You can use the following function to calculate a timedelta in months between two columns of a pandas DataFrame:

def month_diff(x, y):
    end = x.dt.to_period('M').view(dtype='int64')
    start = y.dt.to_period('M').view(dtype='int64')
    return end-start

The following example shows how to use this function in practice.

Example: Calculate Timedelta in Months in Pandas

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'event': ['A', 'B', 'C'],
                   'start_date': ['20210101', '20210201', '20210401'],
                   'end_date': ['20210608', '20210209', '20210801'] })

#convert start date and end date columns to datetime
df['start_date'] = pd.to_datetime(df['start_date'])
df['end_date'] = pd.to_datetime(df['end_date'])

#view DataFrame
print(df)

  event start_date   end_date
0     A 2021-01-01 2021-06-08
1     B 2021-02-01 2021-02-09
2     C 2021-04-01 2021-08-01

Now suppose we’d like to calculate the timedelta (in months) between the start_date and end_date columns.

To do so, we’ll first define the following function:

#define function to calculate timedelta in months between two columns
def month_diff(x, y):
    end = x.dt.to_period('M').view(dtype='int64')
    start = y.dt.to_period('M').view(dtype='int64')
    return end-start

Next, we’ll use this function to calculate the timedelta in months between the start_date and end_date columns:

#calculate month difference between start date and end date columns
df['month_difference'] = month_diff(df.end_date, df.start_date)

#view updated DataFrame
df

    event	start_date	  end_date	month_difference
0	A	2021-01-01	2021-06-08	5
1	B	2021-02-01	2021-02-09	0
2	C	2021-04-01	2021-08-01	4

The month_difference column displays the timedelta (in months) between the start_date and end_date columns.

Additional Resources

The following tutorials explain how to perform other common operations in pandas: