tests: additional tests written (pass) for dim tables transformation. Fact transformation functions not yet tested

1 files changed, 66 insertions, 47 deletions

diff --git a/tests/test_fact_sales_order.py b/tests/test_fact_sales_order.py
index ca53faa..f0796eb 100644
--- a/tests/test_fact_sales_order.py
+++ b/tests/test_fact_sales_order.py
@@ -1,6 +1,7 @@
-from src.dataframes import create_dim_design, create_dim_staff, create_dim_payment_type, create_dim_counterparty, create_dim_currency
+from src.dataframes import *
 import pandas as pd
 from unittest.mock import patch
+from datetime import datetime as dt
 
 class TestCreateDimDesign:
     def test_dim_design_returns_dataframe(self):
@@ -52,59 +53,77 @@ class TestCreatePaymentType:
         assert result.equals(expected_df)
 
 class TestCreateDimCounterparty:
-    def test_create_dim_counterparty_type_returns_correct_columns_and_values(self):
-        data_d = {"counterparty_id": ["Hello", "Bye"], 
+    
+    def test_create_dim_counterparty_type_returns_correct_columns_and_object(self):
+        data_l = pd.DataFrame(data={"counterparty_id": ["Hello", "Bye"], 
              "counterparty_legal_name": ["Hello", "Bye"], 
-             "counterparty_legal_address_line_1": ["Hello", "Bye"], 
-             }
-        data_a = {"address_id":
-                  "address",
-                  }
-        test_df = {"address": pd.DataFrame(data=data_a)}
-        test_df = {}
+             "commercial_contact": ["Hello", "Bye"], 
+             "legal_address_id": ["bond street", "regent street"]})
+        data_a = pd.DataFrame(data={"address_id":["bond street", "regent street"],
+                  "postcode":[98365,93753]})
+        test_df = {"address": data_a,"counterparty":data_l}
         result = create_dim_counterparty(test_df)
 
-        expected_columns = ["counterparty_id", 
-             "counterparty_legal_name", 
-             "counterparty_legal_address_line_1", 
-             "counterparty_legal_address_line_2", 
-             "counterparty_legal_district",
-             "counterparty_legal_city",
-             "counterparty_legal_postal_code",
-             "counterparty_legal_postal_code", 
-             "counterparty_legal_phone_number"]
-        expected_d = {"counterparty_id": ["Hello", "Bye"], 
-             "counterparty_legal_name": ["Hello", "Bye"], 
-             "counterparty_legal_address_line_1": ["Hello", "Bye"], 
-             "counterparty_legal_address_line_2": ["Hello", "Bye"], 
-             "counterparty_legal_district": ["Hello", "Bye"],
-             "counterparty_legal_city": ["Hello", "Bye"],
-             "counterparty_legal_postal_code": ["Hello", "Bye"],
-             "counterparty_legal_postal_code": ["Hello", "Bye"], 
-             "counterparty_legal_phone_number": ["Hello", "Bye"]}
-        expected_df = pd.DataFrame(data=expected_d)
+        expected_columns = ["counterparty_id", "counterparty_legal_name", 
+                            "commercial_contact", "counterparty_legal_postcode"]
+        print(data_l)
+        print(data_a)
         assert isinstance(result, pd.DataFrame)
         assert list(result.columns) == expected_columns
-        assert result.equals(expected_df)
 
-# # figuring out how to mock currency scraper functiom
-# class TestCreateDimCurrency:
-#     @patch("src.dataframes.scrape_currency_names")  
-#     def test_dim_currency_returns_columns_and_values(self):
-#         d = {"currency_id": [1, 2, 3], "currency_code": ["USD", "EUR", "GBP"]}
-#         test_df = {"currency": pd.DataFrame(data=d)}
-#         result = create_dim_currency(test_df)
-#         expected_d = {"currency_id": [1, 2, 3], "currency_code": ["USD", "EUR", "GBP"], "currency_name": ["US Dollar", "Euro", "Pound"]}
-#         expected_df = pd.DataFrame(data=expected_d)
-#         expected_result = expected_df.copy()
-#         assert result.equals(expected_result)  
+class TestCreateDimCurrency:
+    
+    def test_dim_currency_returns_columns_and_values(self):
+        nones = [None,None,None]
+        d = {"currency_id": [1, 2, 3], "currency_code": ["USD", "EUR", "GBP"],"created_at":nones,"last_updated":nones}
+        test_df = {"currency": pd.DataFrame(data=d)}
+        scraper_output = pd.DataFrame({"currency_code":["RUS","USD","PHP","GBP","EUR"],"currency_name":["Rubble","US Dollar","Peso","Pound","Euro"]})
+        result = create_dim_currency(test_df,names=scraper_output).sort_values(by="currency_code",axis=0)
+        expected_d = {"currency_id": [1, 2, 3], "currency_code": ["USD", "EUR", "GBP"], "currency_name": ["US Dollar", "Euro", "Pound"]}
+        expected_df = pd.DataFrame(data=expected_d).sort_values(by="currency_code",axis=0)
+        assert isinstance(result, pd.DataFrame) 
+        assert result.equals(expected_df)  
 
-#     def test_dim_currency_returns_dataframe(self):
-#         d = {"currency_id": [1, 2, 3], "currency_code": ["USD", "EUR", "GBP"]}
-#         test_df = {"currency": pd.DataFrame(data=d)}
-#         result = create_dim_currency(test_df)
-#         assert isinstance(result, pd.DataFrame)  
+    def test_scrape_currency_names_returns_dataframe_with_correct_collumns(self):
+        result = scrape_currency_names()
+        assert isinstance(result,pd.DataFrame)
+        assert list(result.columns) == ['currency_code', 'currency_name']
+
+class TestCreateDimDate:
+
+    def test_returns_required_columns(self):
+        df_one = pd.DataFrame(data={'updated_date':dt(2020, 5, 17),'created_date':dt(2021, 5, 13),'not_dat':None},index=[0])
+        df_two = pd.DataFrame(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)},index=[0])
+        expected_df = pd.DataFrame(data=
+                        [[dt(2020,5,17),2020,5,17,6,'Sunday','May',2],
+                        [dt(2021,5,13),2021,5,13,3,'Thursday','May',2],
+                        [dt(2021,9,13),2021,9,13,0,'Monday','September',3],
+                        [dt(2022,5,17),2022,5,17,1,'Tuesday','May',2],
+                        [dt(2023,5,13),2023,5,13,5,'Saturday','May',2]],
+                        columns=['date_id','year','month','day','day_of_week','day_name','month_name','quarter'])
+        with patch("src.dataframes.create_fact_payment") as mock_fp:
+            with patch("src.dataframes.create_fact_purchase_orders") as mock_fpo:
+                with patch("src.dataframes.create_fact_sales_order") as mock_fso:
+                    mock_fp.return_value = df_one
+                    mock_fpo.return_value = df_two
+                    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)
         
-    
+class TestCreateDimLocation:
 
+    def test_returns_correct_columns_lo(self):
+        dict_df = {'address':pd.DataFrame(data=[['some_time','some_other_time',1,'SE18 9QO']],
+                                          columns=['created_at','last_updated','address_id','postal_code'])}        
+        result = create_dim_location(dict_df)
+        assert list(result.columns) == ['location_id','postal_code']
+    
+class TestCreateDimTransaction:
+     def test_returns_correct_columns_tr(self):
+        dict_df = {'transaction':pd.DataFrame(data=[['some_time','some_other_time',1,'SE18 9QO']],
+                                          columns=['created_at','last_updated','transaction_id','some_other_id'])}        
+        result = create_dim_transaction(dict_df)
+        assert list(result.columns) == ['transaction_id','some_other_id']
     
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