student_db_mange.py

import pandas as pd
import os


class StudentDataBase:
    def __init__(self,filename='student-dataset.csv'):
        self.filename = filename
        self.clean_data_base()
        self.add_avg()
        self.save()

    def clean_data_base(self):
        '''
        clean the Data set from col (info) that i dont care.
        We have 2 Option:
        Option 1: give the header of the col and remove it with columns
        Option 2: give the index of col we want to remove
        '''
        self.students= pd.read_csv(self.filename)
        # Remvoe those col in the begnning (and check if it's not already if i rerun it..)
        columns_to_drop = ['latitude', 'longitude', 'ethnic.group', 'language.grade', 'portfolio.rating', 'coverletter.rating', 'refletter.rating']
        existing_columns_to_drop = [col for col in columns_to_drop if col in self.students.columns]
        if existing_columns_to_drop:
             self.students.drop(columns=existing_columns_to_drop, inplace=True)
        # removing with Index
        
        # indices_to_drop = list(range(8, 15)) + list(range(3, 6))
        # existing_indices_to_drop = [idx for idx in indices_to_drop if idx in self.students.index]
        # if existing_indices_to_drop:
        #     self.students.drop(index=existing_indices_to_drop, inplace=True)
        

        # Mix the removing: 
        # list_idx_remove = list(range(8,13)) + list(range(3,5))

    def save(self):
        self.students.to_csv(self.filename,index=False)

    
    def add_avg(self):
        self.students['Average Grade'] = self.students[["math.grade", "english.grade", "sciences.grade"]].mean(axis=1)

    def removre_student(self,name):
        self.students = self.students[self.students['name'] != name]
        # For memory efficiency :
        #self.students.drop(self.students[self.students["name"] == name].index, inplace=True)

    def update_grade(self,name,english = None ,math=None, science = None):
        
        if self.students[self.students['name'] == name].empty:
            print("No such student exist")
            return
        
        if english:
            self.students.loc[self.students['name'] == name ,'english.grade'] = english
        if math:
            self.students.loc[self.students['name'] == name ,'math.grade'] = math
        if science:
            self.students.loc[self.students['name'] == name ,'sciences.grade'] = science
        
        self.save()

    def get_student_as_panda(self,id):
        '''
        returning a panda type
        '''
        student_data = self.students[self.students['id'] == id]
        if student_data.empty:
            return None
        return student_data
    
    def get_student_as_Student_class(self,id):
        '''
        using argws to pass as arguments to the constructor for the Student class (**stu_dic)
        '''
        student_data = self.students[self.students['id'] == id]
        if student_data.empty:
            return None
        stu_dic = student_data.iloc[0].to_dict()
        # Replace the key "english.grade" with "english"
        if 'english.grade' in stu_dic:
            stu_dic['english'] = stu_dic.pop('english.grade')
        if 'math.grade' in stu_dic:
            stu_dic['math'] = stu_dic.pop('math.grade')
        if 'sciences.grade' in stu_dic:
            stu_dic['science'] = stu_dic.pop('sciences.grade')

        stu_dic.pop('Average Grade')
        print(stu_dic)
        return Student(**stu_dic)
    
    def get_database(self):
        return self.students
    

    def count_country(self, nationality):
        '''
        Option 1: shape will return the dimension of the Data, so if i sort the shape
            of rows will be the number
        Option 2: count() will return the summary of each col to the querey is use:
                # filtered_df = self.students[self.students['nationality'] == nationality]
                # return filtered_df['nationality'].count()
        '''
        return self.students[self.students['nationality'] == nationality].shape[0]
    

class Student(StudentDataBase) :
    def __init__(self,id=None,name=None,nationality=None,city=None,gender=None,age=None,english=None,math=None,science=None):
        super().__init__()
        self.id = id
        self.name = name
        self.nationality = nationality
        self.city = city
        self.age = age
        self.gender = gender
        self.english_grade = english
        self.math_grade = math
        self.science_grade = science
        if id != None:
            self.add_student()


    def add_student(self):
        temp_db = pd.DataFrame([{
            "id" : self.get_next_id(),
            "name": self.name,
            "nationality": self.nationality,
            "city": self.city,
            "gender": self.gender,
            "age": self.age,
            "english.grade": self.english_grade,
            "math.grade": self.math_grade,
            "sciences.grade": self.science_grade,
            "Average Grade" : self.avg()
        }])
        self.students = pd.concat([self.students,temp_db ] , ignore_index=True)
        self.save()

    def __str__(self):
        return f"Student name {self.name} have GPA of {self.avg()}"
    
    def get_next_id(self):
        if self.students.empty:
            return 1
        else:
            return self.students['id'].max()+1

    def avg(self):
        return (self.math_grade + self.english_grade + self.science_grade) / 3





DB = StudentDataBase()
Student(12504,"John Doe", "USA", "New York", "Male", 20, 85, 90, 88)
df = DB.get_database()

print(DB.count_country('Mexico'))
stu = DB.get_student_as_Student_class(179)
(print(stu))