Code

#LIBRARIES
import pandas as pd
import numpy as np

#FUNCTIONS
#Weighted average
def waverage(file):
    list_grades=[]
    list_CFU=[]
    list_summ=[]
    average=0
    num=0
    sum_CFU=0
    for n in file[1]:
        list_grades+=[n]
    #print(list_grades)
    for n in file[2]:
        list_CFU+=[n]
    #print(list_CFU)
    for n in list_CFU:
        list_summ+=[list_grades[num]*n]
        if list_grades[num]!=0:
            sum_CFU+=n
        num+=1
    #print(list_summ)
    average=sum(list_summ)/sum_CFU
    return average
def xgrade (file, media):
    list_grades=[]
    list_CFU=[]
    list_summ=[]
    num=0
    sum_CFU=0
    cfu_r=0
    voto=0
    for n in file[1]:
        list_grades+=[n]
    for n in file[2]:
        list_CFU+=[n]   
    for n in list_CFU:
        list_summ+=[list_grades[num]*n]
        if list_grades[num]!=0:
            sum_CFU+=n
        else:
            cfu_r+=n
        num+=1
    #average=sum(list_summ)/sum_CFU
    grade=(media*sum(list_CFU)-sum(list_summ))/cfu_r
    return grade
def maxaverage(file):
    list_grades=[]
    list_CFU=[]
    list_summ=[]
    for n in file[1]:
        if n!=0:    
            list_grades+=[n]
        else:
            n=30
            list_grades+=[n]
    for n in file[2]:
        list_CFU+=[n]
    num=0
    for n in list_CFU:
        list_summ+=[list_grades[num]*n]
        num+=1    
    maxaverag=sum(list_summ)/sum(list_CFU)
    return maxaverag

#PROGRAM
#read CSV
file_DF = pd.read_csv("file_path.csv", sep=',', header = None, na_values='null', skiprows=1)
#replane the na value with 0
file_DF[1] = file_DF[1].replace(np.nan, 0)
print(file_DF)
average = waverage(file_DF)
objective=input("What is the grade average you would like to achieve?")
grade=xgrade(file_DF, float(objective))
max_reachable_average=maxaverage(file_DF)
print("Current average:", average)
print("To achieve the desired average you should get a grade equal to or higher than:", grade)
print("The highest average you could achieve (getting 30 on each exam) is ", max_reachable_average)