5 Times Python Typing Saved Me from Dumb Bugs

I used to think typing was optional — until it started saving me from myself.

5 Times Python Typing Saved Me from Dumb Bugs

…and why I type all my Python code like it’s a strongly typed language.

If you’re like me, you probably started using Python because it was flexible, quick to write, and didn’t get in your way with boilerplate. That was part of the charm.

But over time, I realized that while Python’s dynamic nature was great for small scripts, it became a silent saboteur as projects scaled. The bugs that crept in weren’t always complex — they were dumb. Embarrassingly dumb. And I started losing confidence in my own code.

Enter: Python Typing.

At first, I resisted. Typing felt “un-Pythonic.” But once I committed, it transformed how I code — and more importantly, how much I trust my code.

Here are five real moments where Python typing saved me from myself.

1. Swapping Parameter Order Without Noticing

The Bug:
I was working on a utility that generates thumbnail images:

def generate_thumbnail(width, height, image_path): 
    ...

One day, while refactoring, I accidentally swapped the order:

def generate_thumbnail(image_path, width, height):

No error. No complaint. Just broken behavior at runtime.

How Typing Saved Me:
Once I annotated the function:

def generate_thumbnail(image_path: str, width: int, height: int) -> None:

Mypy immediately threw a fit when I called it like I used to:

generate_thumbnail(300, 200, "dog.jpg") 
# error: Argument 1 to "generate_thumbnail" has incompatible type "int"; expected "str"

Boom. Instant catch. No more head-scratching over weirdly stretched images.

2. Returning None When I Meant a str

The Bug:
I had a function that was supposed to return a file path. Under rare error conditions, it returned None.

def get_output_path() -> str: 
    if some_weird_condition: 
        return None 
    return "output/data.csv"

Somewhere downstream:

with open(get_output_path()) as f: 
    ...

Python raised a TypeError, but only when the edge case hit in production. Yikes.

How Typing Saved Me:
Once I typed it properly:

def get_output_path() -> Optional[str]:

Now the compiler forces me to handle the None case explicitly:

path = get_output_path() 
if path is None: 
    raise RuntimeError("No output path provided.") 
with open(path) as f: 
    ...

No more surprises. Just cleaner, safer code.

3. Misusing a Dictionary with the Wrong Value Type

The Bug:
I was storing timestamps in a dictionary:

user_last_seen = {}  # user_id -> timestamp

Everything was fine until I accidentally started storing datetime strings instead of datetime objects:

user_last_seen[user_id] = timestamp.isoformat()

Later code that did date arithmetic blew up.

How Typing Saved Me:
By declaring:

from datetime import datetime 
 
user_last_seen: dict[int, datetime] = {}

Mypy immediately complained when I tried assigning a string.

This tiny annotation helped me keep the data structure consistent across hundreds of lines of code.

4. Misunderstanding a Return Type

The Bug:
I was calling a helper function that looked up a user:

def get_user(user_id: int) -> User | None: 
    ...

But I mistakenly treated the result as a guaranteed User:

name = get_user(uid).name

Of course, when the user didn’t exist, I got a 'NoneType' object has no attribute 'name' error.

How Typing Saved Me:
Because of the User | None return type (or Optional[User]), my editor underlined the problem immediately. I added a proper check:

user = get_user(uid) 
if not user: 
    raise ValueError(f"User {uid} not found.") 
name = user.name

Simple. Obvious. But I would’ve missed it without typing.

5. Sending the Wrong Type to an API Wrapper

The Bug:
I was wrapping a REST API call and passed the wrong payload type:

def send_email(payload: EmailPayload) -> bool: 
    ...

At one point, I passed a raw dictionary instead of the EmailPayload dataclass:

send_email({"to": "foo@example.com", "body": "hi"})

Everything looked fine until deep inside the wrapper it expected payload.to, not payload["to"]. Runtime error.

How Typing Saved Me:
With:

def send_email(payload: EmailPayload) -> bool:

And a properly typed EmailPayload class, my IDE (and Mypy) told me instantly: “Dict is not EmailPayload.”

Bonus: Now my autocompletion works perfectly. Fewer mistakes, faster coding.

Conclusion: Type Like the Future You Will Thank You

Typing in Python isn’t about turning it into Java. It’s about catching bugs before they bite — especially the dumb ones.

The kind of bugs you fix and think, “I can’t believe that got through.”

Static typing brings back some of the safety net you miss from statically typed languages, without giving up Python’s expressiveness.

You don’t have to go all-in on day one. Start by annotating key functions. Use mypy or pyright. Let your editor help you.

It’s not about perfection. It’s about prevention.

Typing didn’t just save me from bugs — it saved me time, frustration, and a few gray hairs.

Thanks for reading! If this helped you, share it with your team or leave a comment about your own experience with Python typing. We’ve all been there. 🚀