BUG: Series.loc[dtstr] type depends on monotonicity (#42265)

Author: jbrockmendel

doc/source/whatsnew/v1.4.0.rst

@@ -158,7 +158,7 @@ Interval
 
 Indexing
 ^^^^^^^^
--
+- Bug in indexing on a :class:`Series` or :class:`DataFrame` with a :class:`DatetimeIndex` when passing a string, the return type depended on whether the index was monotonic (:issue:`24892`)
 -
 
 Missing

pandas/core/indexes/datetimes.py

@@ -589,23 +589,8 @@ def _parsed_string_to_bounds(self, reso: Resolution, parsed: datetime):
         return start, end
 
     def _can_partial_date_slice(self, reso: Resolution) -> bool:
-        assert isinstance(reso, Resolution), (type(reso), reso)
-        if (
-            self.is_monotonic
-            and reso.attrname in ["day", "hour", "minute", "second"]
-            and self._resolution_obj >= reso
-        ):
-            # These resolution/monotonicity validations came from GH3931,
-            # GH3452 and GH2369.
-
-            # See also GH14826
-            return False
-
-        if reso.attrname == "microsecond":
-            # _partial_date_slice doesn't allow microsecond resolution, but
-            # _parsed_string_to_bounds allows it.
-            return False
-        return True
+        # History of conversation GH#3452, GH#3931, GH#2369, GH#14826
+        return reso > self._resolution_obj
 
     def _deprecate_mismatched_indexing(self, key) -> None:
         # GH#36148

pandas/tests/indexes/datetimes/test_partial_slicing.py

@@ -20,6 +20,55 @@
 
 
 class TestSlicing:
+    def test_return_type_doesnt_depend_on_monotonicity(self):
+        # GH#24892 we get Series back regardless of whether our DTI is monotonic
+        dti = date_range(start="2015-5-13 23:59:00", freq="min", periods=3)
+        ser = Series(range(3), index=dti)
+
+        # non-monotonic index
+        ser2 = Series(range(3), index=[dti[1], dti[0], dti[2]])
+
+        # key with resolution strictly lower than "min"
+        key = "2015-5-14 00"
+
+        # monotonic increasing index
+        result = ser.loc[key]
+        expected = ser.iloc[1:]
+        tm.assert_series_equal(result, expected)
+
+        # monotonic decreasing index
+        result = ser.iloc[::-1].loc[key]
+        expected = ser.iloc[::-1][:-1]
+        tm.assert_series_equal(result, expected)
+
+        # non-monotonic index
+        result2 = ser2.loc[key]
+        expected2 = ser2.iloc[::2]
+        tm.assert_series_equal(result2, expected2)
+
+    def test_return_type_doesnt_depend_on_monotonicity_higher_reso(self):
+        # GH#24892 we get Series back regardless of whether our DTI is monotonic
+        dti = date_range(start="2015-5-13 23:59:00", freq="min", periods=3)
+        ser = Series(range(3), index=dti)
+
+        # non-monotonic index
+        ser2 = Series(range(3), index=[dti[1], dti[0], dti[2]])
+
+        # key with resolution strictly *higher) than "min"
+        key = "2015-5-14 00:00:00"
+
+        # monotonic increasing index
+        result = ser.loc[key]
+        assert result == 1
+
+        # monotonic decreasing index
+        result = ser.iloc[::-1].loc[key]
+        assert result == 1
+
+        # non-monotonic index
+        result2 = ser2.loc[key]
+        assert result2 == 0
+
     def test_monotone_DTI_indexing_bug(self):
         # GH 19362
         # Testing accessing the first element in a monotonic descending
@@ -38,9 +87,19 @@ def test_monotone_DTI_indexing_bug(self):
         expected = DataFrame({0: list(range(5)), "date": date_index})
         tm.assert_frame_equal(df, expected)
 
-        df = DataFrame({"A": [1, 2, 3]}, index=date_range("20170101", periods=3)[::-1])
-        expected = DataFrame({"A": 1}, index=date_range("20170103", periods=1)[::-1])
-        tm.assert_frame_equal(df.loc["2017-01-03"], expected)
+        # We get a slice because df.index's resolution is hourly and we
+        #  are slicing with a daily-resolution string.  If both were daily,
+        #  we would get a single item back
+        dti = date_range("20170101 01:00:00", periods=3)
+        df = DataFrame({"A": [1, 2, 3]}, index=dti[::-1])
+
+        expected = DataFrame({"A": 1}, index=dti[-1:][::-1])
+        result = df.loc["2017-01-03"]
+        tm.assert_frame_equal(result, expected)
+
+        result2 = df.iloc[::-1].loc["2017-01-03"]
+        expected2 = expected.iloc[::-1]
+        tm.assert_frame_equal(result2, expected2)
 
     def test_slice_year(self):
         dti = date_range(freq="B", start=datetime(2005, 1, 1), periods=500)