NumPy参考 >通用功能（ufunc） > numpy.ufunc.reduceat
reduceat(a, indices, axis=0, dtype=None, out=None)¶
Performs a (local) reduce with specified slices over a single axis.
For i in
ufunc.reduce(a[indices[i]:indices[i+1]]), which becomes the i-th
generalized “row” parallel to axis in the final result (i.e., in a
2-D array, for example, if axis = 0, it becomes the i-th row, but if
axis = 1, it becomes the i-th column). There are three exceptions to this:
i = len(indices) - 1 (so for the last index),
indices[i+1] = a.shape[axis].
indices[i] >= indices[i + 1], the i-th generalized “row” is
indices[i] >= len(a) or
indices[i] < 0, an error is raised.
The shape of the output depends on the size of
indices, and may be
larger than a (this happens if
len(indices) > a.shape[axis]).
The array to act on.
Paired indices, comma separated (not colon), specifying slices to reduce.
The axis along which to apply the reduceat.
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.
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 reduced values. If out was supplied, r is a reference to out.
A descriptive example:
If a is 1-D, the function ufunc.accumulate(a) is the same as
ufunc.reduceat(a, indices)[::2] where
range(len(array) - 1) with a zero placed
in every other element:
indices = zeros(2 * len(a) - 1),
indices[1::2] = range(1, len(a)).
Don’t be fooled by this attribute’s name: reduceat(a) is not necessarily smaller than a.
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])
A 2-D example:
>>> x = np.linspace(0, 15, 16).reshape(4,4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [12., 13., 14., 15.]])
# reduce such that the result has the following five rows: # [row1 + row2 + row3] # [row4] # [row2] # [row3] # [row1 + row2 + row3 + row4]
>>> np.add.reduceat(x, [0, 3, 1, 2, 0]) array([[12., 15., 18., 21.], [12., 13., 14., 15.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [24., 28., 32., 36.]])
# reduce such that result has the following two columns: # [col1 * col2 * col3, col4]
>>> np.multiply.reduceat(x, [0, 3], 1) array([[ 0., 3.], [ 120., 7.], [ 720., 11.], [2184., 15.]])