10 Production-Grade Python Code Styles I Learned from Real-World Experience

WRITE PYTHON LIKE THE PROS!

10 Production-Grade Python Code Styles I Learned from Real-World Experience

Python is a beautiful language — clean, readable, and expressive.

But writing Python code that works in production is a different game.

Over the years, I’ve worked on various real-world projects, from web applications to machine learning models, and I’ve learned (sometimes the hard way) what makes Python code truly production-grade.

Here are ten Python code styles and best practices that have significantly improved my development workflow and code quality.

1. Follow the PEP 8 Style Guide — But Know When to Break It

PEP 8 is the gold standard for Python coding conventions. It covers everything from naming conventions to line spacing, helping keep your code readable and consistent.

Example (Good Code):

def get_user_email(user_id: int) -> str: 
    """Fetch the email of a user by ID.""" 
    return f"user{user_id}@example.com"

Example (Bad Code):

def getUserEmail(id): 
  return 'user'+str(id)+'@example.com'

However, real-world code isn’t always black and white. Sometimes, you need to break PEP 8 for readability. For example, long list comprehensions might look better spread across multiple lines than squeezed into one.

2. Use Type Hints for Clarity and Maintainability

Type hints make your code more readable and help catch bugs early. This is especially useful in large codebases or when working in teams.

Example:

def add_numbers(a: int, b: int) -> int: 
    return a + b

Not only do type hints improve documentation, but they also help IDE autocompletion and static type checkers like mypy.

3. Avoid Mutable Default Arguments

This is one of the most dangerous pitfalls in Python. Using a mutable default argument (like a list or dictionary) can lead to unexpected behavior because Python reuses the same object across function calls.

Example (Bug-Prone Code):

def add_item(item: int, items: list=[]): 
    items.append(item) 
    return items 
 
print(add_item(1))  # [1] 
print(add_item(2))  # [1, 2] (Unexpected!)

Example (Correct Approach):

def add_item(item: int, items=None): 
    if items is None: 
        items = [] 
    items.append(item) 
    return items

4. Use enumerate() Instead of range(len())

Looping through an index is common, but using enumerate() is more Pythonic and eliminates the need for manual indexing.

Example (Old-School Way):

fruits = ["apple", "banana", "cherry"] 
for i in range(len(fruits)): 
    print(i, fruits[i])

Example (Pythonic Way):

fruits = ["apple", "banana", "cherry"] 
for index, fruit in enumerate(fruits): 
    print(index, fruit)

This improves my code readability and prevents off-by-one errors.

5. Use F-Strings for String Formatting

F-strings (introduced in Python 3.6) are faster and more readable than older formatting methods like str.format() or % formatting.

Example (Readable and Efficient):

name = "Aashish" 
age = 25 
print(f"My name is {name} and I am {age} years old.")

Example (Less Readable and Verbose):

print("My name is {} and I am {} years old.".format(name, age))

F-strings also support expressions inside the braces:

print(f"Next year, I will be {age + 1} years old.")

6. Use dataclass for Simple Data Structures

Instead of writing boilerplate code for classes, use dataclasses to auto-generate __init__, __repr__, and other dunder methods.

Example:

from dataclasses import dataclass 
 
@dataclass 
class User: 
    name: str 
    age: int 
user = User(name="Aashish", age=25) 
print(user)  # User(name='Aashish', age=25)

This makes my code cleaner, more maintainable, and less error-prone.

7. Handle Exceptions Like a Pro

In production, exceptions will happen. The key is to handle them gracefully and log useful information.

Example (Bad Exception Handling):

try: 
    result = 10 / 0 
except: 
    print("Something went wrong!")

Example (Better Approach):

import logging 
 
logging.basicConfig(level=logging.ERROR) 
try: 
    result = 10 / 0 
except ZeroDivisionError as e: 
    logging.error(f"Error: {e}")

Always catch specific exceptions and log useful details — this saves debugging time.

8. Optimize Loops with Generators and List Comprehensions

List comprehensions and generator expressions make code more efficient and readable.

Example (Inefficient Loop):

squares = [] 
for i in range(10): 
    squares.append(i * i)

Example (List Comprehension):

squares = [i * i for i in range(10)]

For large data, use generators to save memory:

def infinite_numbers(): 
    num = 0 
    while True: 
        yield num 
        num += 1

9. Use Context Managers (with Statements) for Resource Handling

Manually opening and closing files (or database connections) can lead to resource leaks. Use with statements instead.

Example (Risky File Handling):

file = open("data.txt", "r") 
data = file.read() 
file.close()  # Risky if an exception occurs before this line!

Example (Using with for Safety):

with open("data.txt", "r") as file: 
    data = file.read()

The with statement automatically closes the file, preventing leaks.

10. Keep Your Code Clean with Linters and Formatters

Production-grade code must be consistent and readable. Use tools like:

• Black (for automatic formatting)
• Flake8 (for linting and detecting errors)
• isort (for sorting imports)

Example (Black formatting command):

black my_script.py

These tools save my time in code reviews and reduce style debates.

Or we can replace all these tools with Ruff

Ruff is a blazing-fast linter written in Rust, designed to replace Flake8, Black, and isort in one tool.

pip install ruff

You can use ruff check command to check

$ uv run ruff check 
 
src/numbers/__init__.py:3:8: F401 [*] `os` imported but unused 
Found 1 error. 
[*] 1 fixable with the `--fix` option.

Fix using ruff check — fix command

$ uv run ruff check --fix 
Found 1 error (1 fixed, 0 remaining).

Final Thoughts

Writing production-grade Python code is not just about making it work — it’s about making it maintainable, scalable, and error-free.

These 10 tips have transformed how I write Python in real-world applications. Adopting these practices will not only improve your code quality but also make your development workflow smoother.

What are some Python best practices you’ve learned from experience? Let’s discuss in the comments!