transform

The transform method applies a function to all constituents of a partitioned table.

Syntax

transform(
  func: Callable[[Table], Table],
  dependencies: Optional[Sequence[Union[Table, PartitionedTable]]] = None,
) -> PartitionedTable

Parameters

Parameter	Type	Description
func	Callable[[Table], Table]	A function that takes a table as input and returns a table. The table operations applied within the function must be done from within an execution context. The function must also be stateless, safe for concurrent use, and able to return a valid result for an empty input table.
dependencies optional	Optional[Sequence[Union[Table, PartitionedTable]]]	Additional dependencies that must be satisfied before applying `func` to added or modified constituents during update processing. If `func` uses any other refreshing table or refreshing partitioned table, include it in this argument. The default is `None`.

Returns

A PartitionedTable.

Examples

This first example applies a transformation to add a new column to each constituent of a partitioned table.

from deephaven.execution_context import get_exec_ctx
from deephaven import empty_table

source = empty_table(5).update(["IntCol = i", "StrCol = `value`"])
source_partitioned = source.partition_by(["IntCol"])

ctx = get_exec_ctx()


def add_one(t):
    with ctx:
        return t.update(["IntCol2 = IntCol + 1"])


result_partitioned = source_partitioned.transform(add_one)

result_3 = result_partitioned.get_constituent([3])

This second example applies aggregations to each constituent of a partitioned table via transform.

from deephaven.execution_context import get_exec_ctx
from deephaven import empty_table
from deephaven import agg

import random, string


def rand_symbol() -> str:
    return random.choice(string.ascii_uppercase[:5])


source = empty_table(100).update(
    ["Sym = rand_symbol()", "X = randomInt(0, 100)", "Y = randomDouble(-50.0, 50.0)"]
)

ctx = get_exec_ctx()


def apply_aggs(t):
    with ctx:
        return t.update(["Z = X % 5"]).agg_by(
            aggs=[agg.sum_(["SumX = X"]), agg.count_("Z"), agg.avg(["AvgY = Y"])],
            by=["Sym"],
        )


partitioned_source = source.partition_by(by=["Sym"])

partitioned_result = partitioned_source.transform(func=apply_aggs)

result_A = partitioned_result.get_constituent(["A"])

transform

Syntax

Parameters

Returns

Examples

Related documentation