sum_

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

Note

sum is a reserved Python keyword, so an underscore is used to maintain Python conventions.

Syntax

sum_(cols: Union[str, list[str]]) -> Aggregation

Parameters

ParameterTypeDescription
colsUnion[str, list[str]]

The source column(s) for the calculations.

  • ["X"] will output the total sum of values in the X column for each group.
  • ["Y = X"] will output the total sum of values in the X column for each group and rename it to Y.
  • ["X, A = B"] will output the total sum of values in the X column for each group and the total sum of values in the B value column renaming 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 total sum of values, within an aggregation group, for each input column.

Examples

In this example, agg.sum_ returns the total sum of values of Number as 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]),
    ]
)

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

In this example, agg.sum_ returns the total sum of values of Number (renamed to Z), as 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]),
    ]
)

result = source.agg_by([agg.sum_(cols=["Z = Number"])], by=["X"])

In this example, agg.sum_ returns the total sum of values of Number (renamed to Z), as grouped by X and Y.

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]),
    ]
)

result = source.agg_by([agg.sum_(cols=["Z = Number"])], by=["X", "Y"])

In this example, agg.sum_ returns the total sum of values of Number, and agg.max_ returns the maximum Number , as 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]),
    ]
)

result = source.agg_by(
    [agg.sum_(cols=["SumNumber = Number"]), agg.max_(cols=["MaxNumber = Number"])],
    by=["X"],
)