import pandas as pd import os import s3fs import io import warnings from datetime import date, timedelta, datetime import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates import seaborn as sns def load_files(nb_compagnie): customer = pd.DataFrame() campaigns_brut = pd.DataFrame() campaigns_kpi = pd.DataFrame() products = pd.DataFrame() tickets = pd.DataFrame() # début de la boucle permettant de générer des datasets agrégés pour les 5 compagnies de spectacle for directory_path in nb_compagnie: df_customerplus_clean_0 = display_input_databases(directory_path, file_name = "customerplus_cleaned") df_campaigns_brut = display_input_databases(directory_path, file_name = "campaigns_information", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at']) df_products_purchased_reduced = display_input_databases(directory_path, file_name = "products_purchased_reduced", datetime_col = ['purchase_date']) df_target_information = display_input_databases(directory_path, file_name = "target_information") 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) # 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) # Traitement des index df_tickets_kpi["customer_id"]= directory_path + '_' + df_tickets_kpi['customer_id'].astype('str') df_campaigns_brut["customer_id"]= directory_path + '_' + df_campaigns_brut['customer_id'].astype('str') 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') # 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) return customer, campaigns_kpi, campaigns_brut, tickets, products def save_file_s3(File_name, type_of_activity): image_buffer = io.BytesIO() plt.savefig(image_buffer, format='png') image_buffer.seek(0) FILE_PATH = f"projet-bdc2324-team1/stat_desc/{type_of_activity}/" FILE_PATH_OUT_S3 = FILE_PATH + File_name + type_of_activity + '.png' with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file: s3_file.write(image_buffer.read()) plt.close() def outlier_detection(tickets, company_list, show_diagram=False): outlier_list = list() for company in company_list: total_amount_share = tickets[tickets['number_company']==int(company)].groupby('customer_id')['total_amount'].sum().reset_index() total_amount_share['CA'] = total_amount_share['total_amount'].sum() total_amount_share['share_total_amount'] = total_amount_share['total_amount']/total_amount_share['CA'] total_amount_share_index = total_amount_share.set_index('customer_id') df_circulaire = total_amount_share_index['total_amount'].sort_values(axis = 0, ascending = False) #print('df circulaire : ', df_circulaire.head()) top = df_circulaire[:1] #print('top : ', top) outlier_list.append(top.index[0]) rest = df_circulaire[1:] rest_sum = rest.sum() new_series = pd.concat([top, pd.Series([rest_sum], index=['Autre'])]) if show_diagram: plt.figure(figsize=(3, 3)) plt.pie(new_series, labels=new_series.index, autopct='%1.1f%%', startangle=140, pctdistance=0.5) plt.axis('equal') plt.title(f'Répartition des montants totaux pour la compagnie {company}') plt.show() return outlier_list def valid_customer_detection(products, campaigns_brut): products_valid = products[products['purchase_date']>="2021-05-01"] consumer_valid_product = products_valid['customer_id'].to_list() campaigns_valid = campaigns_brut[campaigns_brut["sent_at"]>="2021-05-01"] consumer_valid_campaigns = campaigns_valid['customer_id'].to_list() consumer_valid = consumer_valid_product + consumer_valid_campaigns return consumer_valid def identify_purchase_during_target_periode(products): products_target_period = products[(products['purchase_date']>="2022-11-01") & (products['purchase_date']<="2023-11-01")] customer_target_period = products_target_period['customer_id'].to_list() return customer_target_period def remove_elements(lst, elements_to_remove): return ''.join([x for x in lst if x not in elements_to_remove]) def compute_nb_clients(customer, type_of_activity): company_nb_clients = customer[customer["purchase_count"]>0].groupby("number_company")["customer_id"].count().reset_index() plt.bar(company_nb_clients["number_company"], company_nb_clients["customer_id"]/1000) plt.xlabel('Company') plt.ylabel("Number of clients (thousands)") plt.title(f"Number of clients Across {type_of_activity} Companies") plt.xticks(company_nb_clients["number_company"], ["{}".format(i) for i in company_nb_clients["number_company"]]) plt.show() save_file_s3("nb_clients_", type_of_activity) def maximum_price_paid(customer, type_of_activity): company_max_price = customer.groupby("number_company")["max_price"].max().reset_index() plt.bar(company_max_price["number_company"], company_max_price["max_price"]) plt.xlabel('Company Number') plt.ylabel("Maximal price of a ticket Prix") plt.title(f"Maximal price of a ticket Across {type_of_activity} Companies") plt.xticks(company_max_price["number_company"], ["{}".format(i) for i in company_max_price["number_company"]]) plt.show() save_file_s3("Maximal_price_", type_of_activity) def mailing_consent(customer, type_of_activity): mailing_consent = customer.groupby("number_company")["opt_in"].mean().reset_index() mailing_consent["opt_in"] *= 100 plt.bar(mailing_consent["number_company"], mailing_consent["opt_in"]) plt.xlabel('Company Number') plt.ylabel('Mailing Consent (%)') plt.title(f'Consent of mailing Across {type_of_activity} Companies') plt.xticks(mailing_consent["number_company"], ["{}".format(i) for i in mailing_consent["number_company"]]) plt.show() save_file_s3("mailing_consent_", type_of_activity) def mailing_consent_by_target(customer): df_graph = customer.groupby(["number_company", "has_purchased_target_period"])["opt_in"].mean().reset_index() # Création du barplot groupé fig, ax = plt.subplots(figsize=(10, 6)) categories = df_graph["number_company"].unique() bar_width = 0.35 bar_positions = np.arange(len(categories)) # Grouper les données par label et créer les barres groupées for label in df_graph["has_purchased_target_period"].unique(): label_data = df_graph[df_graph['has_purchased_target_period'] == label] values = [label_data[label_data['number_company'] == category]['opt_in'].values[0]*100 for category in categories] label_printed = "Purchase" if label else "No purchase" ax.bar(bar_positions, values, bar_width, label=label_printed) # Mise à jour des positions des barres pour le prochain groupe bar_positions = [pos + bar_width for pos in bar_positions] # Ajout des étiquettes, de la légende, etc. ax.set_xlabel('Company Number') ax.set_ylabel('Mailing Consent (%)') ax.set_title(f'Consent of mailing according to target Across {type_of_activity} Companies') ax.set_xticks([pos + bar_width / 2 for pos in np.arange(len(categories))]) ax.set_xticklabels(categories) ax.legend() # Affichage du plot plt.show() save_file_s3("mailing_consent_target_", type_of_activity) def gender_bar(customer, type_of_activity): company_genders = customer.groupby("number_company")[["gender_male", "gender_female", "gender_other"]].mean().reset_index() company_genders["gender_male"] *= 100 company_genders["gender_female"] *= 100 company_genders["gender_other"] *= 100 # Création du barplot plt.bar(company_genders["number_company"], company_genders["gender_male"], label = "Male") plt.bar(company_genders["number_company"], company_genders["gender_female"], bottom = company_genders["gender_male"], label = "Female") plt.bar(company_genders["number_company"], company_genders["gender_other"], bottom = company_genders["gender_male"] + company_genders["gender_female"], label = "Unknown") plt.xlabel('Company Number') plt.ylabel("Frequency (%)") plt.title(f"Gender Distribution of Customers Across {type_of_activity} Companies") plt.legend() plt.xticks(company_genders["number_company"], ["{}".format(i) for i in company_genders["number_company"]]) plt.show() save_file_s3("gender_bar_", type_of_activity) def country_bar(customer, type_of_activity): company_country_fr = customer.groupby("number_company")["country_fr"].mean().reset_index() company_country_fr["country_fr"] *= 100 plt.bar(company_country_fr["number_company"], company_country_fr["country_fr"]) plt.xlabel('Company Number') plt.ylabel("Share of French Customer (%)") plt.title(f"Share of French Customer Across {type_of_activity} Companies") plt.xticks(company_country_fr["number_company"], ["{}".format(i) for i in company_country_fr["number_company"]]) plt.show() save_file_s3("country_bar_", type_of_activity) def lazy_customer_plot(campaigns_kpi, type_of_activity): company_lazy_customers = campaigns_kpi.groupby("number_company")["nb_campaigns_opened"].mean().reset_index() plt.bar(company_lazy_customers["number_company"], company_lazy_customers["nb_campaigns_opened"]) plt.xlabel('Company Number') plt.title(f"Share of Customers who did not Open Mail Across {type_of_activity} Companies") plt.xticks(company_lazy_customers["number_company"], ["{}".format(i) for i in company_lazy_customers["number_company"]]) plt.show() save_file_s3("lazy_customer_", type_of_activity) def campaigns_effectiveness(customer, type_of_activity): campaigns_effectiveness = customer.groupby(["number_company", "has_purchased_target_period"])["opt_in"].mean().reset_index() fig, ax = plt.subplots(figsize=(10, 6)) categories = campaigns_effectiveness["number_company"].unique() bar_width = 0.35 bar_positions = np.arange(len(categories)) # Grouper les données par label et créer les barres groupées for label in campaigns_effectiveness["has_purchased_target_period"].unique(): label_data = campaigns_effectiveness[campaigns_effectiveness['has_purchased_target_period'] == label] values = [label_data[label_data['number_company'] == category]['opt_in'].values[0]*100 for category in categories] label_printed = "Purchase" if label else "No purchase" ax.bar(bar_positions, values, bar_width, label=label_printed) # Mise à jour des positions des barres pour le prochain groupe bar_positions = [pos + bar_width for pos in bar_positions] # Ajout des étiquettes, de la légende, etc. ax.set_xlabel('Company Number') ax.set_ylabel('Share of Consent (%)') ax.set_title(f"Proportion of customers who have given their consent to receive emails, by customer class ({type_of_activity} companies)") ax.set_xticks([pos + bar_width / 2 for pos in np.arange(len(categories))]) ax.set_xticklabels(categories) ax.legend() plt.show() save_file_s3("campaigns_effectiveness_", type_of_activity) def sale_dynamics(products, campaigns_brut, type_of_activity): purchase_min = products.groupby(['customer_id'])['purchase_date'].min().reset_index() purchase_min.rename(columns = {'purchase_date' : 'first_purchase_event'}, inplace = True) purchase_min['first_purchase_event'] = pd.to_datetime(purchase_min['first_purchase_event']) purchase_min['first_purchase_month'] = pd.to_datetime(purchase_min['first_purchase_event'].dt.strftime('%Y-%m')) # Mois du premier mails first_mail_received = campaigns_brut.groupby('customer_id')['sent_at'].min().reset_index() first_mail_received.rename(columns = {'sent_at' : 'first_email_reception'}, inplace = True) first_mail_received['first_email_reception'] = pd.to_datetime(first_mail_received['first_email_reception']) first_mail_received['first_email_month'] = pd.to_datetime(first_mail_received['first_email_reception'].dt.strftime('%Y-%m')) # Fusion known_customer = pd.merge(purchase_min[['customer_id', 'first_purchase_month']], first_mail_received[['customer_id', 'first_email_month']], on = 'customer_id', how = 'outer') # Mois à partir duquel le client est considere comme connu known_customer['known_date'] = pd.to_datetime(known_customer[['first_email_month', 'first_purchase_month']].min(axis = 1), utc = True, format = 'ISO8601') # Nombre de commande par mois purchases_count = pd.merge(products[['customer_id', 'purchase_id', 'purchase_date']].drop_duplicates(), known_customer[['customer_id', 'known_date']], on = ['customer_id'], how = 'inner') purchases_count['is_customer_known'] = purchases_count['purchase_date'] > purchases_count['known_date'] + pd.DateOffset(months=1) purchases_count['purchase_date_month'] = pd.to_datetime(purchases_count['purchase_date'].dt.strftime('%Y-%m')) purchases_count = purchases_count[purchases_count['customer_id'] != 1] # Nombre de commande par mois par type de client nb_purchases_graph = purchases_count.groupby(['purchase_date_month', 'is_customer_known'])['purchase_id'].count().reset_index() nb_purchases_graph.rename(columns = {'purchase_id' : 'nb_purchases'}, inplace = True) nb_purchases_graph_2 = purchases_count.groupby(['purchase_date_month', 'is_customer_known'])['customer_id'].nunique().reset_index() nb_purchases_graph_2.rename(columns = {'customer_id' : 'nb_new_customer'}, inplace = True) # Graphique en nombre de commande purchases_graph = nb_purchases_graph purchases_graph_used = purchases_graph[purchases_graph["purchase_date_month"] >= datetime(2021,3,1)] purchases_graph_used_0 = purchases_graph_used[purchases_graph_used["is_customer_known"]==False] purchases_graph_used_1 = purchases_graph_used[purchases_graph_used["is_customer_known"]==True] merged_data = pd.merge(purchases_graph_used_0, purchases_graph_used_1, on="purchase_date_month", suffixes=("_new", "_old")) plt.bar(merged_data["purchase_date_month"], merged_data["nb_purchases_new"], width=12, label="New Customers") plt.bar(merged_data["purchase_date_month"], merged_data["nb_purchases_old"], bottom=merged_data["nb_purchases_new"], width=12, label="Existing Customers") # commande pr afficher slt plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%y')) plt.xlabel('Month') plt.ylabel("Number of Sales") plt.title(f"Number of Sales Across {type_of_activity} Companies") plt.legend() plt.show() save_file_s3("sale_dynamics_", type_of_activity) def tickets_internet(tickets, type_of_activity): nb_tickets_internet = tickets.groupby("number_company")['prop_purchases_internet'].mean().reset_index() nb_tickets_internet['prop_purchases_internet'] *=100 plt.bar(nb_tickets_internet["number_company"], nb_tickets_internet["prop_purchases_internet"]) plt.xlabel('Company Number') plt.ylabel("Share of Purchases Bought Online (%)") plt.title(f"Share of Online Purchases Across {type_of_activity} Companies") plt.xticks(nb_tickets_internet["number_company"], ["{}".format(i) for i in nb_tickets_internet["number_company"]]) plt.show() save_file_s3("tickets_internet_", type_of_activity) def already_bought_online(tickets, type_of_activity): nb_consumers_online = (tickets.groupby("number_company").agg({'achat_internet' : 'sum', 'customer_id' : 'nunique'} ).reset_index()) nb_consumers_online["Share_consumers_internet"] = (nb_consumers_online["achat_internet"]/ nb_consumers_online["customer_id"])*100 plt.bar(nb_consumers_online["number_company"], nb_consumers_online["Share_consumers_internet"]) plt.xlabel('Company Number') plt.ylabel("Share of Customer who Bought Online at least once (%)") plt.title(f"Share of Customer who Bought Online at least once Across {type_of_activity} Companies") plt.xticks(nb_consumers_online["number_company"], ["{}".format(i) for i in nb_consumers_online["number_company"]]) plt.show() save_file_s3("First_buy_internet_", type_of_activity) def box_plot_price_tickets(tickets, type_of_activity): price_tickets = tickets[(tickets['total_amount'] > 0)] sns.boxplot(data=price_tickets, y="total_amount", x="number_company", showfliers=False, showmeans=True) plt.title(f"Box plot of price tickets Across {type_of_activity} Companies") plt.show() save_file_s3("box_plot_price_tickets_", type_of_activity)