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]
df_sales = dict_of_df[sales]

# 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"})

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()

# 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:
# fact_sales_order