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Version: Python

std

agg.std returns an aggregator that computes the standard deviation of values, within an aggregation group, for each input column.

Syntax

std(cols: List[str]) -> Aggregation

Parameters

ParameterTypeDescription
colsList[str]

The source column(s) for the calculations.

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

Examples

In this example, agg.std returns the standard deviation 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.std(cols=["Number"])], by=["X"])

In this example, agg.std returns the standard deviation of values of Number (renamed to Std), 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.std(cols=["Std = Number"])], by=["X"])

In this example, agg.std returns the standard deviation of values of Number (renamed to Std), 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.std(cols=["Std = Number"])], by=["X", "Y"])

In this example, agg.first returns the standard deviation of values of Number, and AggMed returns the median 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", "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.std(cols=["Std = Number"])], by=["X"])