From 70d1b5f1c4b123e3950dc123bad18469aeeb21fe Mon Sep 17 00:00:00 2001
From: "Dr. Hamed Khalili" <hamedkhalili@uni-koblenz.de>
Date: Mon, 3 Jul 2023 13:27:23 +0000
Subject: [PATCH] Upload New File
---
.../generating_data___month_encoded.ipynb | 2497 +++++++++++++++++
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create mode 100644 p_2/code/generating_data___month_encoded.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "0127991b-fa6f-4dd8-bb75-c12beaffde73",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "7\n"
+ ]
+ }
+ ],
+ "source": [
+ "###******************************HALLO*******************************\n",
+ "\n",
+ "\n",
+ "###***************************BAYESIAN GOVERMENT COVID APPLICATION**************************************************WRITEN BY:\n",
+ "###********************************************************************************************************HAMED KHALILI***********\n",
+ "###***************************INPUTS OF THE PROGRAM********************************************************************************************\n",
+ "#import pandas as pd\n",
+ "#import base64\n",
+ "import pandas as pd\n",
+ "incidence_days_number=7\n",
+ "CBook_i=pd.DataFrame({'index':[] ,'countriesAndTerritories':[], 'dateRep':[], 'cases':[] ,'Rescd':[] ,str(incidence_days_number)+'days_before_mean':[], str(incidence_days_number)+'days_after_mean':[] })\n",
+ "countries=['Netherlands','Germany']#\n",
+ "for tage in [7]:\n",
+ " \n",
+ " incidence_days_number=tage\n",
+ " print(incidence_days_number)\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "#X=['MasksMandatoryAllSpaces','MasksMandatoryClosedSpaces']\n",
+ "\n",
+ "\n",
+ " method=\"hierarchical\"#or#pooled#or#unpooled\n",
+ " ###********************************************************************************************************************************************\n",
+ " #['ClosDaycare','ClosDaycarePartial','ClosPrimPartial','ClosSecPartial','ClosPrim','ClosSec','ClosHighPartial','ClosHigh']\n",
+ " #['RestaurantsCafes','RestaurantsCafesPartial']\n",
+ " #['GymsSportsCentres','GymsSportsCentresPartial']\n",
+ " #['Teleworking','TeleworkingPartial','WorkplaceClosuresPartial','AdaptationOfWorkplace','AdaptationOfWorkplacePartial','WorkplaceClosures']\n",
+ " #['MasksMandatoryClosedSpacesPartial','MasksMandatoryAllSpaces','MasksMandatoryClosedSpaces','MasksMandatoryAllSpacesPartial']\n",
+ " #y_pred =this is almost identical to y_est except we do not specify the observed data. PyMC considers this to be a stochastic node \n",
+ " #(as opposed to an observed node) and as the MCMC sampler runs - it also samples data from y_est.\n",
+ "\n",
+ " \"\"\"\n",
+ " ['Netherlands','Czechia','Lithuania','Austria','Poland','Slovenia','Estonia','Italy','Slovakia','Ireland','Denmark',\n",
+ " 'Iceland','Cyprus','Greece','Belgium','Bulgaria','France','Germany','Latvia','Spain','Norway','Romania','Liechtenstein',\n",
+ " 'Portugal','Luxembourg','Hungary','Malta','Croatia','Finland','Sweden']\n",
+ "\n",
+ " ['EntertainmentVenuesPartial','RestaurantsCafesPartial','EntertainmentVenues','MassGatherAll','ClosSec','GymsSportsCentresPartial','ClosPrim',\n",
+ " 'NonEssentialShopsPartial','ClosPubAnyPartial','RestaurantsCafes','GymsSportsCentres','MassGather50','PrivateGatheringRestrictions',\n",
+ " 'MassGatherAllPartial',\n",
+ " 'ClosHigh','NonEssentialShops','ClosSecPartial','OutdoorOver500','ClosDaycare','BanOnAllEvents','IndoorOver500','QuarantineForInternationalTravellers',\n",
+ " 'ClosHighPartial','IndoorOver100','Teleworking','ClosPubAny','PlaceOfWorshipPartial','MasksMandatoryClosedSpacesPartial','MassGather50Partial',\n",
+ " 'StayHomeOrderPartial','OutdoorOver100','IndoorOver50','ClosPrimPartial','PrivateGatheringRestrictionsPartial','MasksMandatoryClosedSpaces',\n",
+ " 'OutdoorOver1000','TeleworkingPartial','MasksMandatoryAllSpaces','OutdoorOver50','StayHomeOrder','QuarantineForInternationalTravellersPartial',\n",
+ " 'MasksMandatoryAllSpacesPartial','StayHomeGen','PlaceOfWorship','ClosDaycarePartial','IndoorOver1000','BanOnAllEventsPartial',\n",
+ " 'HotelsOtherAccommodationPartial',\n",
+ " 'StayHomeRiskG','ClosureOfPublicTransportPartial','AdaptationOfWorkplace','HotelsOtherAccommodation','MasksVoluntaryClosedSpacesPartial',\n",
+ " 'RegionalStayHomeOrderPartial','AdaptationOfWorkplacePartial','MasksVoluntaryAllSpaces','MasksVoluntaryAllSpacesPartial','MasksVoluntaryClosedSpaces',\n",
+ " 'SocialCircle','WorkplaceClosures','RegionalStayHomeOrder','ClosureOfPublicTransport','StayHomeGenPartial','WorkplaceClosuresPartial',\n",
+ " 'StayHomeRiskGPartial','SocialCirclePartial']\n",
+ "\n",
+ "\n",
+ " \"\"\"\n",
+ " ###************MAIN BODY OF THE PROGRAM****************************************************************************************************\n",
+ "\n",
+ " def add_elemant(element,lis,j):\n",
+ " for i in lis:\n",
+ " if element in i:\n",
+ " return i[j]\n",
+ "\n",
+ "\n",
+ " colors = ['#348ABD', '#A60628', '#7A68A6', '#467821', '#D55E00', '#CC79A7', '#56B4E9', '#009E73', '#F0E442', '#0072B2']\n",
+ " import pandas as pd\n",
+ " import datetime\n",
+ " from datetime import timedelta\n",
+ " import warnings\n",
+ " warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
+ " import seaborn as sns\n",
+ " #import arviz as az\n",
+ " import itertools\n",
+ " import matplotlib.pyplot as plt\n",
+ " import numpy as np\n",
+ " # import pymc3 as pm\n",
+ " # import scipy\n",
+ " #import scipy.stats as stats\n",
+ " from IPython.display import Image\n",
+ " from sklearn import preprocessing\n",
+ " import pandas as pd\n",
+ " import datetime\n",
+ " from IPython.display import display\n",
+ "\n",
+ " \n",
+ " cb =pd.read_excel(r\"C:\\Users\\Hamed\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\Programs\\Anaconda3 (64-bit)\\datac.xlsx\")\n",
+ " measures =pd.read_excel(r\"C:\\Users\\Hamed\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\Programs\\Anaconda3 (64-bit)\\response_graphs.xlsx\")\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " #if method==\"hierarchical\":\n",
+ "\n",
+ "\n",
+ "\n",
+ " #liss=[]\n",
+ "\n",
+ "\n",
+ " responses_plus=[]\n",
+ " responses_minus=[]\n",
+ " liss_plus = pd.DataFrame({'country':[],'mean':[],'std':[],'count+':[],'count-':[],'mean+':[],'mean-':[],'std+':[],'std-':[]})\n",
+ " for index,name in (enumerate(cb['countriesAndTerritories'].value_counts().index.tolist())):\n",
+ " #for name in['Germany']:\n",
+ " land=name\n",
+ " cb_2=cb.loc[(cb['countriesAndTerritories'] == land)]\n",
+ " cb_2=cb_2[['countriesAndTerritories','dateRep','cases']].iloc[::-1]\n",
+ " #cb=cb[(cb['dateRep']>=datetime.date(2020,2,3))] # df[(df['date']>datetime.date(2016,1,1))\n",
+ " cb_2['dateRep'] = pd.to_datetime(cb_2['dateRep'], format='%d/%m/%Y')\n",
+ " #cb_2=cb_2.loc[cb_2['dateRep']>='2020-03-02']\n",
+ " cb_2['cases'].values[cb_2['cases']<0] = cb_2['cases'].values[cb_2['cases']<0]*(-1)\n",
+ " cb_2['cases'].values[cb_2['cases']==0]=1\n",
+ " if cb_2.isnull().values.any():\n",
+ " cb_2['cases']=cb_2['cases'].interpolate()\n",
+ "\n",
+ " cb_2=cb_2.reset_index()\n",
+ " measures_2=measures.loc[(measures['Country'] == land)]\n",
+ " if measures_2.isnull().values.any():\n",
+ " #print(measures_2[measures_2['E'].isna()])\n",
+ " measures_2=measures_2.dropna()\n",
+ " measures_2=measures_2.reset_index()\n",
+ " cooBook=cb_2\n",
+ " lst=[]\n",
+ " cooBook[\"Rescd\"] = [list() for x in range(len(cooBook.index))]\n",
+ " measures_2['A'] = pd.to_datetime(measures_2['A'], format='%d/%m/%Y')\n",
+ " measures_2['E'] = pd.to_datetime(measures_2['E'], format='%d/%m/%Y')\n",
+ " for i in range (0,len(measures_2)):\n",
+ " #col=\"rc\"#(measures['Measure'][i])\n",
+ " A=(measures_2['A'][i])\n",
+ " E=(measures_2['E'][i]++ timedelta(days=1))\n",
+ " #if col not in coBook.columns:\n",
+ " #coBook[col] = pd.Series(dtype='int')\n",
+ "\n",
+ " for j in range (0,len(cooBook)): \n",
+ " date=cooBook['dateRep'][j]\n",
+ " #date_E=coBook['time_iso8601'][j-1]\n",
+ " #if coBook[col][j] != 1:\n",
+ " if date>=A and date<=E:\n",
+ " cooBook[\"Rescd\"][j].append(measures_2['Measure'][i])\n",
+ " CBook=cooBook\n",
+ "\n",
+ " #CBook['daily_cases']=CBook['daily_cases'].diff()\n",
+ " #CBook=CBook[1::]\n",
+ " #CBook=CBook.reset_index()\n",
+ "\n",
+ " CBook[str(incidence_days_number)+'days_before_mean'] = pd.Series(dtype='float')\n",
+ " CBook[str(incidence_days_number)+'days_after_mean'] = pd.Series(dtype='float')\n",
+ " CBook = CBook.replace(np.nan, 0)\n",
+ " for i in range (incidence_days_number,len(CBook)-incidence_days_number):\n",
+ " caesdayNplusone=CBook.loc[i+incidence_days_number, ['cases']].to_list()[0]\n",
+ " averagecasesdayonetoNminusone_forward=sum(CBook.iloc[i:i+incidence_days_number]['cases'].values)/incidence_days_number\n",
+ " averagecasesdayonetoNminusone_backward=sum(CBook.iloc[i-incidence_days_number:i]['cases'].values)/incidence_days_number\n",
+ " #if averagecasesdayonetoNminusone ==0:\n",
+ " #print(\"00000000\")\n",
+ " CBook.loc[i, [str(incidence_days_number)+'days_after_mean']]=averagecasesdayonetoNminusone_forward#/averagecasesdayonetoNminusone_backward-1\n",
+ " CBook.loc[i, [str(incidence_days_number)+'days_before_mean']]=averagecasesdayonetoNminusone_backward\n",
+ " CBook=CBook.loc[ (CBook['dateRep']>=min(CBook[incidence_days_number::]['dateRep']))] \n",
+ "\n",
+ " #operator=\"or\"\n",
+ " #mass='rpn_minus_one'+str(incidence_days_number)\n",
+ " #CBook[mass]=CBook[mass].pct_change()\n",
+ " #CBook=CBook.iloc[1: , :]\n",
+ " #CBook[[mass]] = CBook[[mass]].apply(lambda x: 100*x) \n",
+ " data_collecting_start_date=min(measures_2['A'])\n",
+ " data_collecting_end_date=max(measures_2['E'])\n",
+ " if land== 'Hungary':#03/03/2020-11/06/2021 for Hungary\n",
+ " data_collecting_start_date='2020-03-11'\n",
+ " data_collecting_end_date='2021-06-11'\n",
+ " if land== 'Iceland':#23/07/2020-03/12/2021 for \n",
+ " data_collecting_start_date='2020-07-23'\n",
+ " data_collecting_end_date='2020-12-04' \n",
+ " if land== 'Liechtenstein':# 01/10/2020-24/10/2022 for Liechtenstein \n",
+ " data_collecting_start_date='2020-12-31'\n",
+ " data_collecting_end_date='2022-03-31'#2020-03-10 14.04.2022\n",
+ " if land== 'Cyprus':# 01/10/2020-24/10/2022 for Liechtenstein \n",
+ " data_collecting_start_date='2020-10-03'\n",
+ " data_collecting_end_date='2022-04-14'#2020-03-10 14.04.2022\n",
+ " if land== 'Germany':# 01/10/2020-24/10/2022 for Liechtenstein \n",
+ " data_collecting_start_date='2020-02-24'\n",
+ " if land== 'Ireland':# 01/10/2020-24/10/2022 for Liechtenstein \n",
+ " data_collecting_start_date='2020-03-02'\n",
+ "\n",
+ " CBook=CBook.loc[ (CBook['dateRep']>=data_collecting_start_date) ]\n",
+ " CBook=CBook.loc[ (CBook['dateRep']<=data_collecting_end_date) ]\n",
+ " CBook_i=pd.concat([CBook_i, CBook], axis=0)\n",
+ "#CBook_i\n",
+ "\n",
+ "\n",
+ "import category_encoders as ce\n",
+ "encoder= ce.BinaryEncoder(cols=['countriesAndTerritories'],return_df=True)\n",
+ "data_encoded=encoder.fit_transform(CBook_i) \n",
+ "\n",
+ "\n",
+ "data_encoded=pd.concat([data_encoded, data_encoded['dateRep'].dt.month%12], axis=1)\n",
+ "data_encoded=data_encoded.reset_index()\n",
+ "data_encoded.drop(data_encoded.columns[[0,1]], axis = 1, inplace = True)\n",
+ "data_encoded.columns=['countriesAndTerritories_0', 'countriesAndTerritories_1','countriesAndTerritories_2','countriesAndTerritories_3','countriesAndTerritories_4','dateRep','cases' ,'Rescd',str(incidence_days_number)+'days_before_mean',str(incidence_days_number)+'days_after_mean','month'] \n",
+ "data_encoded=pd.concat([data_encoded, data_encoded['dateRep'].dt.week], axis=1)#CBook_i['dateRep'].dt.week\n",
+ "data_encoded.columns=['countriesAndTerritories_0', 'countriesAndTerritories_1','countriesAndTerritories_2','countriesAndTerritories_3','countriesAndTerritories_4','dateRep','cases' ,'Rescd',str(incidence_days_number)+'days_before_mean',str(incidence_days_number)+'days_after_mean','month','week'] \n",
+ "data_encoded=pd.concat([data_encoded, data_encoded['dateRep'].dt.year], axis=1)#CBook_i['dateRep'].dt.week\n",
+ "data_encoded.columns=['countriesAndTerritories_0', 'countriesAndTerritories_1','countriesAndTerritories_2','countriesAndTerritories_3','countriesAndTerritories_4','dateRep','cases' ,'Rescd',str(incidence_days_number)+'days_before_mean',str(incidence_days_number)+'days_after_mean','month','week','year'] \n",
+ "\n",
+ "data_encoded=pd.concat([data_encoded, CBook_i[['countriesAndTerritories']].reset_index()], axis=1)\n",
+ "\n",
+ "\n",
+ "data_encoded['week'].mask((data_encoded['year'] == 2021) & (data_encoded['month'] ==1) & (data_encoded['week'] ==53) ,1, inplace=True)\n",
+ "data_encoded['week'].mask((data_encoded['year'] == 2022) & (data_encoded['month'] ==1) & (data_encoded['week'] ==52) ,1, inplace=True)\n",
+ "\n",
+ "v = pd.read_excel(r\"C:\\Users\\Hamed\\Downloads\\data(7).xlsx\")\n",
+ "variant_plus = pd.DataFrame({'measure':[], 'v':[],'vd':[]})\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "i=0\n",
+ "while i < len(v):\n",
+ " c=v.iloc[i]['country']\n",
+ " w=v.iloc[i]['year_week']\n",
+ " n=1\n",
+ " j=i\n",
+ " measure=c+w\n",
+ " s=[v.iloc[i]['variant']]\n",
+ " sp=[v.iloc[i]['percent_variant']]\n",
+ " while j+1< len (v) and v.iloc[j+1]['country']+v.iloc[j+1]['year_week']==measure:\n",
+ " # .tolist()==list(name) and :\n",
+ " #print(i)\n",
+ " s.append(v.iloc[j+1]['variant'])\n",
+ " sp.append(v.iloc[j+1]['percent_variant'])\n",
+ " n=n+1\n",
+ " j=j+1\n",
+ " #print(j)\n",
+ " if str(sp[0])=='nan':\n",
+ " s=['not_sequenced']\n",
+ " sp=[100]\n",
+ " info ={'measure':measure,'v':s,'vd':sp}\n",
+ " variant_plus = variant_plus.append(info, ignore_index = True)\n",
+ " i=j+1\n",
+ " \n",
+ "variant_plus.columns=['country_year_week', 'variants','variants_percentage'] \n",
+ "dist=dict()\n",
+ "\n",
+ "for i in range (0, len(variant_plus)):\n",
+ " dist[variant_plus.iloc[i]['country_year_week']]=[variant_plus.iloc[i]['variants'],variant_plus.iloc[i]['variants_percentage']]\n",
+ " \n",
+ " \n",
+ "\n",
+ "def valuation_formula(x, y,w):\n",
+ " a=x + str(y)+ '-'+ str(w)\n",
+ " if a in dist:\n",
+ " return dist[a]\n",
+ " else:\n",
+ " return [['not_sequenced'], [100]]\n",
+ "\n",
+ "data_encoded['va'] = data_encoded.apply(lambda row: valuation_formula(row['countriesAndTerritories'], row['year'],row['week']), axis=1)\n",
+ "\n",
+ "\n",
+ "vaccin=pd.read_excel(r\"C:\\Users\\Hamed\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\Programs\\Anaconda3 (64-bit)\\data(6).xlsx\")\n",
+ "\n",
+ "cou=[]\n",
+ "for idx, name in (enumerate(v['country'].value_counts().index.tolist())):\n",
+ " cou.append( name)\n",
+ "cou_cod=[]\n",
+ "for idx, name in (enumerate(v['country_code'].value_counts().index.tolist())):\n",
+ " cou_cod.append( name)\n",
+ " \n",
+ "vaccin_all=vaccin.loc[(vaccin['TargetGroup'] == 'ALL')]\n",
+ "\n",
+ "vaccin_all=vaccin_all.sort_values(by = ['ReportingCountry','YearWeekISO'])\n",
+ "\n",
+ "dist_vaccin=dict()\n",
+ "\n",
+ "\n",
+ "for index,name in (enumerate(vaccin_all['ReportingCountry'].value_counts().index.tolist())):\n",
+ " #for name in['Germany']:\n",
+ " land=name\n",
+ " vaccin_all_land=vaccin_all.loc[(vaccin_all['ReportingCountry'] == land)]\n",
+ " vaccin_all_land=vaccin_all_land.reset_index()\n",
+ " for i in range (0, len(vaccin_all_land)):\n",
+ " vaccin_all_land.iloc[i]['FirstDose']=vaccin_all_land.iloc[i]['FirstDose']/7\n",
+ " vaccin_all_land['CUMSUM_FirstDose'] = vaccin_all_land['FirstDose'].cumsum()\n",
+ " vaccin_all_land['CUMSUM_FirstDose'] =vaccin_all_land['CUMSUM_FirstDose'] /vaccin_all_land.iloc[0]['Population']\n",
+ " \n",
+ " vaccin_all_land.iloc[i]['SecondDose']=vaccin_all_land.iloc[i]['SecondDose']/7\n",
+ " vaccin_all_land['CUMSUM_SecondDose'] = vaccin_all_land['SecondDose'].cumsum()\n",
+ " vaccin_all_land['CUMSUM_SecondDose'] =vaccin_all_land['CUMSUM_SecondDose'] /vaccin_all_land.iloc[0]['Population']\n",
+ " \n",
+ " vaccin_all_land.iloc[i]['DoseAdditional1']=vaccin_all_land.iloc[i]['DoseAdditional1']/7\n",
+ " vaccin_all_land['CUMSUM_DoseAdditional1'] = vaccin_all_land['DoseAdditional1'].cumsum()\n",
+ " vaccin_all_land['CUMSUM_DoseAdditional1'] =vaccin_all_land['CUMSUM_DoseAdditional1'] /vaccin_all_land.iloc[0]['Population']\n",
+ " \n",
+ " vaccin_all_land.iloc[i]['DoseAdditional2']=vaccin_all_land.iloc[i]['DoseAdditional2']/7\n",
+ " vaccin_all_land['CUMSUM_DoseAdditional2'] = vaccin_all_land['DoseAdditional2'].cumsum()\n",
+ " vaccin_all_land['CUMSUM_DoseAdditional2'] =vaccin_all_land['CUMSUM_DoseAdditional2'] /vaccin_all_land.iloc[0]['Population']\n",
+ " \n",
+ " vaccin_all_land.iloc[i]['DoseAdditional3']=vaccin_all_land.iloc[i]['DoseAdditional3']/7\n",
+ " vaccin_all_land['CUMSUM_DoseAdditional3'] = vaccin_all_land['DoseAdditional3'].cumsum()\n",
+ " vaccin_all_land['CUMSUM_DoseAdditional3'] =vaccin_all_land['CUMSUM_DoseAdditional3'] /vaccin_all_land.iloc[0]['Population']\n",
+ " \n",
+ " dist_vaccin[vaccin_all_land.iloc[i]['ReportingCountry']+vaccin_all_land.iloc[i]['YearWeekISO']]=[vaccin_all_land.iloc[i]['CUMSUM_FirstDose'],vaccin_all_land.iloc[i]['CUMSUM_SecondDose'],vaccin_all_land.iloc[i]['CUMSUM_DoseAdditional1'],vaccin_all_land.iloc[i]['CUMSUM_DoseAdditional2'],vaccin_all_land.iloc[i]['CUMSUM_DoseAdditional3']]\n",
+ "\n",
+ "\n",
+ "for index,name in (enumerate(vaccin_all['ReportingCountry'].value_counts().index.tolist())):\n",
+ " land=name\n",
+ " for y in range(2020,2023):\n",
+ " for w in range(1,57):\n",
+ " #for name in['Germany']:\n",
+ " if (y==2020 and w==1 and name+str(y)+'-W'+str(w) not in dist_vaccin):\n",
+ " dist_vaccin[name+str(y)+'-W'+str(w)]=[0,0,0,0,0]\n",
+ " if (y==2020 and w>1 and name+str(y)+'-W'+str(w) not in dist_vaccin):\n",
+ " dist_vaccin[name+str(y)+'-W'+str(w)]=dist_vaccin[name+str(y)+'-W'+str(w-1)]\n",
+ " if (y==2021 and w==1 and name+str(y)+'-W'+str(w) not in dist_vaccin):\n",
+ " dist_vaccin[name+str(y)+'-W'+str(w)]=dist_vaccin[name+str(y-1)+'-W'+str(56)]\n",
+ " if (y==2021 and w>1 and name+str(y)+'-W'+str(w) not in dist_vaccin):\n",
+ " dist_vaccin[name+str(y)+'-W'+str(w)]=dist_vaccin[name+str(y)+'-W'+str(w-1)]\n",
+ " if (y==2022 and w==1 and name+str(y)+'-W'+str(w) not in dist_vaccin):\n",
+ " dist_vaccin[name+str(y)+'-W'+str(w)]=dist_vaccin[name+str(y-1)+'-W'+str(56)]\n",
+ " if (y==2022 and w>1 and name+str(y)+'-W'+str(w) not in dist_vaccin):\n",
+ " dist_vaccin[name+str(y)+'-W'+str(w)]=dist_vaccin[name+str(y)+'-W'+str(w-1)]\n",
+ "\n",
+ "\n",
+ "def valuation_formula(x,y,w):\n",
+ " x=cou_cod[cou.index(x)]\n",
+ " a=x + str(y)+ '-W'+ str(w)\n",
+ " if a in dist_vaccin:\n",
+ " return dist_vaccin[a]\n",
+ " else:\n",
+ " return [0,0,0,0,0]\n",
+ "\n",
+ "data_encoded['vaccin'] = data_encoded.apply(lambda row: valuation_formula(row['countriesAndTerritories'], row['year'],row['week']), axis=1)\n",
+ "\n",
+ "\n",
+ "pd.set_option('display.max_columns', 500)\n",
+ "v_list=['not_sequenced']\n",
+ "for idx, name in (enumerate(v['variant'].value_counts().index.tolist())):\n",
+ " v_list.append( name)\n",
+ "measures_list=[]\n",
+ "for idx, name in (enumerate(measures['Measure'].value_counts().index.tolist())):\n",
+ " measures_list.append( name)\n",
+ "def vf(x,y):\n",
+ " if x in y:\n",
+ " return 1\n",
+ " else:\n",
+ " return 0\n",
+ "def vf(x,y):\n",
+ " if x in y:\n",
+ " return 1\n",
+ " else:\n",
+ " return 0\n",
+ "\n",
+ "for i in measures_list:\n",
+ " data_encoded[i] = data_encoded.apply(lambda row: vf(i, row['Rescd']), axis=1)\n",
+ "def vaf(x,y):\n",
+ " if x in y[0]:\n",
+ " return y[1][y[0].index(x)]\n",
+ " else:\n",
+ " return 0\n",
+ "\n",
+ "for i in v_list:\n",
+ " data_encoded[i] = data_encoded.apply(lambda row: vaf(i, row['va']), axis=1)\n",
+ "def vac(x,i):\n",
+ " return x[i]\n",
+ " \n",
+ "\n",
+ "for i in range (0,5):\n",
+ " data_encoded['vaccin_'+str(i)] = data_encoded.apply(lambda row: vac(row['vaccin'],i), axis=1)\n",
+ "\n",
+ "data_encoded_i= data_encoded\n",
+ "\n",
+ "encoder= ce.BinaryEncoder(cols=['month'],return_df=True)\n",
+ "data_encoded=encoder.fit_transform(data_encoded) \n",
+ "\n",
+ "data_encoded=pd.concat([data_encoded, data_encoded_i[['month']]], axis=1)\n",
+ "writer = pd.ExcelWriter('data_encoded.xlsx', engine='xlsxwriter')\n",
+ "\n",
+ "data_encoded.to_excel('data_encoded.xlsx', sheet_name='1', index=False)\n",
+ "\n",
+ "#data_encoded\n",
+ "\n",
+ "for i in range(0,len(data_encoded)):\n",
+ " #if data_encoded.iloc[i]['cases']==0:\n",
+ " if data_encoded.iloc[i]['7days_before_mean']==0:#sum(data_encoded.iloc[i-7:i]['cases'].values)/7):\n",
+ " print(i,data_encoded.iloc[i]['countriesAndTerritories'],data_encoded.iloc[i]['dateRep'])\n",
+ "\n",
+ "df=data_encoded\n",
+ "for i in range (0, len(df)-1):\n",
+ " if df.iloc[i]['dateRep']+datetime.timedelta(days=1)!=df.iloc[i+1]['dateRep'] and df.iloc[i]['countriesAndTerritories']==df.iloc[i+1]['countriesAndTerritories']:\n",
+ " print(df.iloc[i]['countriesAndTerritories'],df.iloc[i]['dateRep'],df.iloc[i+1]['dateRep'])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "c05338be-25fc-4cd3-b52a-0e3a2b6c8bd1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_encoded =pd.read_excel(r\"C:\\Users\\Hamed\\data_encoded.xlsx\")\n",
+ "df=data_encoded\n",
+ "nnn_data_encoded=df.head(0)\n",
+ "nnn_data_encoded['rp_zeitraum'] = pd.Series(dtype='float')\n",
+ "nnn_data_encoded = nnn_data_encoded.replace(np.nan, 0)\n",
+ "lll=nnn_data_encoded.columns.values.tolist()\n",
+ "nnn=30\n",
+ "\n",
+ "for index, name in (enumerate(df['countriesAndTerritories'].value_counts().index.tolist())):\n",
+ " #for name in['Germany']:\n",
+ " land=name\n",
+ " #print(land)\n",
+ " data_encoded_land=df.loc[(df['countriesAndTerritories'] == land)].reset_index()\n",
+ " for i in range (nnn-1, len(data_encoded_land)):\n",
+ " #data_encoded_zeitraum=data_encoded_land[i-nnn+1:i:]\n",
+ " rp_zeitraum=data_encoded_land.iloc[i]['7days_after_mean']/data_encoded_land.iloc[i-nnn+1]['7days_before_mean']\n",
+ " v=data_encoded_land[i-nnn+1:i:].drop(['dateRep', 'cases' , 'Rescd' , 'week' ,'year' , 'month', 'countriesAndTerritories', 'va' , 'vaccin' , 'index'], axis=1).mean(axis='index')\n",
+ " a=pd.DataFrame([v]).reset_index()\n",
+ " b=data_encoded_land[i:i+1:][['dateRep', 'cases' , 'Rescd' , 'week' ,'year' , 'month', 'countriesAndTerritories', 'va' , 'vaccin' , 'index']].reset_index()\n",
+ " c=pd.concat([a, b.reindex(a.index)], axis=1)\n",
+ " \n",
+ " c['rp_zeitraum'] = pd.Series(dtype='float')\n",
+ " c = c.replace(np.nan, 0)\n",
+ " c['rp_zeitraum']=c['rp_zeitraum'].apply(lambda x:rp_zeitraum)\n",
+ " c=c[lll]\n",
+ " c=c.drop(['index'], axis=1)\n",
+ " nnn_data_encoded=pd.concat([nnn_data_encoded, c], axis=0)\n",
+ "month_data_encoded=nnn_data_encoded"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "5b33cef1-82e3-4f8c-9c59-8a1847bf2520",
+ "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>countriesAndTerritories_0</th>\n",
+ " <th>countriesAndTerritories_1</th>\n",
+ " <th>countriesAndTerritories_2</th>\n",
+ " <th>countriesAndTerritories_3</th>\n",
+ " <th>countriesAndTerritories_4</th>\n",
+ " <th>dateRep</th>\n",
+ " <th>cases</th>\n",
+ " <th>Rescd</th>\n",
+ " <th>7days_before_mean</th>\n",
+ " <th>7days_after_mean</th>\n",
+ " <th>month_0</th>\n",
+ " <th>month_1</th>\n",
+ " <th>month_2</th>\n",
+ " <th>month_3</th>\n",
+ " <th>week</th>\n",
+ " <th>year</th>\n",
+ " <th>index</th>\n",
+ " <th>countriesAndTerritories</th>\n",
+ " <th>va</th>\n",
+ " <th>vaccin</th>\n",
+ " <th>EntertainmentVenuesPartial</th>\n",
+ " <th>RestaurantsCafesPartial</th>\n",
+ " <th>EntertainmentVenues</th>\n",
+ " <th>MassGatherAll</th>\n",
+ " <th>ClosSec</th>\n",
+ " <th>GymsSportsCentresPartial</th>\n",
+ " <th>ClosPrim</th>\n",
+ " <th>NonEssentialShopsPartial</th>\n",
+ " <th>ClosPubAnyPartial</th>\n",
+ " <th>RestaurantsCafes</th>\n",
+ " <th>GymsSportsCentres</th>\n",
+ " <th>MassGather50</th>\n",
+ " <th>PrivateGatheringRestrictions</th>\n",
+ " <th>MassGatherAllPartial</th>\n",
+ " <th>ClosHigh</th>\n",
+ " <th>NonEssentialShops</th>\n",
+ " <th>ClosSecPartial</th>\n",
+ " <th>OutdoorOver500</th>\n",
+ " <th>ClosDaycare</th>\n",
+ " <th>BanOnAllEvents</th>\n",
+ " <th>IndoorOver500</th>\n",
+ " <th>QuarantineForInternationalTravellers</th>\n",
+ " <th>ClosHighPartial</th>\n",
+ " <th>IndoorOver100</th>\n",
+ " <th>Teleworking</th>\n",
+ " <th>ClosPubAny</th>\n",
+ " <th>PlaceOfWorshipPartial</th>\n",
+ " <th>MasksMandatoryClosedSpacesPartial</th>\n",
+ " <th>MassGather50Partial</th>\n",
+ " <th>StayHomeOrderPartial</th>\n",
+ " <th>OutdoorOver100</th>\n",
+ " <th>IndoorOver50</th>\n",
+ " <th>ClosPrimPartial</th>\n",
+ " <th>PrivateGatheringRestrictionsPartial</th>\n",
+ " <th>MasksMandatoryClosedSpaces</th>\n",
+ " <th>OutdoorOver1000</th>\n",
+ " <th>TeleworkingPartial</th>\n",
+ " <th>MasksMandatoryAllSpaces</th>\n",
+ " <th>OutdoorOver50</th>\n",
+ " <th>StayHomeOrder</th>\n",
+ " <th>QuarantineForInternationalTravellersPartial</th>\n",
+ " <th>MasksMandatoryAllSpacesPartial</th>\n",
+ " <th>StayHomeGen</th>\n",
+ " <th>PlaceOfWorship</th>\n",
+ " <th>ClosDaycarePartial</th>\n",
+ " <th>IndoorOver1000</th>\n",
+ " <th>BanOnAllEventsPartial</th>\n",
+ " <th>HotelsOtherAccommodationPartial</th>\n",
+ " <th>StayHomeRiskG</th>\n",
+ " <th>ClosureOfPublicTransportPartial</th>\n",
+ " <th>AdaptationOfWorkplace</th>\n",
+ " <th>HotelsOtherAccommodation</th>\n",
+ " <th>MasksVoluntaryClosedSpacesPartial</th>\n",
+ " <th>RegionalStayHomeOrderPartial</th>\n",
+ " <th>AdaptationOfWorkplacePartial</th>\n",
+ " <th>MasksVoluntaryAllSpaces</th>\n",
+ " <th>MasksVoluntaryAllSpacesPartial</th>\n",
+ " <th>MasksVoluntaryClosedSpaces</th>\n",
+ " <th>SocialCircle</th>\n",
+ " <th>WorkplaceClosures</th>\n",
+ " <th>RegionalStayHomeOrder</th>\n",
+ " <th>ClosureOfPublicTransport</th>\n",
+ " <th>StayHomeGenPartial</th>\n",
+ " <th>WorkplaceClosuresPartial</th>\n",
+ " <th>StayHomeRiskGPartial</th>\n",
+ " <th>SocialCirclePartial</th>\n",
+ " <th>not_sequenced</th>\n",
+ " <th>B.1.617.2</th>\n",
+ " <th>BA.2</th>\n",
+ " <th>BA.5</th>\n",
+ " <th>Other</th>\n",
+ " <th>B.1.1.7</th>\n",
+ " <th>BA.1</th>\n",
+ " <th>BA.4</th>\n",
+ " <th>BA.2.75</th>\n",
+ " <th>BQ.1</th>\n",
+ " <th>XBB</th>\n",
+ " <th>B.1.351</th>\n",
+ " <th>P.1</th>\n",
+ " <th>XBB.1.5</th>\n",
+ " <th>B.1.525</th>\n",
+ " <th>B.1.621</th>\n",
+ " <th>C.37</th>\n",
+ " <th>B.1.617.1</th>\n",
+ " <th>B.1.616</th>\n",
+ " <th>B.1.620</th>\n",
+ " <th>B.1.427/B.1.429</th>\n",
+ " <th>P.3</th>\n",
+ " <th>UNK</th>\n",
+ " <th>B.1.1.529</th>\n",
+ " <th>BA.3</th>\n",
+ " <th>AY.4.2</th>\n",
+ " <th>BA.4/BA.5</th>\n",
+ " <th>SGTF</th>\n",
+ " <th>B.1.1.7+E484K</th>\n",
+ " <th>BA.2+L452X</th>\n",
+ " <th>B.1.617.3</th>\n",
+ " <th>vaccin_0</th>\n",
+ " <th>vaccin_1</th>\n",
+ " <th>vaccin_2</th>\n",
+ " <th>vaccin_3</th>\n",
+ " <th>vaccin_4</th>\n",
+ " <th>month</th>\n",
+ " <th>rp_zeitraum</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2020-03-29</td>\n",
+ " <td>2599.0</td>\n",
+ " <td>['ClosDaycare', 'ClosHighPartial', 'ClosPubAny...</td>\n",
+ " <td>790.871921</td>\n",
+ " <td>1741.147783</td>\n",
+ " <td>0.034483</td>\n",
+ " <td>0.034483</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.965517</td>\n",
+ " <td>13</td>\n",
+ " <td>2020</td>\n",
+ " <td>NaN</td>\n",
+ " <td>France</td>\n",
+ " <td>[['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ...</td>\n",
+ " <td>[0, 0, 0, 0, 0]</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.448276</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.448276</td>\n",
+ " <td>0.448276</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.413793</td>\n",
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+ " <td>0.448276</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
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+ " <td>0.413793</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.137931</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.896552</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.062069</td>\n",
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+ " <td>93.041379</td>\n",
+ " <td>0.000000</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.606897</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>756.829268</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2020-03-30</td>\n",
+ " <td>4376.0</td>\n",
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+ " <td>904.541872</td>\n",
+ " <td>1891.231527</td>\n",
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+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>14</td>\n",
+ " <td>2020</td>\n",
+ " <td>NaN</td>\n",
+ " <td>France</td>\n",
+ " <td>[['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ...</td>\n",
+ " <td>[0, 0, 0, 0, 0]</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.482759</td>\n",
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+ " <td>0.0</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
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+ " <td>1.0</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
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+ " <td>1.000000</td>\n",
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+ " <td>222.686567</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
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+ " <td>1.000000</td>\n",
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+ " <td>145.469274</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>0.0</td>\n",
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+ " <td>0.0</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3.003448</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>11</td>\n",
+ " <td>0.183333</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2020-12-01</td>\n",
+ " <td>20.0</td>\n",
+ " <td>['ClosPubAnyPartial', 'EntertainmentVenues', '...</td>\n",
+ " <td>29.433498</td>\n",
+ " <td>20.719212</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>49</td>\n",
+ " <td>2020</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Iceland</td>\n",
+ " <td>[['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ...</td>\n",
+ " <td>[0, 0, 0, 0, 0]</td>\n",
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+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3.031034</td>\n",
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+ " <td>0.0</td>\n",
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+ " <td>0</td>\n",
+ " <td>0.216867</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2020-12-02</td>\n",
+ " <td>15.0</td>\n",
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+ " <td>27.137931</td>\n",
+ " <td>20.502463</td>\n",
+ " <td>1.000000</td>\n",
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+ " <td>0.034483</td>\n",
+ " <td>0.965517</td>\n",
+ " <td>49</td>\n",
+ " <td>2020</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Iceland</td>\n",
+ " <td>[['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ...</td>\n",
+ " <td>[0, 0, 0, 0, 0]</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3.024138</td>\n",
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+ " <td>0.254002</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1.0</td>\n",
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+ " <td>1.0</td>\n",
+ " <td>2020-12-03</td>\n",
+ " <td>14.0</td>\n",
+ " <td>['ClosPubAnyPartial', 'EntertainmentVenues', '...</td>\n",
+ " <td>25.426108</td>\n",
+ " <td>20.315271</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.068966</td>\n",
+ " <td>0.931034</td>\n",
+ " <td>49</td>\n",
+ " <td>2020</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Iceland</td>\n",
+ " <td>[['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ...</td>\n",
+ " <td>[0, 0, 0, 0, 0]</td>\n",
+ " <td>0.000000</td>\n",
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+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>99.886207</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3.017241</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
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+ " <td>1.0</td>\n",
+ " <td>2020-12-04</td>\n",
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+ " <td>20.098522</td>\n",
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+ " <td>0.896552</td>\n",
+ " <td>49</td>\n",
+ " <td>2020</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Iceland</td>\n",
+ " <td>[['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ...</td>\n",
+ " <td>[0, 0, 0, 0, 0]</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>99.848276</td>\n",
+ " <td>0.151724</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3.010345</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.265971</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>21549 rows × 124 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " countriesAndTerritories_0 countriesAndTerritories_1 \\\n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ ".. ... ... \n",
+ "0 1.0 1.0 \n",
+ "0 1.0 1.0 \n",
+ "0 1.0 1.0 \n",
+ "0 1.0 1.0 \n",
+ "0 1.0 1.0 \n",
+ "\n",
+ " countriesAndTerritories_2 countriesAndTerritories_3 \\\n",
+ "0 0.0 1.0 \n",
+ "0 0.0 1.0 \n",
+ "0 0.0 1.0 \n",
+ "0 0.0 1.0 \n",
+ "0 0.0 1.0 \n",
+ ".. ... ... \n",
+ "0 1.0 0.0 \n",
+ "0 1.0 0.0 \n",
+ "0 1.0 0.0 \n",
+ "0 1.0 0.0 \n",
+ "0 1.0 0.0 \n",
+ "\n",
+ " countriesAndTerritories_4 dateRep cases \\\n",
+ "0 0.0 2020-03-29 2599.0 \n",
+ "0 0.0 2020-03-30 4376.0 \n",
+ "0 0.0 2020-03-31 7578.0 \n",
+ "0 0.0 2020-04-01 4861.0 \n",
+ "0 0.0 2020-04-02 2116.0 \n",
+ ".. ... ... ... \n",
+ "0 1.0 2020-11-30 21.0 \n",
+ "0 1.0 2020-12-01 20.0 \n",
+ "0 1.0 2020-12-02 15.0 \n",
+ "0 1.0 2020-12-03 14.0 \n",
+ "0 1.0 2020-12-04 14.0 \n",
+ "\n",
+ " Rescd 7days_before_mean \\\n",
+ "0 ['ClosDaycare', 'ClosHighPartial', 'ClosPubAny... 790.871921 \n",
+ "0 ['ClosDaycare', 'ClosHighPartial', 'ClosPubAny... 904.541872 \n",
+ "0 ['ClosDaycare', 'ClosHighPartial', 'ClosPubAny... 1019.802956 \n",
+ "0 ['ClosDaycare', 'ClosHighPartial', 'ClosPubAny... 1140.788177 \n",
+ "0 ['ClosDaycare', 'ClosHighPartial', 'ClosPubAny... 1286.832512 \n",
+ ".. ... ... \n",
+ "0 ['ClosPubAnyPartial', 'EntertainmentVenues', '... 32.187192 \n",
+ "0 ['ClosPubAnyPartial', 'EntertainmentVenues', '... 29.433498 \n",
+ "0 ['ClosPubAnyPartial', 'EntertainmentVenues', '... 27.137931 \n",
+ "0 ['ClosPubAnyPartial', 'EntertainmentVenues', '... 25.426108 \n",
+ "0 ['ClosPubAnyPartial', 'EntertainmentVenues', '... 24.054187 \n",
+ "\n",
+ " 7days_after_mean month_0 month_1 month_2 month_3 week year \\\n",
+ "0 1741.147783 0.034483 0.034483 0.000000 0.965517 13 2020 \n",
+ "0 1891.231527 0.000000 0.000000 0.000000 1.000000 14 2020 \n",
+ "0 2036.330049 0.000000 0.000000 0.000000 1.000000 14 2020 \n",
+ "0 2178.492611 0.000000 0.000000 0.000000 1.000000 14 2020 \n",
+ "0 2300.748768 0.000000 0.000000 0.034483 0.965517 14 2020 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "0 20.995074 1.000000 0.000000 0.000000 1.000000 49 2020 \n",
+ "0 20.719212 1.000000 0.000000 0.000000 1.000000 49 2020 \n",
+ "0 20.502463 1.000000 0.000000 0.034483 0.965517 49 2020 \n",
+ "0 20.315271 1.000000 0.000000 0.068966 0.931034 49 2020 \n",
+ "0 20.098522 1.000000 0.000000 0.103448 0.896552 49 2020 \n",
+ "\n",
+ " index countriesAndTerritories \\\n",
+ "0 NaN France \n",
+ "0 NaN France \n",
+ "0 NaN France \n",
+ "0 NaN France \n",
+ "0 NaN France \n",
+ ".. ... ... \n",
+ "0 NaN Iceland \n",
+ "0 NaN Iceland \n",
+ "0 NaN Iceland \n",
+ "0 NaN Iceland \n",
+ "0 NaN Iceland \n",
+ "\n",
+ " va vaccin \\\n",
+ "0 [['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ... [0, 0, 0, 0, 0] \n",
+ "0 [['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ... [0, 0, 0, 0, 0] \n",
+ "0 [['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ... [0, 0, 0, 0, 0] \n",
+ "0 [['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ... [0, 0, 0, 0, 0] \n",
+ "0 [['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ... [0, 0, 0, 0, 0] \n",
+ ".. ... ... \n",
+ "0 [['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ... [0, 0, 0, 0, 0] \n",
+ "0 [['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ... [0, 0, 0, 0, 0] \n",
+ "0 [['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ... [0, 0, 0, 0, 0] \n",
+ "0 [['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ... [0, 0, 0, 0, 0] \n",
+ "0 [['Other', 'XBB.1.5', 'B.1.351', 'B.1.617.2', ... [0, 0, 0, 0, 0] \n",
+ "\n",
+ " EntertainmentVenuesPartial RestaurantsCafesPartial EntertainmentVenues \\\n",
+ "0 0.000000 0.0 0.448276 \n",
+ "0 0.000000 0.0 0.482759 \n",
+ "0 0.000000 0.0 0.517241 \n",
+ "0 0.000000 0.0 0.551724 \n",
+ "0 0.000000 0.0 0.586207 \n",
+ ".. ... ... ... \n",
+ "0 0.034483 1.0 1.000000 \n",
+ "0 0.000000 1.0 1.000000 \n",
+ "0 0.000000 1.0 1.000000 \n",
+ "0 0.000000 1.0 1.000000 \n",
+ "0 0.000000 1.0 1.000000 \n",
+ "\n",
+ " MassGatherAll ClosSec GymsSportsCentresPartial ClosPrim \\\n",
+ "0 1.0 0.0 0.0 0.0 \n",
+ "0 1.0 0.0 0.0 0.0 \n",
+ "0 1.0 0.0 0.0 0.0 \n",
+ "0 1.0 0.0 0.0 0.0 \n",
+ "0 1.0 0.0 0.0 0.0 \n",
+ ".. ... ... ... ... \n",
+ "0 1.0 0.0 1.0 0.0 \n",
+ "0 1.0 0.0 1.0 0.0 \n",
+ "0 1.0 0.0 1.0 0.0 \n",
+ "0 1.0 0.0 1.0 0.0 \n",
+ "0 1.0 0.0 1.0 0.0 \n",
+ "\n",
+ " NonEssentialShopsPartial ClosPubAnyPartial RestaurantsCafes \\\n",
+ "0 0.0 0.0 0.448276 \n",
+ "0 0.0 0.0 0.482759 \n",
+ "0 0.0 0.0 0.517241 \n",
+ "0 0.0 0.0 0.551724 \n",
+ "0 0.0 0.0 0.586207 \n",
+ ".. ... ... ... \n",
+ "0 1.0 1.0 0.000000 \n",
+ "0 1.0 1.0 0.000000 \n",
+ "0 1.0 1.0 0.000000 \n",
+ "0 1.0 1.0 0.000000 \n",
+ "0 1.0 1.0 0.000000 \n",
+ "\n",
+ " GymsSportsCentres MassGather50 PrivateGatheringRestrictions \\\n",
+ "0 0.448276 0.0 0.413793 \n",
+ "0 0.482759 0.0 0.448276 \n",
+ "0 0.517241 0.0 0.482759 \n",
+ "0 0.551724 0.0 0.517241 \n",
+ "0 0.586207 0.0 0.551724 \n",
+ ".. ... ... ... \n",
+ "0 0.000000 1.0 1.000000 \n",
+ "0 0.000000 1.0 1.000000 \n",
+ "0 0.000000 1.0 1.000000 \n",
+ "0 0.000000 1.0 1.000000 \n",
+ "0 0.000000 1.0 1.000000 \n",
+ "\n",
+ " MassGatherAllPartial ClosHigh NonEssentialShops ClosSecPartial \\\n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ ".. ... ... ... ... \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ " OutdoorOver500 ClosDaycare BanOnAllEvents IndoorOver500 \\\n",
+ "0 0.0 0.448276 0.0 0.0 \n",
+ "0 0.0 0.482759 0.0 0.0 \n",
+ "0 0.0 0.517241 0.0 0.0 \n",
+ "0 0.0 0.551724 0.0 0.0 \n",
+ "0 0.0 0.586207 0.0 0.0 \n",
+ ".. ... ... ... ... \n",
+ "0 0.0 0.000000 0.0 0.0 \n",
+ "0 0.0 0.000000 0.0 0.0 \n",
+ "0 0.0 0.000000 0.0 0.0 \n",
+ "0 0.0 0.000000 0.0 0.0 \n",
+ "0 0.0 0.000000 0.0 0.0 \n",
+ "\n",
+ " QuarantineForInternationalTravellers ClosHighPartial IndoorOver100 \\\n",
+ "0 0.0 0.448276 0.0 \n",
+ "0 0.0 0.482759 0.0 \n",
+ "0 0.0 0.517241 0.0 \n",
+ "0 0.0 0.551724 0.0 \n",
+ "0 0.0 0.586207 0.0 \n",
+ ".. ... ... ... \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "\n",
+ " Teleworking ClosPubAny PlaceOfWorshipPartial \\\n",
+ "0 0.413793 0.448276 0.0 \n",
+ "0 0.448276 0.482759 0.0 \n",
+ "0 0.482759 0.517241 0.0 \n",
+ "0 0.517241 0.551724 0.0 \n",
+ "0 0.551724 0.586207 0.0 \n",
+ ".. ... ... ... \n",
+ "0 0.000000 0.000000 0.0 \n",
+ "0 0.000000 0.000000 0.0 \n",
+ "0 0.000000 0.000000 0.0 \n",
+ "0 0.000000 0.000000 0.0 \n",
+ "0 0.000000 0.000000 0.0 \n",
+ "\n",
+ " MasksMandatoryClosedSpacesPartial MassGather50Partial \\\n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ ".. ... ... \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "\n",
+ " StayHomeOrderPartial OutdoorOver100 IndoorOver50 ClosPrimPartial \\\n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ ".. ... ... ... ... \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ " PrivateGatheringRestrictionsPartial MasksMandatoryClosedSpaces \\\n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ ".. ... ... \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "\n",
+ " OutdoorOver1000 TeleworkingPartial MasksMandatoryAllSpaces \\\n",
+ "0 0.551724 0.0 0.0 \n",
+ "0 0.586207 0.0 0.0 \n",
+ "0 0.620690 0.0 0.0 \n",
+ "0 0.655172 0.0 0.0 \n",
+ "0 0.689655 0.0 0.0 \n",
+ ".. ... ... ... \n",
+ "0 0.000000 0.0 0.0 \n",
+ "0 0.000000 0.0 0.0 \n",
+ "0 0.000000 0.0 0.0 \n",
+ "0 0.000000 0.0 0.0 \n",
+ "0 0.000000 0.0 0.0 \n",
+ "\n",
+ " OutdoorOver50 StayHomeOrder QuarantineForInternationalTravellersPartial \\\n",
+ "0 0.0 0.413793 0.0 \n",
+ "0 0.0 0.448276 0.0 \n",
+ "0 0.0 0.482759 0.0 \n",
+ "0 0.0 0.517241 0.0 \n",
+ "0 0.0 0.551724 0.0 \n",
+ ".. ... ... ... \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "\n",
+ " MasksMandatoryAllSpacesPartial StayHomeGen PlaceOfWorship \\\n",
+ "0 0.0 0.137931 0.0 \n",
+ "0 0.0 0.137931 0.0 \n",
+ "0 0.0 0.137931 0.0 \n",
+ "0 0.0 0.137931 0.0 \n",
+ "0 0.0 0.137931 0.0 \n",
+ ".. ... ... ... \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "0 1.0 0.000000 0.0 \n",
+ "\n",
+ " ClosDaycarePartial IndoorOver1000 BanOnAllEventsPartial \\\n",
+ "0 0.0 1.0 0.0 \n",
+ "0 0.0 1.0 0.0 \n",
+ "0 0.0 1.0 0.0 \n",
+ "0 0.0 1.0 0.0 \n",
+ "0 0.0 1.0 0.0 \n",
+ ".. ... ... ... \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "\n",
+ " HotelsOtherAccommodationPartial StayHomeRiskG \\\n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ ".. ... ... \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "\n",
+ " ClosureOfPublicTransportPartial AdaptationOfWorkplace \\\n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ ".. ... ... \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "\n",
+ " HotelsOtherAccommodation MasksVoluntaryClosedSpacesPartial \\\n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ ".. ... ... \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "\n",
+ " RegionalStayHomeOrderPartial AdaptationOfWorkplacePartial \\\n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ ".. ... ... \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "\n",
+ " MasksVoluntaryAllSpaces MasksVoluntaryAllSpacesPartial \\\n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ ".. ... ... \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "0 0.0 0.0 \n",
+ "\n",
+ " MasksVoluntaryClosedSpaces SocialCircle WorkplaceClosures \\\n",
+ "0 0.0 0.0 0.413793 \n",
+ "0 0.0 0.0 0.448276 \n",
+ "0 0.0 0.0 0.482759 \n",
+ "0 0.0 0.0 0.517241 \n",
+ "0 0.0 0.0 0.551724 \n",
+ ".. ... ... ... \n",
+ "0 0.0 0.0 0.000000 \n",
+ "0 0.0 0.0 0.000000 \n",
+ "0 0.0 0.0 0.000000 \n",
+ "0 0.0 0.0 0.000000 \n",
+ "0 0.0 0.0 0.000000 \n",
+ "\n",
+ " RegionalStayHomeOrder ClosureOfPublicTransport StayHomeGenPartial \\\n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ ".. ... ... ... \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "\n",
+ " WorkplaceClosuresPartial StayHomeRiskGPartial SocialCirclePartial \\\n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ ".. ... ... ... \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 \n",
+ "\n",
+ " not_sequenced B.1.617.2 BA.2 BA.5 Other B.1.1.7 BA.1 \\\n",
+ "0 6.896552 0.0 0.062069 0.0 93.041379 0.000000 0.0 \n",
+ "0 3.448276 0.0 0.072414 0.0 96.479310 0.000000 0.0 \n",
+ "0 0.000000 0.0 0.072414 0.0 99.924138 0.003448 0.0 \n",
+ "0 0.000000 0.0 0.072414 0.0 99.920690 0.006897 0.0 \n",
+ "0 0.000000 0.0 0.072414 0.0 99.917241 0.010345 0.0 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "0 0.000000 0.0 0.000000 0.0 100.000000 0.000000 0.0 \n",
+ "0 0.000000 0.0 0.000000 0.0 99.962069 0.037931 0.0 \n",
+ "0 0.000000 0.0 0.000000 0.0 99.924138 0.075862 0.0 \n",
+ "0 0.000000 0.0 0.000000 0.0 99.886207 0.113793 0.0 \n",
+ "0 0.000000 0.0 0.000000 0.0 99.848276 0.151724 0.0 \n",
+ "\n",
+ " BA.4 BA.2.75 BQ.1 XBB B.1.351 P.1 XBB.1.5 B.1.525 B.1.621 C.37 \\\n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ ".. ... ... ... ... ... ... ... ... ... ... \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ " B.1.617.1 B.1.616 B.1.620 B.1.427/B.1.429 P.3 UNK B.1.1.529 \\\n",
+ "0 0.0 0.0 0.0 0.0 0.0 1.606897 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 1.641379 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 1.682759 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 1.703448 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 1.724138 0.0 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "0 0.0 0.0 0.0 0.0 0.0 3.003448 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 3.031034 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 3.024138 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 3.017241 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 3.010345 0.0 \n",
+ "\n",
+ " BA.3 AY.4.2 BA.4/BA.5 SGTF B.1.1.7+E484K BA.2+L452X B.1.617.3 \\\n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ " vaccin_0 vaccin_1 vaccin_2 vaccin_3 vaccin_4 month rp_zeitraum \n",
+ "0 0.0 0.0 0.0 0.0 0.0 3 756.829268 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 3 336.711111 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 3 222.686567 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 4 145.469274 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 4 126.560606 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "0 0.0 0.0 0.0 0.0 0.0 11 0.183333 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0 0.216867 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0 0.254002 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0 0.270136 \n",
+ "0 0.0 0.0 0.0 0.0 0.0 0 0.265971 \n",
+ "\n",
+ "[21549 rows x 124 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "writer = pd.ExcelWriter('month_data_encoded.xlsx', engine='xlsxwriter')\n",
+ "\n",
+ "month_data_encoded.to_excel('month_data_encoded.xlsx', sheet_name='1', index=False)\n",
+ "month_data_encoded"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "358acefb-05a6-4298-b33a-cffa4ec17134",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.9.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
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