Repository: numpy/numpy
File: numpy/ma/core.py (Python)

Code before:
        ValueError is raised.

        Parameters
        ----------
        shape : int or tuple of ints
            The new shape should be compatible with the original shape. If an
            integer is supplied, then the result will be a 1-D array of that
            length.
        order : {'C', 'F'}, optional
            Determines whether the array data should be viewed as in C
            (row-major) or FORTRAN (column-major) order.

        Returns
        -------
        reshaped_array : array
            A new view on the array.

        See Also
        --------
        reshape : Equivalent function in the masked array module.
        numpy.ndarray.reshape : Equivalent method on ndarray object.
        numpy.reshape : Equivalent function in the NumPy module.

        Notes
        -----
        The reshaping operation cannot guarantee that a copy will not be made,
        to modify the shape in place, use ``a.shape = s``. Note: reshaping in
        place is deprecated.

        Examples
        --------
        >>> import numpy as np
        >>> x = np.ma.array([[1,2],[3,4]], mask=[1,0,0,1])
        >>> x
        masked_array(
          data=[[--, 2],
                [3, --]],
          mask=[[ True, False],
                [False,  True]],
          fill_value=999999)
        >>> x = x.reshape((4,1))
        >>> x
        masked_array(
          data=[[--],
                [2],
                [3],
                [--]],
          mask=[[ True],
                [False],
                [False],
                [ True]],

Review comment (suggestion):
Could we rewrite a bit more, like
```
By default, the reshaping operation will make a copy if a view with different strides
is not possible. To ensure a view, pass ``copy=False``.
```
I don't think you need to mention setting a shape in place at all.

Code after:

        See Also
        --------
        reshape : Equivalent function in the masked array module.
        numpy.ndarray.reshape : Equivalent method on ndarray object.
        numpy.reshape : Equivalent function in the NumPy module.

        Notes
        -----
        By default, the reshaping operation will make a copy if a view
        with different strides is not possible. To ensure a view,
        pass ``copy=False``.

        Examples
        --------
        >>> import numpy as np
        >>> x = np.ma.array([[1,2],[3,4]], mask=[1,0,0,1])
        >>> x
        masked_array(
          data=[[--, 2],
                [3, --]],
          mask=[[ True, False],
                [False,  True]],
          fill_value=999999)
        >>> x = x.reshape((4,1))
        >>> x
        masked_array(
          data=[[--],
                [2],
                [3],
                [--]],
          mask=[[ True],
                [False],
                [False],
                [ True]],
          fill_value=999999)

        """
        result = self._data.reshape(*s, **kwargs).view(type(self))
        result._update_from(self)
        mask = self._mask
        if mask is not nomask:
            result._mask = mask.reshape(*s, **kwargs)
        return result

    def resize(self, newshape, refcheck=True, order=False):
        """
        .. warning::

            This method does nothing, except raise a ValueError exception. A
            masked array does not own its data and therefore cannot safely be