'Cases_Per_Million',
'Deaths_Per_Million')
)
csvwriter.writeheader()
for code in sorted(d_states_latest.keys()):
d = d_states_latest[code]
d['Code'] = code
if code == 'DE-total': # DE as last row
d_de = dict(d)
continue
csvwriter.writerow(
d
)
del d, code
# add # to uncomment the DE total sum last line
d_de['State'] = '# Deutschland'
csvwriter.writerow(d_de)
del d_de
d_ref_states = helper.read_ref_data_de_states()
download_new_data()
d_states_data = read_csv_to_dict()
export_data(d_states_data)
export_latest_data(d_ref_states, d_states_data)
# 1
])
csvwriter.writeheader()
for d in l_time_series:
d2 = d
gesamt = d2['Int Betten gesamt']
belegt = d2['Int Betten belegt']
if 'Int COVID-19 Patienten' in d2 and d2[
'Int COVID-19 Patienten'] != None:
covid = d2['Int COVID-19 Patienten']
d2['Prozent Int COVID-19 Patienten'] = round(
100 * covid / gesamt, 1)
else:
d2['Int COVID-19 Patienten'] = None
d2['Prozent Int COVID-19 Patienten'] = None
d2['Prozent Int Betten belegt'] = round(
100 * belegt / gesamt, 1)
csvwriter.writerow(d2)
d_states_ref = helper.read_ref_data_de_states()
d_states_ref_map_name_code = {}
for code, d in d_states_ref.items():
d_states_ref_map_name_code[d['State']] = code
fetch_betten()
fetch_covid()
calc_de_sum()
export_time_series()
export_data()