Introduction
This project was started to bring together useful Python code snippets that make coding faster, easier, and more enjoyable. You can explore all the cheat sheets at Pysheeet. Contributions are always welcome—feel free to fork the repo and submit a pull request to help it grow!
Plugin
pysheeet is available as a Claude Code plugin. Once installed, Claude automatically uses the cheat sheets to answer Python questions — just ask naturally and the skill triggers based on context.
Installation
As a Claude Code plugin (recommended):
# Step 1: Add the marketplace claude plugin marketplace add crazyguitar/pysheeet # Step 2: Install the plugin claude plugin install pysheeet@pysheeet
Local testing (single session only):
claude --plugin-dir /path/to/pysheeet
Manual installation (requires cloning the repo):
git clone https://github.com/crazyguitar/pysheeet.git mkdir -p ~/.claude/skills cp -r pysheeet/skills/py ~/.claude/skills/py
Python Interview Cheatsheet
Curated Python interview questions indexed by topic — each question links directly to the section of the cheat sheet that answers it. Use it for quick review before an interview, or to drill down on a specific area (GIL, asyncio, decorators, MRO, and more).
What's New In Python 3
This part only provides a quick glance at some important features in Python 3. If you're interested in all of the most important features, please read the official document, What’s New in Python.
Core Python fundamentals including data types, functions, classes, and commonly used patterns for everyday programming tasks.
- From Scratch
- Future
- Typing
- Class
- Function
- Unicode
- List
- Set
- Dictionary
- Heap
- Generator
- Regular expression
System
Date/time handling, file I/O, and operating system interfaces.
- Datetime - Timestamps, formatting, parsing, timezones, timedelta
- Files and I/O - Reading, writing, pathlib, shutil, tempfile
- Operating System - Processes, environment, system calls
Concurrency
Threading, multiprocessing, and concurrent.futures for parallel execution. Covers synchronization primitives, process pools, and bypassing the GIL.
- Threading - Threads, locks, semaphores, events, conditions
- Multiprocessing - Processes, pools, shared memory, IPC
- concurrent.futures - Executors, futures, callbacks
Asyncio
Asynchronous programming with Python's asyncio module. Covers coroutines,
event loops, tasks, networking, and advanced patterns.
- A Hitchhiker's Guide to Asynchronous Programming - Design philosophy and evolution
- Asyncio Basics - Coroutines, tasks, gather, timeouts
- Asyncio Networking - TCP/UDP servers, HTTP, SSL/TLS
- Asyncio Advanced - Synchronization, queues, subprocesses
C/C++ Extensions
Native extensions for performance-critical code. Covers modern pybind11 (used by PyTorch, TensorFlow), ctypes, cffi, Cython, and the traditional Python C API. Also includes a guide for Python developers learning modern C++ syntax.
- ctypes - Load shared libraries without compilation
- Python C API - Traditional C extension reference
- Modern C/C++ Extensions - pybind11, Cython
- Learn C++ from Python - Modern C++ for Python developers
Security
Modern cryptographic practices and common security vulnerabilities. Covers encryption, TLS/SSL, and why legacy patterns are dangerous.
- Modern Cryptography - AES-GCM, RSA-OAEP, Ed25519, Argon2
- TLS/SSL and Certificates - HTTPS servers, certificate generation
- Common Vulnerabilities - Padding oracle, injection, timing attacks
Network
Low-level network programming with Python sockets. Covers TCP/UDP communication, server implementations, asynchronous I/O, SSL/TLS encryption, and packet analysis.
Database
Database access with SQLAlchemy, Python's most popular ORM. Covers connection management, raw SQL, object-relational mapping, and common query patterns.
LLM
Large Language Models (LLM) training, inference, and optimization. Covers PyTorch for model development, distributed training across GPUs, and vLLM/SGLang for high-performance LLM inference and serving.
- PyTorch - Tensors, autograd, neural networks, training loops
- Megatron - NVIDIA Megatron training/fine-tuning framework with enroot/pyxis
- LLM Serving - vLLM and SGLang for production inference with TP/PP/DP/EP
- LLM Benchmark - Benchmark suite for measuring serving performance
HPC
High-Performance Computing tools for cluster management and job scheduling. Covers Slurm workload manager and Ray for distributed computing on GPU clusters.
Blog
Supplementary topics covering Python internals, debugging techniques, and language features that don't fit elsewhere.
- NVSHMEM Multi-NIC Support with AWS EFA
- Is Disaggregated Prefill/Decode a Silver Bullet for LLM Serving?
- Monitoring EFA with NCCL GIN and Nsys
- GPU-Initiated Networking for NCCL on AWS
- PEP 572 and the walrus operator
- Python Interpreter in GNU Debugger
PDF Version
How to run the server
$ virtualenv venv $ . venv/bin/activate $ pip install -r requirements.txt $ make $ python app.py # URL: localhost:5000