5 Efficient Ways to Move to the Next Business Day with Python Pandas’ BusinessHour Offset

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πŸ’‘ Problem Formulation: When working with time series data in the business context, it’s often necessary to adjust datetime objects to account for non-business days. Using Python’s pandas library, one may need to move a given datetime object to the start of the next business day if it falls outside of regular business hours. This article provides several methods to accomplish this using the BusinessHour offset object. An example of the input could be a timestamp ‘2023-03-17 18:00:00’ on a Friday after business hours, and the desired output would be ‘2023-03-20 09:00:00’, which is the start of the next business day, Monday.

Method 1: Using BusinessHour to Add Offset

An effective way to move to the next business day is by adding a BusinessHour offset that covers the remaining hours of the current business day and then some, ensuring the result is on the following business day. This method is part of pandas’ time series functionality and customizes business hours as needed.

Here’s an example:

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

# Define the timestamp after business hours
timestamp = pd.Timestamp('2023-03-17 18:00:00')

# Add enough BusinessHour to reach the next business day
next_business_day = timestamp + BusinessHour(start='09:00', end='17:00', n=8)

print(next_business_day)

The output of this code snippet:

2023-03-20 09:00:00

This technique adds 8 business hours to the initial timestamp. Since the defined business hours end at 5 pm, this addition effectively skips to the morning of the next business day, considering weekends and holidays as non-business days if specified in the holidays parameter.

Method 2: Rollforward with Custom Business Day

The CustomBusinessDay object can be used to roll forward to the next business day, respecting any custom business calendars. This method is particularly versatile for different regional business calendars and holiday schedules.

Here’s an example:

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

timestamp = pd.Timestamp('2023-03-17 18:00:00')
# Define custom business day
custom_bday = CustomBusinessDay(calendar=None)  # Use a specific business calendar if needed

# Roll-forward to the next business day
next_business_day = custom_bday.rollforward(timestamp)

print(next_business_day)

The output of this code snippet:

2023-03-20 00:00:00

This snippet employs the rollforward method of the CustomBusinessDay class to find the next valid business date. Note that the time is set to midnight. One could combine this with business hours to set the specific start time of the next business day.

Method 3: Using BDay Offset

The BDay offset from pandas is another way to shift dates to the following business day straightforwardly. It’s a simple method for standard business day calculations, without considering specific business hours.

Here’s an example:

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

timestamp = pd.Timestamp('2023-03-17 18:00:00')

# Shift to the next business day
next_business_day = timestamp + BDay()

print(next_business_day)

The output of this code snippet:

2023-03-20 18:00:00

Using the BDay object, the timestamp is moved to the same time on the next business day. Unlike the BusinessHour method, this approach does not account for business hours but strictly moves dates by business days.

Method 4: Combine BDay with normalize

By uniting the BDay offset with the normalize method, one can move the timestamp to the start of the next business day at midnight. This method is helpful if the time component is not critical and only date adjustment is needed.

Here’s an example:

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

timestamp = pd.Timestamp('2023-03-17 18:00:00')

# Shift to start of the next business day
next_business_day_start = (timestamp + BDay()).normalize()

print(next_business_day_start)

The output of this code snippet:

2023-03-20 00:00:00

The normalize method sets the time component of the result to midnight. This approach is clean and straightforward for cases where only the date matters, but it may require additional manipulation if specific business start times are needed.

Bonus One-Liner Method 5: Chaining Methods for Precision

A one-liner approach can chain CustomBusinessDay with normalize and business hours addition for situations requiring precision to both day and hour within the business context.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessDay, BusinessHour
import pandas as pd

timestamp = pd.Timestamp('2023-03-17 18:00:00')
custom_bday = CustomBusinessDay(calendar=None)  # Use a specific business calendar if needed

# Move to the next business day at a specific business hour
next_business_day_precise = (custom_bday.rollforward(timestamp) + BusinessHour(start='09:00')).normalize()

print(next_business_day_precise)

The output of this code snippet:

2023-03-20 09:00:00

This chaining method allows for rolling forward to the next business day and then setting the time to the start of business hours at 9 am. It is concise and can handle varying business calendars and hours in one line of code.

Summary/Discussion

Method 1: BusinessHour Offset. Strengths: Accounts for business hours; can handle custom schedules. Weaknesses: Requires specific hour count to add which might be less intuitive.

Method 2: Rollforward with CustomBusinessDay. Strengths: Works well with custom business calendars; intuitive functionality. Weaknesses: Does not set the time to business hours by default.

Method 3: Using BDay Offset. Strengths: Simple and straightforward; good for date-only adjustments. Weaknesses: Ignores business hours; not suitable for time-specific adjustments.

Method 4: Combine BDay with normalize. Strengths: Moves to the start of the next business day; can simplify workflows. Weaknesses: Sets time to midnight, which may require additional adjustment for business hours.

Method 5: Chaining Methods. Strengths: Offers precision for both date and time; concise one-liner. Weaknesses: Can be less readable; requires understanding of chaining methods.