test: fact transformation function for payment test passes, other fact functions are equivalent, no tests written

1 files changed, 85 insertions, 166 deletions

diff --git a/src/dataframes.py b/src/dataframes.py
index ab53063..41f39b8 100644
--- a/src/dataframes.py
+++ b/src/dataframes.py
@@ -2,7 +2,7 @@ import pandas as pd
 from bs4 import BeautifulSoup
 import requests
 
-# Table names:
+#Table names:
 # fact_sales_order
 # fact_purchase_orders
 # fact_payment
@@ -16,214 +16,133 @@ import requests
 # dim_counterparty
 
 
+#no test, same as fact_payment
 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
-    fact_sales_order = df_sales.loc[
-        :,
-        [
-            "sales_record_id",
-            "sales_order_id",
-            "created_date",
-            "created_time",
-            "last_updated_date",
-            "last_updated_time",
-            "sales_staff_id",
-            "counterparty_id",
-            "units_sold",
-            "unit_price",
-            "currency_id",
-            "design_id",
-            "agreed_payment_date",
-            "agreed_delivery_date",
-            "agreed_delivery_location_id",
-        ],
-    ]
-    return fact_sales_order
-
-
-# fact_purchase_order from purchase_order
-
-
+    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"],format='%H-%M-%S')
+    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.reset_index(inplace=True)
+    return df_sales
+
+#no test, same as fact_payment
 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"].date()
-    df_po["created_time"] = df_po["created_at"].dt.time
-    df_po["last_updated_date"] = df_po["last_updated_at"].date()
-    df_po["last_updated_time"] = df_po["last_updated_at"].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_at"], axis=1, inplace=True)
+    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'],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'],format='%H-%M-%S')
+    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.reset_index(inplace=True)
     return df_po
 
-
+#test passed
 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"].date()
-    df_payment["created_time"] = df_payment["created_at"].time
-    df_payment["last_updated_date"] = df_payment["last_updated"].date()
-    df_payment["last_updated_time"] = df_payment["last_updated"].time
-    df_payment["payment_date"] = pd.to_datetime(
-        df_payment["payment_date"], format="%Y-%m-%d"
-    )
-    fact_payment = df_payment.loc[
-        :,
-        [
-            "payment_record_id",
-            "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",
-        ],
-    ]
-    return fact_payment
-
-
-# test passed
-
-
+    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_at"],format='%H-%M-%S')
+    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"],format='%H-%M-%S')
+    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.reset_index(inplace=True)
+    return df_payment
+
+#test passed
 def create_dim_transaction(dict_of_df):
-    df_transaction = dict_of_df["transaction"].drop(
-        labels=["created_at", "last_updated"], axis=1
-    )
+    df_transaction = dict_of_df["transaction"].drop(labels=['created_at', 'last_updated'], axis=1)
     return df_transaction
 
-
-# test passed
+#test passed
 def create_dim_location(dict_of_df):
-    df_loc = (
-        dict_of_df["address"]
-        .drop(labels=["created_at", "last_updated"], axis=1)
-        .rename(columns={"address_id": "location_id"})
-    )
+    df_loc = dict_of_df['address'].drop(labels=['created_at', 'last_updated'], axis=1).rename(columns={'address_id': 'location_id'})
     return df_loc
 
 
 def create_dim_counterparty(dict_of_df):
-    df_prefixed_address = dict_of_df["address"].add_prefix(
-        "counterparty_legal_", axis=1
-    )
-    df_cp = pd.merge(
-        dict_of_df["counterparty"],
-        df_prefixed_address,
-        left_on="legal_address_id",
-        right_on="counterparty_legal_address_id",
-        how="outer",
-    )
-    df_cp.drop(
-        columns=["legal_address_id", "counterparty_legal_address_id"], inplace=True
-    )
+    df_prefixed_address = dict_of_df['address'].add_prefix('counterparty_legal_', axis=1) 
+    df_cp = pd.merge(dict_of_df['counterparty'], 
+            df_prefixed_address, 
+            left_on="legal_address_id", 
+            right_on="counterparty_legal_address_id", 
+            how="outer")
+    df_cp.drop(columns=["legal_address_id","counterparty_legal_address_id"],inplace=True)
     return df_cp
 
-
-# test passed
-
-
+#test passed
 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),
-    ]
-    date_col_names = [
-        col_name for col_name in list(fact_dfs[0].columns) if "date" in col_name
-    ]
+    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]
         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
+#tests passed
 def scrape_currency_names():
-    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"})
-    ]
+    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(
-        {0: "currency_code", 1: "currency_name"}, axis=1
-    )
+    df_cur = sr.str.split(pat=" - ",expand=True).rename({0:'currency_code',1:'currency_name'},axis=1)
     return df_cur
 
+#tests passed
+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,names,left_on='currency_code',right_on='currency_code',how='inner')
+    return dim_cur
+
+#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"]]
+    return dim_payment_type
+
+#tests passed
+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"]]
+    return dim_design
+#tests passed
+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[:, ['staff_id', 'first_name', 'last_name', 'department_name', 'location', 'email_address']]
+    return dim_staff
+
+
 
-# tests passed
 
 
-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, names, left_on="currency_code", right_on="currency_code", how="inner"
-    )
-    return dim_cur
 
 
-# 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"]]
-    return dim_payment_type
 
 
-# tests passed
 
 
-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"]
-    ]
-    return dim_design
 
 
-# tests passed
 
 
-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[
-        :,
-        [
-            "staff_id",
-            "first_name",
-            "last_name",
-            "department_name",
-            "location",
-            "email_address",
-        ],
-    ]
-    return dim_staff