def export_df (list_df, list_prop): #list_df = [['name', df], ..., ], list_prop = [processed, raw]
data_dir = file_fct.get_parent_dir(2, 'data')
list_final_df = []
for df_prop, a_prop in zip(list_df, list_prop):
df_name, df = df_prop
source_df = read_db_list (a_prop)
file_path = file_fct.creation_folder(f'{data_dir}/{a_prop}', [f'{source_df.loc[df_name, "sub_dir"]}'])
export_path = f'{file_path[0]}/{source_df.loc[df_name, "file_name"]}'
export_format = source_df.loc[df_name, "file_name"].split('.')[-1]
if source_df.loc[df_name, 'type'] == 'Pandas':
if export_format == 'csv':
df.to_csv(export_path, index=True)
elif export_format == 'json':
df.to_json(export_path, orient = "table", indent=4)
else:
print(f"File {source_df.loc[df_name, 'file_name']} couldn't be saved, please change extension")
elif source_df.loc[df_name, 'type'] == 'GeoPandas':
if export_format == 'shp':
df.to_file(export_path, index=True)
elif export_format == 'geojson':
df.to_file(export_path, orient = "table", indent=4, driver="GeoJSON")
elif export_format == 'feather':
df.to_feather(export_path)
else:
print(f"File {source_df.loc[df_name, 'file_name']} couldn't be saved, please change extension")
return list_final_df
def import_df (list_df, list_prop):
data_dir = file_fct.get_parent_dir(2, 'data')
list_final_df = []
for df_name, a_prop in zip(list_df, list_prop):
source_df = read_db_list (a_prop)
import_path = os.path.normcase(f'{data_dir}/{a_prop}/{source_df.loc[df_name, "sub_dir"]}/{source_df.loc[df_name, "file_name"]}')
export_format = source_df.loc[df_name, "file_name"].split('.')[-1]
if source_df.loc[df_name, 'type'] == 'Pandas':
if export_format == 'csv':
importing_df = pandas.read_csv(import_path,
sep=source_df.loc[df_name, 'sep'],
encoding=source_df.loc[df_name, 'encoding'])
elif export_format == 'json':
importing_df = pandas.read_json(import_path, orient = "table")
elif source_df.loc[df_name, 'type'] == 'GeoPandas':
if export_format == 'csv' or export_format == 'shp':
importing_df = gpd.read_file(import_path)
elif export_format == 'json' or export_format == 'geojson':
importing_df = gpd.read_json(import_path, orient = "table")
elif export_format == 'feather':
importing_df = gpd.read_feather(import_path)
list_final_df.append(importing_df)
#print(df_name)
#print(importing_df)
return list_final_df
def main(update_limit):
data_dir = file_fct.get_parent_dir(2, 'data') # .../COVID19/data
db_list = df_fct.read_db_list('raw')
last_update = last_update_db(data_dir, db_list)
import_static(data_dir, db_list)
import_daily(data_dir, db_list, last_update, update_limit)
def read_db_list (type):
data_dir = file_fct.get_parent_dir(2, 'data')
if type =='raw':
db_list_path = os.path.normcase(f'{data_dir}/list_raw_data.json')
elif type == 'processed':
db_list_path = os.path.normcase(f'{data_dir}/list_processed_data.json')
db_list = pandas.read_json(db_list_path, orient = "table")
return db_list
def read_db_files ():
data_dir = file_fct.get_parent_dir(2, 'data')
db_file = pandas.read_json(f'{data_dir}/list_files.json', orient = "table")
list_dir, list_files = file_fct.list_dir_files(f'{data_dir}')
if 'list_files_date.json' in list_files:
db_file_date = pandas.read_json(f'{data_dir}/list_files_date.json', orient = "table")
else:
db_file_date = pandas.DataFrame(index=db_file.index, columns=['date'])
db_file_date.loc[:,'date'] = pandas.to_datetime(db_file_date.loc[:,'date'])
return db_file, db_file_date
def import_daily(data_dir, db_list, last_update_db, limit):
raw_data_dir = os.path.normcase(f'{data_dir}/raw')
df_daily = db_list[db_list.loc[:, 'update'] == True]
for a_df_name in df_daily.index:
if a_df_name not in last_update_db.index:
print(
f"Creating and downloading {df_daily.loc[a_df_name, 'file_name']}...",
end='')
df = import_and_save(a_df_name, raw_data_dir, df_daily)
delta_spaces = " " * (len(
f"Creating and downloading {df_daily.loc[a_df_name, 'file_name']}..."
) - len(f"\r{df_daily.loc[a_df_name, 'file_name']} was downloaded")
)
print(
f"\r{df_daily.loc[a_df_name, 'file_name']} was downloaded {delta_spaces}"
)
last_update = get_dates(df, a_df_name, db_list)
last_update_db.loc[a_df_name, 'date'] = last_update
elif last_update_db.loc[a_df_name, 'delta_day'] > limit:
print(f"Downloading {df_daily.loc[a_df_name, 'file_name']}...",
end='')
df = import_and_save(a_df_name, raw_data_dir, df_daily)
delta_spaces = " " * (
len(f"Downloading {df_daily.loc[a_df_name, 'file_name']}...") -
len(f"\r{df_daily.loc[a_df_name, 'file_name']} was downloaded")
)
print(
f"\r{df_daily.loc[a_df_name, 'file_name']} was downloaded {delta_spaces}"
)
last_update = get_dates(df, a_df_name, db_list)
last_update_db.loc[a_df_name, 'date'] = last_update
data_dir = file_fct.get_parent_dir(2, 'data')
last_update_db['delta_day'] = last_update_db.apply(
lambda x: (pandas.to_datetime('today') - x["date"]).days, axis=1)
print(last_update_db)
last_update_db.loc[:, 'date'] = last_update_db.apply(
lambda x: x["date"].strftime("%Y-%m-%d"), axis=1)
last_update_db.to_json(f'{data_dir}/last_update.json',
orient="table",
indent=4)
print('\n\n')
def path_to_file(self):
self.return_path()
root = os.path.normcase(file_fct.get_parent_dir(
2, 'src/data_transfer'))
#print(root)
if self.type_transfer == 'daily':
file_path = os.path.normcase(f'{root}/export_file.txt')
export_file = open(file_path, 'w')
elif self.type_transfer == 'article':
today = date.today().strftime("%Y-%m-%d")
file_path = os.path.normcase(f'{root}/export_file_{today}.txt')
export_file = open(file_path, 'w')
for a_name in self.list_names:
if a_name[-1] != '/':
export_file.write(a_name + '\n')
export_file.close()
def save_db_files (db_file, db_file_date):
data_dir = file_fct.get_parent_dir(2, 'data')
db_file.to_json(f'{data_dir}/list_files.json', orient = "table", indent=4)
db_file_date.to_json(f'{data_dir}/list_files_date.json', orient = "table", indent=4)
