There is a midterm.csv file about the grading result of a course's midterm exam. The first row presents the header. Then, each row presents a student's enrolled name and his or her score. Write a program to read the file and collect all the scores in a list (without header row!) Download the midterm.csv file. Classify those scores according to the criteria given by question 2 in Problem Set 1. Then, draw a histogram with plotly for ranks A, B, C, D, F

Sure! For reading and processing the CSV file, we can use the `csv` module in Python. Here's the code to read and collect all the scores in a list:

import csv

scores = []

with open('midterm.csv', newline='') as csvfile:
    reader = csv.reader(csvfile)
    next(reader)  # skip header row
    for row in reader:
        scores.append(int(row[1]))

print(scores)

This code will create a list `scores` containing all the numeric scores from the CSV file.

For the second part of the question, we need to classify the scores into A, B, C, D, and F categories. According to the criteria given in Problem Set 1, we can use the following classification:

- A: score >= 90
- B: 80 <= score < 90
- C: 70 <= score < 80
- D: 60 <= score < 70
- F: score < 60

Here's the code to classify the scores and count the number of scores in each category:

counts = {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'F': 0}

for score in scores:
    if score >= 90:
        counts['A'] += 1
    elif score >= 80:
        counts['B'] += 1
    elif score >= 70:
        counts['C'] += 1
    elif score >= 60:
        counts['D'] += 1
    else:
        counts['F'] += 1

print(counts)

This code will create a dictionary `counts` containing the number of scores in each category.

Finally, we can use the `plotly` library to create a histogram of the scores. Here's the complete code:

import csv
import plotly.graph_objs as go

scores = []

with open('midterm.csv', newline='') as csvfile:
    reader = csv.reader(csvfile)
    next(reader)  # skip header row
    for row in reader:
        scores.append(int(row[1]))

counts = {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'F': 0}

for score in scores:
    if score >= 90:
        counts['A'] += 1
    elif score >= 80:
        counts['B'] += 1
    elif score >= 70:
        counts['C'] += 1
    elif score >= 60:
        counts['D'] += 1
    else:
        counts['F'] += 1

labels = ['A', 'B', 'C', 'D', 'F']
values = [counts[label] for label in labels]

fig = go.Figure([go.Bar(x=labels, y=values)])
fig.show()

This code will create a histogram of the scores, showing the number of scores in each category.