Top 10 Python Best Practices Every Developer Should Follow

Python is one of the most widely used programming languages, known for its simplicity, readability, and flexibility. However, writing Python code that is maintainable, efficient, and scalable requires following best practices.

In this article, we’ll discuss the top 10 Python best practices that every developer should follow to write clean, efficient, and error-free code.

1. Follow PEP 8 for Clean and Consistent Code

PEP 8 (Python Enhancement Proposal 8) is the official style guide for writing Python code. Following PEP 8 ensures that your code is consistent and readable.

✅ Good Practice:

def get_user_name(user_id: int) -> str:
    """Fetch user name based on user ID."""
    return f"User-{user_id}"

❌ Bad Practice:

def GetUserName(UserID):
 return "User-" + str(UserID)

🔹 Why?

• Uses meaningful function and variable names.
• Uses snake_case for function and variable names.
• Maintains proper indentation and spacing.

📌 Tip: Use tools like Black or autopep8 to automatically format your code.

2. Use Virtual Environments for Dependency Management

A virtual environment helps manage dependencies and avoid conflicts between different projects.

✅ Good Practice:

# Create a virtual environment
python -m venv myenv

# Activate it (Windows)
myenv\Scripts\activate

# Activate it (Mac/Linux)
source myenv/bin/activate

❌ Bad Practice:

• Installing dependencies globally.
• Running multiple projects with conflicting dependencies.

🔹 Why?

• Keeps project dependencies isolated.
• Prevents version conflicts between packages.

📌 Tip: Use pipenv or virtualenvwrapper for better dependency management.

3. Write Readable and Meaningful Variable & Function Names

Use descriptive variable and function names to make your code self-documenting.

✅ Good Practice:

total_price = item_price * quantity
def calculate_discount(price: float, discount: float) -> float:
    return price - (price * discount / 100)

❌ Bad Practice:

tp = ip * q  # Unclear variable names
def disc(p, d):
    return p - (p * d / 100)

🔹 Why?

• Improves code readability.
• Makes debugging and maintenance easier.

📌 Tip: Avoid single-letter variable names unless in loop counters (e.g., for i in range(10)).

4. Use List Comprehensions Instead of Loops

List comprehensions provide a more concise and readable way to create lists.

✅ Good Practice:

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

❌ Bad Practice:

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

🔹 Why?

• Makes code more concise.
• Improves performance.

📌 Tip: Use comprehensions for lists, dictionaries, and sets.

5. Use f-Strings for String Formatting

Introduced in Python 3.6, f-strings are faster and more readable than .format() and % formatting.

✅ Good Practice:

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

❌ Bad Practice:

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

🔹 Why?

• More readable and concise.
• Faster execution compared to other methods.

📌 Tip: Use f-strings instead of .format() and % formatting.

6. Use Exception Handling Properly

Handling exceptions correctly ensures that your program does not crash unexpectedly.

✅ Good Practice:

try:
    result = 10 / 0
except ZeroDivisionError as e:
    print(f"Error: {e}")
finally:
    print("Execution completed")

❌ Bad Practice:

result = 10 / 0  # Will cause a crash

🔹 Why?

• Prevents unexpected program crashes.
• Provides better debugging information.

📌 Tip: Use specific exceptions instead of catching generic Exception.

7. Use Pythonic Ways for File Handling

Use Python’s context manager (with statement) to handle files instead of manually closing them.

✅ Good Practice:

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

❌ Bad Practice:

file = open("data.txt", "r")
content = file.read()
file.close()

🔹 Why?

• Automatically closes the file after use.
• Reduces the risk of file leaks.

📌 Tip: Always handle file errors using try-except.

8. Optimize Performance with Generators

Use generators (yield) instead of lists when dealing with large data.

✅ Good Practice:

def generate_numbers():
    for i in range(10):
        yield i  # Returns one value at a time

gen = generate_numbers()
print(next(gen))  # Output: 0
print(next(gen))  # Output: 1

❌ Bad Practice:

def generate_numbers():
    return [i for i in range(10)]  # Consumes more memory

🔹 Why?

• Saves memory by generating values on demand.
• Improves performance in large datasets.

📌 Tip: Use generators when working with large datasets or streams.

9. Write Unit Tests for Code Reliability

Writing unit tests ensures that your code works correctly and helps prevent regressions.

✅ Good Practice:

import unittest

def add(a, b):
    return a + b

class TestMathOperations(unittest.TestCase):
    def test_add(self):
        self.assertEqual(add(2, 3), 5)

if __name__ == "__main__":
    unittest.main()

❌ Bad Practice:

• Not writing tests.
• Testing manually instead of automating.

🔹 Why?

• Helps detect bugs early.
• Ensures code reliability.

📌 Tip: Use pytest for simpler test writing.

10. Follow DRY (Don’t Repeat Yourself) Principle

Avoid duplicating code by creating reusable functions.

✅ Good Practice:

def calculate_area(length, width):
    return length * width

area1 = calculate_area(5, 10)
area2 = calculate_area(7, 3)

❌ Bad Practice:

area1 = 5 * 10
area2 = 7 * 3  # Code duplication

🔹 Why?

• Reduces code duplication.
• Improves maintainability.

📌 Tip: Identify repeated code and refactor into functions or classes.

Final Thoughts

By following these Python best practices, you can write cleaner, faster, and more maintainable code. Whether you are a beginner or an experienced developer, these guidelines will help you improve your Python programming skills.

📢 Which Python best practices do you follow? Share your thoughts in the comments! 🚀

🔑 Keywords

Python Best Practices, Clean Code in Python, Python Coding Standards, Python Code Optimization, PEP 8, Python Performance Tips.