grade.py

from __future__ import division
import pandas as pd
import numpy as np
import scipy.optimize as sopt

def  gradebook():
    grades = np.array([
        [100,  2.5],
        [100,  2.5],
        [100,  2.5],
        [100,  5],
        [100,  5],
        [100,  15],
        [100,  2.5],
        [np.NaN,  10],
        [np.NaN,  20],
        [np.NaN,  10],
        [np.NaN,  10],
        [np.NaN,  15],
    ], dtype=float)

    index = [
            'mc1hw1',
            'mc1hw2',
            'mc1hw3',
            'mc1p1',
            'mc1p2',
            'mc2p1',
            'mc2hw1',
            'mc2p2',
            'midterm',
            'mc3p1',
            'mc3p2',
            'mc3p3'
    ]
    df = pd.DataFrame(grades, columns=('Grade', 'Percent'), index=index)
    df['Grade'] = df['Grade'] / 100
    df['Percent'] = df['Percent'] / 100
    return df

def calc(grades):
    df = gradebook()
    df.loc[df['Grade'].isnull(), 'Grade'] = grades
    return np.abs(check_grade(df) - .80)

def check_grade(df):
    return (df['Grade'] * df['Percent']).sum()

def optimize():
    df = gradebook()
    nulls = df[df['Grade'].isnull()].shape[0]
    guess = [.5] * nulls
    bounds = ((0.0, 1.0),) * nulls
    res = sopt.minimize(
        calc,
        guess,
        method="SLSQP",
        bounds=bounds,
    )
    return res

def main():
    pass
if __name__ == "__main__":
    main()