style: format code with Autopep8, Black and Ruff Formatter

This commit fixes the style issues introduced in 08c971f according to the output
from Autopep8, Black and Ruff Formatter.

Details: https://github.com/ajschofield/de-project-bentley/pull/102

3 files changed, 123 insertions, 104 deletions

diff --git a/src/dataframes.py b/src/dataframes.py
index 4b32b36..43facd6 100644
--- a/src/dataframes.py
+++ b/src/dataframes.py
@@ -20,8 +20,11 @@ import requests
 def create_fact_sales_order(dict_of_df):
     df_sales = dict_of_df["sales_order"]
     df_sales.index.name = "sales_record_id"
-<<<<<<< HEAD
-    df_sales["created_date"] = df_sales["created_at"].astype("datetime64[ns]").dt.date
+
+
+<< << << < HEAD
+    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
     )
@@ -30,27 +33,29 @@ def create_fact_sales_order(dict_of_df):
     )
     df_sales["last_updated_time"] = (
         df_sales["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time
-=======
-    df_sales["created_date"] = pd.to_datetime(df_sales["created_at"], format="%Y-%m-%d")
-    df_sales["created_time"] = pd.to_datetime(df_sales["created_at"], format="%H-%M-%S")
-    df_sales["last_updated_date"] = pd.to_datetime(
+== == == =
+    df_sales["created_date"]=pd.to_datetime(
+        df_sales["created_at"], format="%Y-%m-%d")
+    df_sales["created_time"]=pd.to_datetime(
+        df_sales["created_at"], format="%H-%M-%S")
+    df_sales["last_updated_date"]=pd.to_datetime(
         df_sales["last_updated"], format="%Y-%m-%d"
     )
-    df_sales["last_updated_time"] = pd.to_datetime(
+    df_sales["last_updated_time"]=pd.to_datetime(
         df_sales["last_updated"], format="%H-%M-%S"
->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter)
+>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter)
     )
-    df_sales["agreed_delivery_date"] = pd.to_datetime(
+    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"]=pd.to_datetime(
         df_sales["agreed_payment_date"], format="%Y-%m-%d"
     )
-<<<<<<< HEAD
-    df_sales = df_sales.drop(labels=["created_at", "last_updated"], axis=1)
-=======
+<< << << < HEAD
+    df_sales=df_sales.drop(labels=["created_at", "last_updated"], axis=1)
+== == == =
     df_sales.drop(labels=["created_at", "last_updated"], axis=1, inplace=True)
->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter)
+>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter)
     df_sales.reset_index(inplace=True)
     return df_sales
 
@@ -59,37 +64,40 @@ 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"
-<<<<<<< HEAD
-    df_po["created_date"] = df_po["created_at"].astype("datetime64[ns]").dt.date
-    df_po["created_time"] = (
+    df_po=dict_of_df["purchase_order"]
+    df_po.index.name="purchase_record_id"
+<< << << < HEAD
+    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_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["created_date"] = pd.to_datetime(df_po["created_at"], format="%Y-%m-%d")
-    df_po["created_time"] = pd.to_datetime(df_po["created_at"], format="%H-%M-%S")
-    df_po["last_updated_date"] = pd.to_datetime(
+== == == =
+    df_po["created_date"]=pd.to_datetime(
+        df_po["created_at"], format="%Y-%m-%d")
+    df_po["created_time"]=pd.to_datetime(
+        df_po["created_at"], format="%H-%M-%S")
+    df_po["last_updated_date"]=pd.to_datetime(
         df_po["last_updated"], format="%Y-%m-%d"
     )
-    df_po["last_updated_time"] = pd.to_datetime(
+    df_po["last_updated_time"]=pd.to_datetime(
         df_po["last_updated"], format="%H-%M-%S"
->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter)
+>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter)
     )
-    df_po["agreed_delivery_date"] = pd.to_datetime(
+    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"]=pd.to_datetime(
         df_po["agreed_payment_date"], format="%Y-%m-%d"
     )
-<<<<<<< HEAD
-    df_po = df_po.drop(labels=["created_at", "last_updated"], axis=1)
-=======
+<< << << < HEAD
+    df_po=df_po.drop(labels=["created_at", "last_updated"], axis=1)
+== == == =
     df_po.drop(labels=["created_at", "last_updated"], axis=1, inplace=True)
->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter)
+>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter)
     df_po.reset_index(inplace=True)
     return df_po
 
@@ -98,42 +106,44 @@ 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"
-<<<<<<< HEAD
-    df_payment["created_date"] = (
+    df_payment=dict_of_df["payment"]
+    df_payment.index.name="payment_record_id"
+<< << << < HEAD
+    df_payment["created_date"]=(
         df_payment["created_at"].astype("datetime64[ns]").dt.date
     )
-    df_payment["created_time"] = (
+    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_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["created_date"] = pd.to_datetime(
+    df_payment["last_updated_time"]=(
+        df_payment["last_updated"].astype(
+            "datetime64[ns]").dt.floor("s").dt.time
+== == == =
+    df_payment["created_date"]=pd.to_datetime(
         df_payment["created_at"], format="%Y-%m-%d"
     )
-    df_payment["created_time"] = pd.to_datetime(
+    df_payment["created_time"]=pd.to_datetime(
         df_payment["created_at"], format="%H-%M-%S"
     )
-    df_payment["last_updated_date"] = pd.to_datetime(
+    df_payment["last_updated_date"]=pd.to_datetime(
         df_payment["last_updated"], format="%Y-%m-%d"
     )
-    df_payment["last_updated_time"] = pd.to_datetime(
+    df_payment["last_updated_time"]=pd.to_datetime(
         df_payment["last_updated"], format="%H-%M-%S"
->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter)
+>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter)
     )
-    df_payment["payment_date"] = pd.to_datetime(
+    df_payment["payment_date"]=pd.to_datetime(
         df_payment["payment_date"], format="%Y-%m-%d"
     )
-<<<<<<< HEAD
-    df_payment = df_payment.drop(labels=["created_at", "last_updated"], axis=1)
-=======
-    df_payment.drop(labels=["created_at", "last_updated"], axis=1, inplace=True)
->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter)
+<< << << < HEAD
+    df_payment=df_payment.drop(labels=["created_at", "last_updated"], axis=1)
+== == == =
+    df_payment.drop(
+        labels=["created_at", "last_updated"], axis=1, inplace=True)
+>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter)
     df_payment.reset_index(inplace=True)
     return df_payment
 
@@ -142,7 +152,7 @@ def create_fact_payment(dict_of_df):
 
 
 def create_dim_transaction(dict_of_df):
-    df_transaction = dict_of_df["transaction"].drop(
+    df_transaction=dict_of_df["transaction"].drop(
         labels=["created_at", "last_updated"], axis=1
     )
     return df_transaction
@@ -152,7 +162,7 @@ def create_dim_transaction(dict_of_df):
 
 
 def create_dim_location(dict_of_df):
-    df_loc = (
+    df_loc=(
         dict_of_df["address"]
         .drop(labels=["created_at", "last_updated"], axis=1)
         .rename(columns={"address_id": "location_id"})
@@ -161,10 +171,10 @@ def create_dim_location(dict_of_df):
 
 
 def create_dim_counterparty(dict_of_df):
-    df_prefixed_address = dict_of_df["address"].drop(labels=["created_at", "last_updated"], axis=1).add_prefix(
+    df_prefixed_address=dict_of_df["address"].drop(labels=["created_at", "last_updated"], axis=1).add_prefix(
         "counterparty_legal_", axis=1
     )
-    df_cp = pd.merge(
+    df_cp=pd.merge(
         dict_of_df["counterparty"],
         df_prefixed_address,
         left_on="legal_address_id",
@@ -181,32 +191,32 @@ def create_dim_counterparty(dict_of_df):
 
 
 def create_dim_date(dict_of_df):
-    fact_dfs = [
+    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 = []
+    list_of_date_columns=[]
     for df in fact_dfs:
-        date_col_names = [
-<<<<<<< HEAD
+        date_col_names=[
+<< << << < HEAD
             col_name for col_name in list(df.columns) if "_date" in col_name
-=======
+== == == =
             col_name for col_name in list(df.columns) if "date" in col_name
->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter)
+>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter)
         ]
         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
 
 
@@ -214,13 +224,13 @@ def create_dim_date(dict_of_df):
 
 
 def scrape_currency_names():
-    response = requests.get("https://www.xe.com/currency/").content
-    soup = BeautifulSoup(response, "html.parser")
-    currency = [
+    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(
+    sr=pd.Series(currency)
+    df_cur=sr.str.split(pat=" - ", expand=True).rename(
         {0: "currency_code", 1: "currency_name"}, axis=1
     )
     return df_cur
@@ -230,8 +240,9 @@ def scrape_currency_names():
 
 
 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=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
@@ -241,8 +252,9 @@ 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"]]
+    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
 
 
@@ -250,8 +262,8 @@ 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[
+    df_design=dict_of_df["design"]
+    dim_design=df_design.loc[
         :, ["design_id", "design_name", "file_name", "file_location"]
     ]
     return dim_design
@@ -261,10 +273,10 @@ def create_dim_design(dict_of_df):
 
 
 def create_dim_staff(dict_of_df):
-    staff_department = pd.merge(
+    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",
diff --git a/tests/test_dataframes.py b/tests/test_dataframes.py
index cc133fe..785a3fd 100644
--- a/tests/test_dataframes.py
+++ b/tests/test_dataframes.py
@@ -54,7 +54,8 @@ class TestCreateDimStaff:
             "email_address": ["Hello", "Bye"],
             "department_id": ["Hello", "Bye"],
         }
-        test_df = {"staff": pd.DataFrame(data=d), "department": pd.DataFrame(data=d2)}
+        test_df = {"staff": pd.DataFrame(
+            data=d), "department": pd.DataFrame(data=d2)}
         result = create_dim_staff(test_df)
         assert isinstance(result, pd.DataFrame)
 
@@ -71,7 +72,8 @@ class TestCreateDimStaff:
             "email_address": ["Hello", "Bye"],
             "department_id": ["Hello", "Bye"],
         }
-        test_df = {"staff": pd.DataFrame(data=d), "department": pd.DataFrame(data=d2)}
+        test_df = {"staff": pd.DataFrame(
+            data=d), "department": pd.DataFrame(data=d2)}
         result = create_dim_staff(test_df)
         expected_d = {
             "staff_id": ["Hello", "Bye"],
@@ -88,7 +90,8 @@ class TestCreateDimStaff:
 
 class TestCreatePaymentType:
     def test_create_dim_payment_type_returns_correct_columns_and_values(self):
-        d = {"payment_type_id": ["Hello", "Bye"], "payment_type_name": ["Hello", "Bye"]}
+        d = {"payment_type_id": ["Hello", "Bye"],
+             "payment_type_name": ["Hello", "Bye"]}
         test_df = {"payment_type": pd.DataFrame(data=d)}
         result = create_dim_payment_type(test_df)
         expected_columns = ["payment_type_id", "payment_type_name"]
@@ -180,11 +183,13 @@ class TestCreateDimDate:
             index=[0],
         )
         df_two = pd.DataFrame(
-            data={"updated_date": dt(2020, 5, 17), "created_date": dt(2021, 9, 13)},
+            data={"updated_date": dt(2020, 5, 17),
+                  "created_date": dt(2021, 9, 13)},
             index=[0],
         )
         df_three = pd.DataFrame(
-            data={"updated_date": dt(2022, 5, 17), "created_date": dt(2023, 5, 13)},
+            data={"updated_date": dt(2022, 5, 17),
+                  "created_date": dt(2023, 5, 13)},
             index=[0],
         )
         expected_df = pd.DataFrame(
@@ -214,7 +219,8 @@ class TestCreateDimDate:
                     mock_fso.return_value = df_three
                     result = create_dim_date({"dum": 0})
                     result.reset_index(inplace=True, drop=True)
-                    assert result.eq(expected_df, axis="columns").all(axis=None)
+                    assert result.eq(
+                        expected_df, axis="columns").all(axis=None)
 
 
 class TestCreateDimLocation:
@@ -222,7 +228,8 @@ class TestCreateDimLocation:
         dict_df = {
             "address": pd.DataFrame(
                 data=[["some_time", "some_other_time", 1, "SE18 9QO"]],
-                columns=["created_at", "last_updated", "address_id", "postal_code"],
+                columns=["created_at", "last_updated",
+                         "address_id", "postal_code"],
             )
         }
         result = create_dim_location(dict_df)
@@ -252,7 +259,7 @@ class TestCreateFactPayment:
             "payment": pd.DataFrame(
                 data=[
                     [
-<<<<<<< HEAD
+                        << << << < HEAD
                         dt.strptime(
                             "2022-11-03 14:20:49.962846", "%Y-%m-%d %H:%M:%S.%f"
                         ),
@@ -262,13 +269,13 @@ class TestCreateFactPayment:
                         1,
                         "SE18 9QO",
                         "2020-07-16",
-=======
+                        == == ===
                         dt(2020, 5, 17, 6, 15, 20),
                         dt(2020, 5, 20, 8, 19, 30),
                         1,
                         "SE18 9QO",
                         "2020-7-16",
->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter)
+                        >>>>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter)
                     ]
                 ],
                 columns=[
@@ -295,10 +302,12 @@ class TestCreateFactPayment:
         for col in list(result.columns):
             assert col in expected_cols
         for col in expected_cols:
-<<<<<<< HEAD
-            if "_date" or "_time" in col:
-                assert result[col].dtype == "O"
-=======
-            if "date" in col:
-                assert result[col].dtype == "datetime64[ns]"
->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter)
+
+
+<< << << < HEAD
+if "_date" or "_time" in col:
+    assert result[col].dtype == "O"
+== == == =
+if "date" in col:
+    assert result[col].dtype == "datetime64[ns]"
+>>>>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter)
diff --git a/tests/test_load_lambda.py b/tests/test_load_lambda.py
index 02cf2c0..65106f7 100644
--- a/tests/test_load_lambda.py
+++ b/tests/test_load_lambda.py
@@ -62,8 +62,6 @@ class TestLambdaHandler:
         assert result == {"error"}
 
 
-
-
 class TestRetrieveSecrets:
     def test_retrieve_secrets_returns_dictionary(self, mock_sm_client):
         secret = {