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Group based on a date/time or integer column

Source code

Description

If you have a time series <t_0, t_1, …, t_n>, then by default the windows created will be:

  • (t_0 - period, t_0\]
  • (t_1 - period, t_1\]
  • (t_n - period, t_n\]

whereas if you pass a non-default offset, then the windows will be:

  • (t_0 + offset, t_0 + offset + period\]
  • (t_1 + offset, t_1 + offset + period\]
  • (t_n + offset, t_n + offset + period\]

Usage

<LazyFrame>$group_by_dynamic(
  index_column,
  ...,
  every,
  period = NULL,
  offset = NULL,
  include_boundaries = FALSE,
  closed = "left",
  label = "left",
  group_by = NULL,
  start_by = "window"
)

Arguments

index_column Column used to group based on the time window. Often of type Date/Datetime. This column must be sorted in ascending order (or, if by is specified, then it must be sorted in ascending order within each group). In case of a rolling group by on indices, dtype needs to be either Int32 or Int64. Note that Int32 gets temporarily cast to Int64, so if performance matters use an Int64 column.
Ignored.
every Interval of the window.
period A character representing the length of the window, must be non-negative. See the Polars duration string language section for details.
offset A character representing the offset of the window, or NULL (default). If NULL, -period is used. See the Polars duration string language section for details.
include_boundaries Add two columns “\_lower_boundary” and “\_upper_boundary” columns that show the boundaries of the window. This will impact performance because it’s harder to parallelize.
closed Define which sides of the temporal interval are closed (inclusive). This can be either “left”, “right”, “both” or “none”.
label Define which label to use for the window:
  • “left”: lower boundary of the window
  • “right”: upper boundary of the window
  • “datapoint”: the first value of the index column in the given window. If you don’t need the label to be at one of the boundaries, choose this option for maximum performance.
group_by Also group by this column/these columns.
start_by The strategy to determine the start of the first window by:
  • “window”: start by taking the earliest timestamp, truncating it with every, and then adding offset. Note that weekly windows start on Monday.
  • “datapoint”: start from the first encountered data point.
  • a day of the week (only takes effect if every contains “w”): “monday” starts the window on the Monday before the first data point, etc.

Details

In case of a rolling operation on an integer column, the windows are defined by:

  • "1i" \# length 1
  • "10i" \# length 10

Value

A LazyGroupBy object

See Also

  • \$rolling()

Examples

library(polars)

lf = pl$LazyFrame(
  time = pl$datetime_range(
    start = strptime("2021-12-16 00:00:00", format = "%Y-%m-%d %H:%M:%S", tz = "UTC"),
    end = strptime("2021-12-16 03:00:00", format = "%Y-%m-%d %H:%M:%S", tz = "UTC"),
    interval = "30m"
  ),
  n = 0:6
)
lf$collect()
#> shape: (7, 2)
#> ┌─────────────────────────┬─────┐
#> │ time                    ┆ n   │
#> │ ---                     ┆ --- │
#> │ datetime[ms, UTC]       ┆ i32 │
#> ╞═════════════════════════╪═════╡
#> │ 2021-12-16 00:00:00 UTC ┆ 0   │
#> │ 2021-12-16 00:30:00 UTC ┆ 1   │
#> │ 2021-12-16 01:00:00 UTC ┆ 2   │
#> │ 2021-12-16 01:30:00 UTC ┆ 3   │
#> │ 2021-12-16 02:00:00 UTC ┆ 4   │
#> │ 2021-12-16 02:30:00 UTC ┆ 5   │
#> │ 2021-12-16 03:00:00 UTC ┆ 6   │
#> └─────────────────────────┴─────┘
# get the sum in the following hour relative to the "time" column
lf$group_by_dynamic("time", every = "1h")$agg(
  vals = pl$col("n"),
  sum = pl$col("n")$sum()
)$collect()
#> shape: (4, 3)
#> ┌─────────────────────────┬───────────┬─────┐
#> │ time                    ┆ vals      ┆ sum │
#> │ ---                     ┆ ---       ┆ --- │
#> │ datetime[ms, UTC]       ┆ list[i32] ┆ i32 │
#> ╞═════════════════════════╪═══════════╪═════╡
#> │ 2021-12-16 00:00:00 UTC ┆ [0, 1]    ┆ 1   │
#> │ 2021-12-16 01:00:00 UTC ┆ [2, 3]    ┆ 5   │
#> │ 2021-12-16 02:00:00 UTC ┆ [4, 5]    ┆ 9   │
#> │ 2021-12-16 03:00:00 UTC ┆ [6]       ┆ 6   │
#> └─────────────────────────┴───────────┴─────┘
# using "include_boundaries = TRUE" is helpful to see the period considered
lf$group_by_dynamic("time", every = "1h", include_boundaries = TRUE)$agg(
  vals = pl$col("n")
)$collect()
#> shape: (4, 4)
#> ┌─────────────────────────┬─────────────────────────┬─────────────────────────┬───────────┐
#> │ _lower_boundary         ┆ _upper_boundary         ┆ time                    ┆ vals      │
#> │ ---                     ┆ ---                     ┆ ---                     ┆ ---       │
#> │ datetime[ms, UTC]       ┆ datetime[ms, UTC]       ┆ datetime[ms, UTC]       ┆ list[i32] │
#> ╞═════════════════════════╪═════════════════════════╪═════════════════════════╪═══════════╡
#> │ 2021-12-16 00:00:00 UTC ┆ 2021-12-16 01:00:00 UTC ┆ 2021-12-16 00:00:00 UTC ┆ [0, 1]    │
#> │ 2021-12-16 01:00:00 UTC ┆ 2021-12-16 02:00:00 UTC ┆ 2021-12-16 01:00:00 UTC ┆ [2, 3]    │
#> │ 2021-12-16 02:00:00 UTC ┆ 2021-12-16 03:00:00 UTC ┆ 2021-12-16 02:00:00 UTC ┆ [4, 5]    │
#> │ 2021-12-16 03:00:00 UTC ┆ 2021-12-16 04:00:00 UTC ┆ 2021-12-16 03:00:00 UTC ┆ [6]       │
#> └─────────────────────────┴─────────────────────────┴─────────────────────────┴───────────┘
# in the example above, the values didn't include the one *exactly* 1h after
# the start because "closed = 'left'" by default.
# Changing it to "right" includes values that are exactly 1h after. Note that
# the value at 00:00:00 now becomes included in the interval [23:00:00 - 00:00:00],
# even if this interval wasn't there originally
lf$group_by_dynamic("time", every = "1h", closed = "right")$agg(
  vals = pl$col("n")
)$collect()
#> shape: (4, 2)
#> ┌─────────────────────────┬───────────┐
#> │ time                    ┆ vals      │
#> │ ---                     ┆ ---       │
#> │ datetime[ms, UTC]       ┆ list[i32] │
#> ╞═════════════════════════╪═══════════╡
#> │ 2021-12-15 23:00:00 UTC ┆ [0]       │
#> │ 2021-12-16 00:00:00 UTC ┆ [1, 2]    │
#> │ 2021-12-16 01:00:00 UTC ┆ [3, 4]    │
#> │ 2021-12-16 02:00:00 UTC ┆ [5, 6]    │
#> └─────────────────────────┴───────────┘
# To keep both boundaries, we use "closed = 'both'". Some values now belong to
# several groups:
lf$group_by_dynamic("time", every = "1h", closed = "both")$agg(
  vals = pl$col("n")
)$collect()
#> shape: (5, 2)
#> ┌─────────────────────────┬───────────┐
#> │ time                    ┆ vals      │
#> │ ---                     ┆ ---       │
#> │ datetime[ms, UTC]       ┆ list[i32] │
#> ╞═════════════════════════╪═══════════╡
#> │ 2021-12-15 23:00:00 UTC ┆ [0]       │
#> │ 2021-12-16 00:00:00 UTC ┆ [0, 1, 2] │
#> │ 2021-12-16 01:00:00 UTC ┆ [2, 3, 4] │
#> │ 2021-12-16 02:00:00 UTC ┆ [4, 5, 6] │
#> │ 2021-12-16 03:00:00 UTC ┆ [6]       │
#> └─────────────────────────┴───────────┘
# Dynamic group bys can also be combined with grouping on normal keys
lf = lf$with_columns(
  groups = as_polars_series(c("a", "a", "a", "b", "b", "a", "a"))
)
lf$collect()
#> shape: (7, 3)
#> ┌─────────────────────────┬─────┬────────┐
#> │ time                    ┆ n   ┆ groups │
#> │ ---                     ┆ --- ┆ ---    │
#> │ datetime[ms, UTC]       ┆ i32 ┆ str    │
#> ╞═════════════════════════╪═════╪════════╡
#> │ 2021-12-16 00:00:00 UTC ┆ 0   ┆ a      │
#> │ 2021-12-16 00:30:00 UTC ┆ 1   ┆ a      │
#> │ 2021-12-16 01:00:00 UTC ┆ 2   ┆ a      │
#> │ 2021-12-16 01:30:00 UTC ┆ 3   ┆ b      │
#> │ 2021-12-16 02:00:00 UTC ┆ 4   ┆ b      │
#> │ 2021-12-16 02:30:00 UTC ┆ 5   ┆ a      │
#> │ 2021-12-16 03:00:00 UTC ┆ 6   ┆ a      │
#> └─────────────────────────┴─────┴────────┘
lf$group_by_dynamic(
  "time",
  every = "1h",
  closed = "both",
  group_by = "groups",
  include_boundaries = TRUE
)$agg(pl$col("n"))$collect()
#> shape: (7, 5)
#> ┌────────┬─────────────────────────┬─────────────────────────┬─────────────────────────┬───────────┐
#> │ groups ┆ _lower_boundary         ┆ _upper_boundary         ┆ time                    ┆ n         │
#> │ ---    ┆ ---                     ┆ ---                     ┆ ---                     ┆ ---       │
#> │ str    ┆ datetime[ms, UTC]       ┆ datetime[ms, UTC]       ┆ datetime[ms, UTC]       ┆ list[i32] │
#> ╞════════╪═════════════════════════╪═════════════════════════╪═════════════════════════╪═══════════╡
#> │ a      ┆ 2021-12-15 23:00:00 UTC ┆ 2021-12-16 00:00:00 UTC ┆ 2021-12-15 23:00:00 UTC ┆ [0]       │
#> │ a      ┆ 2021-12-16 00:00:00 UTC ┆ 2021-12-16 01:00:00 UTC ┆ 2021-12-16 00:00:00 UTC ┆ [0, 1, 2] │
#> │ a      ┆ 2021-12-16 01:00:00 UTC ┆ 2021-12-16 02:00:00 UTC ┆ 2021-12-16 01:00:00 UTC ┆ [2]       │
#> │ a      ┆ 2021-12-16 02:00:00 UTC ┆ 2021-12-16 03:00:00 UTC ┆ 2021-12-16 02:00:00 UTC ┆ [5, 6]    │
#> │ a      ┆ 2021-12-16 03:00:00 UTC ┆ 2021-12-16 04:00:00 UTC ┆ 2021-12-16 03:00:00 UTC ┆ [6]       │
#> │ b      ┆ 2021-12-16 01:00:00 UTC ┆ 2021-12-16 02:00:00 UTC ┆ 2021-12-16 01:00:00 UTC ┆ [3, 4]    │
#> │ b      ┆ 2021-12-16 02:00:00 UTC ┆ 2021-12-16 03:00:00 UTC ┆ 2021-12-16 02:00:00 UTC ┆ [4]       │
#> └────────┴─────────────────────────┴─────────────────────────┴─────────────────────────┴───────────┘
# We can also create a dynamic group by based on an index column
lf = pl$LazyFrame(
  idx = 0:5,
  A = c("A", "A", "B", "B", "B", "C")
)$with_columns(pl$col("idx")$set_sorted())
lf$collect()
#> shape: (6, 2)
#> ┌─────┬─────┐
#> │ idx ┆ A   │
#> │ --- ┆ --- │
#> │ i32 ┆ str │
#> ╞═════╪═════╡
#> │ 0   ┆ A   │
#> │ 1   ┆ A   │
#> │ 2   ┆ B   │
#> │ 3   ┆ B   │
#> │ 4   ┆ B   │
#> │ 5   ┆ C   │
#> └─────┴─────┘
lf$group_by_dynamic(
  "idx",
  every = "2i",
  period = "3i",
  include_boundaries = TRUE,
  closed = "right"
)$agg(A_agg_list = pl$col("A"))$collect()
#> shape: (4, 4)
#> ┌─────────────────┬─────────────────┬─────┬─────────────────┐
#> │ _lower_boundary ┆ _upper_boundary ┆ idx ┆ A_agg_list      │
#> │ ---             ┆ ---             ┆ --- ┆ ---             │
#> │ i32             ┆ i32             ┆ i32 ┆ list[str]       │
#> ╞═════════════════╪═════════════════╪═════╪═════════════════╡
#> │ -2              ┆ 1               ┆ -2  ┆ ["A", "A"]      │
#> │ 0               ┆ 3               ┆ 0   ┆ ["A", "B", "B"] │
#> │ 2               ┆ 5               ┆ 2   ┆ ["B", "B", "C"] │
#> │ 4               ┆ 7               ┆ 4   ┆ ["C"]           │
#> └─────────────────┴─────────────────┴─────┴─────────────────┘