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covid.py
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covid.py
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import functools
from universal import *
# STATIC VARIABLES
BING: str = "bing"
COLUMNS_TO_DROP: str = "columns_to_drop"
COVID: str = "covid"
DATE_COLUMN: str = "date_column"
DTYPE: str = "dtype"
FILE_PATH_OR_FILTER_NAME_CONDITIONS: str = "file_path_or_filter_name_conditions"
FOLDER_PATH: str = "folder_path"
GOOGLE: str = "google"
MAPPING: str = "mapping"
# PARAMS
PARAM_BING_DATA_FILEPATH: str = "bing_data_file_path"
PARAM_COLUMNS_TO_DROP: str = "columns_to_drop"
PARAM_DATA: str = "data"
PARAM_DTYPES: str = "dtypes"
PARAM_FOLDER_COVID_RAW: str = "folder_covid_raw"
PARAM_FOLDER_COVID_AGGREGATE: str = "folder_covid_aggregate"
PARAM_GOOGLE_DATA_FILENAME_FILTER_CONDITIONS: str = "google_data_filename_filter_conditions"
PARAM_GOOGLE_DTYPES: str = "google_dtypes"
PARAM_INNER_OR_OUTER_JOIN: str = "inner_or_outer_join"
# DEFAULTS
RE_camel_to_snake_case_pattern: Pattern = re.compile(r"(?<!^)(?=[A-Z])")
def main(
called_from_main: bool = False,
) -> None:
set_error_file_origin(COVID)
set_error_folder(FOLDER_ERROR)
if called_from_main:
with open(f"{COVID}{HYPHEN}{PARAMETERS}{JSON}") as json_file:
json_data = json.load(json_file)
data_schema: dict = json_data[PARAM_DATA]
folder_covid_raw: str = json_data[PARAM_FOLDER_COVID_RAW]
folder_covid_stitch: str = json_data[PARAM_FOLDER_COVID_AGGREGATE]
inner_or_outer_join: str = json_data[PARAM_INNER_OR_OUTER_JOIN]
json_file.close()
else:
pass
set_error_task_origin(AGGREGATE)
list_parsed_dfs: List[pd.DataFrame] = parse_data(
data_schema=data_schema,
folder=folder_covid_raw,
)
df_merged: pd.DataFrame = functools.reduce(
lambda df_1, df_2: pd.merge(
left=df_1,
right=df_2,
how=inner_or_outer_join,
),
list_parsed_dfs,
)
generate_sub_paths_for_folder(
folder=folder_covid_stitch,
)
nt_filename_aggregate = NT_filename_aggregate(
aggregate=AGGREGATE,
filename_label=COVID,
)
filename_covid_stitch: str = generate_filename(
nt_filename=nt_filename_aggregate,
extension=CSV,
)
df_merged.to_csv(
f"{folder_covid_stitch}{filename_covid_stitch}",
index=False,
)
write_errors_to_disk()
def parse_data(
data_schema: dict,
folder: str,
) -> List[pd.DataFrame]:
list_df: List[pd.DataFrame] = []
source: str
data_scheme: dict
for source, data_scheme in data_schema.items():
filepath: Any = data_scheme.get(FILE_PATH_OR_FILTER_NAME_CONDITIONS, "")
if isinstance(filepath, str):
pass
elif isinstance(filepath, list):
filepath = import_single_file(
folder=folder,
list_filename_filter_conditions=tuple(filepath),
)
else:
log_error(
error=(
f"{source}{HYPHEN}"
f"{FILE_PATH_OR_FILTER_NAME_CONDITIONS}{HYPHEN}"
f"neither_a_list_or_string"
)
)
continue
df: pd.DataFrame
date_column: str = data_scheme.get(DATE_COLUMN, "")
if not date_column:
log_error(error=f"{source}{HYPHEN}{DATE_COLUMN}{HYPHEN}{MISSING}")
continue
dtype: dict = data_scheme.get(DTYPE, {})
if dtype:
df = pd.read_csv(
f"{folder}{filepath}",
dtype=dtype,
parse_dates=[date_column],
infer_datetime_format=True,
)
else:
df = pd.read_csv(
f"{folder}{filepath}",
parse_dates=[date_column],
infer_datetime_format=True,
)
columns_to_drop: List[str] = data_scheme.get(COLUMNS_TO_DROP, [])
if columns_to_drop:
df.drop(
columns=filter(None, columns_to_drop),
inplace=True,
)
mapping: dict = data_scheme.get(MAPPING, {})
if mapping:
df.rename(
columns=mapping,
inplace=True,
)
columns_to_snake_case_mapping: dict = {
column_name: RE_camel_to_snake_case_pattern.sub(UNDERSCORE, column_name).lower()
for column_name in df.columns
}
df.rename(
columns=columns_to_snake_case_mapping,
inplace=True,
)
list_df.append(df)
return list_df
main(
*set_up_main(
name=__name__,
possible_number_of_input_arguments=1,
),
)