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Calculates the approximate quantiles of numerical columns of a DataFrame.
Syntax
approxQuantile(col: Union[str, List[str], Tuple[str]], probabilities: Union[List[float], Tuple[float]], relativeError: float)
Parameters
| Parameter | Type | Description |
|---|---|---|
col |
str, tuple or list | Can be a single column name, or a list of names for multiple columns. |
probabilities |
list or tuple of floats | a list of quantile probabilities. Each number must be a float in the range [0, 1]. For example 0.0 is the minimum, 0.5 is the median, 1.0 is the maximum. |
relativeError |
float | 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 gives 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.
Notes
Null values will be ignored in numerical columns before calculation. For columns only containing null values, an empty list is returned.
Examples
data = [(1,), (2,), (3,), (4,), (5,)]
df = spark.createDataFrame(data, ["values"])
quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
quantiles
# [1.0, 3.0, 5.0]
data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)]
df = spark.createDataFrame(data, ["col1", "col2"])
quantiles = df.approxQuantile(["col1", "col2"], [0.0, 0.5, 1.0], 0.05)
quantiles
# [[1.0, 3.0, 5.0], [10.0, 30.0, 50.0]]