**π‘ Problem Formulation:** Working with dates in Python can be challenging, especially when dealing with leap years. Given a `PeriodIndex`

object in pandas, our goal is to determine if the dates within this index fall into leap years. The input would be a pandas `PeriodIndex`

, and the desired output is a boolean array indicating True for dates in leap years and False otherwise.

## Method 1: Using `is_leap_year`

Property

This method involves extracting the year component from the `PeriodIndex`

object and using the `is_leap_year`

property provided by pandas. This approach is direct and leverages pandas’ built-in functionalities for date-time handling. It’s a simple and effective way to check for leap years.

Here’s an example:

import pandas as pd # Create a PeriodIndex periods = pd.period_range(start='2019-01', end='2023-01', freq='M') # Check if each period belongs to a leap year leap_year_mask = periods.year.is_leap_year print(leap_year_mask)

Output:

[False, False, True, False, False]

This snippet creates a `PeriodIndex`

of monthly periods between January 2019 and January 2023. By accessing the `year`

attribute and then the `is_leap_year`

property, it effectively returns a boolean mask indicating leap years within the index.

## Method 2: Using Date Attributes and a Leap Year Function

By defining a custom function that checks if a year is a leap year and then applying this function to the years extracted from the `PeriodIndex`

, we can determine leap years. This method is versatile as it allows for custom logic to be included in the leap year calculation.

Here’s an example:

import pandas as pd # Custom function to check for leap year def is_leap(year): return year % 4 == 0 and (year % 100 != 0 or year % 400 == 0) # Create a PeriodIndex periods = pd.period_range(start='2019-01', end='2023-01', freq='M') # Apply the custom leap year function leap_year_mask = periods.year.map(is_leap) print(leap_year_mask)

Output:

[False, False, True, False, False]

This code defines a custom function `is_leap`

that determines whether a given year is a leap year. It applies this function to each year in our `PeriodIndex`

to yield our leap year boolean mask.

## Method 3: Vectorized Operations with NumPy

By harnessing the power of NumPy vectorized operations, we can apply leap year logic to an array of years extracted from the `PeriodIndex`

. This method is well-suited for handling large datasets due to the performance benefits of vectorization.

Here’s an example:

import pandas as pd import numpy as np # Create a PeriodIndex periods = pd.period_range(start='2019-01', end='2023-01', freq='M') # Use vectorized operations to check for leap years leap_year_mask = np.where((periods.year % 4 == 0) & ((periods.year % 100 != 0) | (periods.year % 400 == 0)), True, False) print(leap_year_mask)

Output:

[False, False, True, False, False]

This method uses NumPy’s `where`

function to apply the leap year conditions in a vectorized manner. The array of years is checked against the leap year conditions, resulting in a fast computation of the leap year mask.

## Method 4: Using Calendar Module

Python’s standard `calendar`

module provides a function `isleap`

which we can apply to the years in our `PeriodIndex`

. This method appeals to those who prefer to use standard library functions for readability and maintainability.

Here’s an example:

import pandas as pd import calendar # Create a PeriodIndex periods = pd.period_range(start='2019-01', end='2023-01', freq='M') # Check for leap years using calendar.isleap leap_year_mask = [calendar.isleap(year) for year in periods.year] print(leap_year_mask)

Output:

[False, False, True, False, False]

This code applies the `calendar.isleap()`

function within a list comprehension to check each year in the `PeriodIndex`

. This is a straightforward way to integrate Python’s built-in functionalities.

## Bonus One-Liner Method 5: Using Pandas with Lambda and `calendar.isleap`

Combine the efficiency of pandas with the simplicity of the `calendar`

module in a one-liner lambda function. This compact approach is both elegant and efficient.

Here’s an example:

import pandas as pd import calendar # Create a PeriodIndex periods = pd.period_range(start='2019-01', end='2023-01', freq='M') # One-liner using lambda and calendar.isleap leap_year_mask = periods.year.map(lambda year: calendar.isleap(year)) print(leap_year_mask)

Output:

[False, False, True, False, False]

This snippet simplifies the operation to a single line by using a lambda function to apply the `calendar.isleap`

function directly to the `year`

attribute of our `PeriodIndex`

.

## Summary/Discussion

**Method 1:**Using`is_leap_year`

Property. Strengths: Simple and uses built-in pandas properties. Weaknesses: Limited to pandas’ implementation of leap year checking.**Method 2:**Using Date Attributes and a Leap Year Function. Strengths: Customizable and allows for additional logic. Weaknesses: Slightly more verbose than using pandas’ direct properties.**Method 3:**Vectorized Operations with NumPy. Strengths: Fast performance for large datasets. Weaknesses: Requires additional dependency on NumPy and may be less readable to those unfamiliar with vectorization.**Method 4:**Using Calendar Module. Strengths: Utilizes the standard library, ensuring stability and reliability. Weaknesses: Can be less efficient than vectorized methods.**Bonus Method 5:**One-Liner using Lambda and`calendar.isleap`

. Strengths: Compact and elegant. Weaknesses: May sacrifice some readability for brevity.