Simpler

Once we remove all deprecated code, score will be very short since most of the work is done in mean_skill

modelskill/comparison/_collection.py

@@ -817,6 +817,9 @@ def weighted_mean(x):
             agg[metric.__name__] = weighted_mean  # type: ignore
         res = skilldf.groupby(by).agg(agg)
 
+        # TODO is this correct?
+        res.index.name = "model"
+
         # output
         res = cmp._add_as_col_if_not_in_index(df, res, fields=["model", "variable"])
         return SkillTable(res.astype({"n": int}))
@@ -1031,14 +1034,7 @@ def score(
 
         metric_name = metric if isinstance(metric, str) else metric.__name__
 
-        # TODO dict comprehension?
-        score = {}
-        for model in models:
-            mtr_val = df.loc[model][metric_name]
-            if not np.isscalar(mtr_val):
-                # e.g. mean over different variables!
-                mtr_val = mtr_val.values.mean()
-            score[model] = mtr_val
+        score = df[metric_name].to_dict()
 
         return score