IA 241 Final Project - Lafley and Blubaugh - Analyzing COVID-19 Data in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "76571a32",
   "metadata": {},
   "source": [
    "# IA 241 Final Project - Analyzing COVID-19 Data in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c8bef6",
   "metadata": {},
   "source": [
    "## Introduction "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13fb54c6",
   "metadata": {},
   "source": [
    "According to the World Health Organization, COVID-19, a coronavirus, is an infectious disease caused by SARS-CoV-2 virus.\n",
    "\n",
    "The COVID-19 outbreak began in December of 2019, and peaked in December of 2020, prior to the widespread distribution of vaccines, which brought about a gradual decline of the virus.\n",
    "\n",
    "The virus had varying levels of effectiveness across different countries, and this study's particular focus is on South America, specifically Peru and Chile.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a2e8b1",
   "metadata": {},
   "source": [
    "### Sources:\n",
    "\n",
    "[COVID-19 Definition](https://www.who.int/health-topics/coronavirus)\n",
    "\n",
    "[COVID-19 Background Information](https://www.cdc.gov/museum/timeline/covid19.html) \n",
    "\n",
    "## Data:\n",
    "\n",
    "[CDC COVID-19 Data from 2020](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c80fcad",
   "metadata": {},
   "source": [
    "### Import pandas and matplot library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "432bbc20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495aa416",
   "metadata": {},
   "source": [
    "### Import CDC data from S3 bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "26e2069f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\r\n",
      "Requirement already satisfied: xlrd in /home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages (2.0.1)\r\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>dateRep</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>geoId</th>\n",
       "      <th>countryterritoryCode</th>\n",
       "      <th>popData2019</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>Cumulative_number_for_14_days_of_COVID-19_cases_per_100000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14/12/2020</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>746</td>\n",
       "      <td>6</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>9.013779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13/12/2020</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>298</td>\n",
       "      <td>9</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.052776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-12 00:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>113</td>\n",
       "      <td>11</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.868768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-12 00:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.134266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-10-12 00:00:00</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>202</td>\n",
       "      <td>16</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.968658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "               dateRep  day  month  year  cases  deaths  \\\n",
       "0           14/12/2020   14     12  2020    746       6   \n",
       "1           13/12/2020   13     12  2020    298       9   \n",
       "2  2020-12-12 00:00:00   12     12  2020    113      11   \n",
       "3  2020-11-12 00:00:00   11     12  2020     63      10   \n",
       "4  2020-10-12 00:00:00   10     12  2020    202      16   \n",
       "\n",
       "  countriesAndTerritories geoId countryterritoryCode  popData2019  \\\n",
       "0             Afghanistan    AF                  AFG   38041757.0   \n",
       "1             Afghanistan    AF                  AFG   38041757.0   \n",
       "2             Afghanistan    AF                  AFG   38041757.0   \n",
       "3             Afghanistan    AF                  AFG   38041757.0   \n",
       "4             Afghanistan    AF                  AFG   38041757.0   \n",
       "\n",
       "  continentExp  Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \n",
       "0         Asia                                           9.013779           \n",
       "1         Asia                                           7.052776           \n",
       "2         Asia                                           6.868768           \n",
       "3         Asia                                           7.134266           \n",
       "4         Asia                                           6.968658           "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install xlrd\n",
    "df = pd.read_excel('s3://blubaugh-241-python/covid_data.xls')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7c44b9",
   "metadata": {},
   "source": [
    "This data was obtained from the Centers for Disease Control and Prevention (CDC) at:\n",
    "https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac92b1a",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f87bb92a",
   "metadata": {},
   "source": [
    "### What was the average death rate in South America?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e22b8b32",
   "metadata": {},
   "source": [
    "#### Adding a \"death rate\" column into the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b203e682",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['dateRep', 'day', 'month', 'year', 'cases', 'deaths',\n",
      "       'countriesAndTerritories', 'geoId', 'countryterritoryCode',\n",
      "       'popData2019', 'continentExp',\n",
      "       'Cumulative_number_for_14_days_of_COVID-19_cases_per_100000'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e5a36508",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['cases']!= 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c33476",
   "metadata": {},
   "source": [
    "This eliminates instances where the number of cases was zero. This avoids an division by zero error in the next line of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "373665f1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>geoId</th>\n",
       "      <th>countryterritoryCode</th>\n",
       "      <th>popData2019</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>Cumulative_number_for_14_days_of_COVID-19_cases_per_100000</th>\n",
       "      <th>death_rate_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14/12/2020</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>746</td>\n",
       "      <td>6</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>3.020134</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-12 00:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>113</td>\n",
       "      <td>11</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.868768</td>\n",
       "      <td>9.734513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-12 00:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.134266</td>\n",
       "      <td>15.873016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-10-12 00:00:00</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>202</td>\n",
       "      <td>16</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.968658</td>\n",
       "      <td>7.920792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               dateRep  day  month  year  cases  deaths  \\\n",
       "0           14/12/2020   14     12  2020    746       6   \n",
       "1           13/12/2020   13     12  2020    298       9   \n",
       "2  2020-12-12 00:00:00   12     12  2020    113      11   \n",
       "3  2020-11-12 00:00:00   11     12  2020     63      10   \n",
       "4  2020-10-12 00:00:00   10     12  2020    202      16   \n",
       "\n",
       "  countriesAndTerritories geoId countryterritoryCode  popData2019  \\\n",
       "0             Afghanistan    AF                  AFG   38041757.0   \n",
       "1             Afghanistan    AF                  AFG   38041757.0   \n",
       "2             Afghanistan    AF                  AFG   38041757.0   \n",
       "3             Afghanistan    AF                  AFG   38041757.0   \n",
       "4             Afghanistan    AF                  AFG   38041757.0   \n",
       "\n",
       "  continentExp  Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \\\n",
       "0         Asia                                           9.013779            \n",
       "1         Asia                                           7.052776            \n",
       "2         Asia                                           6.868768            \n",
       "3         Asia                                           7.134266            \n",
       "4         Asia                                           6.968658            \n",
       "\n",
       "   death_rate_%  \n",
       "0      0.804290  \n",
       "1      3.020134  \n",
       "2      9.734513  \n",
       "3     15.873016  \n",
       "4      7.920792  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['death_rate_%'] = df['deaths']/df['cases']*100\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a41cc0e",
   "metadata": {},
   "source": [
    "The death_rate_% column has been created in the data frame."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e609a1a",
   "metadata": {},
   "source": [
    "#### Creating the \"South America\" continentExp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43882041",
   "metadata": {},
   "source": [
    "The dataset did not differentiate between North and South America in its 'continentExp' column. \n",
    "\n",
    "In order to answer the question of average death rate in South America, we had to change the label of South American countries from \"America\" to \"South America\".\n",
    "\n",
    "For this study, the twelve countries considered part of South America are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guyana, Paraguay, Suriname, Uruguay, and Venezuela."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b7d40c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "sadf = (df['continentExp'] == 'America') & (df['countriesAndTerritories'].isin(['Argentina', 'Bolivia', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Guyana', 'Paraguay', 'Peru', 'Suriname', 'Uruguay', 'Venezuela']))\n",
    "df.loc[sadf, 'continentExp'] = 'South America'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad56b932",
   "metadata": {},
   "source": [
    "This code changes the 'continentExp' value for these countries from 'America' to 'South America'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1a380611",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>13/12/2020</td>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>3.020134</td>\n",
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       "      <th>2</th>\n",
       "      <td>2020-12-12 00:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>113</td>\n",
       "      <td>11</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.868768</td>\n",
       "      <td>9.734513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-12 00:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.134266</td>\n",
       "      <td>15.873016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-10-12 00:00:00</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>202</td>\n",
       "      <td>16</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.968658</td>\n",
       "      <td>7.920792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44931</th>\n",
       "      <td>30/06/2020</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>2946</td>\n",
       "      <td>187</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>PER</td>\n",
       "      <td>32510462.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>151.868036</td>\n",
       "      <td>6.347590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44932</th>\n",
       "      <td>29/06/2020</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>3430</td>\n",
       "      <td>182</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>PER</td>\n",
       "      <td>32510462.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>152.821575</td>\n",
       "      <td>5.306122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44933</th>\n",
       "      <td>28/06/2020</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>3625</td>\n",
       "      <td>196</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>PER</td>\n",
       "      <td>32510462.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>156.432720</td>\n",
       "      <td>5.406897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44934</th>\n",
       "      <td>27/06/2020</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>3762</td>\n",
       "      <td>178</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>PER</td>\n",
       "      <td>32510462.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>158.764277</td>\n",
       "      <td>4.731526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44935</th>\n",
       "      <td>26/06/2020</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>3913</td>\n",
       "      <td>175</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>PER</td>\n",
       "      <td>32510462.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>165.528254</td>\n",
       "      <td>4.472272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30400 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   dateRep  day  month  year  cases  deaths  \\\n",
       "0               14/12/2020   14     12  2020    746       6   \n",
       "1               13/12/2020   13     12  2020    298       9   \n",
       "2      2020-12-12 00:00:00   12     12  2020    113      11   \n",
       "3      2020-11-12 00:00:00   11     12  2020     63      10   \n",
       "4      2020-10-12 00:00:00   10     12  2020    202      16   \n",
       "...                    ...  ...    ...   ...    ...     ...   \n",
       "44931           30/06/2020   30      6  2020   2946     187   \n",
       "44932           29/06/2020   29      6  2020   3430     182   \n",
       "44933           28/06/2020   28      6  2020   3625     196   \n",
       "44934           27/06/2020   27      6  2020   3762     178   \n",
       "44935           26/06/2020   26      6  2020   3913     175   \n",
       "\n",
       "      countriesAndTerritories geoId countryterritoryCode  popData2019  \\\n",
       "0                 Afghanistan    AF                  AFG   38041757.0   \n",
       "1                 Afghanistan    AF                  AFG   38041757.0   \n",
       "2                 Afghanistan    AF                  AFG   38041757.0   \n",
       "3                 Afghanistan    AF                  AFG   38041757.0   \n",
       "4                 Afghanistan    AF                  AFG   38041757.0   \n",
       "...                       ...   ...                  ...          ...   \n",
       "44931                    Peru    PE                  PER   32510462.0   \n",
       "44932                    Peru    PE                  PER   32510462.0   \n",
       "44933                    Peru    PE                  PER   32510462.0   \n",
       "44934                    Peru    PE                  PER   32510462.0   \n",
       "44935                    Peru    PE                  PER   32510462.0   \n",
       "\n",
       "        continentExp  \\\n",
       "0               Asia   \n",
       "1               Asia   \n",
       "2               Asia   \n",
       "3               Asia   \n",
       "4               Asia   \n",
       "...              ...   \n",
       "44931  South America   \n",
       "44932  South America   \n",
       "44933  South America   \n",
       "44934  South America   \n",
       "44935  South America   \n",
       "\n",
       "       Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \\\n",
       "0                                               9.013779            \n",
       "1                                               7.052776            \n",
       "2                                               6.868768            \n",
       "3                                               7.134266            \n",
       "4                                               6.968658            \n",
       "...                                                  ...            \n",
       "44931                                         151.868036            \n",
       "44932                                         152.821575            \n",
       "44933                                         156.432720            \n",
       "44934                                         158.764277            \n",
       "44935                                         165.528254            \n",
       "\n",
       "       death_rate_%  \n",
       "0          0.804290  \n",
       "1          3.020134  \n",
       "2          9.734513  \n",
       "3         15.873016  \n",
       "4          7.920792  \n",
       "...             ...  \n",
       "44931      6.347590  \n",
       "44932      5.306122  \n",
       "44933      5.406897  \n",
       "44934      4.731526  \n",
       "44935      4.472272  \n",
       "\n",
       "[30400 rows x 13 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(30400)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91918733",
   "metadata": {},
   "source": [
    "We were successful at converting the continentExp to 'South America' and keeping the countries not part of South America as 'America'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dac3d2f",
   "metadata": {},
   "source": [
    "#### Finding average death rate across continents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d707be4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_death_rate_continent = df.groupby('continentExp')['death_rate_%'].mean()\n",
    "\n",
    "\n",
    "avg_death_rate_continent = avg_death_rate_continent[avg_death_rate_continent.index != 'Other']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "bars = ax.bar(avg_death_rate_continent.index, avg_death_rate_continent.values)\n",
    "\n",
    "ax.set_title('Average Death Rate Across Continents')\n",
    "ax.set_xlabel('continentExp')\n",
    "ax.set_ylabel('average death rate as percentage')\n",
    "\n",
    "#This code was written by OpenAI's ChatGPT, and was used to make the graph more accessible.\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    ax.annotate(f'{height:.2f}', xy=(bar.get_x() + bar.get_width() / 2, height),\n",
    "                xytext=(0, 3), textcoords='offset points', ha='center', va='bottom')\n",
    "\n",
    "plt.ylim(0.000, 6.000)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dba806f",
   "metadata": {},
   "source": [
    "The average death rate in South America was 3.79%, the second highest following Europe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f947795",
   "metadata": {},
   "source": [
    "### What was the average death rate in Chile and Peru?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9288e6",
   "metadata": {},
   "source": [
    "Most of the code needed to answer this question has already been utilized above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f20c38",
   "metadata": {},
   "source": [
    "#### Finding the average death rate in Chile and Peru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5ebe27ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_death_rate_South_America = df.loc[df['continentExp'] == 'South America'].groupby('countriesAndTerritories')['death_rate_%'].mean()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "cpax = plt.axes()\n",
    "\n",
    "cpbars = cpax.bar(avg_death_rate_South_America.index, avg_death_rate_South_America.values)\n",
    "\n",
    "cpax.set_title('Average Death Rate Across Countries in South America')\n",
    "cpax.set_xlabel('Country')\n",
    "cpax.set_ylabel('Death rate (%)')\n",
    "\n",
    "#This code was written by OpenAI's ChatGPT, and was used in order to make the graph more accessible.\n",
    "for cpbar in cpbars:\n",
    "    height = cpbar.get_height()\n",
    "    cpax.annotate(f'{height:.2f}', xy=(cpbar.get_x() + cpbar.get_width() / 2, height),\n",
    "                  xytext=(0, 3), textcoords='offset points', ha='center', va='bottom')\n",
    "    \n",
    "plt.ylim(0.000, 10.000)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb0dcd8",
   "metadata": {},
   "source": [
    "Chile and Peru both have average death rates lower than South America as a whole.\n",
    "\n",
    "South America's death rate is 3.79%.\n",
    "Chile's average death rate is 2.50%.\n",
    "Peru's average death rate is 3.56%.\n",
    "\n",
    "As this graph shows, countries such as Guyana, Ecuador, and Bolivia bring the average for South America up significantly.\n",
    "\n",
    "Peru's average is very close to several other countries, while Chile's is noticeably lower."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2bca308",
   "metadata": {},
   "source": [
    "### In what month was the peak of the death rate in Chile and Peru?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0448cf",
   "metadata": {},
   "source": [
    "#### Chile data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bcc49156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>geoId</th>\n",
       "      <th>countryterritoryCode</th>\n",
       "      <th>popData2019</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>Cumulative_number_for_14_days_of_COVID-19_cases_per_100000</th>\n",
       "      <th>death_rate_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11813</th>\n",
       "      <td>14/12/2020</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>2138</td>\n",
       "      <td>40</td>\n",
       "      <td>Chile</td>\n",
       "      <td>CL</td>\n",
       "      <td>CHL</td>\n",
       "      <td>18952035.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>113.386241</td>\n",
       "      <td>1.870907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11814</th>\n",
       "      <td>13/12/2020</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>1807</td>\n",
       "      <td>64</td>\n",
       "      <td>Chile</td>\n",
       "      <td>CL</td>\n",
       "      <td>CHL</td>\n",
       "      <td>18952035.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>109.961806</td>\n",
       "      <td>3.541782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11815</th>\n",
       "      <td>2020-12-12 00:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>1534</td>\n",
       "      <td>8</td>\n",
       "      <td>Chile</td>\n",
       "      <td>CL</td>\n",
       "      <td>CHL</td>\n",
       "      <td>18952035.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>109.492200</td>\n",
       "      <td>0.521512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11816</th>\n",
       "      <td>2020-11-12 00:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>1662</td>\n",
       "      <td>84</td>\n",
       "      <td>Chile</td>\n",
       "      <td>CL</td>\n",
       "      <td>CHL</td>\n",
       "      <td>18952035.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>109.634665</td>\n",
       "      <td>5.054152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11817</th>\n",
       "      <td>2020-10-12 00:00:00</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>1244</td>\n",
       "      <td>10</td>\n",
       "      <td>Chile</td>\n",
       "      <td>CL</td>\n",
       "      <td>CHL</td>\n",
       "      <td>18952035.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>109.149229</td>\n",
       "      <td>0.803859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   dateRep  day  month  year  cases  deaths  \\\n",
       "11813           14/12/2020   14     12  2020   2138      40   \n",
       "11814           13/12/2020   13     12  2020   1807      64   \n",
       "11815  2020-12-12 00:00:00   12     12  2020   1534       8   \n",
       "11816  2020-11-12 00:00:00   11     12  2020   1662      84   \n",
       "11817  2020-10-12 00:00:00   10     12  2020   1244      10   \n",
       "\n",
       "      countriesAndTerritories geoId countryterritoryCode  popData2019  \\\n",
       "11813                   Chile    CL                  CHL   18952035.0   \n",
       "11814                   Chile    CL                  CHL   18952035.0   \n",
       "11815                   Chile    CL                  CHL   18952035.0   \n",
       "11816                   Chile    CL                  CHL   18952035.0   \n",
       "11817                   Chile    CL                  CHL   18952035.0   \n",
       "\n",
       "        continentExp  \\\n",
       "11813  South America   \n",
       "11814  South America   \n",
       "11815  South America   \n",
       "11816  South America   \n",
       "11817  South America   \n",
       "\n",
       "       Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \\\n",
       "11813                                         113.386241            \n",
       "11814                                         109.961806            \n",
       "11815                                         109.492200            \n",
       "11816                                         109.634665            \n",
       "11817                                         109.149229            \n",
       "\n",
       "       death_rate_%  \n",
       "11813      1.870907  \n",
       "11814      3.541782  \n",
       "11815      0.521512  \n",
       "11816      5.054152  \n",
       "11817      0.803859  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chile_data = df.loc[df['countriesAndTerritories'] == 'Chile']\n",
    "chile_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d79ae2",
   "metadata": {},
   "source": [
    "Created the chile_data variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30964c09",
   "metadata": {},
   "source": [
    "#### Peru data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "84cec3fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>geoId</th>\n",
       "      <th>countryterritoryCode</th>\n",
       "      <th>popData2019</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>Cumulative_number_for_14_days_of_COVID-19_cases_per_100000</th>\n",
       "      <th>death_rate_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44764</th>\n",
       "      <td>14/12/2020</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>1928</td>\n",
       "      <td>68</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>PER</td>\n",
       "      <td>32510462.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>69.033162</td>\n",
       "      <td>3.526971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44765</th>\n",
       "      <td>13/12/2020</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>2102</td>\n",
       "      <td>65</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>PER</td>\n",
       "      <td>32510462.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>69.752931</td>\n",
       "      <td>3.092293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44766</th>\n",
       "      <td>2020-12-12 00:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>1832</td>\n",
       "      <td>45</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>PER</td>\n",
       "      <td>32510462.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>69.574527</td>\n",
       "      <td>2.456332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44767</th>\n",
       "      <td>2020-11-12 00:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>1799</td>\n",
       "      <td>44</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>PER</td>\n",
       "      <td>32510462.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>70.020537</td>\n",
       "      <td>2.445803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44768</th>\n",
       "      <td>2020-10-12 00:00:00</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>691</td>\n",
       "      <td>54</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>PER</td>\n",
       "      <td>32510462.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>70.294295</td>\n",
       "      <td>7.814761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   dateRep  day  month  year  cases  deaths  \\\n",
       "44764           14/12/2020   14     12  2020   1928      68   \n",
       "44765           13/12/2020   13     12  2020   2102      65   \n",
       "44766  2020-12-12 00:00:00   12     12  2020   1832      45   \n",
       "44767  2020-11-12 00:00:00   11     12  2020   1799      44   \n",
       "44768  2020-10-12 00:00:00   10     12  2020    691      54   \n",
       "\n",
       "      countriesAndTerritories geoId countryterritoryCode  popData2019  \\\n",
       "44764                    Peru    PE                  PER   32510462.0   \n",
       "44765                    Peru    PE                  PER   32510462.0   \n",
       "44766                    Peru    PE                  PER   32510462.0   \n",
       "44767                    Peru    PE                  PER   32510462.0   \n",
       "44768                    Peru    PE                  PER   32510462.0   \n",
       "\n",
       "        continentExp  \\\n",
       "44764  South America   \n",
       "44765  South America   \n",
       "44766  South America   \n",
       "44767  South America   \n",
       "44768  South America   \n",
       "\n",
       "       Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \\\n",
       "44764                                          69.033162            \n",
       "44765                                          69.752931            \n",
       "44766                                          69.574527            \n",
       "44767                                          70.020537            \n",
       "44768                                          70.294295            \n",
       "\n",
       "       death_rate_%  \n",
       "44764      3.526971  \n",
       "44765      3.092293  \n",
       "44766      2.456332  \n",
       "44767      2.445803  \n",
       "44768      7.814761  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peru_data = df.loc[df['countriesAndTerritories'] == 'Peru']\n",
    "peru_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95dd0ad7",
   "metadata": {},
   "source": [
    "Created the peru_data variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d959a3a3",
   "metadata": {},
   "source": [
    "#### Chile and Peru Death Rate by Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b3f60be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "death_rate_chile_peru = df.groupby(['countriesAndTerritories', 'month'])['death_rate_%'].mean()\n",
    "\n",
    "chile_death_rate = death_rate_chile_peru['Chile']\n",
    "peru_death_rate = death_rate_chile_peru['Peru']\n",
    "\n",
    "plt.plot(chile_death_rate.index, chile_death_rate.values, label='Chile')\n",
    "plt.plot(peru_death_rate.index, peru_death_rate.values, label='Peru')\n",
    "\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Death Rate (%)')\n",
    "plt.title('Chile and Peru Death Rates by Month')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f89965d4",
   "metadata": {},
   "source": [
    "In both Chile and Peru, the death rate began increasing in May of 2020, and peaked in July.\n",
    "\n",
    "Further research revealed that several different factors are likely responsible for this: \n",
    "\n",
    "First, the reporting guidelines for deaths due to COVID-19 were very fluid in both of these countries. This may have led to inconsistent reporting which was then reflected in the dataset.\n",
    "\n",
    "Second, arbitrary death reporting may have also been a political play by the health ministers and government in order to make it seem as though they were dealing with the pandemic adequately.\n",
    "\n",
    "Third, Peru had imposed a strict lockdown that ended in June of 2020. After June 2020 and into July, there is a spike in the death rate. This demonstrates a correlation between the death rate and the end of the lockdown. Conversly, Chile's health minister at the time would not impose strict lockdowns and preferred to maintain normal procedure. This is a possible explanation for why Chile's increase in death rate is more consistent than Peru's.\n",
    "\n",
    "Sources: https://www.bbc.com/news/world-latin-america-53150808 and https://www.aljazeera.com/news/2020/6/13/chile-health-minister-resigns-amid-coronavirus-row-live-updates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "168fb5df",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba77d02e",
   "metadata": {},
   "source": [
    "South America saw the second highest average death rate (3.79%) from COVID-19 in 2020.\n",
    "\n",
    "The death rates of Chile (2.50%) and Peru (3.56%), two neighboring countries within South America, had a lower average death rate than South America as a whole, but were nevertheless hit hard by the virus.\n",
    "\n",
    "The peak in death rate for both of these countries was in July, with Chile seeing a consistent increase in rate from May to July, and Peru seeing a sharp spike from June to July.\n",
    "\n",
    "While the difference in death rate between Peru and Chile was significant, both rates varied together.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd417f2",
   "metadata": {},
   "source": [
    "### Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61045295",
   "metadata": {},
   "source": [
    "We were limited in the number of the sources we had access to because many sources on this topic were in Spanish, which neither of us can read."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26e5c1b",
   "metadata": {},
   "source": [
    "### Suggestions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f77703f",
   "metadata": {},
   "source": [
    "Future analyses could include data from 2021, after the vaccines were beginning to be distributed. This could show the effectiveness of the vaccine and its implementation across different countries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70acaf51",
   "metadata": {},
   "source": [
    "## Authors: \n",
    "    Colin Lafley and Luke Blubaugh, Intelligence Analysis\n",
    "    James Madison University, Class of 2025\n",
    "    May 05, 2023"
   ]
  }
 ],
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