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Version: Python

sum_by

sum_by returns the total sum for each group. Null values are ignored.

caution

Applying this aggregation to a column where the sum can not be computed will result in an error. For example, the sum is not defined for a column of string values.

Syntax

table.sum_by(by: List[str]=[])

Parameters

ParameterTypeDescription
by optionalList[str]

The column(s) by which to group data.

  • [] returns the total sum for all non-key columns (default).
  • ["X"] will output the total sum of each group in column X.
  • ["X", "Y"] will output the total sum of each group designated from the X and Y columns.

Returns

A new table containing the sum for each group.

Examples

In this example, sum_by returns the sum of the whole table. Because a sum can not be computed for the string column X and Y, these columns are dropped before applying sum_by.

from deephaven import new_table
from deephaven.column import string_col, int_col

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.drop_columns(cols=["X", "Y"]).sum_by()

In this example, sum_by returns the sum, as grouped by X. Because a sum can not be computed for the string column Y, this column is dropped before applying sum_by.

from deephaven import new_table
from deephaven.column import string_col, int_col

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.drop_columns(cols=["Y"]).sum_by(by=["X"])

In this example, sum_by returns the sum, as grouped by X and Y.

from deephaven import new_table
from deephaven.column import string_col, int_col

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.sum_by(by=["X", "Y"])