diff --git a/.DS_Store b/.DS_Store
index f9eff7d8caa48695afec3a1f76904ad3a16c43f0..c66136cf486383b3afb2e9e018a604d3a5ae1499 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/Data_Base.xlsx b/Data_Base.xlsx
index d9513acf2c25892a5e2e901484c9020144c85240..120e1ff2f3611fc448aae7f91eb8f85f9ec407ad 100644
Binary files a/Data_Base.xlsx and b/Data_Base.xlsx differ
diff --git a/projet_personnel/algorithme_gestion_etudiants.py b/projet_personnel/algorithme_gestion_etudiants.py
index 1de243ae9225b461f10dfb871a12a74bbc3986b4..f9d71dc86ad65afb85240caae65a2ec8662a9192 100644
--- a/projet_personnel/algorithme_gestion_etudiants.py
+++ b/projet_personnel/algorithme_gestion_etudiants.py
@@ -190,15 +190,15 @@ def register(data, firstname, lastname, date_of_birth, place_of_birth, address,
return
def generate_matricule(firstname, lastname, date_of_birth):
- consonants_name = ''.join([c for c in firstname if c.lower() not in 'aeiouy'])[:3]
- consonants_surname = ''.join([c for c in lastname if c.lower() not in 'aeiouy'])[:2]
- last_consonant_surname = ''.join([c for c in lastname if c.lower() not in 'aeiouy']).lower()[-1] # not sure if it's the consonant of the first name or the last name
- birth_year_last_two_digits = str(date_of_birth)[-2:]
+ consonant_of_firstname = ''.join([c for c in firstname if c.lower() not in 'aeiou'])[:3]
+ consonant_of_lastname = ''.join([c for c in lastname if c.lower() not in 'aeiou'])[:2]
+ last_consonant_of_lastname = ''.join([c for c in lastname if c.lower() not in 'aeiou']).lower()[-1] # ne sait pas si c'est la consonne du prénom ou du nom
+ year_of_birth_string = str(date_of_birth)[-4:]
random_integer = random.randint(0, 10)
- matricule_e = f"{consonants_name}{consonants_surname}{last_consonant_surname}{birth_year_last_two_digits}{random_integer}"
- matricule_e = matricule_e.lower()
+ matricule = f"{consonant_of_lastname }{consonant_of_firstname}{last_consonant_of_lastname}{year_of_birth_string}{random_integer}"
+ matricule = matricule.lower()
- return matricule_e
+ return matricule
# MODIFYING THE DATA OF A STUDENT
def modify(data):
@@ -364,7 +364,7 @@ def modify(data):
data.at[student_index, 'Matricule'] = matricule # Update the Matricule column with the new matricule
- print("be careful because of a modification the registration number has changed :")
+ print("Be careful because of a modification the registration number has changed :")
print(f"the new matricule is : {matricule}")
@@ -374,15 +374,15 @@ def modify(data):
# Mapping for field names to DataFrame column names
field_mapping = {
- 1: 'Name',
- 2: 'Surname',
- 3: 'Date of Birth',
- 4: 'Place of Birth',
- 5: 'Address',
- 6: 'Telephone',
- 7: 'Email',
- 8: 'Gender',
- 9: 'Academic Year',
+ 1: 'Name',
+ 2: 'Surname',
+ 3: 'Date of Birth',
+ 4: 'Place of Birth',
+ 5: 'Address',
+ 6: 'Telephone',
+ 7: 'Email',
+ 8: 'Gender',
+ 9: 'Academic Year',
10: 'Curriculum',
12: 'Campus'
}
@@ -460,11 +460,11 @@ def filter_students(data):
headers = ["Firstname", "Lastname", "Matricule"]
print(tabulate(table_data, headers=headers, tablefmt="pretty"))
- export_choice = input("Do you want to export the list to an Excel file? (yes/no): ").lower()
- if export_choice == "yes":
+ export_choice = input("Do you want to export the list to an Excel file? (YES/NO): ").upper()
+ if export_choice == "YES":
export_to_excel(data)
- read_choice = input("Do you want to read the exported list from an Excel file? (yes/no): ").lower()
- if read_choice == "yes":
+ read_choice = input("Do you want to read the exported list from an Excel file? (YES/NO): ").upper()
+ if read_choice == "YES":
read_exported_list()
@@ -565,7 +565,6 @@ def sort_by_academic_year(data):
export_option(sorted_data)
-# attention il faut pouvoir matcher ceci avec les résultats obtenus
def sort_passed(data):
course_columns = data.columns[11:]
print("Available Courses:")
@@ -604,7 +603,6 @@ def sort_failed(data):
failed_data = data[data[selected_course] < 10]
- # Notez que 'selected_course' doit être passé comme une liste pour inclure la note du cours dans 'columns_to_show'
see_the_data( failed_data, columns_to_show=['Firstname', 'Lastname', selected_course])
export_option( failed_data)
@@ -646,127 +644,180 @@ def see_the_data(data, columns_to_show=None):
# STATS
def statistics_analysis(data):
- print('1. Get basic statistics')
- print('2. Get all student grades')
- print('3. Get all grades for a course')
- print('4. Export all statistics')
+ print_menu([
+ "Get basic statistics",
+ "Get all student grades",
+ "Get all grades for a course",
+ ])
+
+ stats_choice = get_valid_input("Enter the number of what you want to do: ", 1, 3)
+
+ if stats_choice == 1 :
+ results = search_and_display_stats(data, "Statistics")
+ elif stats_choice == 2:
+ results = search_and_display_stats(data, "Grades")
+ elif stats_choice == 3:
+ results = course_grades(data)
+def print_menu(options):
+ for index, option in enumerate(options, start=1):
+ print(f"{index}. {option}")
+
+def get_valid_input(prompt, lower_limit, upper_limit):
while True:
- stats_choice = input("Enter the number of what you want to do: ")
- if stats_choice.isdigit():
- stats_int = int(stats_choice)
- if 1 <= stats_int <= 4:
- print("The choice is valid.")
- break
+ user_input = input(prompt)
+ if user_input.isdigit():
+ user_choice = int(user_input)
+ if lower_limit <= user_choice <= upper_limit:
+ return user_choice
else:
- print("The choice must be one of those proposed.")
-
- if stats_choice == "1":
- basic_stats(data)
- elif stats_choice == "2":
- all_grades(data)
- elif stats_choice == "3":
- course_grades(data)
- elif stats_choice == "4":
- export_stats(data)
-
-def basic_stats(data):
- search_criteria = input("Enter the search criteria for student statistics (Name/Surname/Matricule): ").capitalize()
-
- if search_criteria == "Name":
- name_search = input("Enter the name of the student: ")
- results = data[data['Name'].str.contains(name_search, case=False, na=False)]
- elif search_criteria == "Surname":
- surname_search = input("Enter the surname of the student: ")
- results = data[data['Surname'].str.contains(surname_search, case=False, na=False)]
- elif search_criteria == "Matricule":
- matricule_search = input("Enter the matricule of the student: ")
- results = data[data['Matricule'].str.contains(matricule_search, case=False, na=False)]
+ print(f"The choice must be between {lower_limit} and {upper_limit}.")
+ else:
+ print("Invalid input. Please enter a valid number.")
+
+def search_and_display_stats(data, stats_type):
+ print_menu(["Search by Firstname", "Search by Lastname", "Search by Matricule"])
+ search_criteria = get_valid_input(f"Enter the number of the search criteria for student {stats_type}: ", 1, 3)
+
+ if search_criteria == 1:
+ field_name = "Firstname"
+ elif search_criteria == 2:
+ field_name = "Lastname"
+ elif search_criteria == 3:
+ field_name = "Matricule"
else:
print("Invalid search criteria.")
return
+ search_term = input(f"Enter the {field_name.lower()} of the student: ")
+ results = data[data[field_name].str.contains(search_term, case=False, na=False)]
+
+ if not results.empty:
+ if stats_type == "Statistics":
+ display_statistics(results)
+ elif stats_type == "Grades":
+ display_student_grades(results)
+ else:
+ print(f"No students found with the specified {field_name}.")
+
+def display_statistics(results):
numeric_columns = results.select_dtypes(include=['number']).columns
for index, row in results.iterrows():
- student_name = f"{row['Name']} {row['Surname']}"
+ student_name = f"{row['Firstname']} {row['Lastname']}"
student_grades = [row[column] for column in numeric_columns if not pd.isnull(row[column])]
if student_grades:
- lowest_grade = min(student_grades)
- highest_grade = max(student_grades)
- average_grade = statistics.mean(student_grades)
- median_grade = statistics.median(student_grades)
- std_deviation = statistics.stdev(student_grades)
-
- print(f"\nStatistics for student {student_name}:")
- print(f"Lowest grade: {lowest_grade}")
- print(f"Highest grade: {highest_grade}")
- print(f"Average grade: {average_grade}")
- print(f"Median grade: {median_grade}")
- print(f"Standard deviation of grades: {std_deviation}")
+ print_student_statistics(student_name, student_grades)
else:
print(f"\nNo grades found for student {student_name}.")
+ export_choice = input("Do you want to export these statistics? (YES/NO): ").upper()
+ if export_choice == 'YES':
+ export_stats(results)
+
+def print_student_statistics(student_name, student_grades):
+ lowest_grade = min(student_grades)
+ highest_grade = max(student_grades)
+ average_grade = statistics.mean(student_grades)
+ median_grade = statistics.median(student_grades)
+ std_deviation = statistics.stdev(student_grades)
+
+ table = [
+ ["Lowest grade", lowest_grade],
+ ["Highest grade", highest_grade],
+ ["Average grade", average_grade],
+ ["Median grade", median_grade],
+ ["Standard deviation of grades", std_deviation]
+ ]
+
+ print(f"\nStatistics for student {student_name}:")
+ print(tabulate(table, headers=["Metric", "Value"], tablefmt="pretty"))
+
+def display_student_grades(results):
+ for index, row in results.iterrows():
+ student_name = f"{row['Firstname']} {row['Lastname']}"
+ print(f"\nGrades for student {student_name}:")
+ table = [[column, grade] for column, grade in row.iteritems() if pd.notnull(grade)]
+ print(tabulate(table, headers=["Course", "Grade"], tablefmt="pretty"))
+ export_choice = input("Do you want to export these statistics? (YES/NO): ").upper()
+ if export_choice == 'YES':
+ export_stats(results)
-def all_grades(data):
- search_criteria = input("Enter the search criteria for student grades (Name/Surname/Matricule): ").capitalize()
-
- if search_criteria == "Name":
- name_search = input("Enter the name of the student: ")
- results = data[data['Name'].str.contains(name_search, case=False, na=False)]
- elif search_criteria == "Surname":
- surname_search = input("Enter the surname of the student: ")
- results = data[data['Surname'].str.contains(surname_search, case=False, na=False)]
- elif search_criteria == "Matricule":
- matricule_search = input("Enter the matricule of the student: ")
- results = data[data['Matricule'].str.contains(matricule_search, case=False, na=False)]
- else:
- print("Invalid search criteria.")
- return
+def course_grades(data):
+ print("Here is the list of courses:")
+ print(data.select_dtypes(include=['number']).columns)
- student = data[data['Matricule'].str.contains(matricule_search, case=False, na=False)]
+ course_name = input("For which course do you want to display grades? ")
- if student.empty:
- print(f"No student found with matricule {matricule_search}.")
- return
+ if course_name in data.columns:
+ students_in_course = data[data[course_name].notnull()]
+
+ if not students_in_course.empty:
+ display_course_grades(students_in_course, course_name)
+ else:
+ print(f"No students participated in the course {course_name}.")
+ else:
+ print(f"The specified course ({course_name}) was not found.")
+
+ export_choice = input("Do you want to export these statistics? (YES/NO): ").upper()
+ if export_choice == 'YES':
+ export_stats(students_in_course)
- # Select columns containing grades
- grade_columns = student.select_dtypes(include=['number']).columns
+ return students_in_course
- # Display student's grades
- print(f"Grades for student with matricule {matricule_search}:")
- for column in grade_columns:
- if not pd.isnull(student[column].values[0]):
- print(f"{column}: {student[column].values[0]}")
+def display_course_grades(students_in_course, course_name):
+ print(f"\nGrades of students for the course {course_name}:")
+ table = [
+ ["Matricule", "Grade"],
+ *[[row['Matricule'], row[course_name]] for _, row in students_in_course.iterrows() if not pd.isnull(row[course_name])]
+ ]
+ print(tabulate(table, headers="firstrow", tablefmt="pretty"))
-def course_grades(data):
- numeric_columns = data.select_dtypes(include=['number']).columns.tolist()
- print("Here is the list of courses:")
- for column in numeric_columns:
- print(column)
+def export_stats(data):
+ # Ensure numeric columns only
+ numeric_columns = data.select_dtypes(include=['number']).columns
+
+ # Calculate overall statistics
+ overall_stats = calculate_overall_stats(data, numeric_columns)
+
+ # Save overall statistics to Excel
+ overall_stats.to_excel("overall_statistics.xlsx", index=False)
- course_name = input(print("For which course do you want to display grades?"))
+ # Calculate and save statistics for each student
+ for index, row in data.iterrows():
+ student_name = f"{row['Firstname']} {row['Lastname']}"
+ student_stats = calculate_student_stats(row, numeric_columns)
+ student_stats.to_excel(f"{student_name}_statistics.xlsx", index=False)
- if course_name not in data.columns:
- print(f"The specified course ({course_name}) was not found.")
- return
+ print("Statistics exported successfully.")
- # Filter students who participated in this course (non-null grade)
- students_in_course = data[data[course_name].notnull()]
+def calculate_overall_stats(data, numeric_columns):
+ all_grades = [row[column] for column in numeric_columns for _, row in data.iterrows() if not pd.isnull(row[column])]
+
+ overall_stats = {
+ "Lowest grade": min(all_grades),
+ "Highest grade": max(all_grades),
+ "Average grade": statistics.mean(all_grades),
+ "Median grade": statistics.median(all_grades),
+ "Standard deviation of grades": statistics.stdev(all_grades)
+ }
- if students_in_course.empty:
- print(f"No student participated in the course {course_name}.")
- return
+ return pd.DataFrame(list(overall_stats.items()), columns=["Metric", "Value"])
- # Display grades of students for the specified course
- print(f"Grades of students for the course {course_name}:")
- for index, row in students_in_course.iterrows():
- print(f"Matricule: {row['Matricule']}, Grade: {row[course_name]}")
+def calculate_student_stats(row, numeric_columns):
+ student_grades = [row[column] for column in numeric_columns if not pd.isnull(row[column])]
-def export_stats(data):
+ student_stats = {
+ "Lowest grade": min(student_grades),
+ "Highest grade": max(student_grades),
+ "Average grade": statistics.mean(student_grades),
+ "Median grade": statistics.median(student_grades),
+ "Standard deviation of grades": statistics.stdev(student_grades)
+ }
+
+ return pd.DataFrame(list(student_stats.items()), columns=["Metric", "Value"])
- return
# Example of usage
# Make sure you have defined and loaded your DataFrame 'data' before calling this function
@@ -776,7 +827,7 @@ def export_stats(data):
# Action
def action():
- print("What do you want to do?\nBelow, you will find what is possible followed by the commands to type.\n")
+ print("What do you want to do?\nBelow, you will find what is possible followed by the commands to type.")
print("1. Register a student")
print("2. Modify one or more fields")
print("3. Delete a student")
diff --git a/projet_personnel/algorithme_student_generate.py b/projet_personnel/algorithme_student_generate.py
index b37fb2c5cfdf28ee0d0cba8ee88ee18d4cdcaa04..9206e0c9f049c7115597eadd6fc073c79d619c53 100644
--- a/projet_personnel/algorithme_student_generate.py
+++ b/projet_personnel/algorithme_student_generate.py
@@ -148,12 +148,12 @@ for each in range(number_of_students): # générer des données
# attribution du matricule
- consonant_of_lastname = ''.join([c for c in last_name if c.lower() not in 'aeiou'])[:3]
- consonant_of_firstname = ''.join([c for c in first_name if c.lower() not in 'aeiou'])[:2]
- last_consonant_of_firstname = ''.join([c for c in first_name if c.lower() not in 'aeiou']).lower()[-1] # ne sait pas si c'est la consonne du prénom ou du nom
- year_of_birth_string = str(year_of_birth)[-2:]
+ consonant_of_firstname = ''.join([c for c in first_name if c.lower() not in 'aeiou'])[:3]
+ consonant_of_lastname = ''.join([c for c in last_name if c.lower() not in 'aeiou'])[:2]
+ last_consonant_of_lastname = ''.join([c for c in last_name if c.lower() not in 'aeiou']).lower()[-1] # ne sait pas si c'est la consonne du prénom ou du nom
+ year_of_birth_string = str(year_of_birth)[-4:]
random_integer = random.randint(0, 10)
- matricule = f"{consonant_of_lastname }{consonant_of_firstname}{last_consonant_of_firstname}{year_of_birth_string}{random_integer}"
+ matricule = f"{consonant_of_lastname }{consonant_of_firstname}{last_consonant_of_lastname}{year_of_birth_string}{random_integer}"
matricule = matricule.lower()