pandas.qcut(x, q, labels=None, retbins=False, precision=3)

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 :

x : ndarray or Series

q : integer or array of quantiles

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 boolean, default None

Labels to use for bin edges, or False to return integer bin labels

retbins : bool, optional

Whether to return the bins or not. Can be useful if bins is given as a scalar.

precision : int

The precision at which to store and display the bins labels

Returns :

cat : Categorical


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