diff options
| -rw-r--r-- | src/fact-sales-order.py | 35 |
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() |