Module_1_AP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 1: Data validation and pre-processing technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import library packages\n",
    "import pandas as p\n",
    "import numpy as n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load given dataset\n",
    "data = p.read_excel(\"AirQuality.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before drop the given dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>city</th>\n",
       "      <th>place</th>\n",
       "      <th>lastupdate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "      <th>Pollutants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>70.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>41.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>44.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>OZONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Rajamahendravaram</td>\n",
       "      <td>Anand Kala Kshetram, Rajamahendravaram - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Rajamahendravaram</td>\n",
       "      <td>Anand Kala Kshetram, Rajamahendravaram - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Rajamahendravaram</td>\n",
       "      <td>Anand Kala Kshetram, Rajamahendravaram - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country           State               city  \\\n",
       "0   India  Andhra_Pradesh          Amaravati   \n",
       "1   India  Andhra_Pradesh          Amaravati   \n",
       "2   India  Andhra_Pradesh          Amaravati   \n",
       "3   India  Andhra_Pradesh          Amaravati   \n",
       "4   India  Andhra_Pradesh          Amaravati   \n",
       "5   India  Andhra_Pradesh          Amaravati   \n",
       "6   India  Andhra_Pradesh          Amaravati   \n",
       "7   India  Andhra_Pradesh  Rajamahendravaram   \n",
       "8   India  Andhra_Pradesh  Rajamahendravaram   \n",
       "9   India  Andhra_Pradesh  Rajamahendravaram   \n",
       "\n",
       "                                            place          lastupdate   Avg  \\\n",
       "0                  Secretariat, Amaravati - APPCB 2020-01-28 03:00:00  70.0   \n",
       "1                  Secretariat, Amaravati - APPCB 2020-01-28 03:00:00  76.0   \n",
       "2                  Secretariat, Amaravati - APPCB 2020-01-28 03:00:00  73.0   \n",
       "3                  Secretariat, Amaravati - APPCB 2020-01-28 03:00:00   5.0   \n",
       "4                  Secretariat, Amaravati - APPCB 2020-01-28 03:00:00  41.0   \n",
       "5                  Secretariat, Amaravati - APPCB 2020-01-28 03:00:00  44.0   \n",
       "6                  Secretariat, Amaravati - APPCB 2020-01-28 03:00:00  29.0   \n",
       "7  Anand Kala Kshetram, Rajamahendravaram - APPCB 2020-01-28 03:00:00   NaN   \n",
       "8  Anand Kala Kshetram, Rajamahendravaram - APPCB 2020-01-28 03:00:00   NaN   \n",
       "9  Anand Kala Kshetram, Rajamahendravaram - APPCB 2020-01-28 03:00:00   NaN   \n",
       "\n",
       "     Max   Min Pollutants  \n",
       "0  108.0  42.0      PM2.5  \n",
       "1  102.0  43.0       PM10  \n",
       "2  118.0  46.0        NO2  \n",
       "3    6.0   4.0        NH3  \n",
       "4  109.0   2.0        SO2  \n",
       "5  102.0  18.0         CO  \n",
       "6   35.0  12.0      OZONE  \n",
       "7    NaN   NaN      PM2.5  \n",
       "8    NaN   NaN       PM10  \n",
       "9    NaN   NaN        NO2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(824, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After drop the given dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>city</th>\n",
       "      <th>place</th>\n",
       "      <th>lastupdate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "      <th>Pollutants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>70.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>41.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>44.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>OZONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Rajamahendravaram</td>\n",
       "      <td>Anand Kala Kshetram, Rajamahendravaram - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Rajamahendravaram</td>\n",
       "      <td>Anand Kala Kshetram, Rajamahendravaram - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>108.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>OZONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Tirupati</td>\n",
       "      <td>Tirumala, Tirupati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>46.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country           State               city  \\\n",
       "0    India  Andhra_Pradesh          Amaravati   \n",
       "1    India  Andhra_Pradesh          Amaravati   \n",
       "2    India  Andhra_Pradesh          Amaravati   \n",
       "3    India  Andhra_Pradesh          Amaravati   \n",
       "4    India  Andhra_Pradesh          Amaravati   \n",
       "5    India  Andhra_Pradesh          Amaravati   \n",
       "6    India  Andhra_Pradesh          Amaravati   \n",
       "12   India  Andhra_Pradesh  Rajamahendravaram   \n",
       "13   India  Andhra_Pradesh  Rajamahendravaram   \n",
       "14   India  Andhra_Pradesh           Tirupati   \n",
       "\n",
       "                                             place          lastupdate    Avg  \\\n",
       "0                   Secretariat, Amaravati - APPCB 2020-01-28 03:00:00   70.0   \n",
       "1                   Secretariat, Amaravati - APPCB 2020-01-28 03:00:00   76.0   \n",
       "2                   Secretariat, Amaravati - APPCB 2020-01-28 03:00:00   73.0   \n",
       "3                   Secretariat, Amaravati - APPCB 2020-01-28 03:00:00    5.0   \n",
       "4                   Secretariat, Amaravati - APPCB 2020-01-28 03:00:00   41.0   \n",
       "5                   Secretariat, Amaravati - APPCB 2020-01-28 03:00:00   44.0   \n",
       "6                   Secretariat, Amaravati - APPCB 2020-01-28 03:00:00   29.0   \n",
       "12  Anand Kala Kshetram, Rajamahendravaram - APPCB 2020-01-28 03:00:00   30.0   \n",
       "13  Anand Kala Kshetram, Rajamahendravaram - APPCB 2020-01-28 03:00:00  108.0   \n",
       "14                      Tirumala, Tirupati - APPCB 2020-01-28 03:00:00   46.0   \n",
       "\n",
       "      Max   Min Pollutants  \n",
       "0   108.0  42.0      PM2.5  \n",
       "1   102.0  43.0       PM10  \n",
       "2   118.0  46.0        NO2  \n",
       "3     6.0   4.0        NH3  \n",
       "4   109.0   2.0        SO2  \n",
       "5   102.0  18.0         CO  \n",
       "6    35.0  12.0      OZONE  \n",
       "12  103.0   2.0         CO  \n",
       "13  130.0  51.0      OZONE  \n",
       "14   72.0  28.0      PM2.5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(788, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'State', 'city', 'place', 'lastupdate', 'Avg', 'Max', 'Min',\n",
       "       'Pollutants'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>96.736041</td>\n",
       "      <td>149.675127</td>\n",
       "      <td>50.855330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>114.746642</td>\n",
       "      <td>150.264480</td>\n",
       "      <td>74.279885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>180.250000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>457.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>335.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Avg         Max         Min\n",
       "count  788.000000  788.000000  788.000000\n",
       "mean    96.736041  149.675127   50.855330\n",
       "std    114.746642  150.264480   74.279885\n",
       "min      1.000000    1.000000    1.000000\n",
       "25%     20.000000   35.000000    7.000000\n",
       "50%     50.000000  103.000000   20.000000\n",
       "75%    108.000000  180.250000   54.000000\n",
       "max    457.000000  500.000000  335.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To describe the dataframe\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 788 entries, 0 to 823\n",
      "Data columns (total 9 columns):\n",
      "Country       788 non-null object\n",
      "State         788 non-null object\n",
      "city          788 non-null object\n",
      "place         788 non-null object\n",
      "lastupdate    788 non-null datetime64[ns]\n",
      "Avg           788 non-null float64\n",
      "Max           788 non-null float64\n",
      "Min           788 non-null float64\n",
      "Pollutants    788 non-null object\n",
      "dtypes: datetime64[ns](1), float64(3), object(5)\n",
      "memory usage: 61.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#Checking datatype and information about dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking duplicate values of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "5      False\n",
       "6      False\n",
       "12     False\n",
       "13     False\n",
       "14     False\n",
       "15     False\n",
       "16     False\n",
       "17     False\n",
       "18     False\n",
       "19     False\n",
       "20     False\n",
       "21     False\n",
       "22     False\n",
       "23     False\n",
       "24     False\n",
       "25     False\n",
       "26     False\n",
       "27     False\n",
       "28     False\n",
       "29     False\n",
       "30     False\n",
       "31     False\n",
       "32     False\n",
       "33     False\n",
       "34     False\n",
       "       ...  \n",
       "794    False\n",
       "795    False\n",
       "796    False\n",
       "797    False\n",
       "798    False\n",
       "799    False\n",
       "800    False\n",
       "801    False\n",
       "802    False\n",
       "803    False\n",
       "804    False\n",
       "805    False\n",
       "806    False\n",
       "807    False\n",
       "808    False\n",
       "809    False\n",
       "810    False\n",
       "811    False\n",
       "812    False\n",
       "813    False\n",
       "814    False\n",
       "815    False\n",
       "816    False\n",
       "817    False\n",
       "818    False\n",
       "819    False\n",
       "820    False\n",
       "821    False\n",
       "822    False\n",
       "823    False\n",
       "Length: 788, dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for duplicate data\n",
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find sum of duplicate data\n",
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country       0\n",
       "State         0\n",
       "city          0\n",
       "place         0\n",
       "lastupdate    0\n",
       "Avg           0\n",
       "Max           0\n",
       "Min           0\n",
       "Pollutants    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking sum of missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PM2.5', 'PM10', 'NO2', 'NH3', 'SO2', 'CO', 'OZONE'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Pollutants.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To validate avg pollution values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum value of average pollution is: 1.0\n",
      "Maximum value of average pollution is: 457.0\n"
     ]
    }
   ],
   "source": [
    "#Checking minimum or maximum of average pollution values\n",
    "print(\"Minimum value of average pollution is:\", df.Avg.min())\n",
    "print(\"Maximum value of average pollution is:\", df.Avg.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg pollution range : [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 118.0, 119.0, 120.0, 121.0, 122.0, 124.0, 125.0, 126.0, 127.0, 129.0, 130.0, 131.0, 136.0, 138.0, 139.0, 141.0, 142.0, 144.0, 146.0, 148.0, 149.0, 150.0, 154.0, 156.0, 157.0, 160.0, 161.0, 163.0, 164.0, 165.0, 167.0, 176.0, 177.0, 180.0, 183.0, 185.0, 187.0, 190.0, 193.0, 195.0, 201.0, 203.0, 204.0, 207.0, 214.0, 217.0, 221.0, 223.0, 228.0, 229.0, 231.0, 232.0, 235.0, 238.0, 240.0, 241.0, 247.0, 253.0, 257.0, 265.0, 266.0, 269.0, 272.0, 274.0, 276.0, 277.0, 278.0, 280.0, 287.0, 297.0, 302.0, 304.0, 305.0, 307.0, 308.0, 311.0, 314.0, 317.0, 318.0, 320.0, 321.0, 323.0, 325.0, 329.0, 333.0, 336.0, 337.0, 339.0, 341.0, 342.0, 345.0, 349.0, 350.0, 354.0, 356.0, 358.0, 359.0, 360.0, 361.0, 364.0, 370.0, 371.0, 372.0, 373.0, 374.0, 377.0, 378.0, 379.0, 380.0, 381.0, 383.0, 385.0, 388.0, 389.0, 390.0, 392.0, 393.0, 395.0, 396.0, 397.0, 401.0, 406.0, 408.0, 410.0, 411.0, 413.0, 414.0, 418.0, 420.0, 421.0, 426.0, 431.0, 433.0, 436.0, 439.0, 457.0]\n"
     ]
    }
   ],
   "source": [
    "#check the average pollution range\n",
    "print(\"Avg pollution range :\", sorted(df['Avg'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PM2.5', 'PM10', 'NO2', 'NH3', 'SO2', 'CO', 'OZONE'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Pollutants.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "PMkitchen = df.loc[df['Pollutants'] == 'PM2.5']\n",
    "PMclean = df.loc[df['Pollutants'] == 'PM10']\n",
    "N02 = df.loc[df['Pollutants'] == 'NO2']\n",
    "NH3 = df.loc[df['Pollutants'] == 'NH3']\n",
    "S02 = df.loc[df['Pollutants'] == 'SO2']\n",
    "CO = df.loc[df['Pollutants'] == 'CO']\n",
    "O3 = df.loc[df['Pollutants'] == 'OZONE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'State', 'city', 'place', 'lastupdate', 'Avg', 'Max', 'Min',\n",
       "       'Pollutants'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>counts</th>\n",
       "      <th>freqs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categories</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>126</td>\n",
       "      <td>0.159898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>86</td>\n",
       "      <td>0.109137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>122</td>\n",
       "      <td>0.154822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OZONE</th>\n",
       "      <td>117</td>\n",
       "      <td>0.148477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>102</td>\n",
       "      <td>0.129442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>120</td>\n",
       "      <td>0.152284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>115</td>\n",
       "      <td>0.145939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            counts     freqs\n",
       "categories                  \n",
       "CO             126  0.159898\n",
       "NH3             86  0.109137\n",
       "NO2            122  0.154822\n",
       "OZONE          117  0.148477\n",
       "PM10           102  0.129442\n",
       "PM2.5          120  0.152284\n",
       "SO2            115  0.145939"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.Categorical(df['Pollutants']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CO       126\n",
       "NO2      122\n",
       "PM2.5    120\n",
       "OZONE    117\n",
       "SO2      115\n",
       "PM10     102\n",
       "NH3       86\n",
       "Name: Pollutants, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pollutants'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Avg</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.960141</td>\n",
       "      <td>0.933346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>0.960141</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.859276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min</th>\n",
       "      <td>0.933346</td>\n",
       "      <td>0.859276</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Avg       Max       Min\n",
       "Avg  1.000000  0.960141  0.933346\n",
       "Max  0.960141  1.000000  0.859276\n",
       "Min  0.933346  0.859276  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before Pre-Processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>city</th>\n",
       "      <th>place</th>\n",
       "      <th>lastupdate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "      <th>Pollutants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>70.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>41.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SO2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country           State       city                           place  \\\n",
       "0   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "1   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "2   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "3   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "4   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "\n",
       "           lastupdate   Avg    Max   Min Pollutants  \n",
       "0 2020-01-28 03:00:00  70.0  108.0  42.0      PM2.5  \n",
       "1 2020-01-28 03:00:00  76.0  102.0  43.0       PM10  \n",
       "2 2020-01-28 03:00:00  73.0  118.0  46.0        NO2  \n",
       "3 2020-01-28 03:00:00   5.0    6.0   4.0        NH3  \n",
       "4 2020-01-28 03:00:00  41.0  109.0   2.0        SO2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After Pre-Processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'State', 'city', 'place', 'lastupdate', 'Avg', 'Max', 'Min',\n",
       "       'Pollutants'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "var_mod = ['Country', 'State', 'city', 'place', 'lastupdate', 'Avg', 'Max', 'Min','Pollutants']\n",
    "le = LabelEncoder()\n",
    "for i in var_mod:\n",
    "    df[i] = le.fit_transform(df[i]).astype(str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>city</th>\n",
       "      <th>place</th>\n",
       "      <th>lastupdate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "      <th>Pollutants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>105</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>99</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>115</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country State city place lastupdate Avg  Max Min Pollutants\n",
       "0       0     0    4    99          0  68  105  41          5\n",
       "1       0     0    4    99          0  74   99  42          4\n",
       "2       0     0    4    99          0  71  115  45          2\n",
       "3       0     0    4    99          0   4    5   3          1\n",
       "4       0     0    4    99          0  39  106   1          6"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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