verticapy.vDataFrame.var#
- vDataFrame.var(columns: str | list[str] | None = None, **agg_kwargs) TableSample #
Aggregates the vDataFrame using
VAR
aggregation (Variance), providing insights into the spread or variability of data for the selected columns. The variance 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 variance for specific columns.
data.var( columns = ["x", "y", "z"], )
variance "x" 64.2678571428571 "y" 0.267857142857143 "z" 19.9285714285714 Note
All the calculations are pushed to the database.
Hint
For more precise control, please refer to the
aggregate
method.See also
vDataColumn.
kurtosis()
: Kurtosis for a specific column.vDataColumn.
skewness()
: Skewness for a specific column.vDataFrame.
std()
: Standard Deviation for particular columns.