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import pandas as pd
from src.transform_lambda import get_dataframes
# {"design": "design dataframe", "address": "address dataframe", ....}
dict_of_df = get_dataframes()
# 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["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"]
# 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)
# 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[
"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[
"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["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()
# TO DO:
# fact_sales_order
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