IN PYTHON

The following exercise works on data analytics for students grades. - Start by specifiying a random seed of 1234 using 'np.random.seed(1234) - Generate a list that contains 5 classes. ['math',_programming-,_'science', 'statistics', 'art'). Use the function shuffle inside numpy to shuffle the list order. You can google how to use numpy shuffle. **Note that, since you shuffled the list, you have to use logical indexing to access the correct class grades** - Generate an array that has 100 student IDs from 1 to 100. Each student ID will be representing one student - Generate the grades matrix 100X5. Each element is a random integer between 0 and 10. Each row represents the grades of each student in the five classes. For example row 7 in the matrix grades will represent the grades of the five subjects in order, after shuffling the classes names, for the student who has ID 8. In other words, grades [49,3]. will return the grade of the class with index of 3 for the student who has ID 50 - Calculate the total grade for each student using np. sum and store that in a new matrix called total - Calculate the total letter grade for each student according to the following letter calculations. A greater than 90% and less than 100% Bgreater than 80% and less than 90% greater than 70% and less than 80% D "greater than 60% and less than 70% F less than 60 - Calculate the average for each class - Calculate the variance for each class - How many students failed programming? - How many students failed programming but passed math? - What are the IDs of the students who got A in all the classes? - The professor decides that no one shall fail the programming class. Hence, you need to replace the grades of all students who got less than 6 in programming with 6, calculate the new mean and variance for the programming class and compare them with the old mean and variance for the same class. Why did this happen