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Top 38 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

Best Python Libraries for: Data

1. Apache Spark

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

2. Pandas 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

Parallel computing with task scheduling

Best Python Libraries For: Math

4. Scipy

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

The fundamental package for scientific computing with Python.

Best Python Libraries For: Machine Learning

6. Scikit-Learn

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

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

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

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

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

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

12. H20ai

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

Statsmodels: statistical modeling and econometrics in Python

14. mlpack

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

15. Pattern

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

16. Prophet

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

Best Python Libraries For: Automated Machine Learning

17. TPOT

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

18. auto-sklearn

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

19. Hyperopt-sklearn

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

20. SMAC-3

Sequential Model-based Algorithm Configuration

21. scikit-optimize

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

A Python toolbox for performing gradient-free optimization

23. Optuna

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

Best Python Libraries For: Data Visualization

24. Apache Superset

Apache Superset is a Data Visualization and Data Exploration Platform

25. Matplotlib

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

26. Plotly is an interactive, open-source, and browser-based graphing library for Python

27. Seaborn

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

28. folium

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

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

30. VisPy

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

Fast data visualization and GUI tools for scientific / engineering applications

32. Bokeh

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

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

Best Python Libraries For: Explanation & Exploration

34. eli5

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

35. LIME

Lime: Explaining the predictions of any machine learning classifier

36. SHAP

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

37. YellowBrick

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

38. pandas-profiling

Create HTML profiling reports from pandas DataFrame objects

Source; KDNugget

The Tech Platform

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