numpy.nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>)
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 all-NaN slices or slices with zero degrees of freedom, NaN is returned and a
RuntimeWarning is raised.
New in version 1.8.0.
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 floating-point 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 higher-accuracy accumulator using the
dtype keyword can alleviate this issue.
For this function to work on sub-classes of ndarray, they must define
sum with the kwarg
>>> 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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