Backport PR #48579 on branch 1.5.x (BUG: Fix calling groupBy(...).apply(func) on an empty dataframe invokes func) (#48817)

BUG: Fix calling groupBy(...).apply(func) on an empty dataframe invokes func  (#48579)

(cherry picked from commit 8b0ad71)

Co-authored-by: Dennis Chukwunta <[email protected]>

Author: rhshadrach

doc/source/whatsnew/v1.5.1.rst

@@ -75,6 +75,7 @@ Fixed regressions
 - Fixed regression in :meth:`DataFrame.describe` raising ``TypeError`` when result contains ``NA`` (:issue:`48778`)
 - Fixed regression in :meth:`DataFrame.plot` ignoring invalid ``colormap`` for ``kind="scatter"`` (:issue:`48726`)
 - Fixed performance regression in :func:`factorize` when ``na_sentinel`` is not ``None`` and ``sort=False`` (:issue:`48620`)
+- Fixed Regression in :meth:`DataFrameGroupBy.apply` when user defined function is called on an empty dataframe (:issue:`47985`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/groupby/ops.py

@@ -840,15 +840,14 @@ def apply(
             if not mutated and not _is_indexed_like(res, group_axes, axis):
                 mutated = True
             result_values.append(res)
-
         # getattr pattern for __name__ is needed for functools.partial objects
-        if len(group_keys) == 0 and getattr(f, "__name__", None) not in [
-            "idxmin",
-            "idxmax",
-            "nanargmin",
-            "nanargmax",
+        if len(group_keys) == 0 and getattr(f, "__name__", None) in [
+            "mad",
+            "skew",
+            "sum",
+            "prod",
         ]:
-            # If group_keys is empty, then no function calls have been made,
+            #  If group_keys is empty, then no function calls have been made,
             #  so we will not have raised even if this is an invalid dtype.
             #  So do one dummy call here to raise appropriate TypeError.
             f(data.iloc[:0])

pandas/tests/groupby/test_apply.py

@@ -1331,3 +1331,28 @@ def test_result_name_when_one_group(name):
     expected = Series([1, 2], name=name)
 
     tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "method, op",
+    [
+        ("apply", lambda gb: gb.values[-1]),
+        ("apply", lambda gb: gb["b"].iloc[0]),
+        ("agg", "mad"),
+        ("agg", "skew"),
+        ("agg", "prod"),
+        ("agg", "sum"),
+    ],
+)
+def test_empty_df(method, op):
+    # GH 47985
+    empty_df = DataFrame({"a": [], "b": []})
+    gb = empty_df.groupby("a", group_keys=True)
+    group = getattr(gb, "b")
+
+    result = getattr(group, method)(op)
+    expected = Series(
+        [], name="b", dtype="float64", index=Index([], dtype="float64", name="a")
+    )
+
+    tm.assert_series_equal(result, expected)