Implementation:Pola rs Polars DataFrame Sort by Time

Knowledge Sources	Polars Polars Docs
Domains	Data Engineering, Time Series
Last Updated	2026-02-09 10:00 GMT

Overview

Concrete API for sorting a Polars DataFrame by a temporal column to establish chronological order required by downstream time series operations.

Description

DataFrame.sort(by) sorts the entire DataFrame by the specified column(s). When by refers to a temporal column (Date, Datetime), the sort produces a chronologically ordered DataFrame. This is typically performed immediately after data ingestion and date parsing, before any time-dependent operations are applied.

The sort is not in-place -- it returns a new DataFrame. This aligns with Polars' immutable DataFrame design, where operations produce new frames rather than mutating existing ones.

Usage

Use DataFrame.sort whenever you need to:

• Establish chronological order after reading and parsing temporal data.
• Prepare data as input to group_by_dynamic, rolling, or upsample.
• Reset ordering after joins or filters that may disrupt temporal order.

Code Reference

Source Location

• Repository: Polars
• File: docs/source/src/python/user-guide/transformations/time-series/rolling.py (line 10)

Signature

DataFrame.sort(
    by: str | Expr | Sequence[str | Expr],
    *more_by: str | Expr,
    descending: bool | Sequence[bool] = False,
    nulls_last: bool | Sequence[bool] = False,
    multithreaded: bool = True,
    maintain_order: bool = False,
) -> DataFrame

Import

import polars as pl

I/O Contract

Inputs

Name	Type	Required	Description
by	Expr | Sequence[str | Expr]	Yes	Column name or expression to sort by (typically a datetime column name)
descending	Sequence[bool]	No	If True, sort in descending (newest-first) order (default: False, i.e., ascending/oldest-first)
nulls_last	Sequence[bool]	No	If True, place null values at the end (default: False)
maintain_order	bool	No	If True, preserve the relative order of equal elements -- stable sort (default: False)

Outputs

Name	Type	Description
result	pl.DataFrame	A new DataFrame sorted by the specified column(s) in the requested order

Usage Examples

Basic Temporal Sort

import polars as pl

df = pl.read_csv("apple_stock.csv", try_parse_dates=True)
df = df.sort("Date")
print(df.head())

Sort Before Dynamic Grouping

import polars as pl

df = pl.read_csv("apple_stock.csv", try_parse_dates=True)
df = df.sort("Date")

# Now safe to use group_by_dynamic
annual = df.group_by_dynamic("Date", every="1y").agg(
    pl.col("Close").mean()
)

Descending Sort

import polars as pl

# Sort newest-first for display purposes
df_recent_first = df.sort("Date", descending=True)

Related Pages

Implements Principle

• Principle:Pola_rs_Polars_Time_Based_Sorting