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Version: Python

weighted_sum

agg.weighted_sum returns an aggregator that computes the weighted sum of values, within an aggregation group, for each input column.

Syntax

weighted_sum(wcol: str, cols: List[str]) -> Aggregation

Parameters

ParameterTypeDescription
wcolString

The weight column for the calculation.

colsList[str]

The source column(s) for the calculations.

  • ["X"] will output the weighted sum of values in the X column for each group.
  • ["Y = X"] will output the weighted sum of values in the X column for each group and rename it to Y.
  • ["X, A = B"] will output the weighted sum of values in the X column for each group and the weighted sum of values in the B column and rename it to A.
caution

If an aggregation does not rename the resulting column, the aggregation column will appear in the output table, not the input column. If multiple aggregations on the same column do not rename the resulting columns, an error will result, because the aggregations are trying to create multiple columns with the same name. For example, in table.agg_by([agg.sum_(cols=[“X”]), agg.avg(cols=["X"]), both the sum and the average aggregators produce column X, which results in an error.

Returns

An aggregator that computes the weighted sum of values, within an aggregation group, for each input column.

Examples

In this example, agg.weighted_sum returns the weighted sum of values of Number, as weighed by Weight and grouped by X.

from deephaven import new_table
from deephaven.column import string_col, int_col, double_col
from deephaven import agg as agg

source = new_table([
string_col("X", ["A", "B", "A", "C", "B", "A", "B", "B", "C"]),
string_col("Y", ["M", "N", "O", "N", "P", "M", "O", "P", "M"]),
int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
int_col("Weight", [1, 2, 1, 3, 2, 1, 4, 1, 2]),
])

result = source.agg_by([agg.weighted_sum(wcol="Weight", cols=["Number"])], by=["X"])

In this example, agg.weighted_sum returns the weighted sum of values of Number (renamed to WSumNumber), as weighed by Weight and grouped by X.

from deephaven import new_table
from deephaven.column import string_col, int_col, double_col
from deephaven import agg as agg

source = new_table([
string_col("X", ["A", "B", "A", "C", "B", "A", "B", "B", "C"]),
string_col("Y", ["M", "N", "O", "N", "P", "M", "O", "P", "M"]),
int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
int_col("Weight", [1, 2, 1, 3, 2, 1, 4, 1, 2]),
])

result = source.agg_by([agg.weighted_sum(wcol="Weight", cols=["WSumNumber = Number"])], by=["X"])

In this example, agg.weighted_sum returns the weighted sum of values of Number (renamed to WSumNumber), as weighed by Weight and grouped by X.

from deephaven import new_table
from deephaven.column import string_col, int_col, double_col
from deephaven import agg as agg

source = new_table([
string_col("X", ["A", "B", "A", "C", "B", "A", "B", "B", "C"]),
string_col("Y", ["M", "N", "O", "N", "P", "M", "O", "P", "M"]),
int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
int_col("Weight", [1, 2, 1, 3, 2, 1, 4, 1, 2]),
])

result = source.agg_by([agg.weighted_sum(wcol="Weight", cols=["WSumNumber = Number"]),agg.sum_(cols=["Sum = Number"])], by=["X"])