From 26902dc234c114382c2926923820c3537490c30e Mon Sep 17 00:00:00 2001 From: "deepsource-autofix[bot]" <62050782+deepsource-autofix[bot]@users.noreply.github.com> Date: Tue, 27 Aug 2024 16:01:11 +0000 Subject: style: format code with Autopep8, Black and Ruff Formatter This commit fixes the style issues introduced in 1a145a3 according to the output from Autopep8, Black and Ruff Formatter. Details: https://github.com/ajschofield/de-project-bentley/pull/101 --- src/dataframes.py | 236 ++++++++++++++++++++++++++++++++---------------- src/transform_lambda.py | 5 +- 2 files changed, 161 insertions(+), 80 deletions(-) (limited to 'src') diff --git a/src/dataframes.py b/src/dataframes.py index 9d0f2ac..f122368 100644 --- a/src/dataframes.py +++ b/src/dataframes.py @@ -2,7 +2,7 @@ import pandas as pd from bs4 import BeautifulSoup import requests -#Table names: +# Table names: # fact_sales_order # fact_purchase_orders # fact_payment @@ -16,133 +16,213 @@ import requests # dim_counterparty -#no test, same as fact_payment +# no test, same as fact_payment def create_fact_sales_order(dict_of_df): df_sales = dict_of_df["sales_order"] df_sales.index.name = "sales_record_id" - df_sales["created_date"] = df_sales["created_at"].astype('datetime64[ns]').dt.date - df_sales["created_time"] = df_sales["created_at"].astype('datetime64[ns]').dt.floor('s').dt.time - df_sales["last_updated_date"] = df_sales["last_updated"].astype('datetime64[ns]').dt.date - df_sales["last_updated_time"] = df_sales["last_updated"].astype('datetime64[ns]').dt.floor('s').dt.time - df_sales['agreed_delivery_date'] = pd.to_datetime(df_sales['agreed_delivery_date'],format="%Y-%m-%d") - df_sales['agreed_payment_date'] = pd.to_datetime(df_sales['agreed_payment_date'],format="%Y-%m-%d") - df_sales = df_sales.drop(labels=['created_at','last_updated'],axis=1) + df_sales["created_date"] = df_sales["created_at"].astype("datetime64[ns]").dt.date + df_sales["created_time"] = ( + df_sales["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time + ) + df_sales["last_updated_date"] = ( + df_sales["last_updated"].astype("datetime64[ns]").dt.date + ) + df_sales["last_updated_time"] = ( + df_sales["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time + ) + df_sales["agreed_delivery_date"] = pd.to_datetime( + df_sales["agreed_delivery_date"], format="%Y-%m-%d" + ) + df_sales["agreed_payment_date"] = pd.to_datetime( + df_sales["agreed_payment_date"], format="%Y-%m-%d" + ) + df_sales = df_sales.drop(labels=["created_at", "last_updated"], axis=1) df_sales.reset_index(inplace=True) return df_sales -#no test, same as fact_payment + +# no test, same as fact_payment + + def create_fact_purchase_orders(dict_of_df): - df_po = dict_of_df['purchase_order'] - df_po.index.name = 'purchase_record_id' - df_po['created_date'] = df_po['created_at'].astype('datetime64[ns]').dt.date - df_po['created_time'] = df_po['created_at'].astype('datetime64[ns]').dt.floor('s').dt.time - df_po['last_updated_date'] = df_po['last_updated'].astype('datetime64[ns]').dt.date - df_po['last_updated_time'] = df_po['last_updated'].astype('datetime64[ns]').dt.floor('s').dt.time - df_po['agreed_delivery_date'] = pd.to_datetime(df_po['agreed_delivery_date'],format="%Y-%m-%d") - df_po['agreed_payment_date'] = pd.to_datetime(df_po['agreed_payment_date'],format="%Y-%m-%d") - df_po = df_po.drop(labels=['created_at','last_updated'],axis=1) + df_po = dict_of_df["purchase_order"] + df_po.index.name = "purchase_record_id" + df_po["created_date"] = df_po["created_at"].astype("datetime64[ns]").dt.date + df_po["created_time"] = ( + df_po["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time + ) + df_po["last_updated_date"] = df_po["last_updated"].astype("datetime64[ns]").dt.date + df_po["last_updated_time"] = ( + df_po["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time + ) + df_po["agreed_delivery_date"] = pd.to_datetime( + df_po["agreed_delivery_date"], format="%Y-%m-%d" + ) + df_po["agreed_payment_date"] = pd.to_datetime( + df_po["agreed_payment_date"], format="%Y-%m-%d" + ) + df_po = df_po.drop(labels=["created_at", "last_updated"], axis=1) df_po.reset_index(inplace=True) return df_po -#test passed + +# test passed + + def create_fact_payment(dict_of_df): df_payment = dict_of_df["payment"] df_payment.index.name = "payment_record_id" - df_payment["created_date"] = df_payment["created_at"].astype('datetime64[ns]').dt.date - df_payment["created_time"] = df_payment["created_at"].astype('datetime64[ns]').dt.floor('s').dt.time - df_payment["last_updated_date"] = df_payment["last_updated"].astype('datetime64[ns]').dt.date - df_payment["last_updated_time"] = df_payment["last_updated"].astype('datetime64[ns]').dt.floor('s').dt.time - df_payment['payment_date'] = pd.to_datetime(df_payment['payment_date'],format="%Y-%m-%d") - df_payment = df_payment.drop(labels=['created_at','last_updated'],axis=1) + df_payment["created_date"] = ( + df_payment["created_at"].astype("datetime64[ns]").dt.date + ) + df_payment["created_time"] = ( + df_payment["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time + ) + df_payment["last_updated_date"] = ( + df_payment["last_updated"].astype("datetime64[ns]").dt.date + ) + df_payment["last_updated_time"] = ( + df_payment["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time + ) + df_payment["payment_date"] = pd.to_datetime( + df_payment["payment_date"], format="%Y-%m-%d" + ) + df_payment = df_payment.drop(labels=["created_at", "last_updated"], axis=1) df_payment.reset_index(inplace=True) return df_payment -#test passed + +# test passed + + def create_dim_transaction(dict_of_df): - df_transaction = dict_of_df["transaction"].drop(labels=['created_at', 'last_updated'], axis=1) + df_transaction = dict_of_df["transaction"].drop( + labels=["created_at", "last_updated"], axis=1 + ) return df_transaction -#test passed + +# test passed + + def create_dim_location(dict_of_df): - df_loc = dict_of_df['address'].drop(labels=['created_at', 'last_updated'], axis=1).rename(columns={'address_id': 'location_id'}) + df_loc = ( + dict_of_df["address"] + .drop(labels=["created_at", "last_updated"], axis=1) + .rename(columns={"address_id": "location_id"}) + ) return df_loc def create_dim_counterparty(dict_of_df): - df_prefixed_address = dict_of_df['address'].add_prefix('counterparty_legal_', axis=1) - df_cp = pd.merge(dict_of_df['counterparty'], - df_prefixed_address, - left_on="legal_address_id", - right_on="counterparty_legal_address_id", - how="outer") - df_cp.drop(columns=["legal_address_id","counterparty_legal_address_id"],inplace=True) + df_prefixed_address = dict_of_df["address"].add_prefix( + "counterparty_legal_", axis=1 + ) + df_cp = pd.merge( + dict_of_df["counterparty"], + df_prefixed_address, + left_on="legal_address_id", + right_on="counterparty_legal_address_id", + how="outer", + ) + df_cp.drop( + columns=["legal_address_id", "counterparty_legal_address_id"], inplace=True + ) return df_cp -#test passed + +# test passed + + def create_dim_date(dict_of_df): - fact_dfs = [create_fact_payment(dict_of_df), create_fact_purchase_orders(dict_of_df), create_fact_sales_order(dict_of_df)] + fact_dfs = [ + create_fact_payment(dict_of_df), + create_fact_purchase_orders(dict_of_df), + create_fact_sales_order(dict_of_df), + ] list_of_date_columns = [] for df in fact_dfs: - date_col_names = [col_name for col_name in list(df.columns) if '_date' in col_name] + date_col_names = [ + col_name for col_name in list(df.columns) if "_date" in col_name + ] for col in date_col_names: list_of_date_columns.append(df[col]) - sr_date = pd.array(pd.concat(list_of_date_columns),dtype='datetime64[ns]') - df_date = pd.DataFrame(data=sr_date,columns=['date_id']) + sr_date = pd.array(pd.concat(list_of_date_columns), dtype="datetime64[ns]") + df_date = pd.DataFrame(data=sr_date, columns=["date_id"]) df_date.drop_duplicates(inplace=True) - df_date['year'] = df_date['date_id'].dt.year - df_date['month'] = df_date['date_id'].dt.month - df_date['day'] = df_date['date_id'].dt.day - df_date['day_of_week'] = df_date['date_id'].dt.dayofweek - df_date['day_name'] = df_date['date_id'].dt.day_name() - df_date['month_name'] = df_date['date_id'].dt.month_name() - df_date['quarter'] = df_date['date_id'].dt.quarter + df_date["year"] = df_date["date_id"].dt.year + df_date["month"] = df_date["date_id"].dt.month + df_date["day"] = df_date["date_id"].dt.day + df_date["day_of_week"] = df_date["date_id"].dt.dayofweek + df_date["day_name"] = df_date["date_id"].dt.day_name() + df_date["month_name"] = df_date["date_id"].dt.month_name() + df_date["quarter"] = df_date["date_id"].dt.quarter return df_date -#tests passed -def scrape_currency_names(): - response = requests.get('https://www.xe.com/currency/').content - soup = BeautifulSoup(response,'html.parser') - currency = [item.text for item in soup.findAll('a', attrs={'class' : "sc-299dec64-6 fZPTSw"})] - sr = pd.Series(currency) - df_cur = sr.str.split(pat=" - ",expand=True).rename({0:'currency_code',1:'currency_name'},axis=1) - return df_cur - -#tests passed -def create_dim_currency(dict_of_df,names=scrape_currency_names()): - df_cur = dict_of_df['currency'].drop(labels=['created_at', 'last_updated'], axis=1) - dim_cur = pd.merge(df_cur,names,left_on='currency_code',right_on='currency_code',how='inner') - return dim_cur -#tests passed -def create_dim_payment_type(dict_of_df): - df_payment_type = dict_of_df["payment_type"] - dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]] - return dim_payment_type +# tests passed -#tests passed -def create_dim_design(dict_of_df): - df_design = dict_of_df["design"] - dim_design = df_design.loc[:, ["design_id", "design_name", "file_name", "file_location"]] - return dim_design -#tests passed -def create_dim_staff(dict_of_df): - staff_department = pd.merge(dict_of_df["staff"], dict_of_df["department"], on='department_id', how="left") - dim_staff = staff_department.loc[:, ['staff_id', 'first_name', 'last_name', 'department_name', 'location', 'email_address']] - return dim_staff +def scrape_currency_names(): + response = requests.get("https://www.xe.com/currency/").content + soup = BeautifulSoup(response, "html.parser") + currency = [ + item.text for item in soup.findAll("a", attrs={"class": "sc-299dec64-6 fZPTSw"}) + ] + sr = pd.Series(currency) + df_cur = sr.str.split(pat=" - ", expand=True).rename( + {0: "currency_code", 1: "currency_name"}, axis=1 + ) + return df_cur +# tests passed +def create_dim_currency(dict_of_df, names=scrape_currency_names()): + df_cur = dict_of_df["currency"].drop(labels=["created_at", "last_updated"], axis=1) + dim_cur = pd.merge( + df_cur, names, left_on="currency_code", right_on="currency_code", how="inner" + ) + return dim_cur +# tests passed +def create_dim_payment_type(dict_of_df): + df_payment_type = dict_of_df["payment_type"] + dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]] + return dim_payment_type +# tests passed +def create_dim_design(dict_of_df): + df_design = dict_of_df["design"] + dim_design = df_design.loc[ + :, ["design_id", "design_name", "file_name", "file_location"] + ] + return dim_design +# tests passed +def create_dim_staff(dict_of_df): + staff_department = pd.merge( + dict_of_df["staff"], dict_of_df["department"], on="department_id", how="left" + ) + dim_staff = staff_department.loc[ + :, + [ + "staff_id", + "first_name", + "last_name", + "department_name", + "location", + "email_address", + ], + ] + return dim_staff diff --git a/src/transform_lambda.py b/src/transform_lambda.py index ccf90e5..93b2284 100644 --- a/src/transform_lambda.py +++ b/src/transform_lambda.py @@ -117,7 +117,8 @@ def process_to_parquet_and_upload_to_s3( parquet_file = df.to_parquet( f"{table_name}.parquet", engine="pyarrow" ) # or fastparquet - client.upload_file(f"{table_name}.parquet", bucket, f"{table_name}.parquet") #changed parquet_file variable to the file name + # changed parquet_file variable to the file name + client.upload_file(f"{table_name}.parquet", bucket, f"{table_name}.parquet") status["uploaded"].append(table_name) for table_name, df in mutable_df_dict.items(): @@ -203,7 +204,7 @@ def list_existing_s3_files(bucket_name, client=boto3.client("s3")): existing_files = [obj["Key"] for obj in response["Contents"]] else: logger.error("The bucket is empty") - return [] #changed from None to [] so it is an iterable + return [] # changed from None to [] so it is an iterable except ClientError as e: logger.error(f"Error listing S3 objects: {e}") -- cgit v1.2.3