03_export_to_js_2011.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export to JSON pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data (previously created pandas dataframe exported as a json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('WIP/countries_x_y.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>contcod</th>\n",
       "      <th>year_survey</th>\n",
       "      <th>DummyY</th>\n",
       "      <th>totpop</th>\n",
       "      <th>ventile</th>\n",
       "      <th>ventile_income</th>\n",
       "      <th>pop</th>\n",
       "      <th>pop_share</th>\n",
       "      <th>ppp_factor</th>\n",
       "      <th>ppp_2011</th>\n",
       "      <th>ppp_percentile</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>2.829337</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48940.0</td>\n",
       "      <td>0.141467</td>\n",
       "      <td>0.05</td>\n",
       "      <td>43.857745</td>\n",
       "      <td>1115.880438</td>\n",
       "      <td>32.603788</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>2.829337</td>\n",
       "      <td>2.0</td>\n",
       "      <td>68642.0</td>\n",
       "      <td>0.141467</td>\n",
       "      <td>0.05</td>\n",
       "      <td>43.857745</td>\n",
       "      <td>1565.105538</td>\n",
       "      <td>42.932962</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>2.829337</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78991.0</td>\n",
       "      <td>0.141467</td>\n",
       "      <td>0.05</td>\n",
       "      <td>43.857745</td>\n",
       "      <td>1801.072981</td>\n",
       "      <td>47.121037</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>2.829337</td>\n",
       "      <td>4.0</td>\n",
       "      <td>87698.0</td>\n",
       "      <td>0.141467</td>\n",
       "      <td>0.05</td>\n",
       "      <td>43.857745</td>\n",
       "      <td>1999.601199</td>\n",
       "      <td>50.162916</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>2.829337</td>\n",
       "      <td>5.0</td>\n",
       "      <td>95185.0</td>\n",
       "      <td>0.141467</td>\n",
       "      <td>0.05</td>\n",
       "      <td>43.857745</td>\n",
       "      <td>2170.312209</td>\n",
       "      <td>52.479402</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 contcod  year_survey  DummyY    totpop  ventile  ventile_income  \\\n",
       "0           0     ALB         2012       1  2.829337      1.0         48940.0   \n",
       "1           1     ALB         2012       1  2.829337      2.0         68642.0   \n",
       "2           2     ALB         2012       1  2.829337      3.0         78991.0   \n",
       "3           3     ALB         2012       1  2.829337      4.0         87698.0   \n",
       "4           4     ALB         2012       1  2.829337      5.0         95185.0   \n",
       "\n",
       "        pop  pop_share  ppp_factor     ppp_2011  ppp_percentile     x  \n",
       "0  0.141467       0.05   43.857745  1115.880438       32.603788   2.5  \n",
       "1  0.141467       0.05   43.857745  1565.105538       42.932962   7.5  \n",
       "2  0.141467       0.05   43.857745  1801.072981       47.121037  12.5  \n",
       "3  0.141467       0.05   43.857745  1999.601199       50.162916  17.5  \n",
       "4  0.141467       0.05   43.857745  2170.312209       52.479402  22.5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[['contcod', 'x', 'ppp_percentile']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>contcod</th>\n",
       "      <th>x</th>\n",
       "      <th>ppp_percentile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2.5</td>\n",
       "      <td>32.603788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>7.5</td>\n",
       "      <td>42.932962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>12.5</td>\n",
       "      <td>47.121037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  contcod     x  ppp_percentile\n",
       "0     ALB   2.5       32.603788\n",
       "1     ALB   7.5       42.932962\n",
       "2     ALB  12.5       47.121037"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Round to 2 decimal places everything other than 1st column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['ppp_percentile'] = round(data['ppp_percentile'], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = data['contcod'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ALB', 'ARG', 'ARM', 'AUS', 'AUT', 'BEN', 'BGD', 'BGR', 'BOL',\n",
       "       'BRA'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[:10]"
   ]
  },
  {
   "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>contcod</th>\n",
       "      <th>x</th>\n",
       "      <th>ppp_percentile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2.5</td>\n",
       "      <td>32.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>7.5</td>\n",
       "      <td>42.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  contcod    x  ppp_percentile\n",
       "0     ALB  2.5           32.60\n",
       "1     ALB  7.5           42.93"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the columns for personal convenience:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = ['contcod', 'x', 'y']"
   ]
  },
  {
   "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>contcod</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2.5</td>\n",
       "      <td>32.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>7.5</td>\n",
       "      <td>42.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  contcod    x      y\n",
       "0     ALB  2.5  32.60\n",
       "1     ALB  7.5  42.93"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create custom `dict` for export that looks like this:\n",
    "\n",
    "\n",
    "```\n",
    "{\n",
    "   'IND': \n",
    "       [\n",
    "           {'x': 2.5, 'y': 25.3}, \n",
    "           {'x': 7.5, 'y': 31.5}, \n",
    "           ... \n",
    "       ],\n",
    "   'DEU': [  ...  ],\n",
    "   ...\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_for_export = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for country in countries:\n",
    "  \n",
    "  temp_list = []\n",
    "  \n",
    "  def append_to_list(x, y):\n",
    "    temp_list.append({'x': x, 'y': y})\n",
    "  \n",
    "  data[data['contcod'] == country][['x', 'y']].apply(lambda row: append_to_list(row['x'], row['y']), axis=1)\n",
    "    \n",
    "  dict_for_export[country] = temp_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preview contents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'x': 2.5, 'y': 13.46}, {'x': 7.5, 'y': 32.6}, {'x': 12.5, 'y': 42.1}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_for_export['BRA'][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'x': 87.5, 'y': 97.45}, {'x': 92.5, 'y': 98.34}, {'x': 97.5, 'y': 99.54}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_for_export['DEU'][17:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save to file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = json.dumps(dict_for_export)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./export/DATA_2011.js\", \"w\") as text_file:\n",
    "    text_file.write(\"var DATA = \" + output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create .js file with countries sorted by median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's sort countries by their *median* so they show up in that order on the website chart in the legend\n",
    "\n",
    "And export as `ALL_COUNTRIES_CODES` for consumption in JS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "medians = data.groupby('contcod')['y'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contcod\n",
       "ALB    61.960\n",
       "ARG    78.775\n",
       "ARM    34.465\n",
       "AUS    91.175\n",
       "AUT    95.060\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medians.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(medians)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted = medians.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contcod\n",
       "MDG     2.380\n",
       "RWA     3.295\n",
       "MWI    10.425\n",
       "LSO    11.175\n",
       "GNB    13.405\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contcod\n",
       "DNK    93.605\n",
       "LTU    94.480\n",
       "NOR    94.735\n",
       "AUT    95.060\n",
       "LUX    95.785\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_countries = list(sorted.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MDG', 'RWA', 'MWI', 'LSO', 'GNB']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DNK', 'LTU', 'NOR', 'AUT', 'LUX']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note -- countries are in *increasing* order at the moment, so we `reverse()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_countries.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LUX', 'AUT', 'NOR', 'LTU', 'DNK']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_countries_string = json.dumps(all_countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[\"LUX\", \"AUT\", \"NOR\", \"L'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries_string[:24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./export/DATA_2011_COUNTRIES.js\", \"w\") as text_file:\n",
    "    text_file.write(\"var ALL_COUNTRIES_CODES = \" + all_countries_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate .js list of countries in alphabetical order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "note that we can't just alphabetize the ISO3 strings as they don't always correlate to alphabetical order, e.g. `GBR` is `United Kingdom`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build lookup dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ISO = pd.read_csv('./data/iso2_iso3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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</th>\n",
       "      <th>Alpha-2 code</th>\n",
       "      <th>Alpha-3 code</th>\n",
       "      <th>Numeric code</th>\n",
       "      <th>Latitude (average)</th>\n",
       "      <th>Longitude (average)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>\"AF\"</td>\n",
       "      <td>\"AFG\"</td>\n",
       "      <td>\"4\"</td>\n",
       "      <td>\"33\"</td>\n",
       "      <td>\"65\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>\"AL\"</td>\n",
       "      <td>\"ALB\"</td>\n",
       "      <td>\"8\"</td>\n",
       "      <td>\"41\"</td>\n",
       "      <td>\"20\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>\"DZ\"</td>\n",
       "      <td>\"DZA\"</td>\n",
       "      <td>\"12\"</td>\n",
       "      <td>\"28\"</td>\n",
       "      <td>\"3\"</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country Alpha-2 code Alpha-3 code Numeric code Latitude (average)  \\\n",
       "0  Afghanistan         \"AF\"        \"AFG\"          \"4\"               \"33\"   \n",
       "1      Albania         \"AL\"        \"ALB\"          \"8\"               \"41\"   \n",
       "2      Algeria         \"DZ\"        \"DZA\"         \"12\"               \"28\"   \n",
       "\n",
       "  Longitude (average)  \n",
       "0                \"65\"  \n",
       "1                \"20\"  \n",
       "2                 \"3\"  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ISO.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ISO.columns = ['country', 'iso2', 'iso3', 'u0', 'u1', 'u2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = ISO[['country', 'iso2', 'iso3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>\"AF\"</td>\n",
       "      <td>\"AFG\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>\"AL\"</td>\n",
       "      <td>\"ALB\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>\"DZ\"</td>\n",
       "      <td>\"DZA\"</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country   iso2    iso3\n",
       "0  Afghanistan   \"AF\"   \"AFG\"\n",
       "1      Albania   \"AL\"   \"ALB\"\n",
       "2      Algeria   \"DZ\"   \"DZA\""
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "countries['iso3'] = countries['iso3'].apply(lambda x: x.replace('\"', ''))\n",
    "countries['iso3'] = countries['iso3'].apply(lambda x: x.replace(' ', ''))\n",
    "countries['iso2'] = countries['iso2'].apply(lambda x: x.replace('\"', ''))\n",
    "countries['iso2'] = countries['iso2'].apply(lambda x: x.replace(' ', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>AL</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZ</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country iso2 iso3\n",
       "0  Afghanistan   AF  AFG\n",
       "1      Albania   AL  ALB\n",
       "2      Algeria   DZ  DZA"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add South Sudan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "South Sudan was added in 2011 but its data is not on the `iso2_iso3.csv`\n",
    "\n",
    "We will need this country in our lookup dictionary, as we have the PPP data for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "sudan = { 'country': 'South Sudan', 'iso2': 'SS', 'iso3': 'SSD'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = countries.append(sudan, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZM</td>\n",
       "      <td>ZMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SS</td>\n",
       "      <td>SSD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country iso2 iso3\n",
       "254       Zambia   ZM  ZMB\n",
       "255     Zimbabwe   ZW  ZWE\n",
       "256  South Sudan   SS  SSD"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso3_to_name = pd.Series(countries['country'].values, index=countries['iso3']).to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Dict to create list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LUX', 'AUT', 'NOR', 'LTU', 'DNK', 'USA', 'CAN', 'NLD', 'FIN', 'DEU']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_order = pd.DataFrame(all_countries, columns=['iso3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LUX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LTU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso3\n",
       "0  LUX\n",
       "1  AUT\n",
       "2  NOR\n",
       "3  LTU"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_order.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Luxembourg'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso3_to_name.get('LUX')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_order['name'] = df_order.apply(lambda x: iso3_to_name.get(x['iso3']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LUX</td>\n",
       "      <td>Luxembourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LTU</td>\n",
       "      <td>Lithuania</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso3        name\n",
       "0  LUX  Luxembourg\n",
       "1  AUT     Austria\n",
       "2  NOR      Norway\n",
       "3  LTU   Lithuania"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_order.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_sorted_list = df_order.sort_values(['name'])['iso3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_list_sorted = list(my_sorted_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALB', 'ARG', 'ARM', 'AUS', 'AUT', 'BGD', 'BEN', 'BTN', 'BOL', 'BWA']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_list_sorted[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_sorted_string = json.dumps(final_list_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./export/DATA_2011_COUNTRIES_SORTED.js\", \"w\") as text_file:\n",
    "    text_file.write(\"var ALL_COUNTRIES_CODES_SORTED = \" + countries_sorted_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LIST OF ISO2 NAMES OF COUNTRIES NOT IN DATASET "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll need to have a list of countries not in the dataset so we can color the countries differently on the map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "below list comes from the *mapael* library I use to show countries on the map: https://www.vincentbroute.fr/mapael/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_on_map = [\"AE\",\"AF\",\"AL\",\"AM\",\"AO\",\"AR\",\"AT\",\"AU\",\"AZ\",\"BA\",\"BD\",\"BE\",\"BF\",\"BG\",\"BI\",\"BJ\",\"BN\",\"BO\",\"BR\",\"BS\",\"BT\",\"BW\",\"BY\",\"BZ\",\"CA\",\"CD\",\"CF\",\"CG\",\"CH\",\"CI\",\"CL\",\"CM\",\"CN\",\"CO\",\"CR\",\"CU\",\"CY\",\"CZ\",\"DE\",\"DJ\",\"DK\",\"DO\",\"DZ\",\"EC\",\"EE\",\"EG\",\"EH\",\"ER\",\"ES\",\"ET\",\"FI\",\"FJ\",\"FK\",\"FO\",\"FR\",\"GA\",\"GB\",\"GE\",\"GH\",\"GL\",\"GM\",\"GN\",\"GQ\",\"GR\",\"GT\",\"GW\",\"GY\",\"HN\",\"HR\",\"HT\",\"HU\",\"ID\",\"IE\",\"IL\",\"IN\",\"IQ\",\"IR\",\"IS\",\"IT\",\"JM\",\"JO\",\"JP\",\"KE\",\"KG\",\"KH\",\"KM\",\"KP\",\"KR\",\"KW\",\"KZ\",\"LA\",\"LB\",\"LK\",\"LR\",\"LS\",\"LT\",\"LU\",\"LV\",\"LY\",\"MA\",\"MD\",\"ME\",\"MG\",\"MK\",\"ML\",\"MM\",\"MN\",\"MR\",\"MW\",\"MX\",\"MY\",\"MZ\",\"NA\",\"NC\",\"NE\",\"NG\",\"NI\",\"NL\",\"NO\",\"NP\",\"NZ\",\"OM\",\"PA\",\"PE\",\"PF\",\"PG\",\"PH\",\"PK\",\"PL\",\"PR\",\"PS\",\"PT\",\"PY\",\"QA\",\"RO\",\"RS\",\"RU\",\"RW\",\"SA\",\"SB\",\"SD\",\"SE\",\"SI\",\"SK\",\"SL\",\"SN\",\"SO\",\"SR\",\"SS\",\"SV\",\"SY\",\"SZ\",\"TD\",\"TG\",\"TH\",\"TJ\",\"TL\",\"TM\",\"TN\",\"TR\",\"TT\",\"TW\",\"TZ\",\"UA\",\"UG\",\"US\",\"UY\",\"UZ\",\"VE\",\"VN\",\"VU\",\"WS\",\"YE\",\"ZA\",\"ZM\",\"ZW\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AE', 'AF', 'AL', 'AM', 'AO', 'AR', 'AT', 'AU', 'AZ', 'BA']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_on_map[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_in_dataset = list(data['contcod'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All countries in the dataset are here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lookup dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso2_to_iso3 = pd.Series(countries['iso3'].values, index=countries['iso2']).to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_in_dataset = []\n",
    "\n",
    "for iso2 in countries_on_map:\n",
    "  iso3 = iso2_to_iso3.get(iso2)\n",
    "  if iso3 not in all_countries:\n",
    "    not_in_dataset.append(iso2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(not_in_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a file for JS consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = json.dumps(not_in_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./export/NOT_IN_DATASET.js\", \"w\") as text_file:\n",
    "    text_file.write(\"var COUNTRIES_NOT_IN_DATASET = \" + output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Misc exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See how many countries ever dip below 5th percentlie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['y'] < 5]['contcod'].unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which countries last ventile is above 99.5 income percentile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AUS', 'AUT', 'CAN', 'DEU', 'DNK', 'FRA', 'GBR', 'LTU', 'LUX',\n",
       "       'NOR', 'NZL', 'USA'], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['y'] > 99.5]['contcod'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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