mindspore.numpy.nanmean

mindspore.numpy.nanmean(a, axis=None, dtype=None, keepdims=False)[source]

Computes the arithmetic mean along the specified axis, ignoring NaNs.

Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float32 intermediate and return values are used for integer inputs.

Note

Numpy arguments out is not supported.

Parameters

a (Union[int, float, list, tuple, Tensor]) – Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.
axis (Union[int, tuple of int, None], optional) – Axis or axes along which the mean is computed. The default is to compute the mean of the flattened array.
dtype (mindspore.dtype, optional) – Default: None . Overrides the dtype of the output Tensor.
keepdims (boolean, optional) – Default: False . 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 original a.

Returns

Tensor.

Raises

ValueError – If axes are out of the range of [-a.ndim, a.ndim), or if the axes contain duplicates.

Supported Platforms:: GPU CPU

Examples

>>> import mindspore.numpy as np
>>> a = np.array([[1, np.nan], [3, 4]])
>>> output = np.nanmean(a)
>>> print(output)
2.6666667
>>> output = np.nanmean(a, axis=0)
>>> print(output)
[2. 4.]
>>> output = np.nanmean(a, axis=1)
>>> print(output)
[1.  3.5]