@property
def dtypes(self) -> pd.Series:
    "Return the dtypes in the DataFrame.\n\n        This returns a Series with the data type of each column. The result's index is the original\n        DataFrame's columns. Columns with mixed types are stored with the object dtype.\n\n        Returns\n        -------\n        pd.Series\n            The data type of each column.\n\n        Examples\n        --------\n        >>> df = ps.DataFrame({'a': list('abc'),\n        ...                    'b': list(range(1, 4)),\n        ...                    'c': np.arange(3, 6).astype('i1'),\n        ...                    'd': np.arange(4.0, 7.0, dtype='float64'),\n        ...                    'e': [True, False, True],\n        ...                    'f': pd.date_range('20130101', periods=3)},\n        ...                   columns=['a', 'b', 'c', 'd', 'e', 'f'])\n        >>> df.dtypes\n        a            object\n        b             int64\n        c              int8\n        d           float64\n        e              bool\n        f    datetime64[ns]\n        dtype: object\n        "
    return pd.Series([self._psser_for(label).dtype for label in self._internal.column_labels], index=pd.Index([(label if (len(label) > 1) else label[0]) for label in self._internal.column_labels]))
Return the dtypes in the DataFrame.

This returns a Series with the data type of each column. The result's index is the original
DataFrame's columns. Columns with mixed types are stored with the object dtype.

Returns
-------
pd.Series
    The data type of each column.

Examples
--------
>>> df = ps.DataFrame({'a': list('abc'),
...                    'b': list(range(1, 4)),
...                    'c': np.arange(3, 6).astype('i1'),
...                    'd': np.arange(4.0, 7.0, dtype='float64'),
...                    'e': [True, False, True],
...                    'f': pd.date_range('20130101', periods=3)},
...                   columns=['a', 'b', 'c', 'd', 'e', 'f'])
>>> df.dtypes
a            object
b             int64
c              int8
d           float64
e              bool
f    datetime64[ns]
dtype: object
@property
def dtypes(self) -> pd.Series:
    "Return the dtypes in the DataFrame.\n\n        This returns a Series with the data type of each column. The result's index is the original\n        DataFrame's columns. Columns with mixed types are stored with the object dtype.\n\n        Returns\n        -------\n        pd.Series\n            The data type of each column.\n\n        Examples\n        --------\n        >>> df = ps.DataFrame({'a': list('abc'),\n        ...                    'b': list(range(1, 4)),\n        ...                    'c': np.arange(3, 6).astype('i1'),\n        ...                    'd': np.arange(4.0, 7.0, dtype='float64'),\n        ...                    'e': [True, False, True],\n        ...                    'f': pd.date_range('20130101', periods=3)},\n        ...                   columns=['a', 'b', 'c', 'd', 'e', 'f'])\n        >>> df.dtypes\n        a            object\n        b             int64\n        c              int8\n        d           float64\n        e              bool\n        f    datetime64[ns]\n        dtype: object\n        "
    return pd.Series([self._psser_for(label).dtype for label in self._internal.column_labels], index=pd.Index([(label if (len(label) > 1) else label[0]) for label in self._internal.column_labels]))