numpy.nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<class numpy._globals._NoValue>)
[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 all-NaN slices or slices with zero degrees of freedom, NaN is returned and a RuntimeWarning
is raised.
New in version 1.8.0.
Parameters: |
a : array_like Array containing numbers whose variance is desired. If axis : int, optional Axis along which the variance is computed. The default is to compute the variance of the flattened array. dtype : data-type, optional Type to use in computing the variance. For arrays of integer type the default is out : ndarray, optional Alternate output array in which to place the result. It must have the same shape as the expected output, but the type is cast if necessary. ddof : int, optional “Delta Degrees of Freedom”: the divisor used in the calculation 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 |
---|---|
Returns: |
variance : ndarray, see dtype parameter above If |
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 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 keepdims
>>> a = np.array([[1, np.nan], [3, 4]]) >>> np.var(a) 1.5555555555555554 >>> np.nanvar(a, axis=0) array([ 1., 0.]) >>> np.nanvar(a, axis=1) array([ 0., 0.25])
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https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.nanvar.html