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.
Parallel computing with task scheduling
Best Python Libraries For: Math
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.
The fundamental package for scientific computing with Python.
Best Python Libraries For: Machine Learning
Scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.
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
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.
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.
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
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
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.
Statsmodels: statistical modeling and econometrics in Python
mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
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
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn.
Sequential Model-based Algorithm Configuration
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.
A Python toolbox for performing gradient-free optimization
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
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
Plotly.py is an interactive, open-source, and browser-based graphing library for Python
Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
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.
Bqplot is a 2-D visualization system for Jupyter, based on the constructs of the Grammar of Graphics.
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:
Fast data visualization and GUI tools for scientific / engineering applications
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.
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
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Lime: Explaining the predictions of any machine learning classifier
A game theoretic approach to explain the output of any machine learning model.
Visual analysis and diagnostic tools to facilitate machine learning model selection.
Create HTML profiling reports from pandas DataFrame objects
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