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# pandas.qcut

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

  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 1d ndarray or Series

      Input Numpy array or pandas Series object to be discretized.

    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

      If bin edges are not unique, raise ValueError or drop non-uniques.

  Returns:
    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.

  See also

  `cut`

    Bin values into discrete intervals.

  `Series.quantile`

    Return value at the given quantile.

  Notes

  Out of bounds values will be NA in the resulting Categorical object. See the user guide for more examples.

  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, 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, str): [good < medium < bad]

      >>> pd.qcut(range(5), 4, labels=False)
      array([0, 0, 1, 2, 3])