7 top Python libraries for data science and machine learning
If you want to learn in-demand skills, consider learning data science and machine learning. These fields have become highly sought after in the job market given the increasing amount and importance of data in our world. And if you’re just getting into coding, the Python programming language provides a great entry point for beginners.
In this article, we’ll introduce you to the closely related fields of data science and machine learning. We’ll then explore Python’s dominance in these fields and get to know seven of the top Python libraries for working in them.
Get hands-on with data science and machine learning today.#
Try one of our 300+ courses and learning paths: An Introductory Guide to Data Science and Machine Learning.
Data science and machine learning: An overview#
Data science is a field of applied mathematics and statistics that provides useful information based on the analysis and modeling of large amounts of data. Machine learning is a branch of artificial intelligence and computer science that involves developing computer systems that can learn and adapt using algorithms and statistical models. While these two fields sound unrelated, they’ve become inseparable in recent years. This is because, while data science gathers insights, learning machine learning enables the creation of accurate and actionable predictions.
Data science and machine learning have become increasingly important in the era of Big Data, which is characterized by data sets too big and complex to be analyzed by humans or traditional data management systems. By using the tools of data science and machine learning, we can glean information from data to help make important decisions.
Today, data modeling and analysis are essential to the growth and success of businesses and organizations in almost every sector. You can find applications of data science and machine learning across areas as diverse as health care, road travel, sports, government, and e-commerce.
Some of the real-world applications of data science and machine learning include:
- Google has identified breast cancer tumors that metastasize to nearby lymph nodes using a machine-learning tool called LYNA. The tool identified metastatic cancer with 99% accuracy using its algorithm, but more testing is needed before doctors can use it.
- A company called StreetLight is modeling traffic patterns for cars, bikes, and pedestrians in North America using data science and trillions of data points from smartphones and in-vehicle navigation devices.
- UPS is optimizing package transportation with a platform called Network Planning Tools that uses artificial intelligence and machine learning to work around bad weather and service bottlenecks.
- RSPCT’s shooting-analysis system for basketball transmits data from a sensor on the hoop’s rim to a device that displays shot details and generates predictive insights. The system has been adopted by NBA and college teams.
- The IRS has improved its fraud detection with taxpayer profiles built from public social media data, assorted metadata, emailing analysis, and electronic payment patterns. Based on those profiles, the IRS forecasts individual tax returns, and anyone whose returns diverge wildly gets flagged for auditing. (Privacy advocates have not been pleased.)
- A company called Sovrn created intelligent advertising technology compatible with Google and Amazon’s server-to-server bidding platforms to broker deals between advertisers and outlets.
Why Python is used by data scientists#
Python is not the only language used in data science and machine learning. R is another dominant option, and Java, JavaScript, and C++ also have their places. But Python’s advantages have helped it earn its place as one of the most popular programming languages generally, and in data science and machine learning specifically.
These advantages include:
- Python is relatively easy to learn. Its syntax is concise and resembles English, which helps make learning it more intuitive.
- It has a large community of users. This translates into excellent peer support and documentation.
- Python is portable and allows you to run its code anywhere. This means a Python application can run across Windows, MacOS, and Linux without modifications to its source code (unless there are system-specific calls).
- Python is a free, open-source, and object-oriented programming language.
- Python makes it easy to add modules from other languages, such as C and C++.
- Finally, many of Python’s libraries were literally made for data science and machine learning. We’ll talk more about this advantage in the next section.
7 top Python libraries for data science and machine learning#
In Python, a library is a collection of resources that contain pre-written code. As a programmer, this will save you time because you won’t have to write all your code from scratch. Python’s extensive collection of libraries enables all sorts of functionality, especially in data science and machine learning. Python has interactive libraries for data processing, data modeling, data manipulation, data visualization, machine learning algorithms, and more. Let’s talk about seven of the top Python libraries for these fields.
1. NumPy#
NumPy is a popular open-source library for data processing and modeling that is widely used in data science, machine learning, and deep learning. It’s also compatible with other libraries such as Pandas, Matplotlib, and Scikit-learn, which we’ll discuss later.
NumPy introduces objects for multidimensional arrays and matrices, along with routines that let you perform advanced mathematical and statistical functions on arrays with only a small amount of code. In addition, it contains some linear algebra functions and Fourier transforms.
2. SciPy#
SciPy is another open-source library for data processing and modeling that builds on NumPy for scientific computation applications. It contains more fully-featured versions of the linear algebra modules found in NumPy and many other numerical algorithms.
SciPy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics, and other classes of problems.
It also adds a collection of algorithms and high-level commands for manipulating and visualizing data. For instance, by combining SciPy and NumPy, you can do things like image processing.
3. Pandas#
Pandas is an open-source package for data cleaning, processing, and manipulation. It provides extended, flexible data structures to hold different types of labeled and relational data.
Pandas specializes in manipulating numerical tables and time series, which are common data forms in data science.
Pandas is usually used along with other data science libraries: It’s built on NumPy, and it’s also used in SciPy for statistical analysis and Matplotlib for plotting functions.
4. Matplotlib#
Matplotlib is a data visualization and 2-D plotting library. In fact, it’s considered the most popular and widely used plotting library in the Python community.
Matplotlib stands out for its versatility. Matplotlib can be used in Python scripts, the Python and IPython shells, Jupyter notebooks, and web application servers. In addition, it offers a wide range of charts, including plots, bar charts, pie charts, histograms, scatterplots, error charts, power spectra, and stemplots.
5. Seaborn#
Seaborn is a data visualization library based on Matplotlib and closely integrated with NumPy and Pandas data structures. It provides a high-level interface for creating statistical graphics that assist greatly with exploring and understanding data.
The data graphics available in Seaborn include bar charts, pie charts, histograms, scatterplots, and error charts.
6. TensorFlow#
TensorFlow is a popular machine learning platform developed by Google. Its use cases include natural language processing, image classification, creating neural networks, and more.
This platform provides a flexible “ecosystem” of libraries, tools, and user resources that are highly portable: You can train and deploy models anywhere, no matter what language or platform you use.
TensorFlow lets you build and train high-level machine-learning models using the Keras API, a feature of TensorFlow 2.0. It also provides eager execution, allowing for immediate iteration and easier debugging.
Note: Eager execution is an imperative programming environment that evaluates operations immediately, without needing to build graphs. This means operations return concrete values instead of constructing a computational graph to run later.
For bigger training tasks, TensorFlow provides the Distribution Strategy API, which lets you run training on different hardware configurations without changing your machine learning model.
7. Scikit-learn#
Scikit-learn, also called sklearn, is a library for learning, improving, and executing machine learning models. It builds on NumPy and SciPy by adding a set of algorithms for common machine-learning and data-mining tasks.
Sklearn is the most popular Python library for performing classification, regression, and clustering algorithms. It’s considered a very curated library because developers don’t have to choose between different versions of the same algorithm.
Accelerated computing and next-gen performance tools#
As datasets get larger and models become more complex, speed and scalability are more important than ever. Beyond classic libraries like NumPy and Pandas, today’s data science workflows rely on tools that leverage GPU acceleration, JIT compilation, and distributed computing to process data at scale.
JAX and Flax: These libraries combine NumPy-like APIs with just-in-time compilation, automatic differentiation, and GPU/TPU support, making them ideal for both research and production ML workflows.
Ray and Dask: For tasks that don’t fit in memory or need to run across multiple machines, Ray and Dask make distributed data processing and model training simple and scalable.
Modin and cuDF: If you love Pandas but need more performance, Modin offers parallelized DataFrames on CPUs and GPUs, while cuDF (part of NVIDIA RAPIDS) delivers GPU-accelerated data manipulation.
PyTorch Lightning: A lightweight wrapper for PyTorch that simplifies distributed training and scales deep learning models without boilerplate code.
MLOps, validation, and model monitoring#
Building a model is just the first step — deploying and maintaining it is where the real work begins. Modern ML teams use a range of libraries to validate data, track experiments, and monitor performance in production.
Great Expectations and Deepchecks: Validate data quality, catch anomalies, and prevent data drift before it breaks your model.
Evidently AI: Provides dashboards to track model performance, drift, and bias over time.
MLflow and Weights & Biases (W&B): Popular platforms for experiment tracking, model registry, and deployment pipelines.
Prefect and Airflow: Automate data pipelines and schedule recurring ML jobs reliably.
High-performance data processing beyond Pandas#
While Pandas remains a staple, newer libraries push the boundaries of speed and scalability. They’re built to handle massive datasets and complex analytics without sacrificing developer experience.
Polars: A lightning-fast DataFrame library built in Rust, optimized for multi-threaded execution and query performance.
Vaex: Ideal for out-of-core analytics on datasets that don’t fit into memory.
Modin: Drop-in replacement for Pandas that parallelizes operations automatically across cores or clusters.
Embeddings, vector search, and semantic similarity#
Many modern machine learning applications — from recommendation engines to LLM-powered search — rely on vector representations rather than traditional tabular features. Python’s ecosystem now includes powerful tools to work with these embeddings.
FAISS, Annoy, and HNSWlib: Libraries optimized for nearest-neighbor search in high-dimensional vector spaces, essential for recommendation and similarity systems.
SentenceTransformers and Hugging Face Transformers: Easily generate embeddings from text, images, or multimodal data.
Milvus, Weaviate, and Pinecone: Vector databases that integrate with Python libraries to enable semantic search and retrieval-augmented generation (RAG) applications.
Time series, streaming, and online learning#
Real-world data isn’t always static — it arrives continuously, changes over time, and often needs real-time predictions. These libraries help you work with dynamic, time-dependent data.
Darts and GluonTS: High-level frameworks for time series forecasting with classical models and deep learning architectures.
tsfresh: Automates feature extraction from time series data.
River and scikit-multiflow: Support online and incremental learning, allowing models to update continuously as new data streams in.
Explainability, fairness, and interpretability#
Transparency is now a must in machine learning. Stakeholders need to understand how models make decisions — and regulators often require it.
SHAP and LIME: Provide clear, model-agnostic explanations of feature importance.
Captum: A library built for PyTorch that offers deep interpretability methods for neural networks.
Fairlearn and AIF360: Help identify and mitigate bias in ML models, ensuring ethical and fair predictions.
InterpretML: Combines traditional interpretability techniques with modern explainable AI approaches.
Graph and geometric machine learning#
As data becomes more interconnected, graph-based methods are gaining popularity. Graph libraries allow you to model complex relationships that traditional tabular approaches can’t capture.
NetworkX: A classic library for building and analyzing graph structures.
PyTorch Geometric and DGL: Popular frameworks for building Graph Neural Networks (GNNs).
StellarGraph: High-level API for graph-based machine learning tasks like link prediction, node classification, and recommendation.
Wrapping up and next steps#
Today we’ve given you a brief overview of data science and machine learning through the lens of Python and its top libraries for these fields. Hopefully, our discussion has piqued your interest and you’re considering learning more! We’ve just begun to scrape the surface of what you can do with Python’s libraries for data science and machine learning. There are many other libraries and packages worth exploring, like Scrapy and BeautifulSoup for web scraping and Bokeh for data visualization.
Whether you’re just learning to code or have some Python under your belt, we’ve created the course An Introductory Guide to Data Science and Machine Learning. This course is one of our many data science and machine learning resources, so be sure to check out our other offerings as you progress in your journey.
Happy learning!
