Essential Python libraries for Data Science

When you're a developer looking to dive into data science, you immediately think of Python ^^ (Okay, maybe I'm a bit biased haha). It’s fair to say that Python remains the undisputed star of the field, with a massive and rich ecosystem!
In this article, we’ll take a tour of the must-know Python libraries, organized by usage, to work more efficiently — and with the right tools.

Data manipulation

Let's start with the foundation of data science: data manipulation.

Pandas

One of the most well-known Python libraries, it’s at the heart of handling tabular data. Easy to use, with a huge community (which means tons of documentation).

If it has a limitation, it's that it can slow down with very large datasets. But in practice, it’s rare to hit those limits. Plus, it’s probably still faster than coding your own algorithms from scratch (speaking from experience here...).

Polars

The revelation of the past few years. It’s blazing fast, multi-threaded, and built on Rust.
It’s the perfect library for working with large volumes of data. Its syntax is a little different but remains intuitive (especially in "lazy" mode).

Dask

When you need to parallelize or handle truly oversized datasets, Dask comes to the rescue!
It’s compatible with Pandas (which is great) and can run locally or on a cluster.

Visualization: understanding your data at a glance

Once you've extracted, formatted, and processed your data, you often want to visualize it!

Matplotlib & Seaborn

A classic for quick, static visualizations. Seaborn adds a nice layer of styling and handy statistical functions.

Plotly

With Plotly, you can build dynamic charts to explore your data interactively and in fine detail.

Altair

A declarative and elegant approach to visualization. Very pleasant to use for clean, polished reports (I haven’t personally used it, but that’s the feedback I’ve found).

Machine learning

Machine learning involves training models from data to automate complex tasks.
Let’s take a look at the many powerful Python libraries that simplify training, evaluation, and deployment of models.
(Admittedly, this isn't exactly my main area of expertise.)

Scikit-learn

The cornerstone of ML in Python. All the fundamental algorithms are here, with a stable and consistent API.

XGBoost / LightGBM

Always the champions in Kaggle competitions. Perfect for tabular data.

CatBoost

Optimized for categorical variables. Super handy when you don’t want to bother with encoding.

Deep Learning

Deep learning is a more advanced branch of machine learning, often more complex, and based on deep neural networks.

PyTorch

The darling of research... and increasingly of production. Very flexible, dynamic, and well-documented.

TensorFlow / Keras

More production-oriented, with lots of industrialization tools and great stability. Keras makes it much easier to handle.

Pipelines, Tracking, and Automation

MLflow

To track experiments, manage models, and deploy them. Essential once your projects reach a certain level of complexity.

Kedro

A framework that enforces real project rigor: modular structure, pipelines, clean configuration, testing.

Prefect

The modern orchestrator. Simpler and more Pythonic than Airflow, perfect for automating your workflows.

Bonus: LLMs and Generative AI

In recent years, it's become impossible to ignore LLMs and generative AI...

LangChain / LlamaIndex

To easily connect LLMs to data sources (PDFs, SQL databases, etc.) and build smart agents.

Transformers (Hugging Face)

Thousands of NLP, vision, and audio models available with just a few lines of code. The de facto standard for pretrained deep learning models.

Conclusion

As we've seen throughout this article, Python offers a huge range of libraries — it’s truly a very rich ecosystem.