7 Python Mistakes I Stopped Making After Writing Production Code

Writing scripts is one thing. Shipping production code? That’s a different game.

7 Python Mistakes I Stopped Making After Writing Production Code

When I first started learning Python, I thought I had it all figured out. The syntax was clean, the community was massive, and the tutorials made everything seem easy. I built scripts, small apps, and played around with popular libraries. But then I started working on production code — and reality hit like a KeyError at runtime.

Production Python is a different beast. It’s not about making it work; it’s about making it reliable, readable, and maintainable. Through many code reviews, bug fixes, and late-night debugging sessions, I began noticing a pattern of mistakes I was making.

Here are 7 Python mistakes I stopped making after writing production code — and why you should too.

1. Using Mutable Default Arguments

The mistake:

def append_item(item, items=[]): 
    items.append(item) 
    return items

Why it’s a problem:
The default list is shared across all function calls. This leads to bizarre behavior and hard-to-trace bugs.

The fix:

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

Lesson: Always avoid mutable default arguments unless you really know what you’re doing.

2. Overusing List Comprehensions

The mistake:

result = [transform(x) for x in some_large_iterable if condition(x)]

Why it’s a problem:
List comprehensions are elegant, but they can become unreadable or memory-intensive with complex logic or large data sets.

The fix:

Use generator expressions or regular loops when readability or memory is a concern:

for x in some_large_iterable: 
    if condition(x): 
        yield transform(x)

Lesson: Pythonic doesn’t always mean performant or readable. Choose clarity over cleverness.

3. Catching Bare Exceptions

The mistake:

try: 
    do_something() 
except: 
    handle_error()

Why it’s a problem:
Catching all exceptions — including KeyboardInterrupt, SystemExit, and MemoryError—can hide serious problems and make debugging miserable.

The fix:

try: 
    do_something() 
except ValueError as e: 
    handle_value_error(e)

Lesson: Be specific in your exception handling. It makes code safer and easier to debug.

4. Ignoring Logging (or Misusing print)

The mistake:

print("Something went wrong")

Why it’s a problem:
print is fine for debugging but useless in a real-world, multi-user, multi-threaded environment where logs matter.

The fix:

import logging 
 
logging.basicConfig(level=logging.INFO) 
logger = logging.getLogger(__name__) 
logger.info("Something went wrong")

Lesson: Logging is your best friend in production. Use it early and often.

5. Writing Monolithic Functions

The mistake:

def process_data(): 
    # 50+ lines doing many things

Why it’s a problem:
Monolithic functions are hard to test, debug, and maintain. They’re also a sign that responsibilities aren’t well-defined.

The fix:

Break down functionality into smaller, reusable functions:

def fetch_data(): ... 
def clean_data(): ... 
def transform_data(): ... 
def save_data(): ...

Lesson: Code should be modular, not monumental.

6. Ignoring Type Hints

The mistake:

def add(x, y): 
    return x + y

Why it’s a problem:
Without type hints, functions become harder to understand, refactor, or use in large teams.

The fix:

def add(x: int, y: int) -> int: 
    return x + y

Lesson: Type hints make code self-documenting and are a gateway to tools like mypy and better IDE support.

7. Assuming “It Works Locally” Means “It Works in Production”

The mistake:

“I ran the script, it works. Let’s deploy.”

Why it’s a problem:
Production environments differ — environment variables, OS, permissions, dependencies. What works on your machine can (and often will) fail in production.

The fix:

• Use Docker or virtual environments
• Write integration tests
• Monitor deployments with tools like Sentry or Prometheus

Lesson: If you’re not testing in something close to production, you’re gambling.

Final Thoughts

Writing Python in a learning or hobby context is fun and forgiving. But production code is where your skills are truly tested. It’s not about making things work — it’s about making them work well, work consistently, and work with others.

The best part? Every mistake is a learning opportunity. And the more production code you write, the fewer mistakes you’ll keep making twice.

Happy coding.

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