From c5338ebb198a79604e36d65de39e28baf54f0ecd Mon Sep 17 00:00:00 2001 From: T-Aji Date: Thu, 22 Aug 2024 10:29:34 +0100 Subject: refactor df creation into func --- src/fact-sales-order.py | 104 ++++++++++++++++-------------------------------- 1 file changed, 34 insertions(+), 70 deletions(-) (limited to 'src') diff --git a/src/fact-sales-order.py b/src/fact-sales-order.py index 870f660..7921047 100644 --- a/src/fact-sales-order.py +++ b/src/fact-sales-order.py @@ -1,86 +1,50 @@ import pandas as pd -from src.transform_lambda import get_dataframes -# {"design": "design dataframe", "address": "address dataframe", ....} -dict_of_df = get_dataframes() +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 -# 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] -df_sales = dict_of_df[sales] +def create_dim_staff(dict_of_df): + staff_department = pd.merge(dict_of_df["staff"], dict_of_df["department"], on='department_id', how="outer") + dim_staff = staff_department.loc[:, ['staff_id', 'first_name', 'last_name', 'department_name', 'location', 'email_address']] + return dim_staff -# creates the dim_design dataframe -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.loc[:, 'staff_id', 'first_name', 'last_name', 'department_name', 'location', 'email_address'] - -# creates the dim_currency dataframe -# Using .map to add currency_name column and link it to the currency code -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) - - - -# 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)" -df_address.rename(columns={"address_id": "location_id"}) -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" -) -counterparty_address.rename( - columns={ - "address_line_1": "counterparty_legal_address_line_1", - "address_line_2": "counterparty_legal_address_line_2", - "district": "counterparty_legal_district", - "city": "counterparty_legal_city", - "postal_code": "counterparty_postal_code", - "country": "counterparty_legal_country", - "phone": "counterparty_legal_phone_number", +def create_dim_currency(dict_of_df): + df_currency = dict_of_df["currency"] + dim_currency = df_currency.loc[:, ["currency_id", "currency_code"]] + mappings = { + "GBP": "Pound", + "USD": "US Dollar", + "EUR": "Euro" } -) - -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.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 -df_sales["day"] = df_sales["agreed_delivery_date"].dt.day -df_sales["day_of_week"] = df_sales["agreed_delivery_date"].dt.dayofweek -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() + dim_currency["currency_name"] = dim_currency["currency_code"].map(mappings) + return dim_currency + + +def create_dim_date(dict_of_df): + df_sales = dict_of_df["sales"] + 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 + df_sales["day"] = df_sales["agreed_delivery_date"].dt.day + df_sales["day_of_week"] = df_sales["agreed_delivery_date"].dt.dayofweek + 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() + dim_date = ["date_id", "year", "month", "day", "day_of_week", "day_name", "month_name", "quarter"] #series.dt.quarter() + return dim_date # 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() + # TO DO: +# complete dim_date # fact_sales_order -- cgit v1.2.3