style: format code with Autopep8, Black and Ruff Formatter

This commit fixes the style issues introduced in 4651e2f according to the output
from Autopep8, Black and Ruff Formatter.

Details: https://github.com/ajschofield/de-project-bentley/pull/106

1 files changed, 9 insertions, 6 deletions

diff --git a/src/transform_lambda/transform_lambda.py b/src/transform_lambda/transform_lambda.py
index c25ab39..8a2cae8 100644
--- a/src/transform_lambda/transform_lambda.py
+++ b/src/transform_lambda/transform_lambda.py
@@ -11,7 +11,6 @@ from pg8000.native import Connection, InterfaceError
 from datetime import datetime
 
 
-
 class DBConnectionException(Exception):
     """Wraps pg8000.native Error or DatabaseError."""
 
@@ -115,13 +114,16 @@ def process_to_parquet_and_upload_to_s3(
         if table_name in existing_s3_files:
             status["not_uploaded"].append(table_name)
         else:
-<<<<<<< HEAD:src/transform_lambda/transform_lambda.py
+
+
+<< << << < HEAD: src/transform_lambda/transform_lambda.py
             parquet_file = df.to_parquet(
                 f"{table_name}.parquet", engine="pyarrow"
             )  # or fastparquet
             # changed parquet_file variable to the file name
-            client.upload_file(f"{table_name}.parquet", bucket, f"{table_name}.parquet")
-=======
+            client.upload_file(f"{table_name}.parquet",
+                               bucket, f"{table_name}.parquet")
+== == == =
             parquet_buffer = io.BytesIO()
 
             # or engine="fastparquet"
@@ -129,9 +131,10 @@ def process_to_parquet_and_upload_to_s3(
 
             parquet_buffer.seek(0)
 
-            client.upload_fileobj(parquet_buffer, bucket, f"{table_name}.parquet")
+            client.upload_fileobj(parquet_buffer, bucket,
+                                  f"{table_name}.parquet")
 
->>>>>>> 3f24ec753902feecec4c17e2877e19853bde1bb2:src/transform_lambda.py
+>>>>>> > 3f24ec753902feecec4c17e2877e19853bde1bb2: src/transform_lambda.py
             status["uploaded"].append(table_name)
 
     for table_name, df in mutable_df_dict.items():