Life_Expectancy_vs_ GDP_India.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Does Life Expectancy Depend on GDP?: A Study Taking WDI Data of India "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chandra Adhikari** \n",
    "\n",
    "June 21, 2020 \n",
    "\n",
    "North Carolina, USA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective:\n",
    "\n",
    "**Study of the life expectancy at birth and GDP per capita in India.**\n",
    "\n",
    "**Compare the  life expectancy in India trend with that of the USA.**\n",
    "\n",
    "**Are there any correlation between the life expectancy and GDP per capita in India.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Source:** https://www.kaggle.com/worldbank/world-development-indicators\n",
    "\n",
    "**Folder:** 'world-development-indicators'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial exploration of the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5656458, 6)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicator = pd.read_csv('./world-development-indicators/Indicators.csv') # load the data\n",
    "df_indicator.shape # shape of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
       "      <td>SP.ADO.TFRT</td>\n",
       "      <td>1960</td>\n",
       "      <td>1.335609e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio (% of working-age populat...</td>\n",
       "      <td>SP.POP.DPND</td>\n",
       "      <td>1960</td>\n",
       "      <td>8.779760e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio, old (% of working-age po...</td>\n",
       "      <td>SP.POP.DPND.OL</td>\n",
       "      <td>1960</td>\n",
       "      <td>6.634579e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio, young (% of working-age ...</td>\n",
       "      <td>SP.POP.DPND.YG</td>\n",
       "      <td>1960</td>\n",
       "      <td>8.102333e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Arms exports (SIPRI trend indicator values)</td>\n",
       "      <td>MS.MIL.XPRT.KD</td>\n",
       "      <td>1960</td>\n",
       "      <td>3.000000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Arms imports (SIPRI trend indicator values)</td>\n",
       "      <td>MS.MIL.MPRT.KD</td>\n",
       "      <td>1960</td>\n",
       "      <td>5.380000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Birth rate, crude (per 1,000 people)</td>\n",
       "      <td>SP.DYN.CBRT.IN</td>\n",
       "      <td>1960</td>\n",
       "      <td>4.769789e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>CO2 emissions (kt)</td>\n",
       "      <td>EN.ATM.CO2E.KT</td>\n",
       "      <td>1960</td>\n",
       "      <td>5.956399e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>1960</td>\n",
       "      <td>6.439635e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>CO2 emissions from gaseous fuel consumption (%...</td>\n",
       "      <td>EN.ATM.CO2E.GF.ZS</td>\n",
       "      <td>1960</td>\n",
       "      <td>5.041292e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName CountryCode                                      IndicatorName  \\\n",
       "0  Arab World         ARB  Adolescent fertility rate (births per 1,000 wo...   \n",
       "1  Arab World         ARB  Age dependency ratio (% of working-age populat...   \n",
       "2  Arab World         ARB  Age dependency ratio, old (% of working-age po...   \n",
       "3  Arab World         ARB  Age dependency ratio, young (% of working-age ...   \n",
       "4  Arab World         ARB        Arms exports (SIPRI trend indicator values)   \n",
       "5  Arab World         ARB        Arms imports (SIPRI trend indicator values)   \n",
       "6  Arab World         ARB               Birth rate, crude (per 1,000 people)   \n",
       "7  Arab World         ARB                                 CO2 emissions (kt)   \n",
       "8  Arab World         ARB             CO2 emissions (metric tons per capita)   \n",
       "9  Arab World         ARB  CO2 emissions from gaseous fuel consumption (%...   \n",
       "\n",
       "       IndicatorCode  Year         Value  \n",
       "0        SP.ADO.TFRT  1960  1.335609e+02  \n",
       "1        SP.POP.DPND  1960  8.779760e+01  \n",
       "2     SP.POP.DPND.OL  1960  6.634579e+00  \n",
       "3     SP.POP.DPND.YG  1960  8.102333e+01  \n",
       "4     MS.MIL.XPRT.KD  1960  3.000000e+06  \n",
       "5     MS.MIL.MPRT.KD  1960  5.380000e+08  \n",
       "6     SP.DYN.CBRT.IN  1960  4.769789e+01  \n",
       "7     EN.ATM.CO2E.KT  1960  5.956399e+04  \n",
       "8     EN.ATM.CO2E.PC  1960  6.439635e-01  \n",
       "9  EN.ATM.CO2E.GF.ZS  1960  5.041292e+00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicator[:10] # display the first 10 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Country name and country code are the same, the year is also the same, but indicators are many. I would like to see on the  GDP per capita and the life expentancy. Try to see a few more rows to see more the IndicatorName.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us make sure we have the data of India:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicator['CountryCode'].str.contains(\"IND\").any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicator['CountryName'].str.contains(\"India\").any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many years of data do we have ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many years of data do we have ?\n",
    "years = df_indicator['Year'].unique().tolist()\n",
    "len(years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's the range of years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1960  to  2015\n"
     ]
    }
   ],
   "source": [
    "print(min(years),\" to \",max(years))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets pick a country and an indicator to explore: Life expentancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select Life expectancy for India\n",
    "hist_indicator = 'Life expectancy at birth, total \\(years\\)'\n",
    "hist_country = 'IND'\n",
    "\n",
    "m_LE = df_indicator['IndicatorName'].str.contains(hist_indicator) \n",
    "m_CC = df_indicator['CountryCode'].str.contains(hist_country)\n",
    "\n",
    "# df_LE is just those indicators matching the USA for country code and Life expectancy at birth, total (years) over time.\n",
    "df_LE= df_indicator[m_LE & m_CC]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11684</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1960</td>\n",
       "      <td>41.171951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36635</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1961</td>\n",
       "      <td>41.790488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64177</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1962</td>\n",
       "      <td>42.417415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92622</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1963</td>\n",
       "      <td>43.052732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121419</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1964</td>\n",
       "      <td>43.698415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CountryName CountryCode                            IndicatorName  \\\n",
       "11684        India         IND  Life expectancy at birth, total (years)   \n",
       "36635        India         IND  Life expectancy at birth, total (years)   \n",
       "64177        India         IND  Life expectancy at birth, total (years)   \n",
       "92622        India         IND  Life expectancy at birth, total (years)   \n",
       "121419       India         IND  Life expectancy at birth, total (years)   \n",
       "\n",
       "         IndicatorCode  Year      Value  \n",
       "11684   SP.DYN.LE00.IN  1960  41.171951  \n",
       "36635   SP.DYN.LE00.IN  1961  41.790488  \n",
       "64177   SP.DYN.LE00.IN  1962  42.417415  \n",
       "92622   SP.DYN.LE00.IN  1963  43.052732  \n",
       "121419  SP.DYN.LE00.IN  1964  43.698415  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_LE.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How life expectancy  have changed over time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the years\n",
    "years =df_LE['Year'].values\n",
    "# get the values \n",
    "LifeExp = df_LE['Value'].values\n",
    "\n",
    "# create\n",
    "plt.xlabel('Year',size=12, color='k')\n",
    "plt.ylabel('Life expectancy at birth (Years)',size=12, color='k')\n",
    "plt.bar(years,LifeExp,color='b')\n",
    "plt.xticks(fontsize=12,color='k')\n",
    "plt.yticks(fontsize=12,color='k')\n",
    "plt.title('Life expectancy at birth in India')\n",
    "plt.axis([1959, 2014,0,82])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turns out Life expectancy at birth have been consistently increaseing. From 1960 to 2014, the life expectancy increased consistently from about 40 to 70 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How about life expectancy in USA? Does the USA have the same trend?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select Life expectancy for the USA\n",
    "hist_indicator_2 = 'Life expectancy at birth, total \\(years\\)'\n",
    "hist_country_2 = 'USA'\n",
    "\n",
    "m_LE_2 = df_indicator['IndicatorName'].str.contains(hist_indicator_2) \n",
    "m_CC_2 = df_indicator['CountryCode'].str.contains(hist_country_2)\n",
    "\n",
    "# df_LE_2 is just those indicators matching the USA for country code and Life expectancy at birth, total (years) over time.\n",
    "df_LE_2= df_indicator[m_LE_2 & m_CC_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the years\n",
    "years_2 =df_LE_2['Year'].values\n",
    "# get the values \n",
    "LifeExp_2 = df_LE_2['Value'].values\n",
    "\n",
    "# create\n",
    "plt.xlabel('Year',size=12, color='k')\n",
    "plt.ylabel('Life expectancy at birth (Years)',size=12, color='k')\n",
    "plt.bar(years_2,LifeExp_2,color='b')\n",
    "plt.xticks(fontsize=12,color='k')\n",
    "plt.yticks(fontsize=12,color='k')\n",
    "plt.title('Life expectancy at birth in the USA')\n",
    "plt.axis([1959, 2014,0,82])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, the life expectancy has increased over the year but the rate of increase in life ecpectancy is not as fast as in India. Indeed, it was about 70 years in the USA in 1960, which was only 40 years in India at that time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's get line plots in the same graph to make a comparision easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# switch to a line plot\n",
    "style.use('ggplot')\n",
    "plt.plot(df_LE['Year'].values, df_LE['Value'].values,'g',label=\"India\", linewidth=2)\n",
    "plt.plot(df_LE_2['Year'].values, df_LE_2['Value'].values,'r',label=\"USA\", linewidth=2)\n",
    "\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Year',size=14,color='k')\n",
    "#plt.ylabel(df_LE['IndicatorName'].iloc[0],size=12)\n",
    "plt.ylabel('Life expectancy at birth (years)',size=12,color='k')\n",
    "\n",
    "#label the figure\n",
    "plt.title('Life expectancy in India and the USA', size=14,color='k')\n",
    "plt.xticks(fontsize=12,color='k')\n",
    "plt.yticks(fontsize=12,color='k')\n",
    "\n",
    "#show legend\n",
    "plt.legend(fontsize=12)\n",
    "plt.grid(True, color='w')\n",
    "\n",
    "\n",
    "# to make more honest, start they y axis at 0\n",
    "plt.axis([1959, 2014,0,83])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship between Life expectancy and GDP per capita in India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select GDP Per capita  for India\n",
    "hist_indicator_G = 'GDP per capita \\(constant 2005'\n",
    "hist_country_G = 'IND'\n",
    "\n",
    "mk_G1 = df_indicator['IndicatorName'].str.contains(hist_indicator_G) \n",
    "mk_G2 = df_indicator['CountryCode'].str.contains(hist_country_G)\n",
    "\n",
    "gdp_India = df_indicator[mk_G1 & mk_G2]\n",
    "\n",
    "#plot gdp_India vs df_LE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11616</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>GDP per capita (constant 2005 US$)</td>\n",
       "      <td>NY.GDP.PCAP.KD</td>\n",
       "      <td>1960</td>\n",
       "      <td>228.304470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36555</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>GDP per capita (constant 2005 US$)</td>\n",
       "      <td>NY.GDP.PCAP.KD</td>\n",
       "      <td>1961</td>\n",
       "      <td>232.142053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CountryName CountryCode                       IndicatorName  \\\n",
       "11616       India         IND  GDP per capita (constant 2005 US$)   \n",
       "36555       India         IND  GDP per capita (constant 2005 US$)   \n",
       "\n",
       "        IndicatorCode  Year       Value  \n",
       "11616  NY.GDP.PCAP.KD  1960  228.304470  \n",
       "36555  NY.GDP.PCAP.KD  1961  232.142053  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_India.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11684</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1960</td>\n",
       "      <td>41.171951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36635</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1961</td>\n",
       "      <td>41.790488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CountryName CountryCode                            IndicatorName  \\\n",
       "11684       India         IND  Life expectancy at birth, total (years)   \n",
       "36635       India         IND  Life expectancy at birth, total (years)   \n",
       "\n",
       "        IndicatorCode  Year      Value  \n",
       "11684  SP.DYN.LE00.IN  1960  41.171951  \n",
       "36635  SP.DYN.LE00.IN  1961  41.790488  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_LE.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# switch to a line plot\n",
    "plt.plot(gdp_India['Year'].values, gdp_India['Value'].values,linewidth=2)\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Year',fontsize=12,color='k')\n",
    "plt.ylabel(gdp_India['IndicatorName'].iloc[0],fontsize=12,color='k')\n",
    "\n",
    "#label the figure\n",
    "plt.title('GDP per capita of India')\n",
    "plt.xticks(fontsize=12,color='k')\n",
    "plt.yticks(fontsize=12,color='k')\n",
    "\n",
    "# to make more honest, start they y axis at 0\n",
    "plt.axis([1959, 2015,0,1270])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GDP per capita increases with years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw a scatter plot to see corelation between GDP per capita and life expectancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54\n",
      "54\n"
     ]
    }
   ],
   "source": [
    "gdp_India_Trunc = gdp_India[gdp_India['Year'] < 2014]\n",
    "print(len(gdp_India_Trunc))\n",
    "print(len(df_LE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axis = plt.subplots()\n",
    "# Grid lines, Xticks, Xlabel, Ylabel\n",
    "\n",
    "axis.yaxis.grid(True)\n",
    "axis.set_title('Life expectancy vs. GDP per capita in India',fontsize=14, color='k')\n",
    "axis.set_xlabel(gdp_India_Trunc['IndicatorName'].iloc[0],fontsize=12, color='k')\n",
    "axis.set_ylabel('Life expectancy at birth (years)',color='k',fontsize=12)\n",
    "plt.xticks(fontsize=12,color='k')\n",
    "plt.yticks(fontsize=12,color='k')\n",
    "\n",
    "X = gdp_India_Trunc['Value']\n",
    "Y = df_LE['Value']\n",
    "\n",
    "\n",
    "axis.scatter(X, Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows a good relationship. But how strong is it? We can test this by looking at correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.84850089],\n",
       "       [0.84850089, 1.        ]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(gdp_India_Trunc['Value'],df_LE['Value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The correlation of 0.85 is pretty strong.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*We come to the End of the Jupiter notebook.* **Thank You!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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