Ajout graphiques sur targets

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)
@@ -67,4 +67,6 @@ tickets_internet(tickets, type_of_activity)
 
 already_bought_online(tickets, type_of_activity)
 
-box_plot_price_tickets(tickets, type_of_activity)
+box_plot_price_tickets(tickets, type_of_activity)
+
+target_description(targets, type_of_activity)

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 +50,8 @@ 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') 
+        df_target_KPI["customer_id"]= directory_path + '_' +  df_target_KPI['customer_id'].astype('str')
+
     
     # Concaténation
         customer = pd.concat([customer, df_customerplus_clean], ignore_index=True)
@@ -49,8 +59,9 @@ def load_files(nb_compagnie):
         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 save_file_s3(File_name, type_of_activity):
@@ -356,3 +367,34 @@ 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)
+
+    
+