mindspore.numpy.average(x, axis=None, weights=None, returned=False)[source]

Computes the weighted average along the specified axis.

  • x (Tensor) – A Tensor to be averaged.

  • axis (Union[None, int, tuple(int)]) – Axis along which to average x. Default: None. If the axis is None, it will average over all of the elements of the tensor x. If the axis is negative, it counts from the last to the first axis.

  • weights (Union[None, Tensor]) – Weights associated with the values in x. Default: None. If weights is None, all the data in x are assumed to have a weight equal to one. If weights is 1-D tensor, the length must be the same as the given axis. Otherwise, weights should have the same shape as x.

  • returned (bool) – Default: False. If True, the tuple (average, sum_of_weights) is returned. If False, only the average is returned.


Averaged Tensor. If returned is True, return tuple.

Supported Platforms:

Ascend GPU CPU


>>> import mindspore.numpy as np
>>> input_x = np.array([[1., 2.], [3., 4.]])
>>> output = np.average(input_x, axis=0, weights=input_x, returned=True)
>>> print(output)
(Tensor(shape=[2], dtype=Float32, value= [ 2.50000000e+00,  3.33333325e+00]),
 Tensor(shape=[2], dtype=Float32, value= [ 4.00000000e+00,  6.00000000e+00]))