BUG: df.resample('MS', closed='right') incorrectly places bins

doc/source/whatsnew/v2.1.2.rst

@@ -13,6 +13,7 @@ including other versions of pandas.
 
 Fixed regressions
 ~~~~~~~~~~~~~~~~~
+- Fixed bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.MonthBegin` (:issue:`55271`)
 - Fixed bug where PDEP-6 warning about setting an item of an incompatible dtype was being shown when creating a new conditional column (:issue:`55025`)
 -
 
@@ -21,7 +22,8 @@ Fixed regressions
 
 Bug fixes
 ~~~~~~~~~
--
+- Fixed bug in :meth:`DataFrame.resample` not respecting ``closed`` and ``label`` arguments for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55282`)
+- Fixed bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55281`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/resample.py

@@ -2297,7 +2297,17 @@ def _adjust_bin_edges(
     ) -> tuple[DatetimeIndex, npt.NDArray[np.int64]]:
         # Some hacks for > daily data, see #1471, #1458, #1483
 
-        if self.freq != "D" and is_superperiod(self.freq, "D"):
+        if self.freq.name in ("BM", "ME", "W") or self.freq.name.split("-")[0] in (
+            "BQ",
+            "BA",
+            "Q",
+            "A",
+            "W",
+        ):
+            # If the right end-point is on the last day of the month, roll forwards
+            # until the last moment of that day. Note that we only do this for offsets
+            # which correspond to the end of a super-daily period - "month start", for
+            # example, is excluded.
             if self.closed == "right":
                 # GH 21459, GH 9119: Adjust the bins relative to the wall time
                 edges_dti = binner.tz_localize(None)

pandas/tests/resample/test_datetime_index.py

@@ -660,7 +660,7 @@ def test_resample_reresample(unit):
     s = Series(np.random.default_rng(2).random(len(dti)), dti)
     bs = s.resample("B", closed="right", label="right").mean()
     result = bs.resample("8H").mean()
-    assert len(result) == 22
+    assert len(result) == 25
     assert isinstance(result.index.freq, offsets.DateOffset)
     assert result.index.freq == offsets.Hour(8)
 
@@ -2051,3 +2051,66 @@ def test_resample_M_deprecated():
     with tm.assert_produces_warning(UserWarning, match=depr_msg):
         result = s.resample("2M").mean()
     tm.assert_series_equal(result, expected)
+
+
+def test_resample_ms_closed_right():
+    # https://github.com/pandas-dev/pandas/issues/55271
+    dti = date_range(start="2020-01-31", freq="1min", periods=6000)
+    df = DataFrame({"ts": dti}, index=dti)
+    grouped = df.resample("MS", closed="right")
+    result = grouped.last()
+    expected = DataFrame(
+        {"ts": [datetime(2020, 2, 1), datetime(2020, 2, 4, 3, 59)]},
+        index=DatetimeIndex([datetime(2020, 1, 1), datetime(2020, 2, 1)], freq="MS"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("freq", ["B", "C"])
+def test_resample_c_b_closed_right(freq: str):
+    # https://github.com/pandas-dev/pandas/issues/55281
+    dti = date_range(start="2020-01-31", freq="1min", periods=6000)
+    df = DataFrame({"ts": dti}, index=dti)
+    grouped = df.resample(freq, closed="right")
+    result = grouped.last()
+    expected = DataFrame(
+        {
+            "ts": [
+                datetime(2020, 1, 31),
+                datetime(2020, 2, 3),
+                datetime(2020, 2, 4),
+                datetime(2020, 2, 4, 3, 59),
+            ]
+        },
+        index=DatetimeIndex(
+            [
+                datetime(2020, 1, 30),
+                datetime(2020, 1, 31),
+                datetime(2020, 2, 3),
+                datetime(2020, 2, 4),
+            ],
+            freq=freq,
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_resample_b_55282():
+    # https://github.com/pandas-dev/pandas/issues/55282
+    s = Series(
+        [1, 2, 3, 4, 5, 6], index=date_range("2023-09-26", periods=6, freq="12H")
+    )
+    result = s.resample("B", closed="right", label="right").mean()
+    expected = Series(
+        [1.0, 2.5, 4.5, 6.0],
+        index=DatetimeIndex(
+            [
+                datetime(2023, 9, 26),
+                datetime(2023, 9, 27),
+                datetime(2023, 9, 28),
+                datetime(2023, 9, 29),
+            ],
+            freq="B",
+        ),
+    )
+    tm.assert_series_equal(result, expected)