How to Extract Month from Datetime in pandas Easily

To extract the month from a datetime column in pandas, use the .dt.month accessor on a datetime Series. This returns the month as an integer from 1 to 12 for each date.

Syntax

Use the .dt.month attribute on a pandas Series that contains datetime values.

• Series.dt.month: Extracts the month as an integer (1 to 12) from each datetime value.

python

df['date_column'].dt.month

Example

This example shows how to create a pandas DataFrame with a datetime column and extract the month from it.

python

import pandas as pd

data = {'date_column': ['2023-01-15', '2023-05-20', '2023-12-31']}
df = pd.DataFrame(data)
df['date_column'] = pd.to_datetime(df['date_column'])
df['month'] = df['date_column'].dt.month
print(df)

Output

  date_column  month
0  2023-01-15      1
1  2023-05-20      5
2  2023-12-31     12

Common Pitfalls

One common mistake is trying to use .month directly on a pandas Series without the .dt accessor, which causes an error.

Wrong way:

df['date_column'].month  # This raises an AttributeError

Right way:

df['date_column'].dt.month  # Correct usage

python

import pandas as pd

data = {'date_column': ['2023-01-15']}
df = pd.DataFrame(data)
df['date_column'] = pd.to_datetime(df['date_column'])

# Wrong way (will raise error):
# df['date_column'].month

# Right way:
month = df['date_column'].dt.month
print(month)

Output

0    1
dtype: int64

Quick Reference

Operation	Code Example	Description
Extract month	df['date_column'].dt.month	Gets month as integer (1-12)
Extract year	df['date_column'].dt.year	Gets year as integer
Extract day	df['date_column'].dt.day	Gets day of month as integer
Convert to datetime	pd.to_datetime(df['date_column'])	Converts string to datetime type

Key Takeaways

Use the .dt accessor to work with datetime properties in pandas Series.

Extract the month with .dt.month to get integers from 1 to 12.

Always convert date strings to datetime type before extracting parts.

Avoid using .month directly on Series to prevent errors.