xorbits.pandas.qcut(x, q, labels=None, retbins=False, precision=3, duplicate='raise')[source]#

Quantile-based discretization function.

Discretize variable into equal-sized buckets based on rank or based on sample quantiles. For example 1000 values for 10 quantiles would produce a Categorical object indicating quantile membership for each data point.

  • x (1d ndarray or Series) –

  • q (int or list-like of float) – Number of quantiles. 10 for deciles, 4 for quartiles, etc. Alternately array of quantiles, e.g. [0, .25, .5, .75, 1.] for quartiles.

  • labels (array or False, default None) – Used as labels for the resulting bins. Must be of the same length as the resulting bins. If False, return only integer indicators of the bins. If True, raises an error.

  • retbins (bool, optional) – Whether to return the (bins, labels) or not. Can be useful if bins is given as a scalar.

  • precision (int, optional) – The precision at which to store and display the bins labels.

  • duplicates ({default 'raise', 'drop'}, optional (Not supported yet)) – If bin edges are not unique, raise ValueError or drop non-uniques.


  • out (Categorical or Series or array of integers if labels is False) – The return type (Categorical or Series) depends on the input: a Series of type category if input is a Series else Categorical. Bins are represented as categories when categorical data is returned.

  • bins (ndarray of floats) – Returned only if retbins is True.


Out of bounds values will be NA in the resulting Categorical object


>>> pd.qcut(range(5), 4)  
[(-0.001, 1.0], (-0.001, 1.0], (1.0, 2.0], (2.0, 3.0], (3.0, 4.0]]
Categories (4, interval[float64, right]): [(-0.001, 1.0] < (1.0, 2.0] ...
>>> pd.qcut(range(5), 3, labels=["good", "medium", "bad"])  
[good, good, medium, bad, bad]
Categories (3, object): [good < medium < bad]
>>> pd.qcut(range(5), 4, labels=False)  
array([0, 0, 1, 2, 3])

This docstring was copied from pandas.