fix bug when combining groupby with resample and interpolate with dat… (#35360)

Author: CloseChoice

doc/source/whatsnew/v1.2.0.rst

@@ -249,8 +249,7 @@ Groupby/resample/rolling
 
 - Bug in :meth:`DataFrameGroupBy.count` and :meth:`SeriesGroupBy.sum` returning ``NaN`` for missing categories when grouped on multiple ``Categoricals``. Now returning ``0`` (:issue:`35028`)
 - Bug in :meth:`DataFrameGroupBy.apply` that would some times throw an erroneous ``ValueError`` if the grouping axis had duplicate entries (:issue:`16646`)
--
--
+- Bug when combining methods :meth:`DataFrame.groupby` with :meth:`DataFrame.resample` and :meth:`DataFrame.interpolate` raising an ``TypeError`` (:issue:`35325`)
 - Bug in :meth:`DataFrameGroupBy.apply` where a non-nuisance grouping column would be dropped from the output columns if another groupby method was called before ``.apply()`` (:issue:`34656`)
 
 Reshaping

pandas/core/resample.py

@@ -795,7 +795,7 @@ def interpolate(
         """
         Interpolate values according to different methods.
         """
-        result = self._upsample(None)
+        result = self._upsample("asfreq")
         return result.interpolate(
             method=method,
             axis=axis,

pandas/tests/resample/test_time_grouper.py

@@ -287,3 +287,65 @@ def test_upsample_sum(method, method_args, expected_values):
     result = methodcaller(method, **method_args)(resampled)
     expected = pd.Series(expected_values, index=index)
     tm.assert_series_equal(result, expected)
+
+
+def test_groupby_resample_interpolate():
+    # GH 35325
+    d = {"price": [10, 11, 9], "volume": [50, 60, 50]}
+
+    df = pd.DataFrame(d)
+
+    df["week_starting"] = pd.date_range("01/01/2018", periods=3, freq="W")
+
+    result = (
+        df.set_index("week_starting")
+        .groupby("volume")
+        .resample("1D")
+        .interpolate(method="linear")
+    )
+    expected_ind = pd.MultiIndex.from_tuples(
+        [
+            (50, "2018-01-07"),
+            (50, pd.Timestamp("2018-01-08")),
+            (50, pd.Timestamp("2018-01-09")),
+            (50, pd.Timestamp("2018-01-10")),
+            (50, pd.Timestamp("2018-01-11")),
+            (50, pd.Timestamp("2018-01-12")),
+            (50, pd.Timestamp("2018-01-13")),
+            (50, pd.Timestamp("2018-01-14")),
+            (50, pd.Timestamp("2018-01-15")),
+            (50, pd.Timestamp("2018-01-16")),
+            (50, pd.Timestamp("2018-01-17")),
+            (50, pd.Timestamp("2018-01-18")),
+            (50, pd.Timestamp("2018-01-19")),
+            (50, pd.Timestamp("2018-01-20")),
+            (50, pd.Timestamp("2018-01-21")),
+            (60, pd.Timestamp("2018-01-14")),
+        ],
+        names=["volume", "week_starting"],
+    )
+    expected = pd.DataFrame(
+        data={
+            "price": [
+                10.0,
+                9.928571428571429,
+                9.857142857142858,
+                9.785714285714286,
+                9.714285714285714,
+                9.642857142857142,
+                9.571428571428571,
+                9.5,
+                9.428571428571429,
+                9.357142857142858,
+                9.285714285714286,
+                9.214285714285714,
+                9.142857142857142,
+                9.071428571428571,
+                9.0,
+                11.0,
+            ],
+            "volume": [50.0] * 15 + [60],
+        },
+        index=expected_ind,
+    )
+    tm.assert_frame_equal(result, expected)