pandas.qcut¶
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pandas.qcut(x, q, labels=None, retbins=False, precision=3, duplicates='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.
- Parameters
- x1d ndarray or Series
- qint 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.
- labelsarray 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.
- retbinsbool, optional
Whether to return the (bins, labels) or not. Can be useful if bins is given as a scalar.
- precisionint, optional
The precision at which to store and display the bins labels.
- duplicates{default ‘raise’, ‘drop’}, optional
If bin edges are not unique, raise ValueError or drop non-uniques.
- Returns
- outCategorical 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.
- binsndarray of floats
Returned only if retbins is True.
Notes
Out of bounds values will be NA in the resulting Categorical object
Examples
>>> 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]): [(-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])