diff --git a/notes_tools/tools/df_marks_manip.py b/notes_tools/tools/df_marks_manip.py
index 64e12e6..64f0967 100644
--- a/notes_tools/tools/df_marks_manip.py
+++ b/notes_tools/tools/df_marks_manip.py
@@ -4,6 +4,8 @@
 import pandas as pd
 import numpy as np
 from math import ceil
+import logging
+logger = logging.getLogger(__name__)
 
 # Values manipulations
 
@@ -304,9 +306,11 @@ def compute_eval_marks(df):
 
     eval_m = pd.DataFrame()
     for eval_name in df["Nom"].unique():
+        logger.debug(f"Compute marks for {eval_name}")
         eval_df = df[df["Nom"] == eval_name]
         dates = eval_df["Date"].unique()
-        if len(dates) > 1:
+        logger.debug(f"Find those dates: {dates}")
+        if len(dates) > 1 or dates[0] == "Trimestre":
             # Les devoirs sur la durée, les NaN ne sont pas pénalisants
             # On les enlèves
             eval_df = eval_df.dropna(subset=["Mark"])
@@ -371,9 +375,10 @@ def digest_flat_df(flat_df):
     2      0    E1  N2         1     4.0  01/10/2016   3.0        0.75
     3      1    E2  N2         1     4.0  01/10/2016   NaN         NaN
     """
-    df = flat_df.copy()
-    df["Mark"] = compute_marks(flat_df)
-    df["Latex_rep"] = compute_latex_rep(flat_df)
+    # Remove data with "nn" (non notés)
+    df = flat_df.copy()[flat_df["Note"] != "nn"]
+    df["Mark"] = compute_marks(df)
+    df["Latex_rep"] = compute_latex_rep(df)
     df["Normalized"] = compute_normalized(df)
     df["Uniq_quest"] = compute_question_description(df)