Index should be ignored when using the on keyword argument to resample

Author: aram-cedarwood

doc/source/whatsnew/v3.0.0.rst

@@ -117,6 +117,7 @@ These improvements also fixed certain bugs in groupby:
 - :meth:`.DataFrameGroupBy.agg` would fail when there are multiple groupings, unobserved groups, and ``as_index=False`` (:issue:`36698`)
 - :meth:`.DataFrameGroupBy.groups` with ``sort=False`` would sort groups; they now occur in the order they are observed (:issue:`56966`)
 - :meth:`.DataFrameGroupBy.nunique` would fail when there are multiple groupings, unobserved groups, and ``as_index=False`` (:issue:`52848`)
+- :meth:`.DataFrameGroupBy.resample` with an ``on`` value that is not ``None`` would have incorrect values when the index is out of order (:issue:`59350`)
 - :meth:`.DataFrameGroupBy.sum` would have incorrect values when there are multiple groupings, unobserved groups, and non-numeric data (:issue:`43891`)
 - :meth:`.DataFrameGroupBy.value_counts` would produce incorrect results when used with some categorical and some non-categorical groupings and ``observed=False`` (:issue:`56016`)
 

pandas/core/resample.py

@@ -1927,6 +1927,9 @@ def get_resampler_for_grouping(
     """
     # .resample uses 'on' similar to how .groupby uses 'key'
     tg = TimeGrouper(freq=rule, key=on, **kwargs)
+    # GH 59350: Index should be ignored when using the on keyword argument to resample
+    if on is not None:
+        groupby.obj = groupby.obj.reset_index(drop=True)
     resampler = tg._get_resampler(groupby.obj)
     return resampler._get_resampler_for_grouping(
         groupby=groupby, include_groups=include_groups, key=tg.key

pandas/tests/resample/test_resampler_grouper.py

@@ -689,3 +689,36 @@ def test_groupby_resample_on_index_with_list_of_keys_missing_column():
     rs = gb.resample("2D")
     with pytest.raises(KeyError, match="Columns not found"):
         rs[["val_not_in_dataframe"]]
+
+
+def test_groupby_resample_when_index_is_out_of_order():
+    # GH 59350
+    df = DataFrame(
+        data={
+            "datetime": [
+                pd.to_datetime("2024-07-30T00:00Z"),
+                pd.to_datetime("2024-07-30T00:01Z"),
+            ],
+            "group": ["A", "A"],
+            "numbers": [100, 200],
+        },
+        index=[1, 0],
+    )
+
+    gb = df.groupby("group")
+    rs = gb.resample("1min", on="datetime")
+    result = rs.aggregate({"numbers": "sum"})
+
+    index = pd.MultiIndex.from_arrays(
+        [
+            ["A", "A"],
+            [pd.to_datetime("2024-07-30T00:00Z"), pd.to_datetime("2024-07-30T00:01Z")],
+        ],
+        names=[
+            "group",
+            "datetime",
+        ],
+    )
+    expected = DataFrame({"numbers": [100, 200]}, index=index)
+
+    tm.assert_frame_equal(result, expected)