BUG: Fix wrong error in df drop with non unique datetime index and invalid keys

doc/source/whatsnew/v1.0.0.rst

@@ -714,6 +714,8 @@ Datetimelike
 - Bug in :func:`pandas.to_datetime` failing for `deques` when using ``cache=True`` (the default) (:issue:`29403`)
 - 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`)
 - Bug in :class:`DatetimeIndex` addition when adding a non-optimized :class:`DateOffset` incorrectly dropping timezone information (:issue:`30336`)
+- Bug in :meth:`pandas.core.indexes.base.Index.get_indexer_non_unique` missing condition on target.is_all_date before trying to convert target to asi8 values, which results in wrong error message when dropping with non-unique datetime index (:issue:`30399`)
+
 
 Timedelta
 ^^^^^^^^^

pandas/core/indexes/base.py

@@ -4551,7 +4551,7 @@ def get_indexer_non_unique(self, target):
 
         if is_categorical(target):
             tgt_values = np.asarray(target)
-        elif self.is_all_dates:
+        elif self.is_all_dates and target.is_all_dates:  # GH 30399
             tgt_values = target.asi8
         else:
             tgt_values = target._ndarray_values

pandas/tests/indexes/multi/test_drop.py

@@ -139,3 +139,19 @@ def test_drop_not_lexsorted():
     tm.assert_index_equal(lexsorted_mi, not_lexsorted_mi)
     with tm.assert_produces_warning(PerformanceWarning):
         tm.assert_index_equal(lexsorted_mi.drop("a"), not_lexsorted_mi.drop("a"))
+
+
+def test_drop_with_non_unique_datetime_index_and_invalid_keys():
+    # GH 30399
+
+    # define dataframe with unique datetime index
+    df = pd.DataFrame(
+        np.random.randn(5, 3),
+        columns=["a", "b", "c"],
+        index=pd.date_range("2012", freq="H", periods=5),
+    )
+    # create dataframe with non-unique datetime index
+    df = df.iloc[[0, 2, 2, 3]].copy()
+
+    with pytest.raises(KeyError, match="not found in axis"):
+        df.drop(["a", "b"])  # Dropping with labels not exist in the index