polars.Expr.dt.truncate#

Expr.dt.truncate(
every: str | timedelta | Expr,
offset: str | timedelta | None = None,
*,
use_earliest: bool | None = None,
ambiguous: Ambiguous | Expr = 'raise',
) → Expr[source]#

Divide the date/datetime range into buckets.

Each date/datetime is mapped to the start of its bucket using the corresponding local datetime. Note that weekly buckets start on Monday.

Parameters:
every

Every interval start and period length

offset

Offset the window

use_earliest

Determine how to deal with ambiguous datetimes:

  • None (default): raise

  • True: use the earliest datetime

  • False: use the latest datetime

Deprecated since version 0.19.0: Use ambiguous instead

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 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)

These strings can be combined:

  • 3d12h4m25s # 3 days, 12 hours, 4 minutes, and 25 seconds

Suffix with “_saturating” to indicate that dates too large for their month should saturate at the largest date (e.g. 2022-02-29 -> 2022-02-28) instead of erroring.

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,
... ).to_frame()
>>> df
shape: (7, 1)
┌─────────────────────┐
│ datetime            │
│ ---                 │
│ datetime[μs]        │
╞═════════════════════╡
│ 2001-01-01 00:00:00 │
│ 2001-01-01 03:45:00 │
│ 2001-01-01 07:30:00 │
│ 2001-01-01 11:15:00 │
│ 2001-01-01 15:00:00 │
│ 2001-01-01 18:45:00 │
│ 2001-01-01 22:30:00 │
└─────────────────────┘
>>> df.select(pl.col("datetime").dt.truncate("1h"))
shape: (7, 1)
┌─────────────────────┐
│ datetime            │
│ ---                 │
│ datetime[μs]        │
╞═════════════════════╡
│ 2001-01-01 00:00:00 │
│ 2001-01-01 03:00:00 │
│ 2001-01-01 07:00:00 │
│ 2001-01-01 11:00:00 │
│ 2001-01-01 15:00:00 │
│ 2001-01-01 18:00:00 │
│ 2001-01-01 22:00:00 │
└─────────────────────┘
>>> df.select(pl.col("datetime").dt.truncate("1h")).frame_equal(
...     df.select(pl.col("datetime").dt.truncate(timedelta(hours=1)))
... )
True

>>> df = pl.datetime_range(
...     datetime(2001, 1, 1), datetime(2001, 1, 1, 1), "10m", eager=True
... ).to_frame()
>>> df.select(
...     "datetime", pl.col("datetime").dt.truncate("30m").alias("truncate")
... )
shape: (7, 2)
┌─────────────────────┬─────────────────────┐
│ datetime            ┆ truncate            │
│ ---                 ┆ ---                 │
│ 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 00:00:00 │
│ 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 00:30:00 │
│ 2001-01-01 01:00:00 ┆ 2001-01-01 01:00:00 │
└─────────────────────┴─────────────────────┘

If crossing daylight savings time boundaries, you may want to use use_earliest and combine with dst_offset():

>>> df = (
...     pl.datetime_range(
...         datetime(2020, 10, 25, 0),
...         datetime(2020, 10, 25, 2),
...         "30m",
...         eager=True,
...         time_zone="Europe/London",
...     )
...     .dt.offset_by("15m")
...     .to_frame()
... )
>>> df
shape: (7, 1)
┌─────────────────────────────┐
│ datetime                    │
│ ---                         │
│ datetime[μs, Europe/London] │
╞═════════════════════════════╡
│ 2020-10-25 00:15:00 BST     │
│ 2020-10-25 00:45:00 BST     │
│ 2020-10-25 01:15:00 BST     │
│ 2020-10-25 01:45:00 BST     │
│ 2020-10-25 01:15:00 GMT     │
│ 2020-10-25 01:45:00 GMT     │
│ 2020-10-25 02:15:00 GMT     │
└─────────────────────────────┘

>>> ambiguous_mapping = {
...     timedelta(hours=1): "earliest",
...     timedelta(hours=0): "latest",
... }
>>> df.select(
...     pl.col("datetime").dt.truncate(
...         "30m",
...         ambiguous=(
...             pl.col("datetime").dt.dst_offset().map_dict(ambiguous_mapping)
...         ),
...     )
... )
shape: (7, 1)
┌─────────────────────────────┐
│ datetime                    │
│ ---                         │
│ datetime[μs, Europe/London] │
╞═════════════════════════════╡
│ 2020-10-25 00:00:00 BST     │
│ 2020-10-25 00:30:00 BST     │
│ 2020-10-25 01:00:00 BST     │
│ 2020-10-25 01:30:00 BST     │
│ 2020-10-25 01:00:00 GMT     │
│ 2020-10-25 01:30:00 GMT     │
│ 2020-10-25 02:00:00 GMT     │
└─────────────────────────────┘