import pandas as pd
import os
import math
[docs]def predict_final(input_file, goal):
"""
Calculate what grade the student needs to have for the final assignment in order to reach the goal for overall grade
and return the scores needed for the final assignment.
Parameters
----------
input_file : str
Path to read the saved .csv file as a string.
goal : double
The goal for your overall grade for this course.
Returns
-------
final_score_needed : double
The scores needed for the final assignment.
Otherwise return warning message.
Examples
--------
>>> predict_final('/DSCI100.csv', 90.0)
"""
# Set up input path
cwd = os.getcwd()
path = cwd + input_file
course_info = pd.read_csv(path, index_col=0)
# Check if all assignment weight sum up to 100%
if course_info['Weights (%)'].sum() != 100:
error_msg = "Your total assignment weights are not summing up to 100%! Please update all your assignments."
final_score_needed = error_msg
return final_score_needed
# Check if final score (in the last row) is NA
if not math.isnan(course_info['Grades (%)'].iloc[-1]):
error_msg = "Your final assignment score is already updated. Please directly use calculate_grade()."
final_score_needed = error_msg
return final_score_needed
# Check if there if more than one NA value in the 'Grades (%)' column
if course_info['Grades (%)'].isnull().sum() > 1:
error_msg = "Please update all your assignment scores except the final."
final_score_needed = error_msg
return final_score_needed
# Calculate final_score_needed with given goal
total_scores_except_final = (course_info['Grades (%)'].iloc[:-1] * course_info['Weights (%)'].iloc[:-1]).sum()
final_score_needed = round((goal*100 - total_scores_except_final)/course_info['Weights (%)'].iloc[-1], 1)
return final_score_needed