verticapy.vDataFrame.avg#

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

This operation aggregates the vDataFrame using the AVG aggregation, which calculates the average value for the selected column or columns. It provides insights into the central tendency of the data and is a fundamental statistical measure often used in data analysis and reporting.

Parameters#

columns: SQLColumns, optional: List of the vDataColumns names. If empty, all 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 averages for specific columns.

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

	avg
"x"	10.375
"y"	1.375
"z"	5.75

Note

All the calculations are pushed to the database.

Hint

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