@property
def values(self) -> np.ndarray:
    "\n        Return a Numpy representation of the DataFrame or the Series.\n\n        .. warning:: We recommend using `DataFrame.to_numpy()` or `Series.to_numpy()` instead.\n\n        .. note:: This method should only be used if the resulting NumPy ndarray is expected\n            to be small, as all the data is loaded into the driver's memory.\n\n        Returns\n        -------\n        numpy.ndarray\n\n        Examples\n        --------\n        A DataFrame where all columns are the same type (e.g., int64) results in an array of\n        the same type.\n\n        >>> df = ps.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]])\n\n        A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray\n        of the broadest type that accommodates these mixed types (e.g., object).\n\n        >>> df2 = ps.DataFrame([('parrot',   24.0, 'second'),\n        ...                     ('lion',     80.5, 'first'),\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, 'first'],\n               ['monkey', nan, None]], dtype=object)\n\n        For Series,\n\n        >>> ps.Series([1, 2, 3]).values\n        array([1, 2, 3])\n\n        >>> ps.Series(list('aabc')).values\n        array(['a', 'a', 'b', 'c'], dtype=object)\n        "
    warnings.warn('We recommend using `{}.to_numpy()` instead.'.format(type(self).__name__))
    return self.to_numpy()
Return a Numpy representation of the DataFrame or the Series.

.. warning:: We recommend using `DataFrame.to_numpy()` or `Series.to_numpy()` instead.

.. note:: This method should only be used if the resulting NumPy ndarray is expected
    to be small, as all the data is loaded into the driver's memory.

Returns
-------
numpy.ndarray

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

>>> df = ps.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]])

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 = ps.DataFrame([('parrot',   24.0, 'second'),
...                     ('lion',     80.5, 'first'),
...                     ('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, 'first'],
       ['monkey', nan, None]], dtype=object)

For Series,

>>> ps.Series([1, 2, 3]).values
array([1, 2, 3])

>>> ps.Series(list('aabc')).values
array(['a', 'a', 'b', 'c'], dtype=object)
@property
def values(self) -> np.ndarray:
    "\n        Return a Numpy representation of the DataFrame or the Series.\n\n        .. warning:: We recommend using `DataFrame.to_numpy()` or `Series.to_numpy()` instead.\n\n        .. note:: This method should only be used if the resulting NumPy ndarray is expected\n            to be small, as all the data is loaded into the driver's memory.\n\n        Returns\n        -------\n        numpy.ndarray\n\n        Examples\n        --------\n        A DataFrame where all columns are the same type (e.g., int64) results in an array of\n        the same type.\n\n        >>> df = ps.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]])\n\n        A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray\n        of the broadest type that accommodates these mixed types (e.g., object).\n\n        >>> df2 = ps.DataFrame([('parrot',   24.0, 'second'),\n        ...                     ('lion',     80.5, 'first'),\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, 'first'],\n               ['monkey', nan, None]], dtype=object)\n\n        For Series,\n\n        >>> ps.Series([1, 2, 3]).values\n        array([1, 2, 3])\n\n        >>> ps.Series(list('aabc')).values\n        array(['a', 'a', 'b', 'c'], dtype=object)\n        "
    warnings.warn('We recommend using `{}.to_numpy()` instead.'.format(type(self).__name__))
    return self.to_numpy()