style: format code with Autopep8, Black and Ruff Formatter

This commit fixes the style issues introduced in 8e20c5c according to the output
from Autopep8, Black and Ruff Formatter.

Details: https://github.com/ajschofield/de-project-bentley/pull/93

2 files changed, 236 insertions, 157 deletions

diff --git a/src/dataframes.py b/src/dataframes.py
index 9ce3be0..684f102 100644
--- a/src/dataframes.py
+++ b/src/dataframes.py
@@ -8,7 +8,7 @@ import re
 from datetime import datetime as dt
 import requests
 
-#Table names:
+# Table names:
 # fact_sales_order
 # fact_purchase_orders
 # fact_payment
@@ -21,9 +21,11 @@ import requests
 # dim_currency
 # dim_counterparty
 
+
 def create_dim_transaction(dict_of_df):
     pass
 
+
 def create_fact_sales_order(dict_of_df):
     df_sales = dict_of_df["sales_order"]
     df_sales.index.name = "sales_record_id"
@@ -33,36 +35,46 @@ def create_fact_sales_order(dict_of_df):
     df_sales["last_updated_time"] = pd.to_datetime(df_sales["last_updated"]).dt.time
     pd.merge(dict_of_df["staff"], df_sales["sales_staff_id"], on="staff_id", how="left")
     # df_sales.rename(columns={"staff_id": "sales_staff_id"})
-    fact_sales_order = df_sales.loc[:,[
-        "sales_record_id",
-        "sales_order_id",
-        "created_date",
-        "created_time",
-        "last_updated_date",
-        "last_updated_time",
-        "sales_staff_id",
-        "counterparty_id",
-        "units_sold",
-        "unit_price",
-        "currency_id",
-        "design_id",
-        "agreed_payment_date",
-        "agreed_delivery_date",
-        "agreed_delivery_location_id"
-    ]]
+    fact_sales_order = df_sales.loc[
+        :,
+        [
+            "sales_record_id",
+            "sales_order_id",
+            "created_date",
+            "created_time",
+            "last_updated_date",
+            "last_updated_time",
+            "sales_staff_id",
+            "counterparty_id",
+            "units_sold",
+            "unit_price",
+            "currency_id",
+            "design_id",
+            "agreed_payment_date",
+            "agreed_delivery_date",
+            "agreed_delivery_location_id",
+        ],
+    ]
     return fact_sales_order
 
-## fact_purchase_order from purchase_order
+
+# fact_purchase_order from purchase_order
+
+
 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'].date()
-    df_po['created_time'] = df_po['created_at'].dt.time
-    df_po['last_updated_date'] = df_po['last_updated_at'].date()
-    df_po['last_updated_time'] = df_po['last_updated_at'].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.drop(labels=['created_at','last_updated_at'],axis=1,inplace=True)
+    df_po = dict_of_df["purchase_order"]
+    df_po.index.name = "purchase_record_id"
+    df_po["created_date"] = df_po["created_at"].date()
+    df_po["created_time"] = df_po["created_at"].dt.time
+    df_po["last_updated_date"] = df_po["last_updated_at"].date()
+    df_po["last_updated_time"] = df_po["last_updated_at"].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.drop(labels=["created_at", "last_updated_at"], axis=1, inplace=True)
     return df_po
 
 
@@ -73,69 +85,97 @@ def create_fact_payment(dict_of_df):
     df_payment["created_time"] = pd.to_datetime(df_payment["created_at"]).dt.time
     df_payment["last_updated_date"] = pd.to_datetime(df_payment["last_updated"]).dt.date
     df_payment["last_updated_time"] = pd.to_datetime(df_payment["last_updated"]).dt.time
-    fact_payment = df_payment.loc[:,[
-        "payment_record_id",
-        "payment_id",
-        "created_date",
-        "created_time",
-        "last_updated_date",
-        "last_updated_time",
-        "transaction_id",
-        "counterparty_id",
-        "payment_amount",
-        "currency_id",
-        "payment_type_id",
-        "paid",
-        "payment_date"
-    ]]
+    fact_payment = df_payment.loc[
+        :,
+        [
+            "payment_record_id",
+            "payment_id",
+            "created_date",
+            "created_time",
+            "last_updated_date",
+            "last_updated_time",
+            "transaction_id",
+            "counterparty_id",
+            "payment_amount",
+            "currency_id",
+            "payment_type_id",
+            "paid",
+            "payment_date",
+        ],
+    ]
     return fact_payment
 
-## dim_location from address --> drops 2 columns
+
+# dim_location from address --> drops 2 columns
+
+
 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'}).set_index('location_id')
+    df_loc = (
+        dict_of_df["address"]
+        .drop(labels=["created_at", "last_updated"], axis=1)
+        .rename(columns={"address_id": "location_id"})
+        .set_index("location_id")
+    )
     return df_loc
 
-## dim_counterparty from address and counterparty
+
+# dim_counterparty from address and counterparty
+
+
 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="address_id", 
-            how="outer").set_index('counterparty_id')
+    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="address_id",
+        how="outer",
+    ).set_index("counterparty_id")
     return df_cp
 
 
-## dim_date from purchase_order
+# dim_date from purchase_order
 def create_dim_date(dict_of_df):
-    sr_date = pd.concat([dict_of_df['created_date'],dict_of_df['last_updated_date'],dict_of_df['agreed_delivery_date'],dict_of_df['agreed_payment_date']]).sort()
-    df_date = pd.DataFrame(sr_date,columns='date_id')
-    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.set_index('date_id')
+    sr_date = pd.concat(
+        [
+            dict_of_df["created_date"],
+            dict_of_df["last_updated_date"],
+            dict_of_df["agreed_delivery_date"],
+            dict_of_df["agreed_payment_date"],
+        ]
+    ).sort()
+    df_date = pd.DataFrame(sr_date, columns="date_id")
+    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.set_index("date_id")
+
 
 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"})]
+    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)
+    df_cur = sr.str.split(pat=" - ", expand=True).rename(
+        {0: "currency_code", 1: "currency_name"}, axis=1
+    )
     return df_cur
 
-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').set_index('currency_id')
-    return dim_cur
-
-
-
-
 
+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"
+    ).set_index("currency_id")
+    return dim_cur
 
 
 def create_dim_payment_type(dict_of_df):
@@ -143,6 +183,7 @@ def create_dim_payment_type(dict_of_df):
     dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]]
     return dim_payment_type
 
+
 def create_fact_payment(dict_of_df):
     df_payment = dict_of_df["payment"]
     df_payment.index.name = "payment_record_id"
@@ -150,41 +191,57 @@ def create_fact_payment(dict_of_df):
     df_payment["created_time"] = pd.to_datetime(df_payment["created_at"]).dt.time
     df_payment["last_updated_date"] = pd.to_datetime(df_payment["last_updated"]).dt.date
     df_payment["last_updated_time"] = pd.to_datetime(df_payment["last_updated"]).dt.time
-    fact_payment = df_payment.loc[:,[
-        "payment_record_id",
-        "payment_id",
-        "created_date",
-        "created_time",
-        "last_updated_date",
-        "last_updated_time",
-        "transaction_id",
-        "counterparty_id",
-        "payment_amount",
-        "currency_id",
-        "payment_type_id",
-        "paid",
-        "payment_date"
-    ]]
+    fact_payment = df_payment.loc[
+        :,
+        [
+            "payment_record_id",
+            "payment_id",
+            "created_date",
+            "created_time",
+            "last_updated_date",
+            "last_updated_time",
+            "transaction_id",
+            "counterparty_id",
+            "payment_amount",
+            "currency_id",
+            "payment_type_id",
+            "paid",
+            "payment_date",
+        ],
+    ]
     return fact_payment
 
+
 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"]]
+    dim_design = df_design.loc[
+        :, ["design_id", "design_name", "file_name", "file_location"]
+    ]
     return dim_design
 
+
 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']]
+    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 create_dim_currency(dict_of_df):
     df_currency = dict_of_df["currency"]
     dim_currency = df_currency.loc[:, ["currency_id", "currency_code"]]
-    mappings = {
-        "GBP": "Pound",
-        "USD": "US Dollar",
-        "EUR": "Euro"
-    }
+    mappings = {"GBP": "Pound", "USD": "US Dollar", "EUR": "Euro"}
     dim_currency["currency_name"] = dim_currency["currency_code"].map(mappings)
     return dim_currency
 
@@ -200,39 +257,49 @@ def create_dim_date(dict_of_df):
     df_sales["day_name"] = df_sales["agreed_delivery_date"].dt.day_name()
     df_sales["month_name"] = df_sales["agreed_delivery_date"].dt.month_name()
     df_sales["quarter"] = df_sales["agreed_delivery_date"].dt.quarter()
-    dim_date = ["date_id", "year", "month", "day", "day_of_week", "day_name", "month_name", "quarter"]   #series.dt.quarter()
+    dim_date = [
+        "date_id",
+        "year",
+        "month",
+        "day",
+        "day_of_week",
+        "day_name",
+        "month_name",
+        "quarter",
+    ]  # series.dt.quarter()
     return dim_date
 
 
-# TO DO:                                    
+# TO DO:
 # complete dim_date from merged fact table
 # merge dataframes into one dataframe
 # remove duplicates
 # test dim_date and fact_sales_order
 
+
 def create_sales_star_schema(dict_of_df):
     dim_design = create_dim_design(dict_of_df)
     dim_staff = create_dim_staff(dict_of_df)
     dim_currency = create_dim_currency(dict_of_df)
     dim_date = create_dim_date(dict_of_df)
-    
+
     fact_sales_order = create_fact_sales_order(dict_of_df)
-    
-    fact_sales_order = fact_sales_order.merge(dim_design, on='design_id', how='left')
-    fact_sales_order = fact_sales_order.merge(dim_staff, left_on='sales_staff_id', right_on='staff_id', how='left')
-    fact_sales_order = fact_sales_order.merge(dim_currency, on='currency_id', how='left')
-    fact_sales_order = fact_sales_order.merge(dim_date, left_on='agreed_delivery_date', right_on='date_id', how='left')
-    
-    return fact_sales_order
 
+    fact_sales_order = fact_sales_order.merge(dim_design, on="design_id", how="left")
+    fact_sales_order = fact_sales_order.merge(
+        dim_staff, left_on="sales_staff_id", right_on="staff_id", how="left"
+    )
+    fact_sales_order = fact_sales_order.merge(
+        dim_currency, on="currency_id", how="left"
+    )
+    fact_sales_order = fact_sales_order.merge(
+        dim_date, left_on="agreed_delivery_date", right_on="date_id", how="left"
+    )
+
+    return fact_sales_order
 
 
 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
-
-
-
-
-
diff --git a/src/transform_lambda.py b/src/transform_lambda.py
index d30d91d..3e74ee0 100644
--- a/src/transform_lambda.py
+++ b/src/transform_lambda.py
@@ -6,12 +6,14 @@ import pandas as pd
 import pyarrow as pa
 import pyarrow.parquet as pq
 from src.dataframes import *
+
 # from src.extract_lambda import extract_bucket, DBConnectionException
 import boto3
 from botocore.exceptions import ClientError
 from pg8000.native import Connection, InterfaceError
 from datetime import datetime
 
+
 class DBConnectionException(Exception):
     """Wraps pg8000.native Error or DatabaseError."""
 
@@ -20,6 +22,7 @@ class DBConnectionException(Exception):
         self.message = str(e)
         super().__init__(self.message)
 
+
 logger = logging.getLogger(__name__)
 
 logging.basicConfig(
@@ -45,44 +48,45 @@ tables = [
     "payment_type",
 ]
 
+
 def lambda_handler(event, context):
     db = None
-    
-    try: 
+
+    try:
         db = connect_to_database()
-        bucket = bucket_name('transform')
+        bucket = bucket_name("transform")
         existing_s3_files = list_existing_s3_files(bucket)
 
-        dict_of_df = read_from_s3_subfolder_to_df(tables, extract_bucket(), client=boto3.client("s3"))
+        dict_of_df = read_from_s3_subfolder_to_df(
+            tables, extract_bucket(), client=boto3.client("s3")
+        )
 
         immutable_df_dict = {
-                        'dim_counterparty': create_dim_counterparty(dict_of_df),
-                        'dim_date': create_dim_date(dict_of_df),
-                        'dim_location': create_dim_location(dict_of_df),
-                        'dim_staff': create_dim_staff(dict_of_df),
-                        'dim_design': create_dim_design(dict_of_df)}
-        
+            "dim_counterparty": create_dim_counterparty(dict_of_df),
+            "dim_date": create_dim_date(dict_of_df),
+            "dim_location": create_dim_location(dict_of_df),
+            "dim_staff": create_dim_staff(dict_of_df),
+            "dim_design": create_dim_design(dict_of_df),
+        }
 
         mutable_df_dict = {
-                        'fact_sales_order': create_fact_sales_order(dict_of_df),
-                        'fact_purchase_order': create_fact_purchase_orders(dict_of_df),
-                        'fact_payment': create_fact_payment(dict_of_df),
-                        'dim_currency': create_dim_currency(dict_of_df)}
-        
+            "fact_sales_order": create_fact_sales_order(dict_of_df),
+            "fact_purchase_order": create_fact_purchase_orders(dict_of_df),
+            "fact_payment": create_fact_payment(dict_of_df),
+            "dim_currency": create_dim_currency(dict_of_df),
+        }
+
         status = process_to_parquet_and_upload_to_s3(
-            existing_s3_files,
-            immutable_df_dict,
-            mutable_df_dict,
-            bucket
+            existing_s3_files, immutable_df_dict, mutable_df_dict, bucket
         )
-        
-        if not status['uploaded']:
+
+        if not status["uploaded"]:
             logger.info("No dataframes written to the bucket.")
             return {
-                'statusCode': 204,
-                 "body": json.dumps("No files where uploaded."),
+                "statusCode": 204,
+                "body": json.dumps("No files where uploaded."),
             }
-        
+
         return {
             "statusCode": 200,
             "body": json.dumps(
@@ -90,7 +94,7 @@ def lambda_handler(event, context):
                 'The following tables were not uploaded: '+', '.join([status['not_uploaded']]) if status['not_uploaded'] else ''}"""
             ),
         }
- 
+
     except Exception as e:
         logger.error(f"Error: {e}", exc_info=True)
         return {"statusCode": 500, "body": json.dumps("Internal server error.")}
@@ -99,34 +103,38 @@ def lambda_handler(event, context):
             db.close()
 
 
-def process_to_parquet_and_upload_to_s3(existing_s3_files,
-                                        immutable_df_dict, 
-                                        mutable_df_dict,
-                                        bucket,
-                                        client=boto3.client('s3')):
-    status = {'uploaded': [],
-              'not_uploaded': []}
+def process_to_parquet_and_upload_to_s3(
+    existing_s3_files,
+    immutable_df_dict,
+    mutable_df_dict,
+    bucket,
+    client=boto3.client("s3"),
+):
+    status = {"uploaded": [], "not_uploaded": []}
 
     for table_name, df in immutable_df_dict.items():
         if table_name in existing_s3_files:
-            status['not_uploaded'].append(table_name)
+            status["not_uploaded"].append(table_name)
         else:
-            parquet_file = df.to_parquet(f'{table_name}.parquet', engine='pyarrow') #or fastparquet
-            client.upload_file(parquet_file, bucket, f'{table_name}.parquet')
-            status['uploaded'].append(table_name)
+            parquet_file = df.to_parquet(
+                f"{table_name}.parquet", engine="pyarrow"
+            )  # or fastparquet
+            client.upload_file(parquet_file, bucket, f"{table_name}.parquet")
+            status["uploaded"].append(table_name)
 
     for table_name, df in mutable_df_dict.items():
         s3_key = datetime.strftime(
-                datetime.today(), f"{table_name}/%Y/%m/%d/{table_name}_%H:%M:%S.parquet")
-        parquet_file = df.to_parquet(f'{table_name}.parquet', engine='pyarrow') #or fastparquet
+            datetime.today(), f"{table_name}/%Y/%m/%d/{table_name}_%H:%M:%S.parquet"
+        )
+        parquet_file = df.to_parquet(
+            f"{table_name}.parquet", engine="pyarrow"
+        )  # or fastparquet
         client.upload_file(parquet_file, bucket, s3_key)
-        status['uploaded'].append(table_name)
-
+        status["uploaded"].append(table_name)
 
     return status
 
 
-
 def retrieve_secrets():
     secret_name = "bentley-secrets"
     region_name = "eu-west-2"
@@ -175,19 +183,23 @@ def read_from_s3_subfolder_to_df(tables, bucket, client=boto3.client("s3")):
         table_dfs[table] = pd.concat(list_of_df)
     return table_dfs
 
+
 def bucket_name(bucket_prefix, client=boto3.client("s3")):
     response = client.list_buckets()
     bucket_filter = [
-        bucket["Name"] for bucket in response["Buckets"] if bucket_prefix in bucket["Name"]
+        bucket["Name"]
+        for bucket in response["Buckets"]
+        if bucket_prefix in bucket["Name"]
     ]
 
     return bucket_filter[0]
 
+
 def list_existing_s3_files(bucket_name, client=boto3.client("s3")):
     logging.info("Listing existing S3 files")
 
     try:
-        response = client.list_objects_v2(Bucket=bucket_name) 
+        response = client.list_objects_v2(Bucket=bucket_name)
 
         if "Contents" in response:
             existing_files = [obj["Key"] for obj in response["Contents"]]
@@ -198,4 +210,4 @@ def list_existing_s3_files(bucket_name, client=boto3.client("s3")):
     except ClientError as e:
         logger.error(f"Error listing S3 objects: {e}")
 
-    return existing_files
\ No newline at end of file
+    return existing_files