Top Python Libraries for Data Science, Data Visualization & Machine Learning

The categories included in this post, which we see as taking into account common data science libraries — those likely to be used by practitioners in the data science space for generalized, non-neural network, non-research work — are:

  • Data - libraries for the management, manipulation, and other processing of data

  • Math - while many libraries perform mathematical tasks, this small collection does so exclusively

  • Machine learning - self explanatory; excludes libraries primarily meant for building neural networks or for automating machine learning processes

  • Automated machine learning - libraries that primarily function to automate processes related to machine learning

  • Data visualization - libraries that primarily serve a function related to visualizing data, as opposed to modeling, preprocessing, etc.

  • Explanation & exploration - libraries primarily for exploring and explaining models or data

Our list is made up of libraries that our team decided together by consensus was representative of common and well-used Python libraries. Also, to be included a library must have a Github repository. The categories are in no particular order, and neither are the libraries included within each. We contemplated constructing an ordering arbitrarily by stars or some other metric, but decided against it in order not explicitly stray from placing any perceived value or importance of the libraries within. Their listing here, then, is purely random. Library descriptions are directly from the Github repositories, in some form or another.

Note that visualization below, by Gregory Piatetsky, represents each library by type, plots it by stars and contributors, and its symbol size is reflective of the relative number of commits the library has on Github.

Figure 1: Top Python Libraries for Data Science, Data Visualization & Machine Learning

Plotted by number of stars and number of contributors; relative size by number of contributors

And, so without further ado, here are the 38 top Python libraries for data science, data visualization & machine learning.


1. Apache Spark Stars: 27600, Commits: 28197, Contributors: 1638

Apache Spark - A unified analytics engine for large-scale data processing

2. Pandas Stars: 26800, Commits: 24300, Contributors: 2126

Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

3. Dask Stars: 7300, Commits: 6149, Contributors: 393

Parallel computing with task scheduling


4. Scipy Stars: 7500, Commits: 24247, Contributors: 914

SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.

5. Numpy Stars: 1500, Commits: 24266, Contributors: 1010

The fundamental package for scientific computing with Python.

Machine Learning

6. Scikit-Learn Stars: 42500, Commits: 26162, Contributors: 1881

Scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

7. XGBoost Stars: 19900, Commits: 5015, Contributors: 461

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

8. LightGBM Stars: 11600, Commits: 2066, Contributors: 172

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

9. Catboost Stars: 5400, Commits: 12936, Contributors: 188

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

10. Dlib Stars: 9500, Commits: 7868, Contributors: 146

Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. Can be used with Python via dlib API

11. Annoy Stars: 7700, Commits: 778, Contributors: 53

Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

12. H20ai Stars: 500, Commits: 27894, Contributors: 137

Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

13. StatsModels Stars: 5600, Commits: 13446, Contributors: 247

Statsmodels: statistical modeling and econometrics in Python

14. mlpack Stars: 3400, Commits: 24575, Contributors: 190

mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages

15. Pattern Stars: 7600, Commits: 1434, Contributors: 20

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

16. Prophet Stars: 11500, Commits: 595, Contributors: 106

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Automated Machine Learning

17. TPOT Stars: 7500, Commits: 2282, Contributors: 66

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

18. auto-sklearn Stars: 4100, Commits: 2343, Contributors: 52

auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

19. Hyperopt-sklearn Stars: 1100, Commits: 188, Contributors: 18

Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn.

20. SMAC-3 Stars: 529, Commits: 1882, Contributors: 29

Sequential Model-based Algorithm Configuration

21. scikit-optimize Stars: 1900, Commits: 1540, Contributors: 59

Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization.

22. Nevergrad Stars: 2700, Commits: 663, Contributors: 38

A Python toolbox for performing gradient-free optimization

23. Optuna Stars: 3500, Commits: 7749, Contributors: 97

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning.

Data Visualization

24. Apache Superset Stars: 30300, Commits: 5833, Contributors: 492

Apache Superset is a Data Visualization and Data Exploration Platform

25. Matplotlib Stars: 12300, Commits: 36716, Contributors: 1002

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

26. Plotly Stars: 7900, Commits: 4604, Contributors: 137 is an interactive, open-source, and browser-based graphing library for Python

27. Seaborn Stars: 7700, Commits: 2702, Contributors: 126

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

28. folium Stars: 4900, Commits: 1443, Contributors: 109

Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in a Leaflet map via folium.

29. Bqplot Stars: 2900, Commits: 3178, Contributors: 45

Bqplot is a 2-D visualization system for Jupyter, based on the constructs of the Grammar of Graphics.

30. VisPy Stars: 2500, Commits: 6352, Contributors: 117

VisPy is a high-performance interactive 2D/3D data visualization library. VisPy leverages the computational power of modern Graphics Processing Units (GPUs) through the OpenGL library to display very large datasets. Applications of VisPy include:

31. PyQtgraph Stars: 2200, Commits: 2200, Contributors: 142

Fast data visualization and GUI tools for scientific / engineering applications

32. Bokeh Stars: 1400, Commits: 18726, Contributors: 467

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.

33. Altair Stars: 600, Commits: 3031, Contributors: 106

Altair is a declarative statistical visualization library for Python. With Altair, you can spend more time understanding your data and its meaning.

Explanation & Exploration

34. eli5 Stars: 2200, Commits: 1198, Contributors: 15

A library for debugging/inspecting machine learning classifiers and explaining their predictions

35. LIME Stars: 800, Commits: 501, Contributors: 41

Lime: Explaining the predictions of any machine learning classifier

36. SHAP Stars: 10400, Commits: 1376, Contributors: 96

A game theoretic approach to explain the output of any machine learning model.

37. YellowBrick Stars: 300, Commits: 825, Contributors: 92

Visual analysis and diagnostic tools to facilitate machine learning model selection.

38. pandas-profiling Stars: 6200, Commits: 704, Contributors: 47

Create HTML profiling reports from pandas DataFrame objects

Source: KDnuggets

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