How to convert a datetime to month in Python and Pandas?

In this tutorial we will learn how to cast datetime objects into months / years units. We’ll look into two use cases: date conversion in Python and in pandas DataFrames.

Casting datetime to months in Python

We’ll first create a Python date time object.

import datetime
my_dt = datetime.datetime(2023,11,30,20,45,55)

This will return the following:

2023-11-30 20:45:55

We can easily extract the month value:


This will return the value 11.

A more interesting solution is to use the strftime formatter to extract the month, or month year:

# returns: 11

# returns: 11-2023

# returns: 23-11

Convert datetime objects to months in pandas DataFrames

Create example data

We will start by importing the pandas library and create a DataFrame:

import pandas as pd
my_stamps = pd.date_range(start='1/28/2023', periods = 6, freq = 'B' )
num_interviews = [21, 20, 15, 16, 14, 22]

interviews  = pd.DataFrame (dict (dates = my_stamps, num_interviews = num_interviews))

This will return the following DataFrame rows:


Convert dates to month name

We use the pd.Series dt accessor to invoke the month_name() method:

interviews ['dates'].dt.month_name()

This will return the following Series of objects:

0     January
1     January
2    February
3    February
4    February
5    February
Name: dates, dtype: object

We can assign the Series to our dataframe:

interviews = interviews.assign (month_name = interviews ['dates'].dt.month_name())

Get the month – year value from datetime

IWe saw that we can use the strftime formatter to transform our dates to months and years. Here is an example of a different technique, that uses the to_period() method to cast datetimes to a specific period of times, in our case months.

interviews['month-year'] = interviews ['dates'].dt.to_period('m')

This will render the following Series:

0    2023-01
1    2023-01
2    2023-02
3    2023-02
4    2023-02
5    2023-02
Name: month_year, dtype: period[M]

For completeness, here’s code that helps achieve a similar Series, but made of string objects:

interviews['month_year'] = interviews ['dates'].dt.strftime('%Y-%m')

Group by months in pandas

If we would like to group our DataFrame rows by month, we can use the following snippet:

interviews.groupby(interviews ['dates'].dt.month_name())['num_interviews'].sum()

And this will return the following Series:

February    67
January     41
Name: num_interviews, dtype: int64

Follow up learning

How to turn a pandas DataFrame column to a date object with Python?