**π‘ Problem Formulation:** When working with temporal data, it is often useful to identify leap years within a dataset. This article discusses how to write a Python program that takes a pandas DataFrame filled with years and counts the total number of leap years present. For example, given a DataFrame with a column of years ranging from 2000 to 2020, the desired output is 6 β the count of leap years in that range.

## Method 1: Using a Custom Function with apply()

One can define a custom function that checks if a year is a leap year, then use the `apply()`

method on the DataFrame to count the total leap years. The custom function would test if a year is evenly divisible by 4, not evenly divisible by 100 unless it’s also evenly divisible by 400.

Here’s an example:

import pandas as pd # Define the custom function def is_leap_year(year): return year % 4 == 0 and (year % 100 != 0 or year % 400 == 0) # Create DataFrame df = pd.DataFrame({'Year': range(2000, 2021)}) # Use apply() to count leap years leap_years_count = df['Year'].apply(is_leap_year).sum() print(leap_years_count)

The output of this code snippet would be:`6`

This example defines a function called `is_leap_year()`

that takes a year and returns `True`

if it’s a leap year. It then applies this function to each element in the ‘Year’ column of the DataFrame and sums the resulting boolean values to get the count of leap years.

## Method 2: Using datetime and a List Comprehension

To identify leap years, one can leverage the `calendar`

module that contains a method `isleap()`

. By using a list comprehension in combination with this method on the ‘Year’ column of the DataFrame, we can efficiently filter and count leap years.

Here’s an example:

import pandas as pd import calendar # Create DataFrame df = pd.DataFrame({'Year': range(2000, 2021)}) # List comprehension using calendar.isleap() leap_years_count = sum([calendar.isleap(year) for year in df['Year']]) print(leap_years_count)

The output of this code snippet would be:`6`

Here, a list comprehension is used to iterate over each year in the DataFrame, and `calendar.isleap()`

checks each year. The result is a list of boolean values indicating leap years. The `sum()`

function then counts how many `True`

values are in the list.

## Method 3: Vectorized Operations with NumPy

Vectorized operations in NumPy can be used for efficient computation on arrays. Using NumPy’s `vectorize()`

function, the leap year checking can be applied to the entire array of years at once. This method is suitable for large datasets due to NumPy’s optimized performance.

Here’s an example:

import pandas as pd import numpy as np import calendar # Create DataFrame df = pd.DataFrame({'Year': range(2000, 2021)}) # np.vectorize() with calendar.isleap() vectorized_isleap = np.vectorize(calendar.isleap) leap_years_count = vectorized_isleap(df['Year'].to_numpy()).sum() print(leap_years_count)

The output of this code snippet would be:`6`

Here, `np.vectorize()`

is used to vectorize the `calendar.isleap()`

function. It is then applied to the ‘Year’ column’s values, which have been converted to a NumPy array with `to_numpy()`

. The result is an array of boolean values, which is then summed to get the count of leap years.

## Method 4: Filtering with Pandas Queries

Pandas offers powerful data manipulation tools, including the ability to query DataFrame columns. Using the query method to filter leap years based on the same divisibility rules and then counting the resulting DataFrame’s length provides an intuitive and readable approach.

Here’s an example:

import pandas as pd # Define the DataFrame df = pd.DataFrame({'Year': range(2000, 2021)}) # Query to filter leap years and count leap_years_count = df.query('Year % 4 == 0 and (Year % 100 != 0 or Year % 400 == 0)').shape[0] print(leap_years_count)

The output of this code snippet would be:`6`

The `query()`

method is used here to filter the DataFrame directly using the leap year rule as the query string. It returns only the rows representing leap years, and the `shape[0]`

attribute gives the number of these rows, thus the count of leap years.

## Bonus One-Liner Method 5: Using Pandas Series Aggregation

Combining Pandas and the calendar library, we can use a concise one-liner to perform the same operation. By applying `calendar.isleap()`

directly within the `agg()`

(aggregate) function as a lambda, we achieve a succinct and efficient computation.

Here’s an example:

import pandas as pd import calendar # Create the DataFrame df = pd.DataFrame({'Year': range(2000, 2021)}) # One-liner using agg() with a lambda leap_years_count = df['Year'].agg(lambda x: calendar.isleap(x)).sum() print(leap_years_count)

The output of this code snippet would be:`6`

This one-liner uses `agg()`

and passes a lambda function to it that applies `calendar.isleap()`

on each element of the ‘Year’ column. The result is a series of boolean values, which are then summed to count the leap years.

## Summary/Discussion

**Method 1:**Custom Function with apply(). This method provides clear and educational code but may not be the most efficient for large datasets.**Method 2:**Using datetime and List Comprehension. It is straightforward and Pythonic, making it easy to read, but it is less efficient than vectorized approaches.**Method 3:**Vectorized Operations with NumPy. Highly efficient and suitable for large datasets. The downside is the requirement for additional NumPy knowledge.**Method 4:**Filtering with Pandas Queries. Offers an SQL-like intuitive querying method that’s readable, but potentially less performant than vectorized strategies.**Method 5:**Pandas Series Aggregation. This one-liner is elegant and compact but might be less readable to those unfamiliar with lambda functions or the`agg()`

method.