World Trends in Birth rate and Life Expectancy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## World Trends in Birth Rate and Life Expectancy\n",
    "\n",
    "The focus of this project is to perform some data manipulation, and then provide insights from scatterplots. I will prepare the desired dataframe by merging data sets collected from different sources and cleaning up unnecessary information. Along the way, I will explore the data sets as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the data set\n",
    "data1 = pd.read_csv(\"DemoData.csv\") "
   ]
  },
  {
   "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>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Internet users</th>\n",
       "      <th>Income Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.90000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.90000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.10000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.20000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.00000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>17.716</td>\n",
       "      <td>59.90000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>ARM</td>\n",
       "      <td>13.308</td>\n",
       "      <td>41.90000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>ATG</td>\n",
       "      <td>16.447</td>\n",
       "      <td>63.40000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>13.200</td>\n",
       "      <td>83.00000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>AUT</td>\n",
       "      <td>9.400</td>\n",
       "      <td>80.61880</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>AZE</td>\n",
       "      <td>18.300</td>\n",
       "      <td>58.70000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>BDI</td>\n",
       "      <td>44.151</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>BEL</td>\n",
       "      <td>11.200</td>\n",
       "      <td>82.17020</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Benin</td>\n",
       "      <td>BEN</td>\n",
       "      <td>36.440</td>\n",
       "      <td>4.90000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>BFA</td>\n",
       "      <td>40.551</td>\n",
       "      <td>9.10000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>BGD</td>\n",
       "      <td>20.142</td>\n",
       "      <td>6.63000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>BGR</td>\n",
       "      <td>9.200</td>\n",
       "      <td>53.06150</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>BHR</td>\n",
       "      <td>15.040</td>\n",
       "      <td>90.00004</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bahamas, The</td>\n",
       "      <td>BHS</td>\n",
       "      <td>15.339</td>\n",
       "      <td>72.00000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>BIH</td>\n",
       "      <td>9.062</td>\n",
       "      <td>57.79000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>BLR</td>\n",
       "      <td>12.500</td>\n",
       "      <td>54.17000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Belize</td>\n",
       "      <td>BLZ</td>\n",
       "      <td>23.092</td>\n",
       "      <td>33.60000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>BMU</td>\n",
       "      <td>10.400</td>\n",
       "      <td>95.30000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>BOL</td>\n",
       "      <td>24.236</td>\n",
       "      <td>36.94000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>BRA</td>\n",
       "      <td>14.931</td>\n",
       "      <td>51.04000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>BRB</td>\n",
       "      <td>12.188</td>\n",
       "      <td>73.00000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>BRN</td>\n",
       "      <td>16.405</td>\n",
       "      <td>64.50000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>BTN</td>\n",
       "      <td>18.134</td>\n",
       "      <td>29.90000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>BWA</td>\n",
       "      <td>25.267</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>CAF</td>\n",
       "      <td>34.076</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>SYC</td>\n",
       "      <td>18.600</td>\n",
       "      <td>50.40000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>SYR</td>\n",
       "      <td>24.043</td>\n",
       "      <td>26.20000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Chad</td>\n",
       "      <td>TCD</td>\n",
       "      <td>45.745</td>\n",
       "      <td>2.30000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Togo</td>\n",
       "      <td>TGO</td>\n",
       "      <td>36.080</td>\n",
       "      <td>4.50000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>THA</td>\n",
       "      <td>11.041</td>\n",
       "      <td>28.94000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>TJK</td>\n",
       "      <td>30.792</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>TKM</td>\n",
       "      <td>21.322</td>\n",
       "      <td>9.60000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>TLS</td>\n",
       "      <td>35.755</td>\n",
       "      <td>1.10000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>TON</td>\n",
       "      <td>25.409</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>TTO</td>\n",
       "      <td>14.590</td>\n",
       "      <td>63.80000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>TUN</td>\n",
       "      <td>19.800</td>\n",
       "      <td>43.80000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>TUR</td>\n",
       "      <td>16.836</td>\n",
       "      <td>46.25000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>TZA</td>\n",
       "      <td>39.518</td>\n",
       "      <td>4.40000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>UGA</td>\n",
       "      <td>43.474</td>\n",
       "      <td>16.20000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>UKR</td>\n",
       "      <td>11.100</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>URY</td>\n",
       "      <td>14.374</td>\n",
       "      <td>57.69000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>12.500</td>\n",
       "      <td>84.20000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>UZB</td>\n",
       "      <td>22.500</td>\n",
       "      <td>38.20000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>St. Vincent and the Grenadines</td>\n",
       "      <td>VCT</td>\n",
       "      <td>16.306</td>\n",
       "      <td>52.00000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Venezuela, RB</td>\n",
       "      <td>VEN</td>\n",
       "      <td>19.842</td>\n",
       "      <td>54.90000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Virgin Islands (U.S.)</td>\n",
       "      <td>VIR</td>\n",
       "      <td>10.700</td>\n",
       "      <td>45.30000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>VNM</td>\n",
       "      <td>15.537</td>\n",
       "      <td>43.90000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>VUT</td>\n",
       "      <td>26.739</td>\n",
       "      <td>11.30000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>PSE</td>\n",
       "      <td>30.394</td>\n",
       "      <td>46.60000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>WSM</td>\n",
       "      <td>26.172</td>\n",
       "      <td>15.30000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.50000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.20000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.40000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.50000</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Country Name Country Code  Birth rate  Internet users  \\\n",
       "0                             Aruba          ABW      10.244        78.90000   \n",
       "1                       Afghanistan          AFG      35.253         5.90000   \n",
       "2                            Angola          AGO      45.985        19.10000   \n",
       "3                           Albania          ALB      12.877        57.20000   \n",
       "4              United Arab Emirates          ARE      11.044        88.00000   \n",
       "5                         Argentina          ARG      17.716        59.90000   \n",
       "6                           Armenia          ARM      13.308        41.90000   \n",
       "7               Antigua and Barbuda          ATG      16.447        63.40000   \n",
       "8                         Australia          AUS      13.200        83.00000   \n",
       "9                           Austria          AUT       9.400        80.61880   \n",
       "10                       Azerbaijan          AZE      18.300        58.70000   \n",
       "11                          Burundi          BDI      44.151         1.30000   \n",
       "12                          Belgium          BEL      11.200        82.17020   \n",
       "13                            Benin          BEN      36.440         4.90000   \n",
       "14                     Burkina Faso          BFA      40.551         9.10000   \n",
       "15                       Bangladesh          BGD      20.142         6.63000   \n",
       "16                         Bulgaria          BGR       9.200        53.06150   \n",
       "17                          Bahrain          BHR      15.040        90.00004   \n",
       "18                     Bahamas, The          BHS      15.339        72.00000   \n",
       "19           Bosnia and Herzegovina          BIH       9.062        57.79000   \n",
       "20                          Belarus          BLR      12.500        54.17000   \n",
       "21                           Belize          BLZ      23.092        33.60000   \n",
       "22                          Bermuda          BMU      10.400        95.30000   \n",
       "23                          Bolivia          BOL      24.236        36.94000   \n",
       "24                           Brazil          BRA      14.931        51.04000   \n",
       "25                         Barbados          BRB      12.188        73.00000   \n",
       "26                Brunei Darussalam          BRN      16.405        64.50000   \n",
       "27                           Bhutan          BTN      18.134        29.90000   \n",
       "28                         Botswana          BWA      25.267        15.00000   \n",
       "29         Central African Republic          CAF      34.076         3.50000   \n",
       "..                              ...          ...         ...             ...   \n",
       "165                      Seychelles          SYC      18.600        50.40000   \n",
       "166            Syrian Arab Republic          SYR      24.043        26.20000   \n",
       "167                            Chad          TCD      45.745         2.30000   \n",
       "168                            Togo          TGO      36.080         4.50000   \n",
       "169                        Thailand          THA      11.041        28.94000   \n",
       "170                      Tajikistan          TJK      30.792        16.00000   \n",
       "171                    Turkmenistan          TKM      21.322         9.60000   \n",
       "172                     Timor-Leste          TLS      35.755         1.10000   \n",
       "173                           Tonga          TON      25.409        35.00000   \n",
       "174             Trinidad and Tobago          TTO      14.590        63.80000   \n",
       "175                         Tunisia          TUN      19.800        43.80000   \n",
       "176                          Turkey          TUR      16.836        46.25000   \n",
       "177                        Tanzania          TZA      39.518         4.40000   \n",
       "178                          Uganda          UGA      43.474        16.20000   \n",
       "179                         Ukraine          UKR      11.100        41.00000   \n",
       "180                         Uruguay          URY      14.374        57.69000   \n",
       "181                   United States          USA      12.500        84.20000   \n",
       "182                      Uzbekistan          UZB      22.500        38.20000   \n",
       "183  St. Vincent and the Grenadines          VCT      16.306        52.00000   \n",
       "184                   Venezuela, RB          VEN      19.842        54.90000   \n",
       "185           Virgin Islands (U.S.)          VIR      10.700        45.30000   \n",
       "186                         Vietnam          VNM      15.537        43.90000   \n",
       "187                         Vanuatu          VUT      26.739        11.30000   \n",
       "188              West Bank and Gaza          PSE      30.394        46.60000   \n",
       "189                           Samoa          WSM      26.172        15.30000   \n",
       "190                     Yemen, Rep.          YEM      32.947        20.00000   \n",
       "191                    South Africa          ZAF      20.850        46.50000   \n",
       "192                Congo, Dem. Rep.          COD      42.394         2.20000   \n",
       "193                          Zambia          ZMB      40.471        15.40000   \n",
       "194                        Zimbabwe          ZWE      35.715        18.50000   \n",
       "\n",
       "            Income Group  \n",
       "0            High income  \n",
       "1             Low income  \n",
       "2    Upper middle income  \n",
       "3    Upper middle income  \n",
       "4            High income  \n",
       "5            High income  \n",
       "6    Lower middle income  \n",
       "7            High income  \n",
       "8            High income  \n",
       "9            High income  \n",
       "10   Upper middle income  \n",
       "11            Low income  \n",
       "12           High income  \n",
       "13            Low income  \n",
       "14            Low income  \n",
       "15   Lower middle income  \n",
       "16   Upper middle income  \n",
       "17           High income  \n",
       "18           High income  \n",
       "19   Upper middle income  \n",
       "20   Upper middle income  \n",
       "21   Upper middle income  \n",
       "22           High income  \n",
       "23   Lower middle income  \n",
       "24   Upper middle income  \n",
       "25           High income  \n",
       "26           High income  \n",
       "27   Lower middle income  \n",
       "28   Upper middle income  \n",
       "29            Low income  \n",
       "..                   ...  \n",
       "165          High income  \n",
       "166  Lower middle income  \n",
       "167           Low income  \n",
       "168           Low income  \n",
       "169  Upper middle income  \n",
       "170  Lower middle income  \n",
       "171  Upper middle income  \n",
       "172  Lower middle income  \n",
       "173  Upper middle income  \n",
       "174          High income  \n",
       "175  Upper middle income  \n",
       "176  Upper middle income  \n",
       "177           Low income  \n",
       "178           Low income  \n",
       "179  Lower middle income  \n",
       "180          High income  \n",
       "181          High income  \n",
       "182  Lower middle income  \n",
       "183  Upper middle income  \n",
       "184          High income  \n",
       "185          High income  \n",
       "186  Lower middle income  \n",
       "187  Lower middle income  \n",
       "188  Lower middle income  \n",
       "189  Lower middle income  \n",
       "190  Lower middle income  \n",
       "191  Upper middle income  \n",
       "192           Low income  \n",
       "193  Lower middle income  \n",
       "194           Low income  \n",
       "\n",
       "[195 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "195"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# len() returns the number of rows\n",
    "len(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of columns\n",
    "len(data1.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename the columns\n",
    "data1.columns = ['CountryName','CountryCode','BirthRate', 'InternetUsers','IncomeGroup'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data.head(n) gives you the first n rows of your data set. You can choose n of your interest.\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.5</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.4</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "190       Yemen, Rep.         YEM     32.947           20.0   \n",
       "191      South Africa         ZAF     20.850           46.5   \n",
       "192  Congo, Dem. Rep.         COD     42.394            2.2   \n",
       "193            Zambia         ZMB     40.471           15.4   \n",
       "194          Zimbabwe         ZWE     35.715           18.5   \n",
       "\n",
       "             IncomeGroup  \n",
       "190  Lower middle income  \n",
       "191  Upper middle income  \n",
       "192           Low income  \n",
       "193  Lower middle income  \n",
       "194           Low income  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data.tail() returns the bottom 5 rows. Similar to head() function.\n",
    "data1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 5 columns):\n",
      "CountryName      195 non-null object\n",
      "CountryCode      195 non-null object\n",
      "BirthRate        195 non-null float64\n",
      "InternetUsers    195 non-null float64\n",
      "IncomeGroup      195 non-null object\n",
      "dtypes: float64(2), object(3)\n",
      "memory usage: 7.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#Information about the dataset\n",
    "data1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the dataset is not missing any information. CountryName, CountryCode, and IncomeGroup are recognized as object since these are strings.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.469928</td>\n",
       "      <td>42.076471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.605467</td>\n",
       "      <td>29.030788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.120500</td>\n",
       "      <td>14.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.680000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.759500</td>\n",
       "      <td>66.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49.661000</td>\n",
       "      <td>96.546800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        BirthRate  InternetUsers\n",
       "count  195.000000     195.000000\n",
       "mean    21.469928      42.076471\n",
       "std     10.605467      29.030788\n",
       "min      7.900000       0.900000\n",
       "25%     12.120500      14.520000\n",
       "50%     19.680000      41.000000\n",
       "75%     29.759500      66.225000\n",
       "max     49.661000      96.546800"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe() returns some basic statistical details like \n",
    "# count, mean, standard deviation, min, max, quartiles of a dataset.\n",
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BirthRate</th>\n",
       "      <td>195.0</td>\n",
       "      <td>21.469928</td>\n",
       "      <td>10.605467</td>\n",
       "      <td>7.9</td>\n",
       "      <td>12.1205</td>\n",
       "      <td>19.68</td>\n",
       "      <td>29.7595</td>\n",
       "      <td>49.6610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetUsers</th>\n",
       "      <td>195.0</td>\n",
       "      <td>42.076471</td>\n",
       "      <td>29.030788</td>\n",
       "      <td>0.9</td>\n",
       "      <td>14.5200</td>\n",
       "      <td>41.00</td>\n",
       "      <td>66.2250</td>\n",
       "      <td>96.5468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count       mean        std  min      25%    50%      75%  \\\n",
       "BirthRate      195.0  21.469928  10.605467  7.9  12.1205  19.68  29.7595   \n",
       "InternetUsers  195.0  42.076471  29.030788  0.9  14.5200  41.00  66.2250   \n",
       "\n",
       "                   max  \n",
       "BirthRate      49.6610  \n",
       "InternetUsers  96.5468  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transpose view of the above result\n",
    "data1.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the birth rate ranges between 7 and 50."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Countries_2012_Dataset = list ([\"Aruba\",\"Afghanistan\",\"Angola\",\"Albania\",\"United Arab Emirates\",\"Argentina\",\"Armenia\",\"Antigua and Barbuda\",\"Australia\",\"Austria\",\"Azerbaijan\",\"Burundi\",\"Belgium\",\"Benin\",\"Burkina Faso\",\"Bangladesh\",\"Bulgaria\",\"Bahrain\",\"Bahamas, The\",\"Bosnia and Herzegovina\",\"Belarus\",\"Belize\",\"Bermuda\",\"Bolivia\",\"Brazil\",\"Barbados\",\"Brunei Darussalam\",\"Bhutan\",\"Botswana\",\"Central African Republic\",\"Canada\",\"Switzerland\",\"Chile\",\"China\",\"Cote d'Ivoire\",\"Cameroon\",\"Congo, Rep.\",\"Colombia\",\"Comoros\",\"Cabo Verde\",\"Costa Rica\",\"Cuba\",\"Cayman Islands\",\"Cyprus\",\"Czech Republic\",\"Germany\",\"Djibouti\",\"Denmark\",\"Dominican Republic\",\"Algeria\",\"Ecuador\",\"Egypt, Arab Rep.\",\"Eritrea\",\"Spain\",\"Estonia\",\"Ethiopia\",\"Finland\",\"Fiji\",\"France\",\"Micronesia, Fed. Sts.\",\"Gabon\",\"United Kingdom\",\"Georgia\",\"Ghana\",\"Guinea\",\"Gambia, The\",\"Guinea-Bissau\",\"Equatorial Guinea\",\"Greece\",\"Grenada\",\"Greenland\",\"Guatemala\",\"Guam\",\"Guyana\",\"Hong Kong SAR, China\",\"Honduras\",\"Croatia\",\"Haiti\",\"Hungary\",\"Indonesia\",\"India\",\"Ireland\",\"Iran, Islamic Rep.\",\"Iraq\",\"Iceland\",\"Israel\",\"Italy\",\"Jamaica\",\"Jordan\",\"Japan\",\"Kazakhstan\",\"Kenya\",\"Kyrgyz Republic\",\"Cambodia\",\"Kiribati\",\"Korea, Rep.\",\"Kuwait\",\"Lao PDR\",\"Lebanon\",\"Liberia\",\"Libya\",\"St. Lucia\",\"Liechtenstein\",\"Sri Lanka\",\"Lesotho\",\"Lithuania\",\"Luxembourg\",\"Latvia\",\"Macao SAR, China\",\"Morocco\",\"Moldova\",\"Madagascar\",\"Maldives\",\"Mexico\",\"Macedonia, FYR\",\"Mali\",\"Malta\",\"Myanmar\",\"Montenegro\",\"Mongolia\",\"Mozambique\",\"Mauritania\",\"Mauritius\",\"Malawi\",\"Malaysia\",\"Namibia\",\"New Caledonia\",\"Niger\",\"Nigeria\",\"Nicaragua\",\"Netherlands\",\"Norway\",\"Nepal\",\"New Zealand\",\"Oman\",\"Pakistan\",\"Panama\",\"Peru\",\"Philippines\",\"Papua New Guinea\",\"Poland\",\"Puerto Rico\",\"Portugal\",\"Paraguay\",\"French Polynesia\",\"Qatar\",\"Romania\",\"Russian Federation\",\"Rwanda\",\"Saudi Arabia\",\"Sudan\",\"Senegal\",\"Singapore\",\"Solomon Islands\",\"Sierra Leone\",\"El Salvador\",\"Somalia\",\"Serbia\",\"South Sudan\",\"Sao Tome and Principe\",\"Suriname\",\"Slovak Republic\",\"Slovenia\",\"Sweden\",\"Swaziland\",\"Seychelles\",\"Syrian Arab Republic\",\"Chad\",\"Togo\",\"Thailand\",\"Tajikistan\",\"Turkmenistan\",\"Timor-Leste\",\"Tonga\",\"Trinidad and Tobago\",\"Tunisia\",\"Turkey\",\"Tanzania\",\"Uganda\",\"Ukraine\",\"Uruguay\",\"United States\",\"Uzbekistan\",\"St. Vincent and the Grenadines\",\"Venezuela, RB\",\"Virgin Islands (U.S.)\",\"Vietnam\",\"Vanuatu\",\"West Bank and Gaza\",\"Samoa\",\"Yemen, Rep.\",\"South Africa\",\"Congo, Dem. Rep.\",\"Zambia\",\"Zimbabwe\"])\n",
    "Codes_2012_Dataset = list ([\"ABW\",\"AFG\",\"AGO\",\"ALB\",\"ARE\",\"ARG\",\"ARM\",\"ATG\",\"AUS\",\"AUT\",\"AZE\",\"BDI\",\"BEL\",\"BEN\",\"BFA\",\"BGD\",\"BGR\",\"BHR\",\"BHS\",\"BIH\",\"BLR\",\"BLZ\",\"BMU\",\"BOL\",\"BRA\",\"BRB\",\"BRN\",\"BTN\",\"BWA\",\"CAF\",\"CAN\",\"CHE\",\"CHL\",\"CHN\",\"CIV\",\"CMR\",\"COG\",\"COL\",\"COM\",\"CPV\",\"CRI\",\"CUB\",\"CYM\",\"CYP\",\"CZE\",\"DEU\",\"DJI\",\"DNK\",\"DOM\",\"DZA\",\"ECU\",\"EGY\",\"ERI\",\"ESP\",\"EST\",\"ETH\",\"FIN\",\"FJI\",\"FRA\",\"FSM\",\"GAB\",\"GBR\",\"GEO\",\"GHA\",\"GIN\",\"GMB\",\"GNB\",\"GNQ\",\"GRC\",\"GRD\",\"GRL\",\"GTM\",\"GUM\",\"GUY\",\"HKG\",\"HND\",\"HRV\",\"HTI\",\"HUN\",\"IDN\",\"IND\",\"IRL\",\"IRN\",\"IRQ\",\"ISL\",\"ISR\",\"ITA\",\"JAM\",\"JOR\",\"JPN\",\"KAZ\",\"KEN\",\"KGZ\",\"KHM\",\"KIR\",\"KOR\",\"KWT\",\"LAO\",\"LBN\",\"LBR\",\"LBY\",\"LCA\",\"LIE\",\"LKA\",\"LSO\",\"LTU\",\"LUX\",\"LVA\",\"MAC\",\"MAR\",\"MDA\",\"MDG\",\"MDV\",\"MEX\",\"MKD\",\"MLI\",\"MLT\",\"MMR\",\"MNE\",\"MNG\",\"MOZ\",\"MRT\",\"MUS\",\"MWI\",\"MYS\",\"NAM\",\"NCL\",\"NER\",\"NGA\",\"NIC\",\"NLD\",\"NOR\",\"NPL\",\"NZL\",\"OMN\",\"PAK\",\"PAN\",\"PER\",\"PHL\",\"PNG\",\"POL\",\"PRI\",\"PRT\",\"PRY\",\"PYF\",\"QAT\",\"ROU\",\"RUS\",\"RWA\",\"SAU\",\"SDN\",\"SEN\",\"SGP\",\"SLB\",\"SLE\",\"SLV\",\"SOM\",\"SRB\",\"SSD\",\"STP\",\"SUR\",\"SVK\",\"SVN\",\"SWE\",\"SWZ\",\"SYC\",\"SYR\",\"TCD\",\"TGO\",\"THA\",\"TJK\",\"TKM\",\"TLS\",\"TON\",\"TTO\",\"TUN\",\"TUR\",\"TZA\",\"UGA\",\"UKR\",\"URY\",\"USA\",\"UZB\",\"VCT\",\"VEN\",\"VIR\",\"VNM\",\"VUT\",\"PSE\",\"WSM\",\"YEM\",\"ZAF\",\"COD\",\"ZMB\",\"ZWE\"])\n",
    "Regions_2012_Dataset = list ([\"The Americas\",\"Asia\",\"Africa\",\"Europe\",\"Middle East\",\"The Americas\",\"Asia\",\"The Americas\",\"Oceania\",\"Europe\",\"Asia\",\"Africa\",\"Europe\",\"Africa\",\"Africa\",\"Asia\",\"Europe\",\"Middle East\",\"The Americas\",\"Europe\",\"Europe\",\"The Americas\",\"The Americas\",\"The Americas\",\"The Americas\",\"The Americas\",\"Asia\",\"Asia\",\"Africa\",\"Africa\",\"The Americas\",\"Europe\",\"The Americas\",\"Asia\",\"Africa\",\"Africa\",\"Africa\",\"The Americas\",\"Africa\",\"Africa\",\"The Americas\",\"The Americas\",\"The Americas\",\"Europe\",\"Europe\",\"Europe\",\"Africa\",\"Europe\",\"The Americas\",\"Africa\",\"The Americas\",\"Africa\",\"Africa\",\"Europe\",\"Europe\",\"Africa\",\"Europe\",\"Oceania\",\"Europe\",\"Oceania\",\"Africa\",\"Europe\",\"Asia\",\"Africa\",\"Africa\",\"Africa\",\"Africa\",\"Africa\",\"Europe\",\"The Americas\",\"The Americas\",\"The Americas\",\"Oceania\",\"The Americas\",\"Asia\",\"The Americas\",\"Europe\",\"The Americas\",\"Europe\",\"Asia\",\"Asia\",\"Europe\",\"Middle East\",\"Middle East\",\"Europe\",\"Middle East\",\"Europe\",\"The Americas\",\"Middle East\",\"Asia\",\"Asia\",\"Africa\",\"Asia\",\"Asia\",\"Oceania\",\"Asia\",\"Middle East\",\"Asia\",\"Middle East\",\"Africa\",\"Africa\",\"The Americas\",\"Europe\",\"Asia\",\"Africa\",\"Europe\",\"Europe\",\"Europe\",\"Asia\",\"Africa\",\"Europe\",\"Africa\",\"Asia\",\"The Americas\",\"Europe\",\"Africa\",\"Europe\",\"Asia\",\"Europe\",\"Asia\",\"Africa\",\"Africa\",\"Africa\",\"Africa\",\"Asia\",\"Africa\",\"Oceania\",\"Africa\",\"Africa\",\"The Americas\",\"Europe\",\"Europe\",\"Asia\",\"Oceania\",\"Middle East\",\"Asia\",\"The Americas\",\"The Americas\",\"Asia\",\"Oceania\",\"Europe\",\"The Americas\",\"Europe\",\"The Americas\",\"Oceania\",\"Middle East\",\"Europe\",\"Europe\",\"Africa\",\"Middle East\",\"Africa\",\"Africa\",\"Asia\",\"Oceania\",\"Africa\",\"The Americas\",\"Africa\",\"Europe\",\"Africa\",\"Africa\",\"The Americas\",\"Europe\",\"Europe\",\"Europe\",\"Africa\",\"Africa\",\"Middle East\",\"Africa\",\"Africa\",\"Asia\",\"Asia\",\"Asia\",\"Asia\",\"Oceania\",\"The Americas\",\"Africa\",\"Europe\",\"Africa\",\"Africa\",\"Europe\",\"The Americas\",\"The Americas\",\"Asia\",\"The Americas\",\"The Americas\",\"The Americas\",\"Asia\",\"Oceania\",\"Middle East\",\"Oceania\",\"Middle East\",\"Africa\",\"Africa\",\"Africa\",\"Africa\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the above list into a dataframe\n",
    "data2 = pd.DataFrame ({'CountryName': np.array(Countries_2012_Dataset),\n",
    "                             'CountryCode': np.array(Codes_2012_Dataset),\n",
    "                             'CountryRegion': np.array(Regions_2012_Dataset)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>CountryRegion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>Middle East</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode CountryRegion\n",
       "0                 Aruba         ABW  The Americas\n",
       "1           Afghanistan         AFG          Asia\n",
       "2                Angola         AGO        Africa\n",
       "3               Albania         ALB        Europe\n",
       "4  United Arab Emirates         ARE   Middle East"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "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>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging two datasets\n",
    "merged_data1 = pd.merge(left=data1, right=data2, how='inner', on=\"CountryCode\")"
   ]
  },
  {
   "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>CountryName_x</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "      <th>CountryName_y</th>\n",
       "      <th>CountryRegion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.90000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.90000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.10000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.20000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.00000</td>\n",
       "      <td>High income</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Middle East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>17.716</td>\n",
       "      <td>59.90000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>ARM</td>\n",
       "      <td>13.308</td>\n",
       "      <td>41.90000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>ATG</td>\n",
       "      <td>16.447</td>\n",
       "      <td>63.40000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>13.200</td>\n",
       "      <td>83.00000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>AUT</td>\n",
       "      <td>9.400</td>\n",
       "      <td>80.61880</td>\n",
       "      <td>High income</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>AZE</td>\n",
       "      <td>18.300</td>\n",
       "      <td>58.70000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>BDI</td>\n",
       "      <td>44.151</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>BEL</td>\n",
       "      <td>11.200</td>\n",
       "      <td>82.17020</td>\n",
       "      <td>High income</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Benin</td>\n",
       "      <td>BEN</td>\n",
       "      <td>36.440</td>\n",
       "      <td>4.90000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Benin</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>BFA</td>\n",
       "      <td>40.551</td>\n",
       "      <td>9.10000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>BGD</td>\n",
       "      <td>20.142</td>\n",
       "      <td>6.63000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>BGR</td>\n",
       "      <td>9.200</td>\n",
       "      <td>53.06150</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>BHR</td>\n",
       "      <td>15.040</td>\n",
       "      <td>90.00004</td>\n",
       "      <td>High income</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Middle East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bahamas, The</td>\n",
       "      <td>BHS</td>\n",
       "      <td>15.339</td>\n",
       "      <td>72.00000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Bahamas, The</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>BIH</td>\n",
       "      <td>9.062</td>\n",
       "      <td>57.79000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>BLR</td>\n",
       "      <td>12.500</td>\n",
       "      <td>54.17000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Belize</td>\n",
       "      <td>BLZ</td>\n",
       "      <td>23.092</td>\n",
       "      <td>33.60000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Belize</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>BMU</td>\n",
       "      <td>10.400</td>\n",
       "      <td>95.30000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Bermuda</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>BOL</td>\n",
       "      <td>24.236</td>\n",
       "      <td>36.94000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>BRA</td>\n",
       "      <td>14.931</td>\n",
       "      <td>51.04000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>BRB</td>\n",
       "      <td>12.188</td>\n",
       "      <td>73.00000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Barbados</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>BRN</td>\n",
       "      <td>16.405</td>\n",
       "      <td>64.50000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>BTN</td>\n",
       "      <td>18.134</td>\n",
       "      <td>29.90000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Bhutan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>BWA</td>\n",
       "      <td>25.267</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>CAF</td>\n",
       "      <td>34.076</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>SYC</td>\n",
       "      <td>18.600</td>\n",
       "      <td>50.40000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Seychelles</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>SYR</td>\n",
       "      <td>24.043</td>\n",
       "      <td>26.20000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>Middle East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Chad</td>\n",
       "      <td>TCD</td>\n",
       "      <td>45.745</td>\n",
       "      <td>2.30000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Chad</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Togo</td>\n",
       "      <td>TGO</td>\n",
       "      <td>36.080</td>\n",
       "      <td>4.50000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Togo</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>THA</td>\n",
       "      <td>11.041</td>\n",
       "      <td>28.94000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>TJK</td>\n",
       "      <td>30.792</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>TKM</td>\n",
       "      <td>21.322</td>\n",
       "      <td>9.60000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>TLS</td>\n",
       "      <td>35.755</td>\n",
       "      <td>1.10000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>TON</td>\n",
       "      <td>25.409</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Tonga</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>TTO</td>\n",
       "      <td>14.590</td>\n",
       "      <td>63.80000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>TUN</td>\n",
       "      <td>19.800</td>\n",
       "      <td>43.80000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>TUR</td>\n",
       "      <td>16.836</td>\n",
       "      <td>46.25000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>TZA</td>\n",
       "      <td>39.518</td>\n",
       "      <td>4.40000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>UGA</td>\n",
       "      <td>43.474</td>\n",
       "      <td>16.20000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>UKR</td>\n",
       "      <td>11.100</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>URY</td>\n",
       "      <td>14.374</td>\n",
       "      <td>57.69000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>12.500</td>\n",
       "      <td>84.20000</td>\n",
       "      <td>High income</td>\n",
       "      <td>United States</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>UZB</td>\n",
       "      <td>22.500</td>\n",
       "      <td>38.20000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>St. Vincent and the Grenadines</td>\n",
       "      <td>VCT</td>\n",
       "      <td>16.306</td>\n",
       "      <td>52.00000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>St. Vincent and the Grenadines</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Venezuela, RB</td>\n",
       "      <td>VEN</td>\n",
       "      <td>19.842</td>\n",
       "      <td>54.90000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Venezuela, RB</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Virgin Islands (U.S.)</td>\n",
       "      <td>VIR</td>\n",
       "      <td>10.700</td>\n",
       "      <td>45.30000</td>\n",
       "      <td>High income</td>\n",
       "      <td>Virgin Islands (U.S.)</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>VNM</td>\n",
       "      <td>15.537</td>\n",
       "      <td>43.90000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>VUT</td>\n",
       "      <td>26.739</td>\n",
       "      <td>11.30000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>PSE</td>\n",
       "      <td>30.394</td>\n",
       "      <td>46.60000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>Middle East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>WSM</td>\n",
       "      <td>26.172</td>\n",
       "      <td>15.30000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Samoa</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Middle East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.50000</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.20000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.40000</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.50000</td>\n",
       "      <td>Low income</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      CountryName_x CountryCode  BirthRate  InternetUsers  \\\n",
       "0                             Aruba         ABW     10.244       78.90000   \n",
       "1                       Afghanistan         AFG     35.253        5.90000   \n",
       "2                            Angola         AGO     45.985       19.10000   \n",
       "3                           Albania         ALB     12.877       57.20000   \n",
       "4              United Arab Emirates         ARE     11.044       88.00000   \n",
       "5                         Argentina         ARG     17.716       59.90000   \n",
       "6                           Armenia         ARM     13.308       41.90000   \n",
       "7               Antigua and Barbuda         ATG     16.447       63.40000   \n",
       "8                         Australia         AUS     13.200       83.00000   \n",
       "9                           Austria         AUT      9.400       80.61880   \n",
       "10                       Azerbaijan         AZE     18.300       58.70000   \n",
       "11                          Burundi         BDI     44.151        1.30000   \n",
       "12                          Belgium         BEL     11.200       82.17020   \n",
       "13                            Benin         BEN     36.440        4.90000   \n",
       "14                     Burkina Faso         BFA     40.551        9.10000   \n",
       "15                       Bangladesh         BGD     20.142        6.63000   \n",
       "16                         Bulgaria         BGR      9.200       53.06150   \n",
       "17                          Bahrain         BHR     15.040       90.00004   \n",
       "18                     Bahamas, The         BHS     15.339       72.00000   \n",
       "19           Bosnia and Herzegovina         BIH      9.062       57.79000   \n",
       "20                          Belarus         BLR     12.500       54.17000   \n",
       "21                           Belize         BLZ     23.092       33.60000   \n",
       "22                          Bermuda         BMU     10.400       95.30000   \n",
       "23                          Bolivia         BOL     24.236       36.94000   \n",
       "24                           Brazil         BRA     14.931       51.04000   \n",
       "25                         Barbados         BRB     12.188       73.00000   \n",
       "26                Brunei Darussalam         BRN     16.405       64.50000   \n",
       "27                           Bhutan         BTN     18.134       29.90000   \n",
       "28                         Botswana         BWA     25.267       15.00000   \n",
       "29         Central African Republic         CAF     34.076        3.50000   \n",
       "..                              ...         ...        ...            ...   \n",
       "165                      Seychelles         SYC     18.600       50.40000   \n",
       "166            Syrian Arab Republic         SYR     24.043       26.20000   \n",
       "167                            Chad         TCD     45.745        2.30000   \n",
       "168                            Togo         TGO     36.080        4.50000   \n",
       "169                        Thailand         THA     11.041       28.94000   \n",
       "170                      Tajikistan         TJK     30.792       16.00000   \n",
       "171                    Turkmenistan         TKM     21.322        9.60000   \n",
       "172                     Timor-Leste         TLS     35.755        1.10000   \n",
       "173                           Tonga         TON     25.409       35.00000   \n",
       "174             Trinidad and Tobago         TTO     14.590       63.80000   \n",
       "175                         Tunisia         TUN     19.800       43.80000   \n",
       "176                          Turkey         TUR     16.836       46.25000   \n",
       "177                        Tanzania         TZA     39.518        4.40000   \n",
       "178                          Uganda         UGA     43.474       16.20000   \n",
       "179                         Ukraine         UKR     11.100       41.00000   \n",
       "180                         Uruguay         URY     14.374       57.69000   \n",
       "181                   United States         USA     12.500       84.20000   \n",
       "182                      Uzbekistan         UZB     22.500       38.20000   \n",
       "183  St. Vincent and the Grenadines         VCT     16.306       52.00000   \n",
       "184                   Venezuela, RB         VEN     19.842       54.90000   \n",
       "185           Virgin Islands (U.S.)         VIR     10.700       45.30000   \n",
       "186                         Vietnam         VNM     15.537       43.90000   \n",
       "187                         Vanuatu         VUT     26.739       11.30000   \n",
       "188              West Bank and Gaza         PSE     30.394       46.60000   \n",
       "189                           Samoa         WSM     26.172       15.30000   \n",
       "190                     Yemen, Rep.         YEM     32.947       20.00000   \n",
       "191                    South Africa         ZAF     20.850       46.50000   \n",
       "192                Congo, Dem. Rep.         COD     42.394        2.20000   \n",
       "193                          Zambia         ZMB     40.471       15.40000   \n",
       "194                        Zimbabwe         ZWE     35.715       18.50000   \n",
       "\n",
       "             IncomeGroup                   CountryName_y CountryRegion  \n",
       "0            High income                           Aruba  The Americas  \n",
       "1             Low income                     Afghanistan          Asia  \n",
       "2    Upper middle income                          Angola        Africa  \n",
       "3    Upper middle income                         Albania        Europe  \n",
       "4            High income            United Arab Emirates   Middle East  \n",
       "5            High income                       Argentina  The Americas  \n",
       "6    Lower middle income                         Armenia          Asia  \n",
       "7            High income             Antigua and Barbuda  The Americas  \n",
       "8            High income                       Australia       Oceania  \n",
       "9            High income                         Austria        Europe  \n",
       "10   Upper middle income                      Azerbaijan          Asia  \n",
       "11            Low income                         Burundi        Africa  \n",
       "12           High income                         Belgium        Europe  \n",
       "13            Low income                           Benin        Africa  \n",
       "14            Low income                    Burkina Faso        Africa  \n",
       "15   Lower middle income                      Bangladesh          Asia  \n",
       "16   Upper middle income                        Bulgaria        Europe  \n",
       "17           High income                         Bahrain   Middle East  \n",
       "18           High income                    Bahamas, The  The Americas  \n",
       "19   Upper middle income          Bosnia and Herzegovina        Europe  \n",
       "20   Upper middle income                         Belarus        Europe  \n",
       "21   Upper middle income                          Belize  The Americas  \n",
       "22           High income                         Bermuda  The Americas  \n",
       "23   Lower middle income                         Bolivia  The Americas  \n",
       "24   Upper middle income                          Brazil  The Americas  \n",
       "25           High income                        Barbados  The Americas  \n",
       "26           High income               Brunei Darussalam          Asia  \n",
       "27   Lower middle income                          Bhutan          Asia  \n",
       "28   Upper middle income                        Botswana        Africa  \n",
       "29            Low income        Central African Republic        Africa  \n",
       "..                   ...                             ...           ...  \n",
       "165          High income                      Seychelles        Africa  \n",
       "166  Lower middle income            Syrian Arab Republic   Middle East  \n",
       "167           Low income                            Chad        Africa  \n",
       "168           Low income                            Togo        Africa  \n",
       "169  Upper middle income                        Thailand          Asia  \n",
       "170  Lower middle income                      Tajikistan          Asia  \n",
       "171  Upper middle income                    Turkmenistan          Asia  \n",
       "172  Lower middle income                     Timor-Leste          Asia  \n",
       "173  Upper middle income                           Tonga       Oceania  \n",
       "174          High income             Trinidad and Tobago  The Americas  \n",
       "175  Upper middle income                         Tunisia        Africa  \n",
       "176  Upper middle income                          Turkey        Europe  \n",
       "177           Low income                        Tanzania        Africa  \n",
       "178           Low income                          Uganda        Africa  \n",
       "179  Lower middle income                         Ukraine        Europe  \n",
       "180          High income                         Uruguay  The Americas  \n",
       "181          High income                   United States  The Americas  \n",
       "182  Lower middle income                      Uzbekistan          Asia  \n",
       "183  Upper middle income  St. Vincent and the Grenadines  The Americas  \n",
       "184          High income                   Venezuela, RB  The Americas  \n",
       "185          High income           Virgin Islands (U.S.)  The Americas  \n",
       "186  Lower middle income                         Vietnam          Asia  \n",
       "187  Lower middle income                         Vanuatu       Oceania  \n",
       "188  Lower middle income              West Bank and Gaza   Middle East  \n",
       "189  Lower middle income                           Samoa       Oceania  \n",
       "190  Lower middle income                     Yemen, Rep.   Middle East  \n",
       "191  Upper middle income                    South Africa        Africa  \n",
       "192           Low income                Congo, Dem. Rep.        Africa  \n",
       "193  Lower middle income                          Zambia        Africa  \n",
       "194           Low income                        Zimbabwe        Africa  \n",
       "\n",
       "[195 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'CountryName' appears twice with subscript x and y. We will get rid of one soon. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Country_Code = list ([\"ABW\",\"AFG\",\"AGO\",\"ALB\",\"ARE\",\"ARG\",\"ARM\",\"ATG\",\"AUS\",\"AUT\",\"AZE\",\"BDI\",\"BEL\",\"BEN\",\"BFA\",\"BGD\",\"BGR\",\"BHR\",\"BHS\",\"BIH\",\"BLR\",\"BLZ\",\"BOL\",\"BRA\",\"BRB\",\"BRN\",\"BTN\",\"BWA\",\"CAF\",\"CAN\",\n",
    "                      \"CHE\",\"CHL\",\"CHN\",\"CIV\",\"CMR\",\"COG\",\"COL\",\"COM\",\"CPV\",\"CRI\",\"CUB\",\"CYP\",\"CZE\",\"DEU\",\"DJI\",\n",
    "                      \"DNK\",\"DOM\",\"DZA\",\"ECU\",\"EGY\",\"ERI\",\"ESP\",\"EST\",\"ETH\",\"FIN\",\"FJI\",\"FRA\",\"FSM\",\"GAB\",\"GBR\",\n",
    "                      \"GEO\",\"GHA\",\"GIN\",\"GMB\",\"GNB\",\"GNQ\",\"GRC\",\"GRD\",\"GTM\",\"GUM\",\"GUY\",\"HKG\",\"HND\",\"HRV\",\"HTI\",\n",
    "                      \"HUN\",\"IDN\",\"IND\",\"IRL\",\"IRN\",\"IRQ\",\"ISL\",\"ITA\",\"JAM\",\"JOR\",\"JPN\",\"KAZ\",\"KEN\",\"KGZ\",\"KHM\",\n",
    "                      \"KIR\",\"KOR\",\"KWT\",\"LAO\",\"LBN\",\"LBR\",\"LBY\",\"LCA\",\"LKA\",\"LSO\",\"LTU\",\"LUX\",\"LVA\",\"MAC\",\"MAR\",\n",
    "                      \"MDA\",\"MDG\",\"MDV\",\"MEX\",\"MKD\",\"MLI\",\"MLT\",\"MMR\",\"MNE\",\"MNG\",\"MOZ\",\"MRT\",\"MUS\",\"MWI\",\"MYS\",\n",
    "                      \"NAM\",\"NCL\",\"NER\",\"NGA\",\"NIC\",\"NLD\",\"NOR\",\"NPL\",\"NZL\",\"OMN\",\"PAK\",\"PAN\",\"PER\",\"PHL\",\"PNG\",\n",
    "                      \"POL\",\"PRI\",\"PRT\",\"PRY\",\"PYF\",\"QAT\",\"ROU\",\"RUS\",\"RWA\",\"SAU\",\"SDN\",\"SEN\",\"SGP\",\"SLB\",\"SLE\",\n",
    "                      \"SLV\",\"SOM\",\"SSD\",\"STP\",\"SUR\",\"SVK\",\"SVN\",\"SWE\",\"SWZ\",\"SYR\",\"TCD\",\"TGO\",\"THA\",\"TJK\",\"TKM\",\n",
    "                      \"TLS\",\"TON\",\"TTO\",\"TUN\",\"TUR\",\"TZA\",\"UGA\",\"UKR\",\"URY\",\"USA\",\"UZB\",\"VCT\",\"VEN\",\"VIR\",\"VNM\",\n",
    "                      \"VUT\",\"WSM\",\"YEM\",\"ZAF\",\"COD\",\"ZMB\",\"ZWE\"])\n",
    "\n",
    "\n",
    "Life_Expectancy_At_Birth_1960 = list ([65.5693658536586,32.328512195122,32.9848292682927,62.2543658536585,\n",
    "                                       52.2432195121951,65.2155365853659,65.8634634146342,61.7827317073171,\n",
    "                                       70.8170731707317,68.5856097560976,60.836243902439,41.2360487804878,\n",
    "                                       69.7019512195122,37.2782682926829,34.4779024390244,45.8293170731707,\n",
    "                                       69.2475609756098,52.0893658536585,62.7290487804878,60.2762195121951,\n",
    "                                       67.7080975609756,59.9613658536585,42.1183170731707,54.2054634146342,\n",
    "                                       60.7380487804878,62.5003658536585,32.3593658536585,50.5477317073171,\n",
    "                                       36.4826341463415,71.1331707317073,71.3134146341463,57.4582926829268,\n",
    "                                       43.4658048780488,36.8724146341463,41.523756097561,48.5816341463415,\n",
    "                                       56.716756097561,41.4424390243903,48.8564146341463,60.5761951219512,\n",
    "                                       63.9046585365854,69.5939268292683,70.3487804878049,69.3129512195122,\n",
    "                                       44.0212682926829,72.1765853658537,51.8452682926829,46.1351219512195,\n",
    "                                       53.215,48.0137073170732,37.3629024390244,69.1092682926829,67.9059756097561,\n",
    "                                       38.4057073170732,68.819756097561,55.9584878048781,69.8682926829268,\n",
    "                                       57.5865853658537,39.5701219512195,71.1268292682927,63.4318536585366,\n",
    "                                       45.8314634146342,34.8863902439024,32.0422195121951,37.8404390243902,\n",
    "                                       36.7330487804878,68.1639024390244,59.8159268292683,45.5316341463415,\n",
    "                                       61.2263414634146,60.2787317073171,66.9997073170732,46.2883170731707,\n",
    "                                       64.6086585365854,42.1000975609756,68.0031707317073,48.6403170731707,41.1719512195122,69.691756097561,44.945512195122,48.0306829268293,73.4286585365854,69.1239024390244,64.1918292682927,52.6852682926829,67.6660975609756,58.3675853658537,46.3624146341463,56.1280731707317,41.2320243902439,49.2159756097561,53.0013170731707,60.3479512195122,43.2044634146342,63.2801219512195,34.7831707317073,42.6411951219512,57.303756097561,59.7471463414634,46.5107073170732,69.8473170731707,68.4463902439024,69.7868292682927,64.6609268292683,48.4466341463415,61.8127804878049,39.9746829268293,37.2686341463415,57.0656341463415,60.6228048780488,28.2116097560976,67.6017804878049,42.7363902439024,63.7056097560976,48.3688048780488,35.0037073170732,43.4830975609756,58.7452195121951,37.7736341463415,59.4753414634146,46.8803902439024,58.6390243902439,35.5150487804878,37.1829512195122,46.9988292682927,73.3926829268293,73.549756097561,35.1708292682927,71.2365853658537,42.6670731707317,45.2904634146342,60.8817073170732,47.6915853658537,57.8119268292683,38.462243902439,67.6804878048781,68.7196097560976,62.8089268292683,63.7937073170732,56.3570487804878,61.2060731707317,65.6424390243903,66.0552926829268,42.2492926829268,45.6662682926829,48.1876341463415,38.206,65.6598292682927,49.3817073170732,30.3315365853659,49.9479268292683,36.9658780487805,31.6767073170732,50.4513658536585,59.6801219512195,69.9759268292683,68.9780487804878,73.0056097560976,44.2337804878049,52.768243902439,38.0161219512195,40.2728292682927,54.6993170731707,56.1535365853659,54.4586829268293,33.7271219512195,61.3645365853659,62.6575853658537,42.009756097561,45.3844146341463,43.6538780487805,43.9835609756098,68.2995365853659,67.8963902439025,69.7707317073171,58.8855365853659,57.7238780487805,59.2851219512195,63.7302195121951,59.0670243902439,46.4874878048781,49.969512195122,34.3638048780488,49.0362926829268,41.0180487804878,45.1098048780488,51.5424634146342])\n",
    "\n",
    "\n",
    "Life_Expectancy_At_Birth_2013 = list ([75.3286585365854,60.0282682926829,51.8661707317073,77.537243902439,77.1956341463415,75.9860975609756,74.5613658536585,75.7786585365854,82.1975609756098,80.890243902439,70.6931463414634,56.2516097560976,80.3853658536585,59.3120243902439,58.2406341463415,71.245243902439,74.4658536585366,76.5459512195122,75.0735365853659,76.2769268292683,72.4707317073171,69.9820487804878,67.9134390243903,74.1224390243903,75.3339512195122,78.5466585365854,69.1029268292683,64.3608048780488,49.8798780487805,81.4011219512195,82.7487804878049,81.1979268292683,75.3530243902439,51.2084634146342,55.0418048780488,61.6663902439024,73.8097317073171,62.9321707317073,72.9723658536585,79.2252195121951,79.2563902439025,79.9497804878049,78.2780487804878,81.0439024390244,61.6864634146342,80.3024390243903,73.3199024390244,74.5689512195122,75.648512195122,70.9257804878049,63.1778780487805,82.4268292682927,76.4243902439025,63.4421951219512,80.8317073170732,69.9179268292683,81.9682926829268,68.9733902439024,63.8435853658537,80.9560975609756,74.079512195122,61.1420731707317,58.216487804878,59.9992682926829,54.8384146341464,57.2908292682927,80.6341463414634,73.1935609756098,71.4863902439024,78.872512195122,66.3100243902439,83.8317073170732,72.9428536585366,77.1268292682927,62.4011463414634,75.2682926829268,68.7046097560976,67.6604146341463,81.0439024390244,75.1259756097561,69.4716829268293,83.1170731707317,82.290243902439,73.4689268292683,73.9014146341463,83.3319512195122,70.45,60.9537804878049,70.2024390243902,67.7720487804878,65.7665853658537,81.459756097561,74.462756097561,65.687243902439,80.1288780487805,60.5203902439024,71.6576829268293,74.9127073170732,74.2402926829268,49.3314634146342,74.1634146341464,81.7975609756098,73.9804878048781,80.3391463414634,73.7090487804878,68.811512195122,64.6739024390244,76.6026097560976,76.5326585365854,75.1870487804878,57.5351951219512,80.7463414634146,65.6540975609756,74.7583658536585,69.0618048780488,54.641512195122,62.8027073170732,74.46,61.466,74.567512195122,64.3438780487805,77.1219512195122,60.8281463414634,52.4421463414634,74.514756097561,81.1048780487805,81.4512195121951,69.222,81.4073170731707,76.8410487804878,65.9636829268293,77.4192195121951,74.2838536585366,68.1315609756097,62.4491707317073,76.8487804878049,78.7111951219512,80.3731707317073,72.7991707317073,76.3340731707317,78.4184878048781,74.4634146341463,71.0731707317073,63.3948292682927,74.1776341463415,63.1670487804878,65.878756097561,82.3463414634146,67.7189268292683,50.3631219512195,72.4981463414634,55.0230243902439,55.2209024390244,66.259512195122,70.99,76.2609756097561,80.2780487804878,81.7048780487805,48.9379268292683,74.7157804878049,51.1914878048781,59.1323658536585,74.2469268292683,69.4001707317073,65.4565609756098,67.5223658536585,72.6403414634147,70.3052926829268,73.6463414634147,75.1759512195122,64.2918292682927,57.7676829268293,71.159512195122,76.8361951219512,78.8414634146341,68.2275853658537,72.8108780487805,74.0744146341464,79.6243902439024,75.756487804878,71.669243902439,73.2503902439024,63.583512195122,56.7365853658537,58.2719268292683,59.2373658536585,55.633])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "life_exp_data = pd.DataFrame ({'CountryCode': np.array(Country_Code),\n",
    "                             'LifeExp1960': np.array(Life_Expectancy_At_Birth_1960),\n",
    "                             'LifeExp2013': np.array(Life_Expectancy_At_Birth_2013)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "187"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(life_exp_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LifeExp1960</th>\n",
       "      <th>LifeExp2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.734112</td>\n",
       "      <td>70.763808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.182463</td>\n",
       "      <td>8.590893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.211610</td>\n",
       "      <td>48.937927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>43.474451</td>\n",
       "      <td>64.517354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.699317</td>\n",
       "      <td>73.250390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.048244</td>\n",
       "      <td>76.838622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.549756</td>\n",
       "      <td>83.831707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LifeExp1960  LifeExp2013\n",
       "count   187.000000   187.000000\n",
       "mean     53.734112    70.763808\n",
       "std      12.182463     8.590893\n",
       "min      28.211610    48.937927\n",
       "25%      43.474451    64.517354\n",
       "50%      54.699317    73.250390\n",
       "75%      64.048244    76.838622\n",
       "max      73.549756    83.831707"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "life_exp_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_data2 = pd.merge(left=merged_data1, right=life_exp_data, how='inner', on=\"CountryCode\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 187 entries, 0 to 186\n",
      "Data columns (total 9 columns):\n",
      "CountryName_x    187 non-null object\n",
      "CountryCode      187 non-null object\n",
      "BirthRate        187 non-null float64\n",
      "InternetUsers    187 non-null float64\n",
      "IncomeGroup      187 non-null object\n",
      "CountryName_y    187 non-null object\n",
      "CountryRegion    187 non-null object\n",
      "LifeExp1960      187 non-null float64\n",
      "LifeExp2013      187 non-null float64\n",
      "dtypes: float64(4), object(5)\n",
      "memory usage: 14.6+ KB\n"
     ]
    }
   ],
   "source": [
    "merged_data2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deleting a column\n",
    "del merged_data2['CountryName_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_data2.rename(columns = {'CountryName_x':'CountryName'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1dc365c0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis1 = sns.lmplot(data = merged_data2, x ='BirthRate', y ='LifeExp1960', fit_reg=False, hue='CountryRegion',\\\n",
    "                 legend= False, size=7, aspect=1)\n",
    "plt.title(\"Birth Rate vs Life Expectancy: 1960\", fontsize=17, \\\n",
    "         color =\"DarkBlue\", fontname=\"Console\")\n",
    "plt.ylabel(\"Life Expectancy\", fontsize=15, color=\"Green\")\n",
    "plt.xlabel(\"Birth Rate\", fontsize=15, color=\"Red\")\n",
    "plt.yticks(fontsize=13)\n",
    "plt.xticks(fontsize=13)\n",
    "plt.legend(frameon=True, fancybox=True, shadow=True, \\\n",
    "           framealpha=1, prop={'size':15})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- European countries (red dots) had lower birth rate and higher life expectancy with a maximum of about 74 years. \n",
    "- African countries had significantly higher birth rate and lower life expectancy. \n",
    "- We see that life expectancy in Africa ranged from 30-50 years with some exceptions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1dc36ba8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis2 = sns.lmplot(data = merged_data2, x ='BirthRate', y ='LifeExp2013', fit_reg=False, hue='CountryRegion',\\\n",
    "                 legend= False, size=7, aspect=1)\n",
    "plt.title(\"Birth Rate vs Life Expectancy: 2013\", fontsize=17, \\\n",
    "         color =\"DarkBlue\", fontname=\"Console\")\n",
    "plt.ylabel(\"Life Expectancy\", fontsize=15, color=\"Green\")\n",
    "plt.xlabel(\"Birth Rate\", fontsize=15, color=\"Red\")\n",
    "plt.yticks(fontsize=13)\n",
    "plt.xticks(fontsize=13)\n",
    "plt.legend(frameon=True, fancybox=True, shadow=True, \\\n",
    "           framealpha=1, prop={'size':15})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the European countries are at the top of the chart and African countries are at the bottom."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison of the two charts: \n",
    "\n",
    "Looking at the above charts, we see that\n",
    "\n",
    "- The overall life expectancy has increased accross the world. \n",
    "- European contries has maintained lower birth rate and higher life expectancy with a max life expectancy of 83 years.\n",
    "- African countries still maintain higher birth rate with few exceotions. Life expectancy has somewhat increased.\n",
    "- Asian and Middle Eastern countries have improved a lot in case of life expectancy even though some countries still have higher birth rate.\n",
    "- Countries in America and Oceania follows patterns somewhat similar to the 60's.\n",
    "\n",
    "Finally, it won't be an exaggeration to say that higher birth rate leads to lower life expectancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}