polars.Expr.dt.round#

Expr.dt.round(
every: str | timedelta,
offset: str | timedelta | None = None,
*,
ambiguous: Ambiguous | Expr | None = None,
) Expr[source]#

Divide the date/datetime range into buckets.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

Each date/datetime in the first half of the interval is mapped to the start of its bucket. Each date/datetime in the second half of the interval is mapped to the end of its bucket. Ambiguous results are localised using the DST offset of the original timestamp - for example, rounding '2022-11-06 01:20:00 CST' by '1h' results in '2022-11-06 01:00:00 CST', whereas rounding '2022-11-06 01:20:00 CDT' by '1h' results in '2022-11-06 01:00:00 CDT'.

Parameters:
every

Every interval start and period length

offset

Offset the window

ambiguous

Determine how to deal with ambiguous datetimes:

  • 'raise' (default): raise

  • 'earliest': use the earliest datetime

  • 'latest': use the latest datetime

Returns:
Expr

Expression of data type Date or Datetime.

Notes

The every and offset argument are created with the the following small string formatting 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)

eg: 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 timedelta, datetime
>>> df = (
...     pl.datetime_range(
...         datetime(2001, 1, 1),
...         datetime(2001, 1, 2),
...         timedelta(minutes=225),
...         eager=True,
...     )
...     .alias("datetime")
...     .to_frame()
... )
>>> df.with_columns(pl.col("datetime").dt.round("1h").alias("round"))
shape: (7, 2)
┌─────────────────────┬─────────────────────┐
│ datetime            ┆ round               │
│ ---                 ┆ ---                 │
│ datetime[μs]        ┆ datetime[μs]        │
╞═════════════════════╪═════════════════════╡
│ 2001-01-01 00:00:00 ┆ 2001-01-01 00:00:00 │
│ 2001-01-01 03:45:00 ┆ 2001-01-01 04:00:00 │
│ 2001-01-01 07:30:00 ┆ 2001-01-01 08:00:00 │
│ 2001-01-01 11:15:00 ┆ 2001-01-01 11:00:00 │
│ 2001-01-01 15:00:00 ┆ 2001-01-01 15:00:00 │
│ 2001-01-01 18:45:00 ┆ 2001-01-01 19:00:00 │
│ 2001-01-01 22:30:00 ┆ 2001-01-01 23:00:00 │
└─────────────────────┴─────────────────────┘
>>> df = (
...     pl.datetime_range(
...         datetime(2001, 1, 1), datetime(2001, 1, 1, 1), "10m", eager=True
...     )
...     .alias("datetime")
...     .to_frame()
... )
>>> df.with_columns(pl.col("datetime").dt.round("30m").alias("round"))
shape: (7, 2)
┌─────────────────────┬─────────────────────┐
│ datetime            ┆ round               │
│ ---                 ┆ ---                 │
│ datetime[μs]        ┆ datetime[μs]        │
╞═════════════════════╪═════════════════════╡
│ 2001-01-01 00:00:00 ┆ 2001-01-01 00:00:00 │
│ 2001-01-01 00:10:00 ┆ 2001-01-01 00:00:00 │
│ 2001-01-01 00:20:00 ┆ 2001-01-01 00:30:00 │
│ 2001-01-01 00:30:00 ┆ 2001-01-01 00:30:00 │
│ 2001-01-01 00:40:00 ┆ 2001-01-01 00:30:00 │
│ 2001-01-01 00:50:00 ┆ 2001-01-01 01:00:00 │
│ 2001-01-01 01:00:00 ┆ 2001-01-01 01:00:00 │
└─────────────────────┴─────────────────────┘