polars.Expr.dt.replace#

Expr.dt.replace(
*,
year: int | IntoExpr | None = None,
month: int | IntoExpr | None = None,
day: int | IntoExpr | None = None,
hour: int | IntoExpr | None = None,
minute: int | IntoExpr | None = None,
second: int | IntoExpr | None = None,
microsecond: int | IntoExpr | None = None,
ambiguous: Ambiguous | Expr = 'raise',
) → Expr[source]#

Replace time unit.

Parameters:
year

Column or literal.

month

Column or literal, ranging from 1-12.

day

Column or literal, ranging from 1-31.

hour

Column or literal, ranging from 0-23.

minute

Column or literal, ranging from 0-59.

second

Column or literal, ranging from 0-59.

microsecond

Column or literal, ranging from 0-999999.

ambiguous

Determine how to deal with ambiguous datetimes:

  • 'raise' (default): raise

  • 'earliest': use the earliest datetime

  • 'latest': use the latest datetime

  • 'null': set to null

Returns:
Expr

Expression of data type Date or Datetime with the specified time units replaced.

Examples

>>> from datetime import date
>>> df = pl.DataFrame(
...     {
...         "date": [date(2024, 4, 1), date(2025, 3, 16)],
...         "new_day": [10, 15],
...     }
... )
>>> df.with_columns(pl.col("date").dt.replace(day="new_day").alias("replaced"))
shape: (2, 3)
┌────────────┬─────────┬────────────┐
│ date       ┆ new_day ┆ replaced   │
│ ---        ┆ ---     ┆ ---        │
│ date       ┆ i64     ┆ date       │
╞════════════╪═════════╪════════════╡
│ 2024-04-01 ┆ 10      ┆ 2024-04-10 │
│ 2025-03-16 ┆ 15      ┆ 2025-03-15 │
└────────────┴─────────┴────────────┘
>>> df.with_columns(pl.col("date").dt.replace(year=1800).alias("replaced"))
shape: (2, 3)
┌────────────┬─────────┬────────────┐
│ date       ┆ new_day ┆ replaced   │
│ ---        ┆ ---     ┆ ---        │
│ date       ┆ i64     ┆ date       │
╞════════════╪═════════╪════════════╡
│ 2024-04-01 ┆ 10      ┆ 1800-04-01 │
│ 2025-03-16 ┆ 15      ┆ 1800-03-16 │
└────────────┴─────────┴────────────┘
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