IA 241 Final Project- Nathan Daly and John Fleetwood.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7101b285",
   "metadata": {},
   "source": [
    "   # Analyzing Covid-19 Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82994a35",
   "metadata": {},
   "source": [
    "![JMU_IA_LOGO](https://creative.chbs.jmu.edu/images/logos/intelligence-analysis.png)\n",
    "\n",
    "## Background\n",
    "Coronavirus is an acute disease in humans, which is characterized mainly by fever and cough and is capable of progressing to severe symptoms and in some cases death, especially in older people and those with underlying health conditions. In late 2019, the first identification of Covid-19 occurred in Wuhan, China. Despite efforts to contain the virus, it rapidly spread. On January 30th, 2020, the World Health Organization recognized it as an international concern. On March 11th, 2020, the virus was declared a global pandemic, resulting in significant social and economic disruption. The data presented in this report provides insights into how the virus impacted the world throughout the year 2020.\n",
    "\n",
    "## Data\n",
    "The data is provided by the \n",
    "[European Centre for Disease Prevention and Control](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc09abb",
   "metadata": {},
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "805c55f8",
   "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"
     ]
    },
    {
     "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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-09-12 00:00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>135</td>\n",
       "      <td>13</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.963401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-08-12 00:00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>200</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>7.094835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-07-12 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>210</td>\n",
       "      <td>26</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.215755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-06-12 00:00:00</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>234</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>7.326160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-05-12 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>235</td>\n",
       "      <td>18</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.115865</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",
       "5  2020-09-12 00:00:00    9     12  2020    135      13   \n",
       "6  2020-08-12 00:00:00    8     12  2020    200       6   \n",
       "7  2020-07-12 00:00:00    7     12  2020    210      26   \n",
       "8  2020-06-12 00:00:00    6     12  2020    234      10   \n",
       "9  2020-05-12 00:00:00    5     12  2020    235      18   \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",
       "5             Afghanistan    AF                  AFG   38041757.0   \n",
       "6             Afghanistan    AF                  AFG   38041757.0   \n",
       "7             Afghanistan    AF                  AFG   38041757.0   \n",
       "8             Afghanistan    AF                  AFG   38041757.0   \n",
       "9             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",
       "5         Asia                                           6.963401           \n",
       "6         Asia                                           7.094835           \n",
       "7         Asia                                           7.215755           \n",
       "8         Asia                                           7.326160           \n",
       "9         Asia                                           7.115865           "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "!pip install xlrd\n",
    "df = pd.read_excel('s3://jrf-ia241-bucket/covid_data.xls')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e361a93a",
   "metadata": {},
   "source": [
    "## Q1: What are the top 10 countries with the highest recorded cases from Covid-19 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e01b424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='countriesAndTerritories'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "death_countries= df.groupby('countriesAndTerritories')['cases'].sum()\n",
    "death_countries.nlargest(10).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8152db77",
   "metadata": {},
   "source": [
    "### Calculations\n",
    "The death_countries is calculated to show the maximum deaths in the top 10 countries. This is done by using using df.groupby() to group the variable of 'countriesAndTerritories' which is shown on the x axis and selecting the 'cases' variable and using .sum() to show the total number of cases to the 10^7 power on the y axis. In the second line the .nlargest(10) seperates the top ten countries with the largest cases value. Using the .plot.bar generates a bar chart with 'countriesAndTerritories' and 'cases'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f76828a9",
   "metadata": {},
   "source": [
    "### Explanation\n",
    "The bar graph displays the ten countries with the highest recorded COVID-19 case count for 2020. Of these, the United States recorded the highest amount, almost doubling the runner-up (India). This could be attributed to the vast adoption of COVID-19 testing in American hospitals, schools, and places of work and the large population of the United States."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a38b306",
   "metadata": {},
   "source": [
    "## United States Covid-19 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61b3ef81",
   "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>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>59001</th>\n",
       "      <td>14/12/2020</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>189723</td>\n",
       "      <td>1340</td>\n",
       "      <td>United_States_of_America</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>329064917.0</td>\n",
       "      <td>America</td>\n",
       "      <td>873.211592</td>\n",
       "      <td>0.007063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59002</th>\n",
       "      <td>13/12/2020</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>216017</td>\n",
       "      <td>2315</td>\n",
       "      <td>United_States_of_America</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>329064917.0</td>\n",
       "      <td>America</td>\n",
       "      <td>857.089241</td>\n",
       "      <td>0.010717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59003</th>\n",
       "      <td>2020-12-12 00:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>234633</td>\n",
       "      <td>3343</td>\n",
       "      <td>United_States_of_America</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>329064917.0</td>\n",
       "      <td>America</td>\n",
       "      <td>838.514183</td>\n",
       "      <td>0.014248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59004</th>\n",
       "      <td>2020-11-12 00:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>224680</td>\n",
       "      <td>2748</td>\n",
       "      <td>United_States_of_America</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>329064917.0</td>\n",
       "      <td>America</td>\n",
       "      <td>830.394205</td>\n",
       "      <td>0.012231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59005</th>\n",
       "      <td>2020-10-12 00:00:00</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>220025</td>\n",
       "      <td>3124</td>\n",
       "      <td>United_States_of_America</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>329064917.0</td>\n",
       "      <td>America</td>\n",
       "      <td>794.356027</td>\n",
       "      <td>0.014198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59006</th>\n",
       "      <td>2020-09-12 00:00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>217344</td>\n",
       "      <td>2564</td>\n",
       "      <td>United_States_of_America</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>329064917.0</td>\n",
       "      <td>America</td>\n",
       "      <td>784.195114</td>\n",
       "      <td>0.011797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59007</th>\n",
       "      <td>2020-08-12 00:00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>197334</td>\n",
       "      <td>1433</td>\n",
       "      <td>United_States_of_America</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>329064917.0</td>\n",
       "      <td>America</td>\n",
       "      <td>769.896719</td>\n",
       "      <td>0.007262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59008</th>\n",
       "      <td>2020-07-12 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>173432</td>\n",
       "      <td>1111</td>\n",
       "      <td>United_States_of_America</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>329064917.0</td>\n",
       "      <td>America</td>\n",
       "      <td>762.794473</td>\n",
       "      <td>0.006406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59009</th>\n",
       "      <td>2020-06-12 00:00:00</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>211933</td>\n",
       "      <td>2203</td>\n",
       "      <td>United_States_of_America</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>329064917.0</td>\n",
       "      <td>America</td>\n",
       "      <td>757.944062</td>\n",
       "      <td>0.010395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59010</th>\n",
       "      <td>2020-05-12 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>231930</td>\n",
       "      <td>2680</td>\n",
       "      <td>United_States_of_America</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>329064917.0</td>\n",
       "      <td>America</td>\n",
       "      <td>746.870564</td>\n",
       "      <td>0.011555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   dateRep  day  month  year   cases  deaths  \\\n",
       "59001           14/12/2020   14     12  2020  189723    1340   \n",
       "59002           13/12/2020   13     12  2020  216017    2315   \n",
       "59003  2020-12-12 00:00:00   12     12  2020  234633    3343   \n",
       "59004  2020-11-12 00:00:00   11     12  2020  224680    2748   \n",
       "59005  2020-10-12 00:00:00   10     12  2020  220025    3124   \n",
       "59006  2020-09-12 00:00:00    9     12  2020  217344    2564   \n",
       "59007  2020-08-12 00:00:00    8     12  2020  197334    1433   \n",
       "59008  2020-07-12 00:00:00    7     12  2020  173432    1111   \n",
       "59009  2020-06-12 00:00:00    6     12  2020  211933    2203   \n",
       "59010  2020-05-12 00:00:00    5     12  2020  231930    2680   \n",
       "\n",
       "        countriesAndTerritories geoId countryterritoryCode  popData2019  \\\n",
       "59001  United_States_of_America    US                  USA  329064917.0   \n",
       "59002  United_States_of_America    US                  USA  329064917.0   \n",
       "59003  United_States_of_America    US                  USA  329064917.0   \n",
       "59004  United_States_of_America    US                  USA  329064917.0   \n",
       "59005  United_States_of_America    US                  USA  329064917.0   \n",
       "59006  United_States_of_America    US                  USA  329064917.0   \n",
       "59007  United_States_of_America    US                  USA  329064917.0   \n",
       "59008  United_States_of_America    US                  USA  329064917.0   \n",
       "59009  United_States_of_America    US                  USA  329064917.0   \n",
       "59010  United_States_of_America    US                  USA  329064917.0   \n",
       "\n",
       "      continentExp  \\\n",
       "59001      America   \n",
       "59002      America   \n",
       "59003      America   \n",
       "59004      America   \n",
       "59005      America   \n",
       "59006      America   \n",
       "59007      America   \n",
       "59008      America   \n",
       "59009      America   \n",
       "59010      America   \n",
       "\n",
       "       Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  death_rate  \n",
       "59001                                         873.211592             0.007063  \n",
       "59002                                         857.089241             0.010717  \n",
       "59003                                         838.514183             0.014248  \n",
       "59004                                         830.394205             0.012231  \n",
       "59005                                         794.356027             0.014198  \n",
       "59006                                         784.195114             0.011797  \n",
       "59007                                         769.896719             0.007262  \n",
       "59008                                         762.794473             0.006406  \n",
       "59009                                         757.944062             0.010395  \n",
       "59010                                         746.870564             0.011555  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_data = df.loc[df['countriesAndTerritories'] == 'United_States_of_America']\n",
    "usa_data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468d3a63",
   "metadata": {},
   "source": [
    "### Calculation\n",
    "Seperating the United States data was done by using the df.loc() and selecting the variable 'countriesAndTerritories' and making it equal to value of 'United_States_of_America'. This code seperates the United States data from the rest of the countries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e932835",
   "metadata": {},
   "source": [
    "## Q2: What are the average Covid-19 cases in the US per month?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff8d3d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='month'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_covid_month = usa_data.groupby('month') ['cases'].mean()\n",
    "avg_covid_month.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd5eb22e",
   "metadata": {},
   "source": [
    "### Calculation \n",
    "The avg_covid_month is calculating the average amount of cases on the y axis and the months that they occured on the x axis. Using the usa_data.groupby() grouped the data by the selected column 'months' and the .mean() calculated the mean for the variable 'cases'. The output is a line chart this was done using .plot()."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9900718c",
   "metadata": {},
   "source": [
    "### Explanation\n",
    "The line graph shows the average number of cases occurring on any given day within the United States, organized by month. COVID-19 first emerged as a public health crisis in 2020, so the data reflects an exponential increase in infections over time. There appears to be a small spike in the summer months, possibly due to hot weather forcing people indoors. This decreases slightly in the early autumn, but a massive increase occurs in November and December, as people return indoors due to the cold weather."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f65a2c",
   "metadata": {},
   "source": [
    "## Q3: What is the death rate for Covid-19 in the United States?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e253a7db",
   "metadata": {
    "scrolled": true
   },
   "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>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>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",
       "      <td>0.008043</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",
       "      <td>0.030201</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",
       "      <td>0.097345</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>0.158730</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>0.079208</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    0.008043  \n",
       "1    0.030201  \n",
       "2    0.097345  \n",
       "3    0.158730  \n",
       "4    0.079208  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['death_rate'] = df['deaths']/df['cases']\n",
    "df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dfb1e70b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='month', ylabel='death_rate'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_data.plot.scatter(x='month',y='death_rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "546fda78",
   "metadata": {},
   "source": [
    "### Calculations\n",
    "3a.) Creating the 'death_rate' variable was done by selecting the variable columns 'deaths' and 'cases'. Once we divide the variables we got the amount of deaths per cases.\n",
    "\n",
    "3b.) To create the scatter plot using US data we selected the US data variable of 'usa_data' and using the .plot.scatter and the variables for the scatter plot are 'month' on the x axis and the new variable created 'death_rate' on the y axis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e327481",
   "metadata": {},
   "source": [
    "### Explanation\n",
    "The scatterplot shows an overall downward trend in the COVID-19 death rate over time. There is an initial spike in April, when the U.S. first encountered the virus, but over the course of 2020, it appears the U.S. reduced the number of excess deaths through treatment. Additionally, as most viruses mutate, they become more infectious, but less deadly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "608d427a",
   "metadata": {},
   "source": [
    "## Conclusion "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d21ec89f",
   "metadata": {},
   "source": [
    "### Limitations \n",
    "Limitations in this data stems from the fact that this data only accounts for the year 2020. Developing countries had a harder time identifying and reporting cases and deaths, this leads to data being miss represented and either over or under reported. External limitations due to how skewed data effected certian coutnries' reaction to the pandemic."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "063697ec",
   "metadata": {},
   "source": [
    "### Recommendations\n",
    "Include more data from other years, include data from multiple sources to prevent miss reporting and data collection error. Continued research to how Covid-19 affected different countries in varying ways such as economics and political stability. Allowing for more possible variable and data can give a fuller understanding and can allow more prespectives to be represented "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de660424",
   "metadata": {},
   "source": [
    "## Authors\n",
    "-  Nathan Daly & John Fleetwood\n",
    "-  nathanjdaly2002@gmail.com & fleet2jr@dukes.jmu.edu\n",
    "-  IA 241 Spring 2023\n",
    "-  James Madison University "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_python3",
   "language": "python",
   "name": "conda_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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}