def japan_hospitalizations(params):
data_df = load_utils.default_read_function(params)
data_df['deaths_cumulative'] = data_df['deaths_cumulative'].replace(
to_replace='-', value=0).astype('int32')
data_df = region_utils.join_region_codes(data_df, params)
data_df = region_utils.aggregate_and_append(data_df, params)
return data_df
def default_load_function(params):
df = load_utils.default_read_function(params)
df = region_utils.join_region_codes(df, params)
load_params = params['load']
if 'regions' in load_params:
if 'aggregate_by' in load_params['regions']:
df = region_utils.aggregate_and_append(df, params)
df = load_utils.compute_cumulative_from_new(df, params)
return df
def scotland_hospitalizations(data_path, params):
df = pd.read_excel(data_path, 'Table 2 - Hospital Care', skiprows=4)
df = df.rename(columns={
df.columns[1]: 'icu_current',
df.columns[4]: 'hospitalized_current',
})
df = date_utils.parse_date(df, params)
df = region_utils.join_region_codes(df, params)
return df
def scotland_hospitalizations(params):
data_path = path_utils.most_recent_data(params)['path']
df = pd.read_excel(data_path, 'Table 2 - Hospital Care', skiprows=4)
df = df.rename(columns={
df.columns[1]: "icu_current",
df.columns[4]: "hospitalized_current",
})
df = date_utils.parse_date(df, params)
df = region_utils.join_region_codes(df, params)
return df
def default_load_function(data_path, params):
df = load_utils.default_read_function(data_path, params)
df = region_utils.join_region_codes(df, params)
duplicates = df[df[['region_code', 'date']].duplicated(keep=False)]
if duplicates.shape[0] != 0:
logging.warning(
'Dropping the following duplicate data for %s data source:\n%s',
params['config_key'], duplicates[['region_code', 'date']])
df = df.drop_duplicates(subset=['region_code', 'date'], keep='first')
load_params = params['load']
if 'regions' in load_params:
if 'aggregate_by' in load_params['regions']:
df = region_utils.aggregate_and_append(df, params)
df = load_utils.compute_cumulative_from_new(df, params)
return df