BUG: Allow passing of args/kwargs to groupby.apply with str func

doc/source/whatsnew/v1.5.0.rst

@@ -527,6 +527,7 @@ Groupby/resample/rolling
 - Bug in :meth:`GroupBy.cummin` and :meth:`GroupBy.cummax` with nullable dtypes incorrectly altering the original data in place (:issue:`46220`)
 - Bug in :meth:`GroupBy.cummax` with ``int64`` dtype with leading value being the smallest possible int64 (:issue:`46382`)
 - Bug in :meth:`GroupBy.max` with empty groups and ``uint64`` dtype incorrectly raising ``RuntimeError`` (:issue:`46408`)
+- Bug in :meth:`.GroupBy.apply` would fail when ``func`` was a string and args or kwargs were supplied (:issue:`46479`)
 -
 
 Reshaping

pandas/core/groupby/groupby.py

@@ -1363,10 +1363,19 @@ def apply(self, func, *args, **kwargs) -> NDFrameT:
 
         func = com.is_builtin_func(func)
 
-        # this is needed so we don't try and wrap strings. If we could
-        # resolve functions to their callable functions prior, this
-        # wouldn't be needed
-        if args or kwargs:
+        if isinstance(func, str):
+            if hasattr(self, func):
+                res = getattr(self, func)
+                if callable(res):
+                    return res(*args, **kwargs)
+                elif args or kwargs:
+                    raise ValueError(f"Cannot pass arguments to property {func}")
+                return res
+
+            else:
+                raise TypeError(f"apply func should be callable, not '{func}'")
+
+        elif args or kwargs:
             if callable(func):
 
                 @wraps(func)
@@ -1382,15 +1391,6 @@ def f(g):
                 raise ValueError(
                     "func must be a callable if args or kwargs are supplied"
                 )
-        elif isinstance(func, str):
-            if hasattr(self, func):
-                res = getattr(self, func)
-                if callable(res):
-                    return res()
-                return res
-
-            else:
-                raise TypeError(f"apply func should be callable, not '{func}'")
         else:
 
             f = func

pandas/tests/groupby/test_apply.py

@@ -1247,3 +1247,12 @@ def test_apply_nonmonotonic_float_index(arg, idx):
     expected = DataFrame({"col": arg}, index=idx)
     result = expected.groupby("col").apply(lambda x: x)
     tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("args, kwargs", [([True], {}), ([], {"numeric_only": True})])
+def test_apply_str_with_args(df, args, kwargs):
+    # GH#46479
+    gb = df.groupby("A")
+    result = gb.apply("sum", *args, **kwargs)
+    expected = gb.sum(numeric_only=True)
+    tm.assert_frame_equal(result, expected)