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. 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]]])
array([[ 4,  6],
[ 8, 10]])
>>> np.add.reduce(X) # confirm: default axis value is 0
array([[ 4,  6],
[ 8, 10]])
array([[ 2,  4],
[10, 12]])