polars.date_range#

polars.date_range(
start: date | datetime | IntoExprColumn,
end: date | datetime | IntoExprColumn,
interval: str | timedelta = '1d',
*,
closed: ClosedInterval = 'both',
eager: bool = False,
) Series | Expr[source]#

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 to False (default), return an expression instead.

Returns:
Expr or Series

Column of data type Date.

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 use date_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] │
└─────┴──────────────────────────────────────┘