polars.Expr.rolling_sum#

Expr.rolling_sum(
window_size: int | timedelta | str,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
by: str | None = None,
closed: ClosedInterval = 'left',
warn_if_unsorted: bool = True,
) → Self[source]#

Apply a rolling sum (moving sum) over the values in this array.

A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weight vector. The resulting values will be aggregated to their sum.

If by has not been specified (the default), the window at a given row will include the row itself, and the window_size - 1 elements before it.

If you pass a by column <t_0, t_1, ..., t_n>, then closed="left" means the windows will be:

  • [t_0 - window_size, t_0)

  • [t_1 - window_size, t_1)

  • …

  • [t_n - window_size, t_n)

With closed="right", the left endpoint is not included and the right endpoint is included.

Parameters:
window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or 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)

  • 1i (1 index count)

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”.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that will be multiplied elementwise with the values in the window.

min_periods

The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.

center

Set the labels at the center of the window

by

If the window_size is temporal for instance "5h" or "3s", you must set the column that will be used to determine the windows. This column must of dtype {Date, Datetime}

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

Define which sides of the temporal interval are closed (inclusive); only applicable if by has been set.

warn_if_unsorted

Warn if data is not known to be sorted by by column (if passed). Experimental.

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

If you want to compute multiple aggregation statistics over the same dynamic window, consider using rolling - this method can cache the window size computation.

Examples

>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})
>>> df.with_columns(
...     rolling_sum=pl.col("A").rolling_sum(window_size=2),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_sum │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 3.0         │
│ 3.0 ┆ 5.0         │
│ 4.0 ┆ 7.0         │
│ 5.0 ┆ 9.0         │
│ 6.0 ┆ 11.0        │
└─────┴─────────────┘

Specify weights to multiply the values in the window with:

>>> df.with_columns(
...     rolling_sum=pl.col("A").rolling_sum(
...         window_size=2, weights=[0.25, 0.75]
...     ),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_sum │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 1.75        │
│ 3.0 ┆ 2.75        │
│ 4.0 ┆ 3.75        │
│ 5.0 ┆ 4.75        │
│ 6.0 ┆ 5.75        │
└─────┴─────────────┘

Center the values in the window

>>> df.with_columns(
...     rolling_sum=pl.col("A").rolling_sum(window_size=3, center=True),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_sum │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 6.0         │
│ 3.0 ┆ 9.0         │
│ 4.0 ┆ 12.0        │
│ 5.0 ┆ 15.0        │
│ 6.0 ┆ null        │
└─────┴─────────────┘

Create a DataFrame with a datetime column and a row number column

>>> from datetime import timedelta, datetime
>>> start = datetime(2001, 1, 1)
>>> stop = datetime(2001, 1, 2)
>>> df_temporal = pl.DataFrame(
...     {"date": pl.datetime_range(start, stop, "1h", eager=True)}
... ).with_row_count()
>>> df_temporal
shape: (25, 2)
┌────────┬─────────────────────┐
│ row_nr ┆ date                │
│ ---    ┆ ---                 │
│ u32    ┆ datetime[μs]        │
╞════════╪═════════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 │
│ 1      ┆ 2001-01-01 01:00:00 │
│ 2      ┆ 2001-01-01 02:00:00 │
│ 3      ┆ 2001-01-01 03:00:00 │
│ …      ┆ …                   │
│ 21     ┆ 2001-01-01 21:00:00 │
│ 22     ┆ 2001-01-01 22:00:00 │
│ 23     ┆ 2001-01-01 23:00:00 │
│ 24     ┆ 2001-01-02 00:00:00 │
└────────┴─────────────────────┘

Compute the rolling sum with the default left closure of temporal windows

>>> df_temporal.with_columns(
...     rolling_row_sum=pl.col("row_nr").rolling_sum(
...         window_size="2h", by="date", closed="left"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_sum │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ u32             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ null            │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 0               │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 1               │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 3               │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 39              │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 41              │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 43              │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 45              │
└────────┴─────────────────────┴─────────────────┘

Compute the rolling sum with the closure of windows on both sides

>>> df_temporal.with_columns(
...     rolling_row_sum=pl.col("row_nr").rolling_sum(
...         window_size="2h", by="date", closed="both"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_sum │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ u32             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ 0               │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 1               │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 3               │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 6               │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 60              │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 63              │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 66              │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 69              │
└────────┴─────────────────────┴─────────────────┘