Return a Numpy representation of the DataFrame.

        .. warning::

           We recommend using :meth:`DataFrame.to_numpy` instead.

        Only the values in the DataFrame will be returned, the axes labels
        will be removed.

        Returns
        -------
        numpy.ndarray
            The values of the DataFrame.

        See Also
        --------
        DataFrame.to_numpy : Recommended alternative to this method.
        DataFrame.index : Retrieve the index labels.
        DataFrame.columns : Retrieving the column names.

        Notes
        -----
        The dtype will be a lower-common-denominator dtype (implicit
        upcasting); that is to say if the dtypes (even of numeric types)
        are mixed, the one that accommodates all will be chosen. Use this
        with care if you are not dealing with the blocks.

        e.g. If the dtypes are float16 and float32, dtype will be upcast to
        float32.  If dtypes are int32 and uint8, dtype will be upcast to
        int32. By :func:`numpy.find_common_type` convention, mixing int64
        and uint64 will result in a float64 dtype.

        Examples
        --------
        A DataFrame where all columns are the same type (e.g., int64) results
        in an array of the same type.

        >>> df = pd.DataFrame({'age':    [ 3,  29],
        ...                    'height': [94, 170],
        ...                    'weight': [31, 115]})
        >>> df
           age  height  weight
        0    3      94      31
        1   29     170     115
        >>> df.dtypes
        age       int64
        height    int64
        weight    int64
        dtype: object
        >>> df.values
        array([[  3,  94,  31],
               [ 29, 170, 115]], dtype=int64)

        A DataFrame with mixed type columns(e.g., str/object, int64, float32)
        results in an ndarray of the broadest type that accommodates these
        mixed types (e.g., object).

        >>> df2 = pd.DataFrame([('parrot',   24.0, 'second'),
        ...                     ('lion',     80.5, 1),
        ...                     ('monkey', np.nan, None)],
        ...                   columns=('name', 'max_speed', 'rank'))
        >>> df2.dtypes
        name          object
        max_speed    float64
        rank          object
        dtype: object
        >>> df2.values
        array([['parrot', 24.0, 'second'],
               ['lion', 80.5, 1],
               ['monkey', nan, None]], dtype=object)

def values(self):
        """
        Return a Numpy representation of the DataFrame.

        .. warning::

           We recommend using :meth:`DataFrame.to_numpy` instead.

        Only the values in the DataFrame will be returned, the axes labels
        will be removed.

        Returns
        -------
        numpy.ndarray
            The values of the DataFrame.

        See Also
        --------
        DataFrame.to_numpy : Recommended alternative to this method.
        DataFrame.index : Retrieve the index labels.
        DataFrame.columns : Retrieving the column names.

        Notes
        -----
        The dtype will be a lower-common-denominator dtype (implicit
        upcasting); that is to say if the dtypes (even of numeric types)
        are mixed, the one that accommodates all will be chosen. Use this
        with care if you are not dealing with the blocks.

        e.g. If the dtypes are float16 and float32, dtype will be upcast to
        float32.  If dtypes are int32 and uint8, dtype will be upcast to
        int32. By :func:`numpy.find_common_type` convention, mixing int64
        and uint64 will result in a float64 dtype.

        Examples
        --------
        A DataFrame where all columns are the same type (e.g., int64) results
        in an array of the same type.

        >>> df = pd.DataFrame({'age':    [ 3,  29],
        ...                    'height': [94, 170],
        ...                    'weight': [31, 115]})
        >>> df
           age  height  weight
        0    3      94      31
        1   29     170     115
        >>> df.dtypes
        age       int64
        height    int64
        weight    int64
        dtype: object
        >>> df.values
        array([[  3,  94,  31],
               [ 29, 170, 115]], dtype=int64)

        A DataFrame with mixed type columns(e.g., str/object, int64, float32)
        results in an ndarray of the broadest type that accommodates these
        mixed types (e.g., object).

        >>> df2 = pd.DataFrame([('parrot',   24.0, 'second'),
        ...                     ('lion',     80.5, 1),
        ...                     ('monkey', np.nan, None)],
        ...                   columns=('name', 'max_speed', 'rank'))
        >>> df2.dtypes
        name          object
        max_speed    float64
        rank          object
        dtype: object
        >>> df2.values
        array([['parrot', 24.0, 'second'],
               ['lion', 80.5, 1],
               ['monkey', nan, None]], dtype=object)
        """
        self._consolidate_inplace()
        return self._data.as_array(transpose=self._AXIS_REVERSED)