numpy.nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>)
[source]
Compute the variance along the specified axis, while ignoring NaNs.
Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, otherwise over the specified axis.
For allNaN slices or slices with zero degrees of freedom, NaN is returned and a RuntimeWarning
is raised.
New in version 1.8.0.
Parameters: 


Returns: 

See also
numpy.doc.ufuncs
The variance is the average of the squared deviations from the mean, i.e., var = mean(abs(x  x.mean())**2)
.
The mean is normally calculated as x.sum() / N
, where N = len(x)
. If, however, ddof
is specified, the divisor N  ddof
is used instead. In standard statistical practice, ddof=1
provides an unbiased estimator of the variance of a hypothetical infinite population. ddof=0
provides a maximum likelihood estimate of the variance for normally distributed variables.
Note that for complex numbers, the absolute value is taken before squaring, so that the result is always real and nonnegative.
For floatingpoint input, the variance 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
(see example below). Specifying a higheraccuracy accumulator using the dtype
keyword can alleviate this issue.
For this function to work on subclasses of ndarray, they must define sum
with the kwarg keepdims
>>> a = np.array([[1, np.nan], [3, 4]]) >>> np.nanvar(a) 1.5555555555555554 >>> np.nanvar(a, axis=0) array([1., 0.]) >>> np.nanvar(a, axis=1) array([0., 0.25]) # may vary
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.nanvar.html