DEPR: ftype, ftypes

doc/redirects.csv

@@ -357,10 +357,8 @@ generated/pandas.DataFrame.from_csv,../reference/api/pandas.DataFrame.from_csv
 generated/pandas.DataFrame.from_dict,../reference/api/pandas.DataFrame.from_dict
 generated/pandas.DataFrame.from_items,../reference/api/pandas.DataFrame.from_items
 generated/pandas.DataFrame.from_records,../reference/api/pandas.DataFrame.from_records
-generated/pandas.DataFrame.ftypes,../reference/api/pandas.DataFrame.ftypes
 generated/pandas.DataFrame.ge,../reference/api/pandas.DataFrame.ge
 generated/pandas.DataFrame.get_dtype_counts,../reference/api/pandas.DataFrame.get_dtype_counts
-generated/pandas.DataFrame.get_ftype_counts,../reference/api/pandas.DataFrame.get_ftype_counts
 generated/pandas.DataFrame.get,../reference/api/pandas.DataFrame.get
 generated/pandas.DataFrame.get_value,../reference/api/pandas.DataFrame.get_value
 generated/pandas.DataFrame.get_values,../reference/api/pandas.DataFrame.get_values
@@ -883,10 +881,8 @@ generated/pandas.Panel.first_valid_index,../reference/api/pandas.Panel.first_val
 generated/pandas.Panel.floordiv,../reference/api/pandas.Panel.floordiv
 generated/pandas.Panel.from_dict,../reference/api/pandas.Panel.from_dict
 generated/pandas.Panel.fromDict,../reference/api/pandas.Panel.fromDict
-generated/pandas.Panel.ftypes,../reference/api/pandas.Panel.ftypes
 generated/pandas.Panel.ge,../reference/api/pandas.Panel.ge
 generated/pandas.Panel.get_dtype_counts,../reference/api/pandas.Panel.get_dtype_counts
-generated/pandas.Panel.get_ftype_counts,../reference/api/pandas.Panel.get_ftype_counts
 generated/pandas.Panel.get,../reference/api/pandas.Panel.get
 generated/pandas.Panel.get_value,../reference/api/pandas.Panel.get_value
 generated/pandas.Panel.get_values,../reference/api/pandas.Panel.get_values
@@ -1223,11 +1219,8 @@ generated/pandas.Series.flags,../reference/api/pandas.Series.flags
 generated/pandas.Series.floordiv,../reference/api/pandas.Series.floordiv
 generated/pandas.Series.from_array,../reference/api/pandas.Series.from_array
 generated/pandas.Series.from_csv,../reference/api/pandas.Series.from_csv
-generated/pandas.Series.ftype,../reference/api/pandas.Series.ftype
-generated/pandas.Series.ftypes,../reference/api/pandas.Series.ftypes
 generated/pandas.Series.ge,../reference/api/pandas.Series.ge
 generated/pandas.Series.get_dtype_counts,../reference/api/pandas.Series.get_dtype_counts
-generated/pandas.Series.get_ftype_counts,../reference/api/pandas.Series.get_ftype_counts
 generated/pandas.Series.get,../reference/api/pandas.Series.get
 generated/pandas.Series.get_value,../reference/api/pandas.Series.get_value
 generated/pandas.Series.get_values,../reference/api/pandas.Series.get_values

doc/source/reference/frame.rst

@@ -28,7 +28,6 @@ Attributes and underlying data
    :toctree: api/
 
    DataFrame.dtypes
-   DataFrame.ftypes
    DataFrame.get_dtype_counts
    DataFrame.select_dtypes
    DataFrame.values

doc/source/reference/series.rst

@@ -29,7 +29,6 @@ Attributes
    Series.array
    Series.values
    Series.dtype
-   Series.ftype
    Series.shape
    Series.nbytes
    Series.ndim
@@ -43,7 +42,6 @@ Attributes
    Series.flags
    Series.empty
    Series.dtypes
-   Series.ftypes
    Series.data
    Series.name
    Series.put

doc/source/whatsnew/v1.0.0.rst

@@ -440,6 +440,7 @@ or ``matplotlib.Axes.plot``. See :ref:`plotting.formatters` for more.
 - Removed the previously deprecated :meth:`Series.valid`; use :meth:`Series.dropna` instead (:issue:`18800`)
 - Removed the previously properties :attr:`DataFrame.is_copy`, :attr:`Series.is_copy` (:issue:`18812`)
 - Removed the previously deprecated :meth:`DataFrame.get_ftype_counts`, :meth:`Series.get_ftype_counts` (:issue:`18243`)
+- Removed the previously deprecated :meth:`DataFrame.ftypes`, :meth:`Series.ftypes`, :meth:`Series.ftype` (:issue:`26744`)
 - Removed the previously deprecated :meth:`Index.get_duplicated`, use ``idx[idx.duplicated()].unique()`` instead (:issue:`20239`)
 - Removed the previously deprecated :meth:`Series.clip_upper`, :meth:`Series.clip_lower`, :meth:`DataFrame.clip_upper`, :meth:`DataFrame.clip_lower` (:issue:`24203`)
 - Removed the ability to alter :attr:`DatetimeIndex.freq`, :attr:`TimedeltaIndex.freq`, or :attr:`PeriodIndex.freq` (:issue:`20772`)

pandas/core/generic.py

@@ -171,9 +171,7 @@ class NDFrame(PandasObject, SelectionMixin):
     ]
     _internal_names_set: Set[str] = set(_internal_names)
     _accessors: Set[str] = set()
-    _deprecations: FrozenSet[str] = frozenset(
-        ["get_dtype_counts", "get_values", "ftypes", "ix"]
-    )
+    _deprecations: FrozenSet[str] = frozenset(["get_dtype_counts", "get_values", "ix"])
     _metadata: List[str] = []
     _is_copy = None
     _data: BlockManager
@@ -5582,10 +5580,6 @@ def dtypes(self):
         pandas.Series
             The data type of each column.
 
-        See Also
-        --------
-        DataFrame.ftypes : Dtype and sparsity information.
-
         Examples
         --------
         >>> df = pd.DataFrame({'float': [1.0],
@@ -5603,55 +5597,6 @@ def dtypes(self):
 
         return Series(self._data.get_dtypes(), index=self._info_axis, dtype=np.object_)
 
-    @property
-    def ftypes(self):
-        """
-        Return the ftypes (indication of sparse/dense and dtype) in DataFrame.
-
-        .. deprecated:: 0.25.0
-           Use :func:`dtypes` instead.
-
-        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.
-
-        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
-        """
-        warnings.warn(
-            "DataFrame.ftypes is deprecated and will "
-            "be removed in a future version. "
-            "Use DataFrame.dtypes instead.",
-            FutureWarning,
-            stacklevel=2,
-        )
-
-        from pandas import Series
-
-        return Series(self._data.get_ftypes(), index=self._info_axis, dtype=np.object_)
-
     def _to_dict_of_blocks(self, copy=True):
         """
         Return a dict of dtype -> Constructor Types that

pandas/core/internals/managers.py

@@ -82,7 +82,6 @@ class BlockManager(PandasObject):
 
     get_dtype_counts
     get_dtypes
-    get_ftypes
 
     apply(func, axes, block_filter_fn)
 
@@ -251,10 +250,6 @@ def get_dtypes(self):
         dtypes = np.array([blk.dtype for blk in self.blocks])
         return algos.take_1d(dtypes, self._blknos, allow_fill=False)
 
-    def get_ftypes(self):
-        ftypes = np.array([blk.ftype for blk in self.blocks])
-        return algos.take_1d(ftypes, self._blknos, allow_fill=False)
-
     def __getstate__(self):
         block_values = [b.values for b in self.blocks]
         block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks]
@@ -1546,19 +1541,12 @@ def dtype(self):
     def array_dtype(self):
         return self._block.array_dtype
 
-    @property
-    def ftype(self):
-        return self._block.ftype
-
     def get_dtype_counts(self):
         return {self.dtype.name: 1}
 
     def get_dtypes(self):
         return np.array([self._block.dtype])
 
-    def get_ftypes(self):
-        return np.array([self._block.ftype])
-
     def external_values(self):
         return self._block.external_values()
 

pandas/core/series.py

@@ -158,9 +158,7 @@ class Series(base.IndexOpsMixin, generic.NDFrame):
     _deprecations = (
         base.IndexOpsMixin._deprecations
         | generic.NDFrame._deprecations
-        | frozenset(
-            ["compress", "valid", "ftype", "real", "imag", "put", "ptp", "nonzero"]
-        )
+        | frozenset(["compress", "valid", "real", "imag", "put", "ptp", "nonzero"])
     )
 
     # Override cache_readonly bc Series is mutable
@@ -418,42 +416,6 @@ def name(self, value: Optional[Hashable]) -> None:
             raise TypeError("Series.name must be a hashable type")
         self.attrs["name"] = value
 
-    @property
-    def ftype(self):
-        """
-        Return if the data is sparse|dense.
-
-        .. deprecated:: 0.25.0
-           Use :func:`dtype` instead.
-        """
-        warnings.warn(
-            "Series.ftype is deprecated and will "
-            "be removed in a future version. "
-            "Use Series.dtype instead.",
-            FutureWarning,
-            stacklevel=2,
-        )
-
-        return self._data.ftype
-
-    @property
-    def ftypes(self):
-        """
-        Return if the data is sparse|dense.
-
-        .. deprecated:: 0.25.0
-           Use :func:`dtypes` instead.
-        """
-        warnings.warn(
-            "Series.ftypes is deprecated and will "
-            "be removed in a future version. "
-            "Use Series.dtype instead.",
-            FutureWarning,
-            stacklevel=2,
-        )
-
-        return self._data.ftype
-
     @property
     def values(self):
         """

pandas/io/pytables.py

@@ -2641,7 +2641,9 @@ def write(self, **kwargs):
             "cannot write on an abstract storer: sublcasses should implement"
         )
 
-    def delete(self, where=None, start=None, stop=None, **kwargs):
+    def delete(
+        self, where=None, start: Optional[int] = None, stop: Optional[int] = None
+    ):
         """
         support fully deleting the node in its entirety (only) - where
         specification must be None

pandas/tests/frame/test_dtypes.py

@@ -46,63 +46,33 @@ def test_concat_empty_dataframe_dtypes(self):
         assert result["b"].dtype == np.float64
         assert result["c"].dtype == np.float64
 
-    def test_empty_frame_dtypes_ftypes(self):
+    def test_empty_frame_dtypes(self):
         empty_df = pd.DataFrame()
         tm.assert_series_equal(empty_df.dtypes, pd.Series(dtype=np.object))
 
-        # GH 26705 - Assert .ftypes is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            tm.assert_series_equal(empty_df.ftypes, pd.Series(dtype=np.object))
-
         nocols_df = pd.DataFrame(index=[1, 2, 3])
         tm.assert_series_equal(nocols_df.dtypes, pd.Series(dtype=np.object))
 
-        # GH 26705 - Assert .ftypes is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            tm.assert_series_equal(nocols_df.ftypes, pd.Series(dtype=np.object))
-
         norows_df = pd.DataFrame(columns=list("abc"))
         tm.assert_series_equal(
             norows_df.dtypes, pd.Series(np.object, index=list("abc"))
         )
 
-        # GH 26705 - Assert .ftypes is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            tm.assert_series_equal(
-                norows_df.ftypes, pd.Series("object:dense", index=list("abc"))
-            )
-
         norows_int_df = pd.DataFrame(columns=list("abc")).astype(np.int32)
         tm.assert_series_equal(
             norows_int_df.dtypes, pd.Series(np.dtype("int32"), index=list("abc"))
         )
-        # GH 26705 - Assert .ftypes is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            tm.assert_series_equal(
-                norows_int_df.ftypes, pd.Series("int32:dense", index=list("abc"))
-            )
 
         odict = OrderedDict
         df = pd.DataFrame(odict([("a", 1), ("b", True), ("c", 1.0)]), index=[1, 2, 3])
         ex_dtypes = pd.Series(
             odict([("a", np.int64), ("b", np.bool), ("c", np.float64)])
         )
-        ex_ftypes = pd.Series(
-            odict([("a", "int64:dense"), ("b", "bool:dense"), ("c", "float64:dense")])
-        )
         tm.assert_series_equal(df.dtypes, ex_dtypes)
 
-        # GH 26705 - Assert .ftypes is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            tm.assert_series_equal(df.ftypes, ex_ftypes)
-
         # same but for empty slice of df
         tm.assert_series_equal(df[:0].dtypes, ex_dtypes)
 
-        # GH 26705 - Assert .ftypes is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            tm.assert_series_equal(df[:0].ftypes, ex_ftypes)
-
     def test_datetime_with_tz_dtypes(self):
         tzframe = DataFrame(
             {
@@ -474,22 +444,6 @@ def test_dtypes_gh8722(self, float_string_frame):
             result = df.dtypes
             tm.assert_series_equal(result, Series({0: np.dtype("int64")}))
 
-    def test_ftypes(self, mixed_float_frame):
-        frame = mixed_float_frame
-        expected = Series(
-            dict(
-                A="float32:dense",
-                B="float32:dense",
-                C="float16:dense",
-                D="float64:dense",
-            )
-        ).sort_values()
-
-        # GH 26705 - Assert .ftypes is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            result = frame.ftypes.sort_values()
-        tm.assert_series_equal(result, expected)
-
     def test_astype_float(self, float_frame):
         casted = float_frame.astype(int)
         expected = DataFrame(

pandas/tests/series/test_combine_concat.py

@@ -290,32 +290,20 @@ def test_concat_empty_series_dtypes(self):
         )
         assert result.dtype == "Sparse[float64]"
 
-        # GH 26705 - Assert .ftype is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            assert result.ftype == "float64:sparse"
-
         result = pd.concat(
             [Series(dtype="float64").astype("Sparse"), Series(dtype="float64")]
         )
         # TODO: release-note: concat sparse dtype
         expected = pd.SparseDtype(np.float64)
         assert result.dtype == expected
 
-        # GH 26705 - Assert .ftype is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            assert result.ftype == "float64:sparse"
-
         result = pd.concat(
             [Series(dtype="float64").astype("Sparse"), Series(dtype="object")]
         )
         # TODO: release-note: concat sparse dtype
         expected = pd.SparseDtype("object")
         assert result.dtype == expected
 
-        # GH 26705 - Assert .ftype is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            assert result.ftype == "object:sparse"
-
     def test_combine_first_dt64(self):
         from pandas.core.tools.datetimes import to_datetime
 

pandas/tests/series/test_dtypes.py

@@ -49,14 +49,6 @@ def test_dtype(self, datetime_series):
         assert datetime_series.dtype == np.dtype("float64")
         assert datetime_series.dtypes == np.dtype("float64")
 
-        # GH 26705 - Assert .ftype is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            assert datetime_series.ftype == "float64:dense"
-
-        # GH 26705 - Assert .ftypes is deprecated
-        with tm.assert_produces_warning(FutureWarning):
-            assert datetime_series.ftypes == "float64:dense"
-
     @pytest.mark.parametrize("value", [np.nan, np.inf])
     @pytest.mark.parametrize("dtype", [np.int32, np.int64])
     def test_astype_cast_nan_inf_int(self, dtype, value):