numpy.nanmean(a, axis=None, dtype=None, out=None, keepdims=<class numpy._globals._NoValue>)
[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 all-NaN slices, NaN is returned and a RuntimeWarning
is raised.
New in version 1.8.0.
Parameters: |
a : array_like Array containing numbers whose mean is desired. If axis : int, optional Axis along which the means are computed. The default is to compute the mean of the flattened array. dtype : data-type, optional Type to use in computing the mean. For integer inputs, the default is out : ndarray, optional Alternate output array in which to place the result. The default is 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 If the value is anything but the default, then |
---|---|
Returns: |
m : ndarray, see dtype parameter above If |
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 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])
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https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.nanmean.html