Compute the arithmetic mean along the specified axis, ignoring NaNs.
Returns the average of the tensor elements. The average is taken over
the flattened tensor by default, otherwise over the specified axis.
float64 intermediate and return values are used for integer inputs.
For all-NaN slices, NaN is returned and a RuntimeWarning is raised.
a (array_like) – Tensor containing numbers whose mean is desired. If a is not an
tensor, a conversion is attempted.
axis (int, optional) – Axis along which the means are computed. The default is to compute
the mean of the flattened tensor.
dtype (data-type, optional) – Type to use in computing the mean. For integer inputs, the default
is float64; for inexact inputs, it is the same as the input
out (Tensor, optional) – Alternate output tensor in which to place the result. The default
is None; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
doc.ufuncs for details.
keepdims (bool, optional) –
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.
If the value is anything but the default, then
keepdims will be passed through to the mean or sum methods
of sub-classes of Tensor. If the sub-classes methods
does not implement keepdims any exceptions will be raised.
combine_size (int, optional) – The number of chunks to combine.
m – If out=None, returns a new array containing the mean values,
otherwise a reference to the output array is returned. Nan is
returned for slices that contain only NaNs.
Tensor, see dtype parameter above
Arithmetic mean taken while not ignoring NaNs
The arithmetic mean is the sum of the non-NaN elements along the axis
divided by the number of non-NaN elements.
Note that for floating-point input, the mean is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for float32. Specifying a
higher-precision accumulator using the dtype keyword can alleviate
>>> import mars.tensor as mt
>>> a = mt.array([[1, mt.nan], [3, 4]])
>>> mt.nanmean(a, axis=0).execute()
array([ 2., 4.])
>>> mt.nanmean(a, axis=1).execute()
array([ 1., 3.5])