Python | Pandas Series.dt.days_in_month

Pandas is a commonly used Python library for data manipulation and analysis. The Series object in Pandas represents a one-dimensional array of indexed data.

The Series.dt accessor is used to access the datetime properties of the Series object. One of the useful methods of the Series.dt accessor is days_in_month which returns the number of days in the month of each datetime value in the Series.

Syntax

Series.dt.days_in_month

Parameters

None

Returns

The days in the month for all datetime values in the Series.

Example

import pandas as pd

data = {'date': ['2021-01-08', '2022-02-15', '2022-03-10', '2022-04-25']}
df = pd.DataFrame(data)

df['date'] = pd.to_datetime(df['date'])
df['days_in_month'] = df['date'].dt.days_in_month

print(df)

Output:

        date  days_in_month
0 2021-01-08             31
1 2022-02-15             28
2 2022-03-10             31
3 2022-04-25             30

In the example above, we create a DataFrame with a 'date' column containing dates in string format. We then convert this column to a datetime format using the to_datetime() method. We use the dt accessor to access the days_in_month property of the datetime column and create a new column 'days_in_month' with the number of days in the month for each datetime value.

This is just one example of how the Series.dt.days_in_month method can be used in Pandas. It can be useful in a variety of data analysis tasks such as calculating the number of working days in a month, or identifying the month with the most number of days in a dataset.