jax.numpy.nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, where=None)[source]#

Compute the variance along the specified axis, while ignoring NaNs.

LAX-backend implementation of numpy.nanvar().

Original docstring below.

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.

Added in version 1.8.0.

  • a (array_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.

  • dtype (data-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.

  • ddof ({int, float}, 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.

  • 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 a.

  • where (array_like of bool, optional) – Elements to include in the variance. See ~numpy.ufunc.reduce for details.

  • out (None)


variance – 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.

Return type:

ndarray, see dtype parameter above