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# numpy.bincount

`numpy.``bincount`(x, weights=None)

  Count number of occurrences of each value in array of non-negative ints.

  The number of bins (of size 1) is one larger than the largest value in x. Each bin gives the number of occurrences of its index value in x. If weights is specified the input array is weighted by it, i.e. if a value `n` is found at position `i`, `out[n] += weight[i]` instead of `out[n] += 1`.

  Parameters : | x : array_like, 1 dimension, nonnegative ints Input array. weights : array_like, optional Weights, array of the same shape as x.
  Returns : | out : ndarray of ints The result of binning the input array. The length of out is equal to `np.amax(x)+1`.
  Raises : | ValueError : If the input is not 1-dimensional, or contains elements with negative values. TypeError : If the type of the input is float or complex.

  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

      >>> np.bincount(np.arange(5, dtype=np.float))
      Traceback (most recent call last):
        File "<stdin>", line 1, in <module>
      TypeError: array cannot be safely cast to required type

  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])

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