diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab7-Data-Analysis-Agent/Lab7-Data-Analysis-Agent.ipynb b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab7-Data-Analysis-Agent/Lab7-Data-Analysis-Agent.ipynb
index 764b59c986..2f4163b573 100644
--- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab7-Data-Analysis-Agent/Lab7-Data-Analysis-Agent.ipynb
+++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab7-Data-Analysis-Agent/Lab7-Data-Analysis-Agent.ipynb
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@@ -463,16 +463,16 @@
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@@ -528,10 +528,10 @@
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@@ -539,7 +539,7 @@
    "source": [
     "### Question d'exploration : Valeurs uniques\n",
     "\n",
-    "Explorons maintenant les catégories de produits disponibles. Cette question teste la capacite de l'agent a identifier et utiliser les méthodes appropriees de Pandas (comme `.unique()` ou `.nunique()`).\n"
+    "Explorons maintenant les dates des transactions. Cette question teste la capacite de l'agent a identifier et utiliser les méthodes appropriees de Pandas (comme `.unique()` ou `.nunique()`).\n"
    ]
   },
   {
@@ -548,16 +548,16 @@
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@@ -576,25 +576,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32;1m\u001b[1;3mTu peux utiliser pandas. Exemple :\n",
-      "\n",
-      "Code simple (retourne un tableau numpy) :\n",
-      "```python\n",
-      "df['categorie'].unique()\n",
-      "```\n",
-      "\n",
-      "Pour obtenir une liste Python sans valeurs nulles :\n",
-      "```python\n",
-      "df['categorie'].dropna().unique().tolist()\n",
-      "```\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "Invoking: `python_repl_ast` with `{'query': \"import pandas as pd\\npd.to_datetime(df['date']).dt.date.nunique()\"}`\n",
       "\n",
-      "Ou, pour une liste triée et sans doublons :\n",
-      "```python\n",
-      "sorted(df['categorie'].dropna().unique().tolist())\n",
-      "```\n",
       "\n",
-      "D'après l'extrait affiché, les catégories uniques sont :\n",
-      "['Electronique', 'Livre', 'Maison']\u001b[0m\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3m4\u001b[0m"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3mIl y a 4 dates uniques dans le jeu de données.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -602,8 +595,8 @@
     {
      "data": {
       "text/plain": [
-       "{'input': 'Liste les catégories de produits uniques.',\n",
-       " 'output': \"Tu peux utiliser pandas. Exemple :\\n\\nCode simple (retourne un tableau numpy) :\\n```python\\ndf['categorie'].unique()\\n```\\n\\nPour obtenir une liste Python sans valeurs nulles :\\n```python\\ndf['categorie'].dropna().unique().tolist()\\n```\\n\\nOu, pour une liste triée et sans doublons :\\n```python\\nsorted(df['categorie'].dropna().unique().tolist())\\n```\\n\\nD'après l'extrait affiché, les catégories uniques sont :\\n['Electronique', 'Livre', 'Maison']\"}"
+       "{'input': 'Combien y a-t-il de dates uniques dans les données ?',\n",
+       " 'output': 'Il y a 4 dates uniques dans le jeu de données.'}"
       ]
      },
      "execution_count": 6,
@@ -612,7 +605,7 @@
     }
    ],
    "source": [
-    "agent_executor.invoke({\"input\": \"Liste les catégories de produits uniques.\"})"
+    "agent_executor.invoke({\"input\": \"Combien y a-t-il de dates uniques dans les données ?\"})"
    ]
   },
   {
@@ -620,10 +613,10 @@
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@@ -640,16 +633,16 @@
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      "exception": false,
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@@ -669,11 +662,13 @@
      "output_type": "stream",
      "text": [
       "\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `python_repl_ast` with `{'query': \"result = df.groupby('categorie')['chiffre_affaires'].sum().sort_values(ascending=False)\\nresult.head(1)\"}`\n",
+      "Invoking: `python_repl_ast` with `{'query': \"df.groupby('categorie')['chiffre_affaires'].sum().sort_values(ascending=False)\"}`\n",
       "\n",
       "\n",
       "\u001b[0m\u001b[36;1m\u001b[1;3mcategorie\n",
       "Electronique    1440.0\n",
+      "Livre            248.0\n",
+      "Maison           151.8\n",
       "Name: chiffre_affaires, dtype: float64\u001b[0m"
      ]
     },
@@ -681,12 +676,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32;1m\u001b[1;3mLa catégorie la plus rentable est \"Electronique\" — elle a généré 1440.0 en chiffre d'affaires.\n",
-      "\n",
-      "Commande utilisée :\n",
-      "df.groupby('categorie')['chiffre_affaires'].sum().sort_values(ascending=False)\n",
-      "\n",
-      "Voulez-vous le détail complet par catégorie ?\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mLa catégorie qui a généré le plus de chiffre d'affaires est \"Electronique\" — total = 1440.0 (unités monétaires).\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -695,7 +685,7 @@
      "data": {
       "text/plain": [
        "{'input': \"Quelle est la catégorie qui a généré le plus de chiffre d'affaires ?\",\n",
-       " 'output': 'La catégorie la plus rentable est \"Electronique\" — elle a généré 1440.0 en chiffre d\\'affaires.\\n\\nCommande utilisée :\\ndf.groupby(\\'categorie\\')[\\'chiffre_affaires\\'].sum().sort_values(ascending=False)\\n\\nVoulez-vous le détail complet par catégorie ?'}"
+       " 'output': 'La catégorie qui a généré le plus de chiffre d\\'affaires est \"Electronique\" — total = 1440.0 (unités monétaires).'}"
       ]
      },
      "execution_count": 7,
@@ -712,10 +702,10 @@
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