pyspark.sql.DataFrameStatFunctions.approxQuantile¶
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DataFrameStatFunctions.approxQuantile(col: Union[str, List[str], Tuple[str]], probabilities: Union[List[float], Tuple[float]], relativeError: float) → Union[List[float], List[List[float]]]¶ Calculates the approximate quantiles of numerical columns of a
DataFrame.The result of this algorithm has the following deterministic bound: If the
DataFramehas N elements and if we request the quantile at probability p up to error err, then the algorithm will return a sample x from theDataFrameso that the exact rank of x is close to (p * N). More precisely,floor((p - err) * N) <= rank(x) <= ceil((p + err) * N).
This method implements a variation of the Greenwald-Khanna algorithm (with some speed optimizations). The algorithm was first present in [[https://doi.org/10.1145/375663.375670 Space-efficient Online Computation of Quantile Summaries]] by Greenwald and Khanna.
Note that null values will be ignored in numerical columns before calculation. For columns only containing null values, an empty list is returned.
- Parameters
- col: str, tuple or list
Can be a single column name, or a list of names for multiple columns.
Added support for multiple columns.
- probabilitieslist or tuple
a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the minimum, 0.5 is the median, 1 is the maximum.
- relativeErrorfloat
The relative target precision to achieve (>= 0). If set to zero, the exact quantiles are computed, which could be very expensive. Note that values greater than 1 are accepted but give the same result as 1.
- Returns
- list
the approximate quantiles at the given probabilities. If the input col is a string, the output is a list of floats. If the input col is a list or tuple of strings, the output is also a list, but each element in it is a list of floats, i.e., the output is a list of list of floats.