Polars vs Pandas: Which Library Defines the Future of Data Science?

[geniruphin-junior]

I am new to data science, having joined GitHub only three months ago, and I find the journey both exciting and challenging. After going through many tutorials and documentation, one recurring question keeps coming up in our data science group discussions: between Pandas and Polars, which library is better positioned for the future of data science?

As a beginner, I am curious to hear different perspectives and learn from the community. What do you think about this debate, and how do you see the role of Pandas versus Polars evolving for future data scientists?

[tkarim45]

Great question and a good one for a beginner-focused repo. Short version: learn Pandas first, reach for Polars when you hit its limits. They're not really enemies.

Pandas the incumbent. Massive ecosystem, every tutorial/StackOverflow answer/library integration assumes it (scikit-learn, matplotlib, seaborn all speak Pandas). For learning DS and for datasets that fit comfortably in RAM, it's the right default. Pandas 2.x also added Arrow-backed dtypes and copy-on-write, closing part of the performance gap.

Polars the fast challenger. Rust-backed, multi-threaded, with a lazy query engine that optimizes the whole pipeline before running (predicate/projection pushdown), and it streams datasets larger than RAM. On big data it's often 5–30× faster than Pandas and uses less memory. The expression API is stricter and, once it clicks, arguably cleaner — but the ecosystem is younger and many tools still expect a Pandas DataFrame.

Practical take for a learner:

• Master Pandas first it's the lingua franca; you can't avoid it, and the concepts (Series/DataFrame, groupby, joins) transfer directly.
• Learn Polars when a dataset is slow/too big in Pandas, or when you want its lazy pipelines. pl.from_pandas() / .to_pandas() make interop trivial, and Polars can read/write the same Parquet/CSV.
• The real convergence is Apache Arrow underneath both — the future is columnar + Arrow, and df.__dataframe__/narwhals-style tooling increasingly lets libraries accept either.

So it's not "which wins" it's Pandas for ubiquity and learning, Polars for scale and speed. Knowing both makes you more valuable than betting on one.

(Converting this repo's notebooks to Polars could be a fun future contribution if you want to explore it hands-on though we'd keep the Pandas versions as the primary teaching path.)

[geniruphin-junior]

I will definitely look into Polars! Honestly, I absolutely love this project—it is hands down one of the best repositories out there for learning data science. In the meantime, I am developing a local project called 'data-analyzer-files'. Given your deep expertise in data science, I would be incredibly grateful for any guidance or a helping hand along the way. That would mean the world to me

[geniruphin-junior]

thanks

[tkarim45]

Really glad that helped, and thanks for the kind words. Good to hear the repo's been useful for your learning.

Nice that you're building data-analyzer-files too. Building your own thing is how this stuff actually sticks. If you get stuck or need a hand with something, feel free to reach out. Easiest for me is if you post a specific question in an issue or discussion here, that way others can learn from it too, and CONTRIBUTING.md has pointers if you'd like to contribute back.

On Polars, if you dig into it, converting a notebook or two to Polars (kept next to the Pandas versions) would be a useful contribution and a solid way to learn it in the open. Happy to review a PR like that.

You're clearly motivated, which is the part that's hard to teach. Keep going.

[geniruphin-junior]

Could you please share some resources and advice on how to learn effectively and advance using best practices? Which resources did you use during your own journey? Also, are you a biologist? Based on your work, you really seem to be one!"

[tkarim45]

Apologies for the late reply, been a bit busy lately.

Ha, no, not a biologist. I'm an AI/ML engineer. The medical projects are there because I love applying AI in healthcare, it's a space where good ML can genuinely make a difference, so I keep coming back to it.

On learning, the honest truth is that building beat everything else for me. One project taken end to end (messy data, a model, an honest evaluation) taught me more than a pile of tutorials. So my advice: watch less, build more, and always check your model against a dumb baseline before you trust it. Reading other people's code helped a lot too.

Resources worth your time:

• Foundations: the pandas and scikit-learn docs (the scikit-learn user guide is one of the best free ML texts out there), plus StatQuest on YouTube for the intuition behind the math.
• Courses: Andrew Ng's ML Specialization for fundamentals, fast.ai for hands-on deep learning, and Kaggle Learn for short practical lessons.
• Books: Hands-On Machine Learning by Aurélien Géron is the one I'd start with, and Python for Data Analysis by Wes McKinney (who wrote pandas).
• Practice: Kaggle datasets and competitions, and rebuilding projects from scratch without looking at the solution.

For where you are now, working through this repo's projects in order and then trying a Kaggle competition is a solid path. And keep doing what you're already doing, building in public and shipping to GitHub. Writing up what you found, especially the things that didn't work, is underrated.

[geniruphin-junior]

thanks you

[geniruphin-junior]

Engineer, a question that keeps coming up is: is having the best model what matters most?

[geniruphin-junior]

Sorry to bother you, but I have another question: what is the key difference between an AI/ML Engineer and an MLOps Engineer?

[tkarim45]

Good questions.

On 'is the best model what matters most?', honestly no, and it's one of the most useful things to internalise early. The model is maybe 10% of a real project. What matters more is the quality of your data, how you frame the problem, and whether your evaluation is honest: the right metric, no leakage, and always comparing against a simple baseline. A fancy model on a leaky dataset or the wrong metric is worse than a simple model you actually trust. You'll see this theme all over this repo. Plenty of projects 'win' with plain logistic regression, and a few genuinely lose to a naive baseline, which is exactly the point. Chasing the top leaderboard model is the last 5%, not the first 95%.

On AI/ML Engineer vs MLOps Engineer, they overlap, but roughly:

• An AI/ML Engineer builds the model: understanding the data, engineering features, training and evaluating models, getting the thing to actually work.
• An MLOps Engineer runs the model in production: data and training pipelines, deployment, CI/CD, monitoring, catching drift and failures, scaling, and keeping it reliable over time.

One role is 'make a good model', the other is 'make that model keep working in the real world without breaking'. On a small team one person often does both; at scale they split. Knowing a bit of both makes you more valuable than going deep on only one.

And no need to apologise, ask away.

[geniruphin-junior]

Thank you so much for everything! ✨ Your answers are so wise and inspiring. 🌟 I wish all my classmates could see and work on this repository! 🎒 It helps me a lot, and your projects are truly amazing. 🙌 To be completely honest, since I joined GitHub, you are the person who has helped me the most with your valuable advice. 💡 Above all, you have given me great resources and the mindset of a true resourceful hustler. 💪 Thank you for everything! 🙏 Heartfelt thanks to you! ❤️ Le dim. 19 juil. 2026 à 10:06, Taimour Abdul Karim ***@***.***> a écrit :

…

*tkarim45* left a comment (tkarim45/Beginner-Data-Science-Projects#12) <#12 (comment)> Good questions. On 'is the best model what matters most?', honestly no, and it's one of the most useful things to internalise early. The model is maybe 10% of a real project. What matters more is the quality of your data, how you frame the problem, and whether your evaluation is honest: the right metric, no leakage, and always comparing against a simple baseline. A fancy model on a leaky dataset or the wrong metric is worse than a simple model you actually trust. You'll see this theme all over this repo. Plenty of projects 'win' with plain logistic regression, and a few genuinely lose to a naive baseline, which is exactly the point. Chasing the top leaderboard model is the last 5%, not the first 95%. On AI/ML Engineer vs MLOps Engineer, they overlap, but roughly: - An *AI/ML Engineer* builds the model: understanding the data, engineering features, training and evaluating models, getting the thing to actually work. - An *MLOps Engineer* runs the model in production: data and training pipelines, deployment, CI/CD, monitoring, catching drift and failures, scaling, and keeping it reliable over time. One role is 'make a good model', the other is 'make that model keep working in the real world without breaking'. On a small team one person often does both; at scale they split. Knowing a bit of both makes you more valuable than going deep on only one. And no need to apologise, ask away. — Reply to this email directly, view it on GitHub <#12?email_source=notifications&email_token=CEC4H6F4UUJDXQKPYZZNVOL5FR6SDA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMBRGQ4TMOJUGY32M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KYZTPN52GK4S7MNWGSY3L#issuecomment-5014969467>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/CEC4H6ERZTUK3FSTB4XBSQ35FR6SDAVCNFSNUABFKJSXA33TNF2G64TZHM3TOMBQGAYTANRSHNEXG43VMU5TINZZHAYTCNBYGU4KC5QC> . Triage notifications, keep track of coding agent tasks and review pull requests on the go with GitHub Mobile for iOS <https://github.com/notifications/mobile/ios/CEC4H6AQ7CGI245Z62YWVD35FR6SDA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMBRGQ4TMOJUGY32M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KUZTPN52GK4S7NFXXG> and Android <https://github.com/notifications/mobile/android/CEC4H6HMXE7YKXQGWKY67JT5FR6SDA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMBRGQ4TMOJUGY32M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2K4ZTPN52GK4S7MFXGI4TPNFSA>. Download it today! You are receiving this because you authored the thread.Message ID: ***@***.***>

[geniruphin-junior]

And also, in my opinion, you have so much experience because you are truly so wise. Honestly, imagine someone has an AI project that predicts and recognizes images of medicinal plants 🌿—what should they use: Sklearn (classic) or PyTorch (neural networks)? 🧠 It would help me a lot to answer this question, and it would be incredibly useful for me and my fellow science classmates. 🎒 Without a doubt, in my eyes, you are a senior expert. 🥇 Le mar. 21 juil. 2026 à 20:31, RUPHIN JUNIOR ***@***.***> a écrit :

…

Thank you so much for everything! ✨ Your answers are so wise and inspiring. 🌟 I wish all my classmates could see and work on this repository! 🎒 It helps me a lot, and your projects are truly amazing. 🙌 To be completely honest, since I joined GitHub, you are the person who has helped me the most with your valuable advice. 💡 Above all, you have given me great resources and the mindset of a true resourceful hustler. 💪 Thank you for everything! 🙏 Heartfelt thanks to you! ❤️ Le dim. 19 juil. 2026 à 10:06, Taimour Abdul Karim < ***@***.***> a écrit : > *tkarim45* left a comment (tkarim45/Beginner-Data-Science-Projects#12) > <#12 (comment)> > > Good questions. > > On 'is the best model what matters most?', honestly no, and it's one of > the most useful things to internalise early. The model is maybe 10% of a > real project. What matters more is the quality of your data, how you frame > the problem, and whether your evaluation is honest: the right metric, no > leakage, and always comparing against a simple baseline. A fancy model on a > leaky dataset or the wrong metric is worse than a simple model you actually > trust. You'll see this theme all over this repo. Plenty of projects 'win' > with plain logistic regression, and a few genuinely lose to a naive > baseline, which is exactly the point. Chasing the top leaderboard model is > the last 5%, not the first 95%. > > On AI/ML Engineer vs MLOps Engineer, they overlap, but roughly: > > - An *AI/ML Engineer* builds the model: understanding the data, > engineering features, training and evaluating models, getting the thing to > actually work. > - An *MLOps Engineer* runs the model in production: data and training > pipelines, deployment, CI/CD, monitoring, catching drift and failures, > scaling, and keeping it reliable over time. > > One role is 'make a good model', the other is 'make that model keep > working in the real world without breaking'. On a small team one person > often does both; at scale they split. Knowing a bit of both makes you more > valuable than going deep on only one. > > And no need to apologise, ask away. > > — > Reply to this email directly, view it on GitHub > <#12?email_source=notifications&email_token=CEC4H6F4UUJDXQKPYZZNVOL5FR6SDA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMBRGQ4TMOJUGY32M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KYZTPN52GK4S7MNWGSY3L#issuecomment-5014969467>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/CEC4H6ERZTUK3FSTB4XBSQ35FR6SDAVCNFSNUABFKJSXA33TNF2G64TZHM3TOMBQGAYTANRSHNEXG43VMU5TINZZHAYTCNBYGU4KC5QC> > . > Triage notifications, keep track of coding agent tasks and review pull > requests on the go with GitHub Mobile for iOS > <https://github.com/notifications/mobile/ios/CEC4H6AQ7CGI245Z62YWVD35FR6SDA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMBRGQ4TMOJUGY32M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KUZTPN52GK4S7NFXXG> > and Android > <https://github.com/notifications/mobile/android/CEC4H6HMXE7YKXQGWKY67JT5FR6SDA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMBRGQ4TMOJUGY32M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2K4ZTPN52GK4S7MFXGI4TPNFSA>. > Download it today! > You are receiving this because you authored the thread.Message ID: > ***@***.***> >

[tkarim45]

Thanks for the kind words, glad it's been useful. Now to your question, and it's a good one.

For recognizing images of medicinal plants, go with PyTorch (deep learning). Classic sklearn models on raw pixels don't work well for real photos. Plants differ by leaf shape, texture, and vein patterns, and those are exactly the kinds of visual features a CNN learns on its own. A logistic regression or random forest would need you to hand-engineer those features, which is the hard part.

But here's the practical advice: don't train a network from scratch. Use transfer learning. Take a pretrained model (ResNet18 or EfficientNet), replace the final layer with your plant classes, and fine-tune it on your photos. A few hundred labeled images per plant can get you surprisingly far, and it runs on a free Colab GPU. The Plant Disease CNNs project in this repo does exactly this with EfficientNet if you want a working example.

There's also a nice middle path if you want to start simpler: use a pretrained CNN as a feature extractor (run each image through it, keep the embedding vector) and then train a plain sklearn classifier like logistic regression on those vectors. You get most of the deep-learning benefit with sklearn-level simplicity and almost no compute. Honestly a great first version for a class project.

One thing carries over from my earlier answer though: the framework matters less than the data. Clean labels, enough examples per plant, photos taken in varied lighting and backgrounds, and an honest held-out test set will decide whether the project works far more than sklearn vs PyTorch will.

[geniruphin-junior]

Your explanations are clear and undoubtedly worthy of a senior, and I hope my entire class of AI-passionate scientists will come check out your profile

[tkarim45]

Thanks, that genuinely means a lot. Send your classmates this way, I'm always happy to see more people learning in the open. Good luck with your own projects, and feel free to keep the questions coming.