Handling Leap Years in Python Date Calculations with Pandas
I'm encountering an issue when performing date calculations using the Pandas library in Python, specifically related to leap years. I have a DataFrame with a date column that contains multiple years, and I'm trying to calculate the difference in days between two date columns while accounting for leap years. However, I'm getting unexpected results for dates in leap years. Here's a simplified version of my DataFrame: ```python import pandas as pd data = { 'start_date': ['2020-02-28', '2021-02-28', '2022-02-28'], 'end_date': ['2020-03-01', '2021-03-01', '2022-03-01'] } df = pd.DataFrame(data) df['start_date'] = pd.to_datetime(df['start_date']) df['end_date'] = pd.to_datetime(df['end_date']) ``` Next, I calculate the difference in days: ```python df['day_difference'] = (df['end_date'] - df['start_date']).dt.days print(df) ``` The output I get is: ``` start_date end_date day_difference 0 2020-02-28 2020-03-01 2 1 2021-02-28 2021-03-01 1 2 2022-02-28 2022-03-01 1 ``` As you can see, the leap year calculation seems incorrect for the first row where the date difference should actually be 2 days, not 2. My expectation was that the difference between February 28 and March 1 in a leap year (2020) would yield 2 days, considering February 2020 has 29 days. I've verified the date formats and ensured they are converted properly using `pd.to_datetime()`. Is there something I'm missing in the way Pandas treats date differences when leap years are involved? Any guidance on how to properly get the expected results would be greatly appreciated! Any ideas what could be causing this?