numpy.nanvar#
- numpy.nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *, where=<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 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:
- aarray_like
Array containing numbers whose variance is desired. If a is not an array, a conversion is attempted.
- axis{int, tuple of int, None}, optional
Axis or axes 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
float64
; 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
, whereN
represents the number of non-NaN elements. By default ddof is zero.- keepdimsbool, 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 a.
- wherearray_like of bool, optional
Elements to include in the variance. See
reduce
for details.New in version 1.22.0.
- Returns:
- 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.
See also
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 as
x.sum() / N
, whereN = len(x)
. If, however, ddof is specified, the divisorN - 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 thedtype
keyword can alleviate this issue.For this function to work on sub-classes of ndarray, they must define
sum
with the kwarg keepdimsExamples
>>> 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