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# numpy.ufunc.reduceat

`ufunc.``reduceat`(self, array, indices, axis=None, dtype=None, out=None)

  Reduceat performs a reduce with specified slices over an axis.

  Computes op.reduce(array[indices[i]:indices[i+1]]) for i=0..end with an implicit indices[i+1] = len(array) assumed when i = end - 1.

  If indices[i] >= indices[i + 1] then the result is array[indices[i]] for that value.

  The function op.accumulate(array) is the same as op.reduceat(array, indices)[::2] where indices is range(len(array)-1) with a zero placed in every other sample: indices = zeros(len(array)*2 - 1) indices[1::2] = range(1, len(array))

  The output shape is based on the size of indices.

  Parameters: | array : array_like The array to act on. indices : array_like Paired indices specifying slices to reduce. axis : int, optional The axis along which to apply the reduceat. dtype : data-type-code, optional The type used to represent the intermediate results. Defaults to the data type of the output array if this is provided, or the data type of the input array if no output array is provided. out : ndarray, optional A location into which the result is stored. If not provided a freshly-allocated array is returned.
  Returns: | r : array The reduced values. If out was supplied, r is equal to out.

  Examples

  To take the running sum of four successive values:

      >>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]
      array([ 6, 10, 14, 18])

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