mirror of
https://gitlab.com/cief-data/dbt_cloud.git
synced 2026-08-19 04:14:00 +00:00
CIEF ACC1
81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
# Python script to compute advance working dates
|
|
|
|
# Weekend Example: using Saturday or Sunday and add 2 working days:
|
|
# Monday = 1 working day, Tuesday = 1 working day, Wednesday = Delivery Date
|
|
# Assumption for weekends, base date is NOT considered as one working day
|
|
|
|
# Weekday Example: using Monday and adding 2 working days:
|
|
# Monday = 1 working day, Tuesday = 1 working day, Wednesday = Delivery Date
|
|
# Assumption for weekdays, base date is considered as one working day
|
|
|
|
# Forecasting on future dates without information on holidays will not be accurate.
|
|
|
|
|
|
import pandas as pd
|
|
from datetime import datetime as dt, timedelta
|
|
|
|
|
|
# Function to check if a given date is a weekend [Saturday(5) or Sunday(6)]
|
|
def is_weekend(date):
|
|
return date.weekday() in (5, 6)
|
|
|
|
|
|
# Function to compute the in advance working days
|
|
def compute_working_days(start_date, num_working_days, holiday_list, after_cut_off = 0):
|
|
# after_cut_off = 0 for orders before 4pm
|
|
# after_cut_off = 1 for orders after 4pm
|
|
|
|
counter = 0
|
|
|
|
# For dates on weekend or holiday or after cut off time, additional 1 working day to the loop
|
|
if (after_cut_off == 1) or is_weekend(start_date) or (start_date in holiday_list):
|
|
counter -= 1
|
|
|
|
# Loop to increase n number of working days, if weekend/holiday, skip counter
|
|
while counter < num_working_days:
|
|
start_date = start_date + timedelta(days=1)
|
|
|
|
if is_weekend(start_date) or (start_date in holiday_list):
|
|
continue
|
|
|
|
counter += 1
|
|
|
|
return start_date
|
|
|
|
|
|
# Main function
|
|
def model(dbt, session):
|
|
|
|
# Setting configuration
|
|
dbt.config(materialized="table",
|
|
packages = ["pandas"])
|
|
|
|
# Import data from upstream dbt model
|
|
sp_df_date = dbt.ref("int__dates")
|
|
|
|
# Filter holiday dates into a dataframe
|
|
sp_df_filter = sp_df_date.filter(sp_df_date['IS_COMPANY_HOLIDAY'] == 1)
|
|
sp_df_holiday_date = sp_df_filter.select('DATE_DAY')
|
|
|
|
# Convert snowpark dataframe to pandas dataframe
|
|
pd_df_date = sp_df_date.to_pandas()
|
|
pd_df_holiday_date = sp_df_holiday_date.to_pandas()
|
|
|
|
# Store holiday dates in a list
|
|
holiday_list = pd_df_holiday_date['DATE_DAY'].tolist()
|
|
|
|
# Applying add working days function to df
|
|
add_working_days = [1, 2, 3, 7, 30, 90 ,365]
|
|
|
|
for working_day in add_working_days:
|
|
for after_cut_off in (0, 1):
|
|
|
|
if after_cut_off == 0:
|
|
col_name = f'ADD_{working_day}_WORKING_DAY_INCLUDED_BASE_DATE'
|
|
else:
|
|
col_name = f'ADD_{working_day}_WORKING_DAY_EXCLUDED_BASE_DATE'
|
|
|
|
# Apply function and append new calculated columns in the dataframe
|
|
pd_df_date[col_name] = pd_df_date['DATE_DAY'].apply(lambda start_date: compute_working_days(start_date, working_day, holiday_list, after_cut_off))
|
|
|
|
return pd_df_date |