@property<EOL><INDENT>def ftypes(self):<DEDENT>
from pandas import Series<EOL>return Series(self._data.get_ftypes(), index=self._info_axis,<EOL>dtype=np.object_)<EOL>
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