numpy.nanvar¶

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 ofa distribution. The variance is computed for the flattened array bydefault, otherwise over the specified axis.

For all-NaN slices or slices with zero degrees of freedom, NaN isreturned and aRuntimeWarning is raised.

New in version 1.8.0.

Parameters:

a:array_like Array containing numbers whose variance is desired. Ifa is not anarray, a conversion is attempted. axis:{int, tuple of int, None}, optional Axis or axes along which the variance is computed. The default is to computethe variance of the flattened array. dtype:data-type, optional Type to use in computing the variance. For arrays of integer typethe default isfloat32; for arrays of float types it is the same asthe array type. out:ndarray, optional Alternate output array in which to place the result. It must havethe same shape as the expected output, but the type is cast ifnecessary. ddof:int, optional “Delta Degrees of Freedom”: the divisor used in the calculation isN-ddof, whereN represents the number of non-NaNelements. By defaultddof is zero. keepdims:bool, optional If this is set to True, the axes which are reduced are leftin the result as dimensions with size one. With this option,the result will broadcast correctly against the originala.

Returns:

variance:ndarray, see dtype parameter above Ifout is None, return a new array containing the variance,otherwise return a reference to the output array. If ddof is >= thenumber of non-NaN elements in a slice or the slice contains onlyNaNs, then the result for that slice is NaN.

See also

std

Standard deviation

mean

Average

var

Variance while not ignoring NaNs

nanstd,nanmean

numpy.doc.ufuncs

Section “Output arguments”

Notes

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 asx.sum()/N, whereN=len(x).If, however,ddof is specified, the divisorN-ddof is usedinstead. In standard statistical practice,ddof=1 provides anunbiased estimator of the variance of a hypothetical infinitepopulation.ddof=0 provides a maximum likelihood estimate of thevariance for normally distributed variables.

Note that for complex numbers, the absolute value is taken beforesquaring, so that the result is always real and nonnegative.

For floating-point input, the variance is computed using the sameprecision the input has. Depending on the input data, this can causethe results to be inaccurate, especially forfloat32 (see examplebelow). Specifying a higher-accuracy accumulator using thedtypekeyword can alleviate this issue.

For this function to work on sub-classes of ndarray, they must definesum with the kwargkeepdims

Examples

>>>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])