc57cDqdCg8Pl7+/vwoUKKCYmJj73mPePkK0QoUKqlChgrp37y5fX1+5u7vLz88vuQ4jxXiYtjMkJERBQUH69ddftXDhQnl6etKPvw2xTGb/T4sTPjYSW7EyNjbWxMbGmlGjRpkmTZqYY8eOJXgNdyOe9nE6nebMmTPm4sWL5vr168YYY9asWWNWr15tjLm18urMmTNN1apVzZUrV6z3RkVFEdM73B6P+Hrar18/M3DgQGOMMT///LOpVauWadOmTYLVrVnVNukcDodZuHChuXr1qjEmLnZjxowxzzzzjBk4cKC1Pd6cOXPM8ePHk6OoKd7cuXNNYGCgWbt2rbUtOjraGGPM9evXTcuWLU379u2t125fiflx53K57jpv48//BQsWmIYNG1rb582bZ4oUKWLKlCljtmzZYm0nnvd37tw5U7duXVOnTh3TuXNnExwcbMU7NDTUjB071tSqVctcunQpmUuaMsXExJirV6+a4OBg43A4jDFxcXuYthNxHA6HqVWrlqlSpYoJDQ1NcN4n1ufkun5vDofDPPPMM6Zy5comJCTEGGPM9u3bTdWqVU2NGjVM+fLlTYMGDcyZM2eMMXFxXLx4sSlXrpzVftL3vCUyMtL8/vvv1t+317vErkXbtm2zttNPiuNyue57XU7sXL8dsbwlNDTUtGjRwnz66adW33Lt2rVJuseMjIykj3SH/7XtrFChgtm6dasx5lYf/3FHLJMfiwbaKCwsTEOHDlX37t3VpUuXBCvXxz+K4+7urhdeeEFHjx61FhYzxtz1yymIp53iV2Pt0qWLGjdurDFjxigyMlLPP/+8XnrpJUm3FhO6efOmvL29lSZNGuu9I0eO1ODBg5Ot/ClNTEyMXn/9dW3btk3SrTmXX3zxRXXr1k1S3C/2Y8eO1cWLFzV37lzt2LHD2tew2EACYWFh+uCDD9SnTx917NhRCxcu1KlTp5QuXTq1bt1a/v7+iomJkZubm95++23VrVtXu3bt0vjx463FsCSpW7duKlCgQDIeSfK7VyzPnz+v1q1bq2bNmpo0aZI+//xzSbIWCkyfPr3eeust7dq1S4cOHZIk5if7W1hYmN577z116tRJrVu31po1a3Tt2jXrGlOjRg0ZY6xV6Y8dO6Y0adIoIiJCBw8e1F9//SWJeEpxsRw5cqR69+6t7t27a8eOHQoNDVXOnDk1YcIEnTlzRmnTplWmTJnk5uZmjcJv3769Lly4YNVN3OJwOKxzvUmTJurbt6/2798vHx8ftWnTJklt5+2LsD7OHA6HGjZsqJCQEGtqodufSEqsz8m6C3eLj+e1a9fk6elp9Slr1Kih9evXKygoSEWLFlXFihWVM2dOq542a9ZMERERunDhgiTRn79N7969NWTIEP3www+S4vqT8QtbJXYt2r9/v3X/RD/p1nW9S5cuCa7r8RI716VbbSaxjBO/+K+7u7tq1KhhTev0wgsvJOkec9SoURoyZEjyFD4F+idtZ1hYmM6fPy/p1vRajzNimTJwFbdJRESEmjdvrn379snf319Op1NTpkxR//79dejQIbm7uys2NlYul0tly5ZV586dNW3aNP35558koO6BeNonKipKL7/8smJiYtSzZ0916NBBjRs3VurUqa2G1+VyWY1pWFiY3N3d5ebmpvDwcI0bN85a0AFxQkJCdODAAY0dO1Y//vijtb1q1arW/ILGGJUvXz5B0vnbb7+VxKKAtwsPD1ezZs20b98+Zc+eXX5+fvroo4/Uq1cvHTp0yFqYxdPT07qxik+c7N69W8OHD79rLq7H1b1iuXLlSnXv3l3Xrl3T22+/rSeeeEJjx47V+vXrJcl6zDF+znYeFbslIiJCrVq10okTJ1SwYEF5eXlp5MiR+uqrr6x9MmTIoJCQEB05ckSDBg3Sjh079NFHH6lv376aPn26PvnkE6vePs7Cw8P10ksv6c8//1SaNGkUHBysfv36ad++fZLiFr2aN2+eunfvbr3H09NTxhidOXNGWbNmtRZyQZyIiAi1bdtWN27cUKtWrdSqVStrKogzZ84oVapUcnd3f2DbSULvVsIkf/78mjdvnpxOp3bv3n1XX9IYQ58zCe4Vz99++03GGMXExCggIEB+fn46evSoJCWop+fPn1fevHlJ5N1DYGCgjh49quXLl+u7776TdOvHzAwZMuj69ev3vRatXbtWMTExyVn8FOFB1/X4czmxc5028xaHw6EmTZooV65cmjx5srVQpSSlSpVKEveYD4O20z7EMuUgXW+TjRs3ytPTU1OmTLEWCFm9erVWrVql7t27a+bMmQoMDLQ6/S+88IJ2796t2bNna8CAASxkdwfiaZ/41X4HDhyowMBASXEr0//666/y8fFRlixZFBAQYK1sb4yRh4eHwsPDNX36dOsXQBbDuCVz5sxKnz69Tp48qWHDhum9996zVqKPH+0U3zGNTzq/9957Gj9+vDw8PFS1atVkPoKUY9asWXJ3d9fYsWOVK1cuSXHn+uDBg/XKK69o8eLFCgwMlMvlSjBH+9tvv63w8HDt2bNH0dHRyXwUKUNisXzppZe0atUqDR8+XB988IEGDRqks2fPqkGDBgoLC9M333yjDBkyMEfmbVatWiV3d3dNnDjRWrymVatW2rhxo1q1aiVJCggIUJkyZdStWzelT59e06dPV5EiRVSkSBF5eHioVq1ajG6WtGTJEqVNm1bTpk1T1qxZJUnPP/+81q5dq8qVK8vT01NPP/20pLjF7oKDg1W6dGmdO3dO69evV6pUqazFhxBnzZo18vDw0KhRo5Q3b15JUv78+TVo0CDt2rVLefLksa5HtJ33FxoaqqZNmyp37twaO3asdf3ev3+/KleunGDf+B+L6XPe3/3ieeDAAVWpUsVKPKVLl0716tXThg0b9OSTT6pevXo6e/asVq5cqejoaD3xxBPJfCQpT/w5/euvv+rmzZvy8PBQlSpVJMVdi8qWLZvotYhReg++rsef4/F9Ts71+4uOjtZLL72ktGnTav78+Vbszp07p2vXrsnDw0PZsmVTxowZresP95j3R9tpH2KZsvAT3T8U/0vojRs3dP369QQLDjRv3lyvvfaacuTIYY3Wi7/xLFq0qBo2bKhdu3YpKCiIX53/Rjztd+XKFZ0+fdpaCOiLL77Qiy++qO7du6tdu3Zq27atfvvtN6vx9fb2VkREhIYMGaK1a9dqxYoVdATucO3aNV28eFHPPvuscuXKpVGjRlmP3N0+2un2pPPw4cPl5uZGZ/UOp0+flo+PjzJnzmw9cte8eXOVKVNGsbGx6tq1q/VUQ3xHNX6/4cOHa8GCBSSi/pZYLKOjo9W2bVsZYzRq1Ch169ZNc+fOVbNmzdSlSxf98MMPGj9+/GO/YNjtrl69KofDYT0JIkmVK1dW5syZtWbNGm3cuFFXrlzRs88+q1SpUmnSpEl66qmnrH27du1KZ/VvV69elaenZ4IF2AoUKCCn06nly5drxYoVOnHihCTpjz/+UPv27fX000+rR48e+uGHHzR16lRlypQpuYqfIl25ckUREREJRpTVr19fGTNm1G+//SYp4dM0tJ13i4mJ0Ztvvil/f3+NGzdOGTNmVMaMGVWzZk3t2LFDDofjnqOX6XPe28PGs0GDBipTpoz69++vmjVrqnv37tq6daumTp1q/TAFWYNrypQpo9q1a2vSpEk6c+aMpk6dao10luKesvP29uZalIhr16498Lp+4sQJ7i+TIDo6WsWKFVNYWJguX74sT09Pffnll3r55ZfVuXNnvfLKK2rRooV++uknK57cY94bbad9iGXKw0+d/4DD4dCQIUM0aNAgZc2aVS6XS1evXrWmgPD29laNGjXk5uamGTNmaOTIkRo/fryyZ88uSWrRooX++usv1atXj1+dRTztFBYWps8++0xt27ZVtmzZ5O/vr+DgYIWFhWnQoEFq1aqVatasqbNnz2rt2rVq3769li9frjJlyqhKlSpasWKFQkNDtWrVKlZe1t1zNF6/fl0eHh56+eWXdf36dc2ZM0cjRozQ4MGDVa1aNSvRfPtI58qVK2vVqlXWFBGPs9vrpxR37sc/ehc/CiJjxozq2rWrvvzyS40aNUoTJ060kiPu7u7WPO63J1seR0mNZbdu3fT555/rvffe05QpU/Taa6+pUaNG2r9/v9KkSaOiRYsqW7ZsyXkoKcLt8fTz85PD4dCePXtUpkwZpU2bVuvWrZPT6dQvv/xi7f/RRx8lmFoHcW5vNyMjI3Xx4kUrceJwOLRr1y75+fnp119/lSRNnjxZy5YtU4MGDeTr66tDhw4pZ86cKl++vDUSDbekSZPGSp74+PhYbWLWrFkTzM98++PftJ23hIeHa+XKlWrfvr2KFSuW4Me2wMBAff755woODk4QW+lWvabPmdDDxDP+2lS2bFm98847at68uf744w8VLFhQ5cuXt57QeZyFhYVp3bp1atOmjZWsy5Ytm/bs2aMuXbpo4cKF6tChg2bPni1fX199+umn6t27t+rUqcMPx3e4/VqULl26RK/r7u7uunHjhubMmaNKlSpJ4v7yftKmTasuXbpo+PDhWrx4sZ5++mkNHz5cDRo0UI0aNXT58mWtXbtW3bp1U1BQkAIDA7nHvAfaTvsQyxTq31qN8L8uNDTUNG7c2BQpUsSsW7fOOJ1O88wzz5jOnTtb+0RFRVn//vTTT02dOnXM8uXL73oNxNNOoaGhpmbNmqZfv37G6XQaY4xp1KiR6dq1q9m/f7/p16+ftUqrMcYcOHDAtGjRwrRt29Y4HA5z+PBh07Nnz/uuyvy4iYiIMCNGjDC//PKLte3QoUNm8ODB1t/bt283bdq0MXXr1jXfffedtf3OletZyf5W/ezbt69xuVzm22+/NaVLlzZvvfWWCQ0NNcHBwWbSpEmmatWq5tq1a2b58uWmUqVK5rPPPjPGsGL97f6XWFaoUMGKJRKKj+cbb7xhnatNmzY1lSpVMs8//7x5+umnzUsvvWT27dtnoqKizO+//27atWtnatSoYYKDg5O59ClLfLv5888/G2OMOXnypJk1a5ZxOBzGGGOCgoJM9+7dzYEDB4zT6TQ//fSTeemll0zr1q1NaGhochY9xQoPDzfLly83s2bNMmvXrjXXr183vXr1Mt9++60x5tYq6l26dDGdOnUyxhgTExNjjLl17aH9jBMaGmpq1Khh3n77batOGnMrPqGhoaZBgwbmnXfeuWfM4uOKOP9LPOkP3d/t/fjw8HBjzK0617t3b7NkyRJjTFxftFq1aqZq1aqmSJEiZu/evcYYYnu7+GvRrl27rG1NmjRJ9LresWNHU716da7r9xAREWE2bdpkFi1aZDZs2GCMMWblypXmueeeM927dzcjRowwERER1v779+83L730kunevbuJiooyBw8e5B7zNrSd9iGWKRdTavwP4le89PPzU548eXTixAl5eXlp4MCB+vXXX9W/f39JcY+NOJ1OSdKLL76oQoUKaePGjdZriEM87RO/eEPu3Lk1YMAAa0TOO++8o19//VVt2rTRpUuX5OfnZ72nWLFiqlOnjk6cOKGwsDAVLlxY48aNU8GCBZPrMFKUn3/+WcuXL9eCBQu0e/duSVKRIkXUq1cva58aNWqoW7duypQpk0aMGJFgeo3bPe6LBd5ePwcOHCg3NzeVLFlSvXv31vbt21WpUiXVr19fy5Yt08iRI5UhQwa1bdtW2bNn1+effy6JFevj/a+xzJkzpxVL3HJ7PN9++21r6oE1a9Zo4MCB6ty5s9zc3NS+fXsFBgbK29tbZcqUUbNmzXTjxg399ddfyXwEKUt8u7lo0SLt3r1b+fLlU48ePaw5wps2bar33ntPRYoUkZeXlypWrKi6devqzJkzioyMTObSpzwOh0MvvfSSgoKCtHTpUs2ePVsnTpzQsGHDrLmv40feXb9+XV5eXpLiptEICwvTuHHjdPjwYdpP3TrX8+TJozfeeEPp0qWzHq+Nn7opderUqlixon777TedPn1akqw2QRLzst/mf42nucdUJbi7H58mTRprOjEpbiqiL774Qi6XS0WKFFHRokV17do1FS1aVA6HQxJ9zdvFX4sWLlxoPZm0du1avf322+rates9r+tNmjTRzZs3ua7fweFwqFWrVpo1a5Zmzpyp999/X23atFGzZs1UunRpbd++XX5+fgme5CxevLiqV6+uI0eOKCYmRkWLFtX48eO5xxRtp52IZcpGz/MhORwONWrUSLly5dK0adNUvnx57d27V5JUvnx59enTR1u3btWAAQMkJUyEFixYUFFRUcz/dBviaZ/4xjZfvnwaM2aMsmbNanVQ42OZM2dOXbx4USdPnkzQyObIkUOpU6e2Evrp0qVLlmNIiUqWLKns2bNr//79WrBggfX4d/y8TvE3obcnnT/44AN98803yVbmlOjO+hk/PYa/v786deqkTz/9VEOHDlXfvn21fv161axZ03pv1qxZFRUVlUwlT3mIpb3u13bGL6jWuHFjlS9fXpcvX7amHYm/7qRJk0YZM2aUj49PspU/Jbqz3Yz/sc7NzU1Op1OpUqVS7ty5rSkeJMnX11dp0qQhKXoHl8ulIUOGKH369JoxY4bWr1+vjz/+WGXLlk0wr3X8dCWRkZHWNdzhcGj8+PFatGhRspQ9pbnXuX4nNzc3eXp6qmfPnrpx44aWLFkiSQnqKuIQT3s9KJ7GGJUtW1aRkZFyd3fXgAEDdODAAY0cOVLXr1/XqFGj9PPPPydT6VOm269Fixcv1q5duyRJTZo0UZEiRbiuJ1F0dLTeeOMNpU+fXhMnTtTnn3+uUaNG6ciRIxo5cqTeeecdtWvXTo0bN77rvQEBAfLy8rL6nixOTdtpJ2KZ8jER0UNwOBx69tlnVbx4cY0ZM0bp06dXsWLFtGnTJv3111/KlCmTmjR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      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[:,1:14]=df.groupby(\"City\").transform(lambda x:x.fillna(x.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City               0\n",
       "Datetime           0\n",
       "PM2.5              0\n",
       "PM10           48192\n",
       "NO                 0\n",
       "NO2                0\n",
       "NOx            28025\n",
       "NH3            48192\n",
       "CO                 0\n",
       "SO2                0\n",
       "O3              3852\n",
       "C6H6           65433\n",
       "C7H8           96049\n",
       "C8H10         312656\n",
       "AQI           129080\n",
       "AQI_Bucket    129080\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.iloc[:,:3:14]=df.fillna(df.iloc[:,3:14].mean())\n",
    "df['PM10']=df['PM10'].fillna((df['PM10'].mean()))\n",
    "df['NOx']=df['NOx'].fillna((df['NOx'].mean()))\n",
    "df['NH3']=df['NH3'].fillna((df['NH3'].mean()))\n",
    "df['O3']=df['O3'].fillna((df['O3'].mean()))\n",
    "df['C6H6']=df['C6H6'].fillna((df['C6H6'].mean()))\n",
    "df['C7H8']=df['C7H8'].fillna((df['C7H8'].mean()))\n",
    "df['C8H10']=df['C8H10'].fillna((df['C8H10'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City               0\n",
       "Datetime           0\n",
       "PM2.5              0\n",
       "PM10               0\n",
       "NO                 0\n",
       "NO2                0\n",
       "NOx                0\n",
       "NH3                0\n",
       "CO                 0\n",
       "SO2                0\n",
       "O3                 0\n",
       "C6H6               0\n",
       "C7H8               0\n",
       "C8H10              0\n",
       "AQI           129080\n",
       "AQI_Bucket    129080\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aqi_calc(df):\n",
    "    df[\"PM10_24hr_avg\"] = df.groupby(\"City\")[\"PM10\"].rolling(window = 24, min_periods = 16).mean().values\n",
    "    df[\"PM2.5_24hr_avg\"] = df.groupby(\"City\")[\"PM2.5\"].rolling(window = 24, min_periods = 16).mean().values\n",
    "    df[\"SO2_24hr_avg\"] = df.groupby(\"City\")[\"SO2\"].rolling(window = 24, min_periods = 16).mean().values\n",
    "    df[\"NOx_24hr_avg\"] = df.groupby(\"City\")[\"NOx\"].rolling(window = 24, min_periods = 16).mean().values\n",
    "    df[\"NH3_24hr_avg\"] = df.groupby(\"City\")[\"NH3\"].rolling(window = 24, min_periods = 16).mean().values\n",
    "    df[\"CO_8hr_max\"] = df.groupby(\"City\")[\"CO\"].rolling(window = 8, min_periods = 1).max().values\n",
    "    df[\"O3_8hr_max\"] = df.groupby(\"City\")[\"O3\"].rolling(window = 8, min_periods = 1).max().values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "aqi_calc(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                   0\n",
       "Datetime               0\n",
       "PM2.5                  0\n",
       "PM10                   0\n",
       "NO                     0\n",
       "NO2                    0\n",
       "NOx                    0\n",
       "NH3                    0\n",
       "CO                     0\n",
       "SO2                    0\n",
       "O3                     0\n",
       "C6H6                   0\n",
       "C7H8                   0\n",
       "C8H10                  0\n",
       "AQI               129080\n",
       "AQI_Bucket        129080\n",
       "PM10_24hr_avg        390\n",
       "PM2.5_24hr_avg       390\n",
       "SO2_24hr_avg         390\n",
       "NOx_24hr_avg         390\n",
       "NH3_24hr_avg         390\n",
       "CO_8hr_max             0\n",
       "O3_8hr_max             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Datetime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>NH3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>...</th>\n",
       "      <th>PM2.5_SubIndex</th>\n",
       "      <th>PM10_SubIndex</th>\n",
       "      <th>SO2_SubIndex</th>\n",
       "      <th>NOx_SubIndex</th>\n",
       "      <th>NH3_SubIndex</th>\n",
       "      <th>CO_SubIndex</th>\n",
       "      <th>O3_SubIndex</th>\n",
       "      <th>Checks</th>\n",
       "      <th>AQI_calculated</th>\n",
       "      <th>AQI_bucket_calculated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 16:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.87</td>\n",
       "      <td>17.28</td>\n",
       "      <td>18.32</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.87</td>\n",
       "      <td>17.83</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>42.429688</td>\n",
       "      <td>20.088281</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>103.375</td>\n",
       "      <td>132.470588</td>\n",
       "      <td>7</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 17:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.80</td>\n",
       "      <td>19.04</td>\n",
       "      <td>20.00</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.80</td>\n",
       "      <td>16.14</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>41.120588</td>\n",
       "      <td>20.377206</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>103.375</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 18:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.47</td>\n",
       "      <td>21.24</td>\n",
       "      <td>22.70</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.47</td>\n",
       "      <td>11.93</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>39.664583</td>\n",
       "      <td>20.821528</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>103.375</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 19:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.53</td>\n",
       "      <td>25.63</td>\n",
       "      <td>27.42</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.53</td>\n",
       "      <td>14.99</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>38.563158</td>\n",
       "      <td>21.529605</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>92.500</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 20:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.47</td>\n",
       "      <td>16.22</td>\n",
       "      <td>16.00</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.47</td>\n",
       "      <td>13.66</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>37.488750</td>\n",
       "      <td>21.453125</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>92.500</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 21:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.83</td>\n",
       "      <td>16.50</td>\n",
       "      <td>17.52</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.83</td>\n",
       "      <td>13.28</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>36.494048</td>\n",
       "      <td>21.474405</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>92.500</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 22:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.85</td>\n",
       "      <td>17.97</td>\n",
       "      <td>18.18</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12.23</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>35.530114</td>\n",
       "      <td>21.531250</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>47.500</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01 23:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>1.08</td>\n",
       "      <td>15.52</td>\n",
       "      <td>15.40</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>1.08</td>\n",
       "      <td>10.50</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>34.555978</td>\n",
       "      <td>21.432065</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>54.000</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02 00:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.60</td>\n",
       "      <td>14.84</td>\n",
       "      <td>13.53</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.60</td>\n",
       "      <td>9.12</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>33.591146</td>\n",
       "      <td>21.243750</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>54.000</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02 01:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.18</td>\n",
       "      <td>11.91</td>\n",
       "      <td>8.87</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.18</td>\n",
       "      <td>12.95</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>27.907812</td>\n",
       "      <td>19.811458</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>54.000</td>\n",
       "      <td>248.550000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02 02:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.35</td>\n",
       "      <td>12.54</td>\n",
       "      <td>12.23</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.35</td>\n",
       "      <td>11.65</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>24.040625</td>\n",
       "      <td>19.420833</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>54.000</td>\n",
       "      <td>248.550000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02 03:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.18</td>\n",
       "      <td>12.56</td>\n",
       "      <td>11.55</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.18</td>\n",
       "      <td>9.02</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>21.758854</td>\n",
       "      <td>19.445312</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>54.000</td>\n",
       "      <td>248.550000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02 04:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.17</td>\n",
       "      <td>10.15</td>\n",
       "      <td>8.52</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.17</td>\n",
       "      <td>8.84</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>20.160417</td>\n",
       "      <td>19.409896</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>54.000</td>\n",
       "      <td>96.080000</td>\n",
       "      <td>7</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         City            Datetime      PM2.5        PM10    NO    NO2    NOx  \\\n",
       "15  Ahmedabad 2015-01-01 16:00:00  67.273271  111.493008  0.87  17.28  18.32   \n",
       "16  Ahmedabad 2015-01-01 17:00:00  67.273271  111.493008  0.80  19.04  20.00   \n",
       "17  Ahmedabad 2015-01-01 18:00:00  67.273271  111.493008  0.47  21.24  22.70   \n",
       "18  Ahmedabad 2015-01-01 19:00:00  67.273271  111.493008  0.53  25.63  27.42   \n",
       "19  Ahmedabad 2015-01-01 20:00:00  67.273271  111.493008  0.47  16.22  16.00   \n",
       "20  Ahmedabad 2015-01-01 21:00:00  67.273271  111.493008  0.83  16.50  17.52   \n",
       "21  Ahmedabad 2015-01-01 22:00:00  67.273271  111.493008  0.85  17.97  18.18   \n",
       "22  Ahmedabad 2015-01-01 23:00:00  67.273271  111.493008  1.08  15.52  15.40   \n",
       "23  Ahmedabad 2015-01-02 00:00:00  67.273271  111.493008  0.60  14.84  13.53   \n",
       "24  Ahmedabad 2015-01-02 01:00:00  67.273271  111.493008  0.18  11.91   8.87   \n",
       "25  Ahmedabad 2015-01-02 02:00:00  67.273271  111.493008  0.35  12.54  12.23   \n",
       "26  Ahmedabad 2015-01-02 03:00:00  67.273271  111.493008  0.18  12.56  11.55   \n",
       "27  Ahmedabad 2015-01-02 04:00:00  67.273271  111.493008  0.17  10.15   8.52   \n",
       "\n",
       "          NH3    CO    SO2  ...  PM2.5_SubIndex  PM10_SubIndex  SO2_SubIndex  \\\n",
       "15  23.012932  0.87  17.83  ...      124.244236     107.662005     42.429688   \n",
       "16  23.012932  0.80  16.14  ...      124.244236     107.662005     41.120588   \n",
       "17  23.012932  0.47  11.93  ...      124.244236     107.662005     39.664583   \n",
       "18  23.012932  0.53  14.99  ...      124.244236     107.662005     38.563158   \n",
       "19  23.012932  0.47  13.66  ...      124.244236     107.662005     37.488750   \n",
       "20  23.012932  0.83  13.28  ...      124.244236     107.662005     36.494048   \n",
       "21  23.012932  0.85  12.23  ...      124.244236     107.662005     35.530114   \n",
       "22  23.012932  1.08  10.50  ...      124.244236     107.662005     34.555978   \n",
       "23  23.012932  0.60   9.12  ...      124.244236     107.662005     33.591146   \n",
       "24  23.012932  0.18  12.95  ...      124.244236     107.662005     27.907812   \n",
       "25  23.012932  0.35  11.65  ...      124.244236     107.662005     24.040625   \n",
       "26  23.012932  0.18   9.02  ...      124.244236     107.662005     21.758854   \n",
       "27  23.012932  0.17   8.84  ...      124.244236     107.662005     20.160417   \n",
       "\n",
       "    NOx_SubIndex  NH3_SubIndex CO_SubIndex  O3_SubIndex  Checks  \\\n",
       "15     20.088281      5.753233     103.375   132.470588       7   \n",
       "16     20.377206      5.753233     103.375   249.050000       7   \n",
       "17     20.821528      5.753233     103.375   249.050000       7   \n",
       "18     21.529605      5.753233      92.500   249.050000       7   \n",
       "19     21.453125      5.753233      92.500   249.050000       7   \n",
       "20     21.474405      5.753233      92.500   249.050000       7   \n",
       "21     21.531250      5.753233      47.500   249.050000       7   \n",
       "22     21.432065      5.753233      54.000   249.050000       7   \n",
       "23     21.243750      5.753233      54.000   249.050000       7   \n",
       "24     19.811458      5.753233      54.000   248.550000       7   \n",
       "25     19.420833      5.753233      54.000   248.550000       7   \n",
       "26     19.445312      5.753233      54.000   248.550000       7   \n",
       "27     19.409896      5.753233      54.000    96.080000       7   \n",
       "\n",
       "    AQI_calculated  AQI_bucket_calculated  \n",
       "15           132.0               Moderate  \n",
       "16           249.0                   Poor  \n",
       "17           249.0                   Poor  \n",
       "18           249.0                   Poor  \n",
       "19           249.0                   Poor  \n",
       "20           249.0                   Poor  \n",
       "21           249.0                   Poor  \n",
       "22           249.0                   Poor  \n",
       "23           249.0                   Poor  \n",
       "24           249.0                   Poor  \n",
       "25           249.0                   Poor  \n",
       "26           249.0                   Poor  \n",
       "27           124.0               Moderate  \n",
       "\n",
       "[13 rows x 33 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## PM2.5 Sub-Index calculation\n",
    "def get_PM25_subindex(x):\n",
    "        if x <= 30:\n",
    "            return x * 50 / 30\n",
    "        elif x <= 60:\n",
    "            return 50 + (x - 30) * 50 / 30\n",
    "        elif x <= 90:\n",
    "            return 100 + (x - 60) * 100 / 30\n",
    "        elif x <= 120:\n",
    "            return 200 + (x - 90) * 100 / 30\n",
    "        elif x <= 250:\n",
    "            return 300 + (x - 120) * 100 / 130\n",
    "        elif x > 250:\n",
    "            return 400 + (x - 250) * 100 / 130\n",
    "        else:\n",
    "            return 0\n",
    "\n",
    "\n",
    "df[\"PM2.5_SubIndex\"] = df[\"PM2.5_24hr_avg\"].apply(lambda x: get_PM25_subindex(x))\n",
    "\n",
    "    ## PM10 Sub-Index calculation\n",
    "def get_PM10_subindex(x):\n",
    "        if x <= 50:\n",
    "            return x\n",
    "        elif x <= 100:\n",
    "            return x\n",
    "        elif x <= 250:\n",
    "            return 100 + (x - 100) * 100 / 150\n",
    "        elif x <= 350:\n",
    "            return 200 + (x - 250)\n",
    "        elif x <= 430:\n",
    "            return 300 + (x - 350) * 100 / 80\n",
    "        elif x > 430:\n",
    "            return 400 + (x - 430) * 100 / 80\n",
    "        else:\n",
    "            return 0\n",
    "\n",
    "df[\"PM10_SubIndex\"] = df[\"PM10_24hr_avg\"].apply(lambda x: get_PM10_subindex(x))\n",
    "\n",
    "\n",
    "\n",
    "    ## SO2 Sub-Index calculation\n",
    "def get_SO2_subindex(x):\n",
    "        if x <= 40:\n",
    "            return x * 50 / 40\n",
    "        elif x <= 80:\n",
    "            return 50 + (x - 40) * 50 / 40\n",
    "        elif x <= 380:\n",
    "            return 100 + (x - 80) * 100 / 300\n",
    "        elif x <= 800:\n",
    "            return 200 + (x - 380) * 100 / 420\n",
    "        elif x <= 1600:\n",
    "            return 300 + (x - 800) * 100 / 800\n",
    "        elif x > 1600:\n",
    "            return 400 + (x - 1600) * 100 / 800\n",
    "        else:\n",
    "            return 0\n",
    "\n",
    "df[\"SO2_SubIndex\"] = df[\"SO2_24hr_avg\"].apply(lambda x: get_SO2_subindex(x))\n",
    "    ## NOx Sub-Index calculation\n",
    "def get_NOx_subindex(x):\n",
    "        if x <= 40:\n",
    "            return x * 50 / 40\n",
    "        elif x <= 80:\n",
    "            return 50 + (x - 40) * 50 / 40\n",
    "        elif x <= 180:\n",
    "            return 100 + (x - 80) * 100 / 100\n",
    "        elif x <= 280:\n",
    "            return 200 + (x - 180) * 100 / 100\n",
    "        elif x <= 400:\n",
    "            return 300 + (x - 280) * 100 / 120\n",
    "        elif x > 400:\n",
    "            return 400 + (x - 400) * 100 / 120\n",
    "        else:\n",
    "            return 0\n",
    "\n",
    "df[\"NOx_SubIndex\"] = df[\"NOx_24hr_avg\"].apply(lambda x: get_NOx_subindex(x))\n",
    "\n",
    "    ## NH3 Sub-Index calculation\n",
    "def get_NH3_subindex(x):\n",
    "        if x <= 200:\n",
    "            return x * 50 / 200\n",
    "        elif x <= 400:\n",
    "            return 50 + (x - 200) * 50 / 200\n",
    "        elif x <= 800:\n",
    "            return 100 + (x - 400) * 100 / 400\n",
    "        elif x <= 1200:\n",
    "            return 200 + (x - 800) * 100 / 400\n",
    "        elif x <= 1800:\n",
    "            return 300 + (x - 1200) * 100 / 600\n",
    "        elif x > 1800:\n",
    "            return 400 + (x - 1800) * 100 / 600\n",
    "        else:\n",
    "            return 0\n",
    "\n",
    "df[\"NH3_SubIndex\"] = df[\"NH3_24hr_avg\"].apply(lambda x: get_NH3_subindex(x))\n",
    "\n",
    "    ## CO Sub-Index calculation\n",
    "def get_CO_subindex(x):\n",
    "        if x <= 1:\n",
    "            return x * 50 / 1\n",
    "        elif x <= 2:\n",
    "            return 50 + (x - 1) * 50 / 1\n",
    "        elif x <= 10:\n",
    "            return 100 + (x - 2) * 100 / 8\n",
    "        elif x <= 17:\n",
    "            return 200 + (x - 10) * 100 / 7\n",
    "        elif x <= 34:\n",
    "            return 300 + (x - 17) * 100 / 17\n",
    "        elif x > 34:\n",
    "            return 400 + (x - 34) * 100 / 17\n",
    "        else:\n",
    "            return 0\n",
    "\n",
    "df[\"CO_SubIndex\"] = df[\"CO_8hr_max\"].apply(lambda x: get_CO_subindex(x))\n",
    "\n",
    "    ## O3 Sub-Index calculation\n",
    "def get_O3_subindex(x):\n",
    "        if x <= 50:\n",
    "            return x * 50 / 50\n",
    "        elif x <= 100:\n",
    "            return 50 + (x - 50) * 50 / 50\n",
    "        elif x <= 168:\n",
    "            return 100 + (x - 100) * 100 / 68\n",
    "        elif x <= 208:\n",
    "            return 200 + (x - 168) * 100 / 40\n",
    "        elif x <= 748:\n",
    "            return 300 + (x - 208) * 100 / 539\n",
    "        elif x > 748:\n",
    "            return 400 + (x - 400) * 100 / 539\n",
    "        else:\n",
    "            return 0\n",
    "\n",
    "df[\"O3_SubIndex\"] = df[\"O3_8hr_max\"].apply(lambda x: get_O3_subindex(x))\n",
    "    ## AQI bucketing\n",
    "def get_AQI_bucket(x):\n",
    "        if x <= 50:\n",
    "            return \"Good\"\n",
    "        elif x <= 100:\n",
    "            return \"Satisfactory\"\n",
    "        elif x <= 200:\n",
    "            return \"Moderate\"\n",
    "        elif x <= 300:\n",
    "            return \"Poor\"\n",
    "        elif x <= 400:\n",
    "            return \"Very Poor\"\n",
    "        elif x > 400:\n",
    "            return \"Severe\"\n",
    "        else:\n",
    "            return np.NaN\n",
    "\n",
    "df[\"Checks\"] = (df[\"PM2.5_SubIndex\"] > 0).astype(int) + \\\n",
    "                    (df[\"PM10_SubIndex\"] > 0).astype(int) + \\\n",
    "                    (df[\"SO2_SubIndex\"] > 0).astype(int) + \\\n",
    "                    (df[\"NOx_SubIndex\"] > 0).astype(int) + \\\n",
    "                    (df[\"NH3_SubIndex\"] > 0).astype(int) + \\\n",
    "                    (df[\"CO_SubIndex\"] > 0).astype(int) + \\\n",
    "                    (df[\"O3_SubIndex\"] > 0).astype(int)\n",
    "\n",
    "df[\"AQI_calculated\"] = round(df[[\"PM2.5_SubIndex\", \"PM10_SubIndex\", \"SO2_SubIndex\", \"NOx_SubIndex\",\n",
    "                                     \"NH3_SubIndex\", \"CO_SubIndex\", \"O3_SubIndex\"]].max(axis = 1))\n",
    "df.loc[df[\"PM2.5_SubIndex\"] + df[\"PM10_SubIndex\"] <= 0, \"AQI_calculated\"] = np.NaN\n",
    "df.loc[df.Checks < 3, \"AQI_calculated\"] = np.NaN\n",
    "\n",
    "df[\"AQI_bucket_calculated\"] = df[\"AQI_calculated\"].apply(lambda x: get_AQI_bucket(x))\n",
    "df[~df.AQI_calculated.isna()].head(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['AQI','AQI_Bucket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(707485, 31)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "label_encoder=preprocessing.LabelEncoder()\n",
    "df['City']=label_encoder.fit_transform(df['City'])\n",
    "df['City'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Datetime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>NH3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>...</th>\n",
       "      <th>PM2.5_SubIndex</th>\n",
       "      <th>PM10_SubIndex</th>\n",
       "      <th>SO2_SubIndex</th>\n",
       "      <th>NOx_SubIndex</th>\n",
       "      <th>NH3_SubIndex</th>\n",
       "      <th>CO_SubIndex</th>\n",
       "      <th>O3_SubIndex</th>\n",
       "      <th>Checks</th>\n",
       "      <th>AQI_calculated</th>\n",
       "      <th>AQI_bucket_calculated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-01 16:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.87</td>\n",
       "      <td>17.28</td>\n",
       "      <td>18.32</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.87</td>\n",
       "      <td>17.83</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>42.429688</td>\n",
       "      <td>20.088281</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>103.375</td>\n",
       "      <td>132.470588</td>\n",
       "      <td>7</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-01 17:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.80</td>\n",
       "      <td>19.04</td>\n",
       "      <td>20.00</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.80</td>\n",
       "      <td>16.14</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>41.120588</td>\n",
       "      <td>20.377206</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>103.375</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-01 18:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.47</td>\n",
       "      <td>21.24</td>\n",
       "      <td>22.70</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.47</td>\n",
       "      <td>11.93</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>39.664583</td>\n",
       "      <td>20.821528</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>103.375</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-01 19:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.53</td>\n",
       "      <td>25.63</td>\n",
       "      <td>27.42</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.53</td>\n",
       "      <td>14.99</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>38.563158</td>\n",
       "      <td>21.529605</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>92.500</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-01 20:00:00</td>\n",
       "      <td>67.273271</td>\n",
       "      <td>111.493008</td>\n",
       "      <td>0.47</td>\n",
       "      <td>16.22</td>\n",
       "      <td>16.00</td>\n",
       "      <td>23.012932</td>\n",
       "      <td>0.47</td>\n",
       "      <td>13.66</td>\n",
       "      <td>...</td>\n",
       "      <td>124.244236</td>\n",
       "      <td>107.662005</td>\n",
       "      <td>37.488750</td>\n",
       "      <td>21.453125</td>\n",
       "      <td>5.753233</td>\n",
       "      <td>92.500</td>\n",
       "      <td>249.050000</td>\n",
       "      <td>7</td>\n",
       "      <td>249.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    City            Datetime      PM2.5        PM10    NO    NO2    NOx  \\\n",
       "15     0 2015-01-01 16:00:00  67.273271  111.493008  0.87  17.28  18.32   \n",
       "16     0 2015-01-01 17:00:00  67.273271  111.493008  0.80  19.04  20.00   \n",
       "17     0 2015-01-01 18:00:00  67.273271  111.493008  0.47  21.24  22.70   \n",
       "18     0 2015-01-01 19:00:00  67.273271  111.493008  0.53  25.63  27.42   \n",
       "19     0 2015-01-01 20:00:00  67.273271  111.493008  0.47  16.22  16.00   \n",
       "\n",
       "          NH3    CO    SO2  ...  PM2.5_SubIndex  PM10_SubIndex  SO2_SubIndex  \\\n",
       "15  23.012932  0.87  17.83  ...      124.244236     107.662005     42.429688   \n",
       "16  23.012932  0.80  16.14  ...      124.244236     107.662005     41.120588   \n",
       "17  23.012932  0.47  11.93  ...      124.244236     107.662005     39.664583   \n",
       "18  23.012932  0.53  14.99  ...      124.244236     107.662005     38.563158   \n",
       "19  23.012932  0.47  13.66  ...      124.244236     107.662005     37.488750   \n",
       "\n",
       "    NOx_SubIndex  NH3_SubIndex  CO_SubIndex  O3_SubIndex  Checks  \\\n",
       "15     20.088281      5.753233      103.375   132.470588       7   \n",
       "16     20.377206      5.753233      103.375   249.050000       7   \n",
       "17     20.821528      5.753233      103.375   249.050000       7   \n",
       "18     21.529605      5.753233       92.500   249.050000       7   \n",
       "19     21.453125      5.753233       92.500   249.050000       7   \n",
       "\n",
       "    AQI_calculated  AQI_bucket_calculated  \n",
       "15           132.0               Moderate  \n",
       "16           249.0                   Poor  \n",
       "17           249.0                   Poor  \n",
       "18           249.0                   Poor  \n",
       "19           249.0                   Poor  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Neha_City_hour_after_mean_AQI_Imputation.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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