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Version: Python

median

agg.median returns an aggregator that computes the median value, within an aggregation group, for each input column.

Syntax​

median(cols: Union[str, list[str]], average_evenly_divided = True) -> Aggregation

Parameters​

ParameterTypeDescription
colsUnion[str, list[str]]

The source column(s) for the calculations.

  • ["X"] will output the median value in the X column for each group.
  • ["Y = X"] will output the median value in the X column for each group and rename it to Y.
  • ["X, A = B"] will output the median value in the X column for each group and the median value in the B column while renaming it to A.
average_evenly_dividedbool

When the group size is an even number, whether to average the two middle values for the output value.

  • When set to True, the two middle values are averaged. The default is True.
  • When set to False, the smaller value is used.
    This flag is only valid for numeric types.
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 median value, within an aggregation group, for each input column.

Examples​

In this example, agg.median returns the median Number value 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.median(cols=["Number"])], by=["X"])

In this example, agg.median returns the median Number value (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.median(cols=["Z = Number"])], by=["X"])

In this example, agg.median returns the median 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.median(cols=["Number"])], by=["X", "Y"])

In this example, agg.median returns the median 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", "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.median(cols=["MedNumber = Number"]), agg.max_(cols=["MaxNumber = Number"])],
    by=["X"],
)

This example demonstrates the effect of the average_evenly_divided parameter. The first table returns the median Number with average_evenly_divided set to False; the second table shows the default behavior.

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.median(cols=["MedNumber = Number"], average_evenly_divided=False)], by=["X"]
)
result1 = source.agg_by([agg.median(cols=["MedNumber = Number"])], by=["X"])