Indian_Population_Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_states = gpd.read_file('IND_adm/IND_adm1.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('indian_population.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State / Union Territory</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>380581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>49577103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>1383727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Assam</td>\n",
       "      <td>31205576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>104099452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State / Union Territory  Population\n",
       "0  Andaman and Nicobar Islands      380581\n",
       "1               Andhra Pradesh    49577103\n",
       "2            Arunachal Pradesh     1383727\n",
       "3                        Assam    31205576\n",
       "4                        Bihar   104099452"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_0</th>\n",
       "      <th>ISO</th>\n",
       "      <th>NAME_0</th>\n",
       "      <th>ID_1</th>\n",
       "      <th>NAME_1</th>\n",
       "      <th>TYPE_1</th>\n",
       "      <th>ENGTYPE_1</th>\n",
       "      <th>NL_NAME_1</th>\n",
       "      <th>VARNAME_1</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>Andaman and Nicobar</td>\n",
       "      <td>Union Territor</td>\n",
       "      <td>Union Territory</td>\n",
       "      <td>None</td>\n",
       "      <td>Andaman &amp; Nicobar Islands|Andaman et Nicobar|I...</td>\n",
       "      <td>MULTIPOLYGON (((93.78773 6.85264, 93.78849 6.8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>2</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>State</td>\n",
       "      <td>State</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>MULTIPOLYGON (((80.27458 13.45958, 80.27458 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>105</td>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>3</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>State</td>\n",
       "      <td>State</td>\n",
       "      <td>None</td>\n",
       "      <td>Agence de la FrontiÃ¨re du Nord-Est(French-obs...</td>\n",
       "      <td>POLYGON ((96.15778 29.38310, 96.16380 29.37668...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>105</td>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>4</td>\n",
       "      <td>Assam</td>\n",
       "      <td>State</td>\n",
       "      <td>State</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>MULTIPOLYGON (((89.87145 25.53730, 89.87118 25...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>105</td>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>5</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>State</td>\n",
       "      <td>State</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>MULTIPOLYGON (((88.10548 26.53904, 88.10505 26...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID_0  ISO NAME_0  ID_1               NAME_1          TYPE_1  \\\n",
       "0   105  IND  India     1  Andaman and Nicobar  Union Territor   \n",
       "1   105  IND  India     2       Andhra Pradesh           State   \n",
       "2   105  IND  India     3    Arunachal Pradesh           State   \n",
       "3   105  IND  India     4                Assam           State   \n",
       "4   105  IND  India     5                Bihar           State   \n",
       "\n",
       "         ENGTYPE_1 NL_NAME_1  \\\n",
       "0  Union Territory      None   \n",
       "1            State      None   \n",
       "2            State      None   \n",
       "3            State      None   \n",
       "4            State      None   \n",
       "\n",
       "                                           VARNAME_1  \\\n",
       "0  Andaman & Nicobar Islands|Andaman et Nicobar|I...   \n",
       "1                                               None   \n",
       "2  Agence de la FrontiÃ¨re du Nord-Est(French-obs...   \n",
       "3                                               None   \n",
       "4                                               None   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((93.78773 6.85264, 93.78849 6.8...  \n",
       "1  MULTIPOLYGON (((80.27458 13.45958, 80.27458 13...  \n",
       "2  POLYGON ((96.15778 29.38310, 96.16380 29.37668...  \n",
       "3  MULTIPOLYGON (((89.87145 25.53730, 89.87118 25...  \n",
       "4  MULTIPOLYGON (((88.10548 26.53904, 88.10505 26...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_states.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_states = ind_states[['NAME_1', 'geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8f77108>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind_states.plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_states.rename(columns = {'NAME_1' : 'States'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>States</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Andaman and Nicobar</td>\n",
       "      <td>MULTIPOLYGON (((93.78773 6.85264, 93.78849 6.8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>MULTIPOLYGON (((80.27458 13.45958, 80.27458 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>POLYGON ((96.15778 29.38310, 96.16380 29.37668...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Assam</td>\n",
       "      <td>MULTIPOLYGON (((89.87145 25.53730, 89.87118 25...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>MULTIPOLYGON (((88.10548 26.53904, 88.10505 26...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                States                                           geometry\n",
       "0  Andaman and Nicobar  MULTIPOLYGON (((93.78773 6.85264, 93.78849 6.8...\n",
       "1       Andhra Pradesh  MULTIPOLYGON (((80.27458 13.45958, 80.27458 13...\n",
       "2    Arunachal Pradesh  POLYGON ((96.15778 29.38310, 96.16380 29.37668...\n",
       "3                Assam  MULTIPOLYGON (((89.87145 25.53730, 89.87118 25...\n",
       "4                Bihar  MULTIPOLYGON (((88.10548 26.53904, 88.10505 26..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_states.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'State / Union Territory': 'States'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>States</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>380581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>49577103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>1383727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Assam</td>\n",
       "      <td>31205576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>104099452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        States  Population\n",
       "0  Andaman and Nicobar Islands      380581\n",
       "1               Andhra Pradesh    49577103\n",
       "2            Arunachal Pradesh     1383727\n",
       "3                        Assam    31205576\n",
       "4                        Bihar   104099452"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The state  Andaman and Nicobar  is NOT in the population_data list\n",
      "The state  Orissa  is NOT in the population_data list\n",
      "The state  Telangana  is NOT in the population_data list\n",
      "The state  Uttaranchal  is NOT in the population_data list\n"
     ]
    }
   ],
   "source": [
    "for index, row in ind_states['States'].iteritems():\n",
    "    if row in df['States'].tolist():\n",
    "        pass\n",
    "    else:\n",
    "        print('The state ', row , ' is NOT in the population_data list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace('Andaman and Nicobar Islands', 'Andaman and Nicobar', inplace = True)\n",
    "df.replace('Odisha', 'Orissa', inplace = True)\n",
    "df.replace('Telengana', 'Telangana', inplace = True)\n",
    "df.replace('Uttarakhand', 'Uttaranchal', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in ind_states['States'].iteritems():\n",
    "    if row in df['States'].tolist():\n",
    "        pass\n",
    "    else:\n",
    "        print('The state ', row , ' is NOT in the population_data list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36\n",
      "36\n"
     ]
    }
   ],
   "source": [
    "print(len(df['States']))\n",
    "print(len(ind_states['States']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_states = ind_states.merge(df, on = 'States')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>States</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Andaman and Nicobar</td>\n",
       "      <td>MULTIPOLYGON (((93.78773 6.85264, 93.78849 6.8...</td>\n",
       "      <td>380581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>MULTIPOLYGON (((80.27458 13.45958, 80.27458 13...</td>\n",
       "      <td>49577103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>POLYGON ((96.15778 29.38310, 96.16380 29.37668...</td>\n",
       "      <td>1383727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Assam</td>\n",
       "      <td>MULTIPOLYGON (((89.87145 25.53730, 89.87118 25...</td>\n",
       "      <td>31205576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>MULTIPOLYGON (((88.10548 26.53904, 88.10505 26...</td>\n",
       "      <td>104099452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                States                                           geometry  \\\n",
       "0  Andaman and Nicobar  MULTIPOLYGON (((93.78773 6.85264, 93.78849 6.8...   \n",
       "1       Andhra Pradesh  MULTIPOLYGON (((80.27458 13.45958, 80.27458 13...   \n",
       "2    Arunachal Pradesh  POLYGON ((96.15778 29.38310, 96.16380 29.37668...   \n",
       "3                Assam  MULTIPOLYGON (((89.87145 25.53730, 89.87118 25...   \n",
       "4                Bihar  MULTIPOLYGON (((88.10548 26.53904, 88.10505 26...   \n",
       "\n",
       "   Population  \n",
       "0      380581  \n",
       "1    49577103  \n",
       "2     1383727  \n",
       "3    31205576  \n",
       "4   104099452  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_states.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind_states.plot(column = 'Population', cmap = 'Spectral', legend = True, figsize=(12,8))\n",
    "plt.savefig('population_INDIA.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}