Method var

numpy/ma/core.py:5470–5543

Returns the variance of the array elements along given axis. Masked entries are ignored, and result elements which are not finite will be masked. Refer to `numpy.var` for full documentation. See Also -------- numpy.ndarray.var : correspondi

(self, axis=None, dtype=None, out=None, ddof=0,
            keepdims=np._NoValue, mean=np._NoValue)

            return self - expand_dims(m, axis)

    def var(self, axis=None, dtype=None, out=None, ddof=0,
            keepdims=np._NoValue, mean=np._NoValue):
        """
        Returns the variance of the array elements along given axis.

        Masked entries are ignored, and result elements which are not
        finite will be masked.

        Refer to `numpy.var` for full documentation.

        See Also
        --------
        numpy.ndarray.var : corresponding function for ndarrays
        numpy.var : Equivalent function
        """
        kwargs = {}

        if keepdims is not np._NoValue:
            kwargs['keepdims'] = keepdims

        # Easy case: nomask, business as usual
        if self._mask is nomask:

            if mean is not np._NoValue:
                kwargs['mean'] = mean

            ret = super().var(axis=axis, dtype=dtype, out=out, ddof=ddof,
                              **kwargs)[()]
            if out is not None:
                if isinstance(out, MaskedArray):
                    out.__setmask__(nomask)
                return out
            return ret

        # Some data are masked, yay!
        cnt = self.count(axis=axis, **kwargs) - ddof

        if mean is not np._NoValue:
            danom = self - mean
        else:
            danom = self - self.mean(axis, dtype, keepdims=True)

        if iscomplexobj(self):
            danom = umath.absolute(danom) ** 2
        else:
            danom *= danom
        dvar = divide(danom.sum(axis, **kwargs), cnt).view(type(self))
        # Apply the mask if it's not a scalar
        if dvar.ndim:
            dvar._mask = mask_or(self._mask.all(axis, **kwargs), (cnt <= 0))
            dvar._update_from(self)
        elif getmask(dvar):
            # Make sure that masked is returned when the scalar is masked.
            dvar = masked
            if out is not None:
                if isinstance(out, MaskedArray):
                    out.flat = 0
                    out.__setmask__(True)