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create_dataframes.py
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create_dataframes.py
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import tqdm
import warnings
import os
import glob
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import tqdm
import update_files
def create_dataframe():
'''
Function which returns the values of the 4 features for all 3 regions.
'''
# Check if the most recent files have been downloaded
update_files.update()
cwd = os.getcwd()
regional_data_covid_path = os.path.join(cwd, "regional_data_covid")
recovered_data = []
new_daily_infections_data = []
hospitalized_data = []
deceased_data = []
dates = []
# Load data
warnings.filterwarnings("ignore")
for path in glob.glob(regional_data_covid_path):
for i, subpath in enumerate(tqdm.tqdm(sorted(glob.glob(path + os.sep +"*")))):
# add a new column to each dataframe
new_df = pd.read_csv(subpath)
dates.append(new_df.data[0])
recovered_data.append(np.array(new_df.dimessi_guariti))
new_daily_infections_data.append(np.array(new_df.nuovi_positivi))
hospitalized_data.append(np.array(new_df.totale_ospedalizzati))
deceased_data.append(np.array(new_df.deceduti))
denominazione_regione = np.array(new_df.denominazione_regione)
warnings.filterwarnings("default")
# Creation of the dataframes
recovered_df = pd.DataFrame(recovered_data, columns = denominazione_regione, index = dates)
new_daily_infections_df = pd.DataFrame(new_daily_infections_data, columns = denominazione_regione, index = dates)
hospitalized_df = pd.DataFrame(hospitalized_data, columns = denominazione_regione, index = dates)
deceased_df = pd.DataFrame(deceased_data, columns = denominazione_regione, index = dates)
return recovered_df, new_daily_infections_df, hospitalized_df, deceased_df
def create_dataframe_region(region):
'''
Returns the data related to the resion identified by the index "region".
Input:
region: (int) the index of the selected region (in alphabetical order: region != codice regione).
Output:
two dataframe, according to which kind of infected individuals is needed:
res1 uses "variazione_totale_positivi", res2 uses "nuovi_positivi"
'''
# Check if the most recent files have been downloaded
update_files.update()
cwd = os.getcwd()
regional_data_covid_path = os.path.join(cwd, "regional_data_covid")
recovered_data = []
nuovi_positivi = []
variazione_totale_positivi = []
hospitalized_data = []
deceased_data = []
dates = []
# Load data
warnings.filterwarnings("ignore")
for path in glob.glob(regional_data_covid_path):
for i, subpath in enumerate(tqdm.tqdm(sorted(glob.glob(path + os.sep + "*")))):
# add a new column to each dataframe
new_df = pd.read_csv(subpath)
dates.append(new_df.data[0])
recovered_data.append(np.array(new_df.dimessi_guariti.iloc[region]))
nuovi_positivi.append(np.array(new_df.nuovi_positivi.iloc[region]))
variazione_totale_positivi.append(np.array(new_df.variazione_totale_positivi.iloc[region]))
hospitalized_data.append(np.array(new_df.totale_ospedalizzati.iloc[region]))
deceased_data.append(np.array(new_df.deceduti.iloc[region]))
warnings.filterwarnings("default")
# Create the dataframes
recovered_df = pd.DataFrame(recovered_data, columns=["recovered"], index=dates)
new_positives_df = pd.DataFrame(nuovi_positivi, columns=["new_daily_infections"], index=dates)
variation_total_positives_df = pd.DataFrame(variazione_totale_positivi, columns=["new_daily_infections"], index=dates)
hospitalized_df = pd.DataFrame(hospitalized_data, columns=["hospitalized"], index=dates)
deceased_df = pd.DataFrame(deceased_data, columns=["deceased"], index=dates)
res1 = pd.concat([recovered_df, hospitalized_df, deceased_df, variation_total_positives_df], axis = 1)
res2 = pd.concat([recovered_df, hospitalized_df, deceased_df, new_positives_df], axis = 1)
# res1 uses "variazione_totale_positivi", res2 uses "nuovi_positivi"
return res1, res2
if __name__ == "__main__":
'''
Create 3 csv files, one for every region.
'''
# Lombardia: 8
# Lazio: 6
# Sicilia: 16
d1_sicilia, d2_sicilia = create_dataframe_region(16)
d_sicilia = d2_sicilia
d1_lombardia, d2_lombardia = create_dataframe_region(8)
d_lombardia = d2_lombardia
d1_lazio, d2_lazio = create_dataframe_region(6)
d_lazio = d2_lazio
for feature in range(d_lombardia.shape[1]):
with open("lombardia_csv" + os.sep + "lombardia_"+d_lombardia.columns.values[feature] + ".csv", 'w') as f:
for day in range(d_lombardia.shape[0]):
f.write(str(d_lombardia.iloc[day, feature]) + '\n')
with open("lazio_csv" + os.sep + "lazio_"+d_lazio.columns.values[feature] + ".csv", 'w') as f:
for day in range(d_lazio.shape[0]):
f.write(str(d_lazio.iloc[day, feature]) + '\n')
with open("sicilia_csv" + os.sep + "sicilia_"+d_sicilia.columns.values[feature] + ".csv", 'w') as f:
for day in range(d_sicilia.shape[0]):
f.write(str(d_sicilia.iloc[day, feature]) + '\n')
with open("lombardia_csv" + os.sep + "lombardia_all.csv", "w") as f:
for day in range(d_lombardia.shape[0]):
f.write(str(d_lombardia.iloc[day, 0]) + "," + str(d_lombardia.iloc[day, 1]) + "," + str(d_lombardia.iloc[day, 2]) + "," + str(d_lombardia.iloc[day, 3]) + '\n')