def nanvar(a, axis=None, dtype=None, out=None, ddof=<NUM_LIT:0>, keepdims=False):
arr, mask = _replace_nan(a, <NUM_LIT:0>)<EOL>if mask is None:<EOL><INDENT>return np.var(arr, axis=axis, dtype=dtype, out=out, ddof=ddof,<EOL>keepdims=keepdims)<EOL><DEDENT>if dtype is not None:<EOL><INDENT>dtype = np.dtype(dtype)<EOL><DEDENT>if dtype is not None and not issubclass(dtype.type, np.inexact):<EOL><INDENT>raise TypeError("<STR_LIT>")<EOL><DEDENT>if out is not None and not issubclass(out.dtype.type, np.inexact):<EOL><INDENT>raise TypeError("<STR_LIT>")<EOL><DEDENT>with warnings.catch_warnings():<EOL><INDENT>warnings.simplefilter('<STR_LIT:ignore>')<EOL>cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=True)<EOL>avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=True)<EOL>avg = _divide_by_count(avg, cnt)<EOL>arr -= avg<EOL>arr = _copyto(arr, <NUM_LIT:0>, mask)<EOL>if issubclass(arr.dtype.type, np.complexfloating):<EOL><INDENT>sqr = np.multiply(arr, arr.conj(), out=arr).real<EOL><DEDENT>else:<EOL><INDENT>sqr = np.multiply(arr, arr, out=arr)<EOL><DEDENT>var = np.sum(sqr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)<EOL>if var.ndim < cnt.ndim:<EOL><INDENT>cnt = cnt.squeeze(axis)<EOL><DEDENT>dof = cnt - ddof<EOL>var = _divide_by_count(var, dof)<EOL><DEDENT>isbad = (dof <= <NUM_LIT:0>)<EOL>if np.any(isbad):<EOL><INDENT>warnings.warn("<STR_LIT>", RuntimeWarning)<EOL>var = _copyto(var, np.nan, isbad)<EOL><DEDENT>return var<EOL>
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.

.. versionadded:: 1.8.0

Parameters
----------
a : array_like
    Array containing numbers whose variance is desired.  If `a` is not an
    array, a conversion is attempted.
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 `float32`; for arrays of float types it is the same as
    the array type.
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
    ``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 `arr`.

Returns
-------
variance : ndarray, 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
--------
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 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.

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])