@property
def values(self):
    "\n        Return a Numpy representation of the DataFrame.\n\n        .. warning::\n\n           We recommend using :meth:`DataFrame.to_numpy` instead.\n\n        Only the values in the DataFrame will be returned, the axes labels\n        will be removed.\n\n        Returns\n        -------\n        numpy.ndarray\n            The values of the DataFrame.\n\n        See Also\n        --------\n        DataFrame.to_numpy : Recommended alternative to this method.\n        DataFrame.index : Retrieve the index labels.\n        DataFrame.columns : Retrieving the column names.\n\n        Notes\n        -----\n        The dtype will be a lower-common-denominator dtype (implicit\n        upcasting); that is to say if the dtypes (even of numeric types)\n        are mixed, the one that accommodates all will be chosen. Use this\n        with care if you are not dealing with the blocks.\n\n        e.g. If the dtypes are float16 and float32, dtype will be upcast to\n        float32.  If dtypes are int32 and uint8, dtype will be upcast to\n        int32. By :func:`numpy.find_common_type` convention, mixing int64\n        and uint64 will result in a float64 dtype.\n\n        Examples\n        --------\n        A DataFrame where all columns are the same type (e.g., int64) results\n        in an array of the same type.\n\n        >>> df = pd.DataFrame({'age':    [ 3,  29],\n        ...                    'height': [94, 170],\n        ...                    'weight': [31, 115]})\n        >>> df\n           age  height  weight\n        0    3      94      31\n        1   29     170     115\n        >>> df.dtypes\n        age       int64\n        height    int64\n        weight    int64\n        dtype: object\n        >>> df.values\n        array([[  3,  94,  31],\n               [ 29, 170, 115]], dtype=int64)\n\n        A DataFrame with mixed type columns(e.g., str/object, int64, float32)\n        results in an ndarray of the broadest type that accommodates these\n        mixed types (e.g., object).\n\n        >>> df2 = pd.DataFrame([('parrot',   24.0, 'second'),\n        ...                     ('lion',     80.5, 1),\n        ...                     ('monkey', np.nan, None)],\n        ...                   columns=('name', 'max_speed', 'rank'))\n        >>> df2.dtypes\n        name          object\n        max_speed    float64\n        rank          object\n        dtype: object\n        >>> df2.values\n        array([['parrot', 24.0, 'second'],\n               ['lion', 80.5, 1],\n               ['monkey', nan, None]], dtype=object)\n        "
    self._consolidate_inplace()
    return self._data.as_array(transpose=self._AXIS_REVERSED)
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)
@property
def values(self):
    "\n        Return a Numpy representation of the DataFrame.\n\n        .. warning::\n\n           We recommend using :meth:`DataFrame.to_numpy` instead.\n\n        Only the values in the DataFrame will be returned, the axes labels\n        will be removed.\n\n        Returns\n        -------\n        numpy.ndarray\n            The values of the DataFrame.\n\n        See Also\n        --------\n        DataFrame.to_numpy : Recommended alternative to this method.\n        DataFrame.index : Retrieve the index labels.\n        DataFrame.columns : Retrieving the column names.\n\n        Notes\n        -----\n        The dtype will be a lower-common-denominator dtype (implicit\n        upcasting); that is to say if the dtypes (even of numeric types)\n        are mixed, the one that accommodates all will be chosen. Use this\n        with care if you are not dealing with the blocks.\n\n        e.g. If the dtypes are float16 and float32, dtype will be upcast to\n        float32.  If dtypes are int32 and uint8, dtype will be upcast to\n        int32. By :func:`numpy.find_common_type` convention, mixing int64\n        and uint64 will result in a float64 dtype.\n\n        Examples\n        --------\n        A DataFrame where all columns are the same type (e.g., int64) results\n        in an array of the same type.\n\n        >>> df = pd.DataFrame({'age':    [ 3,  29],\n        ...                    'height': [94, 170],\n        ...                    'weight': [31, 115]})\n        >>> df\n           age  height  weight\n        0    3      94      31\n        1   29     170     115\n        >>> df.dtypes\n        age       int64\n        height    int64\n        weight    int64\n        dtype: object\n        >>> df.values\n        array([[  3,  94,  31],\n               [ 29, 170, 115]], dtype=int64)\n\n        A DataFrame with mixed type columns(e.g., str/object, int64, float32)\n        results in an ndarray of the broadest type that accommodates these\n        mixed types (e.g., object).\n\n        >>> df2 = pd.DataFrame([('parrot',   24.0, 'second'),\n        ...                     ('lion',     80.5, 1),\n        ...                     ('monkey', np.nan, None)],\n        ...                   columns=('name', 'max_speed', 'rank'))\n        >>> df2.dtypes\n        name          object\n        max_speed    float64\n        rank          object\n        dtype: object\n        >>> df2.values\n        array([['parrot', 24.0, 'second'],\n               ['lion', 80.5, 1],\n               ['monkey', nan, None]], dtype=object)\n        "
    self._consolidate_inplace()
    return self._data.as_array(transpose=self._AXIS_REVERSED)