polars.date_range#
- polars.date_range(
- start: date | datetime | IntoExprColumn,
- end: date | datetime | IntoExprColumn,
- interval: str | timedelta = '1d',
- *,
- closed: ClosedInterval = 'both',
- eager: bool = False,
Generate a date range.
- Parameters:
- start
Lower bound of the date range.
- end
Upper bound of the date range.
- interval
Interval of the range periods, specified as a Python
timedelta
object or using the Polars duration string language (see “Notes” section below). Must consist of full days.- closed{‘both’, ‘left’, ‘right’, ‘none’}
Define which sides of the range are closed (inclusive).
- eager
Evaluate immediately and return a
Series
. If set toFalse
(default), return an expression instead.
- Returns:
- Expr or Series
Column of data type
Date
.
See also
Notes
interval
is created according to the following string language:1d (1 calendar day)
1w (1 calendar week)
1mo (1 calendar month)
1q (1 calendar quarter)
1y (1 calendar year)
Or combine them: “1w2d” # 1 week, 2 days
By “calendar day”, we mean the corresponding time on the next day (which may not be 24 hours, due to daylight savings). Similarly for “calendar week”, “calendar month”, “calendar quarter”, and “calendar year”.
Examples
Using Polars duration string to specify the interval:
>>> from datetime import date >>> pl.date_range(date(2022, 1, 1), date(2022, 3, 1), "1mo", eager=True).alias( ... "date" ... ) shape: (3,) Series: 'date' [date] [ 2022-01-01 2022-02-01 2022-03-01 ]
Using
timedelta
object to specify the interval:>>> from datetime import timedelta >>> pl.date_range( ... date(1985, 1, 1), ... date(1985, 1, 10), ... timedelta(days=2), ... eager=True, ... ).alias("date") shape: (5,) Series: 'date' [date] [ 1985-01-01 1985-01-03 1985-01-05 1985-01-07 1985-01-09 ]
Omit
eager=True
if you want to usedate_range
as an expression:>>> df = pl.DataFrame( ... { ... "date": [ ... date(2024, 1, 1), ... date(2024, 1, 2), ... date(2024, 1, 1), ... date(2024, 1, 3), ... ], ... "key": ["one", "one", "two", "two"], ... } ... ) >>> result = ( ... df.group_by("key") ... .agg(pl.date_range(pl.col("date").min(), pl.col("date").max())) ... .sort("key") ... ) >>> with pl.Config(fmt_str_lengths=50): ... print(result) shape: (2, 2) ┌─────┬──────────────────────────────────────┐ │ key ┆ date │ │ --- ┆ --- │ │ str ┆ list[date] │ ╞═════╪══════════════════════════════════════╡ │ one ┆ [2024-01-01, 2024-01-02] │ │ two ┆ [2024-01-01, 2024-01-02, 2024-01-03] │ └─────┴──────────────────────────────────────┘