diff --git a/Spectacle/Stat_desc.ipynb b/Spectacle/Stat_desc.ipynb
index 47e83f4..a80db6c 100644
--- a/Spectacle/Stat_desc.ipynb
+++ b/Spectacle/Stat_desc.ipynb
@@ -7693,7 +7693,269 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 114,
+   "id": "aa6655c0-c602-4485-8b38-3117227464e1",
+   "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>customer_id</th>\n",
+       "      <th>nb_tickets</th>\n",
+       "      <th>nb_purchases</th>\n",
+       "      <th>total_amount</th>\n",
+       "      <th>nb_suppliers</th>\n",
+       "      <th>vente_internet_max</th>\n",
+       "      <th>purchase_date_min</th>\n",
+       "      <th>purchase_date_max</th>\n",
+       "      <th>time_between_purchase</th>\n",
+       "      <th>nb_tickets_internet</th>\n",
+       "      <th>number_compagny</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>2563.803900</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>764880 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       customer_id  nb_tickets  nb_purchases  total_amount  nb_suppliers  \\\n",
+       "0            19482          88            29         872.0             2   \n",
+       "1            19484           3             2          62.0             1   \n",
+       "2            19485         131            21        1878.0             2   \n",
+       "3            19486          10             4          96.0             1   \n",
+       "4            19487           2             1          33.0             1   \n",
+       "...            ...         ...           ...           ...           ...   \n",
+       "99580      6884747           2             1          40.0             1   \n",
+       "99581      6884748           2             1          40.0             1   \n",
+       "99582      6884750           4             1          80.0             1   \n",
+       "99583      6884751           2             1          40.0             1   \n",
+       "99584      6884753           2             1          40.0             1   \n",
+       "\n",
+       "       vente_internet_max  purchase_date_min  purchase_date_max  \\\n",
+       "0                       1        2643.092500         718.149398   \n",
+       "1                       0        1745.021736        1743.045035   \n",
+       "2                       1        2649.044745          85.240845   \n",
+       "3                       0        1944.077604        1742.794225   \n",
+       "4                       0        1742.877766        1742.877766   \n",
+       "...                   ...                ...                ...   \n",
+       "99580                   0           0.193750           0.193750   \n",
+       "99581                   0           0.186806           0.186806   \n",
+       "99582                   0           0.136111           0.136111   \n",
+       "99583                   0           0.122917           0.122917   \n",
+       "99584                   0           0.047222           0.047222   \n",
+       "\n",
+       "       time_between_purchase  nb_tickets_internet  number_compagny  \n",
+       "0                1924.943102                  8.0               10  \n",
+       "1                   1.976701                  0.0               10  \n",
+       "2                2563.803900                 84.0               10  \n",
+       "3                 201.283380                  0.0               10  \n",
+       "4                   0.000000                  0.0               10  \n",
+       "...                      ...                  ...              ...  \n",
+       "99580               0.000000                  0.0               14  \n",
+       "99581               0.000000                  0.0               14  \n",
+       "99582               0.000000                  0.0               14  \n",
+       "99583               0.000000                  0.0               14  \n",
+       "99584               0.000000                  0.0               14  \n",
+       "\n",
+       "[764880 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "products_purchased_reduced_spectacle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be04e2f9-60b9-4b44-ab36-06a365b21e32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Stat sur les canaux de vente"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
    "id": "20a70ec0-38f6-470e-a442-7884a150613a",
    "metadata": {},
    "outputs": [
@@ -7710,14 +7972,196 @@
    ],
    "source": [
     "#Repartition du nombre de canaux de vente selon les entreprise\n",
+    "\n",
+    "# Filtrer les données pour inclure uniquement les valeurs positives de total_amount et exclusion des valeur aberrantes\n",
+    "purchase_canaux = products_purchased_reduced_spectacle[(products_purchased_reduced_spectacle['nb_tickets'] > 0) ]\n",
+    "\n",
     "plt.figure(figsize=(8, 6))\n",
-    "sns.barplot(x='number_compagny', y='nb_suppliers', data=products_purchased_reduced_spectacle, ci=None)  # ci=None pour ne pas afficher les intervalles de confiance\n",
+    "sns.barplot(x='number_compagny', y='nb_suppliers', data=purchase_canaux, ci=None)  # ci=None pour ne pas afficher les intervalles de confiance\n",
     "plt.title('Nombre moyen de canaux de vente par entreprise')\n",
     "plt.xlabel('number_compagny')\n",
     "plt.ylabel('Nombre moyen de caneaux ')\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "ee901539-37d1-4dfa-8e78-38e4947c3d35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    697297.000000\n",
+       "mean          0.110917\n",
+       "std           0.319561\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%           0.000000\n",
+       "max           8.000000\n",
+       "Name: nb_suppliers, dtype: float64"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_set_spectacle[\"nb_suppliers\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "7389053e-54ae-4167-9afd-aa5d194822ef",
+   "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>number_company</th>\n",
+       "      <th>y_has_purchased</th>\n",
+       "      <th>nb_suppliers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.118250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.340136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.033992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.155239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.153296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.220174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.007711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.083750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>14</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   number_company  y_has_purchased  nb_suppliers\n",
+       "0              10              0.0      1.118250\n",
+       "1              10              1.0      1.340136\n",
+       "2              11              0.0      1.033992\n",
+       "3              11              1.0      1.155239\n",
+       "4              12              0.0      0.153296\n",
+       "5              12              1.0      0.220174\n",
+       "6              13              0.0      1.007711\n",
+       "7              13              1.0      1.083750\n",
+       "8              14              0.0      1.000000\n",
+       "9              14              1.0      1.000000"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#repartition des client selon le nombre moyen de canaux utilisé pour l'achat de ticket  par  compagnie sur base de train\n",
+    "\n",
+    "#purchase_train_canaux = train_set_spectacle[(train_set_spectacle['nb_tickets'] > 0) ]\n",
+    "\n",
+    "purchase_train_canaux_filtered= purchase_train_canaux.groupby([\"number_company\", \"y_has_purchased\"])[\"nb_suppliers\"].mean().reset_index()\n",
+    "purchase_train_canaux_filtered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "e4079e46-db8b-4a25-9da6-37b1405c57d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multiple_barplot(purchase_train_canaux_filtered, x=\"number_company\", y=\"nb_suppliers\", var_labels=\"y_has_purchased\",\n",
+    "                dico_labels = {0 : \"clients n'ayant pas acheté\", 1 : \"clients ayant acheté sur la période\"},\n",
+    "                xlabel = \"Numéro de compagnie\", ylabel = \"Nombre moyen de canaux d'achat\", \n",
+    "                title = \"Nombre moyen de canaux d'acht selon y_has_purchased par  compagnies de spectacle (train set)\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b9e84af4-a02b-4f83-81ae-b7a73475d060",