Checking Normalization of CustomBusinessHour Offsets in Pandas

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💡 Problem Formulation: When working with time series data in Python’s pandas library, it is often necessary to understand whether the CustomBusinessHour offset has been normalized. This entails verifying that the offset aligns with the start of a business day, usually midnight. A normalized offset helps in maintaining uniformity in timestamp data across various operations. If, for instance, you have an offset ‘CustomBusinessHour(start=’09:00′)’ and a timestamp ‘2023-03-03 15:45’, you’d want to check if your business hour offset is using a normalized base or not.

Method 1: Using the normalized Parameter

This method involves initializing the CustomBusinessHour offset with the normalized parameter. When normalized is set to True, the offset is forced to normalize to midnight. This method is straightforward and part of pandas’ offset functionality.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessHour
cbh = CustomBusinessHour(normalize=True)
print(cbh)

Output:

<CustomBusinessHour: CBH=09:00-17:00>

This code snippet creates a CustomBusinessHour offset with the normalization parameter set. Even though the time isn’t explicitly set to midnight, setting normalize=True ensures that the offset will base itself at the standardized starting point of a day, which is midnight.

Method 2: Checking with the start_time Attribute

The start_time attribute of CustomBusinessHour can be used to check if the object has been normalized. If the start time is at midnight (00:00:00), one can infer that the offset is normalized.

Here’s an example:

cbh = CustomBusinessHour(start='09:00')
is_normalized = cbh.start_time == '00:00:00'
print(is_normalized)

Output:

False

By comparing the start_time of the CustomBusinessHour object to midnight, the code checks if the offset has been normalized. Since the start_time is set to ’09:00′, the output is False, indicating the offset is not normalized.

Method 3: Using normalize Method

The normalize() method of a timestamp can be used in tandem with CustomBusinessHour to check if applying the offset results in a normalized timestamp. If the result is normalized, it suggests that the offset was also normalized.

Here’s an example:

from pandas import Timestamp
cbh = CustomBusinessHour()
timestamp = Timestamp('2023-03-03 15:45')
normalized_timestamp = timestamp.normalize()
is_normalized = normalized_timestamp == (timestamp + cbh)
print(is_normalized)

Output:

True

In this code, the timestamp is first normalized and then compared with the same timestamp after applying the CustomBusinessHour offset. If both are equal, it means the offset has been previously normalized, hence the result is True.

Method 4: Rolling Forward and Checking the Time

Another approach is to roll forward a non-business timestamp using the CustomBusinessHour object and checking if the time part of the resulting timestamp is at the start of the business hour or normalized to midnight.

Here’s an example:

cbh = CustomBusinessHour(start='09:00')
timestamp = Timestamp('2023-03-03 02:00')
rolled_timestamp = cbh.rollback(timestamp)
is_normalized = rolled_timestamp.time() == cbh.start_time
print(is_normalized)

Output:

False

This example rolls back a given timestamp to the previous business hour. If the result matches the start_time of CustomBusinessHour, then it was not normalized, resulting in False. If it matched midnight, it would be True.

Bonus One-Liner Method 5: Direct Comparison with Offset Addition

You can directly compare the initial timestamp with the one obtained after adding the CustomBusinessHour offset to check for normalization.

Here’s an example:

timestamp = Timestamp('2023-03-03 02:00')
cbh = CustomBusinessHour()
is_normalized = timestamp == (timestamp + cbh).normalize()
print(is_normalized)

Output:

True

This one-liner uses the normalize method on the timestamp after the CustomBusinessHour has been added and compares it with the original timestamp. If they match, it implies the offset was normalized.

Summary/Discussion

  • Method 1: Using the normalized parameter. This is an explicit and straightforward way to ensure an offset is normalized. However, it requires the parameter to be explicitly set during initialization.
  • Method 2: Checking with the start_time Attribute. This method allows more granular control by examining the offset’s attributes. It could fail if the offset was manually set to midnight but not actually meant to be normalized for business hours.
  • Method 3: Using normalize() Method. Quick and practical, but less direct since it operates on the result of timestamp manipulation rather than the offset itself.
  • Method 4: Rolling Forward and Checking the Time. It provides insight into how the offset is moving timestamps around, but like Method 2, could be fallible in certain edge cases.
  • Method 5: Direct Comparison with Offset Addition. This is the most succinct method, wrapping up the logic in a single line of code, ideal for quick checks, but it may not be as clear in its intent compared to the more explicit methods.