COVID_Senegal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1 style=\"font-size:3em\"> COVID-19 au Sénégal </h1></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "\n",
    "# Compare to Netherlands\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "# Find coordinates\n",
    "from geopy.geocoders import Nominatim\n",
    "geolocator = Nominatim(user_agent=\"myapp2\")\n",
    "import time\n",
    "\n",
    "# Plot static maps\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# Plot interactive maps\n",
    "import geopandas as gpd\n",
    "from shapely import wkt\n",
    "from bokeh.io import output_notebook, show, output_file\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.models import GeoJSONDataSource, ColumnDataSource\n",
    "import json\n",
    "from bokeh.models import HoverTool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La table de donnée ci-dessous a été contruite à partir des tweets du Ministère de la Santé et de l'Action Sociale du Sénégal. La source peut être trouvée ici: https://twitter.com/MinisteredelaS1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Positif</th>\n",
       "      <th>Negatif</th>\n",
       "      <th>Age</th>\n",
       "      <th>Homme</th>\n",
       "      <th>Femme</th>\n",
       "      <th>Décédé</th>\n",
       "      <th>Guéri</th>\n",
       "      <th>Nationalité</th>\n",
       "      <th>Resident Senegal</th>\n",
       "      <th>Ville</th>\n",
       "      <th>Facteur</th>\n",
       "      <th>Source/Voyage</th>\n",
       "      <th>Hopital</th>\n",
       "      <th>Temps Hospitalisation (j)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>France</td>\n",
       "      <td>Oui</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Importé</td>\n",
       "      <td>France</td>\n",
       "      <td>FANN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gambie</td>\n",
       "      <td>Non</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Importé</td>\n",
       "      <td>Espagne</td>\n",
       "      <td>FANN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>France</td>\n",
       "      <td>Non</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Importé</td>\n",
       "      <td>France</td>\n",
       "      <td>FANN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>France</td>\n",
       "      <td>Non</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Importé</td>\n",
       "      <td>France</td>\n",
       "      <td>FANN</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Angleterre</td>\n",
       "      <td>Non</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Importé</td>\n",
       "      <td>Angleterre</td>\n",
       "      <td>FANN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Positif  Negatif   Age  Homme  Femme  Décédé  Guéri Nationalité  \\\n",
       "0 2020-03-02        1        0  68.0    1.0    0.0     0.0    1.0      France   \n",
       "1 2020-03-03        0        1   NaN    1.0    0.0     0.0    0.0      Gambie   \n",
       "2 2020-03-03        1        0  80.0    1.0    0.0     0.0    1.0      France   \n",
       "3 2020-03-04        1        0  68.0    0.0    1.0     0.0    1.0      France   \n",
       "4 2020-03-04        1        0  33.0    0.0    1.0     0.0    1.0  Angleterre   \n",
       "5 2020-03-06        0        1   NaN    NaN    NaN     NaN    NaN         NaN   \n",
       "6 2020-03-06        0        1   NaN    NaN    NaN     NaN    NaN         NaN   \n",
       "7 2020-03-07        0        1   NaN    NaN    NaN     NaN    NaN         NaN   \n",
       "8 2020-03-07        0        1   NaN    NaN    NaN     NaN    NaN         NaN   \n",
       "9 2020-03-08        0        1   NaN    NaN    NaN     NaN    NaN         NaN   \n",
       "\n",
       "  Resident Senegal  Ville  Facteur Source/Voyage Hopital  \\\n",
       "0              Oui  Dakar  Importé        France    FANN   \n",
       "1              Non  Dakar  Importé       Espagne    FANN   \n",
       "2              Non  Dakar  Importé        France    FANN   \n",
       "3              Non  Dakar  Importé        France    FANN   \n",
       "4              Non  Dakar  Importé    Angleterre    FANN   \n",
       "5              NaN  Dakar      NaN           NaN     NaN   \n",
       "6              NaN  Dakar      NaN           NaN     NaN   \n",
       "7              NaN  Dakar      NaN           NaN     NaN   \n",
       "8              NaN  Dakar      NaN           NaN     NaN   \n",
       "9              NaN  Dakar      NaN           NaN     NaN   \n",
       "\n",
       "   Temps Hospitalisation (j)  \n",
       "0                        4.0  \n",
       "1                        NaN  \n",
       "2                       10.0  \n",
       "3                       15.0  \n",
       "4                        7.0  \n",
       "5                        NaN  \n",
       "6                        NaN  \n",
       "7                        NaN  \n",
       "8                        NaN  \n",
       "9                        NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"COVID_Dakar.csv\", sep=\";\")\n",
    "df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# II. Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Statistiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Positif</th>\n",
       "      <th>Negatif</th>\n",
       "      <th>Décédé</th>\n",
       "      <th>Guéri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Positif  Negatif  Décédé  Guéri\n",
       "Date                                       \n",
       "2020-03-02      1.0      0.0     0.0    1.0\n",
       "2020-03-03      2.0      1.0     0.0    2.0\n",
       "2020-03-04      4.0      1.0     0.0    4.0\n",
       "2020-03-06      4.0      3.0     0.0    4.0\n",
       "2020-03-07      4.0      5.0     0.0    4.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evol_cases = df[['Date', 'Positif', 'Negatif', 'Décédé', 'Guéri']].groupby(\"Date\").sum().cumsum()\n",
    "evol_cases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre de cas positifs:  67.0\n",
      "Nombre de cas negatifs:  255.0\n",
      "Nombre de décès:  0.0\n",
      "Nombre de guérisons:  5.0\n"
     ]
    }
   ],
   "source": [
    "total_positif = evol_cases.tail(1)['Positif'][0]\n",
    "total_negatif = evol_cases.tail(1)['Negatif'][0]\n",
    "total_decede = evol_cases.tail(1)['Décédé'][0]\n",
    "total_geuri = evol_cases.tail(1)['Guéri'][0]\n",
    "print(\"Nombre de cas positifs: \", total_positif)\n",
    "print(\"Nombre de cas negatifs: \", total_negatif)\n",
    "print(\"Nombre de décès: \", total_decede)\n",
    "print(\"Nombre de guérisons: \", total_geuri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Positif</th>\n",
       "      <th>Negatif</th>\n",
       "      <th>Décédé</th>\n",
       "      <th>Guéri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>36.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>38.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>47.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>56.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>67.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            Positif  Negatif  Décédé  Guéri\n",
       "Date                                       \n",
       "2020-03-18     36.0     92.0     0.0    5.0\n",
       "2020-03-19     38.0    112.0     0.0    5.0\n",
       "2020-03-20     47.0    159.0     0.0    5.0\n",
       "2020-03-21     56.0    181.0     0.0    5.0\n",
       "2020-03-22     67.0    255.0     0.0    5.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evol_cases.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19395641247644257"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "growth = pd.DataFrame(np.sqrt(evol_cases['Positif'].pct_change(periods=2)+1)-1).tail(1)['Positif'][0]\n",
    "growth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare to Netherlands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "website_url = requests.get(\"https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_the_Netherlands\").text\n",
    "soup = BeautifulSoup(website_url,\"lxml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "My_table = soup.find(\"table\",{\"class\":\"wikitable mw-collapsible\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nl = pd.read_html(str(My_table))[0]['Cases[b]']['Total'].apply(lambda x: x.replace(\"[c]\", \"\"))[:-2].apply(lambda x: int(x))\n",
    "df_nl = pd.DataFrame(df_nl)\n",
    "df_nl['Date'] = pd.read_html(str(My_table))[0]['Date[a]']['Date[a]'][:-2]\n",
    "df_nl['Date'] = pd.to_datetime(df_nl['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nl_2 = pd.DataFrame(np.concatenate([np.zeros(12), np.array(df_nl['Total'])]))\n",
    "df_nl_2.columns = [\"Netherlands\"]\n",
    "\n",
    "df_nl = pd.concat([pd.DataFrame(df_nl_2),pd.DataFrame(evol_cases.reset_index()['Positif'])], ignore_index=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>Netherlands</th>\n",
       "      <th>Senegal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>6.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>9.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>24.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>38.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>82.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>128.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>188.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>264.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>321.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>382.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>503.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>614.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>804.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>959.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1413.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>2051.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>2994.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>3631.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>4204.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Netherlands  Senegal\n",
       "0           0.0      1.0\n",
       "1           0.0      2.0\n",
       "2           0.0      4.0\n",
       "3           0.0      4.0\n",
       "4           0.0      4.0\n",
       "5           0.0      4.0\n",
       "6           0.0      4.0\n",
       "7           0.0      4.0\n",
       "8           0.0      5.0\n",
       "9           0.0     10.0\n",
       "10          0.0     21.0\n",
       "11          0.0     24.0\n",
       "12          1.0     26.0\n",
       "13          2.0     27.0\n",
       "14          6.0     31.0\n",
       "15          9.0     36.0\n",
       "16         19.0     38.0\n",
       "17         24.0     47.0\n",
       "18         38.0     56.0\n",
       "19         82.0     67.0\n",
       "20        128.0      NaN\n",
       "21        188.0      NaN\n",
       "22        264.0      NaN\n",
       "23        321.0      NaN\n",
       "24        382.0      NaN\n",
       "25        503.0      NaN\n",
       "26        614.0      NaN\n",
       "27        804.0      NaN\n",
       "28        959.0      NaN\n",
       "29       1135.0      NaN\n",
       "30       1413.0      NaN\n",
       "31       1705.0      NaN\n",
       "32       2051.0      NaN\n",
       "33       2460.0      NaN\n",
       "34       2994.0      NaN\n",
       "35       3631.0      NaN\n",
       "36       4204.0      NaN"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nl.columns = ['Netherlands', 'Senegal']\n",
    "df_nl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(df_nl['Senegal'])\n",
    "plt.plot(df_nl['Netherlands'], linestyle=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nl.to_csv(\"df_nl.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Evolution du nombre de cas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(evol_cases['Positif'], label=\"Positif\")\n",
    "plt.plot(evol_cases['Negatif'], label=\"Negatif\")\n",
    "plt.legend()\n",
    "plt.title(\"Evolution du nombre de cas au Senegal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.9"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df['Age'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(df['Age'].dropna(), bins=30)\n",
    "plt.title(\"Age des patients\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Sexe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Homme    9.0\n",
       "Femme    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Homme', 'Femme']].dropna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.bar([\"Homme\", \"Femme\"], df[['Homme', 'Femme']].dropna().sum(), width=0.5)\n",
    "plt.title(\"Sexe des personnes hospitalisées\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Ville du cas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(df['Ville'].dropna())\n",
    "plt.title(\"Ville du cas\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Resident au Senegal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Oui    22\n",
       "Non     6\n",
       "Name: Resident Senegal, dtype: int64"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Resident Senegal'].dropna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(df['Resident Senegal'].dropna())\n",
    "plt.title(\"Resident au Senegal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Facteur de contamination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "facteur = df[['Date', 'Facteur']].dropna()\n",
    "facteur['Count'] = 1\n",
    "\n",
    "importe = facteur[facteur['Facteur'] == \"Importé\"].groupby(\"Date\").sum().cumsum().reset_index()\n",
    "voyage = facteur[facteur['Facteur'] == \"Contact\"].groupby(\"Date\").sum().cumsum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "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>Date</th>\n",
       "      <th>Importes</th>\n",
       "      <th>Contact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Importes  Contact\n",
       "0  2020-03-02       1.0      0.0\n",
       "1  2020-03-03       3.0      0.0\n",
       "2  2020-03-04       5.0      0.0\n",
       "3  2020-03-11       6.0      0.0\n",
       "13 2020-03-12       6.0      5.0\n",
       "14 2020-03-13       6.0     16.0\n",
       "4  2020-03-14       8.0     17.0\n",
       "5  2020-03-15       9.0     18.0\n",
       "6  2020-03-16      10.0     18.0\n",
       "7  2020-03-17      13.0     19.0\n",
       "8  2020-03-18      16.0     21.0\n",
       "9  2020-03-19      18.0     21.0\n",
       "10 2020-03-20      22.0     26.0\n",
       "11 2020-03-21      27.0     27.0\n",
       "12 2020-03-22      33.0     32.0"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int = pd.merge(importe, voyage, left_on='Date', right_on='Date', how='outer')\n",
    "df_int['Date'] = pd.to_datetime(df_int['Date'], dayfirst=True)\n",
    "df_int = df_int.sort_values(\"Date\").ffill().fillna(0)\n",
    "df_int.columns = [\"Date\", \"Importes\", \"Contact\"]\n",
    "df_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-a361bf22dcc8432baa878310697e54b8\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-a361bf22dcc8432baa878310697e54b8\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.2?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-c3bd1ebec546d1d9d83bdd1c2def77a5\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"key\"}, \"x\": {\"type\": \"temporal\", \"field\": \"Date\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"value\"}}, \"transform\": [{\"fold\": [\"Importes\", \"Contact\"]}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.2.json\", \"datasets\": {\"data-c3bd1ebec546d1d9d83bdd1c2def77a5\": [{\"Date\": \"2020-03-02T00:00:00\", \"Importes\": 1.0, \"Contact\": 0.0}, {\"Date\": \"2020-03-03T00:00:00\", \"Importes\": 3.0, \"Contact\": 0.0}, {\"Date\": \"2020-03-04T00:00:00\", \"Importes\": 5.0, \"Contact\": 0.0}, {\"Date\": \"2020-03-11T00:00:00\", \"Importes\": 6.0, \"Contact\": 0.0}, {\"Date\": \"2020-03-12T00:00:00\", \"Importes\": 6.0, \"Contact\": 5.0}, {\"Date\": \"2020-03-13T00:00:00\", \"Importes\": 6.0, \"Contact\": 16.0}, {\"Date\": \"2020-03-14T00:00:00\", \"Importes\": 8.0, \"Contact\": 17.0}, {\"Date\": \"2020-03-15T00:00:00\", \"Importes\": 9.0, \"Contact\": 18.0}, {\"Date\": \"2020-03-16T00:00:00\", \"Importes\": 10.0, \"Contact\": 18.0}, {\"Date\": \"2020-03-17T00:00:00\", \"Importes\": 13.0, \"Contact\": 19.0}, {\"Date\": \"2020-03-18T00:00:00\", \"Importes\": 16.0, \"Contact\": 21.0}, {\"Date\": \"2020-03-19T00:00:00\", \"Importes\": 18.0, \"Contact\": 21.0}, {\"Date\": \"2020-03-20T00:00:00\", \"Importes\": 22.0, \"Contact\": 26.0}, {\"Date\": \"2020-03-21T00:00:00\", \"Importes\": 27.0, \"Contact\": 27.0}, {\"Date\": \"2020-03-22T00:00:00\", \"Importes\": 33.0, \"Contact\": 32.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(df_int).transform_fold(\n",
    "    ['Importes', 'Contact'],\n",
    ").mark_line().encode(\n",
    "    x='Date:T',\n",
    "    y='value:Q',\n",
    "    color='key:N'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(df['Facteur'].dropna())\n",
    "plt.title(\"Facteur de contamination\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Source/Voyage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(df['Source/Voyage'].dropna())\n",
    "plt.title(\"Source du contact ou origine du voyage\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Source/Voyage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(df['Temps Hospitalisation (j)'].dropna())\n",
    "plt.title(\"Temps d'hospitalisation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Carte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "shapefile = 'app/ne_110m_admin_0_countries.shp'\n",
    "\n",
    "#Read shapefile using Geopandas\n",
    "gdf = gpd.read_file(shapefile)[['ADMIN', 'ADM0_A3', 'geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>country_code</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>FJI</td>\n",
       "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>United Republic of Tanzania</td>\n",
       "      <td>TZA</td>\n",
       "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>SAH</td>\n",
       "      <td>POLYGON ((-8.66559 27.65643, -8.66512 27.58948...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -122.9742...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -120.0000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       country country_code  \\\n",
       "0                         Fiji          FJI   \n",
       "1  United Republic of Tanzania          TZA   \n",
       "2               Western Sahara          SAH   \n",
       "3                       Canada          CAN   \n",
       "4     United States of America          USA   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...  \n",
       "1  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...  \n",
       "2  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...  \n",
       "3  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...  \n",
       "4  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename columns.\n",
    "gdf.columns = ['country', 'country_code', 'geometry']\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gdf[gdf['country']==\"Senegal\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x123e7c5c0>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['geometry'][51]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_crs=gdf.crs\n",
    "\n",
    "#Read data to json.\n",
    "gdf_json = json.loads(gdf.to_json())\n",
    "#Convert to String like object.\n",
    "grid = json.dumps(gdf_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "latitude = []\n",
    "longitude = []\n",
    "\n",
    "for line in np.unique(df['Ville'].dropna()):\n",
    "    location = geolocator.geocode(line + \", Senegal\")\n",
    "\n",
    "    latitude.append(location.latitude)\n",
    "    longitude.append(location.longitude)\n",
    "        \n",
    "    time.sleep(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = pd.DataFrame([np.unique(df['Ville'].dropna()), latitude, longitude]).transpose()\n",
    "cities.columns = ['Ville', 'Latitude', 'Longitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities.to_csv(\"city_coordinates.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_lat(x):\n",
    "    try:\n",
    "        return float(cities[cities['Ville'] == x]['Latitude'])\n",
    "    except TypeError:\n",
    "        return None\n",
    "\n",
    "def find_long(x):\n",
    "    try:\n",
    "        return float(cities[cities['Ville'] == x]['Longitude'])\n",
    "    except TypeError:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Positif</th>\n",
       "      <th>Negatif</th>\n",
       "      <th>Age</th>\n",
       "      <th>Homme</th>\n",
       "      <th>Femme</th>\n",
       "      <th>Décédé</th>\n",
       "      <th>Guéri</th>\n",
       "      <th>Nationalité</th>\n",
       "      <th>Resident Senegal</th>\n",
       "      <th>Ville</th>\n",
       "      <th>Facteur</th>\n",
       "      <th>Source/Voyage</th>\n",
       "      <th>Hopital</th>\n",
       "      <th>Temps Hospitalisation (j)</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>France</td>\n",
       "      <td>Oui</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Importé</td>\n",
       "      <td>France</td>\n",
       "      <td>FANN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.693425</td>\n",
       "      <td>-17.447938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gambie</td>\n",
       "      <td>Non</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Importé</td>\n",
       "      <td>Espagne</td>\n",
       "      <td>FANN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.693425</td>\n",
       "      <td>-17.447938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>France</td>\n",
       "      <td>Non</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Importé</td>\n",
       "      <td>France</td>\n",
       "      <td>FANN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.693425</td>\n",
       "      <td>-17.447938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>France</td>\n",
       "      <td>Non</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Importé</td>\n",
       "      <td>France</td>\n",
       "      <td>FANN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.693425</td>\n",
       "      <td>-17.447938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Angleterre</td>\n",
       "      <td>Non</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Importé</td>\n",
       "      <td>Angleterre</td>\n",
       "      <td>FANN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.693425</td>\n",
       "      <td>-17.447938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>201</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>202</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>203</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>205</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Positif  Negatif   Age  Homme  Femme  Décédé  Guéri  \\\n",
       "0   2020-03-02        1        0  68.0    1.0    0.0     0.0    1.0   \n",
       "1   2020-03-03        0        1   NaN    1.0    0.0     0.0    0.0   \n",
       "2   2020-03-03        1        0  80.0    1.0    0.0     0.0    1.0   \n",
       "3   2020-03-04        1        0  68.0    0.0    1.0     0.0    1.0   \n",
       "4   2020-03-04        1        0  33.0    0.0    1.0     0.0    1.0   \n",
       "..         ...      ...      ...   ...    ...    ...     ...    ...   \n",
       "201 2020-03-21        0        1   NaN    NaN    NaN     NaN    NaN   \n",
       "202 2020-03-21        0        1   NaN    NaN    NaN     NaN    NaN   \n",
       "203 2020-03-21        0        1   NaN    NaN    NaN     NaN    NaN   \n",
       "204 2020-03-21        0        1   NaN    NaN    NaN     NaN    NaN   \n",
       "205 2020-03-21        0        1   NaN    NaN    NaN     NaN    NaN   \n",
       "\n",
       "    Nationalité Resident Senegal  Ville  Facteur Source/Voyage Hopital  \\\n",
       "0        France              Oui  Dakar  Importé        France    FANN   \n",
       "1        Gambie              Non  Dakar  Importé       Espagne    FANN   \n",
       "2        France              Non  Dakar  Importé        France    FANN   \n",
       "3        France              Non  Dakar  Importé        France    FANN   \n",
       "4    Angleterre              Non  Dakar  Importé    Angleterre    FANN   \n",
       "..          ...              ...    ...      ...           ...     ...   \n",
       "201         NaN              NaN    NaN      NaN           NaN     NaN   \n",
       "202         NaN              NaN    NaN      NaN           NaN     NaN   \n",
       "203         NaN              NaN    NaN      NaN           NaN     NaN   \n",
       "204         NaN              NaN    NaN      NaN           NaN     NaN   \n",
       "205         NaN              NaN    NaN      NaN           NaN     NaN   \n",
       "\n",
       "     Temps Hospitalisation (j)   latitude  longitude  \n",
       "0                          4.0  14.693425 -17.447938  \n",
       "1                          NaN  14.693425 -17.447938  \n",
       "2                         10.0  14.693425 -17.447938  \n",
       "3                         15.0  14.693425 -17.447938  \n",
       "4                          7.0  14.693425 -17.447938  \n",
       "..                         ...        ...        ...  \n",
       "201                        NaN        NaN        NaN  \n",
       "202                        NaN        NaN        NaN  \n",
       "203                        NaN        NaN        NaN  \n",
       "204                        NaN        NaN        NaN  \n",
       "205                        NaN        NaN        NaN  \n",
       "\n",
       "[206 rows x 17 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary = df[['Positif', 'Ville']].groupby(\"Ville\").sum().reset_index()\n",
    "summary['latitude'] = summary['Ville'].apply(lambda x: find_lat(x))\n",
    "summary['longitude'] = summary['Ville'].apply(lambda x: find_long(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Ville</th>\n",
       "      <th>Positif</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>29</td>\n",
       "      <td>14.693425</td>\n",
       "      <td>-17.447938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Nianing</td>\n",
       "      <td>1</td>\n",
       "      <td>14.343980</td>\n",
       "      <td>-16.929427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Touba</td>\n",
       "      <td>12</td>\n",
       "      <td>14.864559</td>\n",
       "      <td>-15.876047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ville  Positif   latitude  longitude\n",
       "0    Dakar       29  14.693425 -17.447938\n",
       "1  Nianing        1  14.343980 -16.929427\n",
       "2    Touba       12  14.864559 -15.876047"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"2158\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"2158\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      \n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"2158\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '2158' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.0.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"2158\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"2158\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      \n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"2158\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '2158' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"2158\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"01b613b0-fdfb-4be5-bf1a-7ebf27a869c1\" data-root-id=\"2117\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"9ae5a444-734f-4578-87b0-1b0e78b80ba3\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"2128\"}],\"center\":[{\"id\":\"2131\"},{\"id\":\"2135\"}],\"left\":[{\"id\":\"2132\"}],\"plot_width\":950,\"renderers\":[{\"id\":\"2146\"},{\"id\":\"2151\"},{\"id\":\"2156\"}],\"title\":{\"id\":\"2118\"},\"toolbar\":{\"id\":\"2138\"},\"x_range\":{\"id\":\"2120\"},\"x_scale\":{\"id\":\"2124\"},\"y_range\":{\"id\":\"2122\"},\"y_scale\":{\"id\":\"2126\"}},\"id\":\"2117\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"2116\"},{\"id\":\"2136\"},{\"id\":\"2137\"}]},\"id\":\"2138\",\"type\":\"Toolbar\"},{\"attributes\":{\"axis\":{\"id\":\"2128\"},\"grid_line_color\":null,\"ticker\":null},\"id\":\"2131\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null,\"tooltips\":[[\"Ville\",\"@Ville\"],[\"Nombre de 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       "  var render_items = [{\"docid\":\"9ae5a444-734f-4578-87b0-1b0e78b80ba3\",\"root_ids\":[\"2117\"],\"roots\":{\"2117\":\"01b613b0-fdfb-4be5-bf1a-7ebf27a869c1\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
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      "application/vnd.bokehjs_exec.v0+json": ""
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   "source": [
    "#Input GeoJSON source that contains features for plotting.\n",
    "geosource = GeoJSONDataSource(geojson = grid)\n",
    "pointsource = ColumnDataSource(summary)\n",
    "\n",
    "hover = HoverTool(\n",
    "    tooltips = [('Ville', '@Ville'), ('Nombre de cas positifs', '@Positif')]\n",
    ")\n",
    "\n",
    "#Create figure object.\n",
    "p = figure(title = 'Cas au Sénégal', plot_height = 600 , plot_width = 950, tools=[hover, 'pan', 'wheel_zoom'])\n",
    "p.xgrid.grid_line_color = None\n",
    "p.ygrid.grid_line_color = None\n",
    "\n",
    "patch=p.patches('xs','ys', source = geosource,fill_color = '#fff7bc',\n",
    "          line_color = 'black', line_width = 0.35, fill_alpha = 1, \n",
    "                hover_fill_color=\"#fec44f\")\n",
    "\n",
    "#Add patch renderer to figure. \n",
    "patch = p.patches('xs','ys', source = geosource, fill_color = 'lightgrey',\n",
    "          line_color = 'black', line_width = 0.25, fill_alpha = 1)\n",
    "\n",
    "#p.add_tools(HoverTool(tooltips=[('Confirmed cases','@cas_confirmes'),('Deaths','@deces'), ('Reanimation','@reanimation')], renderers=[patch]))\n",
    "\n",
    "p.circle('longitude','latitude',source=pointsource, size='Positif')\n",
    "#Display figure inline in Jupyter Notebook.\n",
    "\n",
    "output_notebook()\n",
    "#Display figure.\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[(df['country']==\"Italy\") | (df['country']==\"Senegal\") | (df['country']==\"United Kingdom\") | (df['country']==\"France\") | (df['country']==\"Spain\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "import plotly.express as px\n",
    "df = px.data.gapminder().query(\"year == 2007\")\n",
    "\n",
    "fig = px.line_geo(df2, locations=\"iso_alpha\",\n",
    "                  projection=\"orthographic\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
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       "      <td>ARG</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <td>1655</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>74.249</td>\n",
       "      <td>85262356</td>\n",
       "      <td>2441.576404</td>\n",
       "      <td>VNM</td>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1667</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>73.422</td>\n",
       "      <td>4018332</td>\n",
       "      <td>3025.349798</td>\n",
       "      <td>PSE</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1679</td>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>62.698</td>\n",
       "      <td>22211743</td>\n",
       "      <td>2280.769906</td>\n",
       "      <td>YEM</td>\n",
       "      <td>887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1691</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>42.384</td>\n",
       "      <td>11746035</td>\n",
       "      <td>1271.211593</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1703</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country continent  year  lifeExp       pop     gdpPercap  \\\n",
       "11           Afghanistan      Asia  2007   43.828  31889923    974.580338   \n",
       "23               Albania    Europe  2007   76.423   3600523   5937.029526   \n",
       "35               Algeria    Africa  2007   72.301  33333216   6223.367465   \n",
       "47                Angola    Africa  2007   42.731  12420476   4797.231267   \n",
       "59             Argentina  Americas  2007   75.320  40301927  12779.379640   \n",
       "...                  ...       ...   ...      ...       ...           ...   \n",
       "1655             Vietnam      Asia  2007   74.249  85262356   2441.576404   \n",
       "1667  West Bank and Gaza      Asia  2007   73.422   4018332   3025.349798   \n",
       "1679         Yemen, Rep.      Asia  2007   62.698  22211743   2280.769906   \n",
       "1691              Zambia    Africa  2007   42.384  11746035   1271.211593   \n",
       "1703            Zimbabwe    Africa  2007   43.487  12311143    469.709298   \n",
       "\n",
       "     iso_alpha  iso_num  \n",
       "11         AFG        4  \n",
       "23         ALB        8  \n",
       "35         DZA       12  \n",
       "47         AGO       24  \n",
       "59         ARG       32  \n",
       "...        ...      ...  \n",
       "1655       VNM      704  \n",
       "1667       PSE      275  \n",
       "1679       YEM      887  \n",
       "1691       ZMB      894  \n",
       "1703       ZWE      716  \n",
       "\n",
       "[142 rows x 8 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  }
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