Polars · Module

df.drop

Removes one or more columns from a Polars DataFrame and returns the resulting DataFrame. It accepts names, iterables of names, or column selectors, with strict schema validation enabled by default.

How it works

DataFrame.drop returns a new dataframe without the requested columns. The columns argument can be one name, an iterable of names, or a column selector; strict validation controls whether missing names raise an exception.

Syntax

DataFrame.drop(columns: ColumnNameOrSelector | Iterable[ColumnNameOrSelector], strict: bool = True) → DataFrame

Parameters

Name	Type	Description
`columns`	`ColumnNameOrSelector \| Iterable[ColumnNameOrSelector]`	Names of the columns to remove; accepts a column selector or iterable of column selectors.
`strict`	`bool = True`	Whether to validate that all requested column names exist in the current schema.

Returns

DataFrame without the selected columns.

Key points

The method produces a dataframe with selected columns removed.
Use columns to identify the fields to eliminate.
Selectors can target groups of columns instead of listing each name individually.
A selector can remove multiple matching columns in one call.
The documented examples also show multiple column names supplied positionally.

Common mistakes

With the default strict=True, a missing column name causes an exception; use strict=False when absent names should be tolerated.

Example: Drop columns selected by type

import polars as pl
import polars.selectors as cs

df = pl.DataFrame({
    "id": [1, 2],
    "score": [10.5, 8.0],
    "name": ["Ada", "Linus"],
})

result = df.drop(cs.numeric())
print(result)

Removes every numeric column selected by the selector, leaving the string column.

When to use it

Remove unwanted fields

Prepare a modeling table by removing identifiers, labels, or other fields that should not be passed to a model.

Drop columns by schema characteristics

Apply a selector such as cs.numeric() when the columns to remove are defined by type rather than fixed names.

Prune multiple named columns

Use an iterable of names to remove several known columns in a single dataframe transformation.

Interview questions

QWhat does DataFrame.drop do?

It removes columns from a dataframe and returns another dataframe. It does not mutate the original dataframe.

Follow-up: How can you remove all numeric columns? Pass a selector such as polars.selectors.numeric() to columns.

QWhat can the columns argument contain?

columns accepts a single column name, an iterable of column names, or a column selector. This supports both explicit schemas and type- or pattern-based selection.

Follow-up: How would you drop two named columns? Supply them as an iterable, for example df.drop(["a", "b"]), or as positional arguments if supported by the current API example.

QWhat does the strict parameter control?

The default is strict=True, which validates that every named column exists in the current schema. If validation fails, the method throws an exception.

Follow-up: What is the trade-off of strict=False? It makes schema-dependent cleanup more tolerant, but can hide misspelled or unexpectedly absent column names.

polars.DataFrame.drop_in_place
polars.DataFrame.drop_nulls
polars.DataFrame.drop_nans
polars.LazyFrame.drop
polars.selectors.numeric

Source: polars.DataFrame.drop. Checked 2026-10-15.