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Data Collection: Colombia scripts & Automatic #52
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,243 @@ | ||
| import pandas as pd | ||
| import numpy as np | ||
| import datetime | ||
| import sys | ||
| import os | ||
|
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| def get_iso_by_country_name(country_name, mode): | ||
|
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| array_iso = np.array(['CO-AMA', | ||
| 'CO-ANT', | ||
| 'CO-ARA', | ||
| 'CO-SAP', | ||
| 'CO-ATL', | ||
| 'CO-ATL', | ||
| 'CO-DC', | ||
| 'CO-BOL', | ||
| 'CO-BOY', | ||
| 'CO-CAU', | ||
| 'CO-CAL', | ||
| 'CO-CAQ', | ||
| 'CO-BOL', | ||
| 'CO-CAS', | ||
| 'CO-CAU', | ||
| 'CO-CES', | ||
| 'CO-COR', | ||
| 'CO-CUN', | ||
| 'CO-CHO', | ||
| 'CO-HUI', | ||
| 'CO-LAG', | ||
| 'CO-MAG', | ||
| 'CO-MET', | ||
| 'CO-NAR', | ||
| 'CO-NSA', | ||
| 'CO-PUT', | ||
| 'CO-QUI', | ||
| 'CO-RIS', | ||
| 'CO-MAG', | ||
| 'CO-SAN', | ||
| 'CO-SUC', | ||
| 'CO-TOL', | ||
| 'CO-VAC', | ||
| 'CO-VAU']) | ||
|
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| array_brazil_csv = np.array(['Amazonas', | ||
| 'Antioquia', | ||
| 'Arauca', | ||
| 'Archipiélago de San Andrés Providencia y Santa Catalina', | ||
| 'Atlántico', | ||
| 'Barranquilla D.E.', | ||
| 'Bogotá D.C.', | ||
| 'Bolívar', | ||
| 'Boyacá', | ||
| 'Buenaventura D.E.', | ||
| 'Caldas', | ||
| 'Caquetá', | ||
| 'Cartagena D.T. y C.', | ||
| 'Casanare', | ||
| 'Cauca', | ||
| 'Cesar', | ||
| 'Chocó', | ||
| 'Córdoba', | ||
| 'Cundinamarca', | ||
| 'Huila', | ||
| 'La Guajira', | ||
| 'Magdalena', | ||
| 'Meta', | ||
| 'Nariño', | ||
| 'Norte de Santander', | ||
| 'Putumayo', | ||
| 'Quindío', | ||
| 'Risaralda', | ||
| 'Santa Marta D.T. y C.', | ||
| 'Santander', | ||
| 'Sucre', | ||
| 'Tolima', | ||
| 'Valle del Cauca', | ||
| 'Vaupés']) | ||
|
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| array_brazil_fixed = np.array(['Amazonas', | ||
| 'Antioquia', | ||
| 'Arauca', | ||
| 'San Andres y Providencia', | ||
| 'Atlantico', | ||
| 'Atlantico', | ||
| 'Bogota', | ||
| 'Bolivar', | ||
| 'Boyaca', | ||
| 'Cauca', | ||
| 'Caldas', | ||
| 'Caqueta', | ||
| 'Bolivar', | ||
| 'Casanare', | ||
| 'Cauca', | ||
| 'Cesar', | ||
| 'Choco', | ||
| 'Cordoba', | ||
| 'Cundinamarca', | ||
| 'Huila', | ||
| 'La Guajira', | ||
| 'Magdalena', | ||
| 'Meta', | ||
| 'Narino', | ||
| 'Norte de Santander', | ||
| 'Putumayo', | ||
| 'Quindio', | ||
| 'Risaralda', | ||
| 'Magdalena', | ||
| 'Santander', | ||
| 'Sucre', | ||
| 'Tolima', | ||
| 'Valle del Cauca', | ||
| 'Vaupes']) | ||
|
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| df = pd.DataFrame({'ISO 3166-2 Code': array_iso, | ||
| 'Remote': array_brazil_csv, 'Local': array_brazil_fixed}) | ||
|
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| string_iso = '' | ||
|
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| if mode == 'remote': | ||
| string_iso = df[df['Remote'].str.contains( | ||
| country_name)]['ISO 3166-2 Code'].values[0] | ||
| elif mode == 'local': | ||
| string_iso = df[df['Local'].str.contains( | ||
| country_name)]['ISO 3166-2 Code'].values[0] | ||
|
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| return string_iso | ||
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| def generate_list_dates(path): | ||
| # Generate dates from files existing | ||
| date_list_csv = [] | ||
| path, dirs, files = next(os.walk(path)) | ||
| numero_archivos = len(files) | ||
| print('There is {} files on the path and one is README. We iterate {} times...'.format( | ||
| numero_archivos, numero_archivos-1)) | ||
| # dates | ||
| base = (datetime.datetime.today()).date() | ||
| numdays = numero_archivos-1 | ||
| date_list_csv = [str(base - datetime.timedelta(days=x))+str('.csv') | ||
| for x in range(numdays)] | ||
| print('Adding {} dates in a list...'.format(len(date_list_csv))) | ||
| date_list = [] | ||
| for d in date_list_csv: | ||
| date_list.append(d[:-4]) | ||
| print("List of dates:", date_list) | ||
| return date_list_csv, date_list | ||
|
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|
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| def load_and_generatecsv(): | ||
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| today = datetime.datetime.now().strftime('%Y-%m-%d') | ||
|
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| path_dsrp_daily_reports = 'latam_covid_19_data/daily_reports/' | ||
| path_colombia_csv = "https://www.datos.gov.co/api/views/gt2j-8ykr/rows.csv?accessType=DOWNLOAD&bom=true&format=true" | ||
| path_dsrp = "https://raw.githubusercontent.com/DataScienceResearchPeru/covid-19_latinoamerica/master/latam_covid_19_data/daily_reports/2020-03-08.csv" | ||
| path_csv = "utils/scripts/data_collection/data/colombia_temporal/" | ||
| path_per_patient_csv = 'latam_covid_19_data/per_patient/CO.csv' | ||
| data_colombia = pd.read_csv(path_colombia_csv) | ||
| # SAVE FILE | ||
| data_colombia.to_csv(path_per_patient_csv, index=False) | ||
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| data_dsrp = pd.read_csv(path_dsrp) | ||
|
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| array_dates_csv, array_dates = generate_list_dates(path_dsrp_daily_reports) | ||
|
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| total_confirmed=[0]*800 | ||
| total_death=[0]*800 | ||
| total_recover=[0]*800 | ||
|
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| for d in list(np.flip(array_dates)): # array_dates | ||
|
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| temp_dsrp = data_dsrp[data_dsrp['ISO 3166-2 Code'] | ||
| .str.contains('CO-')].copy() | ||
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| temp_dsrp['Confirmed'] = 0 | ||
| temp_dsrp['Deaths'] = 0 | ||
| temp_dsrp['Recovered'] = 0 | ||
|
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| # Colombia | ||
| data = data_colombia[data_colombia['fecha reporte web'].str.contains(d)] | ||
| data = data.fillna('') | ||
| data.reset_index(drop=True) | ||
|
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| data_confirmed = data[data['fecha reporte web'] != '- -'] | ||
| data_confirmed = data_confirmed.groupby(['Departamento o Distrito ']).size().reset_index(name='Confirmed') | ||
|
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| data_death = data_colombia[data_colombia['Fecha de muerte'].str.contains(d)] | ||
| data_death = data_death[data_death['Fecha de muerte'] != '- -'] | ||
| data_death = data_death.fillna('') | ||
| data_death = data_death.groupby(['Departamento o Distrito ']).size().reset_index(name='Deaths') | ||
|
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| data_recovered = data_colombia[data_colombia['Fecha recuperado'].str.contains(d)] | ||
| data_recovered = data_recovered[data_recovered['Fecha recuperado'] != '- -'] | ||
| data_recovered = data_recovered.fillna('') | ||
| data_recovered = data_recovered.groupby(['Departamento o Distrito ']).size().reset_index(name='Recovered') | ||
|
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||
| for r in range(len(data_confirmed)): | ||
| try: | ||
| country_name_confirmed = get_iso_by_country_name(data_confirmed['Departamento o Distrito '][r], 'remote') | ||
| numero_confirmed = data_confirmed.loc[r]['Confirmed'] | ||
| f = temp_dsrp[temp_dsrp['ISO 3166-2 Code'] == country_name_confirmed] | ||
| # print(f.index.values[0]) | ||
| # print(f.index.values[0]) | ||
| total_confirmed[f.index.values[0]]+=numero_confirmed | ||
| temp_dsrp.loc[f.index.values[0], ['Confirmed']] = total_confirmed[f.index.values[0]] | ||
| # temp_dsrp.loc[f.index.values[0], ['Deaths']] = numero_deaths | ||
| temp_dsrp.loc[f.index.values[0], ['Last Update']] = today | ||
| except Exception as e: | ||
| print('ERROR:[{}]:{}'.format(r, e)) | ||
|
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||
| for r in range(len(data_death)): | ||
| try: | ||
| country_name_deaths = get_iso_by_country_name(data_death['Departamento o Distrito '][r], 'remote') | ||
| numero_deaths = data_death.loc[r]['Deaths'] | ||
| f = temp_dsrp[temp_dsrp['ISO 3166-2 Code'] == country_name_deaths] | ||
| # print(f.index.values[0]) | ||
| total_death[f.index.values[0]]+=numero_deaths | ||
| temp_dsrp.loc[f.index.values[0], ['Deaths']] = total_death[f.index.values[0]] | ||
| # temp_dsrp.loc[f.index.values[0], ['Deaths']] = numero_deaths | ||
| temp_dsrp.loc[f.index.values[0], ['Last Update']] = today | ||
| except Exception as e: | ||
| print('ERROR:[{}]:{}'.format(r, e)) | ||
|
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| for r in range(len(data_recovered)): | ||
| try: | ||
| country_name_recovered = get_iso_by_country_name(data_recovered['Departamento o Distrito '][r], 'remote') | ||
| numero_recovered = data_recovered.loc[r]['Recovered'] | ||
| f = temp_dsrp[temp_dsrp['ISO 3166-2 Code'] == country_name_recovered] | ||
| # print(f.index.values[0]) | ||
| total_recover[f.index.values[0]]+=numero_recovered | ||
| temp_dsrp.loc[f.index.values[0], ['Recovered']] = total_recover[f.index.values[0]] | ||
| # temp_dsrp.loc[f.index.values[0], ['Deaths']] = numero_deaths | ||
| temp_dsrp.loc[f.index.values[0], ['Last Update']] = today | ||
| except Exception as e: | ||
| print('ERROR:[{}]:{}'.format(r, e)) | ||
|
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| print(d, end=' - ') | ||
| temp_dsrp = temp_dsrp.fillna('') | ||
| temp_dsrp.to_csv(path_csv+d+'.csv', index=False) | ||
|
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||
| if __name__ == "__main__": | ||
| load_and_generatecsv() |
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