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