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fix labels

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Alexis REVELLE 2024-03-21 08:16:29 +00:00
parent b1e877508b
commit 089a8fd3d6
1 changed files with 46 additions and 42 deletions
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88
utils_stat_desc.py
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@ -122,7 +122,7 @@ def compute_nb_clients(customer, type_of_activity):
plt.xlabel('Company')
plt.ylabel("Number of clients (thousands)")
plt.title(f"Number of clients for {type_of_activity}")
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)
@ -132,9 +132,9 @@ 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.xlabel('Company Number')
plt.ylabel("Maximal price of a ticket Prix")
plt.title(f"Maximal price of a ticket for {type_of_activity}")
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)
@ -142,12 +142,12 @@ def maximum_price_paid(customer, 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')
plt.ylabel('Consent')
plt.title(f'Consent of mailing for {type_of_activity}')
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)
@ -167,16 +167,16 @@ def mailing_consent_by_target(customer):
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"
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')
ax.set_ylabel('Consent')
ax.set_title(f'Consent of mailing according to target for {type_of_activity}')
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()
@ -189,16 +189,20 @@ def mailing_consent_by_target(customer):
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")
company_genders["gender_male"] *= 100
company_genders["gender_female"] *= 100
company_genders["gender_other"] *= 100
plt.xlabel('Company')
plt.ylabel("Gender")
plt.title(f"Gender of Customer for {type_of_activity}")
# 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()
@ -207,11 +211,12 @@ def gender_bar(customer, 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')
plt.ylabel("Share of French Customer")
plt.title(f"Share of French Customer for {type_of_activity}")
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)
@ -221,9 +226,8 @@ 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.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)
@ -244,16 +248,16 @@ def campaigns_effectiveness(customer, type_of_activity):
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 = "purchased" if label else "no purchase"
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')
ax.set_ylabel('Consent')
ax.set_title(f"Number of Customers who have consent to received mails for {type_of_activity} dependy on target")
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()
@ -304,9 +308,9 @@ def sale_dynamics(products, campaigns_brut, type_of_activity):
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_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="Ancien client")
bottom=merged_data["nb_purchases_new"], width=12, label="Existing Customers")
# commande pr afficher slt
@ -314,7 +318,7 @@ def sale_dynamics(products, campaigns_brut, type_of_activity):
plt.xlabel('Month')
plt.ylabel("Number of Sales")
plt.title(f"Number of Sales for {type_of_activity}")
plt.title(f"Number of Sales Across {type_of_activity} Companies")
plt.legend()
plt.show()
save_file_s3("sale_dynamics_", type_of_activity)
@ -322,12 +326,12 @@ def sale_dynamics(products, campaigns_brut, 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')
plt.ylabel("Share of Purchases Bought Online")
plt.title(f"Share of Purchases Bought Online for {type_of_activity}")
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)
@ -337,13 +341,13 @@ 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"]
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')
plt.ylabel("Share of Customer who Bought Online at least once")
plt.title(f"Share of Customer who Bought Online at least once for {type_of_activity}")
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)
@ -352,7 +356,7 @@ def already_bought_online(tickets, 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.title(f"Box plot of price tickets Across {type_of_activity} Companies")
plt.show()
save_file_s3("box_plot_price_tickets_", type_of_activity)