charts-india-districts-census-dhanaji.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script src=\"//code.highcharts.com/stock/highstock.js\"></script>\n",
       "<script src=\"//code.highcharts.com/highcharts-more.js\"></script>\n",
       "<script src=\"//code.highcharts.com/modules/exporting.js\"></script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas_highcharts.display import display_charts\n",
    "\n",
    "# Get needed data file\n",
    "input_file = '..\\data\\india-districts-census-2011.csv'\n",
    "df = pd.read_csv(input_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>District code</th>\n",
       "      <th>Population</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Literate</th>\n",
       "      <th>Male_Literate</th>\n",
       "      <th>Female_Literate</th>\n",
       "      <th>SC</th>\n",
       "      <th>Male_SC</th>\n",
       "      <th>Female_SC</th>\n",
       "      <th>...</th>\n",
       "      <th>Power_Parity_Rs_90000_150000</th>\n",
       "      <th>Power_Parity_Rs_45000_150000</th>\n",
       "      <th>Power_Parity_Rs_150000_240000</th>\n",
       "      <th>Power_Parity_Rs_240000_330000</th>\n",
       "      <th>Power_Parity_Rs_150000_330000</th>\n",
       "      <th>Power_Parity_Rs_330000_425000</th>\n",
       "      <th>Power_Parity_Rs_425000_545000</th>\n",
       "      <th>Power_Parity_Rs_330000_545000</th>\n",
       "      <th>Power_Parity_Above_Rs_545000</th>\n",
       "      <th>Total_Power_Parity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>640.000000</td>\n",
       "      <td>6.400000e+02</td>\n",
       "      <td>6.400000e+02</td>\n",
       "      <td>6.400000e+02</td>\n",
       "      <td>6.400000e+02</td>\n",
       "      <td>6.400000e+02</td>\n",
       "      <td>6.400000e+02</td>\n",
       "      <td>6.400000e+02</td>\n",
       "      <td>6.400000e+02</td>\n",
       "      <td>6.400000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>320.500000</td>\n",
       "      <td>1.891961e+06</td>\n",
       "      <td>9.738598e+05</td>\n",
       "      <td>9.181011e+05</td>\n",
       "      <td>1.193186e+06</td>\n",
       "      <td>6.793182e+05</td>\n",
       "      <td>5.138675e+05</td>\n",
       "      <td>3.146537e+05</td>\n",
       "      <td>1.617739e+05</td>\n",
       "      <td>1.528798e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>786.046875</td>\n",
       "      <td>1696.456250</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>215.300000</td>\n",
       "      <td>509.300000</td>\n",
       "      <td>194.204688</td>\n",
       "      <td>261.245313</td>\n",
       "      <td>455.450000</td>\n",
       "      <td>279.631250</td>\n",
       "      <td>3315.412500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>184.896367</td>\n",
       "      <td>1.544380e+06</td>\n",
       "      <td>8.007785e+05</td>\n",
       "      <td>7.449864e+05</td>\n",
       "      <td>1.068583e+06</td>\n",
       "      <td>5.924144e+05</td>\n",
       "      <td>4.801816e+05</td>\n",
       "      <td>3.129818e+05</td>\n",
       "      <td>1.611216e+05</td>\n",
       "      <td>1.520336e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>1038.854733</td>\n",
       "      <td>1720.535151</td>\n",
       "      <td>638.345281</td>\n",
       "      <td>362.684243</td>\n",
       "      <td>968.538748</td>\n",
       "      <td>424.108001</td>\n",
       "      <td>587.279450</td>\n",
       "      <td>1007.364839</td>\n",
       "      <td>1050.934537</td>\n",
       "      <td>4638.568719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.004000e+03</td>\n",
       "      <td>4.414000e+03</td>\n",
       "      <td>3.590000e+03</td>\n",
       "      <td>4.436000e+03</td>\n",
       "      <td>2.614000e+03</td>\n",
       "      <td>1.822000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>160.750000</td>\n",
       "      <td>8.178610e+05</td>\n",
       "      <td>4.171682e+05</td>\n",
       "      <td>4.017458e+05</td>\n",
       "      <td>4.825982e+05</td>\n",
       "      <td>2.764365e+05</td>\n",
       "      <td>2.008920e+05</td>\n",
       "      <td>8.320850e+04</td>\n",
       "      <td>4.230700e+04</td>\n",
       "      <td>4.267175e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>236.750000</td>\n",
       "      <td>589.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>24.750000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1024.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>320.500000</td>\n",
       "      <td>1.557367e+06</td>\n",
       "      <td>7.986815e+05</td>\n",
       "      <td>7.589200e+05</td>\n",
       "      <td>9.573465e+05</td>\n",
       "      <td>5.483525e+05</td>\n",
       "      <td>4.038590e+05</td>\n",
       "      <td>2.460160e+05</td>\n",
       "      <td>1.255485e+05</td>\n",
       "      <td>1.178550e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>518.000000</td>\n",
       "      <td>1220.500000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>118.500000</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>85.500000</td>\n",
       "      <td>186.500000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>2238.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>480.250000</td>\n",
       "      <td>2.583551e+06</td>\n",
       "      <td>1.338604e+06</td>\n",
       "      <td>1.264277e+06</td>\n",
       "      <td>1.602260e+06</td>\n",
       "      <td>9.188582e+05</td>\n",
       "      <td>6.641550e+05</td>\n",
       "      <td>4.477078e+05</td>\n",
       "      <td>2.284602e+05</td>\n",
       "      <td>2.140502e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>941.250000</td>\n",
       "      <td>2233.250000</td>\n",
       "      <td>296.500000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>564.500000</td>\n",
       "      <td>213.250000</td>\n",
       "      <td>293.000000</td>\n",
       "      <td>497.000000</td>\n",
       "      <td>215.500000</td>\n",
       "      <td>3959.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>640.000000</td>\n",
       "      <td>1.106015e+07</td>\n",
       "      <td>5.865078e+06</td>\n",
       "      <td>5.195070e+06</td>\n",
       "      <td>8.227161e+06</td>\n",
       "      <td>4.591396e+06</td>\n",
       "      <td>3.635765e+06</td>\n",
       "      <td>2.464032e+06</td>\n",
       "      <td>1.266504e+06</td>\n",
       "      <td>1.197528e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>10334.000000</td>\n",
       "      <td>13819.000000</td>\n",
       "      <td>10835.000000</td>\n",
       "      <td>3595.000000</td>\n",
       "      <td>14430.000000</td>\n",
       "      <td>5027.000000</td>\n",
       "      <td>7597.000000</td>\n",
       "      <td>12624.000000</td>\n",
       "      <td>18289.000000</td>\n",
       "      <td>60163.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       District code    Population          Male        Female      Literate  \\\n",
       "count     640.000000  6.400000e+02  6.400000e+02  6.400000e+02  6.400000e+02   \n",
       "mean      320.500000  1.891961e+06  9.738598e+05  9.181011e+05  1.193186e+06   \n",
       "std       184.896367  1.544380e+06  8.007785e+05  7.449864e+05  1.068583e+06   \n",
       "min         1.000000  8.004000e+03  4.414000e+03  3.590000e+03  4.436000e+03   \n",
       "25%       160.750000  8.178610e+05  4.171682e+05  4.017458e+05  4.825982e+05   \n",
       "50%       320.500000  1.557367e+06  7.986815e+05  7.589200e+05  9.573465e+05   \n",
       "75%       480.250000  2.583551e+06  1.338604e+06  1.264277e+06  1.602260e+06   \n",
       "max       640.000000  1.106015e+07  5.865078e+06  5.195070e+06  8.227161e+06   \n",
       "\n",
       "       Male_Literate  Female_Literate            SC       Male_SC  \\\n",
       "count   6.400000e+02     6.400000e+02  6.400000e+02  6.400000e+02   \n",
       "mean    6.793182e+05     5.138675e+05  3.146537e+05  1.617739e+05   \n",
       "std     5.924144e+05     4.801816e+05  3.129818e+05  1.611216e+05   \n",
       "min     2.614000e+03     1.822000e+03  0.000000e+00  0.000000e+00   \n",
       "25%     2.764365e+05     2.008920e+05  8.320850e+04  4.230700e+04   \n",
       "50%     5.483525e+05     4.038590e+05  2.460160e+05  1.255485e+05   \n",
       "75%     9.188582e+05     6.641550e+05  4.477078e+05  2.284602e+05   \n",
       "max     4.591396e+06     3.635765e+06  2.464032e+06  1.266504e+06   \n",
       "\n",
       "          Female_SC         ...          Power_Parity_Rs_90000_150000  \\\n",
       "count  6.400000e+02         ...                            640.000000   \n",
       "mean   1.528798e+05         ...                            786.046875   \n",
       "std    1.520336e+05         ...                           1038.854733   \n",
       "min    0.000000e+00         ...                              0.000000   \n",
       "25%    4.267175e+04         ...                            236.750000   \n",
       "50%    1.178550e+05         ...                            518.000000   \n",
       "75%    2.140502e+05         ...                            941.250000   \n",
       "max    1.197528e+06         ...                          10334.000000   \n",
       "\n",
       "       Power_Parity_Rs_45000_150000  Power_Parity_Rs_150000_240000  \\\n",
       "count                    640.000000                     640.000000   \n",
       "mean                    1696.456250                     294.000000   \n",
       "std                     1720.535151                     638.345281   \n",
       "min                        0.000000                       0.000000   \n",
       "25%                      589.000000                      59.000000   \n",
       "50%                     1220.500000                     149.000000   \n",
       "75%                     2233.250000                     296.500000   \n",
       "max                    13819.000000                   10835.000000   \n",
       "\n",
       "       Power_Parity_Rs_240000_330000  Power_Parity_Rs_150000_330000  \\\n",
       "count                     640.000000                     640.000000   \n",
       "mean                      215.300000                     509.300000   \n",
       "std                       362.684243                     968.538748   \n",
       "min                         0.000000                       0.000000   \n",
       "25%                        24.750000                      95.000000   \n",
       "50%                       118.500000                     278.000000   \n",
       "75%                       262.000000                     564.500000   \n",
       "max                      3595.000000                   14430.000000   \n",
       "\n",
       "       Power_Parity_Rs_330000_425000  Power_Parity_Rs_425000_545000  \\\n",
       "count                     640.000000                     640.000000   \n",
       "mean                      194.204688                     261.245313   \n",
       "std                       424.108001                     587.279450   \n",
       "min                         0.000000                       0.000000   \n",
       "25%                        19.000000                      21.000000   \n",
       "50%                        84.000000                      85.500000   \n",
       "75%                       213.250000                     293.000000   \n",
       "max                      5027.000000                    7597.000000   \n",
       "\n",
       "       Power_Parity_Rs_330000_545000  Power_Parity_Above_Rs_545000  \\\n",
       "count                     640.000000                    640.000000   \n",
       "mean                      455.450000                    279.631250   \n",
       "std                      1007.364839                   1050.934537   \n",
       "min                         0.000000                      0.000000   \n",
       "25%                        44.000000                     18.000000   \n",
       "50%                       186.500000                     60.500000   \n",
       "75%                       497.000000                    215.500000   \n",
       "max                     12624.000000                  18289.000000   \n",
       "\n",
       "       Total_Power_Parity  \n",
       "count          640.000000  \n",
       "mean          3315.412500  \n",
       "std           4638.568719  \n",
       "min              9.000000  \n",
       "25%           1024.250000  \n",
       "50%           2238.500000  \n",
       "75%           3959.000000  \n",
       "max          60163.000000  \n",
       "\n",
       "[8 rows x 116 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"chart_idfuzQlJfN\"</div>\n",
       "    <script type=\"text/javascript\">new Highcharts.Chart({\"legend\":{\"enabled\":true},\"series\":[{\"yAxis\":0,\"name\":\"Female\",\"data\":[[\"ANDAMAN AND NICOBAR ISLANDS\",177710],[\"ANDHRA PRADESH\",42138631],[\"ARUNACHAL PRADESH\",669815],[\"ASSAM\",15266133],[\"BIHAR\",49821295],[\"CHANDIGARH\",474787],[\"CHHATTISGARH\",12712303],[\"DADRA AND NAGAR HAVELI\",149949],[\"DAMAN AND DIU\",92946],[\"GOA\",719405],[\"GUJARAT\",28948432],[\"HARYANA\",11856728],[\"HIMACHAL PRADESH\",3382729],[\"JAMMU AND KASHMIR\",5900640],[\"JHARKHAND\",16057819],[\"KARNATAKA\",30128640],[\"KERALA\",17378649],[\"LAKSHADWEEP\",31350],[\"MADHYA PRADESH\",35014503],[\"MAHARASHTRA\",54131277],[\"MANIPUR\",1417208],[\"MEGHALAYA\",1475057],[\"MIZORAM\",541867],[\"NAGALAND\",953853],[\"NCT OF DELHI\",7800615],[\"ORISSA\",20762082],[\"PONDICHERRY\",635442],[\"PUNJAB\",13103873],[\"RAJASTHAN\",32997440],[\"SIKKIM\",287507],[\"TAMIL NADU\",36009055],[\"TRIPURA\",1799541],[\"UTTAR PRADESH\",95331831],[\"UTTARAKHAND\",4948519],[\"WEST BENGAL\",44467088]]},{\"yAxis\":0,\"name\":\"Male\",\"data\":[[\"ANDAMAN AND NICOBAR ISLANDS\",202871],[\"ANDHRA PRADESH\",42442146],[\"ARUNACHAL PRADESH\",713912],[\"ASSAM\",15939443],[\"BIHAR\",54278157],[\"CHANDIGARH\",580663],[\"CHHATTISGARH\",12832895],[\"DADRA AND NAGAR HAVELI\",193760],[\"DAMAN AND DIU\",150301],[\"GOA\",739140],[\"GUJARAT\",31491260],[\"HARYANA\",13494734],[\"HIMACHAL PRADESH\",3481873],[\"JAMMU AND KASHMIR\",6640662],[\"JHARKHAND\",16930315],[\"KARNATAKA\",30966657],[\"KERALA\",16027412],[\"LAKSHADWEEP\",33123],[\"MADHYA PRADESH\",37612306],[\"MAHARASHTRA\",58243056],[\"MANIPUR\",1438586],[\"MEGHALAYA\",1491832],[\"MIZORAM\",555339],[\"NAGALAND\",1024649],[\"NCT OF DELHI\",8987326],[\"ORISSA\",21212136],[\"PONDICHERRY\",612511],[\"PUNJAB\",14639465],[\"RAJASTHAN\",35550997],[\"SIKKIM\",323070],[\"TAMIL NADU\",36137975],[\"TRIPURA\",1874376],[\"UTTAR PRADESH\",104480510],[\"UTTARAKHAND\",5137773],[\"WEST BENGAL\",46809027]]},{\"yAxis\":0,\"name\":\"TOTAL\",\"data\":[[\"ANDAMAN AND NICOBAR ISLANDS\",380581],[\"ANDHRA PRADESH\",84580777],[\"ARUNACHAL PRADESH\",1383727],[\"ASSAM\",31205576],[\"BIHAR\",104099452],[\"CHANDIGARH\",1055450],[\"CHHATTISGARH\",25545198],[\"DADRA AND NAGAR HAVELI\",343709],[\"DAMAN AND DIU\",243247],[\"GOA\",1458545],[\"GUJARAT\",60439692],[\"HARYANA\",25351462],[\"HIMACHAL PRADESH\",6864602],[\"JAMMU AND KASHMIR\",12541302],[\"JHARKHAND\",32988134],[\"KARNATAKA\",61095297],[\"KERALA\",33406061],[\"LAKSHADWEEP\",64473],[\"MADHYA PRADESH\",72626809],[\"MAHARASHTRA\",112374333],[\"MANIPUR\",2855794],[\"MEGHALAYA\",2966889],[\"MIZORAM\",1097206],[\"NAGALAND\",1978502],[\"NCT OF DELHI\",16787941],[\"ORISSA\",41974218],[\"PONDICHERRY\",1247953],[\"PUNJAB\",27743338],[\"RAJASTHAN\",68548437],[\"SIKKIM\",610577],[\"TAMIL NADU\",72147030],[\"TRIPURA\",3673917],[\"UTTAR PRADESH\",199812341],[\"UTTARAKHAND\",10086292],[\"WEST BENGAL\",91276115]]}],\"chart\":{\"height\":700,\"renderTo\":\"chart_idfuzQlJfN\",\"width\":1000,\"type\":\"column\"},\"yAxis\":[{}],\"title\":{\"text\":\"India's population - Male and Females\"},\"xAxis\":{\"categories\":[\"ANDAMAN AND NICOBAR ISLANDS\",\"ANDHRA PRADESH\",\"ARUNACHAL PRADESH\",\"ASSAM\",\"BIHAR\",\"CHANDIGARH\",\"CHHATTISGARH\",\"DADRA AND NAGAR HAVELI\",\"DAMAN AND DIU\",\"GOA\",\"GUJARAT\",\"HARYANA\",\"HIMACHAL PRADESH\",\"JAMMU AND KASHMIR\",\"JHARKHAND\",\"KARNATAKA\",\"KERALA\",\"LAKSHADWEEP\",\"MADHYA PRADESH\",\"MAHARASHTRA\",\"MANIPUR\",\"MEGHALAYA\",\"MIZORAM\",\"NAGALAND\",\"NCT OF DELHI\",\"ORISSA\",\"PONDICHERRY\",\"PUNJAB\",\"RAJASTHAN\",\"SIKKIM\",\"TAMIL NADU\",\"TRIPURA\",\"UTTAR PRADESH\",\"UTTARAKHAND\",\"WEST BENGAL\"],\"title\":{\"text\":\"State Name\"}}});</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 05 - State wise male and female population in India\n",
    "df_population = df[['State Name', 'Male','Female', 'Population']]\n",
    "mf_population =  df_population.groupby(['State Name'])[['Male','Female','Population']].sum().rename(columns={'Population':'TOTAL'})\n",
    "display_charts(mf_population, title=\"India's population - Male and Females\", kind=\"bar\", figsize = (1000, 700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"chart_idbfvTRsdT\"</div>\n",
       "    <script type=\"text/javascript\">new Highcharts.Chart({\"legend\":{\"enabled\":true},\"series\":[{\"yAxis\":0,\"name\":\"0-29 YEARS\",\"data\":[[\"ANDAMAN AND NICOBAR ISLANDS\",204538],[\"ANDHRA PRADESH\",45815669],[\"ARUNACHAL PRADESH\",904739],[\"ASSAM\",19042032],[\"BIHAR\",66930323],[\"CHANDIGARH\",602411],[\"CHHATTISGARH\",15230475],[\"DADRA AND NAGAR HAVELI\",225103],[\"DAMAN AND DIU\",153064],[\"GOA\",695406],[\"GUJARAT\",34370917],[\"HARYANA\",15006789],[\"HIMACHAL PRADESH\",3648872],[\"JAMMU AND KASHMIR\",7725207],[\"JHARKHAND\",20576647],[\"KARNATAKA\",33676315],[\"KERALA\",15711427],[\"LAKSHADWEEP\",33877],[\"MADHYA PRADESH\",44264116],[\"MAHARASHTRA\",61782561],[\"MANIPUR\",1700955],[\"MEGHALAYA\",2040423],[\"MIZORAM\",680399],[\"NAGALAND\",1289918],[\"NCT OF DELHI\",9665080],[\"ORISSA\",23359384],[\"PONDICHERRY\",627771],[\"PUNJAB\",15143130],[\"RAJASTHAN\",42814434],[\"SIKKIM\",364810],[\"TAMIL NADU\",36336584],[\"TRIPURA\",2093146],[\"UTTAR PRADESH\",126507123],[\"UTTARAKHAND\",6033370],[\"WEST BENGAL\",50551737]]},{\"yAxis\":0,\"name\":\"30-49 YEARS\",\"data\":[[\"ANDAMAN AND NICOBAR ISLANDS\",119161],[\"ANDHRA PRADESH\",23148452],[\"ARUNACHAL PRADESH\",337844],[\"ASSAM\",8016988],[\"BIHAR\",22949064],[\"CHANDIGARH\",303748],[\"CHHATTISGARH\",6505399],[\"DADRA AND NAGAR HAVELI\",87888],[\"DAMAN AND DIU\",66800],[\"GOA\",455405],[\"GUJARAT\",16291242],[\"HARYANA\",6403218],[\"HIMACHAL PRADESH\",1905011],[\"JAMMU AND KASHMIR\",3069723],[\"JHARKHAND\",7766660],[\"KARNATAKA\",16740325],[\"KERALA\",9796202],[\"LAKSHADWEEP\",19244],[\"MADHYA PRADESH\",17814066],[\"MAHARASHTRA\",30272010],[\"MANIPUR\",737266],[\"MEGHALAYA\",632928],[\"MIZORAM\",272113],[\"NAGALAND\",475130],[\"NCT OF DELHI\",4762353],[\"ORISSA\",11179683],[\"PONDICHERRY\",387613],[\"PUNJAB\",7481850],[\"RAJASTHAN\",15938006],[\"SIKKIM\",161866],[\"TAMIL NADU\",21429281],[\"TRIPURA\",1010551],[\"UTTAR PRADESH\",44192083],[\"UTTARAKHAND\",2419886],[\"WEST BENGAL\",25343015]]},{\"yAxis\":0,\"name\":\"50 YEARS & ABOVE\",\"data\":[[\"ANDAMAN AND NICOBAR ISLANDS\",56514],[\"ANDHRA PRADESH\",14845225],[\"ARUNACHAL PRADESH\",139635],[\"ASSAM\",4127732],[\"BIHAR\",13816563],[\"CHANDIGARH\",148945],[\"CHHATTISGARH\",3786209],[\"DADRA AND NAGAR HAVELI\",30346],[\"DAMAN AND DIU\",23027],[\"GOA\",305640],[\"GUJARAT\",9538689],[\"HARYANA\",3909921],[\"HIMACHAL PRADESH\",1300556],[\"JAMMU AND KASHMIR\",1731253],[\"JHARKHAND\",4528093],[\"KARNATAKA\",10632806],[\"KERALA\",7863946],[\"LAKSHADWEEP\",11237],[\"MADHYA PRADESH\",10465765],[\"MAHARASHTRA\",19912066],[\"MANIPUR\",409796],[\"MEGHALAYA\",287754],[\"MIZORAM\",144061],[\"NAGALAND\",211983],[\"NCT OF DELHI\",2343806],[\"ORISSA\",7315980],[\"PONDICHERRY\",231468],[\"PUNJAB\",5076836],[\"RAJASTHAN\",9526395],[\"SIKKIM\",82725],[\"TAMIL NADU\",14314360],[\"TRIPURA\",568604],[\"UTTAR PRADESH\",27491073],[\"UTTARAKHAND\",1616428],[\"WEST BENGAL\",15268912]]}],\"chart\":{\"height\":700,\"renderTo\":\"chart_idbfvTRsdT\",\"width\":1000,\"type\":\"bar\"},\"yAxis\":[{}],\"title\":{\"text\":\"State wise different age groups in India\"},\"xAxis\":{\"categories\":[\"ANDAMAN AND NICOBAR ISLANDS\",\"ANDHRA PRADESH\",\"ARUNACHAL PRADESH\",\"ASSAM\",\"BIHAR\",\"CHANDIGARH\",\"CHHATTISGARH\",\"DADRA AND NAGAR HAVELI\",\"DAMAN AND DIU\",\"GOA\",\"GUJARAT\",\"HARYANA\",\"HIMACHAL PRADESH\",\"JAMMU AND KASHMIR\",\"JHARKHAND\",\"KARNATAKA\",\"KERALA\",\"LAKSHADWEEP\",\"MADHYA PRADESH\",\"MAHARASHTRA\",\"MANIPUR\",\"MEGHALAYA\",\"MIZORAM\",\"NAGALAND\",\"NCT OF DELHI\",\"ORISSA\",\"PONDICHERRY\",\"PUNJAB\",\"RAJASTHAN\",\"SIKKIM\",\"TAMIL NADU\",\"TRIPURA\",\"UTTAR PRADESH\",\"UTTARAKHAND\",\"WEST BENGAL\"],\"title\":{\"text\":\"State Name\"}}});</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 07 - Population of different types of age groups in India\n",
    "\n",
    "df_age_group = df.groupby(['State Name'])[['Age_Group_0_29','Age_Group_30_49','Age_Group_50']].sum().rename(columns={'Age_Group_0_29':'0-29 YEARS','Age_Group_30_49':'30-49 YEARS','Age_Group_50':'50 YEARS & ABOVE'})\n",
    "display_charts(df_age_group, title=\"State wise different age groups in India\", kind=\"barh\",figsize = (1000, 700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"chart_idsbNSIzTJ\"</div>\n",
       "    <script type=\"text/javascript\">new Highcharts.Chart({\"legend\":{\"enabled\":true},\"series\":[{\"yAxis\":0,\"name\":0,\"data\":[[\"Hindus\",966257353],[\"Muslims\",172245158],[\"Christians\",27819588],[\"Sikhs\",20833116],[\"Buddhists\",8442972],[\"Jains\",4451753],[\"Others_Religions\",7937734],[\"Religion_Not_Stated\",2867303]]}],\"chart\":{\"renderTo\":\"chart_idsbNSIzTJ\",\"type\":\"pie\"},\"yAxis\":[{}],\"title\":{\"text\":\"Religions in India\"},\"xAxis\":{\"categories\":[\"Hindus\",\"Muslims\",\"Christians\",\"Sikhs\",\"Buddhists\",\"Jains\",\"Others_Religions\",\"Religion_Not_Stated\"]}});</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 09 - Religion's distribution in India\n",
    "\n",
    "df_reg = df[['Hindus', 'Muslims', 'Christians', 'Sikhs', 'Buddhists','Jains','Others_Religions','Religion_Not_Stated']].sum()\n",
    "df_religion = pd.DataFrame(df_reg)\n",
    "\n",
    "df_religion.rename(columns={0:'RELIGIONS'})\n",
    "\n",
    "display_charts(df_religion, kind='pie', title=\"Religions in India\") "
   ]
  }
 ],
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