verticapy.vDataColumn.max#

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

Aggregates the vDataFrame by applying the ‘MAX’ aggregation, which calculates the maximum value, for the input column. This aggregation provides insights into the highest values within the dataset, aiding in understanding the data distribution.

Returns#

PythonScalar: maximum

Examples#

For this example, let’s generate a dataset and calculate the maximum 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"].max()
Out[3]: 22.0

Note

All the calculations are pushed to the database.

Hint

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