verticapy.vDataColumn.std#

vDataColumn.std() → bool | float | str | timedelta | datetime#

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

Returns#

PythonScalar: std

Examples#

For this example, let’s generate a dataset and calculate the standard deviation of a column:

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],
    }
)


data["x"].std()
Out[3]: 8.01672359152148

Note

All the calculations are pushed to the database.

Hint

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