verticapy.vDataColumn.min#

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

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

Returns#

PythonScalar: minimum

Examples#

For this example, let’s generate a dataset and calculate the minimum 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"].min()
Out[3]: 1.0

Note

All the calculations are pushed to the database.

Hint

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