I still love Python.
I just don’t want to be limited by it.
There, I said it.
For years, Python was my ride-or-die. I built models with it. I cleaned horrendous datasets with it. I survived Jupyter notebooks crashing at the worst possible moments because of it. Python was more than a language — it was home.
But lately, I’ve felt something I didn’t expect: restless.
🐍 Python Isn’t Broken. I Just Grew.
Let’s get this out of the way:
I’m not quitting Python. I’m not throwing shade.
This is more like when you realize your favorite pair of sneakers, the ones that carried you through college and your first job and your second caffeine-fueled promotion… don’t quite fit the way they used to.
Because suddenly, my questions started to change:
What if this notebook was a real app?
Why is this ETL job taking so long?
Could this model live closer to the data?
What if I didn’t use Pandas again?
And that’s when I realized — Python had been perfect for the version of me that was. But maybe not the one I’m becoming.
💡 The World of Data Is Changing — Fast
Data science used to feel like magic. Now it feels like product.
We’re shipping apps. Running in production. Automating. Building tools people actually use.
In this new world, I’m seeing a shift:
Less “write every line by hand.”
More “connect systems, design flows, ship results.”
Less “Which library solves this?”
More “Which tool gets it done better, faster, smarter?”
And not everything fits neatly into Python anymore.
🚀 What I’m Exploring (and Loving)
I’m not trading Python in — I’m adding to the toolbox. Here’s what’s lighting me up lately:
🔸 DuckDB & SQL:
Turns out, a lot of “data science” is just “really smart queries.” DuckDB makes working with data feel instant. No servers. No setup. Just results.
🔸 Polars (and the Rust underneath):
Imagine Pandas, but built for the 2025 version of you. Faster, lighter, and… well, it doesn’t crash on large data. That’s a win.
🔸 Streamlit & friends:
I stopped handing people static notebooks. Now I build tiny apps in 10 minutes. Turns out, when non-tech folks can click buttons and explore their own data, they get it.
🔸 Mojo, Julia, Rust:
Not full-time yet. But I’ve dipped my toes. And the ideas? They're powerful. These languages aren’t just hype — they’re glimpses of where we’re headed.
🧠 The Real Shift: Curiosity Over Comfort
Honestly, the biggest upgrade hasn’t been technical — it’s been mental.
Somewhere along the way, I realized:
The moment you stop learning in this field is the moment you start falling behind.
Python got me here. But “here” isn’t the final stop.
I want to be the kind of data scientist who keeps playing. Who tries new things even when they’re uncomfortable. Who says, “I don’t know this yet,” and means it with excitement.
And maybe… that’s what this whole journey is about anyway.
💬 What About You?
If you're reading this and thinking:
“I’ve been meaning to try DuckDB...”
“I wish my notebooks felt faster and cleaner.”
“Everyone's talking about Mojo and I have no clue what it does…”
Then hey: you’re not alone. The smartest people I know are quietly tinkering behind the scenes. No pressure. No rush. Just curiosity over comfort.
We’re all just trying to evolve with the craft.
TL;DR: Python Was the Beginning, Not the Limit
You don’t have to quit Python to grow.
You just have to let yourself be curious again.
So go try something new.
You might come back to Python with fresh eyes — or you might discover your next big tool, your next wild idea, your next version of yourself.
Either way, it’s worth the click.
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