epidatapreprocprovlevel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl \n",
    "import seaborn as sns\n",
    "# sns.set()\n",
    "sns.set(context='notebook', style='whitegrid', palette='deep', font='sans-serif', font_scale=1.3, color_codes=True, rc=None)\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import geoplot as gpl\n",
    "import json\n",
    "import mapclassify\n",
    "import matplotlib.ticker as ticker\n",
    "from datetime import timedelta\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geo Data Processsing\n",
    "\n",
    "Reading the initial zone shapefile and grouped zone (by FV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Handle naming and grouping\n",
    "gpdzs = gpd.read_file('../../data/GEO DRC/RDC_Macro_Provinces.shp')#[['PROVINCE','Nom','geometry']]\n",
    "gpdzsg = gpd.read_file('../../data/GEO DRC/RDC_Macro_Provinces.shp')#[['PROVINCE','Zone+Peupl','geometry']]\n",
    "\n",
    "gpdzsg = gpdzsg.rename({'PROVINCE':'Nom'}, axis=1)\n",
    "# gpdzsg['Nom'] =  gpdzsg['Nom']+'_g'\n",
    "\n",
    "zone_filtered_gd = gpdzsg\n",
    "\n",
    "# gpdzm = gpd.GeoDataFrame(pd.concat([gpdzs,gpdzsg], ignore_index=True), crs=gpdzsg.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Index</th>\n",
       "      <th>C0</th>\n",
       "      <th>C1</th>\n",
       "      <th>Surface</th>\n",
       "      <th>Nom</th>\n",
       "      <th>Population</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>0.415854</td>\n",
       "      <td>0.011909</td>\n",
       "      <td>52049.63</td>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>2 744 345</td>\n",
       "      <td>POLYGON ((18.68941 1.90628, 18.68649 1.90233, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>0.458879</td>\n",
       "      <td>0.012838</td>\n",
       "      <td>54171.85</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>1 037 000</td>\n",
       "      <td>POLYGON ((20.39471 2.82687, 20.38231 2.81683, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.451896</td>\n",
       "      <td>0.042421</td>\n",
       "      <td>56144.21</td>\n",
       "      <td>Mongala</td>\n",
       "      <td>1 793 564</td>\n",
       "      <td>POLYGON ((21.63564 0.72938, 21.63387 0.72562, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>0.351923</td>\n",
       "      <td>0.003476</td>\n",
       "      <td>102527.71</td>\n",
       "      <td>Equateur</td>\n",
       "      <td>1 626 606</td>\n",
       "      <td>POLYGON ((16.94268 -1.86609, 16.94502 -2.00127...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>0.247170</td>\n",
       "      <td>0.006137</td>\n",
       "      <td>132254.42</td>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>1 316 855</td>\n",
       "      <td>POLYGON ((21.84714 -2.53010, 21.84408 -2.53239...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index        C0        C1    Surface          Nom Population  \\\n",
       "0     22  0.415854  0.011909   52049.63   Sud-Ubangi  2 744 345   \n",
       "1      7  0.458879  0.012838   54171.85  Nord-Ubangi  1 037 000   \n",
       "2      2  0.451896  0.042421   56144.21      Mongala  1 793 564   \n",
       "3     24  0.351923  0.003476  102527.71     Equateur  1 626 606   \n",
       "4      8  0.247170  0.006137  132254.42      Tshuapa  1 316 855   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((18.68941 1.90628, 18.68649 1.90233, ...  \n",
       "1  POLYGON ((20.39471 2.82687, 20.38231 2.81683, ...  \n",
       "2  POLYGON ((21.63564 0.72938, 21.63387 0.72562, ...  \n",
       "3  POLYGON ((16.94268 -1.86609, 16.94502 -2.00127...  \n",
       "4  POLYGON ((21.84714 -2.53010, 21.84408 -2.53239...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpdzsg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking data issues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sud-Ubangi', 'Nord-Ubangi', 'Mongala', 'Equateur', 'Tshuapa', 'Maï-Ndombe', 'Kongo-Central', 'Kinshasa', 'Kwango', 'Kwilu', 'Kasaï', 'Kasaï-Central', 'Kasaï-Oriental', 'Lomami', 'Haut-Lomami', 'Lualaba', 'Haut-Katanga', 'Tanganyika', 'Nord-Kivu', 'Sud-Kivu', 'Maniema', 'Sankuru', 'Tshopo', 'Bas-Uele', 'Haut-Uele', 'Ituri'] size: 26\n"
     ]
    }
   ],
   "source": [
    "list_filtered_zones = list(zone_filtered_gd['Nom'])\n",
    "print(list_filtered_zones,'size:',len(list_filtered_zones))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Epidemiological Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv file\n",
    "df_epi = pd.read_csv('./data/inrbdata.csv',sep = ',')\n",
    "df_epi['Date'] = pd.to_datetime(df_epi.Date)\n",
    "\n",
    "# filter last 5 days\n",
    "df_epi = df_epi[df_epi.Date >= '2020-07-25']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping epi data by `Zones` and `Provinces`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Provinces</th>\n",
       "      <th>Cas confirmés</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Haut Katanga</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Haut Uélé</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ituri</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kasai Central</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kongo Central</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nord Kivu</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sud Kivu</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sud Ubangi</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tshopo</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Provinces  Cas confirmés\n",
       "0   Haut Katanga           44.0\n",
       "1      Haut Uélé            5.0\n",
       "2          Ituri           17.0\n",
       "3  Kasai Central            1.0\n",
       "4       Kinshasa          146.0\n",
       "5  Kongo Central           45.0\n",
       "6      Nord Kivu           21.0\n",
       "7       Sud Kivu            2.0\n",
       "8     Sud Ubangi            1.0\n",
       "9         Tshopo            1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi_agg = df_epi.groupby(['Provinces'], as_index=False).sum()\n",
    "df_epi_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Provinces        Haut KatangaHaut UéléIturiKasai CentralKinshas...\n",
       "Cas confirmés                                                  283\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum cases\n",
    "df_epi_agg.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Haut Katanga',\n",
       " 'Haut Uélé',\n",
       " 'Kasai Central',\n",
       " 'Kongo Central',\n",
       " 'Nord Kivu',\n",
       " 'Sud Kivu',\n",
       " 'Sud Ubangi'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provinces in epi data file but not in shape file\n",
    "set(df_epi_agg.Provinces) - set(zone_filtered_gd['Nom'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bas-Uele',\n",
       " 'Equateur',\n",
       " 'Haut-Katanga',\n",
       " 'Haut-Lomami',\n",
       " 'Haut-Uele',\n",
       " 'Kasaï',\n",
       " 'Kasaï-Central',\n",
       " 'Kasaï-Oriental',\n",
       " 'Kongo-Central',\n",
       " 'Kwango',\n",
       " 'Kwilu',\n",
       " 'Lomami',\n",
       " 'Lualaba',\n",
       " 'Maniema',\n",
       " 'Maï-Ndombe',\n",
       " 'Mongala',\n",
       " 'Nord-Kivu',\n",
       " 'Nord-Ubangi',\n",
       " 'Sankuru',\n",
       " 'Sud-Kivu',\n",
       " 'Sud-Ubangi',\n",
       " 'Tanganyika',\n",
       " 'Tshuapa'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provinces in shape file but not in epi data file\n",
    "set(zone_filtered_gd['Nom']) - set(df_epi_agg.Provinces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming and  matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming some elements to match with those in  Geo data\n",
    "def renamed_zs(zs, els):\n",
    "    zrn = zs\n",
    "    for vtr in els:\n",
    "        zrn = zrn.replace(vtr[0], vtr[1])\n",
    "    return zrn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_zone_de_sante_inrb = list(df_epi_agg.groupby('Provinces').groups.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spelling Correction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# {'Haut Katanga',\n",
    "#  'Haut Lomami',\n",
    "#  'Haut Uélé',\n",
    "#  'Kasai',\n",
    "#  'Kasai Central',\n",
    "#  'Kongo Central',\n",
    "#  'Nord Kivu',\n",
    "#  'Sud Kivu',\n",
    "#  'Sud Ubangi'}\n",
    "\n",
    "\n",
    "# {'Bas-Uele',\n",
    "#  'Haut-Katanga',\n",
    "#  'Haut-Lomami',\n",
    "#  'Haut-Uele',\n",
    "#  'Kasaï',\n",
    "#  'Kasaï-Central',\n",
    "#  'Kasaï-Oriental',\n",
    "#  'Kongo-Central',\n",
    "#  'Lomami',\n",
    "#  'Maniema',\n",
    "#  'Maï-Ndombe',\n",
    "#  'Mongala',\n",
    "#  'Nord-Kivu',\n",
    "#  'Nord-Ubangi',\n",
    "#  'Sankuru',\n",
    "#  'Sud-Kivu',\n",
    "#  'Sud-Ubangi',\n",
    "#  'Tanganyika',\n",
    "#  'Tshuapa'}\n",
    "\n",
    "corrected_list = [('Haut Katanga','Haut-Katanga'),\n",
    "                  ('Haut Lomami','Haut-Lomami'), \n",
    "                  ('Kasai','Kasaï'),  \n",
    "                  \n",
    "                  ('Kasai Central','Kasaï-Central'), \n",
    "                  \n",
    "                  ('Kongo Central','Kongo-Central'), \n",
    "                  \n",
    "                  ('Kasavubu','Kasa-Vubu'), ('N\\'djili','Ndjili'), \n",
    "                  \n",
    "                 ('Nord Kivu','Nord-Kivu'),\n",
    "                  \n",
    "                  ('Sud Kivu','Sud-Kivu'),\n",
    "                  \n",
    "                  ('Sud Ubangi','Sud-Ubangi'),\n",
    "                  \n",
    "                   ('Haut Uélé','Haut-Uele'),\n",
    "                  \n",
    "                \n",
    "                 ]\n",
    "correction_dict = {k:v for k,v in corrected_list}\n",
    "# .str.contains('|'.join(['Mont','sele','Binza','djili','Kasa','Kokolo']))\n",
    "\n",
    "for idx in list(df_epi_agg.index[df_epi_agg['Provinces'].isin([el[0] for el in corrected_list])]):\n",
    "    df_epi_agg.at[idx,'Provinces'] = correction_dict[df_epi_agg.iloc[idx]['Provinces']]\n",
    "#     renamed_zs(df_epi_agg.iloc[idx]['Zone de Santé'], corrected_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Provinces</th>\n",
       "      <th>Cas confirmés</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ituri</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kasaï-Central</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kongo-Central</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tshopo</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Provinces  Cas confirmés\n",
       "0   Haut-Katanga           44.0\n",
       "1      Haut-Uele            5.0\n",
       "2          Ituri           17.0\n",
       "3  Kasaï-Central            1.0\n",
       "4       Kinshasa          146.0\n",
       "5  Kongo-Central           45.0\n",
       "6      Nord-Kivu           21.0\n",
       "7       Sud-Kivu            2.0\n",
       "8     Sud-Ubangi            1.0\n",
       "9         Tshopo            1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi_agg = df_epi_agg.groupby(['Provinces'], as_index=False).sum()\n",
    "df_epi_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_zone_de_sante_inrb = list(df_epi_agg.groupby('Provinces').groups.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zones in Epi data but not in Zones FV shp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check mismatching zones\n",
    "missingzones = [l for l  in list_zone_de_sante_inrb if l not in list(gpdzsg['Nom'])];missingzones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cases in those zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Provinces</th>\n",
       "      <th>Cas confirmés</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Provinces, Cas confirmés]\n",
       "Index: []"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi[df_epi['Provinces'].isin(missingzones)].groupby(['Provinces'],as_index = False).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop those zones for now\n",
    "\n",
    "`TODO` see what to do with those later on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Provinces</th>\n",
       "      <th>Cas confirmés</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ituri</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kasaï-Central</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kongo-Central</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tshopo</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Provinces  Cas confirmés\n",
       "0   Haut-Katanga           44.0\n",
       "1      Haut-Uele            5.0\n",
       "2          Ituri           17.0\n",
       "3  Kasaï-Central            1.0\n",
       "4       Kinshasa          146.0\n",
       "5  Kongo-Central           45.0\n",
       "6      Nord-Kivu           21.0\n",
       "7       Sud-Kivu            2.0\n",
       "8     Sud-Ubangi            1.0\n",
       "9         Tshopo            1.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi_agg = df_epi_agg.drop(df_epi_agg.index[df_epi_agg['Provinces'].isin(missingzones)])\n",
    "df_epi_agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check zones left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list_filtered_zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check mismatching zones\n",
    "list_zone_de_sante_inrb = list(df_epi_agg.groupby('Provinces').groups.keys())\n",
    "[l for l  in list_zone_de_sante_inrb if l not in list_filtered_zones]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zones with 0 case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Nord-Ubangi', 'Mongala', 'Equateur', 'Tshuapa', 'Maï-Ndombe', 'Kwango', 'Kwilu', 'Kasaï', 'Kasaï-Oriental', 'Lomami', 'Haut-Lomami', 'Lualaba', 'Tanganyika', 'Maniema', 'Sankuru', 'Bas-Uele'] 16\n"
     ]
    }
   ],
   "source": [
    "#Zone with 0 case\n",
    "zs_none_touched = [l for l  in list_filtered_zones if l not in list_zone_de_sante_inrb]\n",
    "print(zs_none_touched,len(zs_none_touched))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Provinces</th>\n",
       "      <th>Cas confirmés</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mongala</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Equateur</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Maï-Ndombe</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kwango</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kwilu</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kasaï</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kasaï-Oriental</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lomami</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lualaba</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maniema</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Provinces  Cas confirmés\n",
       "1      Nord-Ubangi              0\n",
       "2          Mongala              0\n",
       "3         Equateur              0\n",
       "4          Tshuapa              0\n",
       "5       Maï-Ndombe              0\n",
       "8           Kwango              0\n",
       "9            Kwilu              0\n",
       "10           Kasaï              0\n",
       "12  Kasaï-Oriental              0\n",
       "13          Lomami              0\n",
       "14     Haut-Lomami              0\n",
       "15         Lualaba              0\n",
       "17      Tanganyika              0\n",
       "20         Maniema              0\n",
       "21         Sankuru              0\n",
       "23        Bas-Uele              0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zs_none_touched = gpdzsg[~gpdzsg.Nom.isin(list_zone_de_sante_inrb)][['Nom']]\n",
    "df_zs_none_touched['Cas confirmés'] = 0\n",
    "df_zs_none_touched = df_zs_none_touched.rename({'Nom':'Provinces'},axis=1)\n",
    "df_zs_none_touched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Provinces</th>\n",
       "      <th>Cas confirmés</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ituri</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kasaï-Central</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kongo-Central</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tshopo</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Provinces  Cas confirmés\n",
       "0   Haut-Katanga           44.0\n",
       "1      Haut-Uele            5.0\n",
       "2          Ituri           17.0\n",
       "3  Kasaï-Central            1.0\n",
       "4       Kinshasa          146.0\n",
       "5  Kongo-Central           45.0\n",
       "6      Nord-Kivu           21.0\n",
       "7       Sud-Kivu            2.0\n",
       "8     Sud-Ubangi            1.0\n",
       "9         Tshopo            1.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Provinces</th>\n",
       "      <th>Cas confirmés</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ituri</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kasaï-Central</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kongo-Central</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tshopo</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mongala</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Equateur</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Maï-Ndombe</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kwango</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kwilu</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kasaï</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kasaï-Oriental</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Lomami</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Lualaba</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Maniema</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Provinces  Cas confirmés\n",
       "0     Haut-Katanga           44.0\n",
       "1        Haut-Uele            5.0\n",
       "2            Ituri           17.0\n",
       "3    Kasaï-Central            1.0\n",
       "4         Kinshasa          146.0\n",
       "5    Kongo-Central           45.0\n",
       "6        Nord-Kivu           21.0\n",
       "7         Sud-Kivu            2.0\n",
       "8       Sud-Ubangi            1.0\n",
       "9           Tshopo            1.0\n",
       "10     Nord-Ubangi            0.0\n",
       "11         Mongala            0.0\n",
       "12        Equateur            0.0\n",
       "13         Tshuapa            0.0\n",
       "14      Maï-Ndombe            0.0\n",
       "15          Kwango            0.0\n",
       "16           Kwilu            0.0\n",
       "17           Kasaï            0.0\n",
       "18  Kasaï-Oriental            0.0\n",
       "19          Lomami            0.0\n",
       "20     Haut-Lomami            0.0\n",
       "21         Lualaba            0.0\n",
       "22      Tanganyika            0.0\n",
       "23         Maniema            0.0\n",
       "24         Sankuru            0.0\n",
       "25        Bas-Uele            0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi_agg = pd.concat([df_epi_agg,df_zs_none_touched], ignore_index=True)\n",
    "df_epi_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Provinces</th>\n",
       "      <th>Cas confirmés</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Provinces, Cas confirmés]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi_agg[df_epi_agg.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_epi_agg[df_epi_agg['Zone de Santé'] == 'Bandalungwa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# zone_filtered_gd[zone_filtered_gd.PROVINCE == 'Kinshasa'][['PROVINCE','Zone+Peupl']].sort_values('Zone+Peupl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([l for l in list_zone_de_sante_inrb if l in list_filtered_zones])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(list_filtered_zones))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [l for l  in list(df_epi_agg['Zone de Santé']) if l not in  list_filtered_zones]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Epidata to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Provinces</th>\n",
       "      <th>Cas confirmés</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Equateur</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ituri</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kasaï</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kasaï-Central</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kasaï-Oriental</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kongo-Central</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kwango</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kwilu</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lomami</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lualaba</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Maniema</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Maï-Ndombe</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mongala</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tshopo</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Provinces  Cas confirmés\n",
       "0         Bas-Uele            0.0\n",
       "1         Equateur            0.0\n",
       "2     Haut-Katanga           44.0\n",
       "3      Haut-Lomami            0.0\n",
       "4        Haut-Uele            5.0\n",
       "5            Ituri           17.0\n",
       "6            Kasaï            0.0\n",
       "7    Kasaï-Central            1.0\n",
       "8   Kasaï-Oriental            0.0\n",
       "9         Kinshasa          146.0\n",
       "10   Kongo-Central           45.0\n",
       "11          Kwango            0.0\n",
       "12           Kwilu            0.0\n",
       "13          Lomami            0.0\n",
       "14         Lualaba            0.0\n",
       "15         Maniema            0.0\n",
       "16      Maï-Ndombe            0.0\n",
       "17         Mongala            0.0\n",
       "18       Nord-Kivu           21.0\n",
       "19     Nord-Ubangi            0.0\n",
       "20         Sankuru            0.0\n",
       "21        Sud-Kivu            2.0\n",
       "22      Sud-Ubangi            1.0\n",
       "23      Tanganyika            0.0\n",
       "24          Tshopo            1.0\n",
       "25         Tshuapa            0.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi_agg = df_epi_agg.groupby(['Provinces'], as_index=False).sum()\n",
    "df_epi_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check duplicated zones\n",
    "check_list = list()\n",
    "for el in list_filtered_zones:#list(df_epi_agg['Zone de Santé']):\n",
    "    if (el in check_list):\n",
    "        print(el)\n",
    "    else:\n",
    "        check_list.append(el)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 26)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list_filtered_zones),len(list(df_epi_agg['Provinces']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(list_filtered_zones) == set(list(df_epi_agg['Provinces']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(list_filtered_zones))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(list(df_epi_agg['Provinces'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[ el for el in list_filtered_zones if el not in  list(df_epi_agg['Provinces'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save only filtered zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>patNames</th>\n",
       "      <th>Cas confirmés</th>\n",
       "      <th>patIDs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Equateur</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ituri</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kasaï</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kasaï-Central</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kasaï-Oriental</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>146.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kongo-Central</td>\n",
       "      <td>45.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kwango</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kwilu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lomami</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lualaba</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Maniema</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Maï-Ndombe</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mongala</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tshopo</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          patNames  Cas confirmés  patIDs\n",
       "0         Bas-Uele            0.0       1\n",
       "1         Equateur            0.0       2\n",
       "2     Haut-Katanga           44.0       3\n",
       "3      Haut-Lomami            0.0       4\n",
       "4        Haut-Uele            5.0       5\n",
       "5            Ituri           17.0       6\n",
       "6            Kasaï            0.0       7\n",
       "7    Kasaï-Central            1.0       8\n",
       "8   Kasaï-Oriental            0.0       9\n",
       "9         Kinshasa          146.0      10\n",
       "10   Kongo-Central           45.0      11\n",
       "11          Kwango            0.0      12\n",
       "12           Kwilu            0.0      13\n",
       "13          Lomami            0.0      14\n",
       "14         Lualaba            0.0      15\n",
       "15         Maniema            0.0      16\n",
       "16      Maï-Ndombe            0.0      17\n",
       "17         Mongala            0.0      18\n",
       "18       Nord-Kivu           21.0      19\n",
       "19     Nord-Ubangi            0.0      20\n",
       "20         Sankuru            0.0      21\n",
       "21        Sud-Kivu            2.0      22\n",
       "22      Sud-Ubangi            1.0      23\n",
       "23      Tanganyika            0.0      24\n",
       "24          Tshopo            1.0      25\n",
       "25         Tshuapa            0.0      26"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi_agg_fil = df_epi_agg[df_epi_agg['Provinces'].isin(list_filtered_zones)][['Provinces','Cas confirmés']]\n",
    "df_epi_agg_fil = pd.DataFrame(df_epi_agg_fil)\n",
    "# df_epi_agg_fil['patIDs'] = [i+1 for i in range(len(list_filtered_zones))]#df_epi_agg_fil.shape[0])]\n",
    "df_epi_agg_fil['patIDs'] = [i+1 for i in range(df_epi_agg_fil.shape[0])]#df_epi_agg_fil.shape[0])]\n",
    "df_epi_agg_fil = df_epi_agg_fil.rename(columns={'Provinces':'patNames'})\n",
    "df_epi_agg_fil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save to csv\n",
    "# './epidemiological model/BEARmod/DRCexample/data/epidata.csv'\n",
    "df_epi_agg_fil.to_csv('./epidemiological_model/BEARmod_DRC/data/epidatamacro.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_epi_agg_fil.to_clipboard(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "metadata": {},
   "outputs": [],
   "source": [
    "#['Destination','deltaVolume_x_plus_y_x_y']\n",
    "# df_obs_agg_merge\n",
    "\n",
    "# df_epi_reduc_agg_fil = df_epi_agg_fil.merge(df_obs_agg_merge[['Destination','deltaVolume_x_plus_y_x_y']], left_on='Zone de Santé', right_on='Destination')\n",
    "# df_epi_reduc_agg_fil.rename(columns={'deltaVolume_x_plus_y_x_y'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>patNames</th>\n",
       "      <th>Cas confirmés</th>\n",
       "      <th>patIDs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Equateur</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ituri</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kasaï</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kasaï-Central</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kasaï-Oriental</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>146.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kongo-Central</td>\n",
       "      <td>45.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kwango</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kwilu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lomami</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lualaba</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Maniema</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Maï-Ndombe</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mongala</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tshopo</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          patNames  Cas confirmés  patIDs\n",
       "0         Bas-Uele            0.0       1\n",
       "1         Equateur            0.0       2\n",
       "2     Haut-Katanga           44.0       3\n",
       "3      Haut-Lomami            0.0       4\n",
       "4        Haut-Uele            5.0       5\n",
       "5            Ituri           17.0       6\n",
       "6            Kasaï            0.0       7\n",
       "7    Kasaï-Central            1.0       8\n",
       "8   Kasaï-Oriental            0.0       9\n",
       "9         Kinshasa          146.0      10\n",
       "10   Kongo-Central           45.0      11\n",
       "11          Kwango            0.0      12\n",
       "12           Kwilu            0.0      13\n",
       "13          Lomami            0.0      14\n",
       "14         Lualaba            0.0      15\n",
       "15         Maniema            0.0      16\n",
       "16      Maï-Ndombe            0.0      17\n",
       "17         Mongala            0.0      18\n",
       "18       Nord-Kivu           21.0      19\n",
       "19     Nord-Ubangi            0.0      20\n",
       "20         Sankuru            0.0      21\n",
       "21        Sud-Kivu            2.0      22\n",
       "22      Sud-Ubangi            1.0      23\n",
       "23      Tanganyika            0.0      24\n",
       "24          Tshopo            1.0      25\n",
       "25         Tshuapa            0.0      26"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi_agg_fil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "283.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epi_agg_fil['Cas confirmés'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>patIDs</th>\n",
       "      <th>patNames</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bas-Uele</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Equateur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Haut-Uele</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Ituri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Kasaï</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Kasaï-Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Kasaï-Oriental</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Kinshasa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Kongo-Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Kwango</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Kwilu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Lomami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Lualaba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Maniema</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Maï-Ndombe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Mongala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Sankuru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Sud-Kivu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Sud-Ubangi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Tanganyika</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Tshopo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Tshuapa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    patIDs        patNames\n",
       "0        1        Bas-Uele\n",
       "1        2        Equateur\n",
       "2        3    Haut-Katanga\n",
       "3        4     Haut-Lomami\n",
       "4        5       Haut-Uele\n",
       "5        6           Ituri\n",
       "6        7           Kasaï\n",
       "7        8   Kasaï-Central\n",
       "8        9  Kasaï-Oriental\n",
       "9       10        Kinshasa\n",
       "10      11   Kongo-Central\n",
       "11      12          Kwango\n",
       "12      13           Kwilu\n",
       "13      14          Lomami\n",
       "14      15         Lualaba\n",
       "15      16         Maniema\n",
       "16      17      Maï-Ndombe\n",
       "17      18         Mongala\n",
       "18      19       Nord-Kivu\n",
       "19      20     Nord-Ubangi\n",
       "20      21         Sankuru\n",
       "21      22        Sud-Kivu\n",
       "22      23      Sud-Ubangi\n",
       "23      24      Tanganyika\n",
       "24      25          Tshopo\n",
       "25      26         Tshuapa"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pats = df_epi_agg_fil[['patIDs', 'patNames']]\n",
    "# df_pats = df_pats.rename(columns={'Zone de Santé':'patNames','ids':'patIDs'})\n",
    "df_pats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population movement data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv file\n",
    "df = pd.read_csv('data/new/Zoning Province/Flux_24h.csv',sep = ';')\n",
    "# Date Conversion\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df['Date_day'] = df['Date'].dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Immobility</th>\n",
       "      <th>Home_Category</th>\n",
       "      <th>Activity_Category</th>\n",
       "      <th>Observation_Zone</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Date_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>3h</td>\n",
       "      <td>Outside_Resident</td>\n",
       "      <td>Destination_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>1049</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>3h</td>\n",
       "      <td>Outside_Resident</td>\n",
       "      <td>Outside_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>1049</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>3h</td>\n",
       "      <td>Outside_Resident</td>\n",
       "      <td>Origin_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>1049</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>3h</td>\n",
       "      <td>Destination_Zone_Resident</td>\n",
       "      <td>Destination_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>3h</td>\n",
       "      <td>Origin_Zone_Resident</td>\n",
       "      <td>Origin_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>1049</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123839</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Ituri</td>\n",
       "      <td>3h</td>\n",
       "      <td>Outside_Resident</td>\n",
       "      <td>Outside_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123840</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Ituri</td>\n",
       "      <td>3h</td>\n",
       "      <td>Origin_Zone_Resident</td>\n",
       "      <td>Origin_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123843</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>3h</td>\n",
       "      <td>Outside_Resident</td>\n",
       "      <td>Outside_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123844</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>3h</td>\n",
       "      <td>Destination_Zone_Resident</td>\n",
       "      <td>Origin_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123845</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>3h</td>\n",
       "      <td>Origin_Zone_Resident</td>\n",
       "      <td>Origin_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51137 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date    Origin Destination Immobility              Home_Category  \\\n",
       "2      2020-02-01  Bas-Uele   Haut-Uele         3h           Outside_Resident   \n",
       "7      2020-02-01  Bas-Uele      Tshopo         3h           Outside_Resident   \n",
       "8      2020-02-01  Bas-Uele      Tshopo         3h           Outside_Resident   \n",
       "9      2020-02-01  Bas-Uele      Tshopo         3h  Destination_Zone_Resident   \n",
       "10     2020-02-01  Bas-Uele      Tshopo         3h       Origin_Zone_Resident   \n",
       "...           ...       ...         ...        ...                        ...   \n",
       "123839 2020-08-11    Tshopo       Ituri         3h           Outside_Resident   \n",
       "123840 2020-08-11    Tshopo       Ituri         3h       Origin_Zone_Resident   \n",
       "123843 2020-08-11    Tshopo     Maniema         3h           Outside_Resident   \n",
       "123844 2020-08-11    Tshopo     Maniema         3h  Destination_Zone_Resident   \n",
       "123845 2020-08-11    Tshopo     Maniema         3h       Origin_Zone_Resident   \n",
       "\n",
       "              Activity_Category Observation_Zone       Mode  Volume  Date_day  \n",
       "2       Destination_Zone_Worker      ZoneGlobale  All_modes    1049         5  \n",
       "7                Outside_Worker      ZoneGlobale  All_modes    1049         5  \n",
       "8            Origin_Zone_Worker      ZoneGlobale  All_modes    1049         5  \n",
       "9       Destination_Zone_Worker      ZoneGlobale  All_modes      42         5  \n",
       "10           Origin_Zone_Worker      ZoneGlobale  All_modes    1049         5  \n",
       "...                         ...              ...        ...     ...       ...  \n",
       "123839           Outside_Worker      ZoneGlobale  All_modes     129         1  \n",
       "123840       Origin_Zone_Worker      ZoneGlobale  All_modes      86         1  \n",
       "123843           Outside_Worker      ZoneGlobale  All_modes      86         1  \n",
       "123844       Origin_Zone_Worker      ZoneGlobale  All_modes     881         1  \n",
       "123845       Origin_Zone_Worker      ZoneGlobale  All_modes      86         1  \n",
       "\n",
       "[51137 rows x 10 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter data for only 3h of Immobility and exclude hors-zone\n",
    "df = df[(df.Immobility == '3h') & (df.Origin != 'Hors_Zone')]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove duplicated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Immobility</th>\n",
       "      <th>Home_Category</th>\n",
       "      <th>Activity_Category</th>\n",
       "      <th>Observation_Zone</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Date_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>3h</td>\n",
       "      <td>Outside_Resident</td>\n",
       "      <td>Destination_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>1049</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>3h</td>\n",
       "      <td>Outside_Resident</td>\n",
       "      <td>Outside_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>1049</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>3h</td>\n",
       "      <td>Outside_Resident</td>\n",
       "      <td>Origin_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>1049</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>3h</td>\n",
       "      <td>Destination_Zone_Resident</td>\n",
       "      <td>Destination_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>3h</td>\n",
       "      <td>Origin_Zone_Resident</td>\n",
       "      <td>Origin_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>1049</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123839</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Ituri</td>\n",
       "      <td>3h</td>\n",
       "      <td>Outside_Resident</td>\n",
       "      <td>Outside_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123840</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Ituri</td>\n",
       "      <td>3h</td>\n",
       "      <td>Origin_Zone_Resident</td>\n",
       "      <td>Origin_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123843</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>3h</td>\n",
       "      <td>Outside_Resident</td>\n",
       "      <td>Outside_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123844</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>3h</td>\n",
       "      <td>Destination_Zone_Resident</td>\n",
       "      <td>Origin_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123845</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>3h</td>\n",
       "      <td>Origin_Zone_Resident</td>\n",
       "      <td>Origin_Zone_Worker</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>All_modes</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51137 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date    Origin Destination Immobility              Home_Category  \\\n",
       "2      2020-02-01  Bas-Uele   Haut-Uele         3h           Outside_Resident   \n",
       "7      2020-02-01  Bas-Uele      Tshopo         3h           Outside_Resident   \n",
       "8      2020-02-01  Bas-Uele      Tshopo         3h           Outside_Resident   \n",
       "9      2020-02-01  Bas-Uele      Tshopo         3h  Destination_Zone_Resident   \n",
       "10     2020-02-01  Bas-Uele      Tshopo         3h       Origin_Zone_Resident   \n",
       "...           ...       ...         ...        ...                        ...   \n",
       "123839 2020-08-11    Tshopo       Ituri         3h           Outside_Resident   \n",
       "123840 2020-08-11    Tshopo       Ituri         3h       Origin_Zone_Resident   \n",
       "123843 2020-08-11    Tshopo     Maniema         3h           Outside_Resident   \n",
       "123844 2020-08-11    Tshopo     Maniema         3h  Destination_Zone_Resident   \n",
       "123845 2020-08-11    Tshopo     Maniema         3h       Origin_Zone_Resident   \n",
       "\n",
       "              Activity_Category Observation_Zone       Mode  Volume  Date_day  \n",
       "2       Destination_Zone_Worker      ZoneGlobale  All_modes    1049         5  \n",
       "7                Outside_Worker      ZoneGlobale  All_modes    1049         5  \n",
       "8            Origin_Zone_Worker      ZoneGlobale  All_modes    1049         5  \n",
       "9       Destination_Zone_Worker      ZoneGlobale  All_modes      42         5  \n",
       "10           Origin_Zone_Worker      ZoneGlobale  All_modes    1049         5  \n",
       "...                         ...              ...        ...     ...       ...  \n",
       "123839           Outside_Worker      ZoneGlobale  All_modes     129         1  \n",
       "123840       Origin_Zone_Worker      ZoneGlobale  All_modes      86         1  \n",
       "123843           Outside_Worker      ZoneGlobale  All_modes      86         1  \n",
       "123844       Origin_Zone_Worker      ZoneGlobale  All_modes     881         1  \n",
       "123845       Origin_Zone_Worker      ZoneGlobale  All_modes      86         1  \n",
       "\n",
       "[51137 rows x 10 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Observation_Zone</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>1049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>3189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Equateur</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>4961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Equateur</td>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11107</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11108</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>1</td>\n",
       "      <td>96014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11109</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11110</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Ituri</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>1</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>1</td>\n",
       "      <td>1053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11112 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Origin  Destination Observation_Zone  Date_day  Volume\n",
       "0     2020-02-01      Bas-Uele    Haut-Uele      ZoneGlobale         5    1049\n",
       "1     2020-02-01      Bas-Uele       Tshopo      ZoneGlobale         5    3189\n",
       "2     2020-02-01      Equateur     Kinshasa      ZoneGlobale         5    4961\n",
       "3     2020-02-01      Equateur   Sud-Ubangi      ZoneGlobale         5     505\n",
       "4     2020-02-01  Haut-Katanga  Haut-Lomami      ZoneGlobale         5      22\n",
       "...          ...           ...          ...              ...       ...     ...\n",
       "11107 2020-08-11      Sud-Kivu      Maniema      ZoneGlobale         1      35\n",
       "11108 2020-08-11      Sud-Kivu    Nord-Kivu      ZoneGlobale         1   96014\n",
       "11109 2020-08-11        Tshopo     Bas-Uele      ZoneGlobale         1      43\n",
       "11110 2020-08-11        Tshopo        Ituri      ZoneGlobale         1     215\n",
       "11111 2020-08-11        Tshopo      Maniema      ZoneGlobale         1    1053\n",
       "\n",
       "[11112 rows x 6 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only consider zone Global (not hotspots)\n",
    "df_g = df[df.Observation_Zone == 'ZoneGlobale'].groupby(by=['Date','Origin','Destination','Observation_Zone','Date_day'], as_index=0).sum()\n",
    "df_g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter movement data by selected zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Observation_Zone</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>1049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>3189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Equateur</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>4961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Equateur</td>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11107</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11108</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>1</td>\n",
       "      <td>96014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11109</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11110</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Ituri</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>1</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>1</td>\n",
       "      <td>1053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7502 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Origin  Destination Observation_Zone  Date_day  Volume\n",
       "0     2020-02-01      Bas-Uele    Haut-Uele      ZoneGlobale         5    1049\n",
       "1     2020-02-01      Bas-Uele       Tshopo      ZoneGlobale         5    3189\n",
       "2     2020-02-01      Equateur     Kinshasa      ZoneGlobale         5    4961\n",
       "3     2020-02-01      Equateur   Sud-Ubangi      ZoneGlobale         5     505\n",
       "4     2020-02-01  Haut-Katanga  Haut-Lomami      ZoneGlobale         5      22\n",
       "...          ...           ...          ...              ...       ...     ...\n",
       "11107 2020-08-11      Sud-Kivu      Maniema      ZoneGlobale         1      35\n",
       "11108 2020-08-11      Sud-Kivu    Nord-Kivu      ZoneGlobale         1   96014\n",
       "11109 2020-08-11        Tshopo     Bas-Uele      ZoneGlobale         1      43\n",
       "11110 2020-08-11        Tshopo        Ituri      ZoneGlobale         1     215\n",
       "11111 2020-08-11        Tshopo      Maniema      ZoneGlobale         1    1053\n",
       "\n",
       "[7502 rows x 6 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered_both = df_g[(df_g['Origin'].isin(list_filtered_zones)) & (df_g['Destination'].isin(list_filtered_zones))]\n",
    "df_filtered_both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Computing Totals Volume for each Origin per day\n",
    "# df_filtered_both_agg = df_filtered_both.groupby(by = ['Date','Date_day',\"Origin\"], as_index=False).sum()\n",
    "# df_filtered_both_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_filtered_both_mg = df_filtered_both.merge(df_filtered_both_agg,left_on=['Date','Origin'], right_on=['Date','Origin'])\n",
    "# df_filtered_both_mg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_filtered_both_mg['VolumeProp'] = df_filtered_both_mg['Volume_x']/df_filtered_both_mg['Volume_y']\n",
    "# df_filtered_both_mg#[(df_filtered_both_mg.Origin == 'Kinshasa') & (df_filtered_both_mg.Date == '2020-01-31')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Net Mobility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_filtered_both = df_g[(df_g['Origin'].isin(list_filtered_zones)) & (df_g['Destination'].isin(list_filtered_zones))]\n",
    "# df_filtered_both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_df_from_to(start_date, end_date, df):\n",
    "    mask_base = (df['Date'] >= start_date) & (df['Date'] <= end_date)\n",
    "    return df.loc[mask_base]\n",
    "\n",
    "def slide_df(df,interval, unit, field = 'date'):\n",
    "    df_copy = pd.DataFrame(df, copy=True)\n",
    "    df_copy[field] = df_copy[field] + pd.Timedelta(interval, unit)\n",
    "    df_copy_slid = df_copy[df_copy[field] > df[field].max()]\n",
    "    return pd.concat([df,df_copy_slid], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Net Mobility for each Zone per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>Volume_x</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Volume_y</th>\n",
       "      <th>Volume_x_plus_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>5</td>\n",
       "      <td>106</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>4238</td>\n",
       "      <td>4344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Equateur</td>\n",
       "      <td>5</td>\n",
       "      <td>570</td>\n",
       "      <td>Equateur</td>\n",
       "      <td>5466</td>\n",
       "      <td>6036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>5</td>\n",
       "      <td>48213</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>34517</td>\n",
       "      <td>82730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>5</td>\n",
       "      <td>30670</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>32563</td>\n",
       "      <td>63233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5</td>\n",
       "      <td>1821</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>2760</td>\n",
       "      <td>4581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3078</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Lualaba</td>\n",
       "      <td>1</td>\n",
       "      <td>42553</td>\n",
       "      <td>Lualaba</td>\n",
       "      <td>44216</td>\n",
       "      <td>86769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3079</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>1</td>\n",
       "      <td>1208</td>\n",
       "      <td>Maniema</td>\n",
       "      <td>985</td>\n",
       "      <td>2193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>1</td>\n",
       "      <td>119886</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>116176</td>\n",
       "      <td>236062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>1</td>\n",
       "      <td>101403</td>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>96049</td>\n",
       "      <td>197452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3082</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>1</td>\n",
       "      <td>4499</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>1311</td>\n",
       "      <td>5810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3083 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Destination  Date_day  Volume_x        Origin  Volume_y  \\\n",
       "0    2020-02-01      Bas-Uele         5       106      Bas-Uele      4238   \n",
       "1    2020-02-01      Equateur         5       570      Equateur      5466   \n",
       "2    2020-02-01  Haut-Katanga         5     48213  Haut-Katanga     34517   \n",
       "3    2020-02-01   Haut-Lomami         5     30670   Haut-Lomami     32563   \n",
       "4    2020-02-01     Haut-Uele         5      1821     Haut-Uele      2760   \n",
       "...         ...           ...       ...       ...           ...       ...   \n",
       "3078 2020-08-11       Lualaba         1     42553       Lualaba     44216   \n",
       "3079 2020-08-11       Maniema         1      1208       Maniema       985   \n",
       "3080 2020-08-11     Nord-Kivu         1    119886     Nord-Kivu    116176   \n",
       "3081 2020-08-11      Sud-Kivu         1    101403      Sud-Kivu     96049   \n",
       "3082 2020-08-11        Tshopo         1      4499        Tshopo      1311   \n",
       "\n",
       "      Volume_x_plus_y  \n",
       "0                4344  \n",
       "1                6036  \n",
       "2               82730  \n",
       "3               63233  \n",
       "4                4581  \n",
       "...               ...  \n",
       "3078            86769  \n",
       "3079             2193  \n",
       "3080           236062  \n",
       "3081           197452  \n",
       "3082             5810  \n",
       "\n",
       "[3083 rows x 7 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered_agg_in = df_filtered_both.groupby(by=['Date','Destination','Date_day'],as_index=0).sum()\n",
    "#df_filtered_agg_in[(df_filtered_agg_in.Destination == 'Gombe') & (df_filtered_agg_in.Date == '2020-02-02')]\n",
    "df_filtered_agg_out = df_filtered_both.groupby(by=['Date','Origin','Date_day'],as_index=0).sum()\n",
    "#df_filtered_agg_out\n",
    "df_filtered_agg_in_out = pd.merge(df_filtered_agg_in,df_filtered_agg_out, left_on=['Date','Destination','Date_day'], right_on=['Date','Origin','Date_day'])\n",
    "\n",
    "#Sum volume inbound and outbound for each zone\n",
    "df_filtered_agg_in_out['Volume_x_plus_y'] = df_filtered_agg_in_out['Volume_x'] + df_filtered_agg_in_out['Volume_y']\n",
    "df_filtered_agg_in_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting data (ref/obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mobility with baseline  (the starting date of lockdown in DRC)\n",
    "ref_start_date = '2020-02-01'\n",
    "ref_end_date = '2020-03-18'\n",
    "\n",
    "# mask_base = (df_filtered_agg_in_out['Date'] >= ref_start_date) & (df_filtered_agg_in_out['Date'] <= ref_end_date)\n",
    "\n",
    "obs_start_date = '2020-03-19'\n",
    "obs_end_date = '2021-01-29'\n",
    "\n",
    "# mask_obs = (df_filtered_agg_in_out['Date'] >= obs_start_date) & (df_filtered_agg_in_out['Date'] <= obs_end_date)\n",
    "\n",
    "df_ref = filter_df_from_to(ref_start_date, ref_end_date,df_filtered_agg_in_out)#df_agg_gen_mob.loc[mask_base]\n",
    "df_obs = filter_df_from_to(obs_start_date, obs_end_date,df_filtered_agg_in_out)#df_agg_gen_mob.loc[mask_obs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ref_agg = df_ref.groupby(by=['Date','Destination','Date_day'],as_index=0).sum().groupby(by=['Destination'],as_index=0).median()\n",
    "df_obs_agg = df_obs.groupby(by=['Date','Destination','Date_day'],as_index=0).sum().groupby(by=['Destination'],as_index=0).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>Volume_x</th>\n",
       "      <th>Volume_y</th>\n",
       "      <th>Volume_x_plus_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>3.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>2260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Equateur</td>\n",
       "      <td>3.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>1669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41765.5</td>\n",
       "      <td>36506.5</td>\n",
       "      <td>79376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28824.0</td>\n",
       "      <td>34335.0</td>\n",
       "      <td>64605.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>2316.0</td>\n",
       "      <td>3350.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Destination  Date_day  Volume_x  Volume_y  Volume_x_plus_y\n",
       "0      Bas-Uele       3.0     129.0    1916.0           2260.0\n",
       "1      Equateur       3.0     452.0    1012.0           1669.0\n",
       "2  Haut-Katanga       3.0   41765.5   36506.5          79376.0\n",
       "3   Haut-Lomami       3.0   28824.0   34335.0          64605.5\n",
       "4     Haut-Uele       3.0     836.0    2316.0           3350.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ref_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>Volume_x</th>\n",
       "      <th>Volume_y</th>\n",
       "      <th>Volume_x_plus_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>3.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Equateur</td>\n",
       "      <td>2.5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>117.5</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36606.0</td>\n",
       "      <td>31904.0</td>\n",
       "      <td>68880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32260.0</td>\n",
       "      <td>41026.0</td>\n",
       "      <td>72612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>3.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>1855.0</td>\n",
       "      <td>3253.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Destination  Date_day  Volume_x  Volume_y  Volume_x_plus_y\n",
       "0      Bas-Uele       3.0      79.0    1535.0           1950.0\n",
       "1      Equateur       2.5      75.0     117.5            352.0\n",
       "2  Haut-Katanga       3.0   36606.0   31904.0          68880.0\n",
       "3   Haut-Lomami       3.0   32260.0   41026.0          72612.0\n",
       "4     Haut-Uele       3.0     666.0    1855.0           3253.5"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obs_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_obs_agg_merge = pd.merge(df_ref_agg, df_obs_agg,left_on='Destination', right_on='Destination')\n",
    "# df_obs_agg_merge['diffVolume_x_plus_y_x_y'] = df_obs_agg_merge['Volume_x_plus_y_y'] - df_obs_agg_merge['Volume_x_plus_y_x']\n",
    "\n",
    "# df_obs_agg_merge['deltaVolume_x_plus_y_x_y'] = df_obs_agg_merge['diffVolume_x_plus_y_x_y']/df_obs_agg_merge['Volume_x_plus_y_x']*100\n",
    "# df_obs_agg_merge = df_obs_agg_merge.sort_values('deltaVolume_x_plus_y_x_y')\n",
    "# df_obs_agg_merge = df_obs_agg_merge[df_obs_agg_merge.Destination.isin(list_filtered_zones)]\n",
    "\n",
    "# #['Destination','deltaVolume_x_plus_y_x_y']\n",
    "# df_obs_agg_merge\n",
    "# # df_obs_agg_merge['Destination','deltaVolume_x_plus_y_x_y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Movement Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>Volume_x</th>\n",
       "      <th>Volume_y</th>\n",
       "      <th>Volume_x_plus_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>2686.0</td>\n",
       "      <td>2869.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>1</td>\n",
       "      <td>825.5</td>\n",
       "      <td>816.0</td>\n",
       "      <td>1286.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>1119.5</td>\n",
       "      <td>1181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>3</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>3649.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>4</td>\n",
       "      <td>913.5</td>\n",
       "      <td>1507.5</td>\n",
       "      <td>2082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Tshopo</td>\n",
       "      <td>5</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>8902.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Tshopo</td>\n",
       "      <td>6</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>701.0</td>\n",
       "      <td>2770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1522.0</td>\n",
       "      <td>1543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>3</td>\n",
       "      <td>151.0</td>\n",
       "      <td>6057.5</td>\n",
       "      <td>6309.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>4</td>\n",
       "      <td>425.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Destination  Date_day  Volume_x  Volume_y  Volume_x_plus_y\n",
       "0      Bas-Uele         0     204.0    2686.0           2869.5\n",
       "1      Bas-Uele         1     825.5     816.0           1286.5\n",
       "2      Bas-Uele         2      61.5    1119.5           1181.0\n",
       "3      Bas-Uele         3     104.0    3545.0           3649.0\n",
       "4      Bas-Uele         4     913.5    1507.5           2082.0\n",
       "..          ...       ...       ...       ...              ...\n",
       "142      Tshopo         5    3498.0    3212.0           8902.0\n",
       "143      Tshopo         6    2460.0     701.0           2770.0\n",
       "144     Tshuapa         0      21.0    1522.0           1543.0\n",
       "145     Tshuapa         3     151.0    6057.5           6309.5\n",
       "146     Tshuapa         4     425.0     425.0            850.0\n",
       "\n",
       "[147 rows x 5 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ref_agg_date_day = df_ref.groupby(by=['Date','Destination','Date_day'],as_index=0).sum().groupby(by=['Destination','Date_day'],as_index=0).median()\n",
    "df_ref_agg_date_day#[df_ref_agg_date_day.Destination == 'Gombe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_obs_agg_date_dest_dd = df_obs.groupby(by=['Date','Destination','Date_day'],as_index=0).sum()\n",
    "# df_obs_agg_date_dest_dd[df_obs_agg_date_dest_dd.Destination == 'Gombe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>Volume_x_x</th>\n",
       "      <th>Volume_y_x</th>\n",
       "      <th>Volume_x_plus_y_x</th>\n",
       "      <th>Volume_x_y</th>\n",
       "      <th>Volume_y_y</th>\n",
       "      <th>Volume_x_plus_y_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>2498</td>\n",
       "      <td>2560</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>3649.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Kwango</td>\n",
       "      <td>3</td>\n",
       "      <td>4676</td>\n",
       "      <td>7478</td>\n",
       "      <td>12154</td>\n",
       "      <td>4101.5</td>\n",
       "      <td>5644.5</td>\n",
       "      <td>10684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Kwilu</td>\n",
       "      <td>3</td>\n",
       "      <td>1241</td>\n",
       "      <td>6202</td>\n",
       "      <td>7443</td>\n",
       "      <td>2050.0</td>\n",
       "      <td>6342.5</td>\n",
       "      <td>8392.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Lomami</td>\n",
       "      <td>3</td>\n",
       "      <td>37741</td>\n",
       "      <td>31685</td>\n",
       "      <td>69426</td>\n",
       "      <td>34207.0</td>\n",
       "      <td>29013.0</td>\n",
       "      <td>60645.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Lualaba</td>\n",
       "      <td>3</td>\n",
       "      <td>35734</td>\n",
       "      <td>39337</td>\n",
       "      <td>75071</td>\n",
       "      <td>37456.0</td>\n",
       "      <td>38494.0</td>\n",
       "      <td>76222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Ituri</td>\n",
       "      <td>1</td>\n",
       "      <td>18001</td>\n",
       "      <td>24567</td>\n",
       "      <td>42568</td>\n",
       "      <td>22998.0</td>\n",
       "      <td>26059.0</td>\n",
       "      <td>47636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>1</td>\n",
       "      <td>29029</td>\n",
       "      <td>50156</td>\n",
       "      <td>79185</td>\n",
       "      <td>31470.0</td>\n",
       "      <td>31847.0</td>\n",
       "      <td>64777.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>1</td>\n",
       "      <td>43535</td>\n",
       "      <td>39009</td>\n",
       "      <td>82544</td>\n",
       "      <td>40820.0</td>\n",
       "      <td>37540.0</td>\n",
       "      <td>79235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Lualaba</td>\n",
       "      <td>1</td>\n",
       "      <td>42553</td>\n",
       "      <td>44216</td>\n",
       "      <td>86769</td>\n",
       "      <td>38547.0</td>\n",
       "      <td>39965.0</td>\n",
       "      <td>77726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>1</td>\n",
       "      <td>32411</td>\n",
       "      <td>26284</td>\n",
       "      <td>58695</td>\n",
       "      <td>36111.0</td>\n",
       "      <td>30894.0</td>\n",
       "      <td>65749.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2275 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Destination  Date_day  Volume_x_x  Volume_y_x  \\\n",
       "0    2020-03-19      Bas-Uele         3          62        2498   \n",
       "120  2020-03-19        Kwango         3        4676        7478   \n",
       "141  2020-03-19         Kwilu         3        1241        6202   \n",
       "162  2020-03-19        Lomami         3       37741       31685   \n",
       "183  2020-03-19       Lualaba         3       35734       39337   \n",
       "...         ...           ...       ...         ...         ...   \n",
       "1661 2020-08-11         Ituri         1       18001       24567   \n",
       "1628 2020-08-11   Haut-Lomami         1       29029       50156   \n",
       "1608 2020-08-11  Haut-Katanga         1       43535       39009   \n",
       "1780 2020-08-11       Lualaba         1       42553       44216   \n",
       "1681 2020-08-11      Kinshasa         1       32411       26284   \n",
       "\n",
       "      Volume_x_plus_y_x  Volume_x_y  Volume_y_y  Volume_x_plus_y_y  \n",
       "0                  2560       104.0      3545.0             3649.0  \n",
       "120               12154      4101.5      5644.5            10684.0  \n",
       "141                7443      2050.0      6342.5             8392.5  \n",
       "162               69426     34207.0     29013.0            60645.5  \n",
       "183               75071     37456.0     38494.0            76222.0  \n",
       "...                 ...         ...         ...                ...  \n",
       "1661              42568     22998.0     26059.0            47636.0  \n",
       "1628              79185     31470.0     31847.0            64777.0  \n",
       "1608              82544     40820.0     37540.0            79235.0  \n",
       "1780              86769     38547.0     39965.0            77726.0  \n",
       "1681              58695     36111.0     30894.0            65749.0  \n",
       "\n",
       "[2275 rows x 9 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trend = pd.merge(df_obs_agg_date_dest_dd,df_ref_agg_date_day,left_on=['Destination','Date_day'],right_on=['Destination','Date_day']).sort_values(by='Date')\n",
    "# df_trend[df_trend.Destination == 'Gombe']\n",
    "df_trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>Volume_x_x</th>\n",
       "      <th>Volume_y_x</th>\n",
       "      <th>Volume_x_plus_y_x</th>\n",
       "      <th>Volume_x_y</th>\n",
       "      <th>Volume_y_y</th>\n",
       "      <th>Volume_x_plus_y_y</th>\n",
       "      <th>deltaV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>2498</td>\n",
       "      <td>2560</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>3649.0</td>\n",
       "      <td>0.701562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Kwango</td>\n",
       "      <td>3</td>\n",
       "      <td>4676</td>\n",
       "      <td>7478</td>\n",
       "      <td>12154</td>\n",
       "      <td>4101.5</td>\n",
       "      <td>5644.5</td>\n",
       "      <td>10684.0</td>\n",
       "      <td>1.137589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Kwilu</td>\n",
       "      <td>3</td>\n",
       "      <td>1241</td>\n",
       "      <td>6202</td>\n",
       "      <td>7443</td>\n",
       "      <td>2050.0</td>\n",
       "      <td>6342.5</td>\n",
       "      <td>8392.5</td>\n",
       "      <td>0.886863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Lomami</td>\n",
       "      <td>3</td>\n",
       "      <td>37741</td>\n",
       "      <td>31685</td>\n",
       "      <td>69426</td>\n",
       "      <td>34207.0</td>\n",
       "      <td>29013.0</td>\n",
       "      <td>60645.5</td>\n",
       "      <td>1.144784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Lualaba</td>\n",
       "      <td>3</td>\n",
       "      <td>35734</td>\n",
       "      <td>39337</td>\n",
       "      <td>75071</td>\n",
       "      <td>37456.0</td>\n",
       "      <td>38494.0</td>\n",
       "      <td>76222.0</td>\n",
       "      <td>0.984899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Ituri</td>\n",
       "      <td>1</td>\n",
       "      <td>18001</td>\n",
       "      <td>24567</td>\n",
       "      <td>42568</td>\n",
       "      <td>22998.0</td>\n",
       "      <td>26059.0</td>\n",
       "      <td>47636.0</td>\n",
       "      <td>0.893610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>1</td>\n",
       "      <td>29029</td>\n",
       "      <td>50156</td>\n",
       "      <td>79185</td>\n",
       "      <td>31470.0</td>\n",
       "      <td>31847.0</td>\n",
       "      <td>64777.0</td>\n",
       "      <td>1.222425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>1</td>\n",
       "      <td>43535</td>\n",
       "      <td>39009</td>\n",
       "      <td>82544</td>\n",
       "      <td>40820.0</td>\n",
       "      <td>37540.0</td>\n",
       "      <td>79235.0</td>\n",
       "      <td>1.041762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Lualaba</td>\n",
       "      <td>1</td>\n",
       "      <td>42553</td>\n",
       "      <td>44216</td>\n",
       "      <td>86769</td>\n",
       "      <td>38547.0</td>\n",
       "      <td>39965.0</td>\n",
       "      <td>77726.0</td>\n",
       "      <td>1.116345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>1</td>\n",
       "      <td>32411</td>\n",
       "      <td>26284</td>\n",
       "      <td>58695</td>\n",
       "      <td>36111.0</td>\n",
       "      <td>30894.0</td>\n",
       "      <td>65749.0</td>\n",
       "      <td>0.892713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2275 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Destination  Date_day  Volume_x_x  Volume_y_x  \\\n",
       "0    2020-03-19      Bas-Uele         3          62        2498   \n",
       "120  2020-03-19        Kwango         3        4676        7478   \n",
       "141  2020-03-19         Kwilu         3        1241        6202   \n",
       "162  2020-03-19        Lomami         3       37741       31685   \n",
       "183  2020-03-19       Lualaba         3       35734       39337   \n",
       "...         ...           ...       ...         ...         ...   \n",
       "1661 2020-08-11         Ituri         1       18001       24567   \n",
       "1628 2020-08-11   Haut-Lomami         1       29029       50156   \n",
       "1608 2020-08-11  Haut-Katanga         1       43535       39009   \n",
       "1780 2020-08-11       Lualaba         1       42553       44216   \n",
       "1681 2020-08-11      Kinshasa         1       32411       26284   \n",
       "\n",
       "      Volume_x_plus_y_x  Volume_x_y  Volume_y_y  Volume_x_plus_y_y    deltaV  \n",
       "0                  2560       104.0      3545.0             3649.0  0.701562  \n",
       "120               12154      4101.5      5644.5            10684.0  1.137589  \n",
       "141                7443      2050.0      6342.5             8392.5  0.886863  \n",
       "162               69426     34207.0     29013.0            60645.5  1.144784  \n",
       "183               75071     37456.0     38494.0            76222.0  0.984899  \n",
       "...                 ...         ...         ...                ...       ...  \n",
       "1661              42568     22998.0     26059.0            47636.0  0.893610  \n",
       "1628              79185     31470.0     31847.0            64777.0  1.222425  \n",
       "1608              82544     40820.0     37540.0            79235.0  1.041762  \n",
       "1780              86769     38547.0     39965.0            77726.0  1.116345  \n",
       "1681              58695     36111.0     30894.0            65749.0  0.892713  \n",
       "\n",
       "[2275 rows x 10 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trend['deltaV'] = (df_trend['Volume_x_plus_y_x'])/df_trend['Volume_x_plus_y_y'] #- df_trend['Volume_x_plus_y_y']\n",
    "# df_trend = df_trend[df_trend.Destination == 'Gombe']\n",
    "\n",
    "# df_trend[(df_trend.Destination == 'Ngiri-Ngiri') & (df_trend.Date == '2020-03-30')]\n",
    "df_trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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_day</th>\n",
       "      <th>Volume_x_x</th>\n",
       "      <th>Volume_y_x</th>\n",
       "      <th>Volume_x_plus_y_x</th>\n",
       "      <th>Volume_x_y</th>\n",
       "      <th>Volume_y_y</th>\n",
       "      <th>Volume_x_plus_y_y</th>\n",
       "      <th>deltaV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2275.000000</td>\n",
       "      <td>2275.000000</td>\n",
       "      <td>2275.000000</td>\n",
       "      <td>2275.000000</td>\n",
       "      <td>2275.000000</td>\n",
       "      <td>2275.000000</td>\n",
       "      <td>2275.000000</td>\n",
       "      <td>2275.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.961319</td>\n",
       "      <td>24714.241319</td>\n",
       "      <td>24620.784615</td>\n",
       "      <td>49335.025934</td>\n",
       "      <td>27685.330549</td>\n",
       "      <td>27334.111868</td>\n",
       "      <td>55089.512088</td>\n",
       "      <td>0.888674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.993679</td>\n",
       "      <td>29672.672116</td>\n",
       "      <td>28345.153841</td>\n",
       "      <td>57685.505605</td>\n",
       "      <td>30733.966111</td>\n",
       "      <td>28538.504462</td>\n",
       "      <td>58769.595659</td>\n",
       "      <td>1.434328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.007576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1610.500000</td>\n",
       "      <td>2212.000000</td>\n",
       "      <td>4071.500000</td>\n",
       "      <td>2245.000000</td>\n",
       "      <td>3212.000000</td>\n",
       "      <td>7920.000000</td>\n",
       "      <td>0.552974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>14279.000000</td>\n",
       "      <td>15183.000000</td>\n",
       "      <td>30392.000000</td>\n",
       "      <td>25008.000000</td>\n",
       "      <td>24259.000000</td>\n",
       "      <td>48965.500000</td>\n",
       "      <td>0.844846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>35501.000000</td>\n",
       "      <td>34627.000000</td>\n",
       "      <td>70748.500000</td>\n",
       "      <td>38531.000000</td>\n",
       "      <td>35433.000000</td>\n",
       "      <td>76048.000000</td>\n",
       "      <td>1.054113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>148650.000000</td>\n",
       "      <td>156635.000000</td>\n",
       "      <td>265505.000000</td>\n",
       "      <td>118974.000000</td>\n",
       "      <td>110672.000000</td>\n",
       "      <td>227173.000000</td>\n",
       "      <td>56.714286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date_day     Volume_x_x     Volume_y_x  Volume_x_plus_y_x  \\\n",
       "count  2275.000000    2275.000000    2275.000000        2275.000000   \n",
       "mean      2.961319   24714.241319   24620.784615       49335.025934   \n",
       "std       1.993679   29672.672116   28345.153841       57685.505605   \n",
       "min       0.000000      21.000000      21.000000          44.000000   \n",
       "25%       1.000000    1610.500000    2212.000000        4071.500000   \n",
       "50%       3.000000   14279.000000   15183.000000       30392.000000   \n",
       "75%       5.000000   35501.000000   34627.000000       70748.500000   \n",
       "max       6.000000  148650.000000  156635.000000      265505.000000   \n",
       "\n",
       "          Volume_x_y     Volume_y_y  Volume_x_plus_y_y       deltaV  \n",
       "count    2275.000000    2275.000000        2275.000000  2275.000000  \n",
       "mean    27685.330549   27334.111868       55089.512088     0.888674  \n",
       "std     30733.966111   28538.504462       58769.595659     1.434328  \n",
       "min        21.000000      21.000000          42.000000     0.007576  \n",
       "25%      2245.000000    3212.000000        7920.000000     0.552974  \n",
       "50%     25008.000000   24259.000000       48965.500000     0.844846  \n",
       "75%     38531.000000   35433.000000       76048.000000     1.054113  \n",
       "max    118974.000000  110672.000000      227173.000000    56.714286  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trend.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>date</th>\n",
       "      <th>type</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>lower_20</th>\n",
       "      <th>upper_20</th>\n",
       "      <th>lower_50</th>\n",
       "      <th>upper_50</th>\n",
       "      <th>lower_90</th>\n",
       "      <th>upper_90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3615</th>\n",
       "      <td>Congo - Kinshasa</td>\n",
       "      <td>11/08/2020</td>\n",
       "      <td>estimate</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3616</th>\n",
       "      <td>Congo - Kinshasa</td>\n",
       "      <td>12/08/2020</td>\n",
       "      <td>estimate</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3617</th>\n",
       "      <td>Congo - Kinshasa</td>\n",
       "      <td>13/08/2020</td>\n",
       "      <td>estimate</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3618</th>\n",
       "      <td>Congo - Kinshasa</td>\n",
       "      <td>14/08/2020</td>\n",
       "      <td>estimate</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3619</th>\n",
       "      <td>Congo - Kinshasa</td>\n",
       "      <td>15/08/2020</td>\n",
       "      <td>estimate</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3695</th>\n",
       "      <td>Congo - Kinshasa</td>\n",
       "      <td>30/10/2020</td>\n",
       "      <td>forecast</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3696</th>\n",
       "      <td>Congo - Kinshasa</td>\n",
       "      <td>31/10/2020</td>\n",
       "      <td>forecast</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3697</th>\n",
       "      <td>Congo - Kinshasa</td>\n",
       "      <td>01/11/2020</td>\n",
       "      <td>forecast</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3698</th>\n",
       "      <td>Congo - Kinshasa</td>\n",
       "      <td>02/11/2020</td>\n",
       "      <td>forecast</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3699</th>\n",
       "      <td>Congo - Kinshasa</td>\n",
       "      <td>03/11/2020</td>\n",
       "      <td>forecast</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country        date      type  median  mean    sd  lower_20  \\\n",
       "3615  Congo - Kinshasa  11/08/2020  estimate    0.95  0.95  0.04      0.95   \n",
       "3616  Congo - Kinshasa  12/08/2020  estimate    0.95  0.95  0.04      0.95   \n",
       "3617  Congo - Kinshasa  13/08/2020  estimate    0.95  0.95  0.04      0.95   \n",
       "3618  Congo - Kinshasa  14/08/2020  estimate    0.95  0.95  0.04      0.95   \n",
       "3619  Congo - Kinshasa  15/08/2020  estimate    0.95  0.95  0.04      0.95   \n",
       "...                ...         ...       ...     ...   ...   ...       ...   \n",
       "3695  Congo - Kinshasa  30/10/2020  forecast    0.98  1.02  0.15      0.95   \n",
       "3696  Congo - Kinshasa  31/10/2020  forecast    0.98  1.02  0.15      0.95   \n",
       "3697  Congo - Kinshasa  01/11/2020  forecast    0.98  1.02  0.15      0.95   \n",
       "3698  Congo - Kinshasa  02/11/2020  forecast    0.98  1.02  0.15      0.95   \n",
       "3699  Congo - Kinshasa  03/11/2020  forecast    0.98  1.02  0.15      0.95   \n",
       "\n",
       "      upper_20  lower_50  upper_50  lower_90  upper_90  \n",
       "3615      0.96      0.94      0.97      0.90      1.01  \n",
       "3616      0.96      0.94      0.97      0.89      1.01  \n",
       "3617      0.96      0.94      0.97      0.89      1.00  \n",
       "3618      0.96      0.94      0.97      0.89      1.01  \n",
       "3619      0.96      0.94      0.97      0.89      1.01  \n",
       "...        ...       ...       ...       ...       ...  \n",
       "3695      0.97      0.93      1.02      0.84      1.24  \n",
       "3696      0.97      0.93      1.02      0.84      1.24  \n",
       "3697      0.97      0.93      1.02      0.84      1.24  \n",
       "3698      0.97      0.93      1.02      0.84      1.24  \n",
       "3699      0.97      0.93      1.02      0.84      1.24  \n",
       "\n",
       "[85 rows x 12 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read R0s\n",
    "\n",
    "r0vs = pd.read_csv('epidemiological_model/BEARmod_DRC/data/rt.csv')\n",
    "r0vs = r0vs[r0vs.country == 'Congo - Kinshasa']\n",
    "r0vs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Movement Reduction data to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mvt_red = df_trend[['Date','Destination','deltaV']].merge(df_pats, left_on='Destination', right_on='patNames').rename(columns={'Destination':'from','Date':'date','patIDs':'name', 'deltaV':'relative_move'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mvt_red_slid = df_mvt_red\n",
    "# df_mvt_red_slid = slide_df(df_mvt_red,6, 'W')\n",
    "# df_mvt_red_slid = slide_df(df_mvt_red_slid,6, 'W')\n",
    "# df_mvt_red_slid = slide_df(df_mvt_red_slid,6, 'W')\n",
    "#df_mvt_red_slid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>from</th>\n",
       "      <th>relative_move</th>\n",
       "      <th>name</th>\n",
       "      <th>patNames</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>0.701562</td>\n",
       "      <td>1</td>\n",
       "      <td>Bas-Uele</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>0.390361</td>\n",
       "      <td>1</td>\n",
       "      <td>Bas-Uele</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>1.186618</td>\n",
       "      <td>1</td>\n",
       "      <td>Bas-Uele</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>0.418966</td>\n",
       "      <td>1</td>\n",
       "      <td>Bas-Uele</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>2.206605</td>\n",
       "      <td>1</td>\n",
       "      <td>Bas-Uele</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2270</th>\n",
       "      <td>2020-08-10</td>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>0.012123</td>\n",
       "      <td>23</td>\n",
       "      <td>Sud-Ubangi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>1.612903</td>\n",
       "      <td>21</td>\n",
       "      <td>Sankuru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0.076570</td>\n",
       "      <td>21</td>\n",
       "      <td>Sankuru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0.196636</td>\n",
       "      <td>21</td>\n",
       "      <td>Sankuru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>2020-08-04</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>10.761538</td>\n",
       "      <td>21</td>\n",
       "      <td>Sankuru</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2275 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date        from  relative_move  name    patNames\n",
       "0    2020-03-19    Bas-Uele       0.701562     1    Bas-Uele\n",
       "1    2020-03-22    Bas-Uele       0.390361     1    Bas-Uele\n",
       "2    2020-03-23    Bas-Uele       1.186618     1    Bas-Uele\n",
       "3    2020-03-24    Bas-Uele       0.418966     1    Bas-Uele\n",
       "4    2020-03-25    Bas-Uele       2.206605     1    Bas-Uele\n",
       "...         ...         ...            ...   ...         ...\n",
       "2270 2020-08-10  Sud-Ubangi       0.012123    23  Sud-Ubangi\n",
       "2271 2020-06-28     Sankuru       1.612903    21     Sankuru\n",
       "2272 2020-06-29     Sankuru       0.076570    21     Sankuru\n",
       "2273 2020-07-23     Sankuru       0.196636    21     Sankuru\n",
       "2274 2020-08-04     Sankuru      10.761538    21     Sankuru\n",
       "\n",
       "[2275 rows x 5 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Save to csv\n",
    "df_mvt_red_slid.to_csv('./epidemiological_model/BEARmod_DRC/data/mvt_redmacro.csv')\n",
    "df_mvt_red_slid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>relative_move</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2275.000000</td>\n",
       "      <td>2275.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.888674</td>\n",
       "      <td>13.504615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.434328</td>\n",
       "      <td>7.107811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.007576</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.552974</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.844846</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.054113</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56.714286</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       relative_move         name\n",
       "count    2275.000000  2275.000000\n",
       "mean        0.888674    13.504615\n",
       "std         1.434328     7.107811\n",
       "min         0.007576     1.000000\n",
       "25%         0.552974     6.000000\n",
       "50%         0.844846    13.000000\n",
       "75%         1.054113    19.000000\n",
       "max        56.714286    26.000000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mvt_red.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = sns.lineplot(x='Date',y='deltaV',data=df_trend, hue = 'Destination',linewidth = 3.2)\n",
    "\n",
    "# ax.set_xticklabels(labels = df_total_flux['Date'], rotation=90)\n",
    "# ax.\n",
    "\n",
    "# sns.scatterplot(x='Date',y='Volume',data=df_temp[df_temp.Destination == 'Kinshasa' ], hue = 'Date_type', ax =ax, palette = ['#ffb300','b'])\n",
    "\n",
    "# ax.axvline('2020-07-01',ymin = 0, ymax = 1,color = '#546e7a', ls = '--', linewidth = 2.5)\n",
    "\n",
    "# ax.axvline('2020-06-10',ymin = 0, ymax = 1,color = 'r')\n",
    "# ax.yaxis.set_major_formatter(ticker.EngFormatter())\n",
    "# ax.set_xticklabels(labels=,rotation=45)\n",
    "# plt.xlim(0)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1307.44x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_zones = ['Kinshasa','Haut-Katanga']\n",
    "g = sns.FacetGrid(df_trend[df_trend.Destination.isin(selected_zones)], row=\"Destination\",aspect=3,height=6,sharey=False,sharex=False)\n",
    "# g.map(sns.lineplot, x='Date',y='deltaV', alpha=.7)\n",
    "g.map(plt.plot, \"Date\", \"deltaV\", alpha=.7)\n",
    "# sns.lineplot(x='Date',y='deltaV',data=df_trend)\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Presence Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Day_type</th>\n",
       "      <th>PresenceType</th>\n",
       "      <th>Type</th>\n",
       "      <th>Activity_Zone</th>\n",
       "      <th>Home_Zone</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>WE</td>\n",
       "      <td>Jour</td>\n",
       "      <td>Local</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>679176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>WE</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>Local</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>350111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>WE</td>\n",
       "      <td>Jour</td>\n",
       "      <td>Local</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>770048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>WE</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>Local</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>226845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>WE</td>\n",
       "      <td>Jour</td>\n",
       "      <td>Local</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208455</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>JO</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>Etranger</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>1209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208456</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>JO</td>\n",
       "      <td>Jour</td>\n",
       "      <td>Etranger</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>1403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208457</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>JO</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>Etranger</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>1782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208458</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>JO</td>\n",
       "      <td>Jour</td>\n",
       "      <td>Etranger</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>3835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208459</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>JO</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>Etranger</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>3402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208460 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date Day_type PresenceType      Type Activity_Zone  Home_Zone  \\\n",
       "0       2020-02-01       WE         Jour     Local     Hors_Zone  Hors_Zone   \n",
       "1       2020-02-01       WE         Nuit     Local     Hors_Zone  Hors_Zone   \n",
       "2       2020-02-01       WE         Jour     Local    Tanganyika  Hors_Zone   \n",
       "3       2020-02-01       WE         Nuit     Local    Tanganyika  Hors_Zone   \n",
       "4       2020-02-01       WE         Jour     Local  Haut-Katanga  Hors_Zone   \n",
       "...            ...      ...          ...       ...           ...        ...   \n",
       "208455  2020-08-11       JO         Nuit  Etranger     Nord-Kivu  Hors_Zone   \n",
       "208456  2020-08-11       JO         Jour  Etranger     Hors_Zone  Nord-Kivu   \n",
       "208457  2020-08-11       JO         Nuit  Etranger     Hors_Zone  Nord-Kivu   \n",
       "208458  2020-08-11       JO         Jour  Etranger     Nord-Kivu  Nord-Kivu   \n",
       "208459  2020-08-11       JO         Nuit  Etranger     Nord-Kivu  Nord-Kivu   \n",
       "\n",
       "              Zone  Volume  \n",
       "0       Tanganyika  679176  \n",
       "1       Tanganyika  350111  \n",
       "2       Tanganyika  770048  \n",
       "3       Tanganyika  226845  \n",
       "4       Tanganyika     464  \n",
       "...            ...     ...  \n",
       "208455   Nord-Kivu    1209  \n",
       "208456   Nord-Kivu    1403  \n",
       "208457   Nord-Kivu    1782  \n",
       "208458   Nord-Kivu    3835  \n",
       "208459   Nord-Kivu    3402  \n",
       "\n",
       "[208460 rows x 8 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading csv file\n",
    "dfp = pd.read_csv('data/new/Zoning Province/Presence_24h.csv',sep = ';')\n",
    "dfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Day_type</th>\n",
       "      <th>PresenceType</th>\n",
       "      <th>Type</th>\n",
       "      <th>Activity_Zone</th>\n",
       "      <th>Home_Zone</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Date, Day_type, PresenceType, Type, Activity_Zone, Home_Zone, Zone, Volume]\n",
       "Index: []"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Duplicates check\n",
    "dfp[dfp.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert to Date\n",
    "\n",
    "dfp['Date'] = pd.to_datetime(dfp['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Day_type</th>\n",
       "      <th>PresenceType</th>\n",
       "      <th>Type</th>\n",
       "      <th>Activity_Zone</th>\n",
       "      <th>Home_Zone</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>WE</td>\n",
       "      <td>Jour</td>\n",
       "      <td>Local</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>679176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>WE</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>Local</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>350111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>WE</td>\n",
       "      <td>Jour</td>\n",
       "      <td>Local</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>770048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>WE</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>Local</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>226845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>WE</td>\n",
       "      <td>Jour</td>\n",
       "      <td>Local</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208455</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>JO</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>Etranger</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>1209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208456</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>JO</td>\n",
       "      <td>Jour</td>\n",
       "      <td>Etranger</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>1403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208457</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>JO</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>Etranger</td>\n",
       "      <td>Hors_Zone</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>1782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208458</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>JO</td>\n",
       "      <td>Jour</td>\n",
       "      <td>Etranger</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>3835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208459</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>JO</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>Etranger</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>3402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174112 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Day_type PresenceType      Type Activity_Zone  Home_Zone  \\\n",
       "0      2020-02-01       WE         Jour     Local     Hors_Zone  Hors_Zone   \n",
       "1      2020-02-01       WE         Nuit     Local     Hors_Zone  Hors_Zone   \n",
       "2      2020-02-01       WE         Jour     Local    Tanganyika  Hors_Zone   \n",
       "3      2020-02-01       WE         Nuit     Local    Tanganyika  Hors_Zone   \n",
       "4      2020-02-01       WE         Jour     Local  Haut-Katanga  Hors_Zone   \n",
       "...           ...      ...          ...       ...           ...        ...   \n",
       "208455 2020-08-11       JO         Nuit  Etranger     Nord-Kivu  Hors_Zone   \n",
       "208456 2020-08-11       JO         Jour  Etranger     Hors_Zone  Nord-Kivu   \n",
       "208457 2020-08-11       JO         Nuit  Etranger     Hors_Zone  Nord-Kivu   \n",
       "208458 2020-08-11       JO         Jour  Etranger     Nord-Kivu  Nord-Kivu   \n",
       "208459 2020-08-11       JO         Nuit  Etranger     Nord-Kivu  Nord-Kivu   \n",
       "\n",
       "              Zone  Volume  \n",
       "0       Tanganyika  679176  \n",
       "1       Tanganyika  350111  \n",
       "2       Tanganyika  770048  \n",
       "3       Tanganyika  226845  \n",
       "4       Tanganyika     464  \n",
       "...            ...     ...  \n",
       "208455   Nord-Kivu    1209  \n",
       "208456   Nord-Kivu    1403  \n",
       "208457   Nord-Kivu    1782  \n",
       "208458   Nord-Kivu    3835  \n",
       "208459   Nord-Kivu    3402  \n",
       "\n",
       "[174112 rows x 8 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter by selected zones\n",
    "\n",
    "dfp_filtered = dfp[dfp['Zone'].isin(list_filtered_zones)]\n",
    "dfp_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Zone</th>\n",
       "      <th>PresenceType</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Jour</td>\n",
       "      <td>1034483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>709632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Equateur</td>\n",
       "      <td>Jour</td>\n",
       "      <td>1803206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Equateur</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>1419771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>Jour</td>\n",
       "      <td>5629768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8223</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>1568133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8224</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Jour</td>\n",
       "      <td>2799508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8225</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>2125865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8226</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>Jour</td>\n",
       "      <td>1675233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8227</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>Nuit</td>\n",
       "      <td>1102533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8228 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date          Zone PresenceType   Volume\n",
       "0    2020-02-01      Bas-Uele         Jour  1034483\n",
       "1    2020-02-01      Bas-Uele         Nuit   709632\n",
       "2    2020-02-01      Equateur         Jour  1803206\n",
       "3    2020-02-01      Equateur         Nuit  1419771\n",
       "4    2020-02-01  Haut-Katanga         Jour  5629768\n",
       "...         ...           ...          ...      ...\n",
       "8223 2020-08-11    Tanganyika         Nuit  1568133\n",
       "8224 2020-08-11        Tshopo         Jour  2799508\n",
       "8225 2020-08-11        Tshopo         Nuit  2125865\n",
       "8226 2020-08-11       Tshuapa         Jour  1675233\n",
       "8227 2020-08-11       Tshuapa         Nuit  1102533\n",
       "\n",
       "[8228 rows x 4 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_dzp = dfp_filtered.groupby([\"Date\",\"Zone\",'PresenceType'],as_index=0).sum()\n",
    "df_agg_dzp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Zone</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>1744115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Equateur</td>\n",
       "      <td>3222977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>9181926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>5263402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>2264021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4109</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Sud-Kivu</td>\n",
       "      <td>10611755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4110</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Sud-Ubangi</td>\n",
       "      <td>3694226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4111</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tanganyika</td>\n",
       "      <td>3933769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4112</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>4925373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4113</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>Tshuapa</td>\n",
       "      <td>2777766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4114 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date          Zone    Volume\n",
       "0    2020-02-01      Bas-Uele   1744115\n",
       "1    2020-02-01      Equateur   3222977\n",
       "2    2020-02-01  Haut-Katanga   9181926\n",
       "3    2020-02-01   Haut-Lomami   5263402\n",
       "4    2020-02-01     Haut-Uele   2264021\n",
       "...         ...           ...       ...\n",
       "4109 2020-08-11      Sud-Kivu  10611755\n",
       "4110 2020-08-11    Sud-Ubangi   3694226\n",
       "4111 2020-08-11    Tanganyika   3933769\n",
       "4112 2020-08-11        Tshopo   4925373\n",
       "4113 2020-08-11       Tshuapa   2777766\n",
       "\n",
       "[4114 rows x 3 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp_agg_f_day = df_agg_dzp#[df_agg_dzp.PresenceType == 'Nuit']\n",
    "dfp_agg_date_zone = dfp_agg_f_day.groupby(by=['Date','Zone'],as_index=0).sum()\n",
    "dfp_agg_date_zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dfp_agg_f_night = df_agg_dzp[df_agg_dzp.PresenceType == 'Nuit']\n",
    "# dfp_agg_f_day = df_agg_dzp[df_agg_dzp.PresenceType == 'Day']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dfp_agg_f_night"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging Presence and Movement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Observation_Zone</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>Volume_x</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Volume_y</th>\n",
       "      <th>tot_from_origin</th>\n",
       "      <th>move_prop</th>\n",
       "      <th>move_prop_tot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>Haut-Katanga</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>689</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>5263402</td>\n",
       "      <td>32563</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.021159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>Lomami</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>28872</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>5263402</td>\n",
       "      <td>32563</td>\n",
       "      <td>0.005485</td>\n",
       "      <td>0.886650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>Lualaba</td>\n",
       "      <td>ZoneGlobale</td>\n",
       "      <td>5</td>\n",
       "      <td>3002</td>\n",
       "      <td>Haut-Lomami</td>\n",
       "      <td>5263402</td>\n",
       "      <td>32563</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>0.092191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date       Origin   Destination Observation_Zone  Date_day  Volume_x  \\\n",
       "7 2020-02-01  Haut-Lomami  Haut-Katanga      ZoneGlobale         5       689   \n",
       "8 2020-02-01  Haut-Lomami        Lomami      ZoneGlobale         5     28872   \n",
       "9 2020-02-01  Haut-Lomami       Lualaba      ZoneGlobale         5      3002   \n",
       "\n",
       "          Zone  Volume_y  tot_from_origin  move_prop  move_prop_tot  \n",
       "7  Haut-Lomami   5263402            32563   0.000131       0.021159  \n",
       "8  Haut-Lomami   5263402            32563   0.005485       0.886650  \n",
       "9  Haut-Lomami   5263402            32563   0.000570       0.092191  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mvt_pres = df_filtered_both.merge(dfp_agg_date_zone,left_on=['Origin','Date'], right_on=['Zone','Date'])\n",
    "\n",
    "#Total movers per day from an origin\n",
    "df_mvt_pres_filtered = (df_mvt_pres[['Date','Origin', 'Volume_x']].groupby(by=['Date','Origin'], as_index=False).sum()).rename(columns={'Volume_x':'tot_from_origin'})\n",
    "df_mvt_pres = df_mvt_pres.merge(df_mvt_pres_filtered,left_on=['Origin','Date'], right_on=['Origin','Date'])\n",
    "#Proportion of movers\n",
    "df_mvt_pres['move_prop'] = df_mvt_pres['Volume_x']/ df_mvt_pres['Volume_y'] # Proportion with respect to presence in the corresponding day\n",
    "df_mvt_pres['move_prop_tot'] = df_mvt_pres['Volume_x']/ df_mvt_pres['tot_from_origin'] # Proportion with respect to total from origin in the corresponding day\n",
    "\n",
    "df_mvt_pres[(df_mvt_pres.Date == '2020-02-1') & (df_mvt_pres.Origin == 'Haut-Lomami')]\n",
    "# df_mvt_pres[(df_mvt_pres.Origin == 'Kinshasa') ]\n",
    "# df_mvt_pres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>movers</th>\n",
       "      <th>fr_users</th>\n",
       "      <th>tot_from_origin</th>\n",
       "      <th>move_prop</th>\n",
       "      <th>move_prop_tot</th>\n",
       "      <th>fr_pat</th>\n",
       "      <th>to_pat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5</td>\n",
       "      <td>1049</td>\n",
       "      <td>1744115</td>\n",
       "      <td>4238</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.247522</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>3</td>\n",
       "      <td>875</td>\n",
       "      <td>1741434</td>\n",
       "      <td>4464</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.196013</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>0</td>\n",
       "      <td>781</td>\n",
       "      <td>1761174</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.199949</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>2</td>\n",
       "      <td>710</td>\n",
       "      <td>1767211</td>\n",
       "      <td>710</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>4</td>\n",
       "      <td>752</td>\n",
       "      <td>1760713</td>\n",
       "      <td>2297</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.327384</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7497</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>14787320</td>\n",
       "      <td>113169</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7498</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>3</td>\n",
       "      <td>83</td>\n",
       "      <td>14313779</td>\n",
       "      <td>103150</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7499</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>14858971</td>\n",
       "      <td>111088</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7500</th>\n",
       "      <td>2020-06-05</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>4939442</td>\n",
       "      <td>497</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.088531</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7501</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>Tshopo</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>4944710</td>\n",
       "      <td>3996</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.010761</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7502 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date     Origin Destination  Date_day  movers  fr_users  \\\n",
       "0    2020-02-01   Bas-Uele   Haut-Uele         5    1049   1744115   \n",
       "1    2020-02-06   Bas-Uele   Haut-Uele         3     875   1741434   \n",
       "2    2020-02-10   Bas-Uele   Haut-Uele         0     781   1761174   \n",
       "3    2020-02-12   Bas-Uele   Haut-Uele         2     710   1767211   \n",
       "4    2020-02-14   Bas-Uele   Haut-Uele         4     752   1760713   \n",
       "...         ...        ...         ...       ...     ...       ...   \n",
       "7497 2020-06-29  Nord-Kivu     Sankuru         0      50  14787320   \n",
       "7498 2020-07-23  Nord-Kivu     Sankuru         3      83  14313779   \n",
       "7499 2020-08-05  Nord-Kivu     Sankuru         2      27  14858971   \n",
       "7500 2020-06-05     Tshopo     Sankuru         4      44   4939442   \n",
       "7501 2020-07-23     Tshopo     Sankuru         3      43   4944710   \n",
       "\n",
       "      tot_from_origin  move_prop  move_prop_tot  fr_pat  to_pat  \n",
       "0                4238   0.000601       0.247522       1       5  \n",
       "1                4464   0.000502       0.196013       1       5  \n",
       "2                3906   0.000443       0.199949       1       5  \n",
       "3                 710   0.000402       1.000000       1       5  \n",
       "4                2297   0.000427       0.327384       1       5  \n",
       "...               ...        ...            ...     ...     ...  \n",
       "7497           113169   0.000003       0.000442      19      21  \n",
       "7498           103150   0.000006       0.000805      19      21  \n",
       "7499           111088   0.000002       0.000243      19      21  \n",
       "7500              497   0.000009       0.088531      25      21  \n",
       "7501             3996   0.000009       0.010761      25      21  \n",
       "\n",
       "[7502 rows x 11 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mvt_epi = df_mvt_pres.merge(df_pats, left_on='Origin', right_on='patNames')\n",
    "df_mvt_epi = df_mvt_epi.merge(df_pats, left_on='Destination', right_on='patNames')\n",
    "df_mvt_epi.drop(['patNames_x','patNames_y','Observation_Zone','Zone'],axis=1, inplace=True) #,'Date_day'\n",
    "#\"fr_users\",\"movers\"\n",
    "df_mvt_epi.rename(columns={'Date':'date','Volume_x':'movers','patIDs_x':'fr_pat','patIDs_y':'to_pat','Volume_y':'fr_users'}, inplace=True)\n",
    "df_mvt_epi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying to repeate mobility pattern for missing date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>movers</th>\n",
       "      <th>fr_users</th>\n",
       "      <th>tot_from_origin</th>\n",
       "      <th>move_prop</th>\n",
       "      <th>move_prop_tot</th>\n",
       "      <th>fr_pat</th>\n",
       "      <th>to_pat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5</td>\n",
       "      <td>1049</td>\n",
       "      <td>1744115</td>\n",
       "      <td>4238</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.247522</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>3</td>\n",
       "      <td>875</td>\n",
       "      <td>1741434</td>\n",
       "      <td>4464</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.196013</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>0</td>\n",
       "      <td>781</td>\n",
       "      <td>1761174</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.199949</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>2</td>\n",
       "      <td>710</td>\n",
       "      <td>1767211</td>\n",
       "      <td>710</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>4</td>\n",
       "      <td>752</td>\n",
       "      <td>1760713</td>\n",
       "      <td>2297</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.327384</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7478</th>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>15901877</td>\n",
       "      <td>25798</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.001667</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7479</th>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>1</td>\n",
       "      <td>239</td>\n",
       "      <td>16052541</td>\n",
       "      <td>31464</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.007596</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7480</th>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>15841221</td>\n",
       "      <td>33640</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7489</th>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>14894498</td>\n",
       "      <td>119260</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7490</th>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>14567409</td>\n",
       "      <td>105622</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2169 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date     Origin Destination  Date_day  movers  fr_users  \\\n",
       "0    2020-02-01   Bas-Uele   Haut-Uele         5    1049   1744115   \n",
       "1    2020-02-06   Bas-Uele   Haut-Uele         3     875   1741434   \n",
       "2    2020-02-10   Bas-Uele   Haut-Uele         0     781   1761174   \n",
       "3    2020-02-12   Bas-Uele   Haut-Uele         2     710   1767211   \n",
       "4    2020-02-14   Bas-Uele   Haut-Uele         4     752   1760713   \n",
       "...         ...        ...         ...       ...     ...       ...   \n",
       "7478 2020-03-09   Kinshasa     Sankuru         0      43  15901877   \n",
       "7479 2020-03-10   Kinshasa     Sankuru         1     239  16052541   \n",
       "7480 2020-03-16   Kinshasa     Sankuru         0      21  15841221   \n",
       "7489 2020-02-18  Nord-Kivu     Sankuru         1      33  14894498   \n",
       "7490 2020-02-23  Nord-Kivu     Sankuru         6      31  14567409   \n",
       "\n",
       "      tot_from_origin  move_prop  move_prop_tot  fr_pat  to_pat  \n",
       "0                4238   0.000601       0.247522       1       5  \n",
       "1                4464   0.000502       0.196013       1       5  \n",
       "2                3906   0.000443       0.199949       1       5  \n",
       "3                 710   0.000402       1.000000       1       5  \n",
       "4                2297   0.000427       0.327384       1       5  \n",
       "...               ...        ...            ...     ...     ...  \n",
       "7478            25798   0.000003       0.001667      10      21  \n",
       "7479            31464   0.000015       0.007596      10      21  \n",
       "7480            33640   0.000001       0.000624      10      21  \n",
       "7489           119260   0.000002       0.000277      19      21  \n",
       "7490           105622   0.000002       0.000293      19      21  \n",
       "\n",
       "[2169 rows x 11 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mvt_epi_wo_npi = df_mvt_epi[df_mvt_epi.date<'2020-03-18']\n",
    "df_mvt_epi_wo_npi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#zone_w_issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mvt_w_issue = df_mvt_epi_wo_npi[(df_mvt_epi_wo_npi['Origin'].isin(zone_w_issue)) | (df_mvt_epi_wo_npi['Destination'].isin(zone_w_issue)) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mvt_w_issue[df_mvt_w_issue.isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for missing dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.date_range('2020-01-31','2020-03-17',)\n",
    "def check_missing_dates(df,start,end):\n",
    "    return set([d.strftime('%Y-%m-%d') for d in pd.date_range(start,end,)]) - set(df.date.dt.date.astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mvt_epi[df_mvt_epi.date == '2020-03-18']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>movers</th>\n",
       "      <th>fr_users</th>\n",
       "      <th>tot_from_origin</th>\n",
       "      <th>move_prop</th>\n",
       "      <th>move_prop_tot</th>\n",
       "      <th>fr_pat</th>\n",
       "      <th>to_pat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [date, Origin, Destination, Date_day, movers, fr_users, tot_from_origin, move_prop, move_prop_tot, fr_pat, to_pat]\n",
       "Index: []"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mvt_epi_wo_npi[df_mvt_epi_wo_npi.date == '2020-02-05']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2020-02-05', '2020-02-28', '2020-03-02'}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_missing_dates(df_mvt_epi_wo_npi,'2020-02-01','2020-03-17')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_mvt_epi_wo_npi.sort_values('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "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>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>movers</th>\n",
       "      <th>fr_users</th>\n",
       "      <th>tot_from_origin</th>\n",
       "      <th>move_prop</th>\n",
       "      <th>move_prop_tot</th>\n",
       "      <th>fr_pat</th>\n",
       "      <th>to_pat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5</td>\n",
       "      <td>1049</td>\n",
       "      <td>1744115</td>\n",
       "      <td>4238</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.247522</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>3</td>\n",
       "      <td>875</td>\n",
       "      <td>1741434</td>\n",
       "      <td>4464</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.196013</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>0</td>\n",
       "      <td>781</td>\n",
       "      <td>1761174</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.199949</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>2</td>\n",
       "      <td>710</td>\n",
       "      <td>1767211</td>\n",
       "      <td>710</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>4</td>\n",
       "      <td>752</td>\n",
       "      <td>1760713</td>\n",
       "      <td>2297</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.327384</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7478</th>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>15901877</td>\n",
       "      <td>25798</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.001667</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7479</th>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>1</td>\n",
       "      <td>239</td>\n",
       "      <td>16052541</td>\n",
       "      <td>31464</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.007596</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7480</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>15841221</td>\n",
       "      <td>33640</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7489</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>14894498</td>\n",
       "      <td>119260</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7490</th>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>14567409</td>\n",
       "      <td>105622</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2169 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date     Origin Destination  Date_day  movers  fr_users  \\\n",
       "0    2020-03-14   Bas-Uele   Haut-Uele         5    1049   1744115   \n",
       "1    2020-03-19   Bas-Uele   Haut-Uele         3     875   1741434   \n",
       "2    2020-03-23   Bas-Uele   Haut-Uele         0     781   1761174   \n",
       "3    2020-03-25   Bas-Uele   Haut-Uele         2     710   1767211   \n",
       "4    2020-03-27   Bas-Uele   Haut-Uele         4     752   1760713   \n",
       "...         ...        ...         ...       ...     ...       ...   \n",
       "7478 2020-04-20   Kinshasa     Sankuru         0      43  15901877   \n",
       "7479 2020-04-21   Kinshasa     Sankuru         1     239  16052541   \n",
       "7480 2020-04-27   Kinshasa     Sankuru         0      21  15841221   \n",
       "7489 2020-03-31  Nord-Kivu     Sankuru         1      33  14894498   \n",
       "7490 2020-04-05  Nord-Kivu     Sankuru         6      31  14567409   \n",
       "\n",
       "      tot_from_origin  move_prop  move_prop_tot  fr_pat  to_pat  \n",
       "0                4238   0.000601       0.247522       1       5  \n",
       "1                4464   0.000502       0.196013       1       5  \n",
       "2                3906   0.000443       0.199949       1       5  \n",
       "3                 710   0.000402       1.000000       1       5  \n",
       "4                2297   0.000427       0.327384       1       5  \n",
       "...               ...        ...            ...     ...     ...  \n",
       "7478            25798   0.000003       0.001667      10      21  \n",
       "7479            31464   0.000015       0.007596      10      21  \n",
       "7480            33640   0.000001       0.000624      10      21  \n",
       "7489           119260   0.000002       0.000277      19      21  \n",
       "7490           105622   0.000002       0.000293      19      21  \n",
       "\n",
       "[2169 rows x 11 columns]"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mvt_epi_wo_npi_slid = pd.DataFrame(df_mvt_epi_wo_npi, copy=True)\n",
    "df_mvt_epi_wo_npi_slid['date'] = df_mvt_epi_wo_npi_slid ['date'] + pd.Timedelta(6, 'W')\n",
    "df_mvt_epi_wo_npi_slid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mvt_epi_slid = df_mvt_epi_slid[df_mvt_epi_slid.date > df_mvt_epi_wo_npi.date.max()] #>= df_mvt_epi_wo_npi.date.max()\n",
    "# df_mvt_epi_slid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "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>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Date_day</th>\n",
       "      <th>movers</th>\n",
       "      <th>fr_users</th>\n",
       "      <th>tot_from_origin</th>\n",
       "      <th>move_prop</th>\n",
       "      <th>move_prop_tot</th>\n",
       "      <th>fr_pat</th>\n",
       "      <th>to_pat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>5</td>\n",
       "      <td>1049</td>\n",
       "      <td>1744115</td>\n",
       "      <td>4238</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.247522</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>3</td>\n",
       "      <td>875</td>\n",
       "      <td>1741434</td>\n",
       "      <td>4464</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.196013</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>0</td>\n",
       "      <td>781</td>\n",
       "      <td>1761174</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.199949</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>2</td>\n",
       "      <td>710</td>\n",
       "      <td>1767211</td>\n",
       "      <td>710</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>Bas-Uele</td>\n",
       "      <td>Haut-Uele</td>\n",
       "      <td>4</td>\n",
       "      <td>752</td>\n",
       "      <td>1760713</td>\n",
       "      <td>2297</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.327384</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7478</th>\n",
       "      <td>2020-11-02</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>15901877</td>\n",
       "      <td>25798</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.001667</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7479</th>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>1</td>\n",
       "      <td>239</td>\n",
       "      <td>16052541</td>\n",
       "      <td>31464</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.007596</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7480</th>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>15841221</td>\n",
       "      <td>33640</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7489</th>\n",
       "      <td>2020-10-13</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>14894498</td>\n",
       "      <td>119260</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7490</th>\n",
       "      <td>2020-10-18</td>\n",
       "      <td>Nord-Kivu</td>\n",
       "      <td>Sankuru</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>14567409</td>\n",
       "      <td>105622</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13362 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date     Origin Destination  Date_day  movers  fr_users  \\\n",
       "0    2020-02-01   Bas-Uele   Haut-Uele         5    1049   1744115   \n",
       "1    2020-02-06   Bas-Uele   Haut-Uele         3     875   1741434   \n",
       "2    2020-02-10   Bas-Uele   Haut-Uele         0     781   1761174   \n",
       "3    2020-02-12   Bas-Uele   Haut-Uele         2     710   1767211   \n",
       "4    2020-02-14   Bas-Uele   Haut-Uele         4     752   1760713   \n",
       "...         ...        ...         ...       ...     ...       ...   \n",
       "7478 2020-11-02   Kinshasa     Sankuru         0      43  15901877   \n",
       "7479 2020-11-03   Kinshasa     Sankuru         1     239  16052541   \n",
       "7480 2020-11-09   Kinshasa     Sankuru         0      21  15841221   \n",
       "7489 2020-10-13  Nord-Kivu     Sankuru         1      33  14894498   \n",
       "7490 2020-10-18  Nord-Kivu     Sankuru         6      31  14567409   \n",
       "\n",
       "      tot_from_origin  move_prop  move_prop_tot  fr_pat  to_pat  \n",
       "0                4238   0.000601       0.247522       1       5  \n",
       "1                4464   0.000502       0.196013       1       5  \n",
       "2                3906   0.000443       0.199949       1       5  \n",
       "3                 710   0.000402       1.000000       1       5  \n",
       "4                2297   0.000427       0.327384       1       5  \n",
       "...               ...        ...            ...     ...     ...  \n",
       "7478            25798   0.000003       0.001667      10      21  \n",
       "7479            31464   0.000015       0.007596      10      21  \n",
       "7480            33640   0.000001       0.000624      10      21  \n",
       "7489           119260   0.000002       0.000277      19      21  \n",
       "7490           105622   0.000002       0.000293      19      21  \n",
       "\n",
       "[13362 rows x 11 columns]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sliding data until december 2020\n",
    "\n",
    "df_mvt_wo_npi_slid = slide_df(df_mvt_epi_wo_npi,6, 'W')\n",
    "df_mvt_wo_npi_slid = slide_df(df_mvt_wo_npi_slid,12, 'W')\n",
    "df_mvt_wo_npi_slid = slide_df(df_mvt_wo_npi_slid,16, 'W')\n",
    "df_mvt_wo_npi_slid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2020-01-31',\n",
       " '2020-02-05',\n",
       " '2020-02-28',\n",
       " '2020-03-02',\n",
       " '2020-03-18',\n",
       " '2020-04-10',\n",
       " '2020-04-13',\n",
       " '2020-04-29',\n",
       " '2020-05-22',\n",
       " '2020-05-25',\n",
       " '2020-06-10',\n",
       " '2020-07-03',\n",
       " '2020-07-06',\n",
       " '2020-07-31',\n",
       " '2020-08-03',\n",
       " '2020-08-19',\n",
       " '2020-09-11',\n",
       " '2020-09-14',\n",
       " '2020-09-30',\n",
       " '2020-10-23',\n",
       " '2020-10-26'}"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_missing_dates(df_mvt_wo_npi_slid,'2020-01-31','2020-11-08')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mvt_epi[(df_mvt_epi.Origin == 'Gombe')].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mvt_wo_npi_slid[(df_mvt_wo_npi_slid.Origin == 'Gombe')&(df_mvt_wo_npi_slid.date == '2020-11-08')].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mvt_epi[(df_mvt_epi.Origin == 'Kinshasa') & (df_mvt_epi.date == '2020-03-27')]['move_prop'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save OD matrix to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mvt_wo_npi_slid[['date','Origin','Destination','fr_pat', 'to_pat','movers','fr_users','move_prop','tot_from_origin','move_prop_tot']].to_csv('./epidemiological_model/BEARmod_DRC/data/mobmatmacro.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "#[(df_mvt_wo_npi_slid.date >= '2020-07-04')&((df_mvt_wo_npi_slid.date <= '2020-11-09'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BEARmod\n",
    "Basic Epidemic, Activity, and Response COVID-19 model\n",
    "\n",
    "This model implements a basic SEIR simulation model, accounting for variable daily movement patterns, recovery rates, and contact rates. Demonstration of this model can be seen in a recent Nature paper [1]\n",
    "\n",
    "For a placeholder dummy dataset and example simulation run, please see \"run_model_small.R\", which uses a dummy movement dataset \"testmove.csv\"\n",
    "\n",
    "## Overall model\n",
    "This model is a metapopulation model of COVID-19 transmission, based on an SEIR modeling framework. Within each patch, this model follows a fairly simple SEIR framework. The primary complexities this model is designed to describe are daily movement patterns, and spatially and temporally heterogeneous reductions in movement and contact rates. Specifically, this model is particularly suited for data that generally come from mobile phone companies.\n",
    "\n",
    "### Baseline patch-level processes\n",
    "Within each patch, this model first calculates the number of infected people who recovered or were otherwise removed from the infectious population (ie. through self-isolatuion) at an average rate r, where r is equal to the inverse of the average infectious period. This is explicitly incorporated as a Bernoulli trial for each infected person with a probability of recovering 1-exp⁡(-r). \n",
    "Then, the model converts exposed people to infectious by similarly incorporating a Bernoulli trial for each exposed individual, where the daily probability of becoming infectious 1-exp⁡(-ε), where ε was the inverse of the average time spent exposed but not infectious. \n",
    "Finally, to end the exposure, infection, and recovery step of the model, newly exposed people are calculated for each city based on the number of infectious people in the city I_i, and the average number of daily contacts that lead to transmission that each infectious person has c. This model then simulates the number of newly exposed people through a random draw from a Poisson distribution for each infectious person where the mean number of new infections per person was c, which was then multiplied by the fraction of people in the patch who are susceptible.\n",
    "The infection processes within each patch therefore approximate the following deterministic, continuous-time model, where c and r varied through time:\n",
    "dS/dt=S-c SI/N\n",
    "dE/dt=c SI/N-εE\n",
    "dI/dt=εE-rI\n",
    "dR/dt=rI\n",
    "\n",
    "### Movement between patches\n",
    "After completing the infection-related processes, the model moves infectious people between cities, using the proportion of people who went from each patch to each other patch measured in the input OD matrix. Infectious people are moved from their current location to each possible destination (including remaining in the same place) using Bernoulli trials for each infectied person, and each possible destination city. \n",
    "Through this model, stochasticity in the numbers and places where COVID-19 appears between simulation runs in this model through variance in numbers of people becoming exposed, infectious, and removed/recovered, as well as variance in numbers of people moving from one city to another.\n",
    "\n",
    "## Input options and formats\n",
    "Note: These parameter specifications are relevant for v 0.92, denoted at the top of the bearmod_fx.R file.\n",
    "\n",
    "First, you will create an empty population list HPop, using InitiatePop(). This function takes as inputs:\n",
    "- pat_locator: A data frame with variables \"patNames\", \"patIDs\" (numeric; sequential from 1:number of patches), and \"pop\" (population per patch)\n",
    "- initialInf: A vector of initially infected people per patch, length equal to the number of patches\n",
    "- initialExp: A vector of initially exposed people per patch, length equal to the number of patches\n",
    "\n",
    "The initial HPop is then fed into the runSim function, which has the following inputs:\n",
    "- HPop\n",
    "- pat_info: This is the same as pat_locator\n",
    "- movement_reduction_df: a data frame with 3 variables, \"date\", \"name\", and \"relative_movement\". \"name\" corresponds to the patNames ID for the patch, and \"relative_movement\" indicates the relative proportion of movement for that day--.3 means all movement for that patch in that day (both incoming and outgoing) will be 30% of the baseline value (specified in mobmat later). This is specified on a per-day basis, and does not have to be complete--any missing day/patch pairs will have 100% of the baseline movement patterns\n",
    "- contact_reduction_df: a data frame with 3 variables, \"date\", \"name\", and \"relative_contact\". Same as movement_reduction_df except this refers to the relative contact rate within a patch for a given day--ie. .5 means half as many contacts per person\n",
    "- mobmat: A data frame with variables \"date\", \"fr_pat\", \"to_pat\", \"move_prop\". fr_pat and to_pat refer to the patch IDs of the origin and destination patches (see patIDs from pat_locator), and move_prop is the proportion of people who move from each origin to each destination on the given day in \"date\". If stayers are not denoted (origin = destination), then the model will designate this as 1 - sum(movement elsewhere) for a given patch. \n",
    "\n",
    "--more parameter definitions coming soon--\n",
    "\n",
    "Contact:\n",
    "Nick W Ruktanonchai; \n",
    "nrukt00 at gmail.com\n",
    "\n",
    "[1] Lai, S., Ruktanonchai, N.W., Zhou, L. et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature (2020). https://doi.org/10.1038/s41586-020-2293-x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}