Upload Scheduler_Covid.py

Scheduler/Scheduler_Covid.py

@@ -1,3 +1,116 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ec9e9a702f0ebc24463fc8e550cbd22c8ca38be3da21a90e660b4b629e3d8253
-size 4915
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 23 09:27:21 2021
+
+@author: benjaminull
+"""
+
+import pandas as pd
+from datetime import datetime
+import numpy as np
+from datetime import timedelta
+import requests
+import io
+import openpyxl
+from pandas import ExcelWriter
+import requests
+from bs4 import BeautifulSoup
+from bs4 import BeautifulSoup
+import pandas as pd
+from selenium import webdriver
+import requests
+
+
+
+def GenerarExcel(ruta_guardado, Pestañas, Data):
+    wb = openpyxl.Workbook()
+    writer = ExcelWriter(ruta_guardado)
+    for pestaña in Pestañas:
+        wb.create_sheet(pestaña)
+    std = wb.get_sheet_by_name('Sheet')
+    wb.remove_sheet(std)
+    wb.save(ruta_guardado)
+    for i in range(len(Pestañas)):
+        print(Data[i])
+        Data[i].to_excel(writer, sheet_name=Pestañas[i])
+    writer.save()
+
+
+def run_data_covid():
+    options = webdriver.ChromeOptions()
+    options.binary_location = r'C:/Program Files/Google/Chrome/Application/chrome.exe'
+    path_to_chromedriver = r'C:/Users/bullm/larrainvial.com/Equipo Quant - Area Estrategias Cuantitativas 2.0/Codigos\Data Alternativa/Transcripts/chromedriver.exe'
+    browser = webdriver.Chrome(executable_path=path_to_chromedriver, chrome_options=options)
+    # Ir a página deseada
+    url = 'https://covid19.apple.com/mobility'
+    browser.get(url)
+    page = requests.get(url)
+    html=browser.page_source
+    soup = BeautifulSoup(html, "html.parser")
+    link = str(soup.find_all("a")[1]).split('"')[1]
+    link_apple = "https://covid19.apple.com/mobility"
+    r = requests.get(link_apple)
+    soup = BeautifulSoup(r.text, 'lxml')
+    data_agg = pd.DataFrame()
+    i = 0
+    for chunk in pd.read_csv(
+        'https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv',
+        usecols=['country_region', 'date',
+                 'retail_and_recreation_percent_change_from_baseline',
+                 'grocery_and_pharmacy_percent_change_from_baseline',
+                 'parks_percent_change_from_baseline',
+                 'transit_stations_percent_change_from_baseline',
+                 'workplaces_percent_change_from_baseline'],
+                 dtype = {"workplaces_percent_change_from_baseline":
+                          "float32",
+                          "parks_percent_change_from_baseline": "float32",
+                          "retail_and_recreation_percent_change_from_baseline":
+                              "float32",
+                          'transit_stations_percent_change_from_baseline':
+                              "float32",
+                         },chunksize = 150000):
+        if i == 65:
+            break
+        i=i+1
+        data_agg = pd.concat([data_agg, chunk], ignore_index=True)
+    data_agg.info(memory_usage="deep") 
+    data_agg.set_index(['country_region', 'date'], inplace=True)
+    data_agg = data_agg.groupby(level=[0, 1]).mean()
+    data_agg.columns = data_agg.columns.str.replace('_percent_change_from_baseline', '_google')
+    yesterday = (datetime.today() - timedelta(2)).strftime("%Y-%m-%d")
+    url=f''+link
+    CONFIRMED_CONTENT = requests.get(url).content
+    data_app = pd.read_csv(io.StringIO(CONFIRMED_CONTENT.decode('utf-8')),
+                           error_bad_lines=False)
+    # Dejamos solo la data a nivel pais
+    data_app.info(memory_usage="deep")
+    data_app = data_app.loc[data_app['geo_type'] == 'country/region']
+    data_app = data_app.drop(columns=['geo_type', 'country',
+                                      'alternative_name', 'sub-region'])
+    data_app = data_app.set_index(['region', 'transportation_type']).stack()
+    data_app = data_app.unstack(level='transportation_type') - 100
+    data_app.index.names = data_agg.index.names
+    data_agg = data_agg.join(data_app)
+    print(data_app.columns)
+    mob_idx_cols = ['retail_and_recreation_google', 'grocery_and_pharmacy_google',
+                    'parks_google', 'transit_stations_google',
+                    'workplaces_google', 'driving', 'transit', 'walking']
+    data_agg['Mobility Index'] = data_agg[mob_idx_cols].mean(1)
+    regiones = {}
+    regiones['Latam'] = ['Argentina', 'Brazil', 'Chile', 'Colombia',
+                         'Mexico', 'Peru']
+    regiones['Europa'] = ['Italy', 'Spain', 'Germany', 'United Kingdom', 'France']
+    regiones['Asia Emergente'] = ['South Korea', 'Taiwan', 'Hong Kong', 'India',
+                                  'Thailand', 'Indonesia']
+    regiones['USA'] = ['United States']
+    # regiones['Israel'] = ['Israel']
+    data_dict = {}
+    for col in data_agg.columns:
+        df = data_agg[col].unstack().T.rolling(7, 3).mean()
+        for region, paises in regiones.items():
+            df[region] = df[paises].mean(1)
+        data_dict[col] = df
+    GenerarExcel("Scheduler/Movilidad_desagrada.xlsx", list(data_dict.keys()),
+                 list(data_dict.values()))
+    np.save('Scheduler/dict_movilidad.npy', data_dict)