MPCS 51042 - Python Programming

Python is one of the most commonly used programming languages today across a huge range of sectors. It is a language designed to be easy to learn that emphasizes readable code via powerful and extensible syntax. This course provides a thorough overview of the Python language with an emphasis on writing idiomatic code.

This course aims to:

This course will cover:

Readings

While there are no required textbooks for this course, I provide suggested readings from the following books.

I’d highly recommend picking up one of these, there is no need to get both:

  • Python Distilled (1st Edition), by David Beazley
  • Learning Python (5th Edition), by Mark Lutz

Additionally, these books may be useful to further explain concepts discussed in this course:

  • Fluent Python (2nd Edition), by Luciano Ramalho (1st Edition OK)
  • Python in a Nutshell (3rd Edition), by Martelli, Ravenscroft, and Holden

Office Hours

Regular office hours start Week 2, unless otherwise indicated.

Who Where When
Summer Han JCL 207 Monday 9:00-11:00am
Pablo Hernandez Pedraza JCL 207 Tuesday 3:30-4:30pm
Keling Yue JCL 205 Wednesday, 10:00am-noon (starting Week 3)
James Turk JCL 398E Wednesday 2:00-4:00pm
Stanley Yi JCL 205 Thursday 9:00-11:00am
Pablo Hernandez Pedraza JCL Common Area 3A Thursday 3:30-4:30pm
James Turk Zoom Friday, 11am-1pm

!!! note

James also has openings for appointments available: <https://cal.com/jamesturk/autumn-office-hours>

Please note that these are limited and they are shared between multiple classes, so please be considerate in your usage and favor the drop-in office hours for help on assignments.

Schedule

Meets Tuesday & Thursday 11:00am-12:20pm @ JCL 011.

Any schedule changes will be announced in class and on Ed Discussion.

Week Topics Assignment Resources & Readings *
1
Sep. 29-Oct 3 		Python Basics
  • Running Python
  • Basic Syntax & Data Types
  • Sequence Data Types
Homework #0 Out Python Distilled Ch. 1-3.3
Learning Python Ch. 1-7, 12, 13, 16
Python Tutorial Parts 1-4.5
2
Oct 6-10 		Python Basics (Part 2)
  • Compound Data Types
  • User Interaction
  • File I/O
Homework #0 Due Oct 10 @ 11:59pm CT
Homework #1 Out 		Python Distilled Ch. 4.1-4.5, 5.1-5.10, 9.6-9.12
Learning Python Ch. 8, 9
Python Tutorial Parts 5, 7
3
Oct 13-17 		Advanced Functions
  • Advanced Arguments
  • Functional Programming
Homework #1 Due Oct 17 @ 11:59pm CT
Homework #2 Out
Quiz #1 - Oct 16 		Python Distilled Ch. 5-6
Learning Python Ch. 14, 17-20, 39
Python Tutorial Part 4
4
Oct 20-24 		Comprehensions, Generators, Decorators
  • Generators & Comprehensions
  • Decorators
Homework #2 Due Oct 24 @ 11:59pm CT
Homework #3 Out 		Python Distilled Ch. 8, 10
Learning Python Ch. 22-24
Python Tutorial Part 6, 10-12
5
Oct 27-Oct 31 		Object Oriented Programming
  • Classes
  • Inheritance
Homework #3 Due Oct 31 @ 11:59pm CT
Homework #4 Out 		Python Distilled Ch. 7
Learning Python Ch. 26-28
Python Tutorial Part 9.1-9.4
6
Nov 3-7 		Advanced Classes
  • Abstract Base Classes
  • Class Based Design
  • Properties
Homework #4 Due Nov 7 @ 11:59pm CT
Homework #5 Out
Quiz #2 - Nov 4 		Python Distilled Ch. 7
Learning Python Ch. 29, 31, 32
Python Tutorial Part 9.5
7
Nov 10-14 		Python Data Model
  • Special Methods
  • Context Managers
  • Exceptions
  • Standard Library
Homework #5 Due Nov 14 @ 11:59pm CT
Homework #6 Out 		Python Distilled Ch. 3.4-3.7, 4
Learning Python Ch. 30, 32-36, 38
Python Tutorial Part 8
8
Nov 17-21 		Python Packages & Ecosystem
  • Standard Library Continued
  • PyPI
  • Testing
Homework #6 Due Nov 21 @ 11:59pm CT Python Distilled Ch. 9.15, 10
Python Tutorial Part 10-12
Nov 24-28 Thanksgiving Break
9
Dec 1-Dec 5 		Special Topics
  • Past topics have included: optimization, NumPy/pandas, polars, data viz

Quiz #3 - Dec 2
10
Dec 8-12 		No Class Final Project
Final Exam TBD

Programming Assignments

There will be seven programming assignments and a final project.

!!! note

For planning purposes, it may be helpful to know Homework #0 and #6 are shorter than the rest.

All programming assignments in this course will be distributed via GitHub Classroom and submitted via Gradescope.

Checking Out an Assignment

  1. When an assignment is made available a link to GitHub classroom will be posted on Ed. Clicking the link will create a new private repository for the assignment.

  2. Once the repository is created, you will want to clone the repository to your machine.

    Visit your repository on GitHub, and click on the green “Code” button. Be sure SSH is selected as pictured. Copy the URL to the repository.

    To check out your project, you can run git clone <URL> from the machine you’re doing your work on.

  3. Once the assignment is checked out, the first step will be uv sync, this will ensure you have the appropriate packages downloaded for the assignment.

  4. Each assignment has a README.md, this contains the full description of the assignment. It is recommended you read all the way through before beginning, careful reading of requirements is a skill being practiced in this class.

Working on an Assignment

General Tips

  • Install dependencies. Once the assignment is checked out, first run uv sync. This will ensure that you have the necessary packages for this project, details will be discussed in class.

  • Read the instructions carefully - Make sure you understand what you’re being asked to do before you start writing code. If you’re not sure, ask for clarification.

  • Check in often - Check your code into git and push to GitHub regularly. This will help you avoid losing work if something goes wrong with your machine.

  • Run the tests - The tests are there to help you. If you’re not sure if your code is correct, run the tests and see if they pass. If they don’t, read the error messages carefully - they may give you a hint as to what’s wrong.

  • Experiment in the REPL - If you’re not sure how a particular function or method works, try it out in the REPL (python3 or ipython). A major advantage of interpreted languages is this ability to experiment interactively.

  • Ask questions of course staff - If anything is not clear, ask questions via Ed! If you are stuck or need help with a concept, office hours may be a good idea as well! See Getting Help for more advice on how to ask for help.

  • Use a Linter. ruff will be introduced and is recommended. Using it (or a linter of your choice) in this course will let you focus on the actual problem, help you write cleaner code, and detect common issues that may result in a lower grade.

pytest Tips

Each assignment comes with a set of tests to be run with pytest.

(If you are using uv, remember to prefix all pytest commands with uv run!)

pytest has some helpful command line options you may wish to use:

  • Passing a filename like tests/test_markov.py will run only the tests in that file.
  • -v will print out more information about each test
  • -vv will print out even more information about each test, particularly useful when comparing expected output vs. actual
  • -s will include any output from print statements (normally suppressed by pytest).
  • -k <pattern> will only run tests whose names match the pattern. (So pytest -k frontend would match test_frontend_template and test_frontend_call but not test_database_setup, etc.)
  • -x will stop running tests after the first failure.

Grading

We will be using specifications grading in this course. The goal of specifications grading is to help students focus on their mastery of the material and identify areas for improvement as the quarter progresses.

Shorthand: focus on skills, not scores.

If you are new to using specifications grading, here’s what you can expect:

  • For each assignment you will be given a set of specifications. Meeting all/most of those will earn the highest grade. This is described in detail below, but aims to allow you to focus on learning and not on the difference between an 89 and a 90 on an assignment. It also more closely resembles how work is evaluated in the real world, where work is either satisfactory or not.
  • Where time permits, we will allow resubmissions of work to learn from mistakes. The specific policy is detailed below.

SNU Scale

Programming Assignments will be evaluated using an SNU scale:

  • Satisfactory (S): While the work has some room for improvement, the student has put in a good-faith effort to complete all the work and demonstrated sufficient mastery of the material.

  • Needs Improvement (N): The student has put in a good-faith effort to complete the work, but revealed a lack of mastery in the material that can be addressed via concrete feedback. The work could become Satisfactory with some major revisions.

  • Ungradable (U): The student did not submit any work, or did not complete a sufficient portion of the work to be graded.

All assignments will be evaluated for completeness and code quality, and given a grade for each.

Completeness will be determined using an automated test suite. “Does your code do what we asked of it?”

Code quality will be evaluated by reviewing the code for:

  1. adherence to the style guide
  2. appropriate choice of algorithms and data structures
  3. appropriate use of functions and classes to structure the computation.

Resubmissions

You will be allowed to resubmit one programming assignment (not the project).

Once a programming assignment is graded and returned to you, there will be a two week window for you to resubmit that assignment. This means that, if you produce work that needs some improvement, you will have a chance to address the grader’s feedback to improve your score on that assignment.

The procedure for resubmissions will be shared via Ed.

Mastery Quizzes

There are three “mastery quizzes”, these quizzes serve two purposes:

  1. Give you a chance to practice on the kinds of questions on a paper programming exam. (for this final, or certain interviews)

  2. Allow you to ensure that you are retaining the important concepts, and not overly relying on StackOverflow/GPT/etc. when solving problems.

Each mastery quiz will be scored out of 30-40 points.

These scores are for primarily your own benefit, there is no penalty for doing poorly or even not taking the quizzes. Make-ups will only be available in exceptional circumstances.

If you get more than 90% of the available points, you have demonstrated mastery of the subjects at hand and will waive that third of the exam.

If you do not receive above 90% on any portion, you may elect to use one of your quiz grades as a substitute for that portion of the exam. (See Example 2 below.)

Final Exam

The final exam will be given in Week 10/finals week. It will scored out of 100 points, and will cover all topics from the course. It will be made up of three parts, corresponding to the three mastery quizzes.

As mentioned, you may opt to take only some portions of it (or skip it altogether) depending on your performance on the quizzes and desired final grade.

Example 1

  • Quiz #1: 28/30 (93%)
  • Quiz #2: 20/35 (57%)
  • Quiz #3: 30/35 (86%)

This student has demonstrated mastery on the topics covered by Quiz #1, and will not need to take that portions of the final exam.

They take the final exam:

  • Part 1: Waived/30
  • Part 2: 32/35
  • Part 3: 29/35

Their final exam score is 30+32+29 = 91.

Example 2

  • Quiz #1: 20/30 (66%)
  • Quiz #2: 14/35 (40%)
  • Quiz #3: 27/35 (77%)

First, this student did not perform poorly!

The mastery quizzes are given quickly after content is introduced, and if it is new to you (which is the hope for most people in this course!) you are unlikely to have already fully digested it.

Instead this student can recognize that the material covered on Quiz 2 is their weakest, and plan accordingly.

They take the final and do much better, having now seen the kinds of questions that may be asked and focusing on the ones they struggled with:

  • Part 1: 28/30
  • Part 2: 35/35
  • Part 3: 26/35

Their grade went up significantly in Part 1 and 2. Part 3 they did a point worse than the quiz on, but overall a marked improvement.

Their final grade here would be: 28 + 35 + 27 = 90.

The 27 is used from their Quiz 3 instead of their slight decrease in Quiz 2.

(This will only be done for one section, you may opt to use this rule to skip a single section of the final if you wish to focus your efforts on two others and accept a sub-90% quiz grade instead.)

Example 3

  • Quiz #1: missed (0%)
  • Quiz #2: 34/35 (97%)
  • Quiz #3: 34/35 (97%)

This individual has earned a 70 on the exam without even taking it (full credit for parts 2 and 3). Depending on what they need for the final grade they want, they may decide to take Part 1 of the final, or skip it altogether.

Final Grade

Your final grade will be determined by 18 programming grades (14 from the 7 assignments, and 4 from the project) and your final exam score.

Assignment Type Number When
Programming Assignment 7 Weeks 1-7
Programming Project 1 Weeks 8-10
Quizzes 3 Weeks 3, 6, 9
Final Exam 1 Week 10

Your final grade is a function of three variables:

  • S the total number of S grades you received.
  • U the maximum number of U grades you received.
  • T Your exam/quiz total, which will be between 0-100.
S >= U <= 50 <= T < 70 70 <= T < 80 80 <= T < 90 T > 90
16 0 B B+ A- A
14 2 B- B B+ A-
12 3 C+ B- B B+
10 4 C C+ B- B
9 5 C- C C+ B-
8 6 D+ C- C C+
  • If you have 16 S, 2 N, 0 U: then you are in the top row. A 90 on the exam will earn you an A.
  • If you miss an assignment entirely and earn 2 U, but have at least 14 S: you are in the second row. A 80 on the exam would earn you an B+ overall.

Anything lower than what is represented in the chart will need to be addressed on a case-by-case basis.

Remember: A C+ is a passing grade for MPCS courses.

Academic Honesty Violation

The minimum penalty for an academic honesty violation is a U on for all grades on the assignment in question and having the highest grade you can earn in the class capped at a B.

You may not use a regrade on an assignment flagged for an academic honesty violation.

There will be no exceptions to this policy.

Curving

In lieu of traditional curving, I reserve the right to lower the thresholds for each grade.

I will not raise the thresholds. (So if you earn an A- according to the above chart, you will get an A- or better.)