BUG: validate Index data is 1D + deprecate multi-dim indexing (#30588)

* BUG: validate Index data is 1D

Author: jbrockmendel

doc/source/whatsnew/v1.0.0.rst

@@ -706,6 +706,7 @@ Deprecations
 - The ``pandas.util.testing`` module has been deprecated. Use the public API in ``pandas.testing`` documented at :ref:`api.general.testing` (:issue:`16232`).
 - ``pandas.SparseArray`` has been deprecated.  Use ``pandas.arrays.SparseArray`` (:class:`arrays.SparseArray`) instead. (:issue:`30642`)
 - The parameter ``is_copy`` of :meth:`DataFrame.take` has been deprecated and will be removed in a future version. (:issue:`27357`)
+- Support for multi-dimensional indexing (e.g. ``index[:, None]``) on a :class:`Index` is deprecated and will be removed in a future version, convert to a numpy array before indexing instead (:issue:`30588`)
 
 **Selecting Columns from a Grouped DataFrame**
 
@@ -1168,6 +1169,7 @@ Other
 - Bug in :meth:`DataFrame.to_csv` when supplied a series with a ``dtype="string"`` and a ``na_rep``, the ``na_rep`` was being truncated to 2 characters. (:issue:`29975`)
 - Bug where :meth:`DataFrame.itertuples` would incorrectly determine whether or not namedtuples could be used for dataframes of 255 columns (:issue:`28282`)
 - Handle nested NumPy ``object`` arrays in :func:`testing.assert_series_equal` for ExtensionArray implementations (:issue:`30841`)
+- Bug in :class:`Index` constructor incorrectly allowing 2-dimensional input arrays (:issue:`13601`, :issue:`27125`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/arrays/categorical.py

@@ -2007,9 +2007,10 @@ def __getitem__(self, key):
         if com.is_bool_indexer(key):
             key = check_bool_array_indexer(self, key)
 
-        return self._constructor(
-            values=self._codes[key], dtype=self.dtype, fastpath=True
-        )
+        result = self._codes[key]
+        if result.ndim > 1:
+            return result
+        return self._constructor(result, dtype=self.dtype, fastpath=True)
 
     def __setitem__(self, key, value):
         """

pandas/core/arrays/datetimelike.py

@@ -543,8 +543,6 @@ def __getitem__(self, key):
         if result.ndim > 1:
             # To support MPL which performs slicing with 2 dim
             # even though it only has 1 dim by definition
-            if is_period:
-                return self._simple_new(result, dtype=self.dtype, freq=freq)
             return result
 
         return self._simple_new(result, dtype=self.dtype, freq=freq)

pandas/core/arrays/interval.py

@@ -500,8 +500,11 @@ def __getitem__(self, value):
 
         # scalar
         if not isinstance(left, ABCIndexClass):
-            if isna(left):
+            if is_scalar(left) and isna(left):
                 return self._fill_value
+            if np.ndim(left) > 1:
+                # GH#30588 multi-dimensional indexer disallowed
+                raise ValueError("multi-dimensional indexing not allowed")
             return Interval(left, right, self.closed)
 
         return self._shallow_copy(left, right)

pandas/core/indexes/base.py

@@ -393,6 +393,9 @@ def __new__(
 
             if kwargs:
                 raise TypeError(f"Unexpected keyword arguments {repr(set(kwargs))}")
+            if subarr.ndim > 1:
+                # GH#13601, GH#20285, GH#27125
+                raise ValueError("Index data must be 1-dimensional")
             return cls._simple_new(subarr, name, **kwargs)
 
         elif hasattr(data, "__array__"):
@@ -608,7 +611,7 @@ def __array_wrap__(self, result, context=None):
         Gets called after a ufunc.
         """
         result = lib.item_from_zerodim(result)
-        if is_bool_dtype(result) or lib.is_scalar(result):
+        if is_bool_dtype(result) or lib.is_scalar(result) or np.ndim(result) > 1:
             return result
 
         attrs = self._get_attributes_dict()
@@ -687,11 +690,10 @@ def astype(self, dtype, copy=True):
             return Index(np.asarray(self), dtype=dtype, copy=copy)
 
         try:
-            return Index(
-                self.values.astype(dtype, copy=copy), name=self.name, dtype=dtype
-            )
+            casted = self.values.astype(dtype, copy=copy)
         except (TypeError, ValueError):
             raise TypeError(f"Cannot cast {type(self).__name__} to dtype {dtype}")
+        return Index(casted, name=self.name, dtype=dtype)
 
     _index_shared_docs[
         "take"
@@ -3902,6 +3904,9 @@ def __getitem__(self, key):
         key = com.values_from_object(key)
         result = getitem(key)
         if not is_scalar(result):
+            if np.ndim(result) > 1:
+                deprecate_ndim_indexing(result)
+                return result
             return promote(result)
         else:
             return result
@@ -5533,3 +5538,17 @@ def _try_convert_to_int_array(
         pass
 
     raise ValueError
+
+
+def deprecate_ndim_indexing(result):
+    if np.ndim(result) > 1:
+        # GH#27125 indexer like idx[:, None] expands dim, but we
+        #  cannot do that and keep an index, so return ndarray
+        # Deprecation GH#30588
+        warnings.warn(
+            "Support for multi-dimensional indexing (e.g. `index[:, None]`) "
+            "on an Index is deprecated and will be removed in a future "
+            "version.  Convert to a numpy array before indexing instead.",
+            DeprecationWarning,
+            stacklevel=3,
+        )

pandas/core/indexes/extension.py

@@ -12,7 +12,7 @@
 from pandas.core.dtypes.generic import ABCSeries
 
 from pandas.core.arrays import ExtensionArray
-from pandas.core.indexes.base import Index
+from pandas.core.indexes.base import Index, deprecate_ndim_indexing
 from pandas.core.ops import get_op_result_name
 
 
@@ -178,6 +178,7 @@ def __getitem__(self, key):
             return type(self)(result, name=self.name)
 
         # Includes cases where we get a 2D ndarray back for MPL compat
+        deprecate_ndim_indexing(result)
         return result
 
     def __iter__(self):

pandas/core/indexes/numeric.py

@@ -73,6 +73,10 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None):
         else:
             subarr = data
 
+        if subarr.ndim > 1:
+            # GH#13601, GH#20285, GH#27125
+            raise ValueError("Index data must be 1-dimensional")
+
         name = maybe_extract_name(name, data, cls)
         return cls._simple_new(subarr, name=name)
 

pandas/tests/indexes/categorical/test_category.py

@@ -975,3 +975,9 @@ def test_engine_type(self, dtype, engine_type):
             ci.values._codes = ci.values._codes.astype("int64")
         assert np.issubdtype(ci.codes.dtype, dtype)
         assert isinstance(ci._engine, engine_type)
+
+    def test_getitem_2d_deprecated(self):
+        # GH#30588 multi-dim indexing is deprecated, but raising is also acceptable
+        idx = self.create_index()
+        with pytest.raises(ValueError, match="cannot mask with array containing NA"):
+            idx[:, None]

pandas/tests/indexes/common.py

@@ -875,3 +875,11 @@ def test_engine_reference_cycle(self):
         nrefs_pre = len(gc.get_referrers(index))
         index._engine
         assert len(gc.get_referrers(index)) == nrefs_pre
+
+    def test_getitem_2d_deprecated(self):
+        # GH#30588
+        idx = self.create_index()
+        with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
+            res = idx[:, None]
+
+        assert isinstance(res, np.ndarray), type(res)

pandas/tests/indexes/datetimes/test_indexing.py

@@ -86,7 +86,9 @@ def test_dti_business_getitem(self):
 
     def test_dti_business_getitem_matplotlib_hackaround(self):
         rng = pd.bdate_range(START, END)
-        values = rng[:, None]
+        with tm.assert_produces_warning(DeprecationWarning):
+            # GH#30588 multi-dimensional indexing deprecated
+            values = rng[:, None]
         expected = rng.values[:, None]
         tm.assert_numpy_array_equal(values, expected)
 
@@ -110,7 +112,9 @@ def test_dti_custom_getitem(self):
 
     def test_dti_custom_getitem_matplotlib_hackaround(self):
         rng = pd.bdate_range(START, END, freq="C")
-        values = rng[:, None]
+        with tm.assert_produces_warning(DeprecationWarning):
+            # GH#30588 multi-dimensional indexing deprecated
+            values = rng[:, None]
         expected = rng.values[:, None]
         tm.assert_numpy_array_equal(values, expected)