verticapy.vDataFrame.std#

vDataFrame.std(columns: str | list[str] | None = None, **agg_kwargs) → TableSample#

Aggregates the vDataFrame 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 vDataColumns names. If empty, all numerical vDataColumns are used.
**agg_kwargs: Any optional parameter to pass to the Aggregate function.

Returns#

TableSample: result.

Examples#

For this example, we will use the following dataset:

import verticapy as vp

data = vp.vDataFrame(
    {
        "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"	8.01672359152148
"y"	0.517549169506766
"z"	4.46414285485707

Note

All the calculations are pushed to the database.

Hint

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