diff --git a/notes_tools/tools/df_marks_manip.py b/notes_tools/tools/df_marks_manip.py
index 60b7538..4ae5162 100644
--- a/notes_tools/tools/df_marks_manip.py
+++ b/notes_tools/tools/df_marks_manip.py
@@ -287,11 +287,12 @@ def compute_eval_marks(df):
     >>> df["Mark"] = compute_marks(df)
     >>> df_exo = compute_exo_marks(df)
     >>> compute_eval_marks(df_exo)
-      Eleve Nom Trimestre  Bareme        Date  Mark Exercice  Niveau
-    0    E1  N1         1     6.0  16/09/2016   5.0    Total       0
-    1    E1  N2         1     4.0  01/10/2016   3.0    Total       0
-    2    E2  N1         1     6.0  16/09/2016   4.5    Total       0
-    3    E2  N2         1     4.0  01/10/2016   3.5    Total       0
+       index Eleve Nom Trimestre  Bareme        Date  Mark
+    0      0    E1  N1         1     6.0  16/09/2016   5.0
+    1      1    E2  N1         1     6.0  16/09/2016   4.5
+    2      0    E1  N2         1     4.0  01/10/2016   3.0
+    3      1    E2  N2         1     4.0  01/10/2016   3.5
+
 
     """
     def date_format(dates):
@@ -313,12 +314,13 @@ def compute_eval_marks(df):
 
         eval_pt = pd.pivot_table(eval_df,
                 index = [ "Eleve", "Nom", "Trimestre"],
-                values = ["Bareme", "Mark", "Normalized", "Date"],
-                aggfunc={"Bareme": np.sum, "Mark": np.sum,"Normalized":np.mean, "Date":lambda x:dates[0]},
+                values = ["Bareme", "Mark", "Date"],
+                aggfunc={"Bareme": np.sum, "Mark": np.sum, "Date":lambda x:dates[0]},
                 )
         eval_pt = eval_pt.reset_index()
         eval_m = pd.concat([eval_m, eval_pt])
 
+    eval_m = eval_m.reset_index()
 
     return eval_m
 
@@ -362,12 +364,12 @@ def digest_flat_df(flat_df):
     5    E2  N1      Ex2   0.0       0.000
     6    E2  N2      Ex1   NaN         NaN
     7    E2  N2      Ex2   NaN         NaN
-    >>> eval_df[['Eleve', "Nom", "Mark", "Normalized"]]
-      Eleve Nom  Mark  Normalized
-    0    E1  N1   5.0    0.833333
-    1    E1  N2   3.0    0.750000
-    2    E2  N1   0.0    0.000000
-    3    E2  N2   NaN         NaN
+    >>> eval_df
+       index Eleve Nom Trimestre  Bareme        Date  Mark  Normalized
+    0      0    E1  N1         1     6.0  16/09/2016   5.0    0.833333
+    1      1    E2  N1         1     6.0  16/09/2016   0.0    0.000000
+    2      0    E1  N2         1     4.0  01/10/2016   3.0    0.750000
+    3      1    E2  N2         1     4.0  01/10/2016   NaN         NaN
     """
     df = flat_df.copy()
     df["Mark"] = compute_marks(flat_df)