transform
The transform method applies a function to all constituents of a partitioned table.
Syntax
transform(func: Callable[[Table], Table]) -> 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. |
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])
- source
- result_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"])
- source
- result_A