test: transform refactoring - it now loads parquet files into s3 bucket

2 files changed, 19 insertions, 19 deletions

diff --git a/src/dataframes.py b/src/dataframes.py
index 1f445a4..9d0f2ac 100644
--- a/src/dataframes.py
+++ b/src/dataframes.py
@@ -20,13 +20,13 @@ import requests
 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"] = pd.to_datetime(df_sales["created_at"].dt.date,format='%Y-%m-%d')
-    df_sales["created_time"] = df_sales["created_at"].dt.floor('s').dt.time
-    df_sales["last_updated_date"] = pd.to_datetime(df_sales["last_updated"].dt.date,format='%Y-%m-%d')
-    df_sales["last_updated_time"] = df_sales["last_updated"].dt.floor('s').dt.time
+    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.drop(labels=['created_at','last_updated'],axis=1,inplace=True)
+    df_sales = df_sales.drop(labels=['created_at','last_updated'],axis=1)
     df_sales.reset_index(inplace=True)
     return df_sales
 
@@ -34,13 +34,13 @@ def create_fact_sales_order(dict_of_df):
 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'] = pd.to_datetime(df_po['created_at'].dt.date,format='%Y-%m-%d')
-    df_po['created_time'] = df_po['created_at'].dt.floor('s').dt.time
-    df_po['last_updated_date'] = pd.to_datetime(df_po['last_updated'].dt.date,format='%Y-%m-%d')
-    df_po['last_updated_time'] = df_po['last_updated'].dt.floor('s').dt.time
+    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.drop(labels=['created_at','last_updated'],axis=1,inplace=True)
+    df_po = df_po.drop(labels=['created_at','last_updated'],axis=1)
     df_po.reset_index(inplace=True)
     return df_po
 
@@ -48,12 +48,12 @@ def create_fact_purchase_orders(dict_of_df):
 def create_fact_payment(dict_of_df):
     df_payment = dict_of_df["payment"]
     df_payment.index.name = "payment_record_id"
-    df_payment["created_date"] = pd.to_datetime(df_payment["created_at"].dt.date,format='%Y-%m-%d')
-    df_payment["created_time"] = df_payment["created_at"].dt.floor('s').dt.time
-    df_payment["last_updated_date"] = pd.to_datetime(df_payment["last_updated"].dt.date,format='%Y-%m-%d')
-    df_payment["last_updated_time"] = df_payment["last_updated"].dt.floor('s').dt.time
+    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.drop(labels=['created_at','last_updated'],axis=1,inplace=True)
+    df_payment = df_payment.drop(labels=['created_at','last_updated'],axis=1)
     df_payment.reset_index(inplace=True)
     return df_payment
 
@@ -83,7 +83,7 @@ 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)]
     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]')
diff --git a/src/transform_lambda.py b/src/transform_lambda.py
index 2cd9272..ccf90e5 100644
--- a/src/transform_lambda.py
+++ b/src/transform_lambda.py
@@ -117,7 +117,7 @@ def process_to_parquet_and_upload_to_s3(
             parquet_file = df.to_parquet(
                 f"{table_name}.parquet", engine="pyarrow"
             )  # or fastparquet
-            client.upload_file(parquet_file, bucket, f"{table_name}.parquet")
+            client.upload_file(f"{table_name}.parquet", bucket, f"{table_name}.parquet") #changed parquet_file variable to the file name
             status["uploaded"].append(table_name)
 
     for table_name, df in mutable_df_dict.items():
@@ -127,7 +127,7 @@ def process_to_parquet_and_upload_to_s3(
         parquet_file = df.to_parquet(
             f"{table_name}.parquet", engine="pyarrow"
         )  # or fastparquet
-        client.upload_file(parquet_file, bucket, s3_key)
+        client.upload_file(f"{table_name}.parquet", bucket, s3_key)
         status["uploaded"].append(table_name)
 
     return status
@@ -203,7 +203,7 @@ 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 None
+            return [] #changed from None to [] so it is an iterable
 
     except ClientError as e:
         logger.error(f"Error listing S3 objects: {e}")