Polars · Module

.dt.month

Extracts the calendar month from Date or Datetime expressions as one-based Int8 values from 1 through 12.

How it works

Call .dt.month() on a date or datetime expression to produce the calendar month for every value. The resulting expression has Int8 type and uses one-based numbering, so January is 1 and December is 12.

Syntax

Expr.dt.month() → Expr

Returns

Expr of data type Int8 containing month numbers from 1 to 12.

Key points

The method derives the calendar month from each temporal value.
Month numbering begins at one rather than zero.
Every non-null result belongs to the inclusive range 1–12.
It is an expression method and returns another Polars expression.
The resulting expression uses the compact Int8 integer type.

Common mistakes

Use this temporal accessor with Date or Datetime columns; it is not documented for other data types.

Example: Extract calendar months

import polars as pl
from datetime import date

df = pl.DataFrame({"date": [date(2001, 1, 1), date(2001, 6, 30), date(2001, 12, 27)]})
result = df.with_columns(pl.col("date").dt.month().alias("month"))
print(result)

This adds an Int8 expression containing the calendar month for each date.

When to use it

Calendar-month grouping

Create a numeric month column for reporting or grouping.

Feature engineering

Derive a compact temporal feature for downstream expressions.

DataFrame enrichment

Project month values while transforming a DataFrame.

Interview questions

QWhat does Expr.dt.month() return?

Expr.dt.month() extracts the month from each Date or Datetime value. It returns an expression with Int8 data type and values from 1 through 12.

Follow-up: How would you obtain month names instead? Use a temporal string-formatting method with an appropriate strftime month-name specifier.

QIs the extracted month zero-based or one-based?

The output is one-based: January produces 1, and December produces 12. This makes the result suitable for ordinary calendar-month grouping without adding an offset.

Follow-up: How would you validate the output domain? Check that the resulting integer values remain within the documented inclusive range 1 to 12.

QHow is this method typically used in a Polars pipeline?

Use it inside a DataFrame expression, such as pl.col("date").dt.month().alias("month"), and pass that expression to with_columns or another expression-consuming operation. The result is a derived column containing month numbers.

Follow-up: How could the derived month be used analytically? Group or aggregate by the resulting month column after creating it.

Series.dt.month
Expr.dt.to_string
Date
Datetime

Source: polars.Expr.dt.month — Polars documentation. Checked 2026-10-15.