nlcpy.nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=nlcpy._NoValue)

Computes 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.


Array containing numbers whose variance is desired. If a is not an array, a conversion is attempted.

axisint, None, optional

Axis along which the variance is computed. The default is to compute the variance of the flattened array.

dtypedata-type, optional

Type to use in computing the variance. For arrays of integer type the default is float32; for arrays of float types it is the same as the array type.

outndarray, 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.

ddofint, optional

"Delta Degrees of Freedom": the divisor used in the calculation is N - ddof, where N represents the number of non-NaN elements. By default, ddof is zero.

keepdimsbool, optional

If this is set to True, the axis which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.

variancendarray, see dtype parameter above

If out is None, return a new array containing the variance, otherwise return a reference to the output array. If ddof is >= the number of non-NaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN.



Standard deviation




Variance while not ignoring NaNs


Computes the standard deviation along the specified axis, while ignoring NaNs.


Computes the arithmetic mean along the specified axis, ignoring NaNs.


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.

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.


  • If axis is neither a scalar nor None : NotImplementedError occurs.

  • For complex numbers, NotImplementedError occurs.


>>> import nlcpy as vp
>>> a = vp.array([[1, vp.nan], [3, 4]])
>>> vp.nanvar(a)    
>>> vp.nanvar(a, axis=0)
array([1., 0.])
>>> vp.nanvar(a, axis=1)
array([0.  , 0.25])