BDC-team-1/Descriptive_statistics/plot.py

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_databases(directory_path, file_name = "customerplus_cleaned")
        df_campaigns_brut = display_databases(directory_path, file_name = "campaigns_information", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])
        df_products_purchased_reduced = display_databases(directory_path, file_name = "products_purchased_reduced", datetime_col = ['purchase_date'])
        df_target_information = display_databases(directory_path, file_name = "target_information")
        
        df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_brut) 
        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 for {type_of_activity}")
    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')
    plt.ylabel("Maximal price of a ticket Prix")
    plt.title(f"Maximal price of a ticket for {type_of_activity}")
    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()

    plt.bar(mailing_consent["number_company"], mailing_consent["opt_in"])

    plt.xlabel('Company')
    plt.ylabel('Consent')
    plt.title(f'Consent of mailing for {type_of_activity}')
    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 = "purchased" 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')
    ax.set_ylabel('Consent')
    ax.set_title(f'Consent of mailing according to target for {type_of_activity}')
    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()
    
    # Création du barplot
    plt.bar(company_genders["number_company"], company_genders["gender_male"], label = "Homme")
    plt.bar(company_genders["number_company"], company_genders["gender_female"], 
            bottom = company_genders["gender_male"], label = "Femme")
    plt.bar(company_genders["number_company"], company_genders["gender_other"], 
            bottom = company_genders["gender_male"] + company_genders["gender_female"], label = "Inconnu")
    
    plt.xlabel('Company')
    plt.ylabel("Gender")
    plt.title(f"Gender of Customer for {type_of_activity}")
    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()
    plt.bar(company_country_fr["number_company"], company_country_fr["country_fr"])
    
    plt.xlabel('Company')
    plt.ylabel("Share of French Customer")
    plt.title(f"Share of French Customer for {type_of_activity}")
    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')
    plt.ylabel("Share of Customers who did not open mail")
    plt.title(f"Share of Customers who did not open mail for {type_of_activity}")
    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")["opt_in"].mean().reset_index()

    plt.bar(campaigns_effectiveness["number_company"], campaigns_effectiveness["opt_in"])
    
    plt.xlabel('Company')
    plt.ylabel("Number of Customers (thousands)")
    plt.title(f"Number of Customers of have bought or have received mails for {type_of_activity}")
    plt.legend()
    plt.xticks(campaigns_effectiveness["number_company"], ["{}".format(i) for i in campaigns_effectiveness["number_company"]])
    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="Nouveau client")
    plt.bar(merged_data["purchase_date_month"], merged_data["nb_purchases_old"], 
            bottom=merged_data["nb_purchases_new"], width=12, label="Ancien client")
    
    
    # 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 for {type_of_activity}")
    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")[["nb_tickets", "nb_tickets_internet"]].sum().reset_index()
    nb_tickets_internet["Share_ticket_internet"] = nb_tickets_internet["nb_tickets_internet"]*100 / nb_tickets_internet["nb_tickets"]

    plt.bar(nb_tickets_internet["number_company"],  nb_tickets_internet["Share_ticket_internet"])
    
    plt.xlabel('Company')
    plt.ylabel("Share of Tickets Bought Online")
    plt.title(f"Share of Tickets Bought Online for {type_of_activity}")
    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 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 for {type_of_activity}")
    plt.xticks(price_tickets["number_company"], ["{}".format(i) for i in price_tickets["number_company"]])
    plt.show()
    save_file_s3("box_plot_price_tickets_", type_of_activity)