polars.date_ranges#

polars.date_ranges(
start: date | datetime | IntoExprColumn,
end: date | datetime | IntoExprColumn,
interval: str | timedelta = '1d',
*,
closed: ClosedInterval = 'both',
time_unit: TimeUnit | None = None,
time_zone: str | None = None,
eager: Literal[False] = False,
) → Expr[source]#
polars.date_ranges(
start: date | datetime | IntoExprColumn,
end: date | datetime | IntoExprColumn,
interval: str | timedelta = '1d',
*,
closed: ClosedInterval = 'both',
time_unit: TimeUnit | None = None,
time_zone: str | None = None,
eager: Literal[True],
) → Series
polars.date_ranges(
start: date | datetime | IntoExprColumn,
end: date | datetime | IntoExprColumn,
interval: str | timedelta = '1d',
*,
closed: ClosedInterval = 'both',
time_unit: TimeUnit | None = None,
time_zone: str | None = None,
eager: bool,
) → Series | Expr

Create a column of date ranges.

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).

closed{‘both’, ‘left’, ‘right’, ‘none’}

Define which sides of the range are closed (inclusive).

time_unit{None, ‘ns’, ‘us’, ‘ms’}

Time unit of the resulting Datetime data type. Only takes effect if the output column is of type Datetime.

time_zone

Time zone of the resulting Datetime data type. Only takes effect if the output column is of type Datetime.

eager

Evaluate immediately and return a Series. If set to False (default), return an expression instead.

Returns:
Expr or Series

Column of data type List(Date) or List(Datetime).

Notes

interval is created according to the following string language:

  • 1ns (1 nanosecond)

  • 1us (1 microsecond)

  • 1ms (1 millisecond)

  • 1s (1 second)

  • 1m (1 minute)

  • 1h (1 hour)

  • 1d (1 calendar day)

  • 1w (1 calendar week)

  • 1mo (1 calendar month)

  • 1q (1 calendar quarter)

  • 1y (1 calendar year)

Or combine them: “3d12h4m25s” # 3 days, 12 hours, 4 minutes, and 25 seconds

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

>>> from datetime import date
>>> df = pl.DataFrame(
...     {
...         "start": [date(2022, 1, 1), date(2022, 1, 2)],
...         "end": date(2022, 1, 3),
...     }
... )
>>> df.with_columns(pl.date_ranges("start", "end"))
shape: (2, 3)
┌────────────┬────────────┬───────────────────────────────────┐
│ start      ┆ end        ┆ date_range                        │
│ ---        ┆ ---        ┆ ---                               │
│ date       ┆ date       ┆ list[date]                        │
╞════════════╪════════════╪═══════════════════════════════════╡
│ 2022-01-01 ┆ 2022-01-03 ┆ [2022-01-01, 2022-01-02, 2022-01… │
│ 2022-01-02 ┆ 2022-01-03 ┆ [2022-01-02, 2022-01-03]          │
└────────────┴────────────┴───────────────────────────────────┘