snowflake.snowpark.DataFrame.approxQuantile¶

DataFrame.approxQuantile(col: ColumnOrName | Iterable[ColumnOrName], percentile: Iterable[float], *, statement_params: Dict[str, str] | None = None) → List[float] | List[List[float]][source]¶

For a specified numeric column and a list of desired quantiles, returns an approximate value for the column at each of the desired quantiles. This function uses the t-Digest algorithm.

Examples:

>>> df = session.create_dataframe([1, 2, 3, 4, 5, 6, 7, 8, 9, 0], schema=["a"])
>>> df.stat.approx_quantile("a", [0, 0.1, 0.4, 0.6, 1])
[-0.5, 0.5, 3.5, 5.5, 9.5]

>>> df2 = session.create_dataframe([[0.1, 0.5], [0.2, 0.6], [0.3, 0.7]], schema=["a", "b"])
>>> df2.stat.approx_quantile(["a", "b"], [0, 0.1, 0.6])
[[0.05, 0.15000000000000002, 0.25], [0.45, 0.55, 0.6499999999999999]]

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Parameters:

• col – The name of the numeric column.

• percentile – A list of float values greater than or equal to 0.0 and less than 1.0.

• statement_params – Dictionary of statement level parameters to be set while executing this action.

Returns:

A list of approximate percentile values if col is a single column name, or a matrix with the dimensions (len(col) * len(percentile) containing the approximate percentile values if col is a list of column names.