numpy.nanmean(a, axis=None, dtype=None, out=None, keepdims=<no value>)
[source]
Compute 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. float64
intermediate and return values are used for integer inputs.
For allNaN slices, NaN is returned and a RuntimeWarning
is raised.
New in version 1.8.0.
Parameters: 


Returns: 

The arithmetic mean is the sum of the nonNaN elements along the axis divided by the number of nonNaN elements.
Note that for floatingpoint 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 higherprecision accumulator using the dtype
keyword can alleviate this issue.
>>> a = np.array([[1, np.nan], [3, 4]]) >>> np.nanmean(a) 2.6666666666666665 >>> np.nanmean(a, axis=0) array([2., 4.]) >>> np.nanmean(a, axis=1) array([1., 3.5]) # may vary
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.nanmean.html