Final Project.ipynb

{
 "cells": [
  {
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mJoOzalFdN011PNGpBLp9xKoJ7vMGwsWcLiVVipLbvjwZHjcBCpFxksmv4aFwlJReW8sTKXk8iZ55pXRsqE3CWz5stzR5vpX5Wp9eXiz3zTOFVWnKLV3bLoluZ4HpSL9LUus9ed+9kMRPWgr8Tfo2Npy5IjwmIcJJr+Eb0a8ZJNPacy2TRrtLJnOqQ1sz0WGxLp3TzR0E6sVtaQ2E4t3TTMBtvpZLQoyi7lTpW6zVHGNvo5G4DI8NNtZklypo6EXdXQ276CWns7V+jEiZMt/8bkdTROddv7L80ed+U8rYJLjOPlJ+goWEFPRngrjkARAQFZoLCvaCJgdXzb/LVfYy8ynp6ergdJvjeP52rHzLnNx8tV9jLzMzljPV0dpB8atOPfUoo5dT40l2rzRup9CPf6nkLQqEQ5I0kh6QrQxMdFjAcCEYJAA5MlhAihFlijF3EM9a5icMlDEy361OPubO8xC9HK/zZv8ANn9z8TjuYyFsPiH/AH0F3Q/ed/iKKknFrJrMxzlabJPMgWaKuLpEmGbg/Rz/ACZfSX39BLWpji8xMo0ISi+C4lib4Zk0ZvYl2Eigt9uwG+IJFenSvmTqKRHWeqIqre5+HiLeMmpvMmuQ0GPu3kQe8YSzPGOWGjovFV9XL8LLqvrXfvPaIvczx/T9W+Iqy41Zv7TMmKlZI7ehoKU533WX5v8AQ42rCzae1DsPRc3a9lvZpaXpKS10s1t6jNoyztx8TOpXR0JU9nU1XmjXw2GhBZK74vaSaq3GQqk47GPekJ9HcVODZuhiacVbVtY2YJjthVw2PUo55MbXw855qUbblnsK9Xjka1VTjeH0iWviIpO7J8HPWimt5iYilKPxo28H2o3MCrU4bvUWXZmdfRVNKpKV+r1XseV+UeIlKjCDVvpeSfuSgOA7p5AVCDogIZWYCtZgTEdVzc/LVfYy8zH5dzto7Fr6WKpR+3CX7Jr83Py1X2MvM5jnNxerhnS/rMa5dlOF/Fo5c868l2r0N9LoRPN4ijWyajg61R2hSm+qMrd+w1EiFyHJmthuSdeWc5Qh0Xcn7iaryVqx+LKEu+D95LVfAhtIcTFiiWMSbEaOq0vj05Lps3HvWRFEiTWY6MSeiRxRJCQhnsnMh+LV/wDEL9Wj0FnnvMhL4NX/AMQv1cTt8RpOhDKdelF8HOCfdcw1M5uxLqJa9GM1qy+5p8U9zKOMxMqCvNa0djmleS4ay80LDTWHf/cUf/rT+8m9JTqq0Jwn0RlGeXUmCy3hyKEdK0WrrWfSnl7syehpCnNeo11GBpDR0sPO6vqS2Ph/ZZUxDT9aL1ZcVl38SyyInWTx8UiqsfdnM4fGyk/Wea2ovQrvayWohax1uEkpJSRY7Dm9F6WUXa+R0OGrqauimcWmTTuOSVzxXTUfwknuc5eLPaltPHMTG7d+L8Tn4x21e89DoKOttfw+pmwoprPfkZWK0XKOcc17+408XhpRzi+wr08dbKfeZItrOJ2akacrRqK3BmRKclk/eN1jdr01NZq/j2My62Aknln0by2M09+Rlq4ecN2aIYSNXAVL7GZNR6uW8XD4lxZKUdZEaNZU55nRP1kWYcezzM+hXurl6hvb6+7/AGZbo2rs8Qov+LL1X5ojp6gsRgnNb6dpd25/k7j7AOEsemPACxAVAIRWYWCQEwOo5u/lqvsJeZh6Xpwqr8JGEmpVZLWSk4tzgsutI3Obz5Wr7GXgzIxcXqvZZSm9mfx4Lb2o59v/AEvmjYnakjKhh4R+LCK6oxRNcQDoGMAFAAEIauEpz+NCL60icQHmCyKM9DUH/Rrscl5i09EUI5qmu1yfiy6BHVXAlry4iUVqLVh6sW7uKyi3xaFSAVEtxG4D6bcXeLs1saya7UNQ4QG9guV1eMfR10q9N5NTup26JrzuV6+lacm9VTS3J2k12raZLAqdGF72sWKtPiWq+N3xvfjsFelav0u8qAGxj2j20izHSE077+t2NXR3KupTeay4p591jBANjANtM9JwPLXCyS9JUcJb04VGu9JnntWSu897+bIgFRkraNpVbXbVuXsdDB6YrYXW1Yxetbffqvwa4hOz3/x2lTE6PjPinxWqW0OILRNBdcvFexpn8o8ZLJxhb7r/AMjDWBrxdotNbnrJe4dKhiGrOMetSjfxNkcT/wCNodvivYpWnMX1NeD9znKuhKjzjbpvKK8B9Dk9K95yj1K8n35G/YckSWAori+/2sVS0piJbrLkvdsq4XARpr1b9tvLMsaq3fcOkIjRTo04dGKX78TJVxNar8Sbffl3LcgsI2DAtM46ICxAQys1mNzFAmI6fm9X4ar7CXgzLxK9SXVU/WUxQOf/ADEu42L4S5My8xbAB0DELYQABjFsI4gAIAsJYAABbCpCAAD4xFSABDQWCwgAIWwaogAAtgURAABbCgAAEUO1QAQBYXVEAGNi2CwAIAURZIAABlgsADESRQAAhn//2Q=="
    }
   },
   "cell_type": "markdown",
   "id": "0ec12c21",
   "metadata": {},
   "source": [
    "# Analyzing and Visualizing COVID-19 Data in France\n",
    "![download.jpg](attachment:download.jpg)\n",
    "\n",
    "# Background of COVID-19 in France\n",
    "\n",
    "COVID started in Wuhan, China in December of 2019 when people began to fall sick with a previously unknown pneumonia. China declared it's first case of COVID on Janurary 7th 2020. COVID-19 surveliance in France started on Janurary 10th 2020. The first cases in France were not diagnosed until 14 days later. \n",
    "##### Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7029452/#:~:text=Specific%20COVID%2D19%20surveillance%20has,days%20later%2C%20on%2024%20January. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a8285bc",
   "metadata": {},
   "source": [
    "# Data Source\n",
    "\n",
    "###### The Data is from European Centre for Disease Prevention and Control \n",
    "https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b7cdb23",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5922e8",
   "metadata": {},
   "source": [
    "\n",
    "# Overview of Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab96830c",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-14</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>2020-12-13</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</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>2020-12-11</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>2020-12-10</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>2020-12-09</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>2020-12-08</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>2020-12-07</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>2020-12-06</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>2020-12-05</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 countriesAndTerritories geoId  \\\n",
       "0 2020-12-14   14     12  2020    746       6             Afghanistan    AF   \n",
       "1 2020-12-13   13     12  2020    298       9             Afghanistan    AF   \n",
       "2 2020-12-12   12     12  2020    113      11             Afghanistan    AF   \n",
       "3 2020-12-11   11     12  2020     63      10             Afghanistan    AF   \n",
       "4 2020-12-10   10     12  2020    202      16             Afghanistan    AF   \n",
       "5 2020-12-09    9     12  2020    135      13             Afghanistan    AF   \n",
       "6 2020-12-08    8     12  2020    200       6             Afghanistan    AF   \n",
       "7 2020-12-07    7     12  2020    210      26             Afghanistan    AF   \n",
       "8 2020-12-06    6     12  2020    234      10             Afghanistan    AF   \n",
       "9 2020-12-05    5     12  2020    235      18             Afghanistan    AF   \n",
       "\n",
       "  countryterritoryCode  popData2019 continentExp  \\\n",
       "0                  AFG   38041757.0         Asia   \n",
       "1                  AFG   38041757.0         Asia   \n",
       "2                  AFG   38041757.0         Asia   \n",
       "3                  AFG   38041757.0         Asia   \n",
       "4                  AFG   38041757.0         Asia   \n",
       "5                  AFG   38041757.0         Asia   \n",
       "6                  AFG   38041757.0         Asia   \n",
       "7                  AFG   38041757.0         Asia   \n",
       "8                  AFG   38041757.0         Asia   \n",
       "9                  AFG   38041757.0         Asia   \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",
       "5                                           6.963401           \n",
       "6                                           7.094835           \n",
       "7                                           7.215755           \n",
       "8                                           7.326160           \n",
       "9                                           7.115865           "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pandas.read_excel('s3://schultz-241-2023-python/COVID-19-geographic-disbtribution-worldwide-2020-12-14.xlsx')\n",
    "df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca2b4fe3",
   "metadata": {},
   "source": [
    "\n",
    "# France's Covid Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa681d97",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20364</th>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>11533</td>\n",
       "      <td>150</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>67012883.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>236.326200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20365</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>13947</td>\n",
       "      <td>194</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>67012883.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>233.716254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20366</th>\n",
       "      <td>2020-12-12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>13406</td>\n",
       "      <td>627</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>67012883.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>231.556968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20367</th>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>13750</td>\n",
       "      <td>292</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>67012883.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>230.263187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20368</th>\n",
       "      <td>2020-12-10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>14595</td>\n",
       "      <td>296</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>67012883.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>229.984136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20369</th>\n",
       "      <td>2020-12-09</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>13713</td>\n",
       "      <td>831</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>67012883.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>232.501562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20370</th>\n",
       "      <td>2020-12-08</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>3411</td>\n",
       "      <td>366</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>67012883.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>225.699885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20371</th>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>11022</td>\n",
       "      <td>174</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>67012883.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>227.253318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20372</th>\n",
       "      <td>2020-12-06</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>12923</td>\n",
       "      <td>214</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>67012883.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>230.439272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20373</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>11221</td>\n",
       "      <td>627</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>67012883.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>237.837850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dateRep  day  month  year  cases  deaths countriesAndTerritories  \\\n",
       "20364 2020-12-14   14     12  2020  11533     150                  France   \n",
       "20365 2020-12-13   13     12  2020  13947     194                  France   \n",
       "20366 2020-12-12   12     12  2020  13406     627                  France   \n",
       "20367 2020-12-11   11     12  2020  13750     292                  France   \n",
       "20368 2020-12-10   10     12  2020  14595     296                  France   \n",
       "20369 2020-12-09    9     12  2020  13713     831                  France   \n",
       "20370 2020-12-08    8     12  2020   3411     366                  France   \n",
       "20371 2020-12-07    7     12  2020  11022     174                  France   \n",
       "20372 2020-12-06    6     12  2020  12923     214                  France   \n",
       "20373 2020-12-05    5     12  2020  11221     627                  France   \n",
       "\n",
       "      geoId countryterritoryCode  popData2019 continentExp  \\\n",
       "20364    FR                  FRA   67012883.0       Europe   \n",
       "20365    FR                  FRA   67012883.0       Europe   \n",
       "20366    FR                  FRA   67012883.0       Europe   \n",
       "20367    FR                  FRA   67012883.0       Europe   \n",
       "20368    FR                  FRA   67012883.0       Europe   \n",
       "20369    FR                  FRA   67012883.0       Europe   \n",
       "20370    FR                  FRA   67012883.0       Europe   \n",
       "20371    FR                  FRA   67012883.0       Europe   \n",
       "20372    FR                  FRA   67012883.0       Europe   \n",
       "20373    FR                  FRA   67012883.0       Europe   \n",
       "\n",
       "       Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \n",
       "20364                                         236.326200           \n",
       "20365                                         233.716254           \n",
       "20366                                         231.556968           \n",
       "20367                                         230.263187           \n",
       "20368                                         229.984136           \n",
       "20369                                         232.501562           \n",
       "20370                                         225.699885           \n",
       "20371                                         227.253318           \n",
       "20372                                         230.439272           \n",
       "20373                                         237.837850           "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "france_data = df.loc[ df['countriesAndTerritories']=='France' ]\n",
    "\n",
    "france_data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03af5fe7",
   "metadata": {},
   "source": [
    "\n",
    "# Deaths in Relation to Number of Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6e42069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='cases', ylabel='deaths'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "france_data.plot.scatter(x='cases',y='deaths')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b5456a0",
   "metadata": {},
   "source": [
    "## Explanation of Calculations\n",
    "This model is a linear regression comparing the cases to the number of deaths in france. \n",
    "\n",
    "## Explanation of Visualizations\n",
    "There does not appear to be a strong correlation between the number of cases and number of deaths. Even with a high number of cases, there were still the same amount of deaths as seen with less cases. This shows that France had good mitigation efforts to prevent death when cases were present."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30dbac41",
   "metadata": {},
   "source": [
    "\n",
    "# Month With Highest Number of Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0cfc1d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7365/1788494786.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  sum_deaths_per_month = france_data.groupby('month').sum()['deaths']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='month'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sum_deaths_per_month = france_data.groupby('month').sum()['deaths']\n",
    "sum_deaths_per_month.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0298c2f6",
   "metadata": {},
   "source": [
    "## Explanation of Calculations\n",
    "This is a line graph illustrating the number of deaths per month. The months are numbered from 1-12 on the x-axis and deaths numbered on the y-axis.\n",
    "\n",
    "## Explanation of Visualization\n",
    "The graph shows that there were a maximum number of deaths during April of over 20,000 deaths. We saw an increase in deaths after Feburary and they did not decrease until the peak in April. They hit a low point from June to September and rose again in October. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca32b25c",
   "metadata": {},
   "source": [
    "\n",
    "# Month With Highest Number of Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c45d67b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7365/2990800422.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  sum_cases_by_month=france_data.groupby('month').sum()['cases']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='month'>"
      ]
     },
     "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": [
    "sum_cases_by_month=france_data.groupby('month').sum()['cases']\n",
    "sum_cases_by_month.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65211611",
   "metadata": {},
   "source": [
    "## Explanation of Calculations\n",
    "This is a bar graph showing the number cases of COVID-19 per month. The numbers on the x-axis relate to the number month in the year it is.\n",
    "\n",
    "## Explanation of Visualization\n",
    "This graph illustrates that the number of cases started to increase in March and April, stayed slighly lower in May-July and then picked back up in August. A spike of cases occured in November with over 800,000 cases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5b0eed",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7381173",
   "metadata": {},
   "source": [
    "In conclusion this analysis specifically focuses on France and has allowed us to visualize trends of COVID-19 throughout 2020. We looked at deaths in relation to number of cases and found that there is not a strong correlation. From this we can conclude that France did a good job at minimizing deaths even when cases were high. We also looked at which months had the highest number of deaths and as the graph illustrates, the winter months saw more deaths than the warmer months. We can conclude that this could possibly be related to other diseases like the flu which also occurs during the winter months. Finally, we looked at the cases per month and observed that the cases did not hit their peak until November. We could conclude that they hit their peak in November because of holiday season and getting together with family.\n",
    "\n",
    "The limitations of our data is that we are unable to break down the numbers in France by economic status, region, or health care opportunities available. There may be outliers in this data due to looking at all of France. \n",
    "\n",
    "Suggestions for further research are that age and other socioeconomic fractors should be taken into account. Vaccinations would also be interesting data to look at in comparison to deaths and cases. More recent years would also be great for next steps of looking at COVID-19 over the years. "
   ]
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