verticapy.vDataFrame.min#

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

Aggregates the vDataFrame by applying the MIN aggregation, which calculates the minimum value, for the specified columns. This aggregation provides insights into the lowest values within the dataset, aiding in understanding the data distribution.

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 minimum for specific columns.

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

	min
"x"	1.0
"y"	1.0
"z"	1.0

Note

All the calculations are pushed to the database.

Hint

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