From da3d85dd2dc515226d16992c5f63b2a8b02a0a38 Mon Sep 17 00:00:00 2001
From: Ellie <ecsymonds@gmail.com>
Date: Wed, 21 Aug 2024 13:41:01 +0100
Subject: add dim tables: design, staff, currency, location (wip)

---
 src/fact-sales-order.py | 54 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 src/fact-sales-order.py

(limited to 'src')

diff --git a/src/fact-sales-order.py b/src/fact-sales-order.py
new file mode 100644
index 0000000..a143889
--- /dev/null
+++ b/src/fact-sales-order.py
@@ -0,0 +1,54 @@
+import pandas as pd
+from src.transform_lambda import get_dataframes
+
+dict_of_df = get_dataframes()  # {"design": "design dataframe", "address": "address dataframe", ....}
+
+
+# iterates through each dataframe in the list of dataframes and assigns them to a variable
+df_design = dict_of_df[design]
+df_currency = dict_of_df[currency]
+df_address = dict_of_df[address]
+df_staff = dict_of_df[staff]
+df_department = dict_of_df[department]
+df_counterparty = dict_of_df[counterparty]
+
+
+# creates the dim_design dataframe
+dim_design = df_design["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']
+
+# 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"]
+
+# creates the dim_location dataframe
+# 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)"
+dim_location = df_address["address_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")
+
+# dim_counterparty = df_counterparty["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"]
+
+
+# TO DO:
+# dim_location
+# dim_date
+# fact_sales_order
\ No newline at end of file
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