began dim_date df

1 files changed, 17 insertions, 18 deletions

diff --git a/src/fact-sales-order.py b/src/fact-sales-order.py
index 30c958f..ef18f02 100644
--- a/src/fact-sales-order.py
+++ b/src/fact-sales-order.py
@@ -14,27 +14,21 @@ df_counterparty = dict_of_df[counterparty]
 df_sales = dict_of_df[sales]
 
 # creates the dim_design dataframe
-dim_design = df_design["design_id", "design_name", "file_name", "file_location"]
+dim_design = df_design.loc[:, "design_id", "design_name", "file_name", "file_location"]
 
 # creates the dim_staff dataframe
 staff_department = pd.merge(df_staff, df_department, on='department_id', how="outer")
-dim_staff = staff_department['staff_id', 'first_name', 'last_name', 'department_name', 'location', 'email_address']
+dim_staff = staff_department.loc[:, 'staff_id', 'first_name', 'last_name', 'department_name', 'location', 'email_address']
 
 # creates the dim_currency dataframe
-# currency names currently hardcoded and not taken from database, is this viable/how else to do this? 
-d = {"currency_id": [1, 2, 3], "currency_code": ["GBP", "USD", "EUR"], "currency_name": ["Pound", "US Dollar", "Euro"]}
-currency_names = pd.DataFrame(data=d)
-join_currency = pd.merge(df_currency, currency_names, on="currency_name", how="outer")
-dim_currency = join_currency["currency_id", "currency_code", "currency_name"]
-
 # Using .map to add currency_name column and link it to the currency code
-# dim_currency = df_currency["currency_id", "currency_code"]
-# mappings = {
-#     "GBP": "Pound",
-#     "USD": "US Dollar",
-#     "EUR": "Euro"
-# }
-# dim_currency["currency_name"] = dim_currency["currency_code"].map(mappings)
+dim_currency = df_currency.loc[:, "currency_id", "currency_code"]
+mappings = {
+    "GBP": "Pound",
+    "USD": "US Dollar",
+    "EUR": "Euro"
+}
+dim_currency["currency_name"] = dim_currency["currency_code"].map(mappings)
 
 
 
@@ -42,7 +36,7 @@ dim_currency = join_currency["currency_id", "currency_code", "currency_name"]
 # need to change address id to location id 
 "dim_location dataframe: (location_id, address_line_1, address_line_2, district, city, postal code, country, phone)"
 df_address.rename(columns={"address_id": "location_id"})
-dim_location = df_address["location_id", "address_line_1", "address_line_2", "district", "city", "postal_code" "country", "phone"]
+dim_location = df_address.loc[:, "location_id", "address_line_1", "address_line_2", "district", "city", "postal_code" "country", "phone"]
 
 # creates the dim_counterparty dataframe
 counterparty_address = pd.merge(df_counterparty, df_address, left_on="legal_address_id", right_on='address_id', how="outer")
@@ -50,12 +44,12 @@ counterparty_address.rename(columns={"address_line_1": "counterparty_legal_addre
                                      "district": "counterparty_legal_district", "city": "counterparty_legal_city", "postal_code": "counterparty_postal_code",
                                      "country": "counterparty_legal_country", "phone": "counterparty_legal_phone_number"})
 
-dim_counterparty = df_counterparty["counterparty_id", "counterparty_legal_name", "counterparty_legal_address_line_1",
+dim_counterparty = df_counterparty.loc[:, "counterparty_id", "counterparty_legal_name", "counterparty_legal_address_line_1",
                                    "counterparty_legal_address_line_2", "counterparty_legal_district", "counterpart_legal_city",
                                    "counterparty_legal_postal_code", "counterparty_legal_country", "counterparty_legal_phone_number"]
 
 # creates the dim_date dataframe
-df_sales = df_sales["agreed_delivery_date"]
+df_sales = df_sales.loc[:, "agreed_delivery_date"]
 df_sales["agreed_delivery_date"] = pd.to_datetime["agreed_delivery_date"]
 df_sales["year"] = df_sales["agreed_delivery_date"].dt.year
 df_sales["month"] = df_sales["agreed_delivery_date"].dt.month
@@ -65,6 +59,11 @@ df_sales["day_name"] = df_sales["agreed_delivery_date"].dt.day_name()
 df_sales["month_name"] = df_sales["agreed_delivery_date"].dt.month_name()
 df_sales["quarter"] = df_sales["agreed_delivery_date"].dt.quarter()
 
+# repeat ln 52 - 60 for each column
+# merge dataframes into one dataframe
+# remove duplicates
+
+
 dim_date = ["date_id", "year", "month", "day", "day_of_week", "day_name", "month_name", "quarter"]   #series.dt.quarter()