updates

doc/source/whatsnew/v1.0.0.rst

@@ -706,6 +706,7 @@ Datetimelike
 - Bug in :attr:`Timestamp.resolution` being a property instead of a class attribute (:issue:`29910`)
 - Bug in :func:`pandas.to_datetime` when called with ``None`` raising ``TypeError`` instead of returning ``NaT`` (:issue:`30011`)
 - Bug in :func:`pandas.to_datetime` failing for `deques` when using ``cache=True`` (the default) (:issue:`29403`)
+- Bug in datetimelike indexes and arrays not validating that the length of a boolean mask matches the array (:issue:`30308`)
 - Bug in :meth:`Series.item` with ``datetime64`` or ``timedelta64`` dtype, :meth:`DatetimeIndex.item`, and :meth:`TimedeltaIndex.item` returning an integer instead of a :class:`Timestamp` or :class:`Timedelta` (:issue:`30175`)
 -
 

pandas/core/arrays/datetimelike.py

@@ -416,7 +416,9 @@ def __getitem__(self, key):
             return self._box_func(val)
 
         if com.is_bool_indexer(key):
-            key = np.asarray(key, dtype=bool)
+            from pandas.core.indexing import check_bool_indexer
+
+            key = check_bool_indexer(self, key)
             if key.all():
                 key = slice(0, None, None)
             else:

pandas/core/common.py

@@ -112,35 +112,28 @@ def is_bool_indexer(key: Any) -> bool:
     bool
         Whether `key` is a valid boolean indexer.
 
-    Raises
-    ------
-    ValueError
-        When the array is an object-dtype ndarray or ExtensionArray
-        and contains missing values.
+    Notes
+    -----
+    This function is inexpensive for `bool` and `BooleanDtype`.
+    It is expensive for object-dtype backed arrays. In this case
+    a scan of the data to check that all the values are bool is
+    needed.
 
     See Also
     --------
     api.extensions.check_bool_array_indexer : Check that `key`
         is a valid mask for an array, and convert to an ndarary.
     """
-    na_msg = "cannot index with vector containing NA / NaN values"
     if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or (
         is_array_like(key) and is_extension_array_dtype(key.dtype)
     ):
         if key.dtype == np.object_:
             key = np.asarray(values_from_object(key))
 
             if not lib.is_bool_array(key):
-                if isna(key).any():
-                    raise ValueError(na_msg)
                 return False
             return True
         elif is_bool_dtype(key.dtype):
-            # an ndarray with bool-dtype by definition has no missing values.
-            # So we only need to check for NAs in ExtensionArrays
-            if is_extension_array_dtype(key.dtype):
-                if np.any(key.isna()):
-                    raise ValueError(na_msg)
             return True
     elif isinstance(key, list):
         try:

pandas/core/frame.py

@@ -2799,10 +2799,6 @@ def _setitem_slice(self, key, value):
     def _setitem_array(self, key, value):
         # also raises Exception if object array with NA values
         if com.is_bool_indexer(key):
-            if len(key) != len(self.index):
-                raise ValueError(
-                    f"Item wrong length {len(key)} instead of {len(self.index)}!"
-                )
             key = check_bool_indexer(self.index, key)
             indexer = key.nonzero()[0]
             self._check_setitem_copy()

pandas/core/indexes/base.py

@@ -3998,6 +3998,8 @@ def __getitem__(self, key):
         corresponding `Index` subclass.
 
         """
+        from pandas.core.indexing import check_bool_indexer
+
         # There's no custom logic to be implemented in __getslice__, so it's
         # not overloaded intentionally.
         getitem = self._data.__getitem__
@@ -4013,7 +4015,7 @@ def __getitem__(self, key):
             return promote(getitem(key))
 
         if com.is_bool_indexer(key):
-            key = np.asarray(key, dtype=bool)
+            key = check_bool_indexer(self, key)
 
         key = com.values_from_object(key)
         result = getitem(key)

pandas/core/indexes/datetimes.py

@@ -1115,6 +1115,8 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None):
     _has_same_tz = ea_passthrough(DatetimeArray._has_same_tz)
 
     def __getitem__(self, key):
+        # if com.is_bool_indexer(key):
+        #     breakpoint()
         result = self._data.__getitem__(key)
         if is_scalar(result):
             return result

pandas/core/indexes/multi.py

@@ -42,6 +42,7 @@
     ensure_index,
 )
 from pandas.core.indexes.frozen import FrozenList
+from pandas.core.indexing import check_bool_indexer
 import pandas.core.missing as missing
 from pandas.core.sorting import (
     get_group_index,
@@ -1934,7 +1935,7 @@ def __getitem__(self, key):
             return tuple(retval)
         else:
             if com.is_bool_indexer(key):
-                key = np.asarray(key, dtype=bool)
+                key = check_bool_indexer(self, key)
                 sortorder = self.sortorder
             else:
                 # cannot be sure whether the result will be sorted

pandas/core/indexing.py

@@ -1595,6 +1595,7 @@ def _validate_key(self, key, axis: int):
             return
 
         if com.is_bool_indexer(key):
+            # XXX: do we need to verify no NA here?
             return
 
         if not is_list_like_indexer(key):
@@ -1681,6 +1682,7 @@ def _getitem_axis(self, key, axis: int):
             self._validate_key(key, axis)
             return self._get_slice_axis(key, axis=axis)
         elif com.is_bool_indexer(key):
+            # check_bool_indexer is called in getbool_axis
             return self._getbool_axis(key, axis=axis)
         elif is_list_like_indexer(key):
 
@@ -2030,6 +2032,7 @@ def _getitem_axis(self, key, axis: int):
             key = np.asarray(key)
 
         if com.is_bool_indexer(key):
+            # check_bool_indexer is called in _getbool_axis
             self._validate_key(key, axis)
             return self._getbool_axis(key, axis=axis)
 

pandas/core/series.py

@@ -879,6 +879,7 @@ def __getitem__(self, key):
             elif key is Ellipsis:
                 return self
             elif com.is_bool_indexer(key):
+                # We check later on.
                 pass
             else:
 

pandas/tests/indexes/common.py

@@ -220,6 +220,11 @@ def test_get_indexer_consistency(self, indices):
         assert isinstance(indexer, np.ndarray)
         assert indexer.dtype == np.intp
 
+    def test_getitem_mask_wrong_length(self, indices):
+        mask = np.array([True])
+        with pytest.raises(IndexError, match="Item wrong length 1"):
+            indices[mask]
+
     def test_ndarray_compat_properties(self):
         idx = self.create_index()
         assert idx.T.equals(idx)