Python Data Science

Machine Learning

General Purpouse Machine Learning

• scikit-learn – Machine learning in Python. 
• Shogun – Machine learning toolbox.
• xLearn – High Performance, Easy-to-use, and Scalable Machine Learning Package.
• cuML – RAPIDS Machine Learning Library.  
• modAL – Modular active learning framework for Python3. 
• Sparkit-learn – PySpark + scikit-learn = Sparkit-learn.  
• mlpack – A scalable C++ machine learning library (Python bindings).
• dlib – 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. 
• hyperlearn – 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels.  
• Reproducible Experiment Platform (REP) – Machine Learning toolbox for Humans. 
• scikit-multilearn – Multi-label classification for python. 
• seqlearn – 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. 
• RuleFit – Implementation of the rulefit. 
• metric-learn – Metric learning algorithms in Python. 
• pyGAM – Generalized Additive Models in Python.
• Karate Club – An unsupervised machine learning library for graph structured data.

Time Series

• tslearn – Machine learning toolkit dedicated to time-series data. 
• tick – Module for statistical learning, with a particular emphasis on time-dependent modelling. 
• Prophet – Automatic Forecasting Procedure.
• PyFlux – Open source time series library for Python.
• bayesloop – Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
• luminol – Anomaly Detection and Correlation library.

Automated Machine Learning

• TPOT – Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. 
• auto-sklearn – An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. 
• MLBox – A powerful Automated Machine Learning python library.

Ensemble Methods

• ML-Ensemble – High performance ensemble learning. 
• 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 Datasets

• 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.  

Random Forests

• rpforest – A forest of random projection trees. 
• 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. 

Extreme Learning Machine

• 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). 

Kernel Methods

• 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. 
• ThunderSVM – A fast SVM Library on GPUs and CPUs.  

Gradient Boosting

• XGBoost – Scalable, Portable and Distributed Gradient Boosting.  
• LightGBM – A fast, distributed, high performance gradient boosting.  
• CatBoost – An open-source gradient boosting on decision trees library.  
• ThunderGBM – Fast GBDTs and Random Forests on GPUs.  

Deep Learning

PyTorch

• PyTorch – Tensors and Dynamic neural networks in Python with strong GPU acceleration. 
• torchvision – Datasets, Transforms and Models specific to Computer Vision. 
• torchtext – Data loaders and abstractions for text and NLP. 
• torchaudio – An audio library for PyTorch. 
• ignite – High-level library to help with training neural networks in PyTorch. 
• PyToune – A Keras-like framework and utilities for PyTorch.
• skorch – A scikit-learn compatible neural network library that wraps pytorch.  
• PyTorchNet – An abstraction to train neural networks. 
• pytorch_geometric – Geometric Deep Learning Extension Library for PyTorch. 
• Catalyst – High-level utils for PyTorch DL & RL research. 

TensorFlow

• TensorFlow – Computation 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. 
• tensorpack – A Neural Net Training Interface on TensorFlow. 
• Polyaxon – A platform that helps you build, manage and monitor deep learning models. 
• NeuPy – NeuPy is a Python library for Artificial Neural Networks and Deep Learning (previously: ). 
• tfdeploy – Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy. 
• tensorflow-upstream – TensorFlow ROCm port.  
• TensorFlow Fold – Deep learning with dynamic computation graphs in TensorFlow. 
• tensorlm – Wrapper library for text generation / language models at char and word level with RNN. 
• TensorLight – A high-level framework for TensorFlow. 
• Mesh TensorFlow – Model Parallelism Made Easier. 
• Ludwig – A toolbox, that allows to train and test deep learning models without the need to write code. 

Keras

• Keras – A high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. 
• keras-contrib – Keras community contributions. 
• 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. 
• Spektral – Deep learning on graphs. 
• qkeras – A quantization deep learning library. 

MXNet

• 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. 
• gluon-cv – Provides implementations of the state-of-the-art deep learning models in computer vision. 
• gluon-nlp – NLP made easy. 
• Xfer – Transfer Learning library for Deep Neural Networks. 
• MXNet – HIP Port of MXNet.  

Chainer

• Chainer – A flexible framework for neural networks.
• ChainerCV – A Library for Deep Learning in Computer Vision.
• ChainerMN – Scalable distributed deep learning with Chainer.

Theano

WARNING: Theano development has been stopped

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

Others

• CNTK – Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit.
• 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).
• nnabla – Neural Network Libraries by Sony.
• Caffe – A fast open framework for deep learning.
• Caffe2 – A lightweight, modular, and scalable deep learning framework (now a part of PyTorch).
• hipCaffe – The HIP port of Caffe. 

Data Manipulation

Data Containers

• pandas – Powerful Python data analysis toolkit.
• cuDF – GPU DataFrame Library.  
• 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.
• pysparkling – A pure Python implementation of Apache Spark’s RDD and DStream interfaces. 
• Arctic – High performance datastore for time series and tick data.
• datatable – Data.table for Python. 
• koalas – pandas API on Apache Spark. 
• modin – Speed up your pandas workflows by changing a single line of code. 
• swifter – A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner.
• pandas_flavor – A package which allow to write your own flavor of Pandas easily.
• pandas-log – A package which allow to provide feedback about basic pandas operations and find both buisness logic and performance issues.

Pipelines

• pdpipe – Sasy pipelines for pandas DataFrames.
• SSPipe – Python pipe (|) operator with support for DataFrames and Numpy and Pytorch.
• pandas-ply – Functional data manipulation for pandas. 
• Dplython – Dplyr for Python. 
• sklearn-pandas – pandas integration with sklearn.  
• Dataset – Helps you conveniently work with random or sequential batches of your data and define data processing.
• pyjanitor – Clean APIs for data cleaning. 
• meza – A Python toolkit for processing tabular data.
• Prodmodel – Build system for data science pipelines.
• dopanda – Hints and tips for using pandas in an analysis environment. 

Feature Engineering

General

• Featuretools – Automated feature engineering.
• skl-groups – A scikit-learn addon to operate on set/”group”-based features. 
• Feature Forge – A set of tools for creating and testing machine learning feature. 
• few – A feature engineering wrapper for sklearn. 
• scikit-mdr – A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction. 
• tsfresh – Automatic extraction of relevant features from time series. 

Feature Selection

• scikit-feature – Feature selection repository in python.
• boruta_py – Implementations of the Boruta all-relevant feature selection method. 
• BoostARoota – A fast xgboost feature selection algorithm. 
• scikit-rebate – A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. 

Visualization

• 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.
• prettyplotlib – Painlessly create beautiful matplotlib plots.
• python-ternary – Ternary plotting library for python with matplotlib.
• missingno – Missing data visualization module for Python.
• chartify – Python library that makes it easy for data scientists to create charts.
• physt – Improved histograms.
• animatplot – A python package for animating plots build on matplotlib.
• plotly – A Python library that makes interactive and publication-quality graphs.

Model Explanation

• Alibi – Algorithms for monitoring and explaining machine learning models.
• anchor – Code for “High-Precision Model-Agnostic Explanations” paper.
• aequitas – Bias and Fairness Audit Toolkit.
• Contrastive Explanation – Contrastive Explanation (Foil Trees). 
• 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. 
• shap – A unified approach to explain the output of any machine learning model. 
• ELI5 – A library for debugging/inspecting machine learning classifiers and explaining their predictions.
• Lime – Explaining the predictions of any machine learning classifier. 
• FairML – FairML is a python toolbox auditing the machine learning models for bias. 
• L2X – Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.
• PDPbox – Partial dependence plot toolbox.
• pyBreakDown – Python implementation of R package breakDown. 
• PyCEbox – Python Individual Conditional Expectation Plot Toolbox.
• Skater – Python Library for Model Interpretation.
• model-analysis – Model analysis tools for TensorFlow. 
• themis-ml – A library that implements fairness-aware machine learning algorithms. 
• treeinterpreter – Interpreting scikit-learn’s decision tree and random forest predictions. 
• AI Explainability 360 – Interpretability and explainability of data and machine learning models.
• Auralisation – Auralisation of learned features in CNN (for audio).
• CapsNet-Visualization – A visualization of the CapsNet layers to better understand how it works.
• lucid – A collection of infrastructure and tools for research in neural network interpretability.
• Netron – Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).
• FlashLight – Visualization Tool for your NeuralNetwork.
• tensorboard-pytorch – Tensorboard for pytorch (and chainer, mxnet, numpy, …).
• mxboard – Logging MXNet data for visualization in TensorBoard. 

Reinforcement Learning

• OpenAI Gym – A toolkit for developing and comparing reinforcement learning algorithms.
• Coach – Easy experimentation with state of the art Reinforcement Learning algorithms.
• garage – A toolkit for reproducible reinforcement learning research.
• OpenAI Baselines – High-quality implementations of reinforcement learning algorithms.
• Stable Baselines – A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
• RLlib – Scalable Reinforcement Learning.
• Horizon – A platform for Applied Reinforcement Learning.
• TF-Agents – A library for Reinforcement Learning in TensorFlow. 
• TensorForce – A TensorFlow library for applied reinforcement learning. 
• TRFL – TensorFlow Reinforcement Learning. 
• Dopamine – A research framework for fast prototyping of reinforcement learning algorithms.
• keras-rl – Deep Reinforcement Learning for Keras. 
• ChainerRL – A deep reinforcement learning library built on top of Chainer.

Distributed Computing

• Horovod – Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. 
• PySpark – Exposes the Spark programming model to Python. 
• Veles – Distributed machine learning platform.
• Jubatus – Framework and Library for Distributed Online Machine Learning.
• DMTK – Microsoft Distributed Machine Learning Toolkit.
• PaddlePaddle – PArallel Distributed Deep LEarning.
• dask-ml – Distributed and parallel machine learning. 
• Distributed – Distributed computation in Python.

Probabilistic Methods

• pomegranate – Probabilistic and graphical models 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. 
• sampled – Decorator for reusable models in PyMC3.
• Edward – A library for probabilistic modeling, inference, and criticism. 
• InferPy – Deep Probabilistic Modelling Made Easy. 
• 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. 
• PyVarInf – Bayesian Deep Learning methods with Variational Inference for PyTorch. 
• emcee – The Python ensemble sampling toolkit for affine-invariant MCMC.
• hsmmlearn – A library for hidden semi-Markov models with explicit durations.
• pyhsmm – Bayesian inference in HSMMs and HMMs.
• GPyTorch – A highly efficient and modular implementation of Gaussian Processes in PyTorch. 
• MXFusion – Modular Probabilistic Programming on MXNet. 
• sklearn-crfsuite – A scikit-learn inspired API for CRFsuite. 

Genetic Programming

• 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.
• sklearn-genetic – Genetic feature selection module for scikit-learn. 

Optimization

• Spearmint – Bayesian optimization.
• BoTorch – Bayesian optimization in PyTorch. 
• scikit-opt – Heuristic Algorithms for optimization.
• SMAC3 – Sequential Model-based Algorithm Configuration.
• Optunity – Is a library containing various optimizers for hyperparameter tuning.
• hyperopt – 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.
• GPflowOpt – Bayesian Optimization using GPflow. 
• POT – Python Optimal Transport library.
• Talos – Hyperparameter Optimization for Keras Models.
• nlopt – Library for nonlinear optimization (global and local, constrained or unconstrained).

Natural Language Processing

• 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.
• pyMorfologik – Python binding for Morfologik.
• skift – Scikit-learn wrappers for Python fastText. 
• Phonemizer – Simple text to phonemes converter for multiple languages.
• flair – Very simple framework for state-of-the-art NLP.
• spaCy – Industrial-Strength Natural Language Processing.

Computer Audition

• 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 – 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.

Computer Vision

• OpenCV – Open Source Computer Vision Library.
• scikit-image – Image Processing SciKit (Toolbox for SciPy).
• imgaug – Image augmentation for machine learning experiments.
• imgaug_extension – Additional augmentations for imgaug.
• Augmentor – Image augmentation library in Python for machine learning.
• albumentations – Fast image augmentation library and easy to use wrapper around other libraries.

Statistics

• pandas_summary – Extension to pandas dataframes describe function. 
• Pandas Profiling – Create HTML profiling reports from pandas DataFrame objects. 
• statsmodels – Statistical modeling and econometrics in Python.
• stockstats – Supply a wrapper StockDataFrame based on the pandas.DataFrame with inline stock statistics/indicators support.
• weightedcalcs – A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.
• scikit-posthocs – Pairwise Multiple Comparisons Post-hoc Tests.
• Alphalens – Performance analysis of predictive (alpha) stock factors.

Distributed Computing

• Horovod – Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. 
• PySpark – Exposes the Spark programming model to Python. 
• Veles – Distributed machine learning platform.
• Jubatus – Framework and Library for Distributed Online Machine Learning.
• DMTK – Microsoft Distributed Machine Learning Toolkit.
• PaddlePaddle – PArallel Distributed Deep LEarning.
• dask-ml – Distributed and parallel machine learning. 
• Distributed – Distributed computation in Python.

Experimentation

• Sacred – A tool to help you configure, organize, log and reproduce experiments.
• Xcessiv – A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling.
• Persimmon – A visual dataflow programming language for sklearn.
• Ax – Adaptive Experimentation Platform. 
• Neptune – A lightweight ML experiment tracking, results visualization and management tool.

Evaluation

• recmetrics – Library of useful metrics and plots for evaluating recommender systems.
• Metrics – Machine learning evaluation metric.
• sklearn-evaluation – Model evaluation made easy: plots, tables and markdown reports. 
• AI Fairness 360 – Fairness metrics for datasets and ML models, explanations and algorithms to mitigate bias in datasets and models.

Computations

• numpy – The fundamental package needed for scientific computing with Python.
• Dask – Parallel computing with task scheduling. 
• bottleneck – Fast NumPy array functions written in C.
• CuPy – NumPy-like API accelerated with CUDA.
• scikit-tensor – Python library for multilinear algebra and tensor factorizations.
• numdifftools – Solve automatic numerical differentiation problems in one or more variables.
• quaternion – Add built-in support for quaternions to numpy.
• adaptive – Tools for adaptive and parallel samping of mathematical functions.

Spatial Analysis

• GeoPandas – Python tools for geographic data. 
• PySal – Python Spatial Analysis Library.

Quantum Computing

• PennyLane – Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
• QML – A Python Toolkit for Quantum Machine Learning.

Conversion

• sklearn-porter – Transpile trained scikit-learn estimators to C, Java, JavaScript and others.
• ONNX – Open Neural Network Exchange.
• MMdnn – A set of tools to help users inter-operate among different deep learning frameworks.