DEPR: enforce indexing deprecations

doc/source/whatsnew/v0.11.0.rst

@@ -368,7 +368,7 @@ Enhancements
   - You can now select with a string from a DataFrame with a datelike index, in a similar way to a Series (:issue:`3070`)
 
     .. ipython:: python
-       :okwarning:
+       :okexcept:
 
         idx = pd.date_range("2001-10-1", periods=5, freq='M')
         ts = pd.Series(np.random.rand(len(idx)), index=idx)

doc/source/whatsnew/v2.0.0.rst

@@ -306,6 +306,7 @@ Removal of prior version deprecations/changes
 - Removed argument ``kind`` from :meth:`Index.get_slice_bound`, :meth:`Index.slice_indexer` and :meth:`Index.slice_locs` (:issue:`41378`)
 - Removed arguments ``prefix``, ``squeeze``, ``error_bad_lines`` and ``warn_bad_lines`` from :func:`read_csv` (:issue:`40413`, :issue:`43427`)
 - Removed argument ``datetime_is_numeric`` from :meth:`DataFrame.describe` and :meth:`Series.describe` as datetime data will always be summarized as numeric data (:issue:`34798`)
+- Disallow passing list ``key`` to :meth:`Series.xs` and :meth:`DataFrame.xs`, pass a tuple instead (:issue:`41789`)
 - Disallow subclass-specific keywords (e.g. "freq", "tz", "names", "closed") in the :class:`Index` constructor (:issue:`38597`)
 - Removed argument ``inplace`` from :meth:`Categorical.remove_unused_categories` (:issue:`37918`)
 - Disallow passing non-round floats to :class:`Timestamp` with ``unit="M"`` or ``unit="Y"`` (:issue:`47266`)
@@ -381,6 +382,8 @@ Removal of prior version deprecations/changes
 - Enforced disallowing a tuple of column labels into :meth:`.DataFrameGroupBy.__getitem__` (:issue:`30546`)
 - Enforced disallowing setting values with ``.loc`` using a positional slice. Use ``.loc`` with labels or ``.iloc`` with positions instead (:issue:`31840`)
 - Enforced disallowing positional indexing with a ``float`` key even if that key is a round number, manually cast to integer instead (:issue:`34193`)
+- Enforced disallowing using a :class:`DataFrame` indexer with ``.iloc``, use ``.loc`` instead for automatic alignment (:issue:`39022`)
+- Enforced disallowing ``set`` or ``dict`` indexers in ``__getitem__`` and ``__setitem__`` methods (:issue:`42825`)
 - Enforced disallowing indexing on a :class:`Index` or positional indexing on a :class:`Series` producing multi-dimensional objects e.g. ``obj[:, None]``, convert to numpy before indexing instead (:issue:`35141`)
 - Enforced disallowing ``dict`` or ``set`` objects in ``suffixes`` in :func:`merge` (:issue:`34810`)
 - Enforced disallowing :func:`merge` to produce duplicated columns through the ``suffixes`` keyword and already existing columns (:issue:`22818`)
@@ -392,6 +395,7 @@ Removal of prior version deprecations/changes
 - Enforced :meth:`Rolling.count` with ``min_periods=None`` to default to the size of the window (:issue:`31302`)
 - Renamed ``fname`` to ``path`` in :meth:`DataFrame.to_parquet`, :meth:`DataFrame.to_stata` and :meth:`DataFrame.to_feather` (:issue:`30338`)
 - Enforced disallowing indexing a :class:`Series` with a single item list with a slice (e.g. ``ser[[slice(0, 2)]]``). Either convert the list to tuple, or pass the slice directly instead (:issue:`31333`)
+- Changed behavior indexing on a :class:`DataFrame` with a :class:`DatetimeIndex` index using a string indexer, previously this operated as a slice on rows, now it operates like any other column key; use ``frame.loc[key]`` for the old behavior (:issue:`36179`)
 - Enforced the ``display.max_colwidth`` option to not accept negative integers (:issue:`31569`)
 - Removed the ``display.column_space`` option in favor of ``df.to_string(col_space=...)`` (:issue:`47280`)
 - Removed the deprecated method ``mad`` from pandas classes (:issue:`11787`)

pandas/core/frame.py

@@ -187,8 +187,7 @@
 )
 from pandas.core.indexing import (
     check_bool_indexer,
-    check_deprecated_indexers,
-    convert_to_index_sliceable,
+    check_dict_or_set_indexers,
 )
 from pandas.core.internals import (
     ArrayManager,
@@ -3703,7 +3702,7 @@ def _iter_column_arrays(self) -> Iterator[ArrayLike]:
             yield self._get_column_array(i)
 
     def __getitem__(self, key):
-        check_deprecated_indexers(key)
+        check_dict_or_set_indexers(key)
         key = lib.item_from_zerodim(key)
         key = com.apply_if_callable(key, self)
 
@@ -3723,17 +3722,18 @@ def __getitem__(self, key):
             elif is_mi and self.columns.is_unique and key in self.columns:
                 return self._getitem_multilevel(key)
         # Do we have a slicer (on rows)?
-        indexer = convert_to_index_sliceable(self, key)
-        if indexer is not None:
+        if isinstance(key, slice):
+            indexer = self.index._convert_slice_indexer(
+                key, kind="getitem", is_frame=True
+            )
             if isinstance(indexer, np.ndarray):
+                # reachable with DatetimeIndex
                 indexer = lib.maybe_indices_to_slice(
                     indexer.astype(np.intp, copy=False), len(self)
                 )
                 if isinstance(indexer, np.ndarray):
                     # GH#43223 If we can not convert, use take
                     return self.take(indexer, axis=0)
-            # either we have a slice or we have a string that can be converted
-            #  to a slice for partial-string date indexing
             return self._slice(indexer, axis=0)
 
         # Do we have a (boolean) DataFrame?
@@ -3903,11 +3903,9 @@ def __setitem__(self, key, value):
         key = com.apply_if_callable(key, self)
 
         # see if we can slice the rows
-        indexer = convert_to_index_sliceable(self, key)
-        if indexer is not None:
-            # either we have a slice or we have a string that can be converted
-            #  to a slice for partial-string date indexing
-            return self._setitem_slice(indexer, value)
+        if isinstance(key, slice):
+            slc = self.index._convert_slice_indexer(key, kind="getitem", is_frame=True)
+            return self._setitem_slice(slc, value)
 
         if isinstance(key, DataFrame) or getattr(key, "ndim", None) == 2:
             self._setitem_frame(key, value)

pandas/core/generic.py

@@ -3930,12 +3930,7 @@ class   animal   locomotion
         labels = self._get_axis(axis)
 
         if isinstance(key, list):
-            warnings.warn(
-                "Passing lists as key for xs is deprecated and will be removed in a "
-                "future version. Pass key as a tuple instead.",
-                FutureWarning,
-                stacklevel=find_stack_level(),
-            )
+            raise TypeError("list keys are not supported in xs, pass a tuple instead")
 
         if level is not None:
             if not isinstance(labels, MultiIndex):

pandas/core/indexing.py

@@ -687,7 +687,7 @@ def _get_setitem_indexer(self, key):
 
         if isinstance(key, tuple):
             for x in key:
-                check_deprecated_indexers(x)
+                check_dict_or_set_indexers(x)
 
         if self.axis is not None:
             key = _tupleize_axis_indexer(self.ndim, self.axis, key)
@@ -813,7 +813,7 @@ def _ensure_listlike_indexer(self, key, axis=None, value=None) -> None:
 
     @final
     def __setitem__(self, key, value) -> None:
-        check_deprecated_indexers(key)
+        check_dict_or_set_indexers(key)
         if isinstance(key, tuple):
             key = tuple(list(x) if is_iterator(x) else x for x in key)
             key = tuple(com.apply_if_callable(x, self.obj) for x in key)
@@ -1004,7 +1004,7 @@ def _getitem_nested_tuple(self, tup: tuple):
         # we should be able to match up the dimensionality here
 
         for key in tup:
-            check_deprecated_indexers(key)
+            check_dict_or_set_indexers(key)
 
         # we have too many indexers for our dim, but have at least 1
         # multi-index dimension, try to see if we have something like
@@ -1062,7 +1062,7 @@ def _convert_to_indexer(self, key, axis: AxisInt):
 
     @final
     def __getitem__(self, key):
-        check_deprecated_indexers(key)
+        check_dict_or_set_indexers(key)
         if type(key) is tuple:
             key = tuple(list(x) if is_iterator(x) else x for x in key)
             key = tuple(com.apply_if_callable(x, self.obj) for x in key)
@@ -1499,12 +1499,9 @@ def _has_valid_setitem_indexer(self, indexer) -> bool:
             raise IndexError("iloc cannot enlarge its target object")
 
         if isinstance(indexer, ABCDataFrame):
-            warnings.warn(
-                "DataFrame indexer for .iloc is deprecated and will be removed in "
-                "a future version.\n"
-                "consider using .loc with a DataFrame indexer for automatic alignment.",
-                FutureWarning,
-                stacklevel=find_stack_level(),
+            raise TypeError(
+                "DataFrame indexer for .iloc is not supported. "
+                "Consider using .loc with a DataFrame indexer for automatic alignment.",
             )
 
         if not isinstance(indexer, tuple):
@@ -2493,40 +2490,6 @@ def _tupleize_axis_indexer(ndim: int, axis: AxisInt, key) -> tuple:
     return tuple(new_key)
 
 
-def convert_to_index_sliceable(obj: DataFrame, key):
-    """
-    If we are index sliceable, then return my slicer, otherwise return None.
-    """
-    idx = obj.index
-    if isinstance(key, slice):
-        return idx._convert_slice_indexer(key, kind="getitem", is_frame=True)
-
-    elif isinstance(key, str):
-
-        # we are an actual column
-        if key in obj.columns:
-            return None
-
-        # We might have a datetimelike string that we can translate to a
-        # slice here via partial string indexing
-        if idx._supports_partial_string_indexing:
-            try:
-                res = idx._get_string_slice(str(key))
-                warnings.warn(
-                    "Indexing a DataFrame with a datetimelike index using a single "
-                    "string to slice the rows, like `frame[string]`, is deprecated "
-                    "and will be removed in a future version. Use `frame.loc[string]` "
-                    "instead.",
-                    FutureWarning,
-                    stacklevel=find_stack_level(),
-                )
-                return res
-            except (KeyError, ValueError, NotImplementedError):
-                return None
-
-    return None
-
-
 def check_bool_indexer(index: Index, key) -> np.ndarray:
     """
     Check if key is a valid boolean indexer for an object with such index and
@@ -2661,27 +2624,24 @@ def need_slice(obj: slice) -> bool:
     )
 
 
-def check_deprecated_indexers(key) -> None:
-    """Checks if the key is a deprecated indexer."""
+def check_dict_or_set_indexers(key) -> None:
+    """
+    Check if the indexer is or contains a dict or set, which is no longer allowed.
+    """
     if (
         isinstance(key, set)
         or isinstance(key, tuple)
         and any(isinstance(x, set) for x in key)
     ):
-        warnings.warn(
-            "Passing a set as an indexer is deprecated and will raise in "
-            "a future version. Use a list instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
+        raise TypeError(
+            "Passing a set as an indexer is not supported. Use a list instead."
         )
+
     if (
         isinstance(key, dict)
         or isinstance(key, tuple)
         and any(isinstance(x, dict) for x in key)
     ):
-        warnings.warn(
-            "Passing a dict as an indexer is deprecated and will raise in "
-            "a future version. Use a list instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
+        raise TypeError(
+            "Passing a dict as an indexer is not supported. Use a list instead."
         )

pandas/core/series.py

@@ -140,7 +140,7 @@
 from pandas.core.indexes.multi import maybe_droplevels
 from pandas.core.indexing import (
     check_bool_indexer,
-    check_deprecated_indexers,
+    check_dict_or_set_indexers,
 )
 from pandas.core.internals import (
     SingleArrayManager,
@@ -914,7 +914,7 @@ def _slice(self, slobj: slice, axis: Axis = 0) -> Series:
         return self._get_values(slobj)
 
     def __getitem__(self, key):
-        check_deprecated_indexers(key)
+        check_dict_or_set_indexers(key)
         key = com.apply_if_callable(key, self)
 
         if key is Ellipsis:
@@ -1057,7 +1057,7 @@ def _get_value(self, label, takeable: bool = False):
             return self.iloc[loc]
 
     def __setitem__(self, key, value) -> None:
-        check_deprecated_indexers(key)
+        check_dict_or_set_indexers(key)
         key = com.apply_if_callable(key, self)
         cacher_needs_updating = self._check_is_chained_assignment_possible()
 

pandas/tests/frame/indexing/test_getitem.py

@@ -142,8 +142,10 @@ def test_getitem_listlike(self, idx_type, levels, float_frame):
         idx_check = list(idx_type(keys))
 
         if isinstance(idx, (set, dict)):
-            with tm.assert_produces_warning(FutureWarning):
-                result = frame[idx]
+            with pytest.raises(TypeError, match="as an indexer is not supported"):
+                frame[idx]
+
+            return
         else:
             result = frame[idx]
 
@@ -467,9 +469,9 @@ def test_getitem_datetime_slice(self):
 class TestGetitemDeprecatedIndexers:
     @pytest.mark.parametrize("key", [{"a", "b"}, {"a": "a"}])
     def test_getitem_dict_and_set_deprecated(self, key):
-        # GH#42825
+        # GH#42825 enforced in 2.0
         df = DataFrame(
             [[1, 2], [3, 4]], columns=MultiIndex.from_tuples([("a", 1), ("b", 2)])
         )
-        with tm.assert_produces_warning(FutureWarning):
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
             df[key]

pandas/tests/frame/indexing/test_indexing.py

@@ -1711,14 +1711,14 @@ def test_loc_on_multiindex_one_level(self):
         tm.assert_frame_equal(result, expected)
 
 
-class TestDepreactedIndexers:
+class TestDeprecatedIndexers:
     @pytest.mark.parametrize(
         "key", [{1}, {1: 1}, ({1}, "a"), ({1: 1}, "a"), (1, {"a"}), (1, {"a": "a"})]
     )
     def test_getitem_dict_and_set_deprecated(self, key):
-        # GH#42825
+        # GH#42825 enforced in 2.0
         df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
-        with tm.assert_produces_warning(FutureWarning):
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
             df.loc[key]
 
     @pytest.mark.parametrize(
@@ -1733,22 +1733,22 @@ def test_getitem_dict_and_set_deprecated(self, key):
         ],
     )
     def test_getitem_dict_and_set_deprecated_multiindex(self, key):
-        # GH#42825
+        # GH#42825 enforced in 2.0
         df = DataFrame(
             [[1, 2], [3, 4]],
             columns=["a", "b"],
             index=MultiIndex.from_tuples([(1, 2), (3, 4)]),
         )
-        with tm.assert_produces_warning(FutureWarning):
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
             df.loc[key]
 
     @pytest.mark.parametrize(
         "key", [{1}, {1: 1}, ({1}, "a"), ({1: 1}, "a"), (1, {"a"}), (1, {"a": "a"})]
     )
-    def test_setitem_dict_and_set_deprecated(self, key):
-        # GH#42825
+    def test_setitem_dict_and_set_disallowed(self, key):
+        # GH#42825 enforced in 2.0
         df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
-        with tm.assert_produces_warning(FutureWarning):
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
             df.loc[key] = 1
 
     @pytest.mark.parametrize(
@@ -1762,12 +1762,12 @@ def test_setitem_dict_and_set_deprecated(self, key):
             ((1, 2), {"a": "a"}),
         ],
     )
-    def test_setitem_dict_and_set_deprecated_multiindex(self, key):
-        # GH#42825
+    def test_setitem_dict_and_set_disallowed_multiindex(self, key):
+        # GH#42825 enforced in 2.0
         df = DataFrame(
             [[1, 2], [3, 4]],
             columns=["a", "b"],
             index=MultiIndex.from_tuples([(1, 2), (3, 4)]),
         )
-        with tm.assert_produces_warning(FutureWarning):
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
             df.loc[key] = 1

pandas/tests/frame/indexing/test_xs.py

@@ -107,8 +107,7 @@ def test_xs_keep_level(self):
         expected = df[:1]
         tm.assert_frame_equal(result, expected)
 
-        with tm.assert_produces_warning(FutureWarning):
-            result = df.xs([2008, "sat"], level=["year", "day"], drop_level=False)
+        result = df.xs((2008, "sat"), level=["year", "day"], drop_level=False)
         tm.assert_frame_equal(result, expected)
 
     def test_xs_view(self, using_array_manager, using_copy_on_write):
@@ -225,8 +224,7 @@ def test_xs_with_duplicates(self, key, level, multiindex_dataframe_random_data):
         expected = concat([frame.xs("one", level="second")] * 2)
 
         if isinstance(key, list):
-            with tm.assert_produces_warning(FutureWarning):
-                result = df.xs(key, level=level)
+            result = df.xs(tuple(key), level=level)
         else:
             result = df.xs(key, level=level)
         tm.assert_frame_equal(result, expected)
@@ -412,6 +410,5 @@ def test_xs_list_indexer_droplevel_false(self):
         # GH#41760
         mi = MultiIndex.from_tuples([("x", "m", "a"), ("x", "n", "b"), ("y", "o", "c")])
         df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=mi)
-        with tm.assert_produces_warning(FutureWarning):
-            with pytest.raises(KeyError, match="y"):
-                df.xs(["x", "y"], drop_level=False, axis=1)
+        with pytest.raises(KeyError, match="y"):
+            df.xs(("x", "y"), drop_level=False, axis=1)

pandas/tests/indexes/datetimes/test_partial_slicing.py

@@ -295,12 +295,10 @@ def test_partial_slicing_dataframe(self):
                     expected = df["a"][theslice]
                     tm.assert_series_equal(result, expected)
 
-                    # Frame should return slice as well
-                    with tm.assert_produces_warning(FutureWarning):
-                        # GH#36179 deprecated this indexing
-                        result = df[ts_string]
-                    expected = df[theslice]
-                    tm.assert_frame_equal(result, expected)
+                    # pre-2.0 df[ts_string] was overloaded to interpret this
+                    #  as slicing along index
+                    with pytest.raises(KeyError, match=ts_string):
+                        df[ts_string]
 
             # Timestamp with resolution more precise than index
             # Compatible with existing key