From 03a5959df25f74d52ed5393c2a5af6b1b9eb34c9 Mon Sep 17 00:00:00 2001
From: T-Aji <tolujbd2@gmail.com>
Date: Wed, 28 Aug 2024 12:48:13 +0100
Subject: refactored functs to include columns instead of drop columns

---
 src/transform_lambda/transform_lambda.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'src/transform_lambda/transform_lambda.py')

diff --git a/src/transform_lambda/transform_lambda.py b/src/transform_lambda/transform_lambda.py
index 93b2284..1453c6c 100644
--- a/src/transform_lambda/transform_lambda.py
+++ b/src/transform_lambda/transform_lambda.py
@@ -42,7 +42,7 @@ TABLES = [
     "department",
     "currency",
     "design",
-    "payment_type",
+    "payment_type"
 ]
 
 
@@ -73,7 +73,8 @@ def lambda_handler(event, context):
             "fact_payment": create_fact_payment(dict_of_df),
             "dim_currency": create_dim_currency(dict_of_df),
         }
-
+        print(immutable_df_dict.values())
+        print(mutable_df_dict.values())
         status = process_to_parquet_and_upload_to_s3(
             existing_s3_files, immutable_df_dict, mutable_df_dict, bucket
         )
-- 
cgit v1.2.3


From 6235a2bb04b60d57a41196b07bbf0296920c6980 Mon Sep 17 00:00:00 2001
From: T-Aji <tolujbd2@gmail.com>
Date: Wed, 28 Aug 2024 17:52:45 +0100
Subject: wip commit

---
 src/load_lambda.py                       | 174 +++++++++++++++++++------------
 src/transform_lambda/dataframes.py       |   8 +-
 src/transform_lambda/transform_lambda.py |   2 +-
 tests/test_transform_lambda.py           |   2 +-
 4 files changed, 115 insertions(+), 71 deletions(-)

(limited to 'src/transform_lambda/transform_lambda.py')

diff --git a/src/load_lambda.py b/src/load_lambda.py
index 272cb8c..cdcf105 100644
--- a/src/load_lambda.py
+++ b/src/load_lambda.py
@@ -7,7 +7,8 @@ import logging
 import json
 import traceback
 from sqlalchemy import create_engine
-
+from datetime import datetime as dt
+import re
 
 logger = logging.getLogger(__name__)
 
@@ -15,10 +16,10 @@ logging.basicConfig(
     format="{asctime} - {levelname} - {message}",
     style="{",
     datefmt="%Y-%m-%d %H:%M",
-    level=logging.DEBUG,
+    level=logging.INFO,
 )
-
-logging.getLogger("botocore").setLevel(logging.INFO)
+# logging.getLogger("botocore").setLevel(logging.INFO)
+# logging.getLogger('sqlalchemy.engine').setLevel(logging.DEBUG)
 
 
 def lambda_handler(event, context):
@@ -38,10 +39,10 @@ def lambda_handler(event, context):
                 ),
             }
         else:
-            logger.error(f"error")
+            logger.error(f"error", exc_info=True)
             return {"error"}
     except Exception as e:
-        logger.error({e})
+        logger.error({e}, exc_info=True)
         return {"statusCode": 500, "body": {e}}
 
 
@@ -58,10 +59,10 @@ def retrieve_secrets(client=None, secret_name=None):
         get_secret_value_response = client.get_secret_value(SecretId=secret_name)
         print(get_secret_value_response)
     except ClientError as e:
-        logger.error(f"Failed to retrieve secret {secret_name}: {str(e)}")
+        logger.error(f"Failed to retrieve secret {secret_name}: {str(e)}", exc_info=True)
         raise e
     except KeyError:
-        logger.error(f"Secret {secret_name} does not contain a SecretString")
+        logger.error(f"Secret {secret_name} does not contain a SecretString", exc_info=True)
         raise ValueError(f"Secret {secret_name} does not contain a SecretString")
 
     return get_secret_value_response["SecretString"]
@@ -86,7 +87,7 @@ def connect_to_db_and_return_engine(sm_secret=None):
         engine = create_engine(conn_str)
         return engine
     except Exception as e:
-        logger.error(f"Interface error: {e}")
+        logger.error(f"Interface error: {e}", exc_info=True)
         raise RuntimeError("Failed to create database engine")
 
 
@@ -97,7 +98,7 @@ def get_transform_bucket(client=None):
     try:
         response = client.list_buckets()
     except ClientError as e:
-        logger.error(f"Error listing S3 buckets: {e}")
+        logger.error(f"Error listing S3 buckets: {e}", exc_info=True)
         raise RuntimeError("Error listing S3 buckets")
 
     transform_bucket_filter = [
@@ -107,7 +108,7 @@ def get_transform_bucket(client=None):
     ]
 
     if not transform_bucket_filter:
-        logger.error("No transform bucket found")
+        logger.error("No transform bucket found", exc_info=True)
         raise ValueError("No transform bucket found")
 
     return transform_bucket_filter[0]
@@ -117,41 +118,78 @@ def get_transform_bucket(client=None):
 # convert parquet files into dataframes
 # return a dictionary of dataframes with name as key, and dataframe object as value
 
+def get_latest_timestamp(existing_files):
+    if existing_files:
+        all_datetimes = []
+        for file_name in existing_files:
+            match = re.search(r"\/(.+/).+_(.+)\.parquet", file_name)
+            if match:
+                datetime_str = "".join(match.group(1, 2))
+                all_datetimes.append(
+                    dt.strptime(datetime_str, "%Y/%m/%d/%H:%M:%S")
+                )
+        return max(all_datetimes) if all_datetimes else dt.min
+    return existing_files
 
 def convert_parquet_files_to_dfs(bucket_name=None, client=None):
+    mutable_df_dict = [
+        "dim_currency",
+        "fact_sales_order",
+        "fact_purchase_order",
+        "fact_payment"
+        
+    ]
+
     try:
         if client is None:
             client = boto3.client("s3")
         if bucket_name is None:
             bucket_name = get_transform_bucket()
         files = client.list_objects_v2(Bucket=bucket_name)
-
+        
         dfs = {}
         if "Contents" in files:
-            for file in files["Contents"]:
-                file_key = file["Key"]
+            s3_key_list = [file["Key"]for file in files["Contents"]]
+            immutables_l = []
+            mutables_d = {prefix:[] for prefix in mutable_df_dict}
+            for tab, s3_key in mutables_d.items():
+                for file in s3_key_list:
+                    if tab in file:
+                        s3_key.append(file)
+                    elif "2024" not in file:
+                        immutables_l.append(file)
+                    else:
+                        continue
+            immutables_l = list(set(immutables_l))
+            print(mutables_d,'mutables_d')
+            latest_s3_keys = []
+            for k,v in mutables_d.items():
+	            latest_s3_keys.append(dt.strftime(get_latest_timestamp(v), f"{k}/%Y/%m/%d/{k}_%H:%M:%S.parquet"))
+            print(latest_s3_keys,'latest')
+            print(immutables_l,'immutables_l')
+            for file_key in latest_s3_keys+immutables_l:
                 try:
                     file_obj = client.get_object(Bucket=bucket_name, Key=file_key)
                     parquet_file = pq.ParquetFile(BytesIO(file_obj["Body"].read()))
                     df = parquet_file.read().to_pandas()
-                    print("df", df)
-                    print("type", type(df))
-                    print(df.columns)
-                    dfs[file_key] = df
+                    df_without_nulls = df.dropna()
+                    #print("df_without_nulls", df_without_nulls)
+                    #print("type", type(df_without_nulls))
+                    #print(df_without_nulls.columns)
+                    dfs[file_key] = df_without_nulls
                 except ClientError as e:
-                    logger.error(f"Unable to retrieve S3 object {file_key}: {e}")
+                    logger.error(f"Unable to retrieve S3 object {file_key}: {e}", exc_info=True)
                 except Exception as e:
-                    logger.error(f"Unable to process file {file_key}: {e}")
+                    logger.error(f"Unable to process file {file_key}: {e}", exc_info=True)
         else:
-            logger.error(f"No files found in {bucket_name}.")
+            logger.error(f"No files found in {bucket_name}.", exc_info=True)
             return {}
     except ValueError as value_error:
-        logger.error(f"Unable to list objects: {value_error}")
+        logger.error(f"Unable to list objects: {value_error}", exc_info=True)
         raise
     except ClientError as client_error:
-        logger.error(f"Unable to list objects: {client_error}")
+        logger.error(f"Unable to list objects: {client_error}", exc_info=True)
         raise
-    print()
     return dfs
 
 
@@ -160,53 +198,57 @@ def upload_dfs_to_database():
     dict_of_dfs = convert_parquet_files_to_dfs()
     db_engine = connect_to_db_and_return_engine()
     immutable_df_dict = [
-        "dim_counterparty.parquet",
-        "dim_date.parquet",  # this needs to be mutable
-        "dim_location.parquet",
-        "dim_staff.parquet",
-        "dim_design.parquet"
+        # #"dim_counterparty.parquet",
+        # "dim_date.parquet",  # this needs to be mutable
+        # "dim_location.parquet",
+        # "dim_staff.parquet",
+        # "dim_design.parquet"
     ]
     mutable_df_dict = [
+        "dim_currency",
         "fact_sales_order",
         "fact_purchase_order",
-        "fact_payment",
-        "dim_currency"
+        "fact_payment"
+        
     ]
-
-    for file_name, df in dict_of_dfs.items():
-        print(df)
-        if file_name in immutable_df_dict:
-            table_name = file_name.split(".")[0]
-            print(table_name, "<<<<<")
-            try:
-                df.to_sql(
-                    table_name,
-                    con=db_engine,
-                    schema="project_team_2",
-                    if_exists="append",
-                    index=False,
-                )
-                upload_status["uploaded"].append(table_name)
-            except Exception as e:
-                logger.error(f"Error uploading dataframe {file_name} to database: {e}")
-                raise
-        elif file_name.rsplit("_", 1)[0] in mutable_df_dict:
-            table_name = file_name.rsplit("_", 1)[0]
-            try:
-                df.to_sql(
-                    table_name,
-                    con=db_engine,
-                    schema="project_team_2",
-                    if_exists="append",
-                    index=False,
-                )
-                upload_status["uploaded"].append(table_name)
-            except Exception as e:
-                logger.error(f"Error uploading dataframe {file_name} to database: {e}")
-                raise
-        else:
-            upload_status["not_uploaded"].append(file_name)
-            logger.error(f"{file_name} does not correspond with table in database")
+    with db_engine.begin() as connection:
+        for file_name, df in dict_of_dfs.items():
+            print(df.dtypes, "dtypes")
+            print(df.head())
+            if file_name in immutable_df_dict:
+                table_name = file_name.split(".")[0]
+                print(table_name, "<<<<<")
+                try:
+                    df.to_sql(
+                        table_name,
+                        con=connection,
+                        schema="project_team_2",
+                        if_exists="append",
+                        index=False,
+                    )
+                    upload_status["uploaded"].append(table_name)
+                    print(upload_status)
+                except Exception as e:
+                    logger.error(f"Error uploading dataframe {file_name} to database: {e}", exc_info=True)
+                    raise
+            elif file_name.split("/")[0] in mutable_df_dict:
+                table_name = file_name.split("/")[0]
+                print(table_name, "<<<<<<<TABLE NAME")
+                try:
+                    df.to_sql(
+                        table_name,
+                        con=connection,
+                        schema="project_team_2",
+                        if_exists="append",
+                        index=False,
+                    )
+                    upload_status["uploaded"].append(table_name)
+                except Exception as e:
+                    logger.error(f"Error uploading dataframe {file_name} to database: {e}", exc_info=True)
+                    raise
+            else:
+                upload_status["not_uploaded"].append(file_name)
+            logger.error(f"{file_name} does not correspond with table in database", exc_info=True)
     db_engine.dispose()
     return upload_status
 
diff --git a/src/transform_lambda/dataframes.py b/src/transform_lambda/dataframes.py
index bf0556b..e89a6b2 100644
--- a/src/transform_lambda/dataframes.py
+++ b/src/transform_lambda/dataframes.py
@@ -18,7 +18,7 @@ import requests
 
 # no test, same as fact_payment
 def create_fact_sales_order(dict_of_df):
-    df_sales = dict_of_df["sales_order"]
+    df_sales = dict_of_df["sales_order"].rename(columns={"staff_id": "sales_staff_id"})
     df_sales.index.name = "sales_record_id"
 
     df_sales["created_date"] = df_sales["created_at"].astype("datetime64[ns]").dt.date
@@ -44,7 +44,7 @@ def create_fact_sales_order(dict_of_df):
             "created_time",
             "last_updated_date",
             "last_updated_time",
-            "staff_id",
+            "sales_staff_id",
             "counterparty_id",
             "units_sold",
             "unit_price",
@@ -55,7 +55,7 @@ def create_fact_sales_order(dict_of_df):
             "agreed_delivery_location_id"
         ],
     ]
-    fact_sales.rename(columns={"staff_id": "sales_staff_id"}).reset_index(inplace=True)
+    fact_sales.reset_index(inplace=True)
     
 
     return fact_sales
@@ -253,6 +253,8 @@ def create_dim_currency(dict_of_df, names=scrape_currency_names()):
         df_cur, names, left_on="currency_code", right_on="currency_code", how="left"
     )
     dim_currency.drop_duplicates(inplace=True)
+    dim_currency.astype({"currency_name": "string", "currency_code": "string"})
+    print(dim_currency.dtypes, "<<<<<<<<<Dtype")
     return dim_currency
 
 
diff --git a/src/transform_lambda/transform_lambda.py b/src/transform_lambda/transform_lambda.py
index 1453c6c..0b5748b 100644
--- a/src/transform_lambda/transform_lambda.py
+++ b/src/transform_lambda/transform_lambda.py
@@ -5,7 +5,7 @@ import logging
 import pandas as pd
 import pyarrow as pa
 import pyarrow.parquet as pq
-from dataframes import *
+from src.transform_lambda.dataframes import *
 from botocore.exceptions import ClientError
 from pg8000.native import Connection, InterfaceError
 from datetime import datetime
diff --git a/tests/test_transform_lambda.py b/tests/test_transform_lambda.py
index 5ed743e..308dc65 100644
--- a/tests/test_transform_lambda.py
+++ b/tests/test_transform_lambda.py
@@ -1,4 +1,4 @@
-from src.transform_lambda import (
+from src.transform_lambda.transform_lambda import (
     read_from_s3_subfolder_to_df,
     list_existing_s3_files,
     bucket_name,
-- 
cgit v1.2.3


From d396cd95d660fb76188ef887fc268d20aeeb5352 Mon Sep 17 00:00:00 2001
From: Ang Bel <anzelikabelotelova@Anzelikas-MacBook-Air.local>
Date: Wed, 28 Aug 2024 22:46:00 +0100
Subject: fix: adds missing dataframes and resolves tables upload to end data
 warehouse in case the table is empty

---
 .gitignore                               |  6 +++++-
 src/load_lambda.py                       | 24 +++++++++++++-----------
 src/transform_lambda/dataframes.py       | 19 ++++++++++++++-----
 src/transform_lambda/transform_lambda.py |  4 +++-
 4 files changed, 35 insertions(+), 18 deletions(-)

(limited to 'src/transform_lambda/transform_lambda.py')

diff --git a/.gitignore b/.gitignore
index 6aa03fc..480ae4b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,4 +14,8 @@ __pycache__/
 
 # OS-Related Files
 .DS_Store
-venv
\ No newline at end of file
+venv
+
+#files
+/dim_*
+/fact_*
\ No newline at end of file
diff --git a/src/load_lambda.py b/src/load_lambda.py
index cdcf105..8f921b8 100644
--- a/src/load_lambda.py
+++ b/src/load_lambda.py
@@ -161,18 +161,15 @@ def convert_parquet_files_to_dfs(bucket_name=None, client=None):
                     else:
                         continue
             immutables_l = list(set(immutables_l))
-            print(mutables_d,'mutables_d')
             latest_s3_keys = []
             for k,v in mutables_d.items():
 	            latest_s3_keys.append(dt.strftime(get_latest_timestamp(v), f"{k}/%Y/%m/%d/{k}_%H:%M:%S.parquet"))
-            print(latest_s3_keys,'latest')
-            print(immutables_l,'immutables_l')
-            for file_key in latest_s3_keys+immutables_l:
+            for file_key in immutables_l+latest_s3_keys:
                 try:
                     file_obj = client.get_object(Bucket=bucket_name, Key=file_key)
                     parquet_file = pq.ParquetFile(BytesIO(file_obj["Body"].read()))
                     df = parquet_file.read().to_pandas()
-                    df_without_nulls = df.dropna()
+                    df_without_nulls = df.dropna(how='all') #>> can't do 'any' (default) because we lose rows in dim_location
                     #print("df_without_nulls", df_without_nulls)
                     #print("type", type(df_without_nulls))
                     #print(df_without_nulls.columns)
@@ -202,12 +199,14 @@ def upload_dfs_to_database():
         # "dim_date.parquet",  # this needs to be mutable
         # "dim_location.parquet",
         # "dim_staff.parquet",
-        # "dim_design.parquet"
+        # "dim_design.parquet",
+        # 'dim_transaction.parquet' #This one was missing,
+        'dim_payment_type.parquet'
     ]
     mutable_df_dict = [
-        "dim_currency",
-        "fact_sales_order",
-        "fact_purchase_order",
+        # "dim_currency",
+        # "fact_sales_order",
+        # "fact_purchase_order",
         "fact_payment"
         
     ]
@@ -215,7 +214,9 @@ def upload_dfs_to_database():
         for file_name, df in dict_of_dfs.items():
             print(df.dtypes, "dtypes")
             print(df.head())
-            if file_name in immutable_df_dict:
+            print(file_name,"<<< FILE NAME")
+            print(immutable_df_dict,"<<<IMMUTABLE_DF_DICT")
+            if  file_name in immutable_df_dict: 
                 table_name = file_name.split(".")[0]
                 print(table_name, "<<<<<")
                 try:
@@ -248,7 +249,8 @@ def upload_dfs_to_database():
                     raise
             else:
                 upload_status["not_uploaded"].append(file_name)
-            logger.error(f"{file_name} does not correspond with table in database", exc_info=True)
+                logger.error(f"{file_name} does not correspond with table in database", exc_info=True)
+            print(upload_status)
     db_engine.dispose()
     return upload_status
 
diff --git a/src/transform_lambda/dataframes.py b/src/transform_lambda/dataframes.py
index e89a6b2..c823b87 100644
--- a/src/transform_lambda/dataframes.py
+++ b/src/transform_lambda/dataframes.py
@@ -19,7 +19,6 @@ import requests
 # no test, same as fact_payment
 def create_fact_sales_order(dict_of_df):
     df_sales = dict_of_df["sales_order"].rename(columns={"staff_id": "sales_staff_id"})
-    df_sales.index.name = "sales_record_id"
 
     df_sales["created_date"] = df_sales["created_at"].astype("datetime64[ns]").dt.date
     df_sales["created_time"] = (
@@ -55,9 +54,11 @@ def create_fact_sales_order(dict_of_df):
             "agreed_delivery_location_id"
         ],
     ]
+    fact_sales.convert_dtypes()
+    fact_sales.index = pd.RangeIndex(1, len(fact_sales.index) + 1)
+    fact_sales.index.name = "sales_record_id"
     fact_sales.reset_index(inplace=True)
-    
-
+    fact_sales.dropna(inplace=True)
     return fact_sales
 
 
@@ -66,7 +67,6 @@ 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"] = 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
@@ -100,7 +100,11 @@ def create_fact_purchase_orders(dict_of_df):
         ]
 
     ]
+    fact_purchase_order.convert_dtypes()
+    fact_purchase_order.index = pd.RangeIndex(1, len(fact_purchase_order.index) + 1)
+    fact_purchase_order.index.name = "purchase_record_id"
     fact_purchase_order.reset_index(inplace=True)
+    fact_purchase_order.dropna(inplace=True)
     return fact_purchase_order
 
 
@@ -109,7 +113,6 @@ 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"] = (
         df_payment["created_at"].astype("datetime64[ns]").dt.date
     )
@@ -141,7 +144,12 @@ def create_fact_payment(dict_of_df):
         "payment_date"
         ]
     ]
+    fact_payment.convert_dtypes()
+    fact_payment.index = pd.RangeIndex(1, len(fact_payment.index) + 1)
+    fact_payment.index.name = "payment_record_id"
     fact_payment.reset_index(inplace=True)
+    fact_payment.dropna(inplace=True)
+    fact_payment = fact_payment.astype({'currency_id':'int','payment_id':'int'})
     return fact_payment
 
 
@@ -157,6 +165,7 @@ def create_dim_transaction(dict_of_df):
         "purchase_order_id"
         ]
     ]
+    #dim_transaction = dim_transaction.astype({"sales_order_id":"Int64","purchase_order_id":"Int64"})
     return dim_transaction
 
 
diff --git a/src/transform_lambda/transform_lambda.py b/src/transform_lambda/transform_lambda.py
index 0b5748b..5ea8cf0 100644
--- a/src/transform_lambda/transform_lambda.py
+++ b/src/transform_lambda/transform_lambda.py
@@ -65,13 +65,15 @@ def lambda_handler(event, context):
             "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_transaction": create_dim_transaction(dict_of_df),
+            "dim_payment_type": create_dim_payment_type(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),
+            "dim_currency": create_dim_currency(dict_of_df)
         }
         print(immutable_df_dict.values())
         print(mutable_df_dict.values())
-- 
cgit v1.2.3


From 42ad135b25044bb1c7ab8a553f038c8da9de0f75 Mon Sep 17 00:00:00 2001
From: "deepsource-autofix[bot]"
 <62050782+deepsource-autofix[bot]@users.noreply.github.com>
Date: Thu, 29 Aug 2024 08:57:48 +0000
Subject: style: format code with Autopep8, Black and Ruff Formatter

This commit fixes the style issues introduced in 48e7dae according to the output
from Autopep8, Black and Ruff Formatter.

Details: https://github.com/ajschofield/de-project-bentley/pull/107
---
 src/load_lambda.py                       |  78 +++++++++++++--------
 src/transform_lambda/dataframes.py       | 116 ++++++++++++++-----------------
 src/transform_lambda/transform_lambda.py |   6 +-
 3 files changed, 105 insertions(+), 95 deletions(-)

(limited to 'src/transform_lambda/transform_lambda.py')

diff --git a/src/load_lambda.py b/src/load_lambda.py
index 941ae97..86189dc 100644
--- a/src/load_lambda.py
+++ b/src/load_lambda.py
@@ -58,10 +58,14 @@ def retrieve_secrets(client=None, secret_name=None):
     try:
         get_secret_value_response = client.get_secret_value(SecretId=secret_name)
     except ClientError as e:
-        logger.error(f"Failed to retrieve secret {secret_name}: {str(e)}", exc_info=True)
+        logger.error(
+            f"Failed to retrieve secret {secret_name}: {str(e)}", exc_info=True
+        )
         raise e
     except KeyError:
-        logger.error(f"Secret {secret_name} does not contain a SecretString", exc_info=True)
+        logger.error(
+            f"Secret {secret_name} does not contain a SecretString", exc_info=True
+        )
         raise ValueError(f"Secret {secret_name} does not contain a SecretString")
 
     return get_secret_value_response["SecretString"]
@@ -117,6 +121,7 @@ def get_transform_bucket(client=None):
 # convert parquet files into dataframes
 # return a dictionary of dataframes with name as key, and dataframe object as value
 
+
 def get_latest_timestamp(existing_files):
     if existing_files:
         all_datetimes = []
@@ -124,19 +129,17 @@ def get_latest_timestamp(existing_files):
             match = re.search(r"\/(.+/).+_(.+)\.parquet", file_name)
             if match:
                 datetime_str = "".join(match.group(1, 2))
-                all_datetimes.append(
-                    dt.strptime(datetime_str, "%Y/%m/%d/%H:%M:%S")
-                )
+                all_datetimes.append(dt.strptime(datetime_str, "%Y/%m/%d/%H:%M:%S"))
         return max(all_datetimes) if all_datetimes else dt.min
     return existing_files
 
+
 def convert_parquet_files_to_dfs(bucket_name=None, client=None):
     mutable_df_dict = [
         "dim_currency",
         "fact_sales_order",
         "fact_purchase_order",
-        "fact_payment"
-        
+        "fact_payment",
     ]
 
     try:
@@ -145,12 +148,12 @@ def convert_parquet_files_to_dfs(bucket_name=None, client=None):
         if bucket_name is None:
             bucket_name = get_transform_bucket()
         files = client.list_objects_v2(Bucket=bucket_name)
-        
+
         dfs = {}
         if "Contents" in files:
-            s3_key_list = [file["Key"]for file in files["Contents"]]
+            s3_key_list = [file["Key"] for file in files["Contents"]]
             immutables_l = []
-            mutables_d = {prefix:[] for prefix in mutable_df_dict}
+            mutables_d = {prefix: [] for prefix in mutable_df_dict}
             for tab, s3_key in mutables_d.items():
                 for file in s3_key_list:
                     if tab in file:
@@ -161,22 +164,31 @@ def convert_parquet_files_to_dfs(bucket_name=None, client=None):
                         continue
             immutables_l = list(set(immutables_l))
             latest_s3_keys = []
-            for k,v in mutables_d.items():
-	            latest_s3_keys.append(dt.strftime(get_latest_timestamp(v), f"{k}/%Y/%m/%d/{k}_%H:%M:%S.parquet"))
-            for file_key in immutables_l+latest_s3_keys:
+            for k, v in mutables_d.items():
+                latest_s3_keys.append(
+                    dt.strftime(
+                        get_latest_timestamp(v), f"{k}/%Y/%m/%d/{k}_%H:%M:%S.parquet"
+                    )
+                )
+            for file_key in immutables_l + latest_s3_keys:
                 try:
                     file_obj = client.get_object(Bucket=bucket_name, Key=file_key)
                     parquet_file = pq.ParquetFile(BytesIO(file_obj["Body"].read()))
                     df = parquet_file.read().to_pandas()
-                    df_without_nulls = df.dropna(how='all') #>> can't do 'any' (default) because we lose rows in dim_location
-                    #print("df_without_nulls", df_without_nulls)
-                    #print("type", type(df_without_nulls))
-                    #print(df_without_nulls.columns)
+                    # >> can't do 'any' (default) because we lose rows in dim_location
+                    df_without_nulls = df.dropna(how="all")
+                    # print("df_without_nulls", df_without_nulls)
+                    # print("type", type(df_without_nulls))
+                    # print(df_without_nulls.columns)
                     dfs[file_key] = df_without_nulls
                 except ClientError as e:
-                    logger.error(f"Unable to retrieve S3 object {file_key}: {e}", exc_info=True)
+                    logger.error(
+                        f"Unable to retrieve S3 object {file_key}: {e}", exc_info=True
+                    )
                 except Exception as e:
-                    logger.error(f"Unable to process file {file_key}: {e}", exc_info=True)
+                    logger.error(
+                        f"Unable to process file {file_key}: {e}", exc_info=True
+                    )
         else:
             logger.error(f"No files found in {bucket_name}.", exc_info=True)
             return {}
@@ -199,23 +211,22 @@ def upload_dfs_to_database():
         "dim_location.parquet",
         "dim_staff.parquet",
         "dim_design.parquet",
-        'dim_transaction.parquet', #This one was missing,
-        'dim_payment_type.parquet'
+        "dim_transaction.parquet",  # This one was missing,
+        "dim_payment_type.parquet",
     ]
     mutable_df_dict = [
         "dim_currency",
         "fact_sales_order",
         "fact_purchase_order",
-        "fact_payment"
-        
+        "fact_payment",
     ]
     with db_engine.begin() as connection:
         for file_name, df in dict_of_dfs.items():
             print(df.dtypes, "dtypes")
             print(df.head())
-            print(file_name,"<<< FILE NAME")
-            print(immutable_df_dict,"<<<IMMUTABLE_DF_DICT")
-            if  file_name in immutable_df_dict: 
+            print(file_name, "<<< FILE NAME")
+            print(immutable_df_dict, "<<<IMMUTABLE_DF_DICT")
+            if file_name in immutable_df_dict:
                 table_name = file_name.split(".")[0]
                 print(table_name, "<<<<<")
                 try:
@@ -229,7 +240,10 @@ def upload_dfs_to_database():
                     upload_status["uploaded"].append(table_name)
                     print(upload_status)
                 except Exception as e:
-                    logger.error(f"Error uploading dataframe {file_name} to database: {e}", exc_info=True)
+                    logger.error(
+                        f"Error uploading dataframe {file_name} to database: {e}",
+                        exc_info=True,
+                    )
                     raise
             elif file_name.split("/")[0] in mutable_df_dict:
                 table_name = file_name.split("/")[0]
@@ -244,11 +258,17 @@ def upload_dfs_to_database():
                     )
                     upload_status["uploaded"].append(table_name)
                 except Exception as e:
-                    logger.error(f"Error uploading dataframe {file_name} to database: {e}", exc_info=True)
+                    logger.error(
+                        f"Error uploading dataframe {file_name} to database: {e}",
+                        exc_info=True,
+                    )
                     raise
             else:
                 upload_status["not_uploaded"].append(file_name)
-                logger.error(f"{file_name} does not correspond with table in database", exc_info=True)
+                logger.error(
+                    f"{file_name} does not correspond with table in database",
+                    exc_info=True,
+                )
             print(upload_status)
     db_engine.dispose()
     return upload_status
diff --git a/src/transform_lambda/dataframes.py b/src/transform_lambda/dataframes.py
index c823b87..6de58e7 100644
--- a/src/transform_lambda/dataframes.py
+++ b/src/transform_lambda/dataframes.py
@@ -36,7 +36,8 @@ def create_fact_sales_order(dict_of_df):
     df_sales["agreed_payment_date"] = pd.to_datetime(
         df_sales["agreed_payment_date"], format="%Y-%m-%d"
     )
-    fact_sales = df_sales.loc[:,
+    fact_sales = df_sales.loc[
+        :,
         [
             "sales_order_id",
             "created_date",
@@ -51,7 +52,7 @@ def create_fact_sales_order(dict_of_df):
             "design_id",
             "agreed_payment_date",
             "agreed_delivery_date",
-            "agreed_delivery_location_id"
+            "agreed_delivery_location_id",
         ],
     ]
     fact_sales.convert_dtypes()
@@ -81,24 +82,24 @@ def create_fact_purchase_orders(dict_of_df):
     df_po["agreed_payment_date"] = pd.to_datetime(
         df_po["agreed_payment_date"], format="%Y-%m-%d"
     )
-    fact_purchase_order = df_po.loc[:,
-        [                           
-        "purchase_order_id",
-        "created_date",
-        "created_time",
-        "last_updated_date",
-        "last_updated_time",
-        "staff_id",
-        "counterparty_id",
-        "item_code",
-        "item_quantity",
-        "item_unit_price",
-        "currency_id",
-        "agreed_delivery_date",
-        "agreed_payment_date",
-        "agreed_delivery_location_id"
-        ]
-
+    fact_purchase_order = df_po.loc[
+        :,
+        [
+            "purchase_order_id",
+            "created_date",
+            "created_time",
+            "last_updated_date",
+            "last_updated_time",
+            "staff_id",
+            "counterparty_id",
+            "item_code",
+            "item_quantity",
+            "item_unit_price",
+            "currency_id",
+            "agreed_delivery_date",
+            "agreed_payment_date",
+            "agreed_delivery_location_id",
+        ],
     ]
     fact_purchase_order.convert_dtypes()
     fact_purchase_order.index = pd.RangeIndex(1, len(fact_purchase_order.index) + 1)
@@ -128,28 +129,29 @@ def create_fact_payment(dict_of_df):
     df_payment["payment_date"] = pd.to_datetime(
         df_payment["payment_date"], format="%Y-%m-%d"
     )
-    fact_payment = df_payment.loc[:,
+    fact_payment = df_payment.loc[
+        :,
         [
-        "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"
-        ]
+            "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.convert_dtypes()
     fact_payment.index = pd.RangeIndex(1, len(fact_payment.index) + 1)
     fact_payment.index.name = "payment_record_id"
     fact_payment.reset_index(inplace=True)
     fact_payment.dropna(inplace=True)
-    fact_payment = fact_payment.astype({'currency_id':'int','payment_id':'int'})
+    fact_payment = fact_payment.astype({"currency_id": "int", "payment_id": "int"})
     return fact_payment
 
 
@@ -157,15 +159,10 @@ def create_fact_payment(dict_of_df):
 
 
 def create_dim_transaction(dict_of_df):
-    dim_transaction = dict_of_df["transaction"].loc[:,
-        [
-        "transaction_id",
-        "transaction_type",
-        "sales_order_id",
-        "purchase_order_id"
-        ]
+    dim_transaction = dict_of_df["transaction"].loc[
+        :, ["transaction_id", "transaction_type", "sales_order_id", "purchase_order_id"]
     ]
-    #dim_transaction = dim_transaction.astype({"sales_order_id":"Int64","purchase_order_id":"Int64"})
+    # dim_transaction = dim_transaction.astype({"sales_order_id":"Int64","purchase_order_id":"Int64"})
     return dim_transaction
 
 
@@ -174,7 +171,8 @@ def create_dim_transaction(dict_of_df):
 
 def create_dim_location(dict_of_df):
     dim_location = (
-        dict_of_df["address"].drop(labels=["created_at", "last_updated"], axis=1)
+        dict_of_df["address"]
+        .drop(labels=["created_at", "last_updated"], axis=1)
         .rename(columns={"address_id": "location_id"})
     )
     return dim_location
@@ -193,7 +191,7 @@ def create_dim_counterparty(dict_of_df):
         left_on="legal_address_id",
         right_on="counterparty_legal_address_id",
         how="inner",
-    )#.dropna(inplace=True)
+    )  # .dropna(inplace=True)
     dim_counterparty = df_cp.drop(
         labels=[
             "legal_address_id",
@@ -201,8 +199,9 @@ def create_dim_counterparty(dict_of_df):
             "created_at",
             "last_updated",
             "commercial_contact",
-            "delivery_contact"
-        ], axis=1
+            "delivery_contact",
+        ],
+        axis=1,
     )
     return dim_counterparty
 
@@ -272,12 +271,7 @@ def create_dim_currency(dict_of_df, names=scrape_currency_names()):
 
 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"
-        ]
-    ]
+    dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]]
     return dim_payment_type
 
 
@@ -286,13 +280,8 @@ def create_dim_payment_type(dict_of_df):
 
 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
 
@@ -304,14 +293,15 @@ 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[:,
+    dim_staff = staff_department.loc[
+        :,
         [
             "staff_id",
             "first_name",
             "last_name",
             "department_name",
             "location",
-            "email_address"
-        ]
+            "email_address",
+        ],
     ]
     return dim_staff
diff --git a/src/transform_lambda/transform_lambda.py b/src/transform_lambda/transform_lambda.py
index 5ea8cf0..2739997 100644
--- a/src/transform_lambda/transform_lambda.py
+++ b/src/transform_lambda/transform_lambda.py
@@ -42,7 +42,7 @@ TABLES = [
     "department",
     "currency",
     "design",
-    "payment_type"
+    "payment_type",
 ]
 
 
@@ -66,14 +66,14 @@ def lambda_handler(event, context):
             "dim_staff": create_dim_staff(dict_of_df),
             "dim_design": create_dim_design(dict_of_df),
             "dim_transaction": create_dim_transaction(dict_of_df),
-            "dim_payment_type": create_dim_payment_type(dict_of_df)
+            "dim_payment_type": create_dim_payment_type(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)
+            "dim_currency": create_dim_currency(dict_of_df),
         }
         print(immutable_df_dict.values())
         print(mutable_df_dict.values())
-- 
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