diff --git a/0_4_Generate_stat_desc.py b/0_4_Generate_stat_desc.py
index 160e568..a675066 100644
--- a/0_4_Generate_stat_desc.py
+++ b/0_4_Generate_stat_desc.py
@@ -25,7 +25,7 @@ type_of_activity = input('Choisissez le type de compagnie : sport ? musique ? mu
 list_of_comp = companies[type_of_activity] 
 
 # Load files
-customer, campaigns_kpi, campaigns_brut, tickets, products = load_files(list_of_comp)
+customer, campaigns_kpi, campaigns_brut, tickets, products, targets = load_files(list_of_comp)
 
 # Identify anonymous customer for each company and remove them from our datasets
 outlier_list = outlier_detection(tickets, list_of_comp)
@@ -69,4 +69,6 @@ tickets_internet(tickets, type_of_activity)
 
 already_bought_online(tickets, type_of_activity)
 
-box_plot_price_tickets(tickets, type_of_activity)
\ No newline at end of file
+box_plot_price_tickets(tickets, type_of_activity)
+
+target_description(targets, type_of_activity)
\ No newline at end of file
diff --git a/Exploration_billet_AJ.ipynb b/Exploration_billet_AJ.ipynb
index 886aaae..f149f5a 100644
--- a/Exploration_billet_AJ.ipynb
+++ b/Exploration_billet_AJ.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 22,
    "id": "b1a5b9d3",
    "metadata": {},
    "outputs": [],
@@ -20,7 +20,9 @@
     "import os\n",
     "import s3fs\n",
     "import re\n",
-    "import warnings"
+    "import warnings\n",
+    "import io\n",
+    "import matplotlib.pyplot as plt\n"
    ]
   },
   {
@@ -97,6 +99,62 @@
     "        df.to_csv(file_out, index = False)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "108fc5ef-c56a-4f03-a867-943d9d6492fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_file_s3(File_name, type_of_activity):\n",
+    "    image_buffer = io.BytesIO()\n",
+    "    plt.savefig(image_buffer, format='png')\n",
+    "    image_buffer.seek(0)\n",
+    "    FILE_PATH = f\"projet-bdc2324-team1/stat_desc/{type_of_activity}/\"\n",
+    "    FILE_PATH_OUT_S3 = FILE_PATH + File_name + type_of_activity + '.png'\n",
+    "    with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:\n",
+    "        s3_file.write(image_buffer.read())\n",
+    "    plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "c99b9cb7-00ab-41cf-bde7-38676f5a3d02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def taux_partner(campany_nb) :\n",
+    "\n",
+    "    is_partner = load_dataset_2(campany_nb, 'customersplus')[['is_partner']].astype(int)\n",
+    "    percentage_partner = (is_partner['is_partner'].mean()) * 100\n",
+    "    \n",
+    "    return percentage_partner\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "6facc27e-f95d-49c5-afe0-8c34b3a0cb94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 0\n",
+    "for nb in [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\"]:\n",
+    "    a += taux_partner(nb)\n",
+    "\n",
+    "print(a/14)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ccf597b0-b459-4ea5-baf0-5ba8c90915e4",
@@ -573,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 47,
    "id": "9d224485-3472-4cc7-9825-1a643bc94fef",
    "metadata": {},
    "outputs": [
@@ -581,16 +639,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "File path :  projet-bdc2324-team1/0_Input/Company_5/customerplus_cleaned.csv\n",
-      "File path :  projet-bdc2324-team1/0_Input/Company_5/target_information.csv\n",
-      "File path :  projet-bdc2324-team1/0_Input/Company_6/customerplus_cleaned.csv\n",
-      "File path :  projet-bdc2324-team1/0_Input/Company_6/target_information.csv\n",
-      "File path :  projet-bdc2324-team1/0_Input/Company_7/customerplus_cleaned.csv\n",
-      "File path :  projet-bdc2324-team1/0_Input/Company_7/target_information.csv\n",
-      "File path :  projet-bdc2324-team1/0_Input/Company_8/customerplus_cleaned.csv\n",
-      "File path :  projet-bdc2324-team1/0_Input/Company_8/target_information.csv\n",
-      "File path :  projet-bdc2324-team1/0_Input/Company_9/customerplus_cleaned.csv\n",
-      "File path :  projet-bdc2324-team1/0_Input/Company_9/target_information.csv\n"
+      "File path :  projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv\n",
+      "File path :  projet-bdc2324-team1/0_Input/Company_10/target_information.csv\n",
+      "File path :  projet-bdc2324-team1/0_Input/Company_11/customerplus_cleaned.csv\n",
+      "File path :  projet-bdc2324-team1/0_Input/Company_11/target_information.csv\n",
+      "File path :  projet-bdc2324-team1/0_Input/Company_12/customerplus_cleaned.csv\n",
+      "File path :  projet-bdc2324-team1/0_Input/Company_12/target_information.csv\n",
+      "File path :  projet-bdc2324-team1/0_Input/Company_13/customerplus_cleaned.csv\n",
+      "File path :  projet-bdc2324-team1/0_Input/Company_13/target_information.csv\n",
+      "File path :  projet-bdc2324-team1/0_Input/Company_14/customerplus_cleaned.csv\n",
+      "File path :  projet-bdc2324-team1/0_Input/Company_14/target_information.csv\n"
      ]
     }
    ],
@@ -599,7 +657,7 @@
     "            'sport': ['5', '6', '7', '8', '9'],\n",
     "            'musique' : ['10', '11', '12', '13', '14']}\n",
     "\n",
-    "nb_compagnie = companies['sport']\n",
+    "nb_compagnie = companies['musique']\n",
     "\n",
     "def load_files(nb_compagnie):\n",
     "    targets = pd.DataFrame()\n",
@@ -632,13 +690,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 48,
    "id": "3c911274-0ebd-49af-9487-26524ba20e74",
    "metadata": {},
    "outputs": [],
    "source": [
     "\n",
-    "def target_description(targets):\n",
+    "def target_description(targets, type_of_activity):\n",
     "\n",
     "    describe_target = targets.groupby('number_company').agg(\n",
     "        prop_target_jeune=('target_jeune', lambda x: (x.sum() / x.count())*100),\n",
@@ -665,12 +723,13 @@
     "    # Adding a legend\n",
     "    plot.legend([\"Youth\", \"School\", \"Enterprise\", \"Family\", \"Optin\", \"Optout\", \"Newsletter\", \"Subscriber\"], title=\"Target Category\")\n",
     "\n",
+    "    # save_file_s3(\"target_category_proportion_\", type_of_activity)\n",
     "    return plot"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 49,
    "id": "af62ecef-9120-4107-af3e-512588a96800",
    "metadata": {},
    "outputs": [
@@ -680,13 +739,13 @@
        "<Axes: title={'center': 'Distribution of Targets by Category'}, xlabel='Company Number', ylabel='Target Proportion'>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -696,7 +755,7 @@
     }
    ],
    "source": [
-    "target_description(targets)"
+    "target_description(targets, 'musique')"
    ]
   },
   {
diff --git a/utils_stat_desc.py b/utils_stat_desc.py
index 469569a..681e623 100644
--- a/utils_stat_desc.py
+++ b/utils_stat_desc.py
@@ -16,6 +16,7 @@ def load_files(nb_compagnie):
     campaigns_kpi = pd.DataFrame()
     products = pd.DataFrame()
     tickets = pd.DataFrame()
+    targets = pd.DataFrame()
     
     # début de la boucle permettant de générer des datasets agrégés pour les 5 compagnies de spectacle
     for directory_path in nb_compagnie:
@@ -27,14 +28,21 @@ def load_files(nb_compagnie):
         df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_brut, max_date=pd.Timestamp.now(tz='UTC')) 
         df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)
         df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0)
-    
-            
+        df_target_KPI = targets_KPI(df_target = df_target_information)
+
+        # Merge and 
+        df_target_KPI = pd.merge(df_customerplus_clean_0[['customer_id']], df_target_KPI, how = 'left', on = 'customer_id')
+        targets_columns = list(df_target_KPI.columns)
+        targets_columns.remove('customer_id')
+        df_target_KPI[targets_columns] = df_target_KPI[targets_columns].fillna(0)
+        
     # creation de la colonne Number compagnie, qui permettra d'agréger les résultats
         df_tickets_kpi["number_company"]=int(directory_path)
         df_campaigns_brut["number_company"]=int(directory_path)
         df_campaigns_kpi["number_company"]=int(directory_path)
         df_customerplus_clean["number_company"]=int(directory_path)
         df_target_information["number_company"]=int(directory_path)
+        df_target_KPI["number_company"]=int(directory_path)
     
     # Traitement des index
         df_tickets_kpi["customer_id"]= directory_path + '_' +  df_tickets_kpi['customer_id'].astype('str')
@@ -42,6 +50,7 @@ def load_files(nb_compagnie):
         df_campaigns_kpi["customer_id"]= directory_path + '_' +  df_campaigns_kpi['customer_id'].astype('str') 
         df_customerplus_clean["customer_id"]= directory_path + '_' +  df_customerplus_clean['customer_id'].astype('str') 
         df_products_purchased_reduced["customer_id"]= directory_path + '_' +  df_products_purchased_reduced['customer_id'].astype('str') 
+<<<<<<< HEAD
         
     # Remove companies' outliers
         df_tickets_kpi = remove_outlier_total_amount(df_tickets_kpi)
@@ -50,14 +59,20 @@ def load_files(nb_compagnie):
         for dataset in [df_campaigns_brut, df_campaigns_kpi, df_customerplus_clean, df_target_information]:
             dataset = dataset[dataset['customer_id'].isin(customer_id)]
         
+=======
+        df_target_KPI["customer_id"]= directory_path + '_' +  df_target_KPI['customer_id'].astype('str')
+
+    
+>>>>>>> main
     # Concaténation
         customer = pd.concat([customer, df_customerplus_clean], ignore_index=True)
         campaigns_kpi = pd.concat([campaigns_kpi, df_campaigns_kpi], ignore_index=True)
         campaigns_brut = pd.concat([campaigns_brut, df_campaigns_brut], ignore_index=True) 
         tickets = pd.concat([tickets, df_tickets_kpi], ignore_index=True)
         products = pd.concat([products, df_products_purchased_reduced], ignore_index=True)
+        targets = pd.concat([targets, df_target_KPI], ignore_index=True)
 
-    return customer, campaigns_kpi, campaigns_brut, tickets, products
+    return customer, campaigns_kpi, campaigns_brut, tickets, products, targets
 
 
 def remove_outlier_total_amount(tickets):
@@ -390,3 +405,34 @@ def box_plot_price_tickets(tickets, type_of_activity):
     plt.show()
     save_file_s3("box_plot_price_tickets_", type_of_activity)
 
+def target_description(targets, type_of_activity):
+
+    describe_target = targets.groupby('number_company').agg(
+        prop_target_jeune=('target_jeune', lambda x: (x.sum() / x.count())*100),
+        prop_target_scolaire=('target_scolaire', lambda x: (x.sum() / x.count())*100),
+        prop_target_entreprise=('target_entreprise', lambda x: (x.sum() / x.count())*100),
+        prop_target_famille=('target_famille', lambda x: (x.sum() / x.count())*100),
+        prop_target_optin=('target_optin', lambda x: (x.sum() / x.count())*100),
+        prop_target_optout=('target_optout', lambda x: (x.sum() / x.count())*100),
+        prop_target_newsletter=('target_newsletter', lambda x: (x.sum() / x.count())*100),
+        prop_target_abonne=('target_abonne', lambda x: (x.sum() / x.count())*100))
+
+    plot = describe_target.plot.bar()
+    
+    # Adding a title
+    plot.set_title(f"Distribution of Targets by Category for {type_of_activity} companies")
+    
+    # Adding labels for x and y axes
+    plot.set_xlabel("Company Number")
+    plot.set_ylabel("Target Proportion")
+
+    plot.set_xticklabels(plot.get_xticklabels(), rotation=0, horizontalalignment='center')
+
+    
+    # Adding a legend
+    plot.legend(["Youth", "School", "Enterprise", "Family", "Optin", "Optout", "Newsletter", "Subscriber"], title="Target Category")
+
+    save_file_s3("target_category_proportion_", type_of_activity)
+
+    
+