BUG: Groupby sum for object type should be None instead of 0 for all nan values

Author: snitish

pandas/_libs/groupby.pyx

@@ -717,8 +717,12 @@ def group_sum(
         raise ValueError("len(index) != len(labels)")
 
     nobs = np.zeros((<object>out).shape, dtype=np.int64)
-    # the below is equivalent to `np.zeros_like(out)` but faster
-    sumx = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
+    if sum_t is object:
+        # For object dtype, fill value should not be 0 (#60229)
+        sumx = np.empty((<object>out).shape, dtype=object)
+    else:
+        # the below is equivalent to `np.zeros_like(out)` but faster
+        sumx = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
     compensation = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
 
     N, K = (<object>values).shape

pandas/tests/groupby/test_categorical.py

@@ -361,6 +361,7 @@ def test_observed(request, using_infer_string, observed):
         expected = cartesian_product_for_groupers(
             expected, [cat1, cat2], list("AB"), fill_value=0
         )
+        expected.loc[expected.C == 0, "C"] = None
 
     tm.assert_frame_equal(result, expected)
 

pandas/tests/groupby/test_timegrouper.py

@@ -113,7 +113,7 @@ def test_groupby_with_timegrouper(self):
                 unit=df.index.unit,
             )
             expected = DataFrame(
-                {"Buyer": 0, "Quantity": 0},
+                {"Buyer": None, "Quantity": 0},
                 index=exp_dti,
             )
             # Cast to object to avoid implicit cast when setting entry to "CarlCarlCarl"