What I Learned After Rewriting a 5,000-Line Python Script

What started as “just a cleanup” turned into a full-on refactor — and a crash course in everything I was doing wrong.

What I Learned After Rewriting a 5,000-Line Python Script

Rewriting a 5,000-line Python script taught me hard truths about code structure, readability, and scalability. Here are the biggest lessons — and how I’d do it differently today.

Spoiler: It wasn’t just about writing better code. It was about rethinking the problem.

You never really know a codebase until you’ve had to rip it apart and stitch it back together.

Recently, I had to rewrite a sprawling, 5,000-line Python script that had grown over the years into a twisted knot of business logic, copy-pasted functions, and “temporary” fixes that had somehow become permanent.

It was the kind of file where scrolling to the bottom took longer than you’d expect, and making one change meant testing everything — because you never knew what might break.

The rewrite wasn’t optional.

The script had become unmaintainable.

Bugs were creeping in faster than we could patch them, onboarding new devs was a nightmare, and performance was degrading under real-world data loads.

So I rolled up my sleeves, took a deep breath, and dove in.

Here’s what I learned.

1. Working Code Isn’t Always Good Code

One of the first mental hurdles I had to overcome was this: just because it works doesn’t mean it’s good.

The original script did work. Barely. But it had grown organically over time — patched, hacked, and hotfixed by different hands.

There was no clear architecture, no separation of concerns, and logic was duplicated in subtle, dangerous ways.

I had to stop treating the existing code like a sacred relic. I needed to treat it like a rough draft.

And like all rough drafts, it needed brutal editing.

2. Start With the Data Flow

Instead of starting with the functions and classes, I zoomed out and mapped the data flow from start to finish:

What inputs did the script take?

What transformations happened along the way?

What were the expected outputs?

This simple exercise revealed inconsistencies and unnecessary complexity.

Some steps were doing redundant work.

Others were formatting and reformatting data multiple times for no good reason.

Once I had the data pipeline sketched out, restructuring became significantly easier.

3. Split and Conquer

5,000 lines in one file is a red flag. It’s not a script — it’s a codebase pretending to be a script.

I refactored the monolith into multiple modules:

• input_handlers.py — all logic related to parsing and validating input.
• transformers.py — pure functions that handled transformations.
• exporters.py — logic for writing output files or saving to DB.
• utils.py — reusable helpers, properly documented.

Suddenly, testing became easier. Debugging became clearer. And each module had a single responsibility.

4. Tests Aren’t Optional — They’re Oxygen

The original script had zero tests. Every change was a leap of faith.

So I wrote tests before I wrote the new code.

Not full TDD, but I at least ensured each function had a unit test, and each pipeline step had integration coverage.

Yes, it took time.

But the confidence it gave me to move fast later on? Priceless.

Also: bugs I didn’t know existed surfaced during test-writing. The kind of subtle logic errors that don’t crash programs, but silently produce wrong results.

5. Logging > Print Statements

Print statements are fine for debugging.

But they’re no substitute for structured logging — especially in a production-grade script.

I introduced Python’s logging module with different levels (INFO, DEBUG, ERROR), and now I can trace failures, monitor performance, and even debug remotely if needed.

Logs tell stories that print statements never will.

6. Don’t Reinvent What Libraries Already Solved

The original code re-implemented features that Python (or libraries like Pandas, Click, or Pydantic) already solved better.

I replaced:

• Manual CLI argument parsing → argparse
• Ad-hoc data validation → pydantic
• Homegrown CSV processing → pandas
• Custom error handling → try/except with specific exceptions

Using standard libraries reduced bugs, increased readability, and made onboarding other devs easier — because they already knew the tools.

7. Documentation Is an Act of Kindness (Even to Your Future Self)

I documented every module, every function, and the overall architecture. Not just what things do, but why.

Comments that explain “why” last longer than code that shows “how”.

A future developer (or future me) will thank me. Probably with tears of joy.

8. Big Refactors = Big Lessons

Rewriting a 5,000-line script taught me more than any course or book could.

It was a crash course in software architecture, clean code, testing, and humility.

Because the truth is: some of that messy old code? I wrote it years ago.

And that realization hit me hard — but also made me proud.

Growth is uncomfortable. But it’s worth it.

Final Thoughts

If you’re stuck with a massive, messy Python script and wondering whether you should refactor or rewrite — start by asking:

• Can I test this?
• Can I read this without scrolling for minutes?
• Can I change one thing without breaking five others?

If the answer is no, maybe it’s time to start over — with purpose, structure, and empathy.

You’ll write better code. And you’ll become a better developer in the process.

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