Version: Python

# std

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

Sample standard deviation is calculated as the square root of the Bessel-corrected sample variance, which can be shown to be an unbiased estimator of population variance under some conditions. However, sample standard deviation is a biased estimator of population standard deviation.

## Syntax​

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

## Parameters​

ParameterTypeDescription
colsUnion[str, list[str]]

The source column(s) for the calculations.

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

## Examples​

In this example, `agg.std` returns the sample standard deviation of values of `Number` as grouped by `X`.

``from deephaven import new_tablefrom deephaven.column import string_col, int_col, double_colfrom deephaven import agg as aggsource = 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 sample standard deviation of values of `Number` (renamed to `Std`), as grouped by `X`.

``from deephaven import new_tablefrom deephaven.column import string_col, int_col, double_colfrom deephaven import agg as aggsource = 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 sample standard deviation of values of `Number` (renamed to `Std`), as grouped by `X` and `Y`.

``from deephaven import new_tablefrom deephaven.column import string_col, int_col, double_colfrom deephaven import agg as aggsource = 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 sample standard deviation of values of `Number`, and `agg.median` returns the median value of `Number`, as grouped by `X`.

``from deephaven import new_tablefrom deephaven.column import string_col, int_col, double_colfrom deephaven import agg as aggsource = 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"]), agg.median(cols=["Median = Number"])], by=["X"])``