def write_data_jh_ts_filtered():
df = get_data()
countries = ['Germany', 'Italy', 'Spain', 'China', 'US']
df = df[df['Country/Region'].isin(countries)]
df = df.drop(columns=["Lat", "Long"])
filename = 'time_series_covid19_confirmed_filtered_countries.csv'
upload_dataframe(df, filename)
return df
def write_data_nrw():
filename1 = 'corona_mags_nrw_gesamt.csv'
df1 = clear_data_nrw_gesamt()
upload_dataframe(df1, filename1)
filename = 'corona_mags_nrw.csv'
df = clear_data()
# Remove 'Gesamt' from DF
df = df[df['Landkreis/ kreisfreie Stadt'] != 'Gesamt']
upload_dataframe(
df, filename, change_notifcation=f'Mags-Daten aktualisiert')
for studio, areas in studios.items():
df_studio = df[df['Landkreis/ kreisfreie Stadt'].isin(areas)]
filename = f'corona_mags_nrw_{studio}.csv'
upload_dataframe(df_studio, filename)
def write_data_rki_ndr_districts_nrw():
# Make compare function
compare = make_df_compare_fn(ignore_columns=['Stand'])
filename_gesamt = 'rki_ndr_districts_nrw_gesamt.csv'
df_gesamt = clear_data_nrw_gesamt()
upload_dataframe(df_gesamt, filename_gesamt, compare=compare)
filename = 'rki_ndr_districts_nrw.csv'
df = clear_data()
# Remove 'Gesamt' from DF
df = df[df['Landkreis/ kreisfreie Stadt'] != 'Gesamt']
upload_dataframe(df,
filename,
change_notification=f'RKI-Daten für NRW aktualisiert',
compare=compare)
for studio, areas in studios.items():
df_studio = df[df['Landkreis/ kreisfreie Stadt'].isin(areas)]
filename_studio = f'rki_ndr_districts_nrw_{studio}.csv'
upload_dataframe(df_studio, filename_studio, compare=compare)
def write_data_jh_global():
df = get_data()
filename = 'covid19_confirmed_global.csv'
upload_dataframe(df, filename)
def write_data_rki_ndr_districts():
df = clear_data()
filename = 'rki_ndr_districts.csv'
upload_dataframe(df, filename)
def write_data_rki():
filename = 'corona_rki.csv'
df = get_data(url)
print(df)
upload_dataframe(df, filename)
def write_data_divi():
# Prep: Get new data
df = clear_data()
# Prep: Create filenames for all dfs
fn_ger_map = 'intensivregister_karte_de.csv'
fn_nrw_map = 'intensivregister_karte_nrw.csv'
fn_ger_all = 'intensivregister_alle_heute.csv'
fn_ger_all_archive = 'intensivregister_alle_archiv.csv'
fn_ger_covid_archive = 'intensivregister_covid_archiv.csv'
# Check if there is new data in divi csv
# Use all unique values in "daten_stand"-column in case there are different dates
dates_list = [
dt.datetime.strptime(date, '%Y-%m-%d %H:%M:%S').date()
for date in df.daten_stand.unique()
]
# use only latest date from all dates in divi file
datenstand_divi = max(dates_list)
# Get yesterdays date and compare
today = dt.datetime.now(pytz.timezone('Europe/Berlin')).date()
yesterday = today - dt.timedelta(days=1)
# Update only if there is new data
if (datenstand_divi > yesterday):
# Germany: latlong + places + all numbers for current day
df_ger_map = df
# NRW: latlong + places + all numbers for current day
df_nrw_map = df[df['bundesland'] == 'Nordrhein-Westfalen']
# Germany: summarize values for current day
# Aggregate data to get totals for Germany
df_ger_all_base = df.agg({
'betten_belegt': 'sum',
'betten_frei': 'sum',
'betten_gesamt': 'sum',
'faelle_covid_aktuell': 'sum',
'faelle_covid_aktuell_invasiv_beatmet': 'sum'
})
# Create dict as base for df in desired format
ger_all_data = {
'Intensivbetten': [
'Freie Betten', 'Patienten (nicht COVID-19)',
'COVID-19 Patienten (nicht beatmet)',
'COVID-19 Patienten (beatmet)'
],
'Anzahl': [
df_ger_all_base['betten_frei'],
df_ger_all_base['betten_belegt'] -
df_ger_all_base['faelle_covid_aktuell'],
df_ger_all_base['faelle_covid_aktuell'] -
df_ger_all_base['faelle_covid_aktuell_invasiv_beatmet'],
df_ger_all_base['faelle_covid_aktuell_invasiv_beatmet']
]
}
# Create df from dict
df_ger_all = pd.DataFrame(data=ger_all_data)
# Store same data plus current date as dict to append to archive
ger_all_today = {
'X.1':
today,
'Freie Betten':
df_ger_all_base['betten_frei'],
'Patienten (nicht COVID-19)':
df_ger_all_base['betten_belegt'] -
df_ger_all_base['faelle_covid_aktuell'],
'COVID-19 Patienten (nicht beatmet)':
df_ger_all_base['faelle_covid_aktuell'] -
df_ger_all_base['faelle_covid_aktuell_invasiv_beatmet'],
'COVID-19 Patienten (beatmet)':
df_ger_all_base['faelle_covid_aktuell_invasiv_beatmet']
}
# Subset df with only Covid cases
sum_ger_covid_today = {
'X.1':
today,
'COVID-19 Patienten (nicht beatmet)':
df_ger_all_base['faelle_covid_aktuell'] -
df_ger_all_base['faelle_covid_aktuell_invasiv_beatmet'],
'COVID-19 Patienten (beatmet)':
df_ger_all_base['faelle_covid_aktuell_invasiv_beatmet']
}
# Fetch archived data and add current row
# Filepath for archive with all columns
fp_all_archive = str(yesterday) + "/" + fn_ger_all_archive
df_ger_all_archive = pd.read_csv(download_file(fp_all_archive))
# Local testing:
# df_ger_all_archive = pd.read_csv('data/divi_archive_all.csv')
# Append today's data to archive
df_ger_all_archive = df_ger_all_archive.append(ger_all_today,
ignore_index=True)
# Filepath for COVID columns only
fp_covid_archive = str(yesterday) + "/" + fn_ger_covid_archive
df_ger_covid_archive = pd.read_csv(download_file(fp_covid_archive))
# Local testing:
# df_ger_covid_archive = pd.read_csv('data/divi_archive_covid.csv')
# Append today's data to archive
df_ger_covid_archive = df_ger_covid_archive.append(sum_ger_covid_today,
ignore_index=True)
# upload all dfs
upload_dataframe(df_ger_map, fn_ger_map)
upload_dataframe(df_nrw_map, fn_nrw_map)
upload_dataframe(df_ger_all, fn_ger_all)
upload_dataframe(df_ger_all_archive, fn_ger_all_archive)
upload_dataframe(df_ger_covid_archive, fn_ger_covid_archive)
def write_data_example():
df = clear_data()
filename = 'example.csv'
upload_dataframe(df, filename)
def write_data_jh_ts_global():
df = get_data()
filename = 'time_series_covid19_confirmed_global.csv'
upload_dataframe(df, filename)
return df
def write_data_rki_ndr_districts():
df = clear_data()
filename = 'rki_ndr_districts.csv'
compare = make_df_compare_fn(ignore_columns=['Stand'])
upload_dataframe(df, filename, compare=compare)
