XSH-733: Explain ExecutionResponse parsing TODOs

* row totals have some limitations for now

Author: Roman Hartig

gooddata-pandas/gooddata_pandas/result_convertor.py

@@ -84,7 +84,7 @@ def accumulate_grand_totals(
         dim_idx_dict = {dim["localIdentifier"]: idx for idx, dim in enumerate(response.dimensions)}
 
         for grand_total in grand_totals:
-            # TODO not sure why totalDimensions is an array
+            # 2-dim results have always 1-dim grand totals (3-dim results have 2-dim gt but DataFrame stores 2D only)
             dims = grand_total["totalDimensions"]
             assert len(dims) == 1
             dim_idx = dim_idx_dict[dims[0]]
@@ -101,7 +101,8 @@ def accumulate_grand_totals(
                 # grand totals not initialized yet; initialize both data and headers by making
                 # a shallow copy from the results
                 self.grand_totals[opposite_dim] = grand_total["data"][:]
-                # TODO: wtf is the deal with this? why can there be multiple elements in the headerGroups list?
+                # TODO: row total measure headers are currently not supported (only aggregation info w/o measure label)
+                #       measure header defs are under ["headerGroups"][>0]
                 self.grand_totals_headers[opposite_dim] = grand_total["dimensionHeaders"][0]["headerGroups"][0][
                     "headers"
                 ][:]

gooddata-pandas/tests/dataframe/test_dataframe_for_exec_def.py

@@ -222,6 +222,40 @@ def test_dataframe_for_exec_def_totals4(gdf: DataFrameFactory):
     _run_and_validate_results(gdf=gdf, exec_def=exec_def, expected=(96, 19))
 
 
+# TODO - not implemented yet
+# def test_dataframe_for_exec_def_totals5(gdf: DataFrameFactory):
+#     """
+#     Execution with multiple measures row totals; the columns have labels of each measure.
+#     """
+#     exec_def = ExecutionDefinition(
+#         attributes=[
+#             Attribute(local_id="a_region", label="region"),
+#             Attribute(local_id="a_cat", label="products.category"),
+#         ],
+#         metrics=[
+#             SimpleMetric(local_id="m_price", item=ObjId(id="price", type="fact")),
+#             SimpleMetric(local_id="m_quantity", item=ObjId(id="quantity", type="fact")),
+#         ],
+#         filters=[],
+#         dimensions=[["a_region"], ["a_cat", "measureGroup"]],
+#         totals=[
+#             TotalDefinition(
+#                 local_id="grand_total1",
+#                 aggregation="sum",
+#                 total_dims=[TotalDimension(idx=1, items=["measureGroup"]), TotalDimension(idx=0, items=["a_region"])],
+#                 metric_local_id="m_price",
+#             ),
+#             TotalDefinition(
+#                 local_id="grand_total2",
+#                 aggregation="sum",
+#                 total_dims=[TotalDimension(idx=1, items=["measureGroup"]), TotalDimension(idx=0, items=["a_region"])],
+#                 metric_local_id="m_quantity",
+#             ),
+#         ],
+#     )
+#     _run_and_validate_results(gdf=gdf, exec_def=exec_def, expected=(?, ?))
+
+
 @gd_vcr.use_cassette(str(_fixtures_dir / "dataframe_for_exec_def_one_dim1.json"))
 def test_dataframe_for_exec_def_one_dim1(gdf: DataFrameFactory):
     exec_def = ExecutionDefinition(