numpy.ufunc.reduce

ufunc.reduce(a, axis=0, dtype=None, out=None)

Reduces a‘s dimension by one, by applying ufunc along one axis.

Let . Then = the result of iterating j over , cumulatively applying ufunc to each . For a one-dimensional array, reduce produces results equivalent to:

r = op.identity # op = ufunc
for i in xrange(len(A)):
  r = op(r, A[i])
return r

For example, add.reduce() is equivalent to sum().

Parameters:

a : array_like

The array to act on.

axis : int, optional

The axis along which to apply the reduction.

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 : ndarray

The reduced array. If out was supplied, r is a reference to it.

Examples

>>> np.multiply.reduce([2,3,5])
30

A multi-dimensional array example:

>>> X = np.arange(8).reshape((2,2,2))
>>> X
array([[[0, 1],
        [2, 3]],
       [[4, 5],
        [6, 7]]])
>>> np.add.reduce(X, 0)
array([[ 4,  6],
       [ 8, 10]])
>>> np.add.reduce(X) # confirm: default axis value is 0
array([[ 4,  6],
       [ 8, 10]])
>>> np.add.reduce(X, 1)
array([[ 2,  4],
       [10, 12]])
>>> np.add.reduce(X, 2)
array([[ 1,  5],
       [ 9, 13]])