Implementation:Pola rs Polars Str to Date Datetime

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

Overview

Concrete APIs for parsing string columns into Polars temporal types (Date, Datetime) and extracting date components using temporal accessors.

Description

Polars provides multiple entry points for temporal parsing. At the CSV reader level, pl.read_csv(source, try_parse_dates=True) enables automatic format inference for all string columns. At the expression level, Expr.str.to_date(format) and Expr.str.to_datetime(format) parse individual columns using explicit format specifiers. Once parsed, the dt accessor namespace exposes component extraction methods such as dt.year() and dt.month().

Usage

Use these APIs whenever you need to:

• Read CSV files with automatic date detection enabled.
• Convert string columns to Date or Datetime types using known format patterns.
• Extract year, month, day, or other temporal components for downstream analysis.

Code Reference

Source Location

• Repository: Polars
• File: docs/source/src/python/user-guide/transformations/time-series/parsing.py (lines 1-43)

Signature

# CSV reader with auto date parsing
pl.read_csv(
    source: str | Path | IO[bytes],
    try_parse_dates: bool = False,
    ...
) -> DataFrame

# String-to-Date parsing
Expr.str.to_date(
    format: str | None = None,
    strict: bool = True,
    exact: bool = True,
    cache: bool = True,
) -> Expr

# String-to-Datetime parsing
Expr.str.to_datetime(
    format: str | None = None,
    time_unit: str = "us",
    time_zone: str | None = None,
    strict: bool = True,
    exact: bool = True,
    cache: bool = True,
) -> Expr

# Temporal component accessors
Expr.dt.year() -> Expr
Expr.dt.month() -> Expr
Expr.dt.day() -> Expr
Expr.dt.hour() -> Expr
Expr.dt.minute() -> Expr
Expr.dt.second() -> Expr

Import

import polars as pl

I/O Contract

Inputs

Name	Type	Required	Description
source	Path | IO[bytes]	Yes	File path or buffer for CSV reading
try_parse_dates	bool	No	If True, attempt to auto-detect and parse date columns during CSV read (default: False)
format	None	No	strftime format string (e.g., "%Y-%m-%d", "%Y-%m-%dT%H:%M:%S%z"); if None, Polars infers the format
strict	bool	No	If True, raise on parse failure; if False, return null (default: True)

Outputs

Name	Type	Description
DataFrame	pl.DataFrame	DataFrame with string columns converted to temporal types (from read_csv)
Expr	pl.Expr	Expression yielding Date, Datetime, or Int32/UInt32 columns (from str.to_date, str.to_datetime, or dt accessors)

Usage Examples

Auto-Detect Dates During CSV Read

import polars as pl

# Auto-detect dates during CSV read
df = pl.read_csv("data.csv", try_parse_dates=True)
print(df.dtypes)  # Date/Datetime columns are automatically detected

Explicit Date Parsing

import polars as pl

# Explicit date parsing with format specifier
df = df.with_columns(
    pl.col("date_str").str.to_date("%Y-%m-%d")
)

Explicit Datetime Parsing

import polars as pl

# Explicit datetime parsing with timezone
df = df.with_columns(
    pl.col("ts_str").str.to_datetime("%Y-%m-%dT%H:%M:%S%z")
)

Extract Temporal Components

import polars as pl

# Extract year and month components
df = df.with_columns(
    pl.col("date").dt.year().alias("year"),
    pl.col("date").dt.month().alias("month"),
)

Related Pages

Implements Principle

• Principle:Pola_rs_Polars_Temporal_Data_Parsing