vastorbit.VastFrame.nunique¶

VastFrame.nunique(columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, approx: bool = True, **agg_kwargs) → TableSample¶

When aggregating the VastFrame using nunique (cardinality), vastorbit employs the COUNT DISTINCT function to determine the number of unique values in a particular column. It also offers the option to use APPROX_DISTINCT, a more efficient approximation method for calculating cardinality.

Hint

This flexibility allows you to optimize the computation based on your specific requirements, keeping in mind that using APPROX_DISTINCT can significantly improve performance when cardinality estimation is sufficient for your analysis.

Important

To calculate the exact cardinality of a column, you should set the parameter approx to False. This will ensure that the cardinality is computed accurately rather than using the approximate method.

Parameters:

columns (SQLColumns, optional) – List of the VastColumns names. If empty, all VastColumns are used.
approx (bool, optional) – If set to True, the approximate cardinality is returned. By setting this parameter to False, the function’s performance can drastically decrease.
**agg_kwargs – Any optional parameter to pass to the Aggregate function.

Returns:

result.

Return type:

TableSample

Examples

For this example, we will use the following dataset:

import vastorbit as vo

data = vo.VastFrame(
    {
        "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 cardinality for specific columns.

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

	approx_unique
"x"	8.0
"y"	2.0
"z"	7.0

Rows: 1-3 | Columns: 2

Note

All the calculations are pushed to the database.

Hint

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