5 Best Ways to Check Whether a Pandas CustomBusinessDay Offset is Normalized

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💡 Problem Formulation: When working with business date ranges in pandas, you may need to determine if a CustomBusinessDay (CDB) offset is normalized, meaning it stands for a standardised time (usually midnight). Accurate checks are crucial in time series forecasting and other financial analyses. Let’s say you’ve set an offset with CustomBusinessDay() and you want to confirm if this offest represents normalized dates, for consistent interval representation in your dataset.

Method 1: Using the normalized attribute

CustomBusinessDay objects in pandas have a normalized attribute that can be checked to determine if the offset is normalized. This attribute returns a Boolean value indicating the normalization status.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessDay

cbd = CustomBusinessDay()
print(cbd.normalized)

Output:

False

This code snippet creates an instance of CustomBusinessDay without any arguments, which sets the normalized attribute to its default value (False). Using the print function, we verify that the offset is not normalized.

Method 2: Inspecting the offset’s kwds dictionary

The kwds dictionary of a CustomBusinessDay object can sometimes contain an entry for ‘normalize’. By checking this dictionary, we can indirectly infer the normalization status.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessDay

cbd = CustomBusinessDay(normalize=True)
print('normalize' in cbd.kwds and cbd.kwds['normalize'])

Output:

True

This example specifically sets the normalize argument to True when creating the CustomBusinessDay instance. By evaluating the 'normalize' key within the kwds attribute, we confirm the normalization status directly.

Method 3: Check normalization while creating an offset object

You can also check the normalization status immediately after creating a CustomBusinessDay offset by passing a check within the same line of instantiation.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessDay

print(CustomBusinessDay(normalize=True).normalized)

Output:

True

This one-liner initializes a CustomBusinessDay object with normalization set to True and immediately checks its normalized attribute, thereby confirming its state inline.

Method 4: Using pandas offsets module functions

The pandas offsets module contains functions related to time offsets. By referencing the module, we can easily check if our CustomBusinessDay object is normalized using its internal methods.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessDay, is_normalized

cbd = CustomBusinessDay(normalize=False)
print(is_normalized(cbd))

Output:

False

In this example, we pass our CustomBusinessDay object to the is_normalized function from pandas’ offsets module, which returns the normalization status.

Bonus One-Liner Method 5: Use lambda and assert for normalization check

If you prefer a more functional approach, you can use a lambda function combined with an assert statement to quickly check if the offset is normalized. (Warning: This method will raise an AssertionError if the condition is False.)

Here’s an example:

from pandas.tseries.offsets import CustomBusinessDay

check_normalized = lambda x: x.normalized
assert check_normalized(CustomBusinessDay(normalize=True)), "Offset is not normalized"

This code defines a lambda function that tests the normalized attribute of a passed-in CustomBusinessDay object. The assert statement is then used to guarantee the offset has been normalized, or else an AssertionError is raised.

Summary/Discussion

  • Method 1: Using the normalized attribute. Straightforward and direct. The simplest way to check normalization. Does not provide context if False.
  • Method 2: Inspecting the offset’s kwds dictionary. A bit indirect. Useful if additional offset parameters are of interest. Can be more verbose.
  • Method 3: Check normalization while creating an offset object. Efficient for inline checks. Not suitable for pre-existing objects. Offers immediate confirmation.
  • Method 4: Using pandas offsets module functions. Utilizes built-in utilities. Requires understanding of the additional module functions. Good for consistency with other offset checks.
  • Bonus Method 5: Use lambda and assert for normalization check. Functional and concise. Will stop execution with an exception if the assertion fails, which could be a drawback in some contexts.