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# pandas.qcut

`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

  Notes

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

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