diff --git a/Clean-Notebook.ipynb b/Exploration_billet_AJ.ipynb
similarity index 97%
rename from Clean-Notebook.ipynb
rename to Exploration_billet_AJ.ipynb
index 1f70494..f8931c2 100644
--- a/Clean-Notebook.ipynb
+++ b/Exploration_billet_AJ.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 1,
    "id": "15103481-8d74-404c-aa09-7601fe7730da",
    "metadata": {},
    "outputs": [],
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "id": "5d83bb1a-d341-446e-91f6-1c428607f6d4",
    "metadata": {},
    "outputs": [],
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 3,
    "id": "699664b9-eee4-4f8d-a207-e524526560c5",
    "metadata": {},
    "outputs": [],
@@ -84,7 +84,7 @@
     }
    ],
    "source": [
-    "liste_database_select = ['suppliers', 'ticket', 'purchase', 'consumption', 'customer', 'event', 'target', 'prod', 'campa']\n",
+    "liste_database_select = ['suppliers', 'ticket', 'purchase', 'consumption', 'type_ofs']\n",
     "\n",
     "# Filtrer la liste pour les éléments contenant au moins un élément de la liste à tester\n",
     "liste_database_filtered = [element for element in liste_database if any(element_part in element for element_part in liste_database_select)]\n",
@@ -124,50 +124,6 @@
     "        globals()[nom_dataframe] = df"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "7d1da9df-f423-4a9f-a2a6-6d8ceeab1c34",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_\n",
-      "__\n",
-      "___\n",
-      "df\n",
-      "df1_purchases\n",
-      "df1_suppliers\n",
-      "df1_tickets\n",
-      "dataframe\n",
-      "_7\n",
-      "_10\n",
-      "_11\n",
-      "_18\n",
-      "_20\n",
-      "df1_customer_target_mappings\n",
-      "df1_customersplus\n",
-      "df1_event_types\n",
-      "df1_events\n",
-      "df1_target_types\n",
-      "df1_targets\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Obtenir toutes les variables globales\n",
-    "variables_globales = globals()\n",
-    "\n",
-    "# Filtrer les variables pour obtenir uniquement les DataFrames\n",
-    "dataframes = {nom: variable for nom, variable in variables_globales.items() if isinstance(variable, pd.DataFrame)}\n",
-    "\n",
-    "# Afficher les noms et les DataFrames\n",
-    "for nom, dataframe in dataframes.items():\n",
-    "    print(f\"{nom}\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "78453f3c-4f89-44ed-a6c6-2a7443b72b52",
@@ -407,97 +363,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "6d7f338e-e4d3-422b-9cdc-dec967c0b28e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>name</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>manually_added</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>label</th>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>itr</th>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>updated_at</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>created_at</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>commission</th>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>identifier</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    0\n",
-       "id                0.0\n",
-       "name              0.0\n",
-       "manually_added    0.0\n",
-       "label           100.0\n",
-       "itr             100.0\n",
-       "updated_at        0.0\n",
-       "created_at        0.0\n",
-       "commission      100.0\n",
-       "identifier        0.0"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "pd.DataFrame(df1_suppliers.isna().mean()*100)"
+    "df1_suppliers.isna().mean()*100"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c645ab7-16bf-4054-9ae2-15a8c32e29c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "676a9869-9a8b-4cd2-8b1c-0644b5229c72",