Curated list of data science software in Python

- scikit-learn compatible (or inspired) API
- Theano based project
- TensorFlow based project
- PyTorch based project
- CuPy based project
- Apache Spark based project
- GPU-accelerated computations (if not based on Theano, Tensorflow, PyTorch or CuPy)
- possible to run on AMD GPU

• Machine Learning
  • General Purpouse ML
  • Automated Machine Learning
  • Ensemble methods
  • Imbalanced datasets
  • Random Forests
  • Extreme Learning Machine
  • Kernel methods
  • Gradient boosting
• Deep Learning
  • Keras
  • TensorFlow
  • Theano
  • PyTorch
  • MXnet
  • Caffe
  • CNTK
  • Chainer
  • Others
• Reinforcement Learning
• Distributed computing systems
• Probabilistic methods
• Genetic Programming
• Optimization
• Natural Language Processing
• Computer Audition
• Computer Vision
• Feature engineering
• Data manipulation & pipelines
• Statistics
• Experiments tools
• Visualization
• Evaluation
• Computations
• Quantum computing
• Conversion

• scikit-learn - machine learning in Python
• Shogun - machine learning toolbox
• xLearn - High Performance, Easy-to-use, and Scalable Machine Learning Package
• Sparkit-learn - PySpark + Scikit-learn = Sparkit-learn
• mlpack - a scalable C++ machine learning library (Python bindings)
• dlib - A toolkit for making real world machine learning and data analysis applications in C++ (Python bindings)
• MLxtend - extension and helper modules for Python's data analysis and machine learning libraries
• tick - module for statistical learning, with a particular emphasis on time-dependent modelling
• sklearn-extensions - a consolidated package of small extensions to scikit-learn
• civisml-extensions - scikit-learn-compatible estimators from Civis Analytics
• scikit-multilearn - multi-label classification for python
• tslearn - machine learning toolkit dedicated to time-series data
• seqlearn - seqlearn is a sequence classification toolkit for Python
• pystruct - Simple structured learning framework for python
• sklearn-expertsys - Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models
• skutil - A set of scikit-learn and h2o extension classes (as well as caret classes for python)
• sklearn-crfsuite - scikit-learn inspired API for CRFsuite
• RuleFit - implementation of the rulefit
• metric-learn - metric learning algorithms in Python

• TPOT - 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
• MLBox - a powerful Automated Machine Learning python library.

• ML-Ensemble - high performance ensemble learning
• brew - Python Ensemble Learning API
• Stacking - Simple and useful stacking library, written in Python.
• stacked_generalization - library for machine learning stacking generalization.
• vecstack - Python package for stacking (machine learning technique)

• imbalanced-learn - module to perform under sampling and over sampling with various techniques
• imbalanced-algorithms - Python-based implementations of algorithms for learning on imbalanced data.

• rpforest - a forest of random projection trees
• Random Forest Clustering - Unsupervised Clustering using Random Forests
• sklearn-random-bits-forest - wrapper of the Random Bits Forest program written by (Wang et al., 2016)
• rgf_python - Python Wrapper of Regularized Greedy Forest

• Python-ELM - Extreme Learning Machine implementation in Python
• Python Extreme Learning Machine (ELM) - a machine learning technique used for classification/regression tasks
• hpelm - High performance implementation of Extreme Learning Machines (fast randomized neural networks).

• pyFM - Factorization machines in python
• fastFM - a library for Factorization Machines
• tffm - TensorFlow implementation of an arbitrary order Factorization Machine
• liquidSVM - an implementation of SVMs
• scikit-rvm - Relevance Vector Machine implementation using the scikit-learn API

• XGBoost - Scalable, Portable and Distributed Gradient Boosting
• LightGBM - a fast, distributed, high performance gradient boosting by Microsoft
• CatBoost - an open-source gradient boosting on decision trees library by Yandex
• TGBoost - Tiny Gradient Boosting Tree

• Keras - a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano
• Hyperas - Keras + Hyperopt: A very simple wrapper for convenient hyperparameter
• Elephas - Distributed Deep learning with Keras & Spark
• Hera - Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.
• dist-keras - Distributed Deep Learning, with a focus on distributed training
• Keras add-ons...

• TensorFlow - omputation using data flow graphs for scalable machine learning by Google
• TensorLayer - Deep Learning and Reinforcement Learning Library for Researcher and Engineer.
• TFLearn - Deep learning library featuring a higher-level API for TensorFlow
• Sonnet - TensorFlow-based neural network library by DeepMind
• TensorForce - a TensorFlow library for applied reinforcement learning
• tensorpack - a Neural Net Training Interface on TensorFlow
• Polyaxon - a platform that helps you build, manage and monitor deep learning models
• Horovod - Distributed training framework for TensorFlow
• tfdeploy - Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy
• hiptensorflow - ROCm/HIP enabled Tensorflow
• TensorFlow Fold - Deep learning with dynamic computation graphs in TensorFlow

WARNING: Theano development has been stopped

• Theano - is a Python library that allows you to define, optimize, and evaluate mathematical expressions
• Lasagne - Lightweight library to build and train neural networks in Theano Lasagne add-ons...
• nolearn - scikit-learn compatible neural network library (mainly for Lasagne)
• Blocks - a Theano framework for building and training neural networks
• platoon - Multi-GPU mini-framework for Theano
• NeuPy - NeuPy is a Python library for Artificial Neural Networks and Deep Learning
• scikit-neuralnetwork - Deep neural networks without the learning cliff
• Theano-MPI - MPI Parallel framework for training deep learning models built in Theano

• PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
• skorch - a scikit-learn compatible neural network library that wraps pytorch
• PyTorchNet - an abstraction to train neural networks

• MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler
• Gluon - a clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet)
• MXbox - simple, efficient and flexible vision toolbox for mxnet framework.
• MXNet - HIP Port of MXNet

• Caffe - a fast open framework for deep learning
• Caffe2 - a lightweight, modular, and scalable deep learning framework
• hipCaffe - the HIP port of Caffe

• CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

• Chainer - a flexible framework for neural networks
• ChainerRL - a deep reinforcement learning library built on top of Chainer.
• ChainerCV - a Library for Deep Learning in Computer Vision
• ChainerMN - scalable distributed deep learning with Chainer
• scikit-chainer - scikit-learn like interface to chainer
• chainer_sklearn - Sklearn (Scikit-learn) like interface for Chainer

• Neon - Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
• Tangent - Source-to-Source Debuggable Derivatives in Pure Python
• autograd - Efficiently computes derivatives of numpy code
• Myia - deep learning framework (pre-alpha)

• OpenAI Gym - a toolkit for developing and comparing reinforcement learning algorithms.

• PySpark - exposes the Spark programming model to Python
• Veles - Distributed machine learning platform by Samsung
• Jubatus - Framework and Library for Distributed Online Machine Learning
• DMTK - Microsoft Distributed Machine Learning Toolkit
• PaddlePaddle - PArallel Distributed Deep LEarning by Baidu
• dask-ml - Distributed and parallel machine learning
• Distributed - Distributed computation in Python

• pomegranate - probabilistic and graphical models for Python
• bayesloop - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models
• PyFlux - Open source time series library for Python
• pyro - a flexible, scalable deep probabilistic programming library built on PyTorch.
• ZhuSuan - Bayesian Deep Learning
• PyMC - Bayesian Stochastic Modelling in Python
• PyMC3 - Python package for Bayesian statistical modeling and Probabilistic Machine Learning
• Edward - A library for probabilistic modeling, inference, and criticism.
• GPflow - Gaussian processes in TensorFlow
• PyStan - Bayesian inference using the No-U-Turn sampler (Python interface)
• gelato - Bayesian dessert for Lasagne
• sklearn-bayes - Python package for Bayesian Machine Learning with scikit-learn API
• skggm - estimation of general graphical models
• pgmpy - a python library for working with Probabilistic Graphical Models.
• skpro - supervised domain-agnostic prediction framework for probabilistic modelling by The Alan Turing Institute
• Aboleth - a bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation
• PtStat - Probabilistic Programming and Statistical Inference in PyTorch
• emcee - The Python ensemble sampling toolkit for affine-invariant MCMC

• gplearn - Genetic Programming in Python
• DEAP - Distributed Evolutionary Algorithms in Python
• karoo_gp - A Genetic Programming platform for Python with GPU support
• monkeys - A strongly-typed genetic programming framework for Python

• Spearmint - Bayesian optimization
• SMAC3 - Sequential Model-based Algorithm Configuration
• Optunity - is a library containing various optimizers for hyperparameter tuning.
• htperopt - Distributed Asynchronous Hyperparameter Optimization in Python
• hyperopt-sklearn - hyper-parameter optimization for sklearn
• sklearn-deap - use evolutionary algorithms instead of gridsearch in scikit-learn
• sigopt_sklearn - SigOpt wrappers for scikit-learn methods
• Bayesian Optimization - A Python implementation of global optimization with gaussian processes.
• SafeOpt - Safe Bayesian Optimization
• scikit-optimize - Sequential model-based optimization with a scipy.optimize interface
• Solid - A comprehensive gradient-free optimization framework written in Python
• PySwarms - A research toolkit for particle swarm optimization in Python
• Platypus - A Free and Open Source Python Library for Multiobjective Optimization

• NLTK - modules, data sets, and tutorials supporting research and development in Natural Language Processing
• CLTK - The Classical Language Toolkik
• gensim - Topic Modelling for Humans
• PSI-Toolkit - a natural language processing toolkit by Adam Mickiewicz University in Poznań
• pyMorfologik - Python binding for Morfologik (Polish morphological analyzer)

• librosa - Python library for audio and music analysis
• Yaafe - Audio features extraction
• aubio - a library for audio and music analysis
• Essentia - library for audio and music analysis, description and synthesis
• LibXtract - is a simple, portable, lightweight library of audio feature extraction functions
• Marsyas - Music Analysis, Retrieval and Synthesis for Audio Signals
• muda - a library for augmenting annotated audio data
• madmom - Python audio and music signal processing library

• OpenCV - Open Source Computer Vision Library
• scikit-image - Image Processing SciKit (Toolbox for SciPy)

• Featuretools - automated feature engineering
• scikit-feature - feature selection repository in python
• skl-groups - scikit-learn addon to operate on set/"group"-based features
• Feature Forge - a set of tools for creating and testing machine learning feature
• boruta_py - implementations of the Boruta all-relevant feature selection method
• BoostARoota - a fast xgboost feature selection algorithm

• pandas - powerful Python data analysis toolkit
• sklearn-pandas - Pandas integration with sklearn
• alexander - wrapper that aims to make scikit-learn fully compatible with pandas
• blaze - NumPy and Pandas interface to Big Data
• pandasql - allows you to query pandas DataFrames using SQL syntax
• pandas-gbq - Pandas Google Big Query
• xpandas - universal 1d/2d data containers with Transformers functionality for data analysis by The Alan Turing Institute
• Fuel - data pipeline framework for machine learning
• Arctic - high performance datastore for time series and tick data
• pdpipe - sasy pipelines for pandas DataFrames.
• meza - a Python toolkit for processing tabular data
• pandas-ply - functional data manipulation for pandas
• Dplython - Dplyr for Python
• pysparkling - a pure Python implementation of Apache Spark's RDD and DStream interfaces
• quinn - pyspark methods to enhance developer productivity

• statsmodels - statistical modeling and econometrics in Python
• stockstats - Supply a wrapper StockDataFrame based on the pandas.DataFrame with inline stock statistics/indicators support.
• simplestatistics - simple statistical functions implemented in readable Python.
• weightedcalcs - pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more

• Sacred - a tool to help you configure, organize, log and reproduce experiments by IDSIA
• Xcessiv - a web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling
• Persimmon - A visual dataflow programming language for sklearn

• Matplotlib - plotting with Python
• seaborn - statistical data visualization using matplotlib
• Bokeh - Interactive Web Plotting for Python
• HoloViews - stop plotting your data - annotate your data and let it visualize itself
• Alphalens - performance analysis of predictive (alpha) stock factors by Quantopian
• yellowbrick - visual analysis and diagnostic tools to facilitate machine learning model selection
• scikit-plot - an intuitive library to add plotting functionality to scikit-learn objects
• Lime - Explaining the predictions of any machine learning classifier

• kaggle-metrics - Metrics for Kaggle competitions
• Metrics - machine learning evaluation metric
• sklearn-evaluation - scikit-learn model evaluation made easy: plots, tables and markdown reports

• numpy - the fundamental package needed for scientific computing with Python.
• Dask - parallel computing with task scheduling
• bottleneck - Fast NumPy array functions written in C
• minpy - NumPy interface with mixed backend execution
• CuPy - NumPy-like API accelerated with CUDA
• scikit-tensor - Python library for multilinear algebra and tensor factorizations

• QML - a Python Toolkit for Quantum Machine Learning

• sklearn-porter - transpile trained scikit-learn estimators to C, Java, JavaScript and others