Question

Please I seek assistance
Python Programing

import os
import numpy as np

def generate_assignment_data(expected_grade_file_path,
                             std_dev, output_file_path):
    """
    Retrieve list of students and their expected grade from file,
    generate a sampled test grade for each student
    drawn from a Gaussian distribution defined by the
    student expected grade as mean, and the given
    standard deviation.

    If the sample is higher than 100, re-sample.
    If the sample is lower than 0 or 5 standard deviations below mean,
    re-sample

    Write the list of student grades to the given
    output file using ID and grade with TAB separation.

    :param expected_grade_file_path: This is our file of student IDs and expected grades
    :param std_dev: Standard deviation used when sampling grades
    :param output_file_path: Where to write the sample grades
    :return: number of student grades generated and
            tuple of mean, median and standard deviation of grades
    """

Answer 1

The required Code, Sample input file & sample output file are given below. Basically, we read the input file using python csv dictreader which reads each row as a dictionary. We then use Numpy random.normal function to draw a sample from the Gaussian distribution :

CODE:

import csv
import os
import numpy as np

def generate_assignment_data(expected_grade_file_path, std_dev, output_file_path):

    with open(expected_grade_file_path, newline='') as csv_in:    #Opening the file having expected grades
        with open(output_file_path, 'w', newline='') as csv_out:   #Opening the file to write in write mode
            fieldnames = ['id', 'expected_grade', 'sampled_grade']   #specifying fieldnames for output file
            writer = csv.DictWriter(csv_out, fieldnames=fieldnames, delimiter='\t')  #creating a writer object for writing output file
            writer.writeheader()
            data = csv.DictReader(csv_in, delimiter=',')  #read data from specified file
            generated_grades = []        #initialize empty list for generated sample grades
            for row in data:
                row['sampled_grade'] = -1
                #keep sampling again till any of the given condition is true
                while (row['sampled_grade'] < 0 or row['sampled_grade'] > 100 or row['sampled_grade'] < 5 * std_dev):
                    row['sampled_grade'] = np.random.normal(float(row['expected_grade']), std_dev)   #generate the sample
                generated_grades.append(row['sampled_grade'])
                print(row)
                writer.writerow(row)
            generated_grades = np.array(generated_grades)  #convert generated grades list to numpy array
            return generated_grades.size,(generated_grades.mean(),np.median(generated_grades),generated_grades.std()) #return required values

#testing
print(generate_assignment_data('students.csv', 5, 'students_out.csv'))

CODE Screenshot:

Sample Input file used for testing:

id,expected_grade
s1,70
s2,80
s3,65
s4,75

Output:

Output file data:

id   expected_grade   sampled_grade
s1   70   72.89787027604346
s2   80   86.3827664017767
s3   65   75.18435378753523
s4   75   77.46518085069853

(*Note: Please up-vote. If any doubt, please let me know in the comments)