@property
def ftypes(self):
    "\n        Return the ftypes (indication of sparse/dense and dtype) in DataFrame.\n\n        This returns a Series with the data type of each column.\n        The result's index is the original DataFrame's columns. Columns\n        with mixed types are stored with the ``object`` dtype.  See\n        :ref:`the User Guide <basics.dtypes>` for more.\n\n        Returns\n        -------\n        pandas.Series\n            The data type and indication of sparse/dense of each column.\n\n        See Also\n        --------\n        DataFrame.dtypes: Series with just dtype information.\n        SparseDataFrame : Container for sparse tabular data.\n\n        Notes\n        -----\n        Sparse data should have the same dtypes as its dense representation.\n\n        Examples\n        --------\n        >>> arr = np.random.RandomState(0).randn(100, 4)\n        >>> arr[arr < .8] = np.nan\n        >>> pd.DataFrame(arr).ftypes\n        0    float64:dense\n        1    float64:dense\n        2    float64:dense\n        3    float64:dense\n        dtype: object\n\n        >>> pd.SparseDataFrame(arr).ftypes\n        0    float64:sparse\n        1    float64:sparse\n        2    float64:sparse\n        3    float64:sparse\n        dtype: object\n        "
    from pandas import Series
    return Series(self._data.get_ftypes(), index=self._info_axis, dtype=np.object_)
Return the ftypes (indication of sparse/dense and dtype) in 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.  See
:ref:`the User Guide <basics.dtypes>` for more.

Returns
-------
pandas.Series
    The data type and indication of sparse/dense of each column.

See Also
--------
DataFrame.dtypes: Series with just dtype information.
SparseDataFrame : Container for sparse tabular data.

Notes
-----
Sparse data should have the same dtypes as its dense representation.

Examples
--------
>>> arr = np.random.RandomState(0).randn(100, 4)
>>> arr[arr < .8] = np.nan
>>> pd.DataFrame(arr).ftypes
0    float64:dense
1    float64:dense
2    float64:dense
3    float64:dense
dtype: object

>>> pd.SparseDataFrame(arr).ftypes
0    float64:sparse
1    float64:sparse
2    float64:sparse
3    float64:sparse
dtype: object
@property
def ftypes(self):
    "\n        Return the ftypes (indication of sparse/dense and dtype) in DataFrame.\n\n        This returns a Series with the data type of each column.\n        The result's index is the original DataFrame's columns. Columns\n        with mixed types are stored with the ``object`` dtype.  See\n        :ref:`the User Guide <basics.dtypes>` for more.\n\n        Returns\n        -------\n        pandas.Series\n            The data type and indication of sparse/dense of each column.\n\n        See Also\n        --------\n        DataFrame.dtypes: Series with just dtype information.\n        SparseDataFrame : Container for sparse tabular data.\n\n        Notes\n        -----\n        Sparse data should have the same dtypes as its dense representation.\n\n        Examples\n        --------\n        >>> arr = np.random.RandomState(0).randn(100, 4)\n        >>> arr[arr < .8] = np.nan\n        >>> pd.DataFrame(arr).ftypes\n        0    float64:dense\n        1    float64:dense\n        2    float64:dense\n        3    float64:dense\n        dtype: object\n\n        >>> pd.SparseDataFrame(arr).ftypes\n        0    float64:sparse\n        1    float64:sparse\n        2    float64:sparse\n        3    float64:sparse\n        dtype: object\n        "
    from pandas import Series
    return Series(self._data.get_ftypes(), index=self._info_axis, dtype=np.object_)