Jordan Bernard ~ Final Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "31f8e2d0",
   "metadata": {},
   "source": [
    "# Visualizing 2020 Covid-19 Data of France in Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e9e241",
   "metadata": {},
   "source": [
    "## by Jordan Bernard"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1601a4a3",
   "metadata": {},
   "source": [
    "## ↓ Data Source ↓"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2afe7a27",
   "metadata": {},
   "source": [
    "This data is from the [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": "5551b87f",
   "metadata": {},
   "source": [
    "![French Mask Covid Image](https://images.law.com/contrib/content/uploads/sites/378/2020/11/Coronavirus-Mask-France-Article-202011171541.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "270545d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b8f506",
   "metadata": {},
   "source": [
    "## A Small View of 2020 French Covid-19 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "70472b76",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pandas.read_excel('s3://ia241jimmybuckets/COVID-19-geographic-disbtribution-worldwide-2020-12-14.xls')\n",
    "france_data = df.loc[ df['countriesAndTerritories']=='France']\n",
    "\n",
    "france_data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e307b51",
   "metadata": {},
   "source": [
    "## Question 1: How many cases have been confirmed (as of Dec. 2020) in France?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8556f679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2376852"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "france_data.sum()['cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9a6da3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of Covid-19 cases in France as of December 2020 is 2,376,852 cases.\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of Covid-19 cases in France as of December 2020 is 2,376,852 cases.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b333e5e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_cases_per_day = france_data.groupby('month').sum()['cases']\n",
    "sum_cases_per_day.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2f79356e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The month with the most confirmed Covid-19 Cases in France was November. As we can see, Covid-19 infections in the country grew towards the Fall likely due to people being tired of lockdowns and sneaking around to parties and clubs.\n"
     ]
    }
   ],
   "source": [
    "print(\"The month with the most confirmed Covid-19 Cases in France was November. As we can see, Covid-19 infections in the country grew towards the Fall likely due to people being tired of lockdowns and sneaking around to parties and clubs.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb125481",
   "metadata": {},
   "source": [
    "## Question 2: How many deaths have been confirmed  (as of Dec. 2020) in France?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6198af07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57911"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "france_data.sum()['deaths']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8de477f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of deaths in France resuting from Covid-19 as of December 2020 is 57,911 deaths.\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of deaths in France resuting from Covid-19 as of December 2020 is 57,911 deaths.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "564727a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_death_per_month = france_data.groupby('month').sum()['deaths']\n",
    "sum_death_per_month.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f7f655b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The month with the most Covid-19 Deaths in France was April which surpassed over 20,000! The virus had just started to spread  around the world and many people were just learning how to protect themselves from this new pathogen. France, like many other  nations around the world, was unprepared for Covid-19.\n"
     ]
    }
   ],
   "source": [
    "print(\"The month with the most Covid-19 Deaths in France was April which surpassed over 20,000! The virus had just started to spread  around the world and many people were just learning how to protect themselves from this new pathogen. France, like many other nations around the world, was unprepared for Covid-19.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2c7210",
   "metadata": {},
   "source": [
    "## Question 3: How are the number of Cases is related to the number of Deaths in France?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "26eedbb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cases', ylabel='deaths'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "france_data.plot.scatter(x='cases',y='deaths',c='month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "db0c8f2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Covid-19 Cases & Deaths in France are related to each other. As the year went on, both the amount of cases and deaths      continued to increase. The total number of cases in France was 2,376,852 while the total number of deaths was 57,911. Using    some basic math (deaths/cases), we can determine that 0.024% of positive Covid-19 cases in France resulted in death.\n"
     ]
    }
   ],
   "source": [
    "print(\"The Covid-19 Cases & Deaths in France are related to each other. As the year went on, both the amount of cases and deaths continued to increase. The total number of cases in France was 2,376,852 while the total number of deaths was 57,911. Using    some basic math (deaths/cases), we can determine that 0.024% of positive Covid-19 cases in France resulted in death.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f073085",
   "metadata": {},
   "source": [
    "# Conclusions and Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c597904e",
   "metadata": {},
   "source": [
    "### The Covid-19 Pandemic hit France just has hard as other western countires. Nobody was prepared for a virus this deadly. Just as was seen in the rest of the world, France initally struggled to contain the virus and the inintal months saw a high death count. As the year moved into the Summer, France and other nations had developed measures (soical distancing and wearing a mask) to mitigate the spread of Covid-19. Towards the Fall however, there was a spike in Covid-19 deaths likely due to people going to mass gatherings like weddings or parties despite the pandemic. A limitaition of this project is that I only knew three ways to create graphs in Python. There are probably better ways to display the data that I will learn about in the future!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "159570db",
   "metadata": {},
   "source": [
    "# Contact Info: bernarje@dukes.jmu.edu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c65411",
   "metadata": {},
   "source": [
    "## ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f9f3028",
   "metadata": {},
   "source": [
    "## Thank You for viewing my IA 241 Final Project! Have An Awesome Summer Professor Wei!\n",
    "## I will see you in the Fall Semester for IA 340 & GEOG 215 😊"
   ]
  }
 ],
 "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}