rolling_count__where_tick
rolling_count__where_tick creates an update_by table operation that keeps a count of values in a rolling window that pass a set of filters in an update_by table operation. This call uses table ticks as the
windowing unit. Ticks are row counts. The rolling count can be calculated using forward and/or backward windows.
Syntax
rolling_count_where_tick(
col: str,
filters: Union[str, Filter, List[str], List[Filter]],
rev_ticks: int = 0,
fwd_ticks: int = 0) -> UpdateByOperation:
Parameters
| Parameter | Type | Description |
|---|---|---|
| col | str | The name of the column that will contain the count of values that pass the filters. |
| filters | Union[str, Filter, Sequence[str], Sequence[Filter]] | Formulas for filtering as a list of Strings. Any filter is permitted, as long as it is not refreshing and does not use row position/key variables or arrays. |
| rev_ticks | int | The look-behind window size in rows. If positive, it defines the maximum number of rows before the current row that will be used. If negative, it defines the minimum number of rows after the current row that will be used. Includes the current row. |
| fwd_ticks | int | The look-forward window size in rows. If positive, it defines the maximum number of rows after the current row that will be used. If negative, it defines the minimum number of rows before the current row that will be used. |
Providing multiple filter strings in the filters parameter results in an AND operation being applied to the
filters. For example,
"Number % 3 == 0", "Number % 5 == 0" returns the count of values where Number is evenly divisible by
both 3 and 5. You can also write this as a single conditional filter ("Number % 3 == 0 && Number % 5 == 0") and
receive the same result.
You can use the || operator to OR multiple filters. For example, Y == `M` || Y == `N` matches when Y equals
M or N.
Returns
An UpdateByOperation to be used in an update_by table operation.
Examples
The following example performs an update_by on the source table using rolling_count_where_tick. The same filter is used but we are providing three different windows for the rolling calculation.
from deephaven.updateby import rolling_count_where_tick
from deephaven import empty_table
source = empty_table(10).update(
["Letter = (i % 2 == 0) ? `A` : `B`", "X = i", "Y = randomInt(0, 100)"]
)
filter_to_apply = ["Y >= 20", "Y < 99"]
op_before = rolling_count_where_tick(
col="count_before", filters=filter_to_apply, rev_ticks=3, fwd_ticks=-1
)
op_after = rolling_count_where_tick(
col="count_after", filters=filter_to_apply, rev_ticks=-1, fwd_ticks=3
)
op_middle = rolling_count_where_tick(
col="count_middle", filters=filter_to_apply, rev_ticks=1, fwd_ticks=1
)
result = source.update_by(ops=[op_before, op_after, op_middle])
- source
- result
The following example performs an OR filter and computes the results for each value in the Letter column separately.
from deephaven.updateby import rolling_count_where_tick
from deephaven import empty_table
source = empty_table(10).update(
["Letter = (i % 2 == 0) ? `A` : `B`", "X = i", "Y = randomInt(0, 100)"]
)
filter_to_apply = ["Y < 20 || Y >= 80"]
op_before = rolling_count_where_tick(
col="count_before", filters=filter_to_apply, rev_ticks=3, fwd_ticks=-1
)
op_after = rolling_count_where_tick(
col="count_after", filters=filter_to_apply, rev_ticks=-1, fwd_ticks=3
)
op_middle = rolling_count_where_tick(
col="count_middle", filters=filter_to_apply, rev_ticks=1, fwd_ticks=1
)
result = source.update_by(ops=[op_before, op_after, op_middle], by="Letter")
- source
- result
The following example uses a complex filter involving multiple columns, with the results bucketed by the Letter column.
from deephaven.updateby import rolling_count_where_tick
from deephaven import empty_table
source = empty_table(10).update(
["Letter = (i % 2 == 0) ? `A` : `B`", "X = i", "Y = randomInt(0, 100)"]
)
filter_to_apply = ["(Y < 20 || Y >= 80 || Y % 7 == 0) && X >= 3"]
op_before = rolling_count_where_tick(
col="count_before", filters=filter_to_apply, rev_ticks=3, fwd_ticks=-1
)
op_after = rolling_count_where_tick(
col="count_after", filters=filter_to_apply, rev_ticks=-1, fwd_ticks=3
)
op_middle = rolling_count_where_tick(
col="count_middle", filters=filter_to_apply, rev_ticks=1, fwd_ticks=1
)
result = source.update_by(ops=[op_before, op_after, op_middle], by="Letter")
- source
- result