15 Essential Ways to Write Better Python Code in 2026

1. Use Descriptive Variable Names

Clear, meaningful variable names are the foundation of readable code. When you revisit your code months later—or when a teammate reviews it—descriptive names make the purpose immediately obvious.

Bad:

tp = 150.75
x = 0.08

Good:

total_price = 150.75
sales_tax_rate = 0.08

Follow Python's convention: use lowercase letters with underscores (snake_case) for variables and functions. This makes your code instantly more professional and readable.

2. Follow PEP 8 Style Guide

PEP 8 is Python's official style guide, establishing conventions for indentation, line length, naming, and more. Consistent styling makes code easier to read and collaborate on.

Key PEP 8 guidelines:

• Use 4 spaces for indentation (not tabs)
• Limit lines to 79 characters for code, 72 for comments
• Use blank lines to separate functions and classes
• Import statements should be at the top of the file

Modern tools to automate PEP 8 compliance:

• Ruff (2024+): Lightning-fast linter and formatter, replacing Flake8 and Black
• Black: Opinionated code formatter
• Pylint: Comprehensive code analysis

# Install and use Ruff (recommended in 2026)
pip install ruff
ruff check .
ruff format .

3. Use List Comprehensions (But Know When to Stop)

List comprehensions create lists concisely and efficiently, making your code more Pythonic. However, readability should always come first.

Good use case:

# Traditional loop
squares = []
for x in range(10):
    squares.append(x**2)
 
# List comprehension (better)
squares = [x**2 for x in range(10)]

When to avoid:

# Too complex - use a regular loop instead
result = [process(item) for sublist in data 
          for item in sublist if condition(item) 
          and other_condition(item)]

Pro tip: Consider generator expressions for large datasets: (x**2 for x in range(1000000)) instead of [x**2 for x in range(1000000)]

4. Minimize Global Variables

Global variables introduce hidden dependencies and make debugging difficult. They can be modified anywhere in your program, leading to unexpected behavior.

Instead of globals, use:

• Function parameters and return values
• Class attributes for stateful data
• Configuration objects or dataclasses

Bad:

counter = 0
 
def increment():
    global counter
    counter += 1

Good:

class Counter:
    def __init__(self):
        self.value = 0
    
    def increment(self):
        self.value += 1

5. Use f-Strings for String Formatting

Introduced in Python 3.6, f-strings are now the standard for string formatting. They're readable, fast, and support expressions directly inside strings.

Evolution of string formatting:

name = "Alice"
age = 30
 
# Old style (avoid)
message = "Hello, %s. You are %d years old." % (name, age)
 
# .format() method (outdated)
message = "Hello, {}. You are {} years old.".format(name, age)
 
# f-strings (modern standard)
message = f"Hello, {name}. You are {age} years old."
 
# With expressions
message = f"Next year, you'll be {age + 1}!"

Python 3.12+ bonus: f-strings now support inline debugging with =

result = f"{calculation()=}"  # Shows: calculation()=42

6. Follow the DRY Principle (Don't Repeat Yourself)

Repeated code is a maintenance nightmare. When you need to fix a bug or update logic, you'll have to find and change it everywhere it appears.

Refactor repeated logic into functions:

# Before (repetitive)
def process_user(user):
    if user.email and '@' in user.email:
        validated_email = user.email.lower().strip()
    # ... more processing
 
def process_admin(admin):
    if admin.email and '@' in admin.email:
        validated_email = admin.email.lower().strip()
    # ... more processing
 
# After (DRY)
def validate_email(email):
    if email and '@' in email:
        return email.lower().strip()
    return None
 
def process_user(user):
    validated_email = validate_email(user.email)
    # ... more processing

7. Use Constants Instead of Hardcoded Values

Hardcoded values scattered throughout your code make updates difficult and error-prone. Define constants at the top of your file or in a dedicated configuration module.

Bad:

def calculate_tax(price):
    return price * 0.08
 
def apply_discount(price):
    if price > 100:
        return price * 0.9
    return price

Good:

TAX_RATE = 0.08
DISCOUNT_THRESHOLD = 100
DISCOUNT_RATE = 0.10
 
def calculate_tax(price):
    return price * TAX_RATE
 
def apply_discount(price):
    if price > DISCOUNT_THRESHOLD:
        return price * (1 - DISCOUNT_RATE)
    return price

For complex configurations, use environment variables or configuration files:

from pathlib import Path
import json
 
config = json.loads(Path("config.json").read_text())
API_KEY = config.get("api_key")

8. Leverage Generators for Memory Efficiency

Generators produce values on-the-fly using yield, making them perfect for large datasets that don't fit in memory.

Memory-intensive approach:

def get_large_dataset():
    data = []
    for i in range(10_000_000):
        data.append(process(i))
    return data  # Entire list in memory

Memory-efficient generator:

def get_large_dataset():
    for i in range(10_000_000):
        yield process(i)  # One item at a time
 
# Usage
for item in get_large_dataset():
    handle(item)

Modern use case with pathlib:

def read_large_file(filepath):
    with open(filepath) as f:
        for line in f:  # File objects are generators!
            yield line.strip()

9. Use enumerate() for Indexed Loops

When you need both the index and value in a loop, enumerate() is cleaner than manual index tracking.

Old way:

items = ['apple', 'banana', 'cherry']
index = 0
for item in items:
    print(f"{index}: {item}")
    index += 1

Pythonic way:

items = ['apple', 'banana', 'cherry']
for index, item in enumerate(items):
    print(f"{index}: {item}")
 
# Start counting from 1 instead of 0
for index, item in enumerate(items, start=1):
    print(f"{index}: {item}")

10. Write Clear Docstrings

Documentation is code's user manual. Well-written docstrings explain what functions do, their parameters, return values, and potential exceptions.

Modern docstring format (Google style):

def calculate_compound_interest(principal, rate, time, frequency=12):
    """Calculate compound interest on an investment.
    
    Args:
        principal (float): Initial investment amount in dollars
        rate (float): Annual interest rate (as decimal, e.g., 0.05 for 5%)
        time (int): Investment period in years
        frequency (int, optional): Compounding frequency per year. Defaults to 12.
    
    Returns:
        float: Final amount after compound interest
    
    Raises:
        ValueError: If principal or rate is negative
    
    Example:
        >>> calculate_compound_interest(1000, 0.05, 10)
        1647.01
    """
    if principal < 0 or rate < 0:
        raise ValueError("Principal and rate must be non-negative")
    
    return principal * (1 + rate / frequency) ** (frequency * time)

Tools for documentation:

• Sphinx: Generate HTML documentation from docstrings
• MkDocs: Modern documentation framework
• Pydantic: Auto-generate API docs with type validation

11. Use Context Managers for Resource Management

Context managers (the with statement) ensure resources like files, database connections, and network sockets are properly cleaned up, even if errors occur.

File handling:

# Without context manager (risky)
f = open('data.txt')
content = f.read()
f.close()  # Might not execute if an error occurs
 
# With context manager (safe)
with open('data.txt') as f:
    content = f.read()
# File automatically closed

Custom context managers (Python 3.10+):

from contextlib import contextmanager
import time
 
@contextmanager
def timer(label):
    start = time.perf_counter()
    try:
        yield
    finally:
        end = time.perf_counter()
        print(f"{label}: {end - start:.4f}s")
 
# Usage
with timer("Data processing"):
    process_large_dataset()

12. Handle Exceptions Gracefully

Robust applications anticipate and handle errors elegantly. Use specific exception types and provide meaningful error messages.

Poor exception handling:

try:
    result = risky_operation()
except:  # Catches everything, even KeyboardInterrupt!
    pass  # Silent failure

Good exception handling:

import logging
 
try:
    result = process_data(user_input)
except ValueError as e:
    logging.error(f"Invalid input format: {e}")
    return {"error": "Please provide valid data format"}
except ConnectionError as e:
    logging.error(f"Network issue: {e}")
    return {"error": "Service temporarily unavailable"}
except Exception as e:
    logging.exception("Unexpected error occurred")
    raise  # Re-raise for debugging

Python 3.11+ enhancement:

try:
    result = complex_operation()
except* ValueError as e:  # Exception groups
    handle_value_errors(e)
except* TypeError as e:
    handle_type_errors(e)

13. Let Your Code Speak (Minimize Redundant Comments)

Self-documenting code with clear names and structure is better than excessive comments. Comments should explain why, not what.

Bad comments:

# Increment counter by 1
counter += 1
 
# Loop through users
for user in users:
    # Print user name
    print(user.name)

Good comments:

# Apply legacy tax calculation for backward compatibility with 2020 system
tax = price * LEGACY_TAX_RATE
 
# Skip processing for deleted users to avoid database lookup overhead
active_users = [u for u in users if not u.is_deleted]

When to comment:

• Complex algorithms or business logic
• Workarounds for known bugs or limitations
• Performance optimizations that aren't obvious
• Reasons for choosing a specific approach

14. Use Dataclasses for Data-Centric Objects

Introduced in Python 3.7, dataclasses eliminate boilerplate code for classes that primarily store data.

Traditional class:

class Product:
    def __init__(self, name, price, quantity):
        self.name = name
        self.price = price
        self.quantity = quantity
    
    def __repr__(self):
        return f"Product(name={self.name}, price={self.price}, quantity={self.quantity})"
    
    def __eq__(self, other):
        return self.name == other.name and self.price == other.price

Dataclass (much cleaner):

from dataclasses import dataclass
 
@dataclass
class Product:
    name: str
    price: float
    quantity: int = 0  # Default value
    
    def total_value(self):
        return self.price * self.quantity
 
# Bonus features
product = Product("Laptop", 999.99, 5)
print(product)  # Automatic __repr__

Python 3.10+ enhancements:

from dataclasses import dataclass
 
@dataclass(slots=True)  # Faster, less memory
class User:
    username: str
    email: str

15. Embrace Type Hints for Better Code Quality

Type hints (Python 3.5+) make code more maintainable and enable powerful tooling. Modern Python development heavily relies on type checking.

Basic type hints:

def greet(name: str, age: int) -> str:
    return f"Hello, {name}! You are {age} years old."
 
def process_items(items: list[str]) -> dict[str, int]:
    return {item: len(item) for item in items}

Advanced type hints (Python 3.10+):

from typing import Optional, Union
 
def find_user(user_id: int) -> User | None:  # Union syntax
    return database.get(user_id)
 
def process_data(data: str | bytes | list[str]) -> dict:
    # Handle multiple types
    pass

Type checking tools:

# mypy: Static type checker
pip install mypy
mypy your_script.py
 
# pyright: Fast, modern type checker from Microsoft
pip install pyright
pyright

Real-world benefit: IDEs like VS Code and PyCharm provide autocomplete, inline errors, and refactoring support when you use type hints.

Bonus Tips for Modern Python Development

16. Use Virtual Environments Always

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scriptsctivate

17. Leverage Pattern Matching (Python 3.10+)

match status_code:
    case 200:
        return "Success"
    case 404:
        return "Not found"
    case 500 | 502 | 503:
        return "Server error"
    case _:
        return "Unknown status"

18. Use pathlib for File Operations

from pathlib import Path
 
# Better than os.path
config_file = Path("config") / "settings.json"
if config_file.exists():
    content = config_file.read_text()

Frequently Asked Questions (FAQ)

What is the most important Python best practice for beginners?

Start with writing descriptive variable names and following PEP 8 guidelines. These foundational practices make your code readable and professional from day one. Use tools like Ruff or Black to automatically format your code.

Should I use type hints in all my Python projects?

Type hints are highly recommended for production code and collaborative projects. They improve code documentation, enable better IDE support, and catch errors early with static type checkers like mypy. For small personal scripts, they're optional but still beneficial.

What's the difference between list comprehensions and generator expressions?

List comprehensions create the entire list in memory: [x**2 for x in range(1000)]. Generator expressions produce values on-demand: (x**2 for x in range(1000)). Use generators for large datasets to save memory.

How do I choose between a function and a class in Python?

Use functions for simple operations that transform input to output. Use classes when you need to maintain state, bundle related data and methods together, or create multiple instances with similar behavior.

Is Python 3.13 worth upgrading to?

Yes! Newer Python versions offer performance improvements, better error messages, and modern features like pattern matching (3.10+) and exception groups (3.11+). Always use the latest stable version when starting new projects.

What tools should every Python developer use in 2026?

Essential tools include: Ruff (linting/formatting), mypy or pyright (type checking), pytest (testing), virtual environments (venv or poetry), and a good IDE like VS Code or PyCharm with Python extensions.

How can I make my Python code run faster?

Focus on algorithmic efficiency first, then consider: using generators for large data, leveraging built-in functions (they're optimized in C), using list comprehensions appropriately, and profiling with tools like cProfile to identify bottlenecks.

When should I write comments in my code?

Write comments to explain why you made specific decisions, document complex algorithms, note workarounds for bugs, or clarify non-obvious business logic. Avoid commenting on what the code does—make the code self-documenting through clear naming instead.

Conclusion

Writing better Python code is a continuous journey. These 15 practices, combined with modern Python features, will help you create code that's cleaner, more maintainable, and more professional. Start by incorporating one or two techniques into your workflow, then gradually adopt more as they become second nature.

Remember: the goal isn't just to make code work—it's to make code that's a pleasure to read, maintain, and extend. Your future self (and your teammates) will thank you.

What's your favorite Python best practice? Share in the comments below!

Last updated: January 2026 | Python 3.13 compatible