verticapy.vDataFrame.all¶

vDataFrame.all(columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'], **agg_kwargs) → TableSample¶

Applies the BOOL_AND aggregation method to the vDataFrame. BOOL_AND, or Boolean AND, evaluates whether all the conditions within a set of Boolean values are true. This is useful when you need to ascertain if every condition holds. It is particularly handy when working with binary data or to ensure that all specified conditions are met within the dataset.

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": [True, False, False],
        "y": [False, False, False],
        "z": [True, True, True],
    }
)

Now, let’s use the all aggregator for specific columns.

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

	bool_and
"x"	0.0
"y"	0.0
"z"	1.0

Note

All the calculations are pushed to the database.

Hint

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