Completely lost on how to do the second ask in this question:

1. Print the name and grade percentage of the student with the highest total of points.
2. Find the average score of each assignment.
3. Find and apply a curve to each student's total score, such that the best student has 100% of the total points.

Starting Code:

# student_grades contains scores (out of 100) for 5 assignments
student_grades = {
    'Andrew': [56, 79, 90, 22, 50],
    'Nisreen': [88, 62, 68, 75, 78],
    'Alan': [95, 88, 92, 85, 85],
    'Chang': [76, 88, 85, 82, 90],
    'Tricia': [99, 92, 95, 89, 99]
}

CodePudding user response：

import pandas as pd
student_grades = {
    'Andrew': [56, 79, 90, 22, 50],
    'Nisreen': [88, 62, 68, 75, 78],
    'Alan': [95, 88, 92, 85, 85],
    'Chang': [76, 88, 85, 82, 90],
    'Tricia': [99, 92, 95, 89, 99]
}
df = pd.DataFrame(student_grades)
df['average score'] = df.mean(axis=1)
print(df)

Output:

   Andrew  Nisreen  Alan  Chang  Tricia  average score
0      56       88    95     76      99           82.8
1      79       62    88     88      92           81.8
2      90       68    92     85      95           86.0
3      22       75    85     82      89           70.6
4      50       78    85     90      99           80.4

CodePudding user response：

Here is a solution with pure python

print([sum(assignment) / len(assignment) for assignment in zip(*student_grades.values())])

Basically we are trying to do what I outline in this picture - group the scores vertically so that we can find the average. Whenever you want to group across many lists, the zip function comes in handy