pyspark.pandas.Series.quantile¶

Series.quantile(q: Union[float, Iterable[float]] = 0.5, accuracy: int = 10000) → Union[int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None, pyspark.pandas.series.Series]¶

Return value at the given quantile.

Note

Unlike pandas’, the quantile in pandas-on-Spark is an approximated quantile based upon approximate percentile computation because computing quantile across a large dataset is extremely expensive.

Parameters

qfloat or array-like, default 0.5 (50% quantile): 0 <= q <= 1, the quantile(s) to compute.
accuracyint, optional: Default accuracy of approximation. Larger value means better accuracy. The relative error can be deduced by 1.0 / accuracy.

Returns

float or Series: If the current object is a Series and q is an array, a Series will be returned where the index is q and the values are the quantiles, otherwise a float will be returned.

Examples

>>> s = ps.Series([1, 2, 3, 4, 5])
>>> s.quantile(.5)
3.0

>>> (s + 1).quantile(.5)
4.0

>>> s.quantile([.25, .5, .75])
0.25    2.0
0.50    3.0
0.75    4.0
dtype: float64

>>> (s + 1).quantile([.25, .5, .75])
0.25    3.0
0.50    4.0
0.75    5.0
dtype: float64

pyspark.pandas.Series.is_unique

pyspark.pandas.Series.rank