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Version: Python

avg

agg.avgreturns an aggregator that computes the average (mean) of values, within an aggregation group, for each input column.

Syntax​

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

Parameters​

ParameterTypeDescription
colsUnion[str, list[str]]

The source column(s) for the calculations.

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

Examples​

In this example, agg.avgreturns the average value 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.avg(cols=["Number"])], by=["X"])

In this example, agg.avgreturns the average value of Number (renamed to Avg), 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.avg(cols=["Avg = Number"])], by=["X"])

In this example, agg.avgreturns the average value of Number, 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", "P", "O", "N", "P", "M", "O", "P", "N"]),
        int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
    ]
)

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

In this example, agg.avgreturns the average value of Number, and agg.std returns the sample standard deviation 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

agg_list = [agg.avg(cols=["AvgNumber = Number"]), agg.std(cols=["StdNumber = Number"])]

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

result = source.agg_by(aggs=agg_list, by=["X"])