Python Best Practices in 2025

Writing clean, maintainable Python code is more important than ever. Let's explore the best practices that will make your code more readable, efficient, and professional.

1. Use Type Hints

Type hints improve code readability and catch bugs early:

def calculate_total(items: list[dict], tax_rate: float) -> float:
    """Calculate total price with tax."""
    subtotal = sum(item['price'] for item in items)
    return subtotal * (1 + tax_rate)

2. Follow PEP 8 Style Guide

Consistent formatting matters:

# Good
def process_user_data(user_id, include_metadata=False):
    pass

# Bad
def ProcessUserData(userId,includeMetadata=False):
    pass

Use tools like black and ruff to automatically format your code.

3. Write Descriptive Docstrings

Document your functions properly:

def fetch_user_profile(user_id: int) -> dict:
    """
    Fetch user profile from the database.

    Args:
        user_id: The unique identifier for the user

    Returns:
        A dictionary containing user profile data

    Raises:
        UserNotFoundError: If user doesn't exist
    """
    pass

4. Use Context Managers

Always use context managers for resources:

# Good
with open('file.txt', 'r') as f:
    content = f.read()

# Bad
f = open('file.txt', 'r')
content = f.read()
f.close()

5. Embrace List Comprehensions

Use list comprehensions for cleaner code:

# Good
squares = [x**2 for x in range(10)]

# Less pythonic
squares = []
for x in range(10):
    squares.append(x**2)

6. Use f-strings for Formatting

F-strings are fast and readable:

name = "World"
message = f"Hello, {name}!"  # Preferred
message = "Hello, {}!".format(name)  # Okay
message = "Hello, " + name + "!"  # Avoid

7. Handle Exceptions Properly

Be specific with exceptions:

# Good
try:
    result = risky_operation()
except ValueError as e:
    logger.error(f"Invalid value: {e}")
    raise
except KeyError as e:
    logger.error(f"Missing key: {e}")
    return None

# Bad
try:
    result = risky_operation()
except:
    pass

8. Use Pathlib for File Operations

Modern path handling:

from pathlib import Path

# Good
config_path = Path('config') / 'settings.json'
if config_path.exists():
    content = config_path.read_text()

# Old way
import os
config_path = os.path.join('config', 'settings.json')
if os.path.exists(config_path):
    with open(config_path, 'r') as f:
        content = f.read()

9. Use Dataclasses for Data Structures

Simplify class definitions:

from dataclasses import dataclass

@dataclass
class User:
    id: int
    name: str
    email: str
    active: bool = True

10. Write Unit Tests

Always test your code:

import pytest

def test_calculate_total():
    items = [{'price': 10}, {'price': 20}]
    result = calculate_total(items, tax_rate=0.1)
    assert result == 33.0

11. Use Virtual Environments

Isolate project dependencies:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

12. Keep Functions Small and Focused

Each function should do one thing well:

# Good
def validate_email(email: str) -> bool:
    """Validate email format."""
    return '@' in email and '.' in email.split('@')[1]

def send_email(to: str, subject: str, body: str) -> None:
    """Send an email."""
    if not validate_email(to):
        raise ValueError("Invalid email")
    # Send email logic

Conclusion

Following these best practices will make your Python code more professional, maintainable, and enjoyable to work with. Start incorporating them into your projects today!

What are your favorite Python best practices? Let me know in the comments!

Python Best Practices in 2025: Writing Clean and Maintainable Code