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Version: Python

group

agg.group returns an aggregator that computes an array of all values within an aggregation group, for each input column.

Syntax​

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

Parameters​

ParameterTypeDescription
colsUnion[str, list[str]]

The source column(s) for the calculations.

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

Examples​

In this example, agg.group returns an array of values of Y 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", None]),
        string_col("Y", ["M", "N", None, "N", "P", "M", None, "P", "M"]),
        int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
    ]
)

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

In this example, agg.group returns an array of values of Y (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", None]),
        string_col("Y", ["M", "N", None, "N", "P", "M", None, "P", "M"]),
        int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
    ]
)

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

In this example, agg.group returns an array of values of Y (renamed to Letters), and an array of values of Number (renamed to Numbers), 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", None]),
        string_col("Y", ["M", "N", None, "N", "P", "M", None, "P", "M"]),
        int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
    ]
)
result = source.agg_by([agg.group(cols=["Letters = Y", "Numbers = Number"])], by=["X"])

In this example, agg.group returns an array of values (the Number column renamed to Numbers), 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", None]),
        string_col("Y", ["M", "N", None, "N", "P", "M", None, "P", "M"]),
        int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
    ]
)

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

In this example, agg.group returns an array of values Number (renamed to Numbers), and agg.max_ returns the maximum Number integer, 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", None]),
        string_col("Y", ["M", "N", None, "N", "P", "M", None, "P", "M"]),
        int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
    ]
)
result = source.agg_by(
    [agg.group(cols=["Numbers = Number"]), agg.max_(cols=["MaxNumber = Number"])],
    by=["X"],
)