covid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "COVID_DATA_FOLDER = './COVID-19/csse_covid_19_data/csse_covid_19_daily_reports'\n",
    "covid_data = pd.read_csv(f'{COVID_DATA_FOLDER}/07-24-2020.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Last_Update</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Combined_Key</th>\n",
       "      <th>Incidence_Rate</th>\n",
       "      <th>Case-Fatality_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45001.0</td>\n",
       "      <td>Abbeville</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-07-25 04:47:39</td>\n",
       "      <td>34.223334</td>\n",
       "      <td>-82.461707</td>\n",
       "      <td>260</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>Abbeville, South Carolina, US</td>\n",
       "      <td>1060.056265</td>\n",
       "      <td>0.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22001.0</td>\n",
       "      <td>Acadia</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-07-25 04:47:39</td>\n",
       "      <td>30.295065</td>\n",
       "      <td>-92.414197</td>\n",
       "      <td>2111</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>Acadia, Louisiana, US</td>\n",
       "      <td>3402.369248</td>\n",
       "      <td>2.936997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51001.0</td>\n",
       "      <td>Accomack</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-07-25 04:47:39</td>\n",
       "      <td>37.767072</td>\n",
       "      <td>-75.632346</td>\n",
       "      <td>1059</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1044.0</td>\n",
       "      <td>Accomack, Virginia, US</td>\n",
       "      <td>3277.014482</td>\n",
       "      <td>1.416431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16001.0</td>\n",
       "      <td>Ada</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-07-25 04:47:39</td>\n",
       "      <td>43.452658</td>\n",
       "      <td>-116.241552</td>\n",
       "      <td>6732</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>6687.0</td>\n",
       "      <td>Ada, Idaho, US</td>\n",
       "      <td>1397.878265</td>\n",
       "      <td>0.668449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19001.0</td>\n",
       "      <td>Adair</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-07-25 04:47:39</td>\n",
       "      <td>41.330756</td>\n",
       "      <td>-94.471059</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Adair, Iowa, US</td>\n",
       "      <td>279.642058</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      FIPS     Admin2  Province_State Country_Region          Last_Update  \\\n",
       "0  45001.0  Abbeville  South Carolina             US  2020-07-25 04:47:39   \n",
       "1  22001.0     Acadia       Louisiana             US  2020-07-25 04:47:39   \n",
       "2  51001.0   Accomack        Virginia             US  2020-07-25 04:47:39   \n",
       "3  16001.0        Ada           Idaho             US  2020-07-25 04:47:39   \n",
       "4  19001.0      Adair            Iowa             US  2020-07-25 04:47:39   \n",
       "\n",
       "         Lat       Long_  Confirmed  Deaths  Recovered  Active  \\\n",
       "0  34.223334  -82.461707        260       2          0   258.0   \n",
       "1  30.295065  -92.414197       2111      62          0  2049.0   \n",
       "2  37.767072  -75.632346       1059      15          0  1044.0   \n",
       "3  43.452658 -116.241552       6732      45          0  6687.0   \n",
       "4  41.330756  -94.471059         20       0          0    20.0   \n",
       "\n",
       "                    Combined_Key  Incidence_Rate  Case-Fatality_Ratio  \n",
       "0  Abbeville, South Carolina, US     1060.056265             0.769231  \n",
       "1          Acadia, Louisiana, US     3402.369248             2.936997  \n",
       "2         Accomack, Virginia, US     3277.014482             1.416431  \n",
       "3                 Ada, Idaho, US     1397.878265             0.668449  \n",
       "4                Adair, Iowa, US      279.642058             0.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Confirmed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>35981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>4570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>26159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Confirmed\n",
       "Country_Region           \n",
       "Afghanistan         35981\n",
       "Albania              4570\n",
       "Algeria             26159\n",
       "Andorra               897\n",
       "Angola                880"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_covid_data = covid_data.loc[:,('Country_Region', 'Confirmed')]\n",
    "processed_covid_data = processed_covid_data.groupby('Country_Region').sum()\n",
    "processed_covid_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Country_Region'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/covid/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Country_Region'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-d7902e42cbb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcovid_countries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocessed_covid_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Country_Region'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcovid_countries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcovid_countries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/covid/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/covid/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Country_Region'"
     ]
    }
   ],
   "source": [
    "covid_countries = processed_covid_data['Country_Region'].unique()\n",
    "covid_countries.sort()\n",
    "covid_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "POP_DATA_FILE = './population/data/population.csv'\n",
    "pop_data = pd.read_csv(POP_DATA_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>1960</td>\n",
       "      <td>92197753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>1961</td>\n",
       "      <td>94724510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>1962</td>\n",
       "      <td>97334442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>1963</td>\n",
       "      <td>100034179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>1964</td>\n",
       "      <td>102832760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country Name Country Code  Year      Value\n",
       "0   Arab World          ARB  1960   92197753\n",
       "1   Arab World          ARB  1961   94724510\n",
       "2   Arab World          ARB  1962   97334442\n",
       "3   Arab World          ARB  1963  100034179\n",
       "4   Arab World          ARB  1964  102832760"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/patrick/miniconda3/envs/covid/lib/python3.8/site-packages/pandas/core/indexing.py:845: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/home/patrick/miniconda3/envs/covid/lib/python3.8/site-packages/pandas/core/indexing.py:966: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "COUNTRY_MAP = {'United States': 'US'}\n",
    "processed_pop_data = pop_data[pop_data['Year']==2018]\n",
    "processed_pop_data.loc[:,'Country_Region'] = processed_pop_data.loc[:,'Country Name'].apply(lambda x: COUNTRY_MAP[x] if x in COUNTRY_MAP else x)\n",
    "processed_pop_data.loc[:,:] = processed_pop_data.loc[:,('Country_Region', 'Value')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'American Samoa', 'Andorra',\n",
       "       'Angola', 'Antigua and Barbuda', 'Arab World', 'Argentina',\n",
       "       'Armenia', 'Aruba', 'Australia', 'Austria', 'Azerbaijan',\n",
       "       'Bahamas, The', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus',\n",
       "       'Belgium', 'Belize', 'Benin', 'Bermuda', 'Bhutan', 'Bolivia',\n",
       "       'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'British Virgin Islands', 'Brunei Darussalam', 'Bulgaria',\n",
       "       'Burkina Faso', 'Burundi', 'Cabo Verde', 'Cambodia', 'Cameroon',\n",
       "       'Canada', 'Caribbean small states', 'Cayman Islands',\n",
       "       'Central African Republic', 'Central Europe and the Baltics',\n",
       "       'Chad', 'Channel Islands', 'Chile', 'China', 'Colombia', 'Comoros',\n",
       "       'Congo, Dem. Rep.', 'Congo, Rep.', 'Costa Rica', \"Cote d'Ivoire\",\n",
       "       'Croatia', 'Cuba', 'Curacao', 'Cyprus', 'Czech Republic',\n",
       "       'Denmark', 'Djibouti', 'Dominica', 'Dominican Republic',\n",
       "       'Early-demographic dividend', 'East Asia & Pacific',\n",
       "       'East Asia & Pacific (IDA & IBRD countries)',\n",
       "       'East Asia & Pacific (excluding high income)', 'Ecuador',\n",
       "       'Egypt, Arab Rep.', 'El Salvador', 'Equatorial Guinea', 'Estonia',\n",
       "       'Eswatini', 'Ethiopia', 'Euro area', 'Europe & Central Asia',\n",
       "       'Europe & Central Asia (IDA & IBRD countries)',\n",
       "       'Europe & Central Asia (excluding high income)', 'European Union',\n",
       "       'Faroe Islands', 'Fiji', 'Finland',\n",
       "       'Fragile and conflict affected situations', 'France',\n",
       "       'French Polynesia', 'Gabon', 'Gambia, The', 'Georgia', 'Germany',\n",
       "       'Ghana', 'Gibraltar', 'Greece', 'Greenland', 'Grenada', 'Guam',\n",
       "       'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti',\n",
       "       'Heavily indebted poor countries (HIPC)', 'High income',\n",
       "       'Honduras', 'Hong Kong SAR, China', 'Hungary', 'IBRD only',\n",
       "       'IDA & IBRD total', 'IDA blend', 'IDA only', 'IDA total',\n",
       "       'Iceland', 'India', 'Indonesia', 'Iran, Islamic Rep.', 'Iraq',\n",
       "       'Ireland', 'Isle of Man', 'Israel', 'Italy', 'Jamaica', 'Japan',\n",
       "       'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',\n",
       "       'Korea, Dem. People’s Rep.', 'Korea, Rep.', 'Kosovo', 'Kuwait',\n",
       "       'Kyrgyz Republic', 'Lao PDR', 'Late-demographic dividend',\n",
       "       'Latin America & Caribbean',\n",
       "       'Latin America & Caribbean (excluding high income)',\n",
       "       'Latin America & the Caribbean (IDA & IBRD countries)', 'Latvia',\n",
       "       'Least developed countries: UN classification', 'Lebanon',\n",
       "       'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n",
       "       'Low & middle income', 'Low income', 'Lower middle income',\n",
       "       'Luxembourg', 'Macao SAR, China', 'Madagascar', 'Malawi',\n",
       "       'Malaysia', 'Maldives', 'Mali', 'Malta', 'Marshall Islands',\n",
       "       'Mauritania', 'Mauritius', 'Mexico', 'Micronesia, Fed. Sts.',\n",
       "       'Middle East & North Africa',\n",
       "       'Middle East & North Africa (IDA & IBRD countries)',\n",
       "       'Middle East & North Africa (excluding high income)',\n",
       "       'Middle income', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro',\n",
       "       'Morocco', 'Mozambique', 'Myanmar', 'Namibia', 'Nauru', 'Nepal',\n",
       "       'Netherlands', 'New Caledonia', 'New Zealand', 'Nicaragua',\n",
       "       'Niger', 'Nigeria', 'North America', 'North Macedonia',\n",
       "       'Northern Mariana Islands', 'Norway', 'OECD members', 'Oman',\n",
       "       'Other small states', 'Pacific island small states', 'Pakistan',\n",
       "       'Palau', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n",
       "       'Philippines', 'Poland', 'Portugal', 'Post-demographic dividend',\n",
       "       'Pre-demographic dividend', 'Puerto Rico', 'Qatar', 'Romania',\n",
       "       'Russian Federation', 'Rwanda', 'Samoa', 'San Marino',\n",
       "       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n",
       "       'Seychelles', 'Sierra Leone', 'Singapore',\n",
       "       'Sint Maarten (Dutch part)', 'Slovak Republic', 'Slovenia',\n",
       "       'Small states', 'Solomon Islands', 'Somalia', 'South Africa',\n",
       "       'South Asia', 'South Asia (IDA & IBRD)', 'South Sudan', 'Spain',\n",
       "       'Sri Lanka', 'St. Kitts and Nevis', 'St. Lucia',\n",
       "       'St. Martin (French part)', 'St. Vincent and the Grenadines',\n",
       "       'Sub-Saharan Africa', 'Sub-Saharan Africa (IDA & IBRD countries)',\n",
       "       'Sub-Saharan Africa (excluding high income)', 'Sudan', 'Suriname',\n",
       "       'Sweden', 'Switzerland', 'Syrian Arab Republic', 'Tajikistan',\n",
       "       'Tanzania', 'Thailand', 'Timor-Leste', 'Togo', 'Tonga',\n",
       "       'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Turkmenistan',\n",
       "       'Turks and Caicos Islands', 'Tuvalu', 'US', 'Uganda', 'Ukraine',\n",
       "       'United Arab Emirates', 'United Kingdom', 'Upper middle income',\n",
       "       'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela, RB', 'Vietnam',\n",
       "       'Virgin Islands (U.S.)', 'West Bank and Gaza', 'World',\n",
       "       'Yemen, Rep.', 'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_countries = processed_pop_data['Country_Region'].unique()\n",
    "pop_countries.sort()\n",
    "pop_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'covid_countries' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-5f3ee037d547>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mreportable_countries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpop_countries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovid_countries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mreportable_countries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'covid_countries' is not defined"
     ]
    }
   ],
   "source": [
    "reportable_countries = set(pop_countries) & set(covid_countries)\n",
    "reportable_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'US' in pop_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(processed_covid_data, processed_pop_data, on='Country_Region')\n",
    "processed_data = data\n",
    "processed_data['Percent Population Confirmed'] = processed_data['Confirmed']/processed_data['Value']*100\n",
    "top_25 = processed_data.sort_values('Percent Population Confirmed', ascending=False)[:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(15,5))\n",
    "g = sns.barplot(x=\"Country_Region\", y=\"Percent Population Confirmed\", data=top_25)\n",
    "_ = g.set_xticklabels(top_25['Country_Region'], rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.savefig('percents.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}