DEPR: Index.to_native_types (#36418)

doc/source/whatsnew/v1.2.0.rst

@@ -212,6 +212,7 @@ Deprecations
 - Deprecated parameter ``dtype`` in :~meth:`Index.copy` on method all index classes. Use the :meth:`Index.astype` method instead for changing dtype(:issue:`35853`)
 - Date parser functions :func:`~pandas.io.date_converters.parse_date_time`, :func:`~pandas.io.date_converters.parse_date_fields`, :func:`~pandas.io.date_converters.parse_all_fields` and :func:`~pandas.io.date_converters.generic_parser` from ``pandas.io.date_converters`` are deprecated and will be removed in a future version; use :func:`to_datetime` instead (:issue:`35741`)
 - :meth:`DataFrame.lookup` is deprecated and will be removed in a future version, use :meth:`DataFrame.melt` and :meth:`DataFrame.loc` instead (:issue:`18682`)
+- The :meth:`Index.to_native_types` is deprecated. Use ``.astype(str)`` instead (:issue:`28867`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/indexes/base.py

@@ -1021,6 +1021,8 @@ def to_native_types(self, slicer=None, **kwargs):
         """
         Format specified values of `self` and return them.
 
+        .. deprecated:: 1.2.0
+
         Parameters
         ----------
         slicer : int, array-like
@@ -1042,6 +1044,12 @@ def to_native_types(self, slicer=None, **kwargs):
         numpy.ndarray
             Formatted values.
         """
+        warnings.warn(
+            "The 'to_native_types' method is deprecated and will be removed in "
+            "a future version. Use 'astype(str)' instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
         values = self
         if slicer is not None:
             values = values[slicer]

pandas/io/formats/csvs.py

@@ -154,7 +154,7 @@ def _refine_cols(self, cols: Optional[Sequence[Label]]) -> Sequence[Label]:
 
         if cols is not None:
             if isinstance(cols, ABCIndexClass):
-                cols = cols.to_native_types(**self._number_format)
+                cols = cols._format_native_types(**self._number_format)
             else:
                 cols = list(cols)
             self.obj = self.obj.loc[:, cols]
@@ -163,7 +163,7 @@ def _refine_cols(self, cols: Optional[Sequence[Label]]) -> Sequence[Label]:
         # and make sure sure cols is just a list of labels
         cols = self.obj.columns
         if isinstance(cols, ABCIndexClass):
-            return cols.to_native_types(**self._number_format)
+            return cols._format_native_types(**self._number_format)
         else:
             assert isinstance(cols, Sequence)
             return list(cols)
@@ -341,5 +341,5 @@ def _save_chunk(self, start_i: int, end_i: int) -> None:
         res = df._mgr.to_native_types(**self._number_format)
         data = [res.iget_values(i) for i in range(len(res.items))]
 
-        ix = self.data_index.to_native_types(slicer=slicer, **self._number_format)
+        ix = self.data_index[slicer]._format_native_types(**self._number_format)
         libwriters.write_csv_rows(data, ix, self.nlevels, self.cols, self.writer)

pandas/tests/indexes/datetimes/test_formats.py

@@ -10,41 +10,53 @@
 import pandas._testing as tm
 
 
-def test_to_native_types():
+def test_to_native_types_method_deprecated():
     index = pd.date_range(freq="1D", periods=3, start="2017-01-01")
-
-    # First, with no arguments.
     expected = np.array(["2017-01-01", "2017-01-02", "2017-01-03"], dtype=object)
 
-    result = index.to_native_types()
-    tm.assert_numpy_array_equal(result, expected)
+    with tm.assert_produces_warning(FutureWarning):
+        result = index.to_native_types()
 
-    # No NaN values, so na_rep has no effect
-    result = index.to_native_types(na_rep="pandas")
     tm.assert_numpy_array_equal(result, expected)
 
     # Make sure slicing works
     expected = np.array(["2017-01-01", "2017-01-03"], dtype=object)
 
-    result = index.to_native_types([0, 2])
+    with tm.assert_produces_warning(FutureWarning):
+        result = index.to_native_types([0, 2])
+
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_native_types():
+    index = pd.date_range(freq="1D", periods=3, start="2017-01-01")
+
+    # First, with no arguments.
+    expected = np.array(["2017-01-01", "2017-01-02", "2017-01-03"], dtype=object)
+
+    result = index._format_native_types()
+    tm.assert_numpy_array_equal(result, expected)
+
+    # No NaN values, so na_rep has no effect
+    result = index._format_native_types(na_rep="pandas")
     tm.assert_numpy_array_equal(result, expected)
 
     # Make sure date formatting works
     expected = np.array(["01-2017-01", "01-2017-02", "01-2017-03"], dtype=object)
 
-    result = index.to_native_types(date_format="%m-%Y-%d")
+    result = index._format_native_types(date_format="%m-%Y-%d")
     tm.assert_numpy_array_equal(result, expected)
 
     # NULL object handling should work
     index = DatetimeIndex(["2017-01-01", pd.NaT, "2017-01-03"])
     expected = np.array(["2017-01-01", "NaT", "2017-01-03"], dtype=object)
 
-    result = index.to_native_types()
+    result = index._format_native_types()
     tm.assert_numpy_array_equal(result, expected)
 
     expected = np.array(["2017-01-01", "pandas", "2017-01-03"], dtype=object)
 
-    result = index.to_native_types(na_rep="pandas")
+    result = index._format_native_types(na_rep="pandas")
     tm.assert_numpy_array_equal(result, expected)
 
 

pandas/tests/indexes/interval/test_formats.py

@@ -73,6 +73,6 @@ def test_repr_missing(self, constructor, expected):
     def test_to_native_types(self, tuples, closed, expected_data):
         # GH 28210
         index = IntervalIndex.from_tuples(tuples, closed=closed)
-        result = index.to_native_types()
+        result = index._format_native_types()
         expected = np.array(expected_data)
         tm.assert_numpy_array_equal(result, expected)

pandas/tests/indexes/period/test_formats.py

@@ -12,35 +12,29 @@ def test_to_native_types():
     # First, with no arguments.
     expected = np.array(["2017-01-01", "2017-01-02", "2017-01-03"], dtype="=U10")
 
-    result = index.to_native_types()
+    result = index._format_native_types()
     tm.assert_numpy_array_equal(result, expected)
 
     # No NaN values, so na_rep has no effect
-    result = index.to_native_types(na_rep="pandas")
-    tm.assert_numpy_array_equal(result, expected)
-
-    # Make sure slicing works
-    expected = np.array(["2017-01-01", "2017-01-03"], dtype="=U10")
-
-    result = index.to_native_types([0, 2])
+    result = index._format_native_types(na_rep="pandas")
     tm.assert_numpy_array_equal(result, expected)
 
     # Make sure date formatting works
     expected = np.array(["01-2017-01", "01-2017-02", "01-2017-03"], dtype="=U10")
 
-    result = index.to_native_types(date_format="%m-%Y-%d")
+    result = index._format_native_types(date_format="%m-%Y-%d")
     tm.assert_numpy_array_equal(result, expected)
 
     # NULL object handling should work
     index = PeriodIndex(["2017-01-01", pd.NaT, "2017-01-03"], freq="D")
     expected = np.array(["2017-01-01", "NaT", "2017-01-03"], dtype=object)
 
-    result = index.to_native_types()
+    result = index._format_native_types()
     tm.assert_numpy_array_equal(result, expected)
 
     expected = np.array(["2017-01-01", "pandas", "2017-01-03"], dtype=object)
 
-    result = index.to_native_types(na_rep="pandas")
+    result = index._format_native_types(na_rep="pandas")
     tm.assert_numpy_array_equal(result, expected)