From 35638f2a2d625c3d9cb80ecd670307ee7a0be2ec Mon Sep 17 00:00:00 2001
From: ajoubrel-ensae <antoine.joubrel@ensae.fr>
Date: Sun, 31 Mar 2024 16:34:55 +0000
Subject: [PATCH] =?UTF-8?q?Passage=20=C3=A0=20demande=20input=20=C3=A0=20b?=
 =?UTF-8?q?oucle=20sur=20activite?=
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---
 4_Descriptive_Statistics.py | 104 ++++++++++++++++++------------------
 1 file changed, 53 insertions(+), 51 deletions(-)

diff --git a/4_Descriptive_Statistics.py b/4_Descriptive_Statistics.py
index eb67195..45af7ee 100644
--- a/4_Descriptive_Statistics.py
+++ b/4_Descriptive_Statistics.py
@@ -21,54 +21,56 @@ companies = {'musee' : ['1', '2', '3', '4'], # , '101'
             'musique' : ['10', '11', '12', '13', '14']}
 
 
-type_of_activity = input('Choisissez le type de compagnie : sport ? musique ? musee ?')
-list_of_comp = companies[type_of_activity] 
-
-# Load files
-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)
-
-# Identify valid customer (customer who bought tickets after starting date or received mails after starting date)
-customer_valid_list = valid_customer_detection(products, campaigns_brut)
-
-databases = [customer, campaigns_kpi, campaigns_brut, tickets, products]
-
-for dataset in databases:
-    dataset['customer_id'] = dataset['customer_id'].apply(lambda x: remove_elements(x, outlier_list))# remove outlier
-    dataset = dataset[dataset['customer_id'].isin(customer_valid_list)] # keep only valid customer
-    #print(f'shape of {dataset} : ', dataset.shape)
-
-# Identify customer who bought during the period of y
-customer_target_period = identify_purchase_during_target_periode(products)
-customer['has_purchased_target_period'] = np.where(customer['customer_id'].isin(customer_target_period), 1, 0)
-
-# Generate graph and automatically saved them in the bucket
-compute_nb_clients(customer, type_of_activity)
-
-#maximum_price_paid(customer, type_of_activity)
-
-target_proportion(customer, type_of_activity)
-
-mailing_consent(customer, type_of_activity)
-
-mailing_consent_by_target(customer)
-
-gender_bar(customer, type_of_activity)
-
-country_bar(customer, type_of_activity)
-
-lazy_customer_plot(campaigns_kpi, type_of_activity)
-
-campaigns_effectiveness(customer, type_of_activity)
-
-sale_dynamics(products, campaigns_brut, type_of_activity)
-
-tickets_internet(tickets, type_of_activity)
-
-already_bought_online(tickets, type_of_activity)
-
-box_plot_price_tickets(tickets, type_of_activity)
-
-target_description(targets, type_of_activity)
\ No newline at end of file
+# type_of_activity = input('Choisissez le type de compagnie : sport ? musique ? musee ?')
+for type_of_activity in ['musee', 'sport', 'musique'] :
+    
+    list_of_comp = companies[type_of_activity] 
+    
+    # Load files
+    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)
+    
+    # Identify valid customer (customer who bought tickets after starting date or received mails after starting date)
+    customer_valid_list = valid_customer_detection(products, campaigns_brut)
+    
+    databases = [customer, campaigns_kpi, campaigns_brut, tickets, products]
+    
+    for dataset in databases:
+        dataset['customer_id'] = dataset['customer_id'].apply(lambda x: remove_elements(x, outlier_list))# remove outlier
+        dataset = dataset[dataset['customer_id'].isin(customer_valid_list)] # keep only valid customer
+        #print(f'shape of {dataset} : ', dataset.shape)
+    
+    # Identify customer who bought during the period of y
+    customer_target_period = identify_purchase_during_target_periode(products)
+    customer['has_purchased_target_period'] = np.where(customer['customer_id'].isin(customer_target_period), 1, 0)
+    
+    # Generate graph and automatically saved them in the bucket
+    compute_nb_clients(customer, type_of_activity)
+    
+    #maximum_price_paid(customer, type_of_activity)
+    
+    target_proportion(customer, type_of_activity)
+    
+    mailing_consent(customer, type_of_activity)
+    
+    mailing_consent_by_target(customer)
+    
+    gender_bar(customer, type_of_activity)
+    
+    country_bar(customer, type_of_activity)
+    
+    lazy_customer_plot(campaigns_kpi, type_of_activity)
+    
+    campaigns_effectiveness(customer, type_of_activity)
+    
+    sale_dynamics(products, campaigns_brut, type_of_activity)
+    
+    tickets_internet(tickets, type_of_activity)
+    
+    already_bought_online(tickets, type_of_activity)
+    
+    box_plot_price_tickets(tickets, type_of_activity)
+    
+    target_description(targets, type_of_activity)
\ No newline at end of file