mindspore.numpy.sum(a, axis=None, dtype=None, keepdims=False, initial=None)

Returns sum of array elements over a given axis.


Numpy arguments out, where, casting, order, subok, signature, and extobj are not supported.

  • x (Union[int, float, bool, list, tuple, Tensor]) – Elements to sum.

  • axis (Union[None, int, tuple(int)]) – Axis or axes along which a sum is performed. Default: None. If None, sum all of the elements of the input array. If axis is negative it counts from the last to the first axis. If axis is a tuple of integers, a sum is performed on all of the axes specified in the tuple instead of a single axis or all the axes as before.

  • dtype (mindspore.dtype, optional) – Defaults to None. Overrides the dtype of the output Tensor.

  • keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then keepdims will not be passed through to the sum method of sub-classes of ndarray, however any non-default value will be. If the sub-class method does not implement keepdims any exceptions will be raised. Default: False.

  • initial (scalar) – Starting value for the sum, if None, which refers to the first element of the reduction. Default: None.


Tensor. An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.

  • TypeError – If input is not array_like or axis is not int or tuple of integers or keepdims is not integer or initial is not scalar.

  • ValueError – If any axis is out of range or duplicate axes exist.

Supported Platforms:

Ascend GPU CPU


>>> import mindspore.numpy as np
>>> print(np.sum([0.5, 1.5]))
>>> x = np.arange(10).reshape(2, 5).astype('float32')
>>> print(np.sum(x, axis=1))
[10. 35.]