style: format code with Autopep8, Black and Ruff Formatter

This commit fixes the style issues introduced in 8f75a47 according to the output
from Autopep8, Black and Ruff Formatter.

Details: https://github.com/ajschofield/de-project-bentley/pull/96

2 files changed, 41 insertions, 38 deletions

diff --git a/src/dataframes.py b/src/dataframes.py
index fc84f48..f2cae5d 100644
--- a/src/dataframes.py
+++ b/src/dataframes.py
@@ -16,14 +16,15 @@ import requests
 # dim_counterparty
 
 
-
 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
     df_sales["created_time"] = pd.to_datetime(df_sales["created_at"]).dt.time
-    df_sales["last_updated_date"] = pd.to_datetime(df_sales["last_updated"]).dt.date
-    df_sales["last_updated_time"] = pd.to_datetime(df_sales["last_updated"]).dt.time
+    df_sales["last_updated_date"] = pd.to_datetime(
+        df_sales["last_updated"]).dt.date
+    df_sales["last_updated_time"] = pd.to_datetime(
+        df_sales["last_updated"]).dt.time
     fact_sales_order = df_sales.loc[
         :,
         [
@@ -70,10 +71,14 @@ 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
-    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
+    df_payment["created_date"] = pd.to_datetime(
+        df_payment["created_at"]).dt.date
+    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[
         :,
         [
@@ -95,7 +100,6 @@ def create_fact_payment(dict_of_df):
     return fact_payment
 
 
-
 # test passed
 
 
@@ -117,10 +121,10 @@ def create_dim_location(dict_of_df):
 
 
 def create_dim_counterparty(dict_of_df):
-    df_prefixed_address = dict_of_df["address"].add_prefix(
+    df_prefixed_address=dict_of_df["address"].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",
@@ -137,40 +141,40 @@ 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),
     ]
-    date_col_names = [
+    date_col_names=[
         col_name for col_name in list(fact_dfs[0].columns) if "date" in col_name
     ]
-    list_of_date_columns = []
+    list_of_date_columns=[]
     for df in fact_dfs:
         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
 
 
 # tests passed
 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
@@ -179,8 +183,9 @@ 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
@@ -189,8 +194,9 @@ def create_dim_currency(dict_of_df, names=scrape_currency_names()):
 # tests passed
 
 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
 
 
@@ -199,8 +205,8 @@ 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
@@ -210,10 +216,10 @@ def create_dim_design(dict_of_df):
 # tests passed
 
 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_fact_sales_order.py b/tests/test_fact_sales_order.py
index 77395a1..a245379 100644
--- a/tests/test_fact_sales_order.py
+++ b/tests/test_fact_sales_order.py
@@ -4,7 +4,6 @@ from unittest.mock import patch
 from datetime import datetime as dt
 
 
-
 class TestCreateDimDesign:
     def test_dim_design_returns_dataframe(self):
         d = {
@@ -135,7 +134,6 @@ class TestCreateDimCounterparty:
 
 
 class TestCreateDimCurrency:
-
     def test_dim_currency_returns_columns_and_values(self):
         nones = [None, None, None]
         d = {
@@ -246,4 +244,3 @@ class TestCreateDimTransaction:
         }
         result = create_dim_transaction(dict_df)
         assert list(result.columns) == ["transaction_id", "some_other_id"]
-