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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploratory Data Analysis on the World Bank Data\n",
"\n",
"The objective of this project is to collect data from the World Bank Open APIs and prepare and analyse the data using Python."
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import requests\n",
"\n",
"from IPython.display import display\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Task-1: Data Identification\n",
"\n",
"For this analysis, the following API has been selected: https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-api-documentation\n",
"\n",
"The World Bank data consists of demographic and other statistical data related to Population, Employment, Health, GDP, Energy Consumption, etc. for all the countries from the year 1960 to 2018. These categories are called indicators and are each defined by a code.\n",
"\n",
"The following indicators have been chosen for analysis:\n",
"* SP.POP.TOTL - Total Population\n",
"* SP.POP.TOTL.FE.IN - Total Female Population\n",
"* SP.POP.TOTL.MA.IN - Total Male Population\n",
"* SP.DYN.CBRT.IN Birth Rate\n",
"* SP.DYN.CDRT.IN Death Rate\n",
"* SE.COM.DURS - Compulsory Education Duration\n",
"* SL.IND.EMPL.ZS - Employment in Industry(%)\n",
"* SL.AGR.EMPL.ZS - Employment in Agriculture(%)\n",
"* SL.AGR.EMPL.FE.ZS - Female Employment in Agriculture(%)\n",
"* SL.IND.EMPL.FE.ZS - Female Employment in Industry(%)\n",
"* SL.UEM.TOTL.ZS - Unemployment(%)\n",
"* NY.GDP.MKTP.CD - GDP in USD\n",
"* NY.ADJ.NNTY.PC.KD.ZG - National Income per Capita\n",
"* NY.GSR.NFCY.CD - Net income from Abroad\n",
"* NV.AGR.TOTL.CD - Agriculture value added(in USD)\n",
"* EG.USE.ELEC.KH.PC - Electric Power Consumption(kWH per capita)\n",
"* EG.FEC.RNEW.ZS - Renewable Energy Consumption (%)\n",
"* EG.USE.COMM.FO.ZS - Fossil Fuel Consumption (%)\n",
" \n",
"The following countries have been chosen for analysis:\n",
"* US - United States of America\n",
"* IN - India\n",
"* CN - China\n",
"* JP - Japan\n",
"* CA - Canada\n",
"* GB - Great Britain\n",
"* ZA - South Africa\n",
"\n",
"The base API URL is http://api.worldbank.org/v2/ and is followed by the country code and indicator code to obtain the data for each year.\n",
"No authentication is required to use the API."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Task-2: Data Collection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining Constants\n",
"\n",
"Since the API uses codes, we have created a mapping between the codes and meaningful names to use while parsing and storing the data. "
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"# Base URL used in all the API calls\n",
"BASE_URL='http://api.worldbank.org/v2/'\n",
"\n",
"# List of indicators according to the features defined above\n",
"INDICATOR_CODES=['SP.POP.TOTL', 'SP.POP.TOTL.FE.IN', 'SP.POP.TOTL.MA.IN',\n",
" 'SP.DYN.CBRT.IN', 'SP.DYN.CDRT.IN',\n",
" 'SE.COM.DURS',\n",
" 'SL.IND.EMPL.ZS', 'SL.AGR.EMPL.ZS', 'SL.AGR.EMPL.FE.ZS', 'SL.IND.EMPL.FE.ZS', 'SL.UEM.TOTL.ZS',\n",
" 'NY.GDP.MKTP.CD',\n",
" 'NY.ADJ.NNTY.PC.KD.ZG', 'NY.GSR.NFCY.CD', 'NV.AGR.TOTL.CD',\n",
" 'EG.USE.ELEC.KH.PC', 'EG.FEC.RNEW.ZS', 'EG.USE.COMM.FO.ZS']\n",
"\n",
"COUNTRY_LIST=['USA', 'India', 'China', 'Japan', 'Canada', 'Great Britain', 'South Africa']\n",
"\n",
"# mapping of feature codes to more meaningful names\n",
"featureMap={\n",
" \"SP.POP.TOTL\": \"Total Population\",\n",
" \"SP.POP.TOTL.FE.IN\": \"Female Population\",\n",
" \"SP.POP.TOTL.MA.IN\": \"Male Population\",\n",
" \"SP.DYN.CBRT.IN\": \"Birth Rate\",\n",
" \"SP.DYN.CDRT.IN\": \"Death Rate\",\n",
" \"SE.COM.DURS\": \"Compulsory Education Dur.\",\n",
" \"SL.IND.EMPL.ZS\":\"Employment in Industry(%)\",\n",
" \"SL.AGR.EMPL.ZS\": \"Employment in Agriculture(%)\",\n",
" \"SL.AGR.EMPL.FE.ZS\": \"Female Employment in Agriculture(%)\",\n",
" \"SL.IND.EMPL.FE.ZS\": \"Female Employment in Industry(%)\",\n",
" \"SL.UEM.TOTL.ZS\": \"Unemployment(%)\",\n",
" \"NY.GDP.MKTP.CD\": \"GDP in USD\",\n",
" \"NY.ADJ.NNTY.PC.KD.ZG\":\"National Income per Capita\",\n",
" \"NY.GSR.NFCY.CD\":\"Net income from Abroad\",\n",
" \"NV.AGR.TOTL.CD\":\"Agriculture value added(in USD)\",\n",
" \"EG.USE.ELEC.KH.PC\":\"Electric Power Consumption(kWH per capita)\",\n",
" \"EG.FEC.RNEW.ZS\":\"Renewable Energy Consumption (%)\",\n",
" \"EG.USE.COMM.FO.ZS\":\"Fossil Fuel Consumption (%)\"\n",
"}\n",
"\n",
"# Mapping of country codes to their actual names\n",
"countryMap={\n",
" \"US\": \"USA\",\n",
" \"IN\":\"India\",\n",
" \"CN\": \"China\",\n",
" \"JP\": \"Japan\",\n",
" \"CA\": \"Canada\",\n",
" \"GB\": \"Great Britain\",\n",
" \"ZA\": \"South Africa\"\n",
"}\n",
"\n",
"# constant parameters used in sending the request.\n",
"params = dict()\n",
"# to ensure we receive a JSON response\n",
"params['format']='json'\n",
"# The data we fetch is for 59 years.\n",
"# Hence we change the default page size of 50 to 100 to ensure we need only one API call per feature.\n",
"params['per_page']='100'\n",
"# Range of years for which the data is needed\n",
"params['date']='1960:2018'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fetch data through API calls\n",
" \n",
"The following functions have been written to make an API call per feature and fetch the data. Each of these functions performs the following tasks:\n",
"\n",
"* **getCountrywiseDF():** Calls the loadJSONData function for the country specified and returns a dataframe with the complete data for one country.\n",
"* **loadJSONData():** Forms the appropriate URL using the base URL, country code and indicator code and sends the request to the endpoint. It returns a list of list of values for all features."
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"# Function to get JSON data from the endpoint\n",
"def loadJSONData(country_code): \n",
" dataList=[]\n",
" \n",
" # iterate over each indicator code specified in the contant INDICATOR_CODES defined above\n",
" for indicator in INDICATOR_CODES: \n",
" \n",
" # form the URL in the desired format\n",
" # E.g: http://api.worldbank.org/v2/countries/us/indicators/SP.POP.TOTL?format=json&per_page=200&date=1960:2018\n",
" url=BASE_URL+'countries/'+country_code.lower()+'/indicators/'+indicator\n",
" \n",
" # send the request using the resquests module\n",
" response = requests.get(url, params=params)\n",
" \n",
" # validate the response status code\n",
" # The API returns a status_code 200 even for error messages,\n",
" # however, the response body contains a field called \"message\" that includes the details of the error\n",
" # check if message is not present in the response\n",
" if response.status_code == 200 and (\"message\" not in response.json()[0].keys()):\n",
" # print(\"Successfully got data for: \" + str(featureMap[indicator]))\n",
" \n",
" # list of values for one feature\n",
" indicatorVals=[]\n",
" \n",
" # the response is an array containing two arrays - [[{page: 1, ...}], [{year: 2018, SP.POP.TOTL: 123455}, ...]]\n",
" # hence we check if the length of the response is >1\n",
" if len(response.json()) > 1:\n",
" \n",
" # if yes, iterate over each object in the response\n",
" # each object gives one single value for each year\n",
" for obj in response.json()[1]:\n",
" \n",
" # check for empty values\n",
" if obj['value'] is \"\" or obj['value'] is None:\n",
" indicatorVals.append(None)\n",
" else:\n",
" # if a value is present, add it to the list of indicator values\n",
" indicatorVals.append(float(obj['value']))\n",
" dataList.append(indicatorVals)\n",
" else:\n",
" # print an error message if the API call failed\n",
" print(\"Error in Loading the data. Status Code: \" + str(response.status_code))\n",
" \n",
" # Once all the features have been obtained, add the values for the \"Year\"\n",
" # The API returns the indicator values from the most recent year. Hence, we create a list of years in reverse order\n",
" dataList.append([year for year in range(2018, 1959, -1)])\n",
" # return the list of lists of feature values [[val1,val2,val3...], [val1,val2,val3...], [val1,val2,val3...], ...]\n",
" return dataList\n",
"\n",
"#----------------------------------------------------------------------------------------------------\n",
"\n",
"# function to invokde the loadJSONData function and form the final DataFrame for each country\n",
"def getCountrywiseDF(country_code):\n",
" \n",
" # The resulting dataframe needs to have meaningful column names\n",
" # hence we create a list of column names from the map defined above\n",
" col_list=list(featureMap.values())\n",
" # append the year column name\n",
" col_list.append('Year')\n",
" \n",
" print(\"------------------Loading data for: \"+countryMap[country_code]+\"-----------------------\")\n",
" \n",
" # for the given country call the loadJSONData function and fetch the data from the API\n",
" dataList=loadJSONData(country_code)\n",
" \n",
" # transform the list of lists of features into a DataFrame\n",
" # np.column_stack is used to add each list as a column \n",
" df=pd.DataFrame(np.column_stack(dataList), columns=col_list)\n",
" \n",
" # add the country column by extracting the country name from the map using the country code\n",
" df['Country'] = countryMap[country_code]\n",
" \n",
" # display the resulting dataframe\n",
" display(df.head())\n",
" \n",
" # return the formed dataframe for the given country\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"------------------Loading data for: USA-----------------------\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>Total Population</th>\n",
" <th>Female Population</th>\n",
" <th>Male Population</th>\n",
" <th>Birth Rate</th>\n",
" <th>Death Rate</th>\n",
" <th>Compulsory Education Dur.</th>\n",
" <th>Employment in Industry(%)</th>\n",
" <th>Employment in Agriculture(%)</th>\n",
" <th>Female Employment in Agriculture(%)</th>\n",
" <th>Female Employment in Industry(%)</th>\n",
" <th>Unemployment(%)</th>\n",
" <th>GDP in USD</th>\n",
" <th>National Income per Capita</th>\n",
" <th>Net income from Abroad</th>\n",
" <th>Agriculture value added(in USD)</th>\n",
" <th>Electric Power Consumption(kWH per capita)</th>\n",
" <th>Renewable Energy Consumption (%)</th>\n",
" <th>Fossil Fuel Consumption (%)</th>\n",
" <th>Year</th>\n",
" <th>Country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>327,167,434.0</td>\n",
" <td>165,285,036.0</td>\n",
" <td>161,882,398.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>12.0</td>\n",
" <td>19.4</td>\n",
" <td>1.4</td>\n",
" <td>0.8</td>\n",
" <td>8.5</td>\n",
" <td>3.9</td>\n",
" <td>20,544,343,456,936.5</td>\n",
" <td>None</td>\n",
" <td>267,900,000,000.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2018</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>325,147,121.0</td>\n",
" <td>164,275,323.0</td>\n",
" <td>160,871,798.0</td>\n",
" <td>11.8</td>\n",
" <td>8.5</td>\n",
" <td>12.0</td>\n",
" <td>19.7</td>\n",
" <td>1.4</td>\n",
" <td>0.8</td>\n",
" <td>8.7</td>\n",
" <td>4.4</td>\n",
" <td>19,485,393,853,000.0</td>\n",
" <td>2.2</td>\n",
" <td>243,296,000,000.0</td>\n",
" <td>178,579,605,000.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>323,071,342.0</td>\n",
" <td>163,245,729.0</td>\n",
" <td>159,825,613.0</td>\n",
" <td>12.2</td>\n",
" <td>8.5</td>\n",
" <td>12.0</td>\n",
" <td>19.8</td>\n",
" <td>1.4</td>\n",
" <td>0.8</td>\n",
" <td>8.7</td>\n",
" <td>4.9</td>\n",
" <td>18,707,188,235,000.0</td>\n",
" <td>-0.1</td>\n",
" <td>218,168,000,000.0</td>\n",
" <td>175,923,170,000.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2016</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>320,742,673.0</td>\n",
" <td>162,097,641.0</td>\n",
" <td>158,645,032.0</td>\n",
" <td>12.4</td>\n",
" <td>8.4</td>\n",
" <td>12.0</td>\n",
" <td>19.9</td>\n",
" <td>1.4</td>\n",
" <td>0.7</td>\n",
" <td>8.8</td>\n",
" <td>5.3</td>\n",
" <td>18,219,297,584,000.0</td>\n",
" <td>2.4</td>\n",
" <td>224,626,000,000.0</td>\n",
" <td>189,947,276,000.0</td>\n",
" <td>None</td>\n",
" <td>8.7</td>\n",
" <td>82.4</td>\n",
" <td>2015</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>318,386,421.0</td>\n",
" <td>160,946,480.0</td>\n",
" <td>157,439,941.0</td>\n",
" <td>12.5</td>\n",
" <td>8.2</td>\n",
" <td>12.0</td>\n",
" <td>20.0</td>\n",
" <td>1.4</td>\n",
" <td>0.7</td>\n",
" <td>8.9</td>\n",
" <td>6.2</td>\n",
" <td>17,521,746,534,000.0</td>\n",
" <td>2.9</td>\n",
" <td>241,062,000,000.0</td>\n",
" <td>208,273,224,000.0</td>\n",
" <td>12,994.0</td>\n",
" <td>8.8</td>\n",
" <td>83.1</td>\n",
" <td>2014</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total Population Female Population Male Population Birth Rate Death Rate \\\n",
"0 327,167,434.0 165,285,036.0 161,882,398.0 None None \n",
"1 325,147,121.0 164,275,323.0 160,871,798.0 11.8 8.5 \n",
"2 323,071,342.0 163,245,729.0 159,825,613.0 12.2 8.5 \n",
"3 320,742,673.0 162,097,641.0 158,645,032.0 12.4 8.4 \n",
"4 318,386,421.0 160,946,480.0 157,439,941.0 12.5 8.2 \n",
"\n",
" Compulsory Education Dur. Employment in Industry(%) \\\n",
"0 12.0 19.4 \n",
"1 12.0 19.7 \n",
"2 12.0 19.8 \n",
"3 12.0 19.9 \n",
"4 12.0 20.0 \n",
"\n",
" Employment in Agriculture(%) Female Employment in Agriculture(%) \\\n",
"0 1.4 0.8 \n",
"1 1.4 0.8 \n",
"2 1.4 0.8 \n",
"3 1.4 0.7 \n",
"4 1.4 0.7 \n",
"\n",
" Female Employment in Industry(%) Unemployment(%) GDP in USD \\\n",
"0 8.5 3.9 20,544,343,456,936.5 \n",
"1 8.7 4.4 19,485,393,853,000.0 \n",
"2 8.7 4.9 18,707,188,235,000.0 \n",
"3 8.8 5.3 18,219,297,584,000.0 \n",
"4 8.9 6.2 17,521,746,534,000.0 \n",
"\n",
" National Income per Capita Net income from Abroad \\\n",
"0 None 267,900,000,000.0 \n",
"1 2.2 243,296,000,000.0 \n",
"2 -0.1 218,168,000,000.0 \n",
"3 2.4 224,626,000,000.0 \n",
"4 2.9 241,062,000,000.0 \n",
"\n",
" Agriculture value added(in USD) Electric Power Consumption(kWH per capita) \\\n",
"0 None None \n",
"1 178,579,605,000.0 None \n",
"2 175,923,170,000.0 None \n",
"3 189,947,276,000.0 None \n",
"4 208,273,224,000.0 12,994.0 \n",
"\n",
" Renewable Energy Consumption (%) Fossil Fuel Consumption (%) Year Country \n",
"0 None None 2018 USA \n",
"1 None None 2017 USA \n",
"2 None None 2016 USA \n",
"3 8.7 82.4 2015 USA \n",
"4 8.8 83.1 2014 USA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"------------------Loading data for: India-----------------------\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Total Population</th>\n",
" <th>Female Population</th>\n",
" <th>Male Population</th>\n",
" <th>Birth Rate</th>\n",
" <th>Death Rate</th>\n",
" <th>Compulsory Education Dur.</th>\n",
" <th>Employment in Industry(%)</th>\n",
" <th>Employment in Agriculture(%)</th>\n",
" <th>Female Employment in Agriculture(%)</th>\n",
" <th>Female Employment in Industry(%)</th>\n",
" <th>Unemployment(%)</th>\n",
" <th>GDP in USD</th>\n",
" <th>National Income per Capita</th>\n",
" <th>Net income from Abroad</th>\n",
" <th>Agriculture value added(in USD)</th>\n",
" <th>Electric Power Consumption(kWH per capita)</th>\n",
" <th>Renewable Energy Consumption (%)</th>\n",
" <th>Fossil Fuel Consumption (%)</th>\n",
" <th>Year</th>\n",
" <th>Country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1,352,617,328.0</td>\n",
" <td>649,574,719.0</td>\n",
" <td>703,042,609.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>8.0</td>\n",
" <td>24.7</td>\n",
" <td>43.9</td>\n",
" <td>57.1</td>\n",
" <td>18.7</td>\n",
" <td>2.6</td>\n",
" <td>2,718,732,231,257.6</td>\n",
" <td>None</td>\n",
" <td>-27,691,371,968.3</td>\n",
" <td>396,987,486,510.1</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2018</td>\n",
" <td>India</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1,338,658,835.0</td>\n",
" <td>642,787,641.0</td>\n",
" <td>695,871,194.0</td>\n",
" <td>18.1</td>\n",
" <td>7.2</td>\n",
" <td>8.0</td>\n",
" <td>24.5</td>\n",
" <td>44.5</td>\n",
" <td>57.6</td>\n",
" <td>18.7</td>\n",
" <td>2.6</td>\n",
" <td>2,652,242,857,923.9</td>\n",
" <td>4.0</td>\n",
" <td>-28,673,153,563.2</td>\n",
" <td>414,265,878,403.3</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017</td>\n",
" <td>India</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1,324,509,589.0</td>\n",
" <td>635,908,885.0</td>\n",
" <td>688,600,704.0</td>\n",
" <td>18.3</td>\n",
" <td>7.2</td>\n",
" <td>8.0</td>\n",
" <td>24.3</td>\n",
" <td>45.1</td>\n",
" <td>58.2</td>\n",
" <td>18.6</td>\n",
" <td>2.7</td>\n",
" <td>2,290,432,075,123.8</td>\n",
" <td>6.4</td>\n",
" <td>-26,300,115,592.2</td>\n",
" <td>372,190,852,855.1</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2016</td>\n",
" <td>India</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1,310,152,403.0</td>\n",
" <td>628,929,065.0</td>\n",
" <td>681,223,338.0</td>\n",
" <td>18.6</td>\n",
" <td>7.2</td>\n",
" <td>8.0</td>\n",
" <td>24.3</td>\n",
" <td>45.6</td>\n",
" <td>58.6</td>\n",
" <td>18.7</td>\n",
" <td>2.8</td>\n",
" <td>2,103,587,813,812.8</td>\n",
" <td>7.6</td>\n",
" <td>-24,405,488,068.3</td>\n",
" <td>340,244,981,475.1</td>\n",
" <td>None</td>\n",
" <td>36.0</td>\n",
" <td>None</td>\n",
" <td>2015</td>\n",
" <td>India</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1,295,604,184.0</td>\n",
" <td>621,854,638.0</td>\n",
" <td>673,749,546.0</td>\n",
" <td>19.0</td>\n",
" <td>7.2</td>\n",
" <td>8.0</td>\n",
" <td>24.4</td>\n",
" <td>46.1</td>\n",
" <td>59.1</td>\n",
" <td>18.7</td>\n",
" <td>2.8</td>\n",
" <td>2,039,127,446,298.6</td>\n",
" <td>5.8</td>\n",
" <td>-24,112,069,639.3</td>\n",
" <td>342,408,945,451.4</td>\n",
" <td>804.5</td>\n",
" <td>36.7</td>\n",
" <td>73.6</td>\n",
" <td>2014</td>\n",
" <td>India</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total Population Female Population Male Population Birth Rate Death Rate \\\n",
"0 1,352,617,328.0 649,574,719.0 703,042,609.0 None None \n",
"1 1,338,658,835.0 642,787,641.0 695,871,194.0 18.1 7.2 \n",
"2 1,324,509,589.0 635,908,885.0 688,600,704.0 18.3 7.2 \n",
"3 1,310,152,403.0 628,929,065.0 681,223,338.0 18.6 7.2 \n",
"4 1,295,604,184.0 621,854,638.0 673,749,546.0 19.0 7.2 \n",
"\n",
" Compulsory Education Dur. Employment in Industry(%) \\\n",
"0 8.0 24.7 \n",
"1 8.0 24.5 \n",
"2 8.0 24.3 \n",
"3 8.0 24.3 \n",
"4 8.0 24.4 \n",
"\n",
" Employment in Agriculture(%) Female Employment in Agriculture(%) \\\n",
"0 43.9 57.1 \n",
"1 44.5 57.6 \n",
"2 45.1 58.2 \n",
"3 45.6 58.6 \n",
"4 46.1 59.1 \n",
"\n",
" Female Employment in Industry(%) Unemployment(%) GDP in USD \\\n",
"0 18.7 2.6 2,718,732,231,257.6 \n",
"1 18.7 2.6 2,652,242,857,923.9 \n",
"2 18.6 2.7 2,290,432,075,123.8 \n",
"3 18.7 2.8 2,103,587,813,812.8 \n",
"4 18.7 2.8 2,039,127,446,298.6 \n",
"\n",
" National Income per Capita Net income from Abroad \\\n",
"0 None -27,691,371,968.3 \n",
"1 4.0 -28,673,153,563.2 \n",
"2 6.4 -26,300,115,592.2 \n",
"3 7.6 -24,405,488,068.3 \n",
"4 5.8 -24,112,069,639.3 \n",
"\n",
" Agriculture value added(in USD) Electric Power Consumption(kWH per capita) \\\n",
"0 396,987,486,510.1 None \n",
"1 414,265,878,403.3 None \n",
"2 372,190,852,855.1 None \n",
"3 340,244,981,475.1 None \n",
"4 342,408,945,451.4 804.5 \n",
"\n",
" Renewable Energy Consumption (%) Fossil Fuel Consumption (%) Year Country \n",
"0 None None 2018 India \n",
"1 None None 2017 India \n",
"2 None None 2016 India \n",
"3 36.0 None 2015 India \n",
"4 36.7 73.6 2014 India "
]
},
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"------------------Loading data for: China-----------------------\n"
]
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Total Population</th>\n",
" <th>Female Population</th>\n",
" <th>Male Population</th>\n",
" <th>Birth Rate</th>\n",
" <th>Death Rate</th>\n",
" <th>Compulsory Education Dur.</th>\n",
" <th>Employment in Industry(%)</th>\n",
" <th>Employment in Agriculture(%)</th>\n",
" <th>Female Employment in Agriculture(%)</th>\n",
" <th>Female Employment in Industry(%)</th>\n",
" <th>Unemployment(%)</th>\n",
" <th>GDP in USD</th>\n",
" <th>National Income per Capita</th>\n",
" <th>Net income from Abroad</th>\n",
" <th>Agriculture value added(in USD)</th>\n",
" <th>Electric Power Consumption(kWH per capita)</th>\n",
" <th>Renewable Energy Consumption (%)</th>\n",
" <th>Fossil Fuel Consumption (%)</th>\n",
" <th>Year</th>\n",
" <th>Country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1,392,730,000.0</td>\n",
" <td>677,972,245.0</td>\n",
" <td>714,757,755.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>9.0</td>\n",
" <td>28.6</td>\n",
" <td>26.8</td>\n",
" <td>24.2</td>\n",
" <td>28.3</td>\n",
" <td>4.4</td>\n",
" <td>13,608,151,864,637.9</td>\n",
" <td>None</td>\n",
" <td>-51,298,699,628.7</td>\n",
" <td>978,452,524,165.8</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2018</td>\n",
" <td>China</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1,386,395,000.0</td>\n",
" <td>674,707,818.0</td>\n",
" <td>711,687,182.0</td>\n",
" <td>12.4</td>\n",
" <td>7.1</td>\n",
" <td>9.0</td>\n",
" <td>29.0</td>\n",
" <td>27.0</td>\n",
" <td>24.4</td>\n",
" <td>28.8</td>\n",
" <td>4.4</td>\n",
" <td>12,143,491,448,186.1</td>\n",
" <td>4.8</td>\n",
" <td>-9,688,110,315.4</td>\n",
" <td>918,794,756,465.6</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017</td>\n",
" <td>China</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1,378,665,000.0</td>\n",
" <td>670,780,903.0</td>\n",
" <td>707,884,097.0</td>\n",
" <td>12.9</td>\n",
" <td>7.1</td>\n",
" <td>9.0</td>\n",
" <td>29.1</td>\n",
" <td>27.7</td>\n",
" <td>25.1</td>\n",
" <td>29.1</td>\n",
" <td>4.5</td>\n",
" <td>11,137,945,669,350.6</td>\n",
" <td>7.5</td>\n",
" <td>-44,951,463,616.5</td>\n",
" <td>905,097,449,018.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2016</td>\n",
" <td>China</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1,371,220,000.0</td>\n",
" <td>667,009,225.0</td>\n",
" <td>704,210,775.0</td>\n",
" <td>12.1</td>\n",
" <td>7.1</td>\n",
" <td>9.0</td>\n",
" <td>29.2</td>\n",
" <td>28.6</td>\n",
" <td>26.0</td>\n",
" <td>29.3</td>\n",
" <td>4.6</td>\n",
" <td>11,015,542,352,468.9</td>\n",
" <td>7.4</td>\n",
" <td>-41,788,839,823.4</td>\n",
" <td>927,733,440,385.4</td>\n",
" <td>None</td>\n",
" <td>12.4</td>\n",
" <td>None</td>\n",
" <td>2015</td>\n",
" <td>China</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1,364,270,000.0</td>\n",
" <td>663,493,378.0</td>\n",
" <td>700,776,622.0</td>\n",
" <td>12.4</td>\n",
" <td>7.2</td>\n",
" <td>9.0</td>\n",
" <td>29.4</td>\n",
" <td>29.5</td>\n",
" <td>26.8</td>\n",
" <td>29.7</td>\n",
" <td>4.6</td>\n",
" <td>10,438,529,153,237.6</td>\n",
" <td>7.2</td>\n",
" <td>13,298,824,755.0</td>\n",
" <td>905,464,400,820.4</td>\n",
" <td>3,927.0</td>\n",
" <td>12.2</td>\n",
" <td>87.7</td>\n",
" <td>2014</td>\n",
" <td>China</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total Population Female Population Male Population Birth Rate Death Rate \\\n",
"0 1,392,730,000.0 677,972,245.0 714,757,755.0 None None \n",
"1 1,386,395,000.0 674,707,818.0 711,687,182.0 12.4 7.1 \n",
"2 1,378,665,000.0 670,780,903.0 707,884,097.0 12.9 7.1 \n",
"3 1,371,220,000.0 667,009,225.0 704,210,775.0 12.1 7.1 \n",
"4 1,364,270,000.0 663,493,378.0 700,776,622.0 12.4 7.2 \n",
"\n",
" Compulsory Education Dur. Employment in Industry(%) \\\n",
"0 9.0 28.6 \n",
"1 9.0 29.0 \n",
"2 9.0 29.1 \n",
"3 9.0 29.2 \n",
"4 9.0 29.4 \n",
"\n",
" Employment in Agriculture(%) Female Employment in Agriculture(%) \\\n",
"0 26.8 24.2 \n",
"1 27.0 24.4 \n",
"2 27.7 25.1 \n",
"3 28.6 26.0 \n",
"4 29.5 26.8 \n",
"\n",
" Female Employment in Industry(%) Unemployment(%) GDP in USD \\\n",
"0 28.3 4.4 13,608,151,864,637.9 \n",
"1 28.8 4.4 12,143,491,448,186.1 \n",
"2 29.1 4.5 11,137,945,669,350.6 \n",
"3 29.3 4.6 11,015,542,352,468.9 \n",
"4 29.7 4.6 10,438,529,153,237.6 \n",
"\n",
" National Income per Capita Net income from Abroad \\\n",
"0 None -51,298,699,628.7 \n",
"1 4.8 -9,688,110,315.4 \n",
"2 7.5 -44,951,463,616.5 \n",
"3 7.4 -41,788,839,823.4 \n",
"4 7.2 13,298,824,755.0 \n",
"\n",
" Agriculture value added(in USD) Electric Power Consumption(kWH per capita) \\\n",
"0 978,452,524,165.8 None \n",
"1 918,794,756,465.6 None \n",
"2 905,097,449,018.0 None \n",
"3 927,733,440,385.4 None \n",
"4 905,464,400,820.4 3,927.0 \n",
"\n",
" Renewable Energy Consumption (%) Fossil Fuel Consumption (%) Year Country \n",
"0 None None 2018 China \n",
"1 None None 2017 China \n",
"2 None None 2016 China \n",
"3 12.4 None 2015 China \n",
"4 12.2 87.7 2014 China "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"------------------Loading data for: Japan-----------------------\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Total Population</th>\n",
" <th>Female Population</th>\n",
" <th>Male Population</th>\n",
" <th>Birth Rate</th>\n",
" <th>Death Rate</th>\n",
" <th>Compulsory Education Dur.</th>\n",
" <th>Employment in Industry(%)</th>\n",
" <th>Employment in Agriculture(%)</th>\n",
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" <th>Female Employment in Industry(%)</th>\n",
" <th>Unemployment(%)</th>\n",
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" <th>National Income per Capita</th>\n",
" <th>Net income from Abroad</th>\n",
" <th>Agriculture value added(in USD)</th>\n",
" <th>Electric Power Consumption(kWH per capita)</th>\n",
" <th>Renewable Energy Consumption (%)</th>\n",
" <th>Fossil Fuel Consumption (%)</th>\n",
" <th>Year</th>\n",
" <th>Country</th>\n",
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" <td>0</td>\n",
" <td>126,529,100.0</td>\n",
" <td>64,731,350.0</td>\n",
" <td>61,797,750.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>9.0</td>\n",
" <td>24.5</td>\n",
" <td>3.4</td>\n",
" <td>3.0</td>\n",
" <td>14.0</td>\n",
" <td>2.4</td>\n",
" <td>4,971,323,079,771.9</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2018</td>\n",
" <td>Japan</td>\n",
" </tr>\n",
" <tr>\n",