Hey, I’m Kayla. I code for work and for fun. I’ve built apps with TypeScript. I’ve shipped tools and data stuff with Python. I’ve broken things in both. I’ve fixed messes in both. So here’s my honest, first-hand review.
If you’d like to see a longer side-by-side breakdown that digs into everything from setup pain to deploy speed, I put together my notes in this real-life comparison.
Quick vibe check
- TypeScript feels like a safety net. It nags me a bit, but it saves me from facepalm bugs.
- Python feels like a warm hoodie. It’s fast to write. It’s easy to read. But sometimes, a tiny bug hides in the shadows.
I use both each week. I’ll tell you how and why.
The dashboard week: TypeScript helped me sleep
I made a small dashboard at work with React and TypeScript. It showed live orders, refunds, and a tiny sparkline chart. Pretty standard. We had a weird bug: a user with no saved settings made the page crash on load. In plain JS, it would crash at runtime. With TypeScript, the compiler told me, “Hey, settings might be undefined. Handle it.” It stopped me before I shipped it.
Did it slow me down on day one? Yep. I had to add types for props, API calls, and chart data. Taking a moment to sprinkle in thoughtful inline comments made those types feel lighter to future-me. I argued with tsconfig too. But after that, it felt calm. Fewer bugs. Better hints in VS Code. When I changed the API shape, the code lit up red in the right spots. I didn’t have to hunt.
Real tools I used:
- React with TypeScript
- Zod for runtime checks
- Jest for tests
- ESLint + Prettier (and a little side-eye at tsconfig)
One more thing: I liked that “null vs undefined” gets called out. It’s annoying. But helpful. It’s like a coach who tells you to tie your shoes.
The data mess morning: Python let me move fast
I had a pile of CSV files. Sales, returns, and promo codes. Some rows were broken. Some dates were funky. I opened a Jupyter notebook with Python, used pandas, and got answers in under an hour. Cleaned data. Joined tables. Made a quick chart. Done.
If you ever need to sling those CSV rows straight into DynamoDB with a TypeScript AWS Lambda, here’s a handy walk-through that saved me a ton of trial-and-error.
That same day, I wrote a script that renamed 500 photos and pushed them to S3. Python again. Why? It’s simple. The code reads like plain talk. My teammate, who doesn’t code full-time, could read it and tweak it.
Real tools I used:
- pandas and numpy
- Jupyter for fast feedback
- requests for HTTP calls
- pytest for tests
- black + ruff for clean code
A bug I hit: NaN vs None vs empty string. That bit me. A count looked off by 1,000. I added pydantic models to validate rows and used mypy to add types later. That cleaned it up. So yes, Python can have types too. You just don’t have to start there.
Web APIs: both can shine
I built a small chat service as a weekend thing. Two versions.
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TypeScript version: Next.js front end, Express API, shared types for messages. I used Zod schemas to check requests. I loved that types flowed from API to UI. When I changed a field name, everything failed fast during build. Nice.
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Python version: FastAPI with pydantic models. Super clean. Docs were auto-made. The speed felt great. I could write a new route in minutes. For simple APIs, this felt easy and kind.
Which did I ship? The TS version, because my front end used the same types. It kept the team in sync. But the FastAPI version was quicker to write. I still use it for internal tools.
Speed, teams, and those weird gotchas
- Speed of coding: Python wins for quick scripts and data work. Less setup. Less fuss.
- Speed at runtime: For math, Python with numpy is fast. For lots of small web calls, Node with TypeScript feels snappy.
- Team size: With more devs, TypeScript helps keep code from drifting. Fewer “what’s this object?” moments.
- Debug stories:
- TypeScript once saved me from calling a function that sometimes returned null. It warned me. I added a check. No bug report.
- Python once let a bad date string slide into a report. It looked fine. It wasn’t. I added pydantic and tests. Fixed.
Gotchas I hit:
- Node version mismatch. Everything broke. NVM fixed it.
- Python envs got messy. I now use pyenv + venv or Poetry. Much calmer.
- TypeScript generics are great, but they can melt your brain on Friday at 4 pm.
- And if constructors with long positional parameter lists have you pulling your hair out, you can try named arguments in TypeScript—here’s a quick field report on how that feels.
- Python list slicing is cute… until an off-by-one sneaks in.
Tooling that felt good
- TypeScript: VS Code IntelliSense is gold. Jest runs fast. ESLint nags me, but it’s right.
- Python: Jupyter for play, pytest for real. black and ruff keep code clean. FastAPI is a joy.
If you want an even deeper toolbox breakdown (and some sharp refactoring tricks), check out the articles over at Improving Code.
When I pick TypeScript
- Front ends with React, Vue, or Svelte.
- Shared types across API and UI.
- Large codebases with many hands.
- Event-heavy apps: chats, sockets, live dashboards.
A real example: our holiday sale dashboard. High traffic. Lots of states. TS let us refactor without fear. I slept fine.
When I pick Python
- Data work: cleaning, joins, notebooks.
- Small scripts: backups, renames, quick HTTP jobs.
- APIs that need speed to build, not fancy types.
- ML or stats tasks. It owns that space.
A real example: tax-time data checks. I wrote one notebook. It saved me hours. No drama.
Can you mix them? Oh yes
Most weeks, I do:
- FastAPI backend (Python)
- React front end (TypeScript)
- A small Python worker for heavy jobs (Celery + Redis)
- Shared API docs, not shared code
That split feels smooth. Use each where it shines.
My plain answer
- If you’re doing data or quick tasks: choose Python.
- If you’re building a front end or a big app with a team: choose TypeScript.
- If you want both speed and safety: start loose, then add types. Python can add types later. TypeScript starts strict.
You know what? You can’t really go wrong. Both are strong. Both are friendly with good tools. Pick the one that lets you ship without stress.
Little checklist before you choose
- Do you need charts, UI, or a big app? TypeScript.
- Do you need to crunch files today? Python.
- Do you share code with many devs? TypeScript helps.
- Do you work solo and need a fast script? Python is sweet.
- Do you hate runtime surprises? TypeScript. Or add mypy/pydantic to Python.
If you’re still stuck, start with Python for one week. Then try a small TypeScript app the next. Feel the difference. Your hands will tell you the truth.
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That’s my take. Real code. Real bugs. Real wins.