WHO_data_preprocessor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series Name</th>\n",
       "      <th>Series Code</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>2000 [YR2000]</th>\n",
       "      <th>2001 [YR2001]</th>\n",
       "      <th>2002 [YR2002]</th>\n",
       "      <th>2003 [YR2003]</th>\n",
       "      <th>2004 [YR2004]</th>\n",
       "      <th>2005 [YR2005]</th>\n",
       "      <th>...</th>\n",
       "      <th>2008 [YR2008]</th>\n",
       "      <th>2009 [YR2009]</th>\n",
       "      <th>2010 [YR2010]</th>\n",
       "      <th>2011 [YR2011]</th>\n",
       "      <th>2012 [YR2012]</th>\n",
       "      <th>2013 [YR2013]</th>\n",
       "      <th>2014 [YR2014]</th>\n",
       "      <th>2015 [YR2015]</th>\n",
       "      <th>2016 [YR2016]</th>\n",
       "      <th>2017 [YR2017]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>World</td>\n",
       "      <td>WLD</td>\n",
       "      <td>6.121683e+09</td>\n",
       "      <td>6.201340e+09</td>\n",
       "      <td>6.280530e+09</td>\n",
       "      <td>6.359899e+09</td>\n",
       "      <td>6.439825e+09</td>\n",
       "      <td>6.520299e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>6.766297e+09</td>\n",
       "      <td>6.849569e+09</td>\n",
       "      <td>6.932870e+09</td>\n",
       "      <td>7.014984e+09</td>\n",
       "      <td>7.099558e+09</td>\n",
       "      <td>7.185138e+09</td>\n",
       "      <td>7.271323e+09</td>\n",
       "      <td>7.357559e+09</td>\n",
       "      <td>7.444157e+09</td>\n",
       "      <td>7.530360e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>Data from database: World Development Indicators</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>Last Updated: 07/25/2018</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Series Name  Series Code  \\\n",
       "263                                 Population, total  SP.POP.TOTL   \n",
       "264                                               NaN          NaN   \n",
       "265                                               NaN          NaN   \n",
       "266                                               NaN          NaN   \n",
       "267  Data from database: World Development Indicators          NaN   \n",
       "268                          Last Updated: 07/25/2018          NaN   \n",
       "\n",
       "    Country Name Country Code  2000 [YR2000]  2001 [YR2001]  2002 [YR2002]  \\\n",
       "263        World          WLD   6.121683e+09   6.201340e+09   6.280530e+09   \n",
       "264          NaN          NaN            NaN            NaN            NaN   \n",
       "265          NaN          NaN            NaN            NaN            NaN   \n",
       "266          NaN          NaN            NaN            NaN            NaN   \n",
       "267          NaN          NaN            NaN            NaN            NaN   \n",
       "268          NaN          NaN            NaN            NaN            NaN   \n",
       "\n",
       "     2003 [YR2003]  2004 [YR2004]  2005 [YR2005]      ...        \\\n",
       "263   6.359899e+09   6.439825e+09   6.520299e+09      ...         \n",
       "264            NaN            NaN            NaN      ...         \n",
       "265            NaN            NaN            NaN      ...         \n",
       "266            NaN            NaN            NaN      ...         \n",
       "267            NaN            NaN            NaN      ...         \n",
       "268            NaN            NaN            NaN      ...         \n",
       "\n",
       "     2008 [YR2008]  2009 [YR2009]  2010 [YR2010]  2011 [YR2011]  \\\n",
       "263   6.766297e+09   6.849569e+09   6.932870e+09   7.014984e+09   \n",
       "264            NaN            NaN            NaN            NaN   \n",
       "265            NaN            NaN            NaN            NaN   \n",
       "266            NaN            NaN            NaN            NaN   \n",
       "267            NaN            NaN            NaN            NaN   \n",
       "268            NaN            NaN            NaN            NaN   \n",
       "\n",
       "     2012 [YR2012]  2013 [YR2013]  2014 [YR2014]  2015 [YR2015]  \\\n",
       "263   7.099558e+09   7.185138e+09   7.271323e+09   7.357559e+09   \n",
       "264            NaN            NaN            NaN            NaN   \n",
       "265            NaN            NaN            NaN            NaN   \n",
       "266            NaN            NaN            NaN            NaN   \n",
       "267            NaN            NaN            NaN            NaN   \n",
       "268            NaN            NaN            NaN            NaN   \n",
       "\n",
       "     2016 [YR2016]  2017 [YR2017]  \n",
       "263   7.444157e+09   7.530360e+09  \n",
       "264            NaN            NaN  \n",
       "265            NaN            NaN  \n",
       "266            NaN            NaN  \n",
       "267            NaN            NaN  \n",
       "268            NaN            NaN  \n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_all_ages_df = pd.read_csv('WHO_mortality_data\\ghe2016_deaths_country_allages.csv')\n",
    "country_conv = pd.read_csv('WHO_mortality_data\\iso3_ctry_codes.csv')\n",
    "populations = pd.read_csv('WHO_mortality_data\\worldbank_population_data.csv', na_values=['..'])\n",
    "country_conv = country_conv.drop('m49_code', axis=1)\n",
    "populations.tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this printout of the last 6 rows of population data, we see that the last 5 rows are just fluff. We can remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series Name</th>\n",
       "      <th>Series Code</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>2000 [YR2000]</th>\n",
       "      <th>2001 [YR2001]</th>\n",
       "      <th>2002 [YR2002]</th>\n",
       "      <th>2003 [YR2003]</th>\n",
       "      <th>2004 [YR2004]</th>\n",
       "      <th>2005 [YR2005]</th>\n",
       "      <th>...</th>\n",
       "      <th>2008 [YR2008]</th>\n",
       "      <th>2009 [YR2009]</th>\n",
       "      <th>2010 [YR2010]</th>\n",
       "      <th>2011 [YR2011]</th>\n",
       "      <th>2012 [YR2012]</th>\n",
       "      <th>2013 [YR2013]</th>\n",
       "      <th>2014 [YR2014]</th>\n",
       "      <th>2015 [YR2015]</th>\n",
       "      <th>2016 [YR2016]</th>\n",
       "      <th>2017 [YR2017]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>South Asia (IDA &amp; IBRD)</td>\n",
       "      <td>TSA</td>\n",
       "      <td>1.386626e+09</td>\n",
       "      <td>1.412104e+09</td>\n",
       "      <td>1.437568e+09</td>\n",
       "      <td>1.462907e+09</td>\n",
       "      <td>1.487975e+09</td>\n",
       "      <td>1.512671e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.584359e+09</td>\n",
       "      <td>1.607664e+09</td>\n",
       "      <td>1.630807e+09</td>\n",
       "      <td>1.653799e+09</td>\n",
       "      <td>1.676615e+09</td>\n",
       "      <td>1.699310e+09</td>\n",
       "      <td>1.721848e+09</td>\n",
       "      <td>1.744200e+09</td>\n",
       "      <td>1.766394e+09</td>\n",
       "      <td>1.788389e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>SSF</td>\n",
       "      <td>6.706496e+08</td>\n",
       "      <td>6.886160e+08</td>\n",
       "      <td>7.070998e+08</td>\n",
       "      <td>7.261406e+08</td>\n",
       "      <td>7.457881e+08</td>\n",
       "      <td>7.660805e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>8.309656e+08</td>\n",
       "      <td>8.539537e+08</td>\n",
       "      <td>8.776284e+08</td>\n",
       "      <td>9.019899e+08</td>\n",
       "      <td>9.270399e+08</td>\n",
       "      <td>9.527341e+08</td>\n",
       "      <td>9.790179e+08</td>\n",
       "      <td>1.005850e+09</td>\n",
       "      <td>1.033213e+09</td>\n",
       "      <td>1.061108e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Sub-Saharan Africa (excluding high income)</td>\n",
       "      <td>SSA</td>\n",
       "      <td>6.705685e+08</td>\n",
       "      <td>6.885348e+08</td>\n",
       "      <td>7.070161e+08</td>\n",
       "      <td>7.260578e+08</td>\n",
       "      <td>7.457056e+08</td>\n",
       "      <td>7.659976e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>8.308786e+08</td>\n",
       "      <td>8.538664e+08</td>\n",
       "      <td>8.775386e+08</td>\n",
       "      <td>9.019025e+08</td>\n",
       "      <td>9.269516e+08</td>\n",
       "      <td>9.526441e+08</td>\n",
       "      <td>9.789266e+08</td>\n",
       "      <td>1.005757e+09</td>\n",
       "      <td>1.033118e+09</td>\n",
       "      <td>1.061012e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Sub-Saharan Africa (IDA &amp; IBRD countries)</td>\n",
       "      <td>TSS</td>\n",
       "      <td>6.706496e+08</td>\n",
       "      <td>6.886160e+08</td>\n",
       "      <td>7.070998e+08</td>\n",
       "      <td>7.261406e+08</td>\n",
       "      <td>7.457881e+08</td>\n",
       "      <td>7.660805e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>8.309656e+08</td>\n",
       "      <td>8.539537e+08</td>\n",
       "      <td>8.776284e+08</td>\n",
       "      <td>9.019899e+08</td>\n",
       "      <td>9.270399e+08</td>\n",
       "      <td>9.527341e+08</td>\n",
       "      <td>9.790179e+08</td>\n",
       "      <td>1.005850e+09</td>\n",
       "      <td>1.033213e+09</td>\n",
       "      <td>1.061108e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>UMC</td>\n",
       "      <td>2.260806e+09</td>\n",
       "      <td>2.280191e+09</td>\n",
       "      <td>2.298542e+09</td>\n",
       "      <td>2.316548e+09</td>\n",
       "      <td>2.334314e+09</td>\n",
       "      <td>2.352105e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>2.404283e+09</td>\n",
       "      <td>2.422394e+09</td>\n",
       "      <td>2.440543e+09</td>\n",
       "      <td>2.459176e+09</td>\n",
       "      <td>2.478492e+09</td>\n",
       "      <td>2.498184e+09</td>\n",
       "      <td>2.517980e+09</td>\n",
       "      <td>2.537437e+09</td>\n",
       "      <td>2.556922e+09</td>\n",
       "      <td>2.576203e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>World</td>\n",
       "      <td>WLD</td>\n",
       "      <td>6.121683e+09</td>\n",
       "      <td>6.201340e+09</td>\n",
       "      <td>6.280530e+09</td>\n",
       "      <td>6.359899e+09</td>\n",
       "      <td>6.439825e+09</td>\n",
       "      <td>6.520299e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>6.766297e+09</td>\n",
       "      <td>6.849569e+09</td>\n",
       "      <td>6.932870e+09</td>\n",
       "      <td>7.014984e+09</td>\n",
       "      <td>7.099558e+09</td>\n",
       "      <td>7.185138e+09</td>\n",
       "      <td>7.271323e+09</td>\n",
       "      <td>7.357559e+09</td>\n",
       "      <td>7.444157e+09</td>\n",
       "      <td>7.530360e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Series Name  Series Code  \\\n",
       "258  Population, total  SP.POP.TOTL   \n",
       "259  Population, total  SP.POP.TOTL   \n",
       "260  Population, total  SP.POP.TOTL   \n",
       "261  Population, total  SP.POP.TOTL   \n",
       "262  Population, total  SP.POP.TOTL   \n",
       "263  Population, total  SP.POP.TOTL   \n",
       "\n",
       "                                   Country Name Country Code  2000 [YR2000]  \\\n",
       "258                     South Asia (IDA & IBRD)          TSA   1.386626e+09   \n",
       "259                          Sub-Saharan Africa          SSF   6.706496e+08   \n",
       "260  Sub-Saharan Africa (excluding high income)          SSA   6.705685e+08   \n",
       "261   Sub-Saharan Africa (IDA & IBRD countries)          TSS   6.706496e+08   \n",
       "262                         Upper middle income          UMC   2.260806e+09   \n",
       "263                                       World          WLD   6.121683e+09   \n",
       "\n",
       "     2001 [YR2001]  2002 [YR2002]  2003 [YR2003]  2004 [YR2004]  \\\n",
       "258   1.412104e+09   1.437568e+09   1.462907e+09   1.487975e+09   \n",
       "259   6.886160e+08   7.070998e+08   7.261406e+08   7.457881e+08   \n",
       "260   6.885348e+08   7.070161e+08   7.260578e+08   7.457056e+08   \n",
       "261   6.886160e+08   7.070998e+08   7.261406e+08   7.457881e+08   \n",
       "262   2.280191e+09   2.298542e+09   2.316548e+09   2.334314e+09   \n",
       "263   6.201340e+09   6.280530e+09   6.359899e+09   6.439825e+09   \n",
       "\n",
       "     2005 [YR2005]      ...        2008 [YR2008]  2009 [YR2009]  \\\n",
       "258   1.512671e+09      ...         1.584359e+09   1.607664e+09   \n",
       "259   7.660805e+08      ...         8.309656e+08   8.539537e+08   \n",
       "260   7.659976e+08      ...         8.308786e+08   8.538664e+08   \n",
       "261   7.660805e+08      ...         8.309656e+08   8.539537e+08   \n",
       "262   2.352105e+09      ...         2.404283e+09   2.422394e+09   \n",
       "263   6.520299e+09      ...         6.766297e+09   6.849569e+09   \n",
       "\n",
       "     2010 [YR2010]  2011 [YR2011]  2012 [YR2012]  2013 [YR2013]  \\\n",
       "258   1.630807e+09   1.653799e+09   1.676615e+09   1.699310e+09   \n",
       "259   8.776284e+08   9.019899e+08   9.270399e+08   9.527341e+08   \n",
       "260   8.775386e+08   9.019025e+08   9.269516e+08   9.526441e+08   \n",
       "261   8.776284e+08   9.019899e+08   9.270399e+08   9.527341e+08   \n",
       "262   2.440543e+09   2.459176e+09   2.478492e+09   2.498184e+09   \n",
       "263   6.932870e+09   7.014984e+09   7.099558e+09   7.185138e+09   \n",
       "\n",
       "     2014 [YR2014]  2015 [YR2015]  2016 [YR2016]  2017 [YR2017]  \n",
       "258   1.721848e+09   1.744200e+09   1.766394e+09   1.788389e+09  \n",
       "259   9.790179e+08   1.005850e+09   1.033213e+09   1.061108e+09  \n",
       "260   9.789266e+08   1.005757e+09   1.033118e+09   1.061012e+09  \n",
       "261   9.790179e+08   1.005850e+09   1.033213e+09   1.061108e+09  \n",
       "262   2.517980e+09   2.537437e+09   2.556922e+09   2.576203e+09  \n",
       "263   7.271323e+09   7.357559e+09   7.444157e+09   7.530360e+09  \n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations.drop(populations.tail(5).index, inplace=True)\n",
    "populations.tail(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "populations = populations.drop(['Series Code',  'Series Name'], axis=1)\n",
    "populations.rename(columns=lambda x: x.replace('20', 'pop20'), inplace=True)\n",
    "populations.rename(columns=lambda x: x.split()[0] if 'pop' in x else x, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Years starting with 'dths' represent the estimated number of deaths for that year. \n",
    "* Years starting with 'low' or 'upp\" represent the lower or uppe bound on the estimate for that year. (we can probably trim these)\n",
    "* iso3: This represents the country\n",
    "* cause2015: This is a numeric value that maps 1:1 to the 'causename' column\n",
    "\n",
    "Data for this visualization was from http://terrance.who.int/mediacentre/data/ghe/\n",
    "\n",
    "Population Data from Worldbook http://databank.worldbank.org/data/reports.aspx?source=2&series=SP.POP.TOTL#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216                                             Zimbabwe\n",
       "217                                           Arab World\n",
       "218                               Caribbean small states\n",
       "219                       Central Europe and the Baltics\n",
       "220                           Early-demographic dividend\n",
       "221                                  East Asia & Pacific\n",
       "222          East Asia & Pacific (excluding high income)\n",
       "223           East Asia & Pacific (IDA & IBRD countries)\n",
       "224                                            Euro area\n",
       "225                                Europe & Central Asia\n",
       "226        Europe & Central Asia (excluding high income)\n",
       "227         Europe & Central Asia (IDA & IBRD countries)\n",
       "228                                       European Union\n",
       "229             Fragile and conflict affected situations\n",
       "230               Heavily indebted poor countries (HIPC)\n",
       "231                                          High income\n",
       "232                                            IBRD only\n",
       "233                                     IDA & IBRD total\n",
       "234                                            IDA blend\n",
       "235                                             IDA only\n",
       "236                                            IDA total\n",
       "237                            Late-demographic dividend\n",
       "238                            Latin America & Caribbean\n",
       "239    Latin America & Caribbean (excluding high income)\n",
       "240    Latin America & the Caribbean (IDA & IBRD coun...\n",
       "241         Least developed countries: UN classification\n",
       "242                                  Low & middle income\n",
       "243                                           Low income\n",
       "244                                  Lower middle income\n",
       "245                           Middle East & North Africa\n",
       "246    Middle East & North Africa (excluding high inc...\n",
       "247    Middle East & North Africa (IDA & IBRD countries)\n",
       "248                                        Middle income\n",
       "249                                        North America\n",
       "250                                       Not classified\n",
       "251                                         OECD members\n",
       "252                                   Other small states\n",
       "253                          Pacific island small states\n",
       "254                            Post-demographic dividend\n",
       "255                             Pre-demographic dividend\n",
       "256                                         Small states\n",
       "257                                           South Asia\n",
       "258                              South Asia (IDA & IBRD)\n",
       "259                                   Sub-Saharan Africa\n",
       "260           Sub-Saharan Africa (excluding high income)\n",
       "261            Sub-Saharan Africa (IDA & IBRD countries)\n",
       "262                                  Upper middle income\n",
       "263                                                World\n",
       "Name: Country Name, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations['Country Name'].tail(48)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the printout above, we see that Zimbabwe is the last country, and all of the populations after that are aggregated. We want populations for just the countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "zimbabwe = populations.index[populations['Country Name'] == 'Zimbabwe'][0]\n",
    "populations = populations[:zimbabwe+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_values(df, name):\n",
    "    print(\"Missing values in \" + str(name))\n",
    "    print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in populations\n",
      "Country Name    0\n",
      "Country Code    0\n",
      "pop2000         0\n",
      "pop2001         0\n",
      "pop2002         0\n",
      "pop2003         0\n",
      "pop2004         0\n",
      "pop2005         0\n",
      "pop2006         0\n",
      "pop2007         0\n",
      "pop2008         0\n",
      "pop2009         0\n",
      "pop2010         0\n",
      "pop2011         0\n",
      "pop2012         1\n",
      "pop2013         1\n",
      "pop2014         1\n",
      "pop2015         1\n",
      "pop2016         1\n",
      "pop2017         1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing_values(populations, 'populations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>pop2000</th>\n",
       "      <th>pop2001</th>\n",
       "      <th>pop2002</th>\n",
       "      <th>pop2003</th>\n",
       "      <th>pop2004</th>\n",
       "      <th>pop2005</th>\n",
       "      <th>pop2006</th>\n",
       "      <th>pop2007</th>\n",
       "      <th>pop2008</th>\n",
       "      <th>pop2009</th>\n",
       "      <th>pop2010</th>\n",
       "      <th>pop2011</th>\n",
       "      <th>pop2012</th>\n",
       "      <th>pop2013</th>\n",
       "      <th>pop2014</th>\n",
       "      <th>pop2015</th>\n",
       "      <th>pop2016</th>\n",
       "      <th>pop2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>ERI</td>\n",
       "      <td>3392801.0</td>\n",
       "      <td>3497124.0</td>\n",
       "      <td>3614639.0</td>\n",
       "      <td>3738265.0</td>\n",
       "      <td>3858623.0</td>\n",
       "      <td>3969007.0</td>\n",
       "      <td>4066648.0</td>\n",
       "      <td>4153332.0</td>\n",
       "      <td>4232636.0</td>\n",
       "      <td>4310334.0</td>\n",
       "      <td>4390840.0</td>\n",
       "      <td>4474690.0</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Name Country Code    pop2000    pop2001    pop2002    pop2003  \\\n",
       "61      Eritrea          ERI  3392801.0  3497124.0  3614639.0  3738265.0   \n",
       "\n",
       "      pop2004    pop2005    pop2006    pop2007    pop2008    pop2009  \\\n",
       "61  3858623.0  3969007.0  4066648.0  4153332.0  4232636.0  4310334.0   \n",
       "\n",
       "      pop2010    pop2011  pop2012  pop2013  pop2014  pop2015  pop2016  pop2017  \n",
       "61  4390840.0  4474690.0      NaN      NaN      NaN      NaN      NaN      NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations[populations['pop2012'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, we see that population values are missing just for Eritrea, and from from 2012 onward. I can find and input these values. According to this [UN population tracker](http://data.un.org/Data.aspx?q=population&d=PopDiv&f=variableID%3a12), the median annual population values for Eritrea are as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "populations.loc[(populations['Country Code'] == 'ERI'), 'pop2012'] = 4560977\n",
    "populations.loc[(populations['Country Code'] == 'ERI'), 'pop2013'] = 4650998\n",
    "populations.loc[(populations['Country Code'] == 'ERI'), 'pop2014'] = 4746045\n",
    "populations.loc[(populations['Country Code'] == 'ERI'), 'pop2015'] = 4846976\n",
    "populations.loc[(populations['Country Code'] == 'ERI'), 'pop2016'] = 4954645\n",
    "populations.loc[(populations['Country Code'] == 'ERI'), 'pop2017'] = 5068831"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>pop2000</th>\n",
       "      <th>pop2001</th>\n",
       "      <th>pop2002</th>\n",
       "      <th>pop2003</th>\n",
       "      <th>pop2004</th>\n",
       "      <th>pop2005</th>\n",
       "      <th>pop2006</th>\n",
       "      <th>pop2007</th>\n",
       "      <th>pop2008</th>\n",
       "      <th>pop2009</th>\n",
       "      <th>pop2010</th>\n",
       "      <th>pop2011</th>\n",
       "      <th>pop2012</th>\n",
       "      <th>pop2013</th>\n",
       "      <th>pop2014</th>\n",
       "      <th>pop2015</th>\n",
       "      <th>pop2016</th>\n",
       "      <th>pop2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Country Name, Country Code, pop2000, pop2001, pop2002, pop2003, pop2004, pop2005, pop2006, pop2007, pop2008, pop2009, pop2010, pop2011, pop2012, pop2013, pop2014, pop2015, pop2016, pop2017]\n",
       "Index: []"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations[populations['pop2017'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in country_conv\n",
      "country_name    0\n",
      "iso3            1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing_values(country_conv, 'country_conv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in cm_all_ages_df\n",
      "iso3         0\n",
      "cause2015    0\n",
      "causename    0\n",
      "sex          0\n",
      "age          0\n",
      "agegroup     0\n",
      "dths2000     0\n",
      "low2000      0\n",
      "upp2000      0\n",
      "dths2001     0\n",
      "low2001      0\n",
      "upp2001      0\n",
      "dths2002     0\n",
      "low2002      0\n",
      "upp2002      0\n",
      "dths2003     0\n",
      "low2003      0\n",
      "upp2003      0\n",
      "dths2004     0\n",
      "low2004      0\n",
      "upp2004      0\n",
      "dths2005     0\n",
      "low2005      0\n",
      "upp2005      0\n",
      "dths2006     0\n",
      "low2006      0\n",
      "upp2006      0\n",
      "dths2007     0\n",
      "low2007      0\n",
      "upp2007      0\n",
      "dths2008     0\n",
      "low2008      0\n",
      "upp2008      0\n",
      "dths2009     0\n",
      "low2009      0\n",
      "upp2009      0\n",
      "dths2010     0\n",
      "low2010      0\n",
      "upp2010      0\n",
      "dths2011     0\n",
      "low2011      0\n",
      "upp2011      0\n",
      "dths2012     0\n",
      "low2012      0\n",
      "upp2012      0\n",
      "dths2013     0\n",
      "low2013      0\n",
      "upp2013      0\n",
      "dths2014     0\n",
      "low2014      0\n",
      "upp2014      0\n",
      "dths2015     0\n",
      "low2015      0\n",
      "upp2015      0\n",
      "dths2016     0\n",
      "low2016      0\n",
      "upp2016      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing_values(cm_all_ages_df, 'cm_all_ages_df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso3</th>\n",
       "      <th>cause2015</th>\n",
       "      <th>causename</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>agegroup</th>\n",
       "      <th>dths2000</th>\n",
       "      <th>low2000</th>\n",
       "      <th>upp2000</th>\n",
       "      <th>dths2001</th>\n",
       "      <th>...</th>\n",
       "      <th>upp2013</th>\n",
       "      <th>dths2014</th>\n",
       "      <th>low2014</th>\n",
       "      <th>upp2014</th>\n",
       "      <th>dths2015</th>\n",
       "      <th>low2015</th>\n",
       "      <th>upp2015</th>\n",
       "      <th>dths2016</th>\n",
       "      <th>low2016</th>\n",
       "      <th>upp2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>0</td>\n",
       "      <td>All Causes</td>\n",
       "      <td>BTSX</td>\n",
       "      <td>99</td>\n",
       "      <td>AGEAll</td>\n",
       "      <td>239647.000000</td>\n",
       "      <td>210309.55</td>\n",
       "      <td>272668.280</td>\n",
       "      <td>245432.671875</td>\n",
       "      <td>...</td>\n",
       "      <td>265108.280</td>\n",
       "      <td>231586.656250</td>\n",
       "      <td>197272.860</td>\n",
       "      <td>270225.440</td>\n",
       "      <td>234660.859375</td>\n",
       "      <td>198184.640</td>\n",
       "      <td>275727.970</td>\n",
       "      <td>248220.078125</td>\n",
       "      <td>209616.050</td>\n",
       "      <td>291652.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>0</td>\n",
       "      <td>All Causes</td>\n",
       "      <td>FMLE</td>\n",
       "      <td>99</td>\n",
       "      <td>AGEAll</td>\n",
       "      <td>113851.320312</td>\n",
       "      <td>100009.72</td>\n",
       "      <td>129574.590</td>\n",
       "      <td>116775.023438</td>\n",
       "      <td>...</td>\n",
       "      <td>124350.300</td>\n",
       "      <td>107917.742188</td>\n",
       "      <td>92040.750</td>\n",
       "      <td>125965.010</td>\n",
       "      <td>108679.703125</td>\n",
       "      <td>91906.617</td>\n",
       "      <td>127743.900</td>\n",
       "      <td>112863.117188</td>\n",
       "      <td>95438.273</td>\n",
       "      <td>132658.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>0</td>\n",
       "      <td>All Causes</td>\n",
       "      <td>MLE</td>\n",
       "      <td>99</td>\n",
       "      <td>AGEAll</td>\n",
       "      <td>125795.671875</td>\n",
       "      <td>110487.74</td>\n",
       "      <td>143163.230</td>\n",
       "      <td>128657.656250</td>\n",
       "      <td>...</td>\n",
       "      <td>140834.560</td>\n",
       "      <td>123668.875000</td>\n",
       "      <td>105451.520</td>\n",
       "      <td>144341.670</td>\n",
       "      <td>125981.140625</td>\n",
       "      <td>106511.150</td>\n",
       "      <td>148070.380</td>\n",
       "      <td>135356.953125</td>\n",
       "      <td>114424.000</td>\n",
       "      <td>159084.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>10</td>\n",
       "      <td>Communicable, maternal, perinatal and nutritio...</td>\n",
       "      <td>BTSX</td>\n",
       "      <td>99</td>\n",
       "      <td>AGEAll</td>\n",
       "      <td>147164.015625</td>\n",
       "      <td>126559.47</td>\n",
       "      <td>170114.450</td>\n",
       "      <td>150989.515625</td>\n",
       "      <td>...</td>\n",
       "      <td>121107.680</td>\n",
       "      <td>98689.867188</td>\n",
       "      <td>82724.961</td>\n",
       "      <td>117088.220</td>\n",
       "      <td>95241.054688</td>\n",
       "      <td>79333.539</td>\n",
       "      <td>113709.350</td>\n",
       "      <td>90307.523438</td>\n",
       "      <td>74716.375</td>\n",
       "      <td>108548.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>10</td>\n",
       "      <td>Communicable, maternal, perinatal and nutritio...</td>\n",
       "      <td>FMLE</td>\n",
       "      <td>99</td>\n",
       "      <td>AGEAll</td>\n",
       "      <td>75758.437500</td>\n",
       "      <td>65171.02</td>\n",
       "      <td>87545.391</td>\n",
       "      <td>77510.140625</td>\n",
       "      <td>...</td>\n",
       "      <td>60563.539</td>\n",
       "      <td>49176.316406</td>\n",
       "      <td>41219.387</td>\n",
       "      <td>58346.621</td>\n",
       "      <td>47331.613281</td>\n",
       "      <td>39423.008</td>\n",
       "      <td>56514.309</td>\n",
       "      <td>44754.605469</td>\n",
       "      <td>37023.516</td>\n",
       "      <td>53801.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso3  cause2015                                          causename   sex  \\\n",
       "0  AFG          0                                         All Causes  BTSX   \n",
       "1  AFG          0                                         All Causes  FMLE   \n",
       "2  AFG          0                                         All Causes   MLE   \n",
       "3  AFG         10  Communicable, maternal, perinatal and nutritio...  BTSX   \n",
       "4  AFG         10  Communicable, maternal, perinatal and nutritio...  FMLE   \n",
       "\n",
       "   age agegroup       dths2000    low2000     upp2000       dths2001  \\\n",
       "0   99   AGEAll  239647.000000  210309.55  272668.280  245432.671875   \n",
       "1   99   AGEAll  113851.320312  100009.72  129574.590  116775.023438   \n",
       "2   99   AGEAll  125795.671875  110487.74  143163.230  128657.656250   \n",
       "3   99   AGEAll  147164.015625  126559.47  170114.450  150989.515625   \n",
       "4   99   AGEAll   75758.437500   65171.02   87545.391   77510.140625   \n",
       "\n",
       "     ...         upp2013       dths2014     low2014     upp2014  \\\n",
       "0    ...      265108.280  231586.656250  197272.860  270225.440   \n",
       "1    ...      124350.300  107917.742188   92040.750  125965.010   \n",
       "2    ...      140834.560  123668.875000  105451.520  144341.670   \n",
       "3    ...      121107.680   98689.867188   82724.961  117088.220   \n",
       "4    ...       60563.539   49176.316406   41219.387   58346.621   \n",
       "\n",
       "        dths2015     low2015     upp2015       dths2016     low2016    upp2016  \n",
       "0  234660.859375  198184.640  275727.970  248220.078125  209616.050  291652.72  \n",
       "1  108679.703125   91906.617  127743.900  112863.117188   95438.273  132658.88  \n",
       "2  125981.140625  106511.150  148070.380  135356.953125  114424.000  159084.94  \n",
       "3   95241.054688   79333.539  113709.350   90307.523438   74716.375  108548.45  \n",
       "4   47331.613281   39423.008   56514.309   44754.605469   37023.516   53801.02  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_all_ages_df.head()"
   ]
  },
  {
   "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 Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>pop2000</th>\n",
       "      <th>pop2001</th>\n",
       "      <th>pop2002</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>20093756.0</td>\n",
       "      <td>20966463.0</td>\n",
       "      <td>21979923.0</td>\n",
       "      <td>23064851.0</td>\n",
       "      <td>24118979.0</td>\n",
       "      <td>25070798.0</td>\n",
       "      <td>25893450.0</td>\n",
       "      <td>26616792.0</td>\n",
       "      <td>27294031.0</td>\n",
       "      <td>28004331.0</td>\n",
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       "      <td>29708599.0</td>\n",
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       "      <td>31731688.0</td>\n",
       "      <td>32758020.0</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>34656032.0</td>\n",
       "      <td>35530081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>3089027.0</td>\n",
       "      <td>3060173.0</td>\n",
       "      <td>3051010.0</td>\n",
       "      <td>3039616.0</td>\n",
       "      <td>3026939.0</td>\n",
       "      <td>3011487.0</td>\n",
       "      <td>2992547.0</td>\n",
       "      <td>2970017.0</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>2927519.0</td>\n",
       "      <td>2913021.0</td>\n",
       "      <td>2905195.0</td>\n",
       "      <td>2900401.0</td>\n",
       "      <td>2895092.0</td>\n",
       "      <td>2889104.0</td>\n",
       "      <td>2880703.0</td>\n",
       "      <td>2876101.0</td>\n",
       "      <td>2873457.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>31183660.0</td>\n",
       "      <td>31592153.0</td>\n",
       "      <td>31995046.0</td>\n",
       "      <td>32403514.0</td>\n",
       "      <td>32831096.0</td>\n",
       "      <td>33288437.0</td>\n",
       "      <td>33777915.0</td>\n",
       "      <td>34300076.0</td>\n",
       "      <td>34860715.0</td>\n",
       "      <td>35465760.0</td>\n",
       "      <td>36117637.0</td>\n",
       "      <td>36819558.0</td>\n",
       "      <td>37565847.0</td>\n",
       "      <td>38338562.0</td>\n",
       "      <td>39113313.0</td>\n",
       "      <td>39871528.0</td>\n",
       "      <td>40606052.0</td>\n",
       "      <td>41318142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>ASM</td>\n",
       "      <td>57521.0</td>\n",
       "      <td>58175.0</td>\n",
       "      <td>58731.0</td>\n",
       "      <td>59117.0</td>\n",
       "      <td>59264.0</td>\n",
       "      <td>59118.0</td>\n",
       "      <td>58650.0</td>\n",
       "      <td>57903.0</td>\n",
       "      <td>57030.0</td>\n",
       "      <td>56227.0</td>\n",
       "      <td>55637.0</td>\n",
       "      <td>55320.0</td>\n",
       "      <td>55230.0</td>\n",
       "      <td>55307.0</td>\n",
       "      <td>55437.0</td>\n",
       "      <td>55537.0</td>\n",
       "      <td>55599.0</td>\n",
       "      <td>55641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>65390.0</td>\n",
       "      <td>67341.0</td>\n",
       "      <td>70049.0</td>\n",
       "      <td>73182.0</td>\n",
       "      <td>76244.0</td>\n",
       "      <td>78867.0</td>\n",
       "      <td>80991.0</td>\n",
       "      <td>82683.0</td>\n",
       "      <td>83861.0</td>\n",
       "      <td>84462.0</td>\n",
       "      <td>84449.0</td>\n",
       "      <td>83751.0</td>\n",
       "      <td>82431.0</td>\n",
       "      <td>80788.0</td>\n",
       "      <td>79223.0</td>\n",
       "      <td>78014.0</td>\n",
       "      <td>77281.0</td>\n",
       "      <td>76965.0</td>\n",
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      "text/plain": [
       "     Country Name Country Code     pop2000     pop2001     pop2002  \\\n",
       "0     Afghanistan          AFG  20093756.0  20966463.0  21979923.0   \n",
       "1         Albania          ALB   3089027.0   3060173.0   3051010.0   \n",
       "2         Algeria          DZA  31183660.0  31592153.0  31995046.0   \n",
       "3  American Samoa          ASM     57521.0     58175.0     58731.0   \n",
       "4         Andorra          AND     65390.0     67341.0     70049.0   \n",
       "\n",
       "      pop2003     pop2004     pop2005     pop2006     pop2007     pop2008  \\\n",
       "0  23064851.0  24118979.0  25070798.0  25893450.0  26616792.0  27294031.0   \n",
       "1   3039616.0   3026939.0   3011487.0   2992547.0   2970017.0   2947314.0   \n",
       "2  32403514.0  32831096.0  33288437.0  33777915.0  34300076.0  34860715.0   \n",
       "3     59117.0     59264.0     59118.0     58650.0     57903.0     57030.0   \n",
       "4     73182.0     76244.0     78867.0     80991.0     82683.0     83861.0   \n",
       "\n",
       "      pop2009     pop2010     pop2011     pop2012     pop2013     pop2014  \\\n",
       "0  28004331.0  28803167.0  29708599.0  30696958.0  31731688.0  32758020.0   \n",
       "1   2927519.0   2913021.0   2905195.0   2900401.0   2895092.0   2889104.0   \n",
       "2  35465760.0  36117637.0  36819558.0  37565847.0  38338562.0  39113313.0   \n",
       "3     56227.0     55637.0     55320.0     55230.0     55307.0     55437.0   \n",
       "4     84462.0     84449.0     83751.0     82431.0     80788.0     79223.0   \n",
       "\n",
       "      pop2015     pop2016     pop2017  \n",
       "0  33736494.0  34656032.0  35530081.0  \n",
       "1   2880703.0   2876101.0   2873457.0  \n",
       "2  39871528.0  40606052.0  41318142.0  \n",
       "3     55537.0     55599.0     55641.0  \n",
       "4     78014.0     77281.0     76965.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mort_df = pd.merge(left=country_conv, right=cm_all_ages_df, on='iso3', how='inner')\n",
    "# cm_filter = ['iso3', 'country_name', 'cause2015', 'causename', 'sex']   \n",
    "# # df_base = mort_df[cm_filter]\n",
    "# # df_base.head()\n",
    "# dths_filter = [col for col in mort_df if col.startswith('dths')]\n",
    "# cm_filter.extend(dths_filter)\n",
    "# mort_dths_df = mort_df[cm_filter]\n",
    "# mort_dths_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>AFG</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>All Causes</td>\n",
       "      <td>FMLE</td>\n",
       "      <td>113851.320312</td>\n",
       "      <td>116775.023438</td>\n",
       "      <td>113870.007812</td>\n",
       "      <td>111135.078125</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>All Causes</td>\n",
       "      <td>MLE</td>\n",
       "      <td>125795.671875</td>\n",
       "      <td>128657.656250</td>\n",
       "      <td>123940.078125</td>\n",
       "      <td>122607.867188</td>\n",
       "      <td>124426.312500</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>10</td>\n",
       "      <td>Communicable, maternal, perinatal and nutritio...</td>\n",
       "      <td>BTSX</td>\n",
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       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>10</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso3 country_name  cause2015  \\\n",
       "0  AFG  Afghanistan          0   \n",
       "1  AFG  Afghanistan          0   \n",
       "2  AFG  Afghanistan          0   \n",
       "3  AFG  Afghanistan         10   \n",
       "4  AFG  Afghanistan         10   \n",
       "\n",
       "                                           causename   sex       dths2000  \\\n",
       "0                                         All Causes  BTSX  239647.000000   \n",
       "1                                         All Causes  FMLE  113851.320312   \n",
       "2                                         All Causes   MLE  125795.671875   \n",
       "3  Communicable, maternal, perinatal and nutritio...  BTSX  147164.015625   \n",
       "4  Communicable, maternal, perinatal and nutritio...  FMLE   75758.437500   \n",
       "\n",
       "        dths2001       dths2002       dths2003       dths2004     ...      \\\n",
       "0  245432.671875  237810.093750  233742.953125  236298.718750     ...       \n",
       "1  116775.023438  113870.007812  111135.078125  111872.414062     ...       \n",
       "2  128657.656250  123940.078125  122607.867188  124426.312500     ...       \n",
       "3  150989.515625  143539.828125  134649.640625  131553.750000     ...       \n",
       "4   77510.140625   73599.914062   68790.765625   66971.218750     ...       \n",
       "\n",
       "      pop2008     pop2009     pop2010     pop2011     pop2012     pop2013  \\\n",
       "0  27294031.0  28004331.0  28803167.0  29708599.0  30696958.0  31731688.0   \n",
       "1  27294031.0  28004331.0  28803167.0  29708599.0  30696958.0  31731688.0   \n",
       "2  27294031.0  28004331.0  28803167.0  29708599.0  30696958.0  31731688.0   \n",
       "3  27294031.0  28004331.0  28803167.0  29708599.0  30696958.0  31731688.0   \n",
       "4  27294031.0  28004331.0  28803167.0  29708599.0  30696958.0  31731688.0   \n",
       "\n",
       "      pop2014     pop2015     pop2016     pop2017  \n",
       "0  32758020.0  33736494.0  34656032.0  35530081.0  \n",
       "1  32758020.0  33736494.0  34656032.0  35530081.0  \n",
       "2  32758020.0  33736494.0  34656032.0  35530081.0  \n",
       "3  32758020.0  33736494.0  34656032.0  35530081.0  \n",
       "4  32758020.0  33736494.0  34656032.0  35530081.0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mort_df = pd.merge(left=country_conv, right=cm_all_ages_df, on='iso3', how='inner')\n",
    "cm_filter = ['iso3', 'country_name', 'cause2015', 'causename', 'sex']   \n",
    "df_base = mort_df[cm_filter]\n",
    "dths_filter = [col for col in mort_df if col.startswith('dths')]\n",
    "cm_filter.extend(dths_filter)\n",
    "mort_dths_df = mort_df[cm_filter]\n",
    "mort_pop_dths_df = pd.merge(left=mort_dths_df, right=populations, left_on='iso3', right_on='Country Code', how='inner')\n",
    "mort_pop_dths_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_cols = [x for x in mort_pop_dths_df.columns if 'pop' in x]\n",
    "for pop_col in pop_cols:\n",
    "    mort_pop_dths_df[pop_col] = mort_pop_dths_df[pop_col].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 118035 entries, 0 to 118034\n",
      "Data columns (total 42 columns):\n",
      "iso3            118035 non-null object\n",
      "country_name    118035 non-null object\n",
      "cause2015       118035 non-null int64\n",
      "causename       118035 non-null object\n",
      "sex             118035 non-null object\n",
      "dths2000        118035 non-null float64\n",
      "dths2001        118035 non-null float64\n",
      "dths2002        118035 non-null float64\n",
      "dths2003        118035 non-null float64\n",
      "dths2004        118035 non-null float64\n",
      "dths2005        118035 non-null float64\n",
      "dths2006        118035 non-null float64\n",
      "dths2007        118035 non-null float64\n",
      "dths2008        118035 non-null float64\n",
      "dths2009        118035 non-null float64\n",
      "dths2010        118035 non-null float64\n",
      "dths2011        118035 non-null float64\n",
      "dths2012        118035 non-null float64\n",
      "dths2013        118035 non-null float64\n",
      "dths2014        118035 non-null float64\n",
      "dths2015        118035 non-null float64\n",
      "dths2016        118035 non-null float64\n",
      "Country Name    118035 non-null object\n",
      "Country Code    118035 non-null object\n",
      "pop2000         118035 non-null int32\n",
      "pop2001         118035 non-null int32\n",
      "pop2002         118035 non-null int32\n",
      "pop2003         118035 non-null int32\n",
      "pop2004         118035 non-null int32\n",
      "pop2005         118035 non-null int32\n",
      "pop2006         118035 non-null int32\n",
      "pop2007         118035 non-null int32\n",
      "pop2008         118035 non-null int32\n",
      "pop2009         118035 non-null int32\n",
      "pop2010         118035 non-null int32\n",
      "pop2011         118035 non-null int32\n",
      "pop2012         118035 non-null int32\n",
      "pop2013         118035 non-null int32\n",
      "pop2014         118035 non-null int32\n",
      "pop2015         118035 non-null int32\n",
      "pop2016         118035 non-null int32\n",
      "pop2017         118035 non-null int32\n",
      "dtypes: float64(17), int32(18), int64(1), object(6)\n",
      "memory usage: 30.6+ MB\n"
     ]
    }
   ],
   "source": [
    "mort_pop_dths_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now that I've merged both the population data and the death counts, I can normalize the death counts to population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# start_dths = mort_pop_dths_df.columns.get_loc('dths2000')\n",
    "# start_pops = mort_pop_dths_df.columns.get_loc('pop2000')\n",
    "mort_per_capita = df_base\n",
    "for i in range(2000, 2017):\n",
    "    dths = str('dths' + str(i))\n",
    "    pops = str('pop' + str(i))\n",
    "    mort_per_capita[str(i)] = mort_pop_dths_df[dths]*100000/mort_pop_dths_df[pops]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mort_per_capita['causename'] = mort_per_capita['causename'].str.replace(',', ' ')\n",
    "mort_per_capita['causename'] = mort_per_capita['causename'].str.replace('  ', ' ')\n",
    "mort_per_capita['causename'] = mort_per_capita['causename'].str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mort_per_capita = mort_per_capita[mort_per_capita['sex'] == 'BTSX']\n",
    "mort_per_capita.drop('sex', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "mort_per_capita['avg_death_rate'] = (mort_per_capita['2000'] + mort_per_capita['2001'] + mort_per_capita['2002'] + \n",
    "                                     mort_per_capita['2003'] + mort_per_capita['2004'] + mort_per_capita['2005'] +\n",
    "                                     mort_per_capita['2006'] + mort_per_capita['2007'] + mort_per_capita['2008'] +\n",
    "                                     mort_per_capita['2009'] + mort_per_capita['2010'] + mort_per_capita['2011'] +\n",
    "                                     mort_per_capita['2012'] + mort_per_capita['2013'] + mort_per_capita['2014'] +\n",
    "                                     mort_per_capita['2015'] + mort_per_capita['2016'])/17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso3</th>\n",
       "      <th>country_name</th>\n",
       "      <th>cause2015</th>\n",
       "      <th>causename</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>...</th>\n",
       "      <th>2008</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>avg_death_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>All_Causes</td>\n",
       "      <td>1192.644123</td>\n",
       "      <td>1170.596451</td>\n",
       "      <td>1081.942342</td>\n",
       "      <td>1013.416272</td>\n",
       "      <td>979.721068</td>\n",
       "      <td>952.217957</td>\n",
       "      <td>...</td>\n",
       "      <td>874.432834</td>\n",
       "      <td>853.048130</td>\n",
       "      <td>818.273517</td>\n",
       "      <td>789.043593</td>\n",
       "      <td>760.385555</td>\n",
       "      <td>720.814553</td>\n",
       "      <td>706.961704</td>\n",
       "      <td>695.569787</td>\n",
       "      <td>716.239176</td>\n",
       "      <td>893.179204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>10</td>\n",
       "      <td>Communicable_maternal_perinatal_and_nutritiona...</td>\n",
       "      <td>732.386795</td>\n",
       "      <td>720.147769</td>\n",
       "      <td>653.049731</td>\n",
       "      <td>583.787169</td>\n",
       "      <td>545.436646</td>\n",
       "      <td>520.940052</td>\n",
       "      <td>...</td>\n",
       "      <td>445.971136</td>\n",
       "      <td>433.776877</td>\n",
       "      <td>410.423075</td>\n",
       "      <td>391.717027</td>\n",
       "      <td>367.278148</td>\n",
       "      <td>323.670685</td>\n",
       "      <td>301.269329</td>\n",
       "      <td>282.308691</td>\n",
       "      <td>260.582410</td>\n",
       "      <td>467.748444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>20</td>\n",
       "      <td>Infectious_and_parasitic_diseases</td>\n",
       "      <td>333.847529</td>\n",
       "      <td>331.252373</td>\n",
       "      <td>276.718849</td>\n",
       "      <td>216.671950</td>\n",
       "      <td>186.358589</td>\n",
       "      <td>174.989148</td>\n",
       "      <td>...</td>\n",
       "      <td>148.026098</td>\n",
       "      <td>154.977535</td>\n",
       "      <td>151.564184</td>\n",
       "      <td>151.478107</td>\n",
       "      <td>142.848158</td>\n",
       "      <td>112.475168</td>\n",
       "      <td>105.486382</td>\n",
       "      <td>97.911103</td>\n",
       "      <td>85.594435</td>\n",
       "      <td>176.615555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>30</td>\n",
       "      <td>Tuberculosis</td>\n",
       "      <td>69.478187</td>\n",
       "      <td>64.138800</td>\n",
       "      <td>58.110515</td>\n",
       "      <td>58.656732</td>\n",
       "      <td>51.796310</td>\n",
       "      <td>47.514201</td>\n",
       "      <td>...</td>\n",
       "      <td>41.193541</td>\n",
       "      <td>45.501200</td>\n",
       "      <td>44.076683</td>\n",
       "      <td>45.320107</td>\n",
       "      <td>45.454852</td>\n",
       "      <td>43.832857</td>\n",
       "      <td>43.430857</td>\n",
       "      <td>39.483323</td>\n",
       "      <td>33.414956</td>\n",
       "      <td>47.864609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>40</td>\n",
       "      <td>STDs_excluding_HIV</td>\n",
       "      <td>7.780710</td>\n",
       "      <td>7.862510</td>\n",
       "      <td>8.019748</td>\n",
       "      <td>8.397833</td>\n",
       "      <td>8.865039</td>\n",
       "      <td>8.964597</td>\n",
       "      <td>...</td>\n",
       "      <td>8.684912</td>\n",
       "      <td>8.389714</td>\n",
       "      <td>7.838088</td>\n",
       "      <td>7.315537</td>\n",
       "      <td>6.995274</td>\n",
       "      <td>6.989030</td>\n",
       "      <td>6.955069</td>\n",
       "      <td>6.660916</td>\n",
       "      <td>6.386785</td>\n",
       "      <td>7.877237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   iso3 country_name  cause2015  \\\n",
       "0   AFG  Afghanistan          0   \n",
       "3   AFG  Afghanistan         10   \n",
       "6   AFG  Afghanistan         20   \n",
       "9   AFG  Afghanistan         30   \n",
       "12  AFG  Afghanistan         40   \n",
       "\n",
       "                                            causename         2000  \\\n",
       "0                                          All_Causes  1192.644123   \n",
       "3   Communicable_maternal_perinatal_and_nutritiona...   732.386795   \n",
       "6                   Infectious_and_parasitic_diseases   333.847529   \n",
       "9                                        Tuberculosis    69.478187   \n",
       "12                                 STDs_excluding_HIV     7.780710   \n",
       "\n",
       "           2001         2002         2003        2004        2005  \\\n",
       "0   1170.596451  1081.942342  1013.416272  979.721068  952.217957   \n",
       "3    720.147769   653.049731   583.787169  545.436646  520.940052   \n",
       "6    331.252373   276.718849   216.671950  186.358589  174.989148   \n",
       "9     64.138800    58.110515    58.656732   51.796310   47.514201   \n",
       "12     7.862510     8.019748     8.397833    8.865039    8.964597   \n",
       "\n",
       "         ...              2008        2009        2010        2011  \\\n",
       "0        ...        874.432834  853.048130  818.273517  789.043593   \n",
       "3        ...        445.971136  433.776877  410.423075  391.717027   \n",
       "6        ...        148.026098  154.977535  151.564184  151.478107   \n",
       "9        ...         41.193541   45.501200   44.076683   45.320107   \n",
       "12       ...          8.684912    8.389714    7.838088    7.315537   \n",
       "\n",
       "          2012        2013        2014        2015        2016  avg_death_rate  \n",
       "0   760.385555  720.814553  706.961704  695.569787  716.239176      893.179204  \n",
       "3   367.278148  323.670685  301.269329  282.308691  260.582410      467.748444  \n",
       "6   142.848158  112.475168  105.486382   97.911103   85.594435      176.615555  \n",
       "9    45.454852   43.832857   43.430857   39.483323   33.414956       47.864609  \n",
       "12    6.995274    6.989030    6.955069    6.660916    6.386785        7.877237  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mort_per_capita.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This contains the number of deaths per 100,000 by cause\n",
    "mort_per_capita.to_csv('WHO_mortality_data\\mort_by_cause_per_capita_allages.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mort_per_capita = pd.read_csv('WHO_mortality_data\\mort_by_cause_per_capita_allages.csv', encoding='latin-1', index_col=0)"
   ]
  }
 ],
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