7 Python Debugging Tricks That Saved My Career!

If you’re tired of writing print("here") everywhere, you need these.

7 Python Debugging Tricks That Saved My Career!
Photo by Jason Goodman on Unsplash

I’ve lost sleep over bugs. These tricks brought it back — and saved me from disaster more than once.

7 Python Debugging Tricks That Saved My Career!

Debugging isn’t just a skill — it’s a survival tactic.

Over the years, I’ve shipped dozens of Python projects, big and small.

And while writing clean, efficient code is great, what truly saved me time and reputation were the debugging skills I picked up the hard way.

In this article, I’ll share the 7 Python debugging tricks that, quite literally, saved my career — from sleepless nights and production fire drills to crucial interviews and sprint deadlines.


1. breakpoint() — The Built-In Lifesaver

When Python 3.7 introduced breakpoint(), I didn’t realize it would become one of my most-used tools.

def calculate_tax(income): 
    breakpoint() 
    return income * 0.15

No more importing pdb or inserting import pdb; pdb.set_trace(). Just drop breakpoint() anywhere, and you’re inside an interactive debugging shell. You can inspect variables, run commands, and control flow like a boss.

Bonus Tip: Set the PYTHONBREAKPOINT=0 environment variable to disable all breakpoints without changing code. Perfect for production environments.

2. traceback + logging = Context Goldmine

A simple print() only tells you what happened — not where or why. That’s where traceback and logging shine.

import traceback 
import logging 
 
try: 
    risky_function() 
except Exception as e: 
    logging.error("Error occurred:\n%s", traceback.format_exc())

This combo gives you stack traces in your logs, making it easier to diagnose bugs in production or long-running scripts.

3. Use repr() for Real Insights

There’s a world of difference between print(obj) and print(repr(obj)).

print(obj)      # Might show a friendly string 
print(repr(obj))  # Shows internal structure, great for debugging

repr() helps when you’re working with strings, data structures, or any object that overrides __str__() for pretty printing.
It reveals the raw, unfiltered truth.

4. F-Strings with Debug Info: Python 3.8+ Superpower

Python 3.8 added an underrated gem: f-string debugging.

name = "Aashish" 
age = 25 
print(f"{name=}, {age=}") 
# Output: name='Aashish', age=25

Clean. Concise. Informative.

Perfect for dumping variable state without typing redundant info.

5. warnings — Catch the Silent Killers

Sometimes your code doesn’t crash — it just behaves weirdly. That’s often due to warnings you ignore.

import warnings 
 
warnings.filterwarnings("error")  # Turn warnings into exceptions

Suddenly, that harmless deprecation warning becomes a fixable bug, not a future nightmare. It’s saved me during library upgrades and migrations more times than I can count.

6. contextlib for Temporary Patches

Need to override behavior temporarily during debugging? contextlib to the rescue.

from contextlib import contextmanager 
 
@contextmanager 
def debug_mode(): 
    print("Debug mode ON") 
    yield 
    print("Debug mode OFF") 
 
with debug_mode(): 
    run_my_code()

This is a cleaner alternative to manual try-finally blocks, especially when debugging context-sensitive logic.

7. Use ipdb Instead of pdb (Once You Try It, You Won’t Go Back)

If you’re still using pdb, let me introduce you to its smarter sibling: ipdb.

pip install ipdb

Then in your code:

import ipdb; ipdb.set_trace()

Why it’s better:

  • Tab completion
  • Syntax highlighting
  • History navigation
  • Integrated with IPython

In short, it’s like going from Notepad to VS Code — still lightweight, but so much more powerful.


Final Thoughts: Debugging Is a Mindset

Mastering debugging isn’t just about tools — it’s about asking the right questions:

  • What changed recently?
  • What’s different in this environment?
  • What assumptions am I making?

These 7 tricks didn’t just fix bugs — they helped me build resilience, think clearly under pressure, and earn the trust of teammates and clients alike.

So next time you’re knee-deep in a Python mystery, remember:
You’re not stuck. You’re just one breakpoint away from clarity.


Which debugging trick do you swear by? Let me know in the comments!
If this helped you, don’t forget to clap, share, and follow for more Python tips.

Photo by Shubham Dhage on Unsplash