Country Birth Rate Exploratory Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Country Birth Rate Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('DemographicData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        CountryName 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  \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]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Rename columns\n",
    "data.columns = [['CountryName', 'CountryCode', 'BirthRate', 'InternetUsers','IncomeGroup']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical summary\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f+VhVWbPVGv/8Z/Nm23rXrmXkPe/n6MdvITpxMm+A7T3vfEbe8/6ir59emD8w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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbfb95f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization 1\n",
    "vis1 = sns.lmplot(data = data, x = 'BirthRate', y = 'InternetUsers', \n",
    "                  fit_reg = False, hue = 'IncomeGroup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create lists of country codes, life expectancy in 1960, and life expectancy in 2013\n",
    "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\",\"CHE\",\"CHL\",\"CHN\",\"CIV\",\"CMR\",\"COG\",\"COL\",\"COM\",\"CPV\",\"CRI\",\"CUB\",\"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\",\"GTM\",\"GUM\",\"GUY\",\"HKG\",\"HND\",\"HRV\",\"HTI\",\"HUN\",\"IDN\",\"IND\",\"IRL\",\"IRN\",\"IRQ\",\"ISL\",\"ITA\",\"JAM\",\"JOR\",\"JPN\",\"KAZ\",\"KEN\",\"KGZ\",\"KHM\",\"KIR\",\"KOR\",\"KWT\",\"LAO\",\"LBN\",\"LBR\",\"LBY\",\"LCA\",\"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\",\"SSD\",\"STP\",\"SUR\",\"SVK\",\"SVN\",\"SWE\",\"SWZ\",\"SYR\",\"TCD\",\"TGO\",\"THA\",\"TJK\",\"TKM\",\"TLS\",\"TON\",\"TTO\",\"TUN\",\"TUR\",\"TZA\",\"UGA\",\"UKR\",\"URY\",\"USA\",\"UZB\",\"VCT\",\"VEN\",\"VIR\",\"VNM\",\"VUT\",\"WSM\",\"YEM\",\"ZAF\",\"COD\",\"ZMB\",\"ZWE\"])\n",
    "Life_Expectancy_At_Birth_1960 = list ([65.5693658536586,32.328512195122,32.9848292682927,62.2543658536585,52.2432195121951,65.2155365853659,65.8634634146342,61.7827317073171,70.8170731707317,68.5856097560976,60.836243902439,41.2360487804878,69.7019512195122,37.2782682926829,34.4779024390244,45.8293170731707,69.2475609756098,52.0893658536585,62.7290487804878,60.2762195121951,67.7080975609756,59.9613658536585,42.1183170731707,54.2054634146342,60.7380487804878,62.5003658536585,32.3593658536585,50.5477317073171,36.4826341463415,71.1331707317073,71.3134146341463,57.4582926829268,43.4658048780488,36.8724146341463,41.523756097561,48.5816341463415,56.716756097561,41.4424390243903,48.8564146341463,60.5761951219512,63.9046585365854,69.5939268292683,70.3487804878049,69.3129512195122,44.0212682926829,72.1765853658537,51.8452682926829,46.1351219512195,53.215,48.0137073170732,37.3629024390244,69.1092682926829,67.9059756097561,38.4057073170732,68.819756097561,55.9584878048781,69.8682926829268,57.5865853658537,39.5701219512195,71.1268292682927,63.4318536585366,45.8314634146342,34.8863902439024,32.0422195121951,37.8404390243902,36.7330487804878,68.1639024390244,59.8159268292683,45.5316341463415,61.2263414634146,60.2787317073171,66.9997073170732,46.2883170731707,64.6086585365854,42.1000975609756,68.0031707317073,48.6403170731707,41.1719512195122,69.691756097561,44.945512195122,48.0306829268293,73.4286585365854,69.1239024390244,64.1918292682927,52.6852682926829,67.6660975609756,58.3675853658537,46.3624146341463,56.1280731707317,41.2320243902439,49.2159756097561,53.0013170731707,60.3479512195122,43.2044634146342,63.2801219512195,34.7831707317073,42.6411951219512,57.303756097561,59.7471463414634,46.5107073170732,69.8473170731707,68.4463902439024,69.7868292682927,64.6609268292683,48.4466341463415,61.8127804878049,39.9746829268293,37.2686341463415,57.0656341463415,60.6228048780488,28.2116097560976,67.6017804878049,42.7363902439024,63.7056097560976,48.3688048780488,35.0037073170732,43.4830975609756,58.7452195121951,37.7736341463415,59.4753414634146,46.8803902439024,58.6390243902439,35.5150487804878,37.1829512195122,46.9988292682927,73.3926829268293,73.549756097561,35.1708292682927,71.2365853658537,42.6670731707317,45.2904634146342,60.8817073170732,47.6915853658537,57.8119268292683,38.462243902439,67.6804878048781,68.7196097560976,62.8089268292683,63.7937073170732,56.3570487804878,61.2060731707317,65.6424390243903,66.0552926829268,42.2492926829268,45.6662682926829,48.1876341463415,38.206,65.6598292682927,49.3817073170732,30.3315365853659,49.9479268292683,36.9658780487805,31.6767073170732,50.4513658536585,59.6801219512195,69.9759268292683,68.9780487804878,73.0056097560976,44.2337804878049,52.768243902439,38.0161219512195,40.2728292682927,54.6993170731707,56.1535365853659,54.4586829268293,33.7271219512195,61.3645365853659,62.6575853658537,42.009756097561,45.3844146341463,43.6538780487805,43.9835609756098,68.2995365853659,67.8963902439025,69.7707317073171,58.8855365853659,57.7238780487805,59.2851219512195,63.7302195121951,59.0670243902439,46.4874878048781,49.969512195122,34.3638048780488,49.0362926829268,41.0180487804878,45.1098048780488,51.5424634146342])\n",
    "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": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create list of countries, country codes, and regions\n",
    "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": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build dataframe with the lists\n",
    "country_data = 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": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryRegion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>The Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Middle East</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryCode           CountryName CountryRegion\n",
       "0         ABW                 Aruba  The Americas\n",
       "1         AFG           Afghanistan          Asia\n",
       "2         AGO                Angola        Africa\n",
       "3         ALB               Albania        Europe\n",
       "4         ARE  United Arab Emirates   Middle East"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>78.90000</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": [
       "                        CountryName 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  \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": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>78.9</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>5.9</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>19.1</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>57.2</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>88.0</td>\n",
       "      <td>High income</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Middle East</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CountryName_x 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         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  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge dataframes\n",
    "merged_data = pd.merge(left = data, right = country_data, how = 'inner', on ='CountryCode')\n",
    "merged_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K7mjCzDp+LMd4StJI0ma2kXDHSZAkkXAxNeHH7U3Out/O/W42rbUD595DBnufdxFPYpcX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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1c46a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization 2\n",
    "vis2 = sns.lmplot(data = merged_data, x = 'BirthRate', y = 'InternetUsers',\n",
    "                  fit_reg = False, hue = 'CountryRegion',palette = 'rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create three new lists\n",
    "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\",\"CHE\",\"CHL\",\"CHN\",\"CIV\",\"CMR\",\"COG\",\"COL\",\"COM\",\"CPV\",\"CRI\",\"CUB\",\"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\",\"GTM\",\"GUM\",\"GUY\",\"HKG\",\"HND\",\"HRV\",\"HTI\",\"HUN\",\"IDN\",\"IND\",\"IRL\",\"IRN\",\"IRQ\",\"ISL\",\"ITA\",\"JAM\",\"JOR\",\"JPN\",\"KAZ\",\"KEN\",\"KGZ\",\"KHM\",\"KIR\",\"KOR\",\"KWT\",\"LAO\",\"LBN\",\"LBR\",\"LBY\",\"LCA\",\"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\",\"SSD\",\"STP\",\"SUR\",\"SVK\",\"SVN\",\"SWE\",\"SWZ\",\"SYR\",\"TCD\",\"TGO\",\"THA\",\"TJK\",\"TKM\",\"TLS\",\"TON\",\"TTO\",\"TUN\",\"TUR\",\"TZA\",\"UGA\",\"UKR\",\"URY\",\"USA\",\"UZB\",\"VCT\",\"VEN\",\"VIR\",\"VNM\",\"VUT\",\"WSM\",\"YEM\",\"ZAF\",\"COD\",\"ZMB\",\"ZWE\"])\n",
    "Life_Expectancy_At_Birth_1960 = list ([65.5693658536586,32.328512195122,32.9848292682927,62.2543658536585,52.2432195121951,65.2155365853659,65.8634634146342,61.7827317073171,70.8170731707317,68.5856097560976,60.836243902439,41.2360487804878,69.7019512195122,37.2782682926829,34.4779024390244,45.8293170731707,69.2475609756098,52.0893658536585,62.7290487804878,60.2762195121951,67.7080975609756,59.9613658536585,42.1183170731707,54.2054634146342,60.7380487804878,62.5003658536585,32.3593658536585,50.5477317073171,36.4826341463415,71.1331707317073,71.3134146341463,57.4582926829268,43.4658048780488,36.8724146341463,41.523756097561,48.5816341463415,56.716756097561,41.4424390243903,48.8564146341463,60.5761951219512,63.9046585365854,69.5939268292683,70.3487804878049,69.3129512195122,44.0212682926829,72.1765853658537,51.8452682926829,46.1351219512195,53.215,48.0137073170732,37.3629024390244,69.1092682926829,67.9059756097561,38.4057073170732,68.819756097561,55.9584878048781,69.8682926829268,57.5865853658537,39.5701219512195,71.1268292682927,63.4318536585366,45.8314634146342,34.8863902439024,32.0422195121951,37.8404390243902,36.7330487804878,68.1639024390244,59.8159268292683,45.5316341463415,61.2263414634146,60.2787317073171,66.9997073170732,46.2883170731707,64.6086585365854,42.1000975609756,68.0031707317073,48.6403170731707,41.1719512195122,69.691756097561,44.945512195122,48.0306829268293,73.4286585365854,69.1239024390244,64.1918292682927,52.6852682926829,67.6660975609756,58.3675853658537,46.3624146341463,56.1280731707317,41.2320243902439,49.2159756097561,53.0013170731707,60.3479512195122,43.2044634146342,63.2801219512195,34.7831707317073,42.6411951219512,57.303756097561,59.7471463414634,46.5107073170732,69.8473170731707,68.4463902439024,69.7868292682927,64.6609268292683,48.4466341463415,61.8127804878049,39.9746829268293,37.2686341463415,57.0656341463415,60.6228048780488,28.2116097560976,67.6017804878049,42.7363902439024,63.7056097560976,48.3688048780488,35.0037073170732,43.4830975609756,58.7452195121951,37.7736341463415,59.4753414634146,46.8803902439024,58.6390243902439,35.5150487804878,37.1829512195122,46.9988292682927,73.3926829268293,73.549756097561,35.1708292682927,71.2365853658537,42.6670731707317,45.2904634146342,60.8817073170732,47.6915853658537,57.8119268292683,38.462243902439,67.6804878048781,68.7196097560976,62.8089268292683,63.7937073170732,56.3570487804878,61.2060731707317,65.6424390243903,66.0552926829268,42.2492926829268,45.6662682926829,48.1876341463415,38.206,65.6598292682927,49.3817073170732,30.3315365853659,49.9479268292683,36.9658780487805,31.6767073170732,50.4513658536585,59.6801219512195,69.9759268292683,68.9780487804878,73.0056097560976,44.2337804878049,52.768243902439,38.0161219512195,40.2728292682927,54.6993170731707,56.1535365853659,54.4586829268293,33.7271219512195,61.3645365853659,62.6575853658537,42.009756097561,45.3844146341463,43.6538780487805,43.9835609756098,68.2995365853659,67.8963902439025,69.7707317073171,58.8855365853659,57.7238780487805,59.2851219512195,63.7302195121951,59.0670243902439,46.4874878048781,49.969512195122,34.3638048780488,49.0362926829268,41.0180487804878,45.1098048780488,51.5424634146342])\n",
    "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": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert lists to dataframe\n",
    "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": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "187"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(life_exp_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "life_exp_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge new dataframe to previously merged dataframe\n",
    "merge_data = pd.merge(left = merged_data, right =life_exp_data, how = 'inner', on = 'CountryCode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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": [
    "merge_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Delete extra country name column\n",
    "del merge_data['CountryName_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename CountryName_x\n",
    "merge_data.rename(columns = {'CountryName_x':'CountryName'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>CountryRegion</th>\n",
       "      <th>LifeExp1960</th>\n",
       "      <th>LifeExp2013</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",
       "      <td>The Americas</td>\n",
       "      <td>65.569366</td>\n",
       "      <td>75.328659</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",
       "      <td>Asia</td>\n",
       "      <td>32.328512</td>\n",
       "      <td>60.028268</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",
       "      <td>Africa</td>\n",
       "      <td>32.984829</td>\n",
       "      <td>51.866171</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",
       "      <td>Europe</td>\n",
       "      <td>62.254366</td>\n",
       "      <td>77.537244</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",
       "      <td>Middle East</td>\n",
       "      <td>52.243220</td>\n",
       "      <td>77.195634</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 CountryRegion  LifeExp1960  LifeExp2013  \n",
       "0          High income  The Americas    65.569366    75.328659  \n",
       "1           Low income          Asia    32.328512    60.028268  \n",
       "2  Upper middle income        Africa    32.984829    51.866171  \n",
       "3  Upper middle income        Europe    62.254366    77.537244  \n",
       "4          High income   Middle East    52.243220    77.195634  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3m0kMkgP9RjHc6XGle6LXzHaElxhMo3JZC8yUx9me91EJIYTIhwS8IkmXvNns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\nLE+nlxCrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1e4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization 3\n",
    "vis3 = sns.lmplot(data = merge_data, x = 'BirthRate', y = 'LifeExp1960',\n",
    "                  fit_reg = False, hue = 'CountryRegion',palette = 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SK5xfWxtcYXu4EX32SimnO3wzvrcQpSillgBfBN6ktb4E+GPgS5njtfgroLvM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\nUYV/AGPAlzMIIznnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1a8940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization 4\n",
    "vis4 = sns.lmplot(data = merge_data, x = 'BirthRate', y = 'LifeExp1960',\n",
    "                         fit_reg = False, hue = 'CountryRegion',palette = 'rainbow')"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}