Python 3 docs
Language reference, library reference, tutorial.
Free · For students
Python Resources Built for University Coursework
Nine in-depth references plus curated external links covering the artefacts students hit during real Python homework. Cheatsheets you keep open while coding. Twenty common errors with the actual fix. Dev setup, debugging, workflow, originality, scaffolds you can copy, and the autograder survival guide for Gradescope submissions.
In-depth guides
9 deep references, no filler
Each guide solves a specific problem students hit on real coursework. Tested code, worked examples, concrete fixes. Free to read.
Python Cheatsheet
One-page reference for syntax students forget mid-assignment. Data types, control flow, functions, OOP, comprehensions, stdlib patterns. 35 runnable snippets.
20 Common Python Errors
Top 20 errors students hit on coursework. NameError, KeyError, IndentationError, RecursionError. Diagnosis, broken/fixed code, prevention line.
Python Dev Setup
Install Python 3, create venv, manage pip and requirements.txt, configure VS Code, run pytest, format with ruff. Copy-pastable CLI examples.
Big-O Cheatsheet
Time complexity for every Python builtin: list, dict, set, str, tuple, deque, heapq. Average and worst case. Common quadratic blunders to avoid.
Homework Workflow
End-to-end Python assignment workflow: read brief, scope I/O, sketch pseudocode, write tests first, implement, verify, submission prep. 7 steps.
Debugging Guide
Debug Python with breakpoint() and pdb, VS Code debugger, the logging module. Annotated pdb session, common bug patterns, traceback reading.
Originality Guide
How MOSS, JPlag, and Turnitin work. Why honest code still flags as similar. The 5-step originality workflow plus the in-class explanation test.
Project Starter Templates
Six copy-runnable scaffolds: Flask app, Django app, sklearn ML pipeline, Jupyter DS notebook, pytest project, CLI with argparse.
Python Autograder Survival Guide
Why your Python code passes locally but fails the autograder. 15 edge cases that break Gradescope submissions, with a broken/fixed code example for each one. Pre-submission checklist.
Course shortcuts
Quick links by university course
Direct links to the official course homepage for the Python and DS courses we see most. Tell us your course in the brief; we match its conventions on every delivery.
CS50P
Programming with Python (Harvard)
6.100LIntro to CS and Programming (MIT)
CS106AProgramming Methodology (Stanford)
DATA 100Principles and Techniques of Data Science (UC Berkeley)
CS229Machine Learning (Stanford)
CS231nConvolutional Neural Networks (Stanford)
CS50WWeb Programming with Python and JavaScript (Harvard)
CSE 163Intermediate Data Programming (UW)
Curated external links
Docs, books, courses, channels, tools
Outbound links that actually teach. No SEO spam, no listicle filler. Each entry has the one-line reason it earned a spot.
Official documentation
PEP 8 style guide
The canonical Python style guide. Read once, follow forever.
Python HOWTO index
Topic-focused guides: logging, sorting, regex, descriptors, sockets.
CPython source on GitHub
Read how stdlib is actually implemented when docs are ambiguous.
Library docs students hit most
pandas user guide
DataFrame, groupby, merge, time-series. The DS reference.
NumPy user guide
Arrays, broadcasting, ufuncs, linalg.
scikit-learn user guide
Estimator API, pipelines, model selection, metrics.
Flask docs
Routes, Blueprints, Jinja2, Flask-SQLAlchemy.
Django docs
Models, ORM, views, REST framework, admin.
matplotlib user guide
Pyplot interface, axes, ticks, colormaps.
pytest docs
Fixtures, parametrize, monkeypatch, mocking.
asyncio docs
Coroutines, tasks, the event loop, async patterns.
Books worth owning
Python for Data Analysis (Wes McKinney)
The pandas creator. Free online. Best DS book.
Fluent Python (Luciano Ramalho, 2nd ed)
Idiomatic Python. The bridge from "works" to "Pythonic".
Hands-On ML with Scikit-Learn, Keras, TensorFlow (Aurelien Geron)
The standard applied-ML textbook. Worth the price.
Two Scoops of Django (Greenfeld)
The Django best-practices canon.
Free university courses
CS50P: Python (Harvard)
Free. Tested by half a million students. Best intro.
MIT 6.0001: Intro to CS and Programming
MIT OpenCourseWare. Free lectures and assignments.
UC Berkeley DATA 100
Principles and techniques of data science. Free notes + labs.
Stanford CS229 (Andrew Ng)
The classical ML reference course. Free lectures.
fast.ai Practical Deep Learning
Top-down deep learning. Free.
YouTube channels that teach, not pad
mCoding
James Murphy. Deep Python explainers, no filler.
ArjanCodes
Code design, refactoring patterns, real-world Python.
Real Python
Article-quality video tutorials across the stack.
Corey Schafer
Django and Flask deep tutorials. The pre-fastapi standard.
3Blue1Brown
Math for ML and DS. Linear algebra, calculus, neural nets visualized.
Datasets and practice
Kaggle datasets
Tens of thousands of CSV/Parquet datasets for DS and ML coursework.
UCI ML Repository
Classic ML benchmark datasets. Iris, Wine, Adult, MNIST.
OpenML
Datasets with metadata and benchmark results.
data.gov
US government datasets. Great for civic-tech and stats homework.
LeetCode (Python track)
For algorithms and data structures coursework practice.
Tools and IDEs
VS Code
Free. The de facto Python IDE. See our setup guide for config.
PyCharm Community
Free community edition. Heavier than VS Code, deeper refactors.
Jupyter
Notebook environment for DS and ML. JupyterLab is the modern UI.
Google Colab
Free Jupyter notebooks with GPU. Great for ML coursework.
ruff
Modern linter + formatter. Replaces flake8, black, isort, pyupgrade.
mypy
Static type checker. Pair with type hints to catch bugs early.
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