made factor column optional

pymrio/tools/ioutil.py

@@ -1021,6 +1021,8 @@ def convert(df_orig, df_map, agg_func="sum", drop_not_bridged=True):
         stressor ... original index name
         compartment ... original index name
         factor ... the factor for multiplication/characterization
+            If no factor is given, the factor is assumed to be 1.
+            This can be used, to simplify renaming/aggregation mappings.
         impact__stressor ... the new index name,
             replacing the previous index name "stressor".
             Thus here "stressor" will be renamed to "impact", and the row index
@@ -1094,7 +1096,10 @@ def convert(df_orig, df_map, agg_func="sum", drop_not_bridged=True):
         # the loop for getting all (potential regex) matches and multiplication
         for row in df_cur_map.iterrows():
             matched_entries = index_fullmatch(df_ix=df_orig, **row[1].to_dict())
-            mul_entries = matched_entries * row[1].factor
+            try:
+                mul_entries = matched_entries * row[1].factor
+            except AttributeError:
+                mul_entries = matched_entries
             collector.append(mul_entries)
 
         df_collected = pd.concat(collector, axis=0)

tests/test_util.py

@@ -419,6 +419,24 @@ def test_convert_rename():
     )
 
 
+
+    # without factor should give the same result
+    rename_bridge_simple_wo_factor = pd.DataFrame(
+        columns=["em_type", "stressor__em_type"],
+        data=[
+            ["em1", "emission-1"],
+            ["em2", "emission2"],
+            ["emA", "emission A"],
+        ],
+    )
+
+    char_res_keep_comp_wo_factor = convert(to_char, rename_bridge_simple_wo_factor, drop_not_bridged=False)
+
+    pdt.assert_frame_equal(
+        char_res_keep_comp_wo_factor,
+        char_res_keep_comp)
+
+
 def test_convert_characterize():
     """Testing the characterization of one table"""