Rolling Dates Backward with Python Pandas CustomBusinessHour

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πŸ’‘ Problem Formulation: In data analysis, it’s often necessary to adjust datetime objects based on business hours. For instance, one might need to roll a provided date backward to the last business hour if it falls outside of custom business hours. This article explores how to use Python’s pandas library, specifically the CustomBusinessHour class, to handle such a scenario effectively. An input example would be a Timestamp ‘2021-03-01 07:00:00’ and desired output ‘2021-02-26 17:00:00’, assuming business hours end at 5 PM and the weekend is non-business time.

Method 1: Using CustomBusinessHour with rollforward Rollback

CustomBusinessHour in Pandas is a powerful tool that can be tailored to fit non-standard business hours and holidays. It provides methods like rollback(), designed to roll dates backward to the previous business hour.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessHour
from pandas import Timestamp

business_hours = CustomBusinessHour(end='17:00')

given_date = Timestamp('2021-03-01 07:00:00')
rolled_back_date = business_hours.rollback(given_date)

print(rolled_back_date)

The output of this code snippet would be:

2021-02-26 17:00:00

This code first defines custom business hours that end at 5 PM and have standard weekend rules. Then, it uses rollback() on a date outside those hours to adjust it to the closest previous business hour.

Method 2: Combining CustomBusinessHour with Datetime Index Rollback

Another way to achieve rollback of dates in Python is to create a datetime index and apply CustomBusinessHour directly to it. This can be useful when working with multiple dates.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessHour
from pandas import Timestamp, date_range

business_hours = CustomBusinessHour(end='17:00')

date_series = date_range(start='2021-03-01 07:00:00', periods=1, freq=business_hours)
rolled_back_date = business_hours.rollback(date_series[0])

print(rolled_back_date)

The output of this code snippet would be:

2021-02-26 17:00:00

This snippet creates a DatetimeIndex object with a single Timestamp and applies the rollback() method of CustomBusinessHour to find the latest business hour before the given date.

Method 3: Using Custom Business Hours with a Predefined Holiday Calendar

For more complex schedules, including holidays, we can integrate pandas CustomBusinessHour with a custom holiday calendar.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessHour
from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday
from pandas import Timestamp

class MyHolidayCalendar(AbstractHolidayCalendar):
    rules = [
        Holiday('New Year', month=1, day=1)
    ]

business_hours = CustomBusinessHour(end='17:00', calendar=MyHolidayCalendar())

given_date = Timestamp('2021-01-02 07:00:00')
rolled_back_date = business_hours.rollback(given_date)

print(rolled_back_date)

The output of this code snippet would be:

2020-12-31 17:00:00

This code creates a custom holiday calendar and passes it to the CustomBusinessHour class. When rolling back, it avoids holidays and rolls the date back to the last valid business hour before the holiday.

Method 4: Applying Rollback Operations on a Series

When dealing with an entire series of dates, the rollback operation can be applied to each element using the apply() method along with a lambda function.

Here’s an example:

import pandas as pd
from pandas.tseries.offsets import CustomBusinessHour

business_hours = CustomBusinessHour(end='17:00')

series_dates = pd.Series([Timestamp('2021-03-01 07:00:00'), Timestamp('2021-03-02 08:00:00')])
rolled_back_series = series_dates.apply(lambda x: business_hours.rollback(x))

print(rolled_back_series)

The output would be a Series:

0   2021-02-26 17:00:00
1   2021-03-01 17:00:00
dtype: datetime64[ns]

This code converts a list of timestamps into a pandas Series and then rolls each date back to the previous business hour using the apply method, yielding a new Series of adjusted dates.

Bonus One-Liner Method 5: Using Previous Close Method

While not a direct feature of CustomBusinessHour, the previous_close() method from the trading_calendars package can be used to roll a datetime back to the last close of trading session.

Here’s an example:

from trading_calendars import get_calendar

trading_cal = get_calendar('NYSE')
given_date = Timestamp('2021-03-01 07:00:00')
rolled_back_date = trading_cal.previous_close(given_date)

print(rolled_back_date)

The output of this code snippet:

2021-02-26 21:00:00

This code snippet makes use of the trading_calendars package to quickly find the last close time for a given timestamp, showing an alternative for financial date rollbacks.

Summary/Discussion

  • Method 1: CustomBusinessHour rollback. Simple and direct. May not handle holidays without additional setup.
  • Method 2: Datetime Index with CustomBusinessHour. Ideal for manipulating ranges of dates. A bit more complex to setup.
  • Method 3: CustomBusinessHour with Holiday Calendar. Provides holiday awareness. Requires holiday definition.
  • Method 4: Series apply with rollback. Useful for datasets. Can be less efficient with very large datasets.
  • Method 5: previous_close in trading_calendars. Specifically for financial dates. Requires additional library.