vastorbit.VastFrame.std¶

VastFrame.std(columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, **agg_kwargs) → TableSample¶

Aggregates the VastFrame using STDDEV aggregation (Standard Deviation), providing insights into the spread or variability of data for the selected columns. The standard deviation is a measure of how much individual data points deviate from the mean, helping to assess data consistency and variation.

Parameters:

columns (SQLColumns, optional) – List of the VastColumns names. If empty, all numerical VastColumns are used.
**agg_kwargs – Any optional parameter to pass to the Aggregate function.

Returns:

result.

Return type:

TableSample

Examples

For this example, we will use the following dataset:

import vastorbit as vo

data = vo.VastFrame(
    {
        "x": [1, 2, 4, 9, 10, 15, 20, 22],
        "y": [1, 2, 1, 2, 1, 1, 2, 1],
        "z": [10, 12, 2, 1, 9, 8, 1, 3],
    }
)

Now, let’s calculate the standard deviation for specific columns.

data.std(
    columns = ["x", "y", "z"],
)

	stddev
"x"	8.016723591521485
"y"	0.5175491695067657
"z"	4.464142854857069

Rows: 1-3 | Columns: 2

Note

All the calculations are pushed to the database.

Hint

For more precise control, please refer to the aggregate method.