investigate_data_poverty.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Poverty Worldwide\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "mpl.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All Countries of the World with region designations\n",
    "# All Country's populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df = pd.read_csv(\"country_regions.csv\")\n",
    "populations_df = pd.read_csv(\"data_population/population_total.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_df.drop(columns=['World bank, 3 income groups 2017'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_df.drop(columns=['UN member since','members_oecd_g77','geo','World bank region','World bank, 4 income groups 2017'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df.rename(columns={'name':'country'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geo</th>\n",
       "      <th>country</th>\n",
       "      <th>four_regions</th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>six_regions</th>\n",
       "      <th>members_oecd_g77</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>UN member since</th>\n",
       "      <th>World bank region</th>\n",
       "      <th>World bank, 4 income groups 2017</th>\n",
       "      <th>World bank, 3 income groups 2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>afg</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>asia</td>\n",
       "      <td>asia_west</td>\n",
       "      <td>south_asia</td>\n",
       "      <td>g77</td>\n",
       "      <td>33.00000</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>19/11/1946</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alb</td>\n",
       "      <td>Albania</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>others</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>14/12/1955</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dza</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_north</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>g77</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>8/10/1962</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>and</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_west</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>others</td>\n",
       "      <td>42.50779</td>\n",
       "      <td>1.52109</td>\n",
       "      <td>28/7/1993</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ago</td>\n",
       "      <td>Angola</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>g77</td>\n",
       "      <td>-12.50000</td>\n",
       "      <td>18.50000</td>\n",
       "      <td>1/12/1976</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geo      country four_regions       eight_regions  \\\n",
       "0  afg  Afghanistan         asia           asia_west   \n",
       "1  alb      Albania       europe         europe_east   \n",
       "2  dza      Algeria       africa        africa_north   \n",
       "3  and      Andorra       europe         europe_west   \n",
       "4  ago       Angola       africa  africa_sub_saharan   \n",
       "\n",
       "                six_regions members_oecd_g77  Latitude  Longitude  \\\n",
       "0                south_asia              g77  33.00000   66.00000   \n",
       "1       europe_central_asia           others  41.00000   20.00000   \n",
       "2  middle_east_north_africa              g77  28.00000    3.00000   \n",
       "3       europe_central_asia           others  42.50779    1.52109   \n",
       "4        sub_saharan_africa              g77 -12.50000   18.50000   \n",
       "\n",
       "  UN member since           World bank region  \\\n",
       "0      19/11/1946                  South Asia   \n",
       "1      14/12/1955       Europe & Central Asia   \n",
       "2       8/10/1962  Middle East & North Africa   \n",
       "3       28/7/1993       Europe & Central Asia   \n",
       "4       1/12/1976          Sub-Saharan Africa   \n",
       "\n",
       "  World bank, 4 income groups 2017  World bank, 3 income groups 2017  \n",
       "0                       Low income                               NaN  \n",
       "1              Upper middle income                               NaN  \n",
       "2              Upper middle income                               NaN  \n",
       "3                      High income                               NaN  \n",
       "4              Lower middle income                               NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Economic Data - Poverty percentages, 1.90/day, 3.20/day, & 5.50/day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "poverty190 = pd.read_csv(\"data_income/poverty_percent_below_190_a_day.csv\")\n",
    "poverty320 = pd.read_csv(\"data_income/poverty_percent_below_320_a_day.csv\")\n",
    "poverty550 = pd.read_csv(\"data_income/poverty_percent_below_550_a_day.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>1970</th>\n",
       "      <th>1971</th>\n",
       "      <th>1972</th>\n",
       "      <th>1973</th>\n",
       "      <th>1974</th>\n",
       "      <th>1975</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  1967  1968  1969  1970  1971  1972  1973  1974  1975  ...  \\\n",
       "0    Albania   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "1    Algeria   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2     Angola   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3  Argentina   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "4    Armenia   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "\n",
       "    2009   2010   2011   2012   2013   2014   2015   2016   2017   2018  \n",
       "0    NaN    NaN    NaN  0.011    NaN  0.019  0.013  0.011  0.017    NaN  \n",
       "1    NaN    NaN  0.005    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "2    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  0.476  \n",
       "3  0.025  0.022  0.009  0.008  0.007  0.007    NaN  0.007  0.005  0.010  \n",
       "4  0.019  0.019  0.022  0.016  0.022  0.023  0.019  0.018  0.014  0.021  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty190.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge country data with Poverty data to categorize countries into Regions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "poverty190 = pd.merge(country_df,poverty190,on='country')\n",
    "poverty320 = pd.merge(country_df,poverty320,on='country')\n",
    "poverty550 = pd.merge(country_df,poverty550,on='country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>four_regions</th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>six_regions</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>1970</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>41.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_north</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>28.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-12.50</td>\n",
       "      <td>18.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>americas</td>\n",
       "      <td>america_south</td>\n",
       "      <td>america</td>\n",
       "      <td>-34.00</td>\n",
       "      <td>-64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>40.25</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country four_regions       eight_regions               six_regions  \\\n",
       "0    Albania       europe         europe_east       europe_central_asia   \n",
       "1    Algeria       africa        africa_north  middle_east_north_africa   \n",
       "2     Angola       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "3  Argentina     americas       america_south                   america   \n",
       "4    Armenia       europe         europe_east       europe_central_asia   \n",
       "\n",
       "   Latitude  Longitude  1967  1968  1969  1970  ...   2009   2010   2011  \\\n",
       "0     41.00       20.0   NaN   NaN   NaN   NaN  ...    NaN    NaN    NaN   \n",
       "1     28.00        3.0   NaN   NaN   NaN   NaN  ...    NaN    NaN  0.005   \n",
       "2    -12.50       18.5   NaN   NaN   NaN   NaN  ...    NaN    NaN    NaN   \n",
       "3    -34.00      -64.0   NaN   NaN   NaN   NaN  ...  0.025  0.022  0.009   \n",
       "4     40.25       45.0   NaN   NaN   NaN   NaN  ...  0.019  0.019  0.022   \n",
       "\n",
       "    2012   2013   2014   2015   2016   2017   2018  \n",
       "0  0.011    NaN  0.019  0.013  0.011  0.017    NaN  \n",
       "1    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "2    NaN    NaN    NaN    NaN    NaN    NaN  0.476  \n",
       "3  0.008  0.007  0.007    NaN  0.007  0.005  0.010  \n",
       "4  0.016  0.022  0.023  0.019  0.018  0.014  0.021  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty190.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pulling out the US & Canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>four_regions</th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>six_regions</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>United States</td>\n",
       "      <td>americas</td>\n",
       "      <td>america_north</td>\n",
       "      <td>america</td>\n",
       "      <td>39.76</td>\n",
       "      <td>-98.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country four_regions  eight_regions six_regions  Latitude  \\\n",
       "152  United States     americas  america_north     america     39.76   \n",
       "\n",
       "     Longitude  2010  2011  2012  2013  2014  2015   2016  2017  2018  \n",
       "152      -98.5  0.01   NaN   NaN  0.01   NaN   NaN  0.012   NaN   NaN  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty190.loc[poverty190.country == 'United States']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>four_regions</th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>six_regions</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Canada</td>\n",
       "      <td>americas</td>\n",
       "      <td>america_north</td>\n",
       "      <td>america</td>\n",
       "      <td>60.10867</td>\n",
       "      <td>-113.64258</td>\n",
       "      <td>0.002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country four_regions  eight_regions six_regions  Latitude  Longitude  \\\n",
       "22  Canada     americas  america_north     america  60.10867 -113.64258   \n",
       "\n",
       "     2010  2011  2012   2013  2014  2015  2016  2017  2018  \n",
       "22  0.002   NaN   NaN  0.005   NaN   NaN   NaN   NaN   NaN  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty190.loc[poverty190.country == 'Canada']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace NaNs with 0 for Canada & US"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "158    False\n",
       "159    False\n",
       "160    False\n",
       "161    False\n",
       "162    False\n",
       "Name: country, Length: 163, dtype: bool"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#poverty190['United States']['2011']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many values are in each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eight_regions</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>africa_north</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_north</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_south</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asia_west</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_east</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_west</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    country\n",
       "eight_regions              \n",
       "africa_north              5\n",
       "africa_sub_saharan       45\n",
       "america_north            15\n",
       "america_south            12\n",
       "asia_west                21\n",
       "east_asia_pacific        22\n",
       "europe_east              23\n",
       "europe_west              20"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty190.groupby('eight_regions')[['country']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2010</th>\n",
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       "      <th>2018</th>\n",
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       "      <th>africa_north</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>9</td>\n",
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       "      <td>6</td>\n",
       "      <td>2</td>\n",
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       "      <th>america_north</th>\n",
       "      <td>8</td>\n",
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       "      <td>7</td>\n",
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       "      <td>5</td>\n",
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       "      <th>america_south</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
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       "      <th>asia_west</th>\n",
       "      <td>8</td>\n",
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       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_east</th>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_west</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2010  2011  2012  2013  2014  2015  2016  2017  2018\n",
       "eight_regions                                                           \n",
       "africa_north           2     1     2     2     0     2     0     2     0\n",
       "africa_sub_saharan     9     9     6     3     9    10     6     6     2\n",
       "america_north          8     5     7     7     8     5     8     5     6\n",
       "america_south          6     9     8     9     8     8     8     9     8\n",
       "asia_west              8     6     6     4     6     5     8     4     1\n",
       "east_asia_pacific     10     5     8     9     7     7     5     3     4\n",
       "europe_east           18    19    21    20    21    21    20    19     7\n",
       "europe_west           20    20    19    20    19    20    19    16     0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty190.groupby('eight_regions')[['2010','2011','2012','2013','2014','2015','2016','2017','2018']].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many null values are in each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <td>36</td>\n",
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       "      <td>43</td>\n",
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       "      <th>america_north</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_south</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asia_west</th>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_east</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_west</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2010  2011  2012  2013  2014  2015  2016  2017  2018\n",
       "eight_regions                                                           \n",
       "africa_north           3     4     3     3     5     3     5     3     5\n",
       "africa_sub_saharan    36    36    39    42    36    35    39    39    43\n",
       "america_north          7    10     8     8     7    10     7    10     9\n",
       "america_south          6     3     4     3     4     4     4     3     4\n",
       "asia_west             13    15    15    17    15    16    13    17    20\n",
       "east_asia_pacific     12    17    14    13    15    15    17    19    18\n",
       "europe_east            5     4     2     3     2     2     3     4    16\n",
       "europe_west            0     0     1     0     1     0     1     4    20"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty190.groupby('eight_regions')[['2010','2011','2012','2013','2014','2015','2016','2017','2018']].apply(lambda x: x.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "poverty190[poverty]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuing with other poverty datasets, 3.20/day & 5.50/day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>four_regions</th>\n",
       "      <th>eight_regions</th>\n",
       "      <th>six_regions</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>1970</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>41.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.104</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_north</td>\n",
       "      <td>middle_east_north_africa</td>\n",
       "      <td>28.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>africa</td>\n",
       "      <td>africa_sub_saharan</td>\n",
       "      <td>sub_saharan_africa</td>\n",
       "      <td>-12.50</td>\n",
       "      <td>18.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>americas</td>\n",
       "      <td>america_south</td>\n",
       "      <td>america</td>\n",
       "      <td>-34.00</td>\n",
       "      <td>-64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>40.25</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country four_regions       eight_regions               six_regions  \\\n",
       "0    Albania       europe         europe_east       europe_central_asia   \n",
       "1    Algeria       africa        africa_north  middle_east_north_africa   \n",
       "2     Angola       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "3  Argentina     americas       america_south                   america   \n",
       "4    Armenia       europe         europe_east       europe_central_asia   \n",
       "\n",
       "   Latitude  Longitude  1967  1968  1969  1970  ...   2009   2010   2011  \\\n",
       "0     41.00       20.0   NaN   NaN   NaN   NaN  ...    NaN    NaN    NaN   \n",
       "1     28.00        3.0   NaN   NaN   NaN   NaN  ...    NaN    NaN  0.039   \n",
       "2    -12.50       18.5   NaN   NaN   NaN   NaN  ...    NaN    NaN    NaN   \n",
       "3    -34.00      -64.0   NaN   NaN   NaN   NaN  ...  0.057  0.053  0.024   \n",
       "4     40.25       45.0   NaN   NaN   NaN   NaN  ...  0.192  0.205  0.188   \n",
       "\n",
       "    2012   2013   2014   2015   2016   2017   2018  \n",
       "0  0.077    NaN  0.140  0.103  0.112  0.104    NaN  \n",
       "1    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "2    NaN    NaN    NaN    NaN    NaN    NaN  0.698  \n",
       "3  0.027  0.023  0.026    NaN  0.024  0.024  0.030  \n",
       "4  0.171  0.162  0.164  0.135  0.141  0.123  0.130  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty320.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>Albania</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.388</td>\n",
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       "      <td>Algeria</td>\n",
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       "      <td>28.00</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>africa_sub_saharan</td>\n",
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       "      <td>-12.50</td>\n",
       "      <td>18.5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>americas</td>\n",
       "      <td>america_south</td>\n",
       "      <td>america</td>\n",
       "      <td>-34.00</td>\n",
       "      <td>-64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>europe</td>\n",
       "      <td>europe_east</td>\n",
       "      <td>europe_central_asia</td>\n",
       "      <td>40.25</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.504</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country four_regions       eight_regions               six_regions  \\\n",
       "0    Albania       europe         europe_east       europe_central_asia   \n",
       "1    Algeria       africa        africa_north  middle_east_north_africa   \n",
       "2     Angola       africa  africa_sub_saharan        sub_saharan_africa   \n",
       "3  Argentina     americas       america_south                   america   \n",
       "4    Armenia       europe         europe_east       europe_central_asia   \n",
       "\n",
       "   Latitude  Longitude  1967  1968  1969  1970  ...   2009   2010   2011  \\\n",
       "0     41.00       20.0   NaN   NaN   NaN   NaN  ...    NaN    NaN    NaN   \n",
       "1     28.00        3.0   NaN   NaN   NaN   NaN  ...    NaN    NaN  0.292   \n",
       "2    -12.50       18.5   NaN   NaN   NaN   NaN  ...    NaN    NaN    NaN   \n",
       "3    -34.00      -64.0   NaN   NaN   NaN   NaN  ...  0.133  0.127  0.088   \n",
       "4     40.25       45.0   NaN   NaN   NaN   NaN  ...  0.621  0.634  0.608   \n",
       "\n",
       "    2012   2013   2014   2015   2016   2017   2018  \n",
       "0  0.391    NaN  0.403  0.383  0.388  0.380    NaN  \n",
       "1    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "2    NaN    NaN    NaN    NaN    NaN    NaN  0.872  \n",
       "3  0.084  0.082  0.089    NaN  0.085  0.077  0.096  \n",
       "4  0.565  0.546  0.523  0.483  0.435  0.500  0.504  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty550.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "poverty190.groupby('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 163 entries, 0 to 162\n",
      "Data columns (total 58 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   country        163 non-null    object \n",
      " 1   four_regions   163 non-null    object \n",
      " 2   eight_regions  163 non-null    object \n",
      " 3   six_regions    163 non-null    object \n",
      " 4   Latitude       163 non-null    float64\n",
      " 5   Longitude      163 non-null    float64\n",
      " 6   1967           1 non-null      float64\n",
      " 7   1968           0 non-null      float64\n",
      " 8   1969           1 non-null      float64\n",
      " 9   1970           0 non-null      float64\n",
      " 10  1971           1 non-null      float64\n",
      " 11  1972           0 non-null      float64\n",
      " 12  1973           0 non-null      float64\n",
      " 13  1974           2 non-null      float64\n",
      " 14  1975           2 non-null      float64\n",
      " 15  1976           0 non-null      float64\n",
      " 16  1977           1 non-null      float64\n",
      " 17  1978           1 non-null      float64\n",
      " 18  1979           5 non-null      float64\n",
      " 19  1980           3 non-null      float64\n",
      " 20  1981           7 non-null      float64\n",
      " 21  1982           2 non-null      float64\n",
      " 22  1983           4 non-null      float64\n",
      " 23  1984           5 non-null      float64\n",
      " 24  1985           12 non-null     float64\n",
      " 25  1986           15 non-null     float64\n",
      " 26  1987           23 non-null     float64\n",
      " 27  1988           10 non-null     float64\n",
      " 28  1989           18 non-null     float64\n",
      " 29  1990           17 non-null     float64\n",
      " 30  1991           21 non-null     float64\n",
      " 31  1992           36 non-null     float64\n",
      " 32  1993           26 non-null     float64\n",
      " 33  1994           28 non-null     float64\n",
      " 34  1995           39 non-null     float64\n",
      " 35  1996           32 non-null     float64\n",
      " 36  1997           28 non-null     float64\n",
      " 37  1998           39 non-null     float64\n",
      " 38  1999           33 non-null     float64\n",
      " 39  2000           48 non-null     float64\n",
      " 40  2001           35 non-null     float64\n",
      " 41  2002           50 non-null     float64\n",
      " 42  2003           55 non-null     float64\n",
      " 43  2004           72 non-null     float64\n",
      " 44  2005           74 non-null     float64\n",
      " 45  2006           72 non-null     float64\n",
      " 46  2007           71 non-null     float64\n",
      " 47  2008           72 non-null     float64\n",
      " 48  2009           75 non-null     float64\n",
      " 49  2010           81 non-null     float64\n",
      " 50  2011           74 non-null     float64\n",
      " 51  2012           77 non-null     float64\n",
      " 52  2013           74 non-null     float64\n",
      " 53  2014           78 non-null     float64\n",
      " 54  2015           78 non-null     float64\n",
      " 55  2016           74 non-null     float64\n",
      " 56  2017           64 non-null     float64\n",
      " 57  2018           28 non-null     float64\n",
      "dtypes: float64(54), object(4)\n",
      "memory usage: 75.1+ KB\n"
     ]
    }
   ],
   "source": [
    "poverty190.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limited data in years 1967-2009. Focusing attention on 2010 thru 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Used this to clear columns 1967 thru 1999, & subsequently latitude & longitude\n",
    "poverty190.drop(poverty190.iloc[:,6:39], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "poverty320.drop(poverty320.iloc[:,6:39], axis=1, inplace=True)\n",
    "poverty550.drop(poverty550.iloc[:,6:39], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      country\n",
      "1     Algeria\n",
      "38   Djibouti\n",
      "41      Egypt\n",
      "97    Morocco\n",
      "144   Tunisia\n",
      "                      country\n",
      "2                      Angola\n",
      "12                      Benin\n",
      "16                   Botswana\n",
      "19               Burkina Faso\n",
      "20                    Burundi\n",
      "21                   Cameroon\n",
      "23                 Cape Verde\n",
      "24   Central African Republic\n",
      "25                       Chad\n",
      "29                    Comoros\n",
      "30           Congo, Dem. Rep.\n",
      "31                Congo, Rep.\n",
      "33              Cote d'Ivoire\n",
      "44                   Ethiopia\n",
      "48                      Gabon\n",
      "49                     Gambia\n",
      "52                      Ghana\n",
      "55                     Guinea\n",
      "56              Guinea-Bissau\n",
      "73                      Kenya\n",
      "80                    Lesotho\n",
      "81                    Liberia\n",
      "84                 Madagascar\n",
      "85                     Malawi\n",
      "88                       Mali\n",
      "90                 Mauritania\n",
      "91                  Mauritius\n",
      "98                 Mozambique\n",
      "100                   Namibia\n",
      "104                     Niger\n",
      "105                   Nigeria\n",
      "117                    Rwanda\n",
      "120     Sao Tome and Principe\n",
      "121                   Senegal\n",
      "123                Seychelles\n",
      "124              Sierra Leone\n",
      "128              South Africa\n",
      "131                     Sudan\n",
      "133                 Swaziland\n",
      "138                  Tanzania\n",
      "141                      Togo\n",
      "148                    Uganda\n",
      "160                    Zambia\n",
      "161                  Zimbabwe\n",
      "162               South Sudan\n",
      "                 country\n",
      "11                Belize\n",
      "22                Canada\n",
      "32            Costa Rica\n",
      "39    Dominican Republic\n",
      "42           El Salvador\n",
      "54             Guatemala\n",
      "58                 Haiti\n",
      "59              Honduras\n",
      "69               Jamaica\n",
      "92                Mexico\n",
      "103            Nicaragua\n",
      "108               Panama\n",
      "118            St. Lucia\n",
      "143  Trinidad and Tobago\n",
      "152        United States\n",
      "       country\n",
      "3    Argentina\n",
      "14     Bolivia\n",
      "17      Brazil\n",
      "26       Chile\n",
      "28    Colombia\n",
      "40     Ecuador\n",
      "57      Guyana\n",
      "110   Paraguay\n",
      "111       Peru\n",
      "132   Suriname\n",
      "153    Uruguay\n",
      "156  Venezuela\n",
      "                  country\n",
      "8              Bangladesh\n",
      "13                 Bhutan\n",
      "62                  India\n",
      "64                   Iran\n",
      "65                   Iraq\n",
      "67                 Israel\n",
      "71                 Jordan\n",
      "72             Kazakhstan\n",
      "76        Kyrgyz Republic\n",
      "79                Lebanon\n",
      "87               Maldives\n",
      "101                 Nepal\n",
      "107              Pakistan\n",
      "130             Sri Lanka\n",
      "136                 Syria\n",
      "137            Tajikistan\n",
      "146          Turkmenistan\n",
      "150  United Arab Emirates\n",
      "154            Uzbekistan\n",
      "157             Palestine\n",
      "159                 Yemen\n",
      "                   country\n",
      "5                Australia\n",
      "27                   China\n",
      "45                    Fiji\n",
      "63               Indonesia\n",
      "70                   Japan\n",
      "74                Kiribati\n",
      "75             South Korea\n",
      "77                     Lao\n",
      "86                Malaysia\n",
      "93   Micronesia, Fed. Sts.\n",
      "95                Mongolia\n",
      "99                 Myanmar\n",
      "109       Papua New Guinea\n",
      "112            Philippines\n",
      "119                  Samoa\n",
      "127        Solomon Islands\n",
      "139               Thailand\n",
      "140            Timor-Leste\n",
      "142                  Tonga\n",
      "147                 Tuvalu\n",
      "155                Vanuatu\n",
      "158                Vietnam\n",
      "                    country\n",
      "0                   Albania\n",
      "4                   Armenia\n",
      "7                Azerbaijan\n",
      "9                   Belarus\n",
      "15   Bosnia and Herzegovina\n",
      "18                 Bulgaria\n",
      "34                  Croatia\n",
      "36           Czech Republic\n",
      "43                  Estonia\n",
      "46                  Finland\n",
      "50                  Georgia\n",
      "60                  Hungary\n",
      "78                   Latvia\n",
      "82                Lithuania\n",
      "94                  Moldova\n",
      "96               Montenegro\n",
      "115                 Romania\n",
      "116                  Russia\n",
      "122                  Serbia\n",
      "125         Slovak Republic\n",
      "126                Slovenia\n",
      "145                  Turkey\n",
      "149                 Ukraine\n",
      "            country\n",
      "6           Austria\n",
      "10          Belgium\n",
      "35           Cyprus\n",
      "37          Denmark\n",
      "47           France\n",
      "51          Germany\n",
      "53           Greece\n",
      "61          Iceland\n",
      "66          Ireland\n",
      "68            Italy\n",
      "83       Luxembourg\n",
      "89            Malta\n",
      "102     Netherlands\n",
      "106          Norway\n",
      "113          Poland\n",
      "114        Portugal\n",
      "129           Spain\n",
      "134          Sweden\n",
      "135     Switzerland\n",
      "151  United Kingdom\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "Empty DataFrame\n",
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many values do I have for each region?\n",
    "poverty190.groupby('eight_regions')[['country']].apply(lambda x: print(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eight_regions\n",
       "africa_north           5\n",
       "africa_sub_saharan    45\n",
       "america_north         15\n",
       "america_south         12\n",
       "asia_west             21\n",
       "east_asia_pacific     22\n",
       "europe_east           23\n",
       "europe_west           20\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many countries do I have for each region\n",
    "poverty190.groupby('eight_regions')['country'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 163 entries, 0 to 162\n",
      "Data columns (total 25 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   country        163 non-null    object \n",
      " 1   four_regions   163 non-null    object \n",
      " 2   eight_regions  163 non-null    object \n",
      " 3   six_regions    163 non-null    object \n",
      " 4   Latitude       163 non-null    float64\n",
      " 5   Longitude      163 non-null    float64\n",
      " 6   2000           48 non-null     float64\n",
      " 7   2001           35 non-null     float64\n",
      " 8   2002           50 non-null     float64\n",
      " 9   2003           55 non-null     float64\n",
      " 10  2004           72 non-null     float64\n",
      " 11  2005           74 non-null     float64\n",
      " 12  2006           72 non-null     float64\n",
      " 13  2007           71 non-null     float64\n",
      " 14  2008           72 non-null     float64\n",
      " 15  2009           75 non-null     float64\n",
      " 16  2010           81 non-null     float64\n",
      " 17  2011           74 non-null     float64\n",
      " 18  2012           77 non-null     float64\n",
      " 19  2013           74 non-null     float64\n",
      " 20  2014           78 non-null     float64\n",
      " 21  2015           78 non-null     float64\n",
      " 22  2016           74 non-null     float64\n",
      " 23  2017           64 non-null     float64\n",
      " 24  2018           28 non-null     float64\n",
      "dtypes: float64(21), object(4)\n",
      "memory usage: 33.1+ KB\n"
     ]
    }
   ],
   "source": [
    "poverty190.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove years 2000-2009"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iloc includes bottom number but not TOP number, indicies 4 through 38, 38 is not included\n",
    "poverty190.drop(poverty190.iloc[:,6:16], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 163 entries, 0 to 162\n",
      "Data columns (total 15 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   country        163 non-null    object \n",
      " 1   four_regions   163 non-null    object \n",
      " 2   eight_regions  163 non-null    object \n",
      " 3   six_regions    163 non-null    object \n",
      " 4   Latitude       163 non-null    float64\n",
      " 5   Longitude      163 non-null    float64\n",
      " 6   2010           81 non-null     float64\n",
      " 7   2011           74 non-null     float64\n",
      " 8   2012           77 non-null     float64\n",
      " 9   2013           74 non-null     float64\n",
      " 10  2014           78 non-null     float64\n",
      " 11  2015           78 non-null     float64\n",
      " 12  2016           74 non-null     float64\n",
      " 13  2017           64 non-null     float64\n",
      " 14  2018           28 non-null     float64\n",
      "dtypes: float64(11), object(4)\n",
      "memory usage: 20.4+ KB\n"
     ]
    }
   ],
   "source": [
    "poverty190.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "poverty320.drop(poverty320.iloc[:,6:16], axis=1, inplace=True)\n",
    "poverty550.drop(poverty550.iloc[:,6:16], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 163 entries, 0 to 162\n",
      "Data columns (total 15 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   country        163 non-null    object \n",
      " 1   four_regions   163 non-null    object \n",
      " 2   eight_regions  163 non-null    object \n",
      " 3   six_regions    163 non-null    object \n",
      " 4   Latitude       163 non-null    float64\n",
      " 5   Longitude      163 non-null    float64\n",
      " 6   2010           81 non-null     float64\n",
      " 7   2011           74 non-null     float64\n",
      " 8   2012           77 non-null     float64\n",
      " 9   2013           74 non-null     float64\n",
      " 10  2014           78 non-null     float64\n",
      " 11  2015           78 non-null     float64\n",
      " 12  2016           74 non-null     float64\n",
      " 13  2017           64 non-null     float64\n",
      " 14  2018           28 non-null     float64\n",
      "dtypes: float64(11), object(4)\n",
      "memory usage: 20.4+ KB\n"
     ]
    }
   ],
   "source": [
    "poverty320.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 163 entries, 0 to 162\n",
      "Data columns (total 15 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   country        163 non-null    object \n",
      " 1   four_regions   163 non-null    object \n",
      " 2   eight_regions  163 non-null    object \n",
      " 3   six_regions    163 non-null    object \n",
      " 4   Latitude       163 non-null    float64\n",
      " 5   Longitude      163 non-null    float64\n",
      " 6   2010           81 non-null     float64\n",
      " 7   2011           74 non-null     float64\n",
      " 8   2012           77 non-null     float64\n",
      " 9   2013           74 non-null     float64\n",
      " 10  2014           78 non-null     float64\n",
      " 11  2015           78 non-null     float64\n",
      " 12  2016           74 non-null     float64\n",
      " 13  2017           64 non-null     float64\n",
      " 14  2018           28 non-null     float64\n",
      "dtypes: float64(11), object(4)\n",
      "memory usage: 20.4+ KB\n"
     ]
    }
   ],
   "source": [
    "poverty550.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the average percentage for each region for 2010-2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eight_regions</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>africa_north</th>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>11.850000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <td>50.155556</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>43.233333</td>\n",
       "      <td>29.855556</td>\n",
       "      <td>29.540000</td>\n",
       "      <td>41.683333</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>43.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_north</th>\n",
       "      <td>4.350000</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>4.085714</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>4.175000</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>3.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_south</th>\n",
       "      <td>4.433333</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>2.922222</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>3.137500</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>2.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asia_west</th>\n",
       "      <td>5.950000</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <td>4.930000</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.028571</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>1.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_east</th>\n",
       "      <td>1.544444</td>\n",
       "      <td>1.373684</td>\n",
       "      <td>1.619048</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>1.552381</td>\n",
       "      <td>1.361905</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>0.957143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_west</th>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>0.347368</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.331579</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.352632</td>\n",
       "      <td>0.331250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         2010       2011       2012       2013       2014  \\\n",
       "eight_regions                                                               \n",
       "africa_north         1.850000   0.500000   9.800000  11.850000        NaN   \n",
       "africa_sub_saharan  50.155556  43.744444  39.650000  43.233333  29.855556   \n",
       "america_north        4.350000   5.820000   8.585714   4.085714   5.200000   \n",
       "america_south        4.433333   3.866667   3.950000   2.922222   2.875000   \n",
       "asia_west            5.950000   5.183333   1.666667   2.375000   3.466667   \n",
       "east_asia_pacific    4.930000   4.380000   6.850000   6.111111   6.200000   \n",
       "europe_east          1.544444   1.373684   1.619048   1.575000   1.552381   \n",
       "europe_west          0.295000   0.345000   0.347368   0.375000   0.331579   \n",
       "\n",
       "                         2015       2016       2017       2018  \n",
       "eight_regions                                                   \n",
       "africa_north         0.750000        NaN  10.150000        NaN  \n",
       "africa_sub_saharan  29.540000  41.683333  24.666667  43.850000  \n",
       "america_north        4.680000   4.175000   4.660000   3.866667  \n",
       "america_south        2.925000   3.137500   2.533333   2.700000  \n",
       "asia_west            2.300000   2.312500   0.825000   0.900000  \n",
       "east_asia_pacific    3.028571   1.920000   2.566667   1.750000  \n",
       "europe_east          1.361905   1.255000   1.157895   0.957143  \n",
       "europe_west          0.390000   0.352632   0.331250        NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_2010up = poverty190.groupby('eight_regions')[['2010','2011','2012','2013','2014','2015','2016','2017','2018']].apply(lambda x: x.mean() * 100)\n",
    "pov190_2010up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Africa_North has missing data (NaN) in 2014,16,18. Replace with mean of all other values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use the indexing method to specifiy the row & columns to operate on, using the same data to create the\n",
    "## mean values to fillna!!! \n",
    "pov190_2010up.loc['africa_north',:].fillna(pov190_2010up.loc['africa_north',:].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same as above cell. See 3 cells up to see the NaNs that need filling\n",
    "pov190_2010up.loc['europe_west',:].fillna(pov190_2010up.loc['europe_west',:].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eight_regions</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>africa_north</th>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>11.850000</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>5.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <td>50.155556</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>43.233333</td>\n",
       "      <td>29.855556</td>\n",
       "      <td>29.540000</td>\n",
       "      <td>41.683333</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>43.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_north</th>\n",
       "      <td>4.350000</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>4.085714</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>4.175000</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>3.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>america_south</th>\n",
       "      <td>4.433333</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>2.922222</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>3.137500</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>2.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asia_west</th>\n",
       "      <td>5.950000</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <td>4.930000</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.028571</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>1.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_east</th>\n",
       "      <td>1.544444</td>\n",
       "      <td>1.373684</td>\n",
       "      <td>1.619048</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>1.552381</td>\n",
       "      <td>1.361905</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>0.957143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>europe_west</th>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>0.347368</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.331579</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.352632</td>\n",
       "      <td>0.331250</td>\n",
       "      <td>0.345979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         2010       2011       2012       2013       2014  \\\n",
       "eight_regions                                                               \n",
       "africa_north         1.850000   0.500000   9.800000  11.850000   5.816667   \n",
       "africa_sub_saharan  50.155556  43.744444  39.650000  43.233333  29.855556   \n",
       "america_north        4.350000   5.820000   8.585714   4.085714   5.200000   \n",
       "america_south        4.433333   3.866667   3.950000   2.922222   2.875000   \n",
       "asia_west            5.950000   5.183333   1.666667   2.375000   3.466667   \n",
       "east_asia_pacific    4.930000   4.380000   6.850000   6.111111   6.200000   \n",
       "europe_east          1.544444   1.373684   1.619048   1.575000   1.552381   \n",
       "europe_west          0.295000   0.345000   0.347368   0.375000   0.331579   \n",
       "\n",
       "                         2015       2016       2017       2018  \n",
       "eight_regions                                                   \n",
       "africa_north         0.750000   5.816667  10.150000   5.816667  \n",
       "africa_sub_saharan  29.540000  41.683333  24.666667  43.850000  \n",
       "america_north        4.680000   4.175000   4.660000   3.866667  \n",
       "america_south        2.925000   3.137500   2.533333   2.700000  \n",
       "asia_west            2.300000   2.312500   0.825000   0.900000  \n",
       "east_asia_pacific    3.028571   1.920000   2.566667   1.750000  \n",
       "europe_east          1.361905   1.255000   1.157895   0.957143  \n",
       "europe_west          0.390000   0.352632   0.331250   0.345979  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_2010up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>eight_regions</th>\n",
       "      <th>africa_north</th>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <th>america_north</th>\n",
       "      <th>america_south</th>\n",
       "      <th>asia_west</th>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <th>europe_east</th>\n",
       "      <th>europe_west</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.850000</td>\n",
       "      <td>50.155556</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>4.433333</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>1.544444</td>\n",
       "      <td>0.295000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.373684</td>\n",
       "      <td>0.345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>9.800000</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>1.619048</td>\n",
       "      <td>0.347368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>11.850000</td>\n",
       "      <td>43.233333</td>\n",
       "      <td>4.085714</td>\n",
       "      <td>2.922222</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>5.816667</td>\n",
       "      <td>29.855556</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.552381</td>\n",
       "      <td>0.331579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>29.540000</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>3.028571</td>\n",
       "      <td>1.361905</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>5.816667</td>\n",
       "      <td>41.683333</td>\n",
       "      <td>4.175000</td>\n",
       "      <td>3.137500</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>0.352632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>10.150000</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>0.331250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>5.816667</td>\n",
       "      <td>43.850000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.345979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "eight_regions  africa_north  africa_sub_saharan  america_north  america_south  \\\n",
       "2010               1.850000           50.155556       4.350000       4.433333   \n",
       "2011               0.500000           43.744444       5.820000       3.866667   \n",
       "2012               9.800000           39.650000       8.585714       3.950000   \n",
       "2013              11.850000           43.233333       4.085714       2.922222   \n",
       "2014               5.816667           29.855556       5.200000       2.875000   \n",
       "2015               0.750000           29.540000       4.680000       2.925000   \n",
       "2016               5.816667           41.683333       4.175000       3.137500   \n",
       "2017              10.150000           24.666667       4.660000       2.533333   \n",
       "2018               5.816667           43.850000       3.866667       2.700000   \n",
       "\n",
       "eight_regions  asia_west  east_asia_pacific  europe_east  europe_west  \n",
       "2010            5.950000           4.930000     1.544444     0.295000  \n",
       "2011            5.183333           4.380000     1.373684     0.345000  \n",
       "2012            1.666667           6.850000     1.619048     0.347368  \n",
       "2013            2.375000           6.111111     1.575000     0.375000  \n",
       "2014            3.466667           6.200000     1.552381     0.331579  \n",
       "2015            2.300000           3.028571     1.361905     0.390000  \n",
       "2016            2.312500           1.920000     1.255000     0.352632  \n",
       "2017            0.825000           2.566667     1.157895     0.331250  \n",
       "2018            0.900000           1.750000     0.957143     0.345979  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_trans = pov190_2010up.transpose()\n",
    "pov190_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>eight_regions</th>\n",
       "      <th>africa_north</th>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <th>america_north</th>\n",
       "      <th>america_south</th>\n",
       "      <th>asia_west</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.850000</td>\n",
       "      <td>50.155556</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>4.433333</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>1.544444</td>\n",
       "      <td>0.295000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.373684</td>\n",
       "      <td>0.345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>9.800000</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>1.619048</td>\n",
       "      <td>0.347368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>11.850000</td>\n",
       "      <td>43.233333</td>\n",
       "      <td>4.085714</td>\n",
       "      <td>2.922222</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>5.816667</td>\n",
       "      <td>29.855556</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.552381</td>\n",
       "      <td>0.331579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>29.540000</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>3.028571</td>\n",
       "      <td>1.361905</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>5.816667</td>\n",
       "      <td>41.683333</td>\n",
       "      <td>4.175000</td>\n",
       "      <td>3.137500</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>0.352632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>10.150000</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>0.331250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>5.816667</td>\n",
       "      <td>43.850000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.345979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "eight_regions  africa_north  africa_sub_saharan  america_north  america_south  \\\n",
       "2010               1.850000           50.155556       4.350000       4.433333   \n",
       "2011               0.500000           43.744444       5.820000       3.866667   \n",
       "2012               9.800000           39.650000       8.585714       3.950000   \n",
       "2013              11.850000           43.233333       4.085714       2.922222   \n",
       "2014               5.816667           29.855556       5.200000       2.875000   \n",
       "2015               0.750000           29.540000       4.680000       2.925000   \n",
       "2016               5.816667           41.683333       4.175000       3.137500   \n",
       "2017              10.150000           24.666667       4.660000       2.533333   \n",
       "2018               5.816667           43.850000       3.866667       2.700000   \n",
       "\n",
       "eight_regions  asia_west  east_asia_pacific  europe_east  europe_west  \n",
       "2010            5.950000           4.930000     1.544444     0.295000  \n",
       "2011            5.183333           4.380000     1.373684     0.345000  \n",
       "2012            1.666667           6.850000     1.619048     0.347368  \n",
       "2013            2.375000           6.111111     1.575000     0.375000  \n",
       "2014            3.466667           6.200000     1.552381     0.331579  \n",
       "2015            2.300000           3.028571     1.361905     0.390000  \n",
       "2016            2.312500           1.920000     1.255000     0.352632  \n",
       "2017            0.825000           2.566667     1.157895     0.331250  \n",
       "2018            0.900000           1.750000     0.957143     0.345979  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_trans.rename(columns={'eight_regions':'year'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>eight_regions</th>\n",
       "      <th>africa_north</th>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <th>america_north</th>\n",
       "      <th>america_south</th>\n",
       "      <th>asia_west</th>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <th>europe_east</th>\n",
       "      <th>europe_west</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.850000</td>\n",
       "      <td>50.155556</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>4.433333</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>1.544444</td>\n",
       "      <td>0.295000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.373684</td>\n",
       "      <td>0.345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>9.800000</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>1.619048</td>\n",
       "      <td>0.347368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>11.850000</td>\n",
       "      <td>43.233333</td>\n",
       "      <td>4.085714</td>\n",
       "      <td>2.922222</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>5.816667</td>\n",
       "      <td>29.855556</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.552381</td>\n",
       "      <td>0.331579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>29.540000</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>3.028571</td>\n",
       "      <td>1.361905</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>5.816667</td>\n",
       "      <td>41.683333</td>\n",
       "      <td>4.175000</td>\n",
       "      <td>3.137500</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>0.352632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>10.150000</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>0.331250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>5.816667</td>\n",
       "      <td>43.850000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.345979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "eight_regions  africa_north  africa_sub_saharan  america_north  america_south  \\\n",
       "2010               1.850000           50.155556       4.350000       4.433333   \n",
       "2011               0.500000           43.744444       5.820000       3.866667   \n",
       "2012               9.800000           39.650000       8.585714       3.950000   \n",
       "2013              11.850000           43.233333       4.085714       2.922222   \n",
       "2014               5.816667           29.855556       5.200000       2.875000   \n",
       "2015               0.750000           29.540000       4.680000       2.925000   \n",
       "2016               5.816667           41.683333       4.175000       3.137500   \n",
       "2017              10.150000           24.666667       4.660000       2.533333   \n",
       "2018               5.816667           43.850000       3.866667       2.700000   \n",
       "\n",
       "eight_regions  asia_west  east_asia_pacific  europe_east  europe_west  \n",
       "2010            5.950000           4.930000     1.544444     0.295000  \n",
       "2011            5.183333           4.380000     1.373684     0.345000  \n",
       "2012            1.666667           6.850000     1.619048     0.347368  \n",
       "2013            2.375000           6.111111     1.575000     0.375000  \n",
       "2014            3.466667           6.200000     1.552381     0.331579  \n",
       "2015            2.300000           3.028571     1.361905     0.390000  \n",
       "2016            2.312500           1.920000     1.255000     0.352632  \n",
       "2017            0.825000           2.566667     1.157895     0.331250  \n",
       "2018            0.900000           1.750000     0.957143     0.345979  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 9 entries, 2010 to 2018\n",
      "Data columns (total 8 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   africa_north        9 non-null      float64\n",
      " 1   africa_sub_saharan  9 non-null      float64\n",
      " 2   america_north       9 non-null      float64\n",
      " 3   america_south       9 non-null      float64\n",
      " 4   asia_west           9 non-null      float64\n",
      " 5   east_asia_pacific   9 non-null      float64\n",
      " 6   europe_east         9 non-null      float64\n",
      " 7   europe_west         9 non-null      float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 648.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "pov190_trans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>eight_regions</th>\n",
       "      <th>index</th>\n",
       "      <th>africa_north</th>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <th>america_north</th>\n",
       "      <th>america_south</th>\n",
       "      <th>asia_west</th>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <th>europe_east</th>\n",
       "      <th>europe_west</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>50.155556</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>4.433333</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>1.544444</td>\n",
       "      <td>0.295000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.373684</td>\n",
       "      <td>0.345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>1.619048</td>\n",
       "      <td>0.347368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>11.850000</td>\n",
       "      <td>43.233333</td>\n",
       "      <td>4.085714</td>\n",
       "      <td>2.922222</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>29.855556</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.552381</td>\n",
       "      <td>0.331579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>29.540000</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>3.028571</td>\n",
       "      <td>1.361905</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>41.683333</td>\n",
       "      <td>4.175000</td>\n",
       "      <td>3.137500</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>0.352632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>0.331250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>43.850000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.345979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "eight_regions index  africa_north  africa_sub_saharan  america_north  \\\n",
       "0              2010      1.850000           50.155556       4.350000   \n",
       "1              2011      0.500000           43.744444       5.820000   \n",
       "2              2012      9.800000           39.650000       8.585714   \n",
       "3              2013     11.850000           43.233333       4.085714   \n",
       "4              2014      5.816667           29.855556       5.200000   \n",
       "5              2015      0.750000           29.540000       4.680000   \n",
       "6              2016      5.816667           41.683333       4.175000   \n",
       "7              2017     10.150000           24.666667       4.660000   \n",
       "8              2018      5.816667           43.850000       3.866667   \n",
       "\n",
       "eight_regions  america_south  asia_west  east_asia_pacific  europe_east  \\\n",
       "0                   4.433333   5.950000           4.930000     1.544444   \n",
       "1                   3.866667   5.183333           4.380000     1.373684   \n",
       "2                   3.950000   1.666667           6.850000     1.619048   \n",
       "3                   2.922222   2.375000           6.111111     1.575000   \n",
       "4                   2.875000   3.466667           6.200000     1.552381   \n",
       "5                   2.925000   2.300000           3.028571     1.361905   \n",
       "6                   3.137500   2.312500           1.920000     1.255000   \n",
       "7                   2.533333   0.825000           2.566667     1.157895   \n",
       "8                   2.700000   0.900000           1.750000     0.957143   \n",
       "\n",
       "eight_regions  europe_west  \n",
       "0                 0.295000  \n",
       "1                 0.345000  \n",
       "2                 0.347368  \n",
       "3                 0.375000  \n",
       "4                 0.331579  \n",
       "5                 0.390000  \n",
       "6                 0.352632  \n",
       "7                 0.331250  \n",
       "8                 0.345979  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190_trans.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "pov190 = pov190_trans.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>eight_regions</th>\n",
       "      <th>index</th>\n",
       "      <th>africa_north</th>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <th>america_north</th>\n",
       "      <th>america_south</th>\n",
       "      <th>asia_west</th>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <th>europe_east</th>\n",
       "      <th>europe_west</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>50.155556</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>4.433333</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>1.544444</td>\n",
       "      <td>0.295000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.373684</td>\n",
       "      <td>0.345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>1.619048</td>\n",
       "      <td>0.347368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>11.850000</td>\n",
       "      <td>43.233333</td>\n",
       "      <td>4.085714</td>\n",
       "      <td>2.922222</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>29.855556</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.552381</td>\n",
       "      <td>0.331579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>29.540000</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>3.028571</td>\n",
       "      <td>1.361905</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>41.683333</td>\n",
       "      <td>4.175000</td>\n",
       "      <td>3.137500</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>0.352632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>0.331250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>43.850000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.345979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "eight_regions index  africa_north  africa_sub_saharan  america_north  \\\n",
       "0              2010      1.850000           50.155556       4.350000   \n",
       "1              2011      0.500000           43.744444       5.820000   \n",
       "2              2012      9.800000           39.650000       8.585714   \n",
       "3              2013     11.850000           43.233333       4.085714   \n",
       "4              2014      5.816667           29.855556       5.200000   \n",
       "5              2015      0.750000           29.540000       4.680000   \n",
       "6              2016      5.816667           41.683333       4.175000   \n",
       "7              2017     10.150000           24.666667       4.660000   \n",
       "8              2018      5.816667           43.850000       3.866667   \n",
       "\n",
       "eight_regions  america_south  asia_west  east_asia_pacific  europe_east  \\\n",
       "0                   4.433333   5.950000           4.930000     1.544444   \n",
       "1                   3.866667   5.183333           4.380000     1.373684   \n",
       "2                   3.950000   1.666667           6.850000     1.619048   \n",
       "3                   2.922222   2.375000           6.111111     1.575000   \n",
       "4                   2.875000   3.466667           6.200000     1.552381   \n",
       "5                   2.925000   2.300000           3.028571     1.361905   \n",
       "6                   3.137500   2.312500           1.920000     1.255000   \n",
       "7                   2.533333   0.825000           2.566667     1.157895   \n",
       "8                   2.700000   0.900000           1.750000     0.957143   \n",
       "\n",
       "eight_regions  europe_west  \n",
       "0                 0.295000  \n",
       "1                 0.345000  \n",
       "2                 0.347368  \n",
       "3                 0.375000  \n",
       "4                 0.331579  \n",
       "5                 0.390000  \n",
       "6                 0.352632  \n",
       "7                 0.331250  \n",
       "8                 0.345979  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pov190.rename(columns={'index':'year','eight_regions':'index'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>eight_regions</th>\n",
       "      <th>year</th>\n",
       "      <th>africa_north</th>\n",
       "      <th>africa_sub_saharan</th>\n",
       "      <th>america_north</th>\n",
       "      <th>america_south</th>\n",
       "      <th>asia_west</th>\n",
       "      <th>east_asia_pacific</th>\n",
       "      <th>europe_east</th>\n",
       "      <th>europe_west</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>50.155556</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>4.433333</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>1.544444</td>\n",
       "      <td>0.295000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>43.744444</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.373684</td>\n",
       "      <td>0.345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>1.619048</td>\n",
       "      <td>0.347368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>11.850000</td>\n",
       "      <td>43.233333</td>\n",
       "      <td>4.085714</td>\n",
       "      <td>2.922222</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>29.855556</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.552381</td>\n",
       "      <td>0.331579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>29.540000</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>2.925000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>3.028571</td>\n",
       "      <td>1.361905</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>41.683333</td>\n",
       "      <td>4.175000</td>\n",
       "      <td>3.137500</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>0.352632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>0.331250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>43.850000</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.345979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "eight_regions  year  africa_north  africa_sub_saharan  america_north  \\\n",
       "0              2010      1.850000           50.155556       4.350000   \n",
       "1              2011      0.500000           43.744444       5.820000   \n",
       "2              2012      9.800000           39.650000       8.585714   \n",
       "3              2013     11.850000           43.233333       4.085714   \n",
       "4              2014      5.816667           29.855556       5.200000   \n",
       "5              2015      0.750000           29.540000       4.680000   \n",
       "6              2016      5.816667           41.683333       4.175000   \n",
       "7              2017     10.150000           24.666667       4.660000   \n",
       "8              2018      5.816667           43.850000       3.866667   \n",
       "\n",
       "eight_regions  america_south  asia_west  east_asia_pacific  europe_east  \\\n",
       "0                   4.433333   5.950000           4.930000     1.544444   \n",
       "1                   3.866667   5.183333           4.380000     1.373684   \n",
       "2                   3.950000   1.666667           6.850000     1.619048   \n",
       "3                   2.922222   2.375000           6.111111     1.575000   \n",
       "4                   2.875000   3.466667           6.200000     1.552381   \n",
       "5                   2.925000   2.300000           3.028571     1.361905   \n",
       "6                   3.137500   2.312500           1.920000     1.255000   \n",
       "7                   2.533333   0.825000           2.566667     1.157895   \n",
       "8                   2.700000   0.900000           1.750000     0.957143   \n",
       "\n",
       "eight_regions  europe_west  \n",
       "0                 0.295000  \n",
       "1                 0.345000  \n",
       "2                 0.347368  \n",
       "3                 0.375000  \n",
       "4                 0.331579  \n",
       "5                 0.390000  \n",
       "6                 0.352632  \n",
       "7                 0.331250  \n",
       "8                 0.345979  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov190"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations on Poverty data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Years')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "\n",
    "pov190.plot(x='year', y='africa_north', kind='line', figsize=(10,10), ax=ax)\n",
    "pov190.plot(x='year', y='africa_sub_saharan', kind='line', figsize=(16,10), ax=ax)\n",
    "pov190.plot(x='year', y='asia_west', kind='line', figsize=(16,10), ax=ax)\n",
    "pov190.plot(x='year', y='east_asia_pacific', kind='line', figsize=(16,10), ax=ax)\n",
    "\n",
    "plt.title(\"Extreme Poverty Trends <$1.90/day & less>\\n World Regions\")\n",
    "plt.ylabel(\"Percentage of region's population\")\n",
    "plt.xlabel(\"Years\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pov190' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-c30c1b1c7fd3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpov190\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mpov190\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pov190' is not defined"
     ]
    }
   ],
   "source": [
    "pov190.index.name = None\n",
    "pov190"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Years')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "\n",
    "pov190.plot(x='year', y='america_north', kind='line', figsize=(10,10), ax=ax)\n",
    "pov190.plot(x='year', y='america_south', kind='line', figsize=(16,10), ax=ax)\n",
    "pov190.plot(x='year', y='europe_east', kind='line', figsize=(16,10), ax=ax)\n",
    "pov190.plot(x='year', y='europe_west', kind='line', figsize=(16,10), ax=ax)\n",
    "\n",
    "plt.title(\"Extreme Poverty Trends <$1.90/day & less>\\n World Regions\")\n",
    "plt.ylabel(\"Percentage of region's population\")\n",
    "plt.xlabel(\"Years\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}