python
Python Development
Write clean, performant, idiomatic Python code.
When to Use
- Writing Python code
- Refactoring Python projects
- Performance optimization
- Setting up Python tooling
- Code review for Python
Python Best Practices
Code Style
- Follow PEP 8
- Use type hints (Python 3.9+)
- Prefer f-strings over .format()
- Use pathlib over os.path
Modern Features
# Type hints
def process(data: list[dict]) -> dict[str, int]:
...
# Dataclasses
from dataclasses import dataclass
@dataclass
class User:
name: str
email: str
active: bool = True
# Context managers
from contextlib import contextmanager
@contextmanager
def timer():
start = time.time()
yield
print(f"Elapsed: {time.time() - start:.2f}s")
# Generators for memory efficiency
def read_large_file(path):
with open(path) as f:
yield from f
Error Handling
# Custom exceptions
class ValidationError(Exception):
def __init__(self, field: str, message: str):
self.field = field
self.message = message
super().__init__(f"{field}: {message}")
# Proper exception chaining
try:
process()
except ValueError as e:
raise ProcessingError("Failed to process") from e
Project Structure
project/
├── src/
│ └── package/
│ ├── __init__.py
│ └── module.py
├── tests/
│ └── test_module.py
├── pyproject.toml
└── README.md
Tooling Setup
# pyproject.toml
[tool.ruff]
line-length = 88
select = ["E", "F", "I", "UP"]
[tool.mypy]
strict = true
[tool.pytest.ini_options]
testpaths = ["tests"]
Testing Pattern
import pytest
@pytest.fixture
def sample_data():
return {"key": "value"}
def test_process(sample_data):
result = process(sample_data)
assert result["status"] == "success"
Examples
Input: "Refactor this Python code" Action: Apply PEP 8, add type hints, simplify logic, improve error handling
Input: "Make this faster" Action: Profile, identify bottlenecks, use generators/caching, verify improvement
More from htlin222/dotfiles
cpp
Write modern C++ with RAII, smart pointers, and STL. Use for C++ development, memory safety, or performance optimization.
130refactor
Refactor code for quality and maintainability. Use for cleanup and tech debt reduction.
74data-science
Data analysis, SQL queries, BigQuery operations, and data insights. Use for data analysis tasks and queries.
52c-lang
Write efficient C code with proper memory management and system calls. Use for C optimization, memory issues, or system programming.
46quarto-book
Generate Quarto Book project structure with chapters, configuration, and output settings. Use when user wants to create a book, multi-chapter document, technical manual, or asks about Quarto book setup.
45scientific-figure-assembly
Assemble multi-panel scientific figures with panel labels (A, B, C) at publication quality (300 DPI) using R. Use when combining individual plots into journal-ready figures.
43