# pandas.cut

`pandas.``cut`(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False)

  Return indices of half-open bins to which each value of x belongs.

  Parameters:

  x : array-like

  > Input array to be binned. It has to be 1-dimensional.

  bins : int or sequence of scalars

  > If bins is an int, it defines the number of equal-width bins in the range of x. However, in this case, the range of x is extended by .1% on each side to include the min or max values of x. If bins is a sequence it defines the bin edges allowing for non-uniform bin width. No extension of the range of x is done in this case.

  right : bool, optional

  > Indicates whether the bins include the rightmost edge or not. If right == True (the default), then the bins [1,2,3,4] indicate (1,2], (2,3], (3,4].

  labels : array or boolean, 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.

  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

  include_lowest : bool

  > Whether the first interval should be left-inclusive or not.

  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.

  Notes

  The cut function can be useful for going from a continuous variable to a categorical variable. For example, cut could convert ages to groups of age ranges.

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

  Examples

      >>> pd.cut(np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]), 3, retbins=True)
      ([(0.191, 3.367], (0.191, 3.367], (0.191, 3.367], (3.367, 6.533], (6.533, 9.7], (0.191, 3.367]]
      Categories (3, object): [(0.191, 3.367] < (3.367, 6.533] < (6.533, 9.7]],
      array([ 0.1905    ,  3.36666667,  6.53333333,  9.7       ]))
      >>> pd.cut(np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]), 3, labels=["good","medium","bad"])
      [good, good, good, medium, bad, good]
      Categories (3, object): [good < medium < bad]
      >>> pd.cut(np.ones(5), 4, labels=False)
      array([1, 1, 1, 1, 1], dtype=int64)