verticapy.vDataFrame.skewness#

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

Utilizes the skewness aggregation method to analyze and aggregate the vDataFrame. Skewness, a measure of the asymmetry in the data’s distribution, helps us understand the data’s deviation from a perfectly symmetrical distribution. When we aggregate the vDataFrame using skewness, we gain insights into the data’s tendency to be skewed to the left or right, or if it follows a normal distribution.

Warning

To compute skewness, VerticaPy needs to execute multiple queries. It necessitates, at a minimum, a query that includes a subquery to perform this type of aggregation. This complexity is the reason why calculating skewness is typically slower than some other types of aggregations.

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 skewness for specific columns.

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

	skewness
"x"	0.328467287554527
"y"	0.644061188719529
"z"	0.168607546065438

Note

All the calculations are pushed to the database.

Hint

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