def get_values(self):
    '\n        Return an ndarray after converting sparse values to dense.\n\n        This is the same as ``.values`` for non-sparse data. For sparse\n        data contained in a `SparseArray`, the data are first\n        converted to a dense representation.\n\n        Returns\n        -------\n        numpy.ndarray\n            Numpy representation of DataFrame.\n\n        See Also\n        --------\n        values : Numpy representation of DataFrame.\n        SparseArray : Container for sparse data.\n\n        Examples\n        --------\n        >>> df = pd.DataFrame({\'a\': [1, 2], \'b\': [True, False],\n        ...                    \'c\': [1.0, 2.0]})\n        >>> df\n           a      b    c\n        0  1   True  1.0\n        1  2  False  2.0\n\n        >>> df.get_values()\n        array([[1, True, 1.0], [2, False, 2.0]], dtype=object)\n\n        >>> df = pd.DataFrame({"a": pd.SparseArray([1, None, None]),\n        ...                    "c": [1.0, 2.0, 3.0]})\n        >>> df\n             a    c\n        0  1.0  1.0\n        1  NaN  2.0\n        2  NaN  3.0\n\n        >>> df.get_values()\n        array([[ 1.,  1.],\n               [nan,  2.],\n               [nan,  3.]])\n        '
    return self.values
Return an ndarray after converting sparse values to dense.

This is the same as ``.values`` for non-sparse data. For sparse
data contained in a `SparseArray`, the data are first
converted to a dense representation.

Returns
-------
numpy.ndarray
    Numpy representation of DataFrame.

See Also
--------
values : Numpy representation of DataFrame.
SparseArray : Container for sparse data.

Examples
--------
>>> df = pd.DataFrame({'a': [1, 2], 'b': [True, False],
...                    'c': [1.0, 2.0]})
>>> df
   a      b    c
0  1   True  1.0
1  2  False  2.0

>>> df.get_values()
array([[1, True, 1.0], [2, False, 2.0]], dtype=object)

>>> df = pd.DataFrame({"a": pd.SparseArray([1, None, None]),
...                    "c": [1.0, 2.0, 3.0]})
>>> df
     a    c
0  1.0  1.0
1  NaN  2.0
2  NaN  3.0

>>> df.get_values()
array([[ 1.,  1.],
       [nan,  2.],
       [nan,  3.]])
def get_values(self):
    '\n        Return an ndarray after converting sparse values to dense.\n\n        This is the same as ``.values`` for non-sparse data. For sparse\n        data contained in a `SparseArray`, the data are first\n        converted to a dense representation.\n\n        Returns\n        -------\n        numpy.ndarray\n            Numpy representation of DataFrame.\n\n        See Also\n        --------\n        values : Numpy representation of DataFrame.\n        SparseArray : Container for sparse data.\n\n        Examples\n        --------\n        >>> df = pd.DataFrame({\'a\': [1, 2], \'b\': [True, False],\n        ...                    \'c\': [1.0, 2.0]})\n        >>> df\n           a      b    c\n        0  1   True  1.0\n        1  2  False  2.0\n\n        >>> df.get_values()\n        array([[1, True, 1.0], [2, False, 2.0]], dtype=object)\n\n        >>> df = pd.DataFrame({"a": pd.SparseArray([1, None, None]),\n        ...                    "c": [1.0, 2.0, 3.0]})\n        >>> df\n             a    c\n        0  1.0  1.0\n        1  NaN  2.0\n        2  NaN  3.0\n\n        >>> df.get_values()\n        array([[ 1.,  1.],\n               [nan,  2.],\n               [nan,  3.]])\n        '
    return self.values