See Also
    --------
    histogram, digitize, unique

    Examples
    --------
    >>> np.bincount(np.arange(5))
    array([1, 1, 1, 1, 1])
    >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
    array([1, 3, 1, 1, 0, 0, 0, 1])

    >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
    >>> np.bincount(x).size == np.amax(x)+1
    True

    The input array needs to be of integer dtype, otherwise a
    TypeError is raised:

    >>> np.bincount(np.arange(5, dtype=float))
    Traceback (most recent call last):
      ...
    TypeError: Cannot cast array data from dtype('float64') to dtype('int64')
    according to the rule 'safe'

    A possible use of ``bincount`` is to perform sums over
    variable-size chunks of an array, using the ``weights`` keyword.

    >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
    >>> x = np.array([0, 1, 1, 2, 2, 2])
    >>> np.bincount(x,  weights=w)
    array([ 0.3,  0.7,  1.1])

    ro