Python Patterns

Python Development Patterns

Idiomatic Python patterns and best practices for building robust, efficient, and maintainable applications.

When to Activate

• Writing new Python code
• Reviewing Python code
• Refactoring existing Python code
• Designing Python packages/modules

Core Principles

1. Readability Counts

Python prioritizes readability. Code should be obvious and easy to understand.

# Good: Clear and readable
def get_active_users(users: list[User]) -> list[User]:
    """Return only active users from the provided list."""
    return [user for user in users if user.is_active]


# Bad: Clever but confusing
def get_active_users(u):
    return [x for x in u if x.a]

2. Explicit is Better Than Implicit

Avoid magic; be clear about what your code does.

# Good: Explicit configuration
import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Bad: Hidden side effects
import some_module
some_module.setup()  # What does this do?

3. EAFP - Easier to Ask Forgiveness Than Permission

Python prefers exception handling over checking conditions.

# Good: EAFP style
def get_value(dictionary: dict, key: str) -> Any:
    try:
        return dictionary[key]
    except KeyError:
        return default_value

# Bad: LBYL (Look Before You Leap) style
def get_value(dictionary: dict, key: str) -> Any:
    if key in dictionary:
        return dictionary[key]
    else:
        return default_value

Type Hints

Basic Type Annotations

from typing import Optional, List, Dict, Any

def process_user(
    user_id: str,
    data: Dict[str, Any],
    active: bool = True
) -> Optional[User]:
    """Process a user and return the updated User or None."""
    if not active:
        return None
    return User(user_id, data)

Modern Type Hints (Python 3.9+)

# Python 3.9+ - Use built-in types
def process_items(items: list[str]) -> dict[str, int]:
    return {item: len(item) for item in items}

# Python 3.8 and earlier - Use typing module
from typing import List, Dict

def process_items(items: List[str]) -> Dict[str, int]:
    return {item: len(item) for item in items}

Type Aliases and TypeVar

from typing import TypeVar, Union

# Type alias for complex types
JSON = Union[dict[str, Any], list[Any], str, int, float, bool, None]

def parse_json(data: str) -> JSON:
    return json.loads(data)

# Generic types
T = TypeVar('T')

def first(items: list[T]) -> T | None:
    """Return the first item or None if list is empty."""
    return items[0] if items else None

Protocol-Based Duck Typing

from typing import Protocol

class Renderable(Protocol):
    def render(self) -> str:
        """Render the object to a string."""

def render_all(items: list[Renderable]) -> str:
    """Render all items that implement the Renderable protocol."""
    return "\n".join(item.render() for item in items)

Error Handling Patterns

Specific Exception Handling

# Good: Catch specific exceptions
def load_config(path: str) -> Config:
    try:
        with open(path) as f:
            return Config.from_json(f.read())
    except FileNotFoundError as e:
        raise ConfigError(f"Config file not found: {path}") from e
    except json.JSONDecodeError as e:
        raise ConfigError(f"Invalid JSON in config: {path}") from e

# Bad: Bare except
def load_config(path: str) -> Config:
    try:
        with open(path) as f:
            return Config.from_json(f.read())
    except:
        return None  # Silent failure!

Exception Chaining

def process_data(data: str) -> Result:
    try:
        parsed = json.loads(data)
    except json.JSONDecodeError as e:
        # Chain exceptions to preserve the traceback
        raise ValueError(f"Failed to parse data: {data}") from e

Custom Exception Hierarchy

class AppError(Exception):
    """Base exception for all application errors."""
    pass

class ValidationError(AppError):
    """Raised when input validation fails."""
    pass

class NotFoundError(AppError):
    """Raised when a requested resource is not found."""
    pass

# Usage
def get_user(user_id: str) -> User:
    user = db.find_user(user_id)
    if not user:
        raise NotFoundError(f"User not found: {user_id}")
    return user

Context Managers

Resource Management

# Good: Using context managers
def process_file(path: str) -> str:
    with open(path, 'r') as f:
        return f.read()

# Bad: Manual resource management
def process_file(path: str) -> str:
    f = open(path, 'r')
    try:
        return f.read()
    finally:
        f.close()

Custom Context Managers

from contextlib import contextmanager

@contextmanager
def timer(name: str):
    """Context manager to time a block of code."""
    start = time.perf_counter()
    yield
    elapsed = time.perf_counter() - start
    print(f"{name} took {elapsed:.4f} seconds")

# Usage
with timer("data processing"):
    process_large_dataset()

Context Manager Classes

class DatabaseTransaction:
    def __init__(self, connection):
        self.connection = connection

    def __enter__(self):
        self.connection.begin_transaction()
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        if exc_type is None:
            self.connection.commit()
        else:
            self.connection.rollback()
        return False  # Don't suppress exceptions

# Usage
with DatabaseTransaction(conn):
    user = conn.create_user(user_data)
    conn.create_profile(user.id, profile_data)

Comprehensions and Generators

List Comprehensions

# Good: List comprehension for simple transformations
names = [user.name for user in users if user.is_active]

# Bad: Manual loop
names = []
for user in users:
    if user.is_active:
        names.append(user.name)

# Complex comprehensions should be expanded
# Bad: Too complex
result = [x * 2 for x in items if x > 0 if x % 2 == 0]

# Good: Use a generator function
def filter_and_transform(items: Iterable[int]) -> list[int]:
    result = []
    for x in items:
        if x > 0 and x % 2 == 0:
            result.append(x * 2)
    return result

Generator Expressions

# Good: Generator for lazy evaluation
total = sum(x * x for x in range(1_000_000))

# Bad: Creates large intermediate list
total = sum([x * x for x in range(1_000_000)])

Generator Functions

def read_large_file(path: str) -> Iterator[str]:
    """Read a large file line by line."""
    with open(path) as f:
        for line in f:
            yield line.strip()

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

Data Classes and Named Tuples

Data Classes

from dataclasses import dataclass, field
from datetime import datetime

@dataclass
class User:
    """User entity with automatic __init__, __repr__, and __eq__."""
    id: str
    name: str
    email: str
    created_at: datetime = field(default_factory=datetime.now)
    is_active: bool = True

# Usage
user = User(
    id="123",
    name="Alice",
    email="alice@example.com"
)

Data Classes with Validation

@dataclass
class User:
    email: str
    age: int

    def __post_init__(self):
        # Validate email format
        if "@" not in self.email:
            raise ValueError(f"Invalid email: {self.email}")
        # Validate age range
        if self.age < 0 or self.age > 150:
            raise ValueError(f"Invalid age: {self.age}")

Named Tuples

from typing import NamedTuple

class Point(NamedTuple):
    """Immutable 2D point."""
    x: float
    y: float

    def distance(self, other: 'Point') -> float:
        return ((self.x - other.x) ** 2 + (self.y - other.y) ** 2) ** 0.5

# Usage
p1 = Point(0, 0)
p2 = Point(3, 4)
print(p1.distance(p2))  # 5.0

Decorators

Function Decorators

import functools
import time

def timer(func: Callable) -> Callable:
    """Decorator to time function execution."""
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        elapsed = time.perf_counter() - start
        print(f"{func.__name__} took {elapsed:.4f}s")
        return result
    return wrapper

@timer
def slow_function():
    time.sleep(1)

# slow_function() prints: slow_function took 1.0012s

Parameterized Decorators

def repeat(times: int):
    """Decorator to repeat a function multiple times."""
    def decorator(func: Callable) -> Callable:
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            results = []
            for _ in range(times):
                results.append(func(*args, **kwargs))
            return results
        return wrapper
    return decorator

@repeat(times=3)
def greet(name: str) -> str:
    return f"Hello, {name}!"

# greet("Alice") returns ["Hello, Alice!", "Hello, Alice!", "Hello, Alice!"]

Class-Based Decorators

class CountCalls:
    """Decorator that counts how many times a function is called."""
    def __init__(self, func: Callable):
        functools.update_wrapper(self, func)
        self.func = func
        self.count = 0

    def __call__(self, *args, **kwargs):
        self.count += 1
        print(f"{self.func.__name__} has been called {self.count} times")
        return self.func(*args, **kwargs)

@CountCalls
def process():
    pass

# Each call to process() prints the call count

Concurrency Patterns

Threading for I/O-Bound Tasks

import concurrent.futures
import threading

def fetch_url(url: str) -> str:
    """Fetch a URL (I/O-bound operation)."""
    import urllib.request
    with urllib.request.urlopen(url) as response:
        return response.read().decode()

def fetch_all_urls(urls: list[str]) -> dict[str, str]:
    """Fetch multiple URLs concurrently using threads."""
    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
        future_to_url = {executor.submit(fetch_url, url): url for url in urls}
        results = {}
        for future in concurrent.futures.as_completed(future_to_url):
            url = future_to_url[future]
            try:
                results[url] = future.result()
            except Exception as e:
                results[url] = f"Error: {e}"
    return results

Multiprocessing for CPU-Bound Tasks

def process_data(data: list[int]) -> int:
    """CPU-intensive computation."""
    return sum(x ** 2 for x in data)

def process_all(datasets: list[list[int]]) -> list[int]:
    """Process multiple datasets using multiple processes."""
    with concurrent.futures.ProcessPoolExecutor() as executor:
        results = list(executor.map(process_data, datasets))
    return results

Async/Await for Concurrent I/O

import asyncio

async def fetch_async(url: str) -> str:
    """Fetch a URL asynchronously."""
    import aiohttp
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.text()

async def fetch_all(urls: list[str]) -> dict[str, str]:
    """Fetch multiple URLs concurrently."""
    tasks = [fetch_async(url) for url in urls]
    results = await asyncio.gather(*tasks, return_exceptions=True)
    return dict(zip(urls, results))

Package Organization

Standard Project Layout

myproject/
├── src/
│   └── mypackage/
│       ├── __init__.py
│       ├── main.py
│       ├── api/
│       │   ├── __init__.py
│       │   └── routes.py
│       ├── models/
│       │   ├── __init__.py
│       │   └── user.py
│       └── utils/
│           ├── __init__.py
│           └── helpers.py
├── tests/
│   ├── __init__.py
│   ├── conftest.py
│   ├── test_api.py
│   └── test_models.py
├── pyproject.toml
├── README.md
└── .gitignore

Import Conventions

# Good: Import order - stdlib, third-party, local
import os
import sys
from pathlib import Path

import requests
from fastapi import FastAPI

from mypackage.models import User
from mypackage.utils import format_name

# Good: Use isort for automatic import sorting
# pip install isort

init.py for Package Exports

# mypackage/__init__.py
"""mypackage - A sample Python package."""

__version__ = "1.0.0"

# Export main classes/functions at package level
from mypackage.models import User, Post
from mypackage.utils import format_name

__all__ = ["User", "Post", "format_name"]

Memory and Performance

Using slots for Memory Efficiency

# Bad: Regular class uses __dict__ (more memory)
class Point:
    def __init__(self, x: float, y: float):
        self.x = x
        self.y = y

# Good: __slots__ reduces memory usage
class Point:
    __slots__ = ['x', 'y']

    def __init__(self, x: float, y: float):
        self.x = x
        self.y = y

Generator for Large Data

# Bad: Returns full list in memory
def read_lines(path: str) -> list[str]:
    with open(path) as f:
        return [line.strip() for line in f]

# Good: Yields lines one at a time
def read_lines(path: str) -> Iterator[str]:
    with open(path) as f:
        for line in f:
            yield line.strip()

Avoid String Concatenation in Loops

# Bad: O(n²) due to string immutability
result = ""
for item in items:
    result += str(item)

# Good: O(n) using join
result = "".join(str(item) for item in items)

# Good: Using StringIO for building
from io import StringIO

buffer = StringIO()
for item in items:
    buffer.write(str(item))
result = buffer.getvalue()

Python Tooling Integration

Essential Commands

# Code formatting
black .
isort .

# Linting
ruff check .
pylint mypackage/

# Type checking
mypy .

# Testing
pytest --cov=mypackage --cov-report=html

# Security scanning
bandit -r .

# Dependency management
pip-audit
safety check

pyproject.toml Configuration

[project]
name = "mypackage"
version = "1.0.0"
requires-python = ">=3.9"
dependencies = [
    "requests>=2.31.0",
    "pydantic>=2.0.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.4.0",
    "pytest-cov>=4.1.0",
    "black>=23.0.0",
    "ruff>=0.1.0",
    "mypy>=1.5.0",
]

[tool.black]
line-length = 88
target-version = ['py39']

[tool.ruff]
line-length = 88
select = ["E", "F", "I", "N", "W"]

[tool.mypy]
python_version = "3.9"
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = true

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "--cov=mypackage --cov-report=term-missing"

Quick Reference: Python Idioms

Anti-Patterns to Avoid

# Bad: Mutable default arguments
def append_to(item, items=[]):
    items.append(item)
    return items

# Good: Use None and create new list
def append_to(item, items=None):
    if items is None:
        items = []
    items.append(item)
    return items

# Bad: Checking type with type()
if type(obj) == list:
    process(obj)

# Good: Use isinstance
if isinstance(obj, list):
    process(obj)

# Bad: Comparing to None with ==
if value == None:
    process()

# Good: Use is
if value is None:
    process()

# Bad: from module import *
from os.path import *

# Good: Explicit imports
from os.path import join, exists

# Bad: Bare except
try:
    risky_operation()
except:
    pass

# Good: Specific exception
try:
    risky_operation()
except SpecificError as e:
    logger.error(f"Operation failed: {e}")

Remember: Python code should be readable, explicit, and follow the principle of least surprise. When in doubt, prioritize clarity over cleverness.

Python Testing Patterns (pytest)

Comprehensive testing strategies for Python applications using pytest, TDD methodology, and best practices.

Test-Driven Development (TDD)

Always follow the TDD cycle:

1. RED: Write a failing test for the desired behavior
2. GREEN: Write minimal code to make the test pass
3. REFACTOR: Improve code while keeping tests green

# Step 1: Write failing test (RED)
def test_add_numbers():
    result = add(2, 3)
    assert result == 5

# Step 2: Write minimal implementation (GREEN)
def add(a, b):
    return a + b

# Step 3: Refactor if needed (REFACTOR)

Coverage Requirements

• Target: 80%+ code coverage
• Critical paths: 100% coverage required
• Use pytest --cov to measure coverage

pytest --cov=mypackage --cov-report=term-missing --cov-report=html

pytest Fundamentals

import pytest

def test_addition():
    """Test basic addition."""
    assert 2 + 2 == 4

# Exception testing
with pytest.raises(ValueError, match="invalid input"):
    raise ValueError("invalid input provided")

Fixtures

@pytest.fixture
def database():
    """Fixture with setup and teardown."""
    db = Database(":memory:")
    db.create_tables()
    yield db  # Provide to test
    db.close()  # Teardown

def test_database_query(database):
    result = database.query("SELECT * FROM users")
    assert len(result) > 0

Parametrization

@pytest.mark.parametrize("input,expected", [
    ("hello", "HELLO"),
    ("world", "WORLD"),
    ("PyThOn", "PYTHON"),
])
def test_uppercase(input, expected):
    assert input.upper() == expected

Mocking

from unittest.mock import patch

@patch("mypackage.external_api_call")
def test_with_mock(api_call_mock):
    api_call_mock.return_value = {"status": "success"}
    result = my_function()
    api_call_mock.assert_called_once()
    assert result["status"] == "success"

Async Tests

@pytest.mark.asyncio
async def test_async_function():
    result = await async_add(2, 3)
    assert result == 5

Running Tests

pytest                          # Run all tests
pytest -v                       # Verbose output
pytest --cov=mypackage          # With coverage
pytest -m "not slow"            # Skip slow tests
pytest -x                       # Stop on first failure
pytest --lf                     # Run last failed
pytest -k "test_user"           # Pattern matching

Best Practices

DO:

• Follow TDD: red-green-refactor
• Test one thing per test
• Use descriptive names: test_user_login_with_invalid_credentials_fails
• Mock external dependencies
• Test edge cases: empty inputs, None values, boundaries
• Aim for 80%+ coverage on critical paths

DON'T:

• Test implementation details (test behavior)
• Share state between tests
• Ignore test failures
• Use print in tests (use assertions)
• Write overly specific mocks that break easily