Python Core

Quick Start

Prerequisites:

• Python 3.10+ installed (python --version)
• Dependency manager: uv (recommended), poetry, or pip+venv
• IDE with Python language server (VS Code, PyCharm)

Tools Used: Read, Write, Edit, Bash (for uv/poetry/pip commands), LSP diagnostics

Dependency Management Decision Tree:

New project? → Use uv: uv init, uv add, uv sync
Existing poetry? → Use poetry: poetry install, poetry add
Legacy/simple? → Use pip+venv: python -m venv, pip install

Basic Usage:

1. Set up environment (see decision tree)
2. Write code with type hints
3. Write tests first (TDD)
4. Run tests: pytest
5. Verify types: mypy .

What I Do

• Create Python 3.10+ applications (data science, backend, scripting, automation)
• Manage dependencies with uv, poetry, or pip
• Implement type hints (typing module, generics, protocols)
• Write tests with pytest (fixtures, parametrize, mocking)
• Work with pandas DataFrames (selection, groupby, merge)
• Use Python patterns (decorators, comprehensions, generators, dataclasses)
• Handle errors with logging integration
• Follow TDD workflow (Red-Green-Refactor)

When to Use Me

Use this skill when you:

• Create, refactor, or debug Python 3.10+ code
• Set up projects with dependency management (uv, poetry, pip)
• Implement type hints or fix type errors
• Write pytest tests (fixtures, parametrize, mocking)
• Work with pandas DataFrames
• Use Python patterns (decorators, generators, dataclasses)
• Handle errors with logging
• Follow TDD workflows

Python Version Features

Quick Reference Tables

Built-in Functions

String Operations

Collection Methods

Type Hints Basics

from typing import Optional

def greet(name: str) -> str:
    return f"Hello {name}"

def process_items(items: list[int]) -> dict[str, int]:
    return {"count": len(items), "sum": sum(items)}

def find_user(user_id: int) -> Optional[str]:
    return users.get(user_id)

def parse_value(val: str | int) -> int:  # Python 3.10+
    return int(val)

See references/type-hints.md for Generics and Protocols.

TDD Workflow (Red-Green-Refactor)

1. Red: Write failing test
2. Green: Write minimal code to pass
3. Refactor: Improve while keeping tests green

# Red: Test fails (function doesn't exist)
def test_total():
    assert calculate_total([10, 20, 30]) == 60

# Green: Make it pass
def calculate_total(items):
    return sum(items)

# Refactor: Add types and edge cases
def calculate_total(items: list[int]) -> int:
    return sum(items) if items else 0

See references/pytest.md for fixtures and mocking.

Error Handling with Logging

import logging

logger = logging.getLogger(__name__)

# Try/Except/Finally
try:
    result = risky_operation()
except ValueError as e:
    logger.error(f"Invalid value: {e}")
    raise
finally:
    cleanup_resources()

# Custom Exceptions
class DataValidationError(Exception):
    pass

def validate_data(data: dict) -> None:
    if "required_field" not in data:
        raise DataValidationError("required_field missing")

# Context Manager for cleanup
from contextlib import contextmanager

@contextmanager
def db_connection(url: str):
    conn = connect(url)
    try:
        yield conn
    finally:
        conn.close()

Examples

Example 1: Dataclass with Type Hints

from dataclasses import dataclass
from typing import List, Optional

@dataclass
class User:
    id: int
    name: str
    email: str
    tags: List[str] = None
    
    def __post_init__(self):
        if self.tags is None:
            self.tags = []

# Usage
user = User(id=1, name="Alice", email="alice@example.com")
user.tags.append("admin")

Example 2: Decorator Pattern

import functools
import time
import logging

logger = logging.getLogger(__name__)

def timing_decorator(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        elapsed = time.time() - start
        logger.info(f"{func.__name__} took {elapsed:.2f}s")
        return result
    return wrapper

@timing_decorator
def slow_function():
    time.sleep(1)
    return "done"

Example 3: List Comprehension with Filtering

# Filter and transform in one line
numbers = [1, 2, 3, 4, 5, 6]
even_squares = [x**2 for x in numbers if x % 2 == 0]
# Result: [4, 16, 36]

# Dict comprehension
users = [("alice", 25), ("bob", 30)]
user_dict = {name: age for name, age in users}
# Result: {"alice": 25, "bob": 30}

Example 4: Generator for Memory Efficiency

def read_large_file(file_path: str):
    with open(file_path, 'r') as f:
        for line in f:
            yield line.strip()

for line in read_large_file("huge.txt"):
    process(line)

See references/patterns.md for more patterns.

Common Errors

Related Skills

• pytest-testing: Fixtures, parametrize, mocking
• pandas-data-analysis: Advanced DataFrame operations
• fastapi-backend: REST APIs with FastAPI
• django-web: Web applications with Django
• github-actions: CI/CD for Python

References

• references/patterns.md - Context managers, decorators, comprehensions, generators, dataclasses, async/await
• references/type-hints.md - Advanced typing (Generics, Protocols, TypeVar, type guards)
• references/pytest.md - Fixtures, parametrize, mocking, assertions
• references/pandas.md - DataFrame operations, groupby, merge, performance
• references/dependency-management.md - uv, poetry, pip workflows and pyproject.toml