Previous topic


Next topic




ufunc.accumulate(array, axis=0, dtype=None, out=None)

Accumulate the result of applying the operator to all elements.

For a one-dimensional array, accumulate produces results equivalent to:

r = np.empty(len(A))
t = op.identity        # op = the ufunc being applied to A's  elements
for i in range(len(A)):
    t = op(t, A[i])
    r[i] = t
return r

For example, add.accumulate() is equivalent to np.cumsum().

For a multi-dimensional array, accumulate is applied along only one axis (axis zero by default; see Examples below) so repeated use is necessary if one wants to accumulate over multiple axes.


The array to act on.

axisint, optional

The axis along which to apply the accumulation; default is zero.

dtypedata-type code, optional

The data-type used to represent the intermediate results. Defaults to the data-type of the output array if such is provided, or the the data-type of the input array if no output array is provided.

outndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If not provided or None, a freshly-allocated array is returned. For consistency with ufunc.__call__, if given as a keyword, this may be wrapped in a 1-element tuple.

Changed in version 1.13.0: Tuples are allowed for keyword argument.


The accumulated values. If out was supplied, r is a reference to out.


1-D array examples:

>>> np.add.accumulate([2, 3, 5])
array([ 2,  5, 10])
>>> np.multiply.accumulate([2, 3, 5])
array([ 2,  6, 30])

2-D array examples:

>>> I = np.eye(2)
>>> I
array([[1.,  0.],
       [0.,  1.]])

Accumulate along axis 0 (rows), down columns:

>>> np.add.accumulate(I, 0)
array([[1.,  0.],
       [1.,  1.]])
>>> np.add.accumulate(I) # no axis specified = axis zero
array([[1.,  0.],
       [1.,  1.]])

Accumulate along axis 1 (columns), through rows:

>>> np.add.accumulate(I, 1)
array([[1.,  1.],
       [0.,  1.]])