last update of the project

projet_personnel/algorithme_student_generate.py

@@ -3,55 +3,7 @@
 import pandas as pd
 import random
 import numpy as np
-
-firstname_of_students = ['Smith', 'Johnson', 'Williams', 'Jones', 'Brown', 'Davis', 'Miller', 'Wilson', 'Moore', 'Taylor',
-    'Anderson', 'Thomas', 'Jackson', 'White', 'Harris', 'Martin', 'Thompson', 'Garcia', 'Martinez', 'Robinson',
-    'Clark', 'Rodriguez', 'Lewis', 'Lee', 'Walker', 'Hall', 'Allen', 'Young', 'Hernandez', 'King',
-    'Wright', 'Lopez', 'Hill', 'Scott', 'Green', 'Adams', 'Baker', 'Gonzalez', 'Nelson', 'Carter',
-    'Mitchell', 'Perez', 'Roberts', 'Turner', 'Phillips', 'Campbell', 'Parker', 'Evans', 'Edwards', 'Collins',
-    'Stewart', 'Sanchez', 'Morris', 'Rogers', 'Reed', 'Cook', 'Morgan', 'Bell', 'Murphy', 'Bailey',
-    'Rivera', 'Cooper', 'Richardson', 'Cox', 'Howard', 'Ward', 'Torres', 'Peterson', 'Gray', 'Ramirez',
-    'James', 'Watson', 'Brooks', 'Kelly', 'Sanders', 'Price', 'Bennett', 'Wood', 'Barnes', 'Ross',
-    'Henderson', 'Coleman', 'Jenkins', 'Perry', 'Powell', 'Long', 'Patterson', 'Hughes', 'Flores', 'Washington',
-    'Butler', 'Simmons', 'Foster', 'Gonzales', 'Bryant', 'Alexander', 'Russell', 'Griffin', 'Diaz', 'Hayes',
-    'Myers', 'Ford', 'Hamilton', 'Graham', 'Sullivan', 'Wallace', 'Woods', 'Cole', 'West', 'Jordan',
-    'Owens', 'Reynolds', 'Fisher', 'Ellis', 'Harrison', 'Gibson', 'McDonald', 'Cruz', 'Marshall', 'Ortiz',
-    'Gomez', 'Murray', 'Freeman', 'Wells', 'Webb', 'Simpson', 'Stevens', 'Tucker', 'Porter', 'Hunter',
-    'Hicks', 'Crawford', 'Henry', 'Boyd', 'Mason', 'Morales', 'Kennedy', 'Warren', 'Dixon', 'Ramos',
-    'Reyes', 'Burns', 'Gordon', 'Shaw', 'Holmes', 'Rice', 'Robertson', 'Hunt', 'Black', 'Daniels',
-    'Palmer', 'Mills', 'Nichols', 'Grant', 'Knight', 'Ferguson', 'Rose', 'Stone', 'Hawkins', 'Dunn',
-    'Perkins', 'Hudson', 'Spencer', 'Gardner', 'Stephens', 'Payne', 'Pierce', 'Berry', 'Matthews', 'Arnold',
-    'Wagner', 'Willis', 'Ray', 'Watkins', 'Olson', 'Carroll', 'Duncan', 'Snyder', 'Hart', 'Cunningham',
-    'Bradley', 'Lane', 'Andrews', 'Ruiz', 'Harper', 'Fox', 'Riley', 'Armstrong', 'Carpenter', 'Weaver',
-    'Greene', 'Lawrence', 'Elliott', 'Chavez', 'Sims', 'Austin', 'Peters', 'Kelley', 'Franklin', 'Lawson',
-    'Fields', 'Gutierrez', 'Ryan', 'Schmidt', 'Carr', 'Vasquez', 'Castillo', 'Wheeler', 'Chapman', 'Oliver',
-    'Montgomery', 'Richards', 'Williamson', 'Johnston', 'Banks', 'Meyer', 'Bishop', 'McCoy', 'Howell', 'Alvarez',
-    'Morrison', 'Hansen', 'Fernandez', 'Garza', 'Harvey', 'Little', 'Burton', 'Stanley', 'Nguyen', 'George',
-    'Jacobs', 'Reid', 'Kim', 'Fuller', 'Lynch', 'Dean', 'Gilbert', 'Garrett', 'Romero', 'Welch']
-
-last_name_of_students = ['Emma', 'Liam', 'Olivia', 'Noah', 'Ava', 'Isabella', 'Sophia', 'Jackson', 'Mia', 'Lucas',
-    'Aiden', 'Ethan', 'Luna', 'Mason', 'Harper', 'Evelyn', 'Oliver', 'Aria', 'Amelia', 'Caden',
-    'Grayson', 'Charlotte', 'Chloe', 'Ella', 'Sebastian', 'Carter', 'Abigail', 'Scarlett', 'Madison',
-    'Lily', 'Avery', 'Ella', 'Aubrey', 'Mila', 'Layla', 'Riley', 'Zoe', 'Sofia', 'Jackson', 'Liam',
-    'Leah', 'Zoe', 'Henry', 'Gabriel', 'Grace', 'Hannah', 'Mila', 'Alexa', 'Eva', 'Liam', 'Mia',
-    'Logan', 'Benjamin', 'Emily', 'Nora', 'Elijah', 'Lucy', 'Owen', 'Elizabeth', 'Stella', 'Levi',
-    'William', 'Lily', 'Zoey', 'Parker', 'Brooklyn', 'Samuel', 'Luke', 'Madelyn', 'Eleanor', 'Nathan',
-    'Isaac', 'Ryan', 'Ariana', 'Addison', 'Scarlett', 'Julian', 'Natalie', 'Victoria', 'Adam', 'Alexis',
-    'Aaron', 'Aaliyah', 'Robert', 'Samantha', 'Christopher', 'Hazel', 'Serenity', 'Jordan', 'Leah', 'Chase',
-    'Lillian', 'Liam', 'Skylar', 'Bella', 'Zachary', 'Caleb', 'Tyler', 'Connor', 'Eli', 'Aaliyah',
-    'Ezekiel', 'Eva', 'Landon', 'Grace', 'Makayla', 'Nicholas', 'Natalie', 'Nathan', 'Avery', 'Cameron',
-    'Hudson', 'Violet', 'Claire', 'Gavin', 'Eva', 'Zachary', 'Alexa', 'Brooklyn', 'Kylie', 'Peyton',
-    'Emma', 'John', 'Aubrey', 'Carter', 'Mila', 'Elena', 'Paisley', 'Grayson', 'Hailey', 'Elijah',
-    'Ellie', 'Julian', 'Adrian', 'Aria', 'Liliana', 'Bentley', 'Kinsley', 'Savannah', 'Eliana', 'Sofia',
-    'Liam', 'Ella', 'Evelyn', 'Avery', 'Austin', 'Scarlett', 'Leah', 'Aiden', 'Audrey', 'Amelie',
-    'Julian', 'Mila', 'Olivia', 'Anthony', 'Caleb', 'Zoe', 'Autumn', 'Samantha', 'Aria', 'Cooper',
-    'Eva', 'Charlie', 'Madison', 'Landon', 'Penelope', 'Kaylee', 'Lily', 'Ryder', 'Hannah', 'Maya',
-    'Christopher', 'Eleanor', 'Levi', 'Aubree', 'Emma', 'Sofia', 'Evan', 'Ella', 'Gabriel', 'Zachary',
-    'Taylor', 'Liam', 'Makayla', 'Zoe', 'Oliver', 'Isaiah', 'Brooklyn', 'Lily', 'Victoria', 'Cameron',
-    'Madison', 'Eva', 'Gabriel', 'Zoe', 'Brooklyn', 'Sophie', 'Ella', 'Logan', 'Madison', 'Julian',
-    'Alexis', 'Landon', 'Grace', 'Lucas', 'Aria', 'Aaliyah', 'Adrian', 'Riley', 'Nora', 'Isabella',
-    'Eva', 'Layla', 'Aiden', 'Amelia', 'Chloe', 'Levi', 'Lillian', 'Liam', 'Zoey', 'Hazel', 'Carter',
-    'Grace', 'Peyton', 'Julian', 'Eliana', 'Eva', 'Harper', 'Henry', 'Olivia', 'Lily', 'Liam', 'Abigail']
+from faker import Faker
 
 school_years = ['BAC1', 'BAC2', 'BAC3', 'MA1', 'MA2']
 
@@ -93,15 +45,17 @@ school_years = ['BAC1', 'BAC2', 'BAC3', 'MA1', 'MA2']
 # Générer des combinaisons aléatoires de noms et prénoms pour plus de 1000 personnes
 data_generated = []
 
+fake = Faker()
+
 number_of_students = 1000
 
 for each in range(number_of_students): # générer des données
 
     # nom de la personnes
-    last_name = random.choice(last_name_of_students)
+    last_name = fake.last_name()
 
     # prenom de la personnes
-    first_name = random.choice(firstname_of_students)
+    first_name = fake.first_name()
 
     # année de cours
     academic_year = np.random.choice(school_years, p=proportions)
@@ -193,7 +147,7 @@ for each in range(number_of_students): # générer des données
             grades[courses] = random.randint(0, 20)
 
 
-    data_generated.append({"Firstname": last_name, "Lastname": first_name, "Academic Year" : academic_year, "Curriculum" : curriculum, "Place of Birth" : city_of_birth , "Telephone": phone, "Address": adress_of_student, "Gender" : gender_of_student, "Email" : email_formated, "Campus" : campus, "Date of Birth" : complete_date_of_birth, "Matricule" : matricule, **grades})
+    data_generated.append({"Firstname": first_name, "Lastname": last_name, "Academic Year" : academic_year, "Curriculum" : curriculum, "Place of Birth" : city_of_birth , "Telephone": phone, "Address": adress_of_student, "Gender" : gender_of_student, "Email" : email_formated, "Campus" : campus, "Date of Birth" : complete_date_of_birth, "Matricule" : matricule, **grades})
 
 # Créer un DataFrame pandas
 df = pd.DataFrame(data_generated)