Python > Data Science

An open-source, low-code machine learning library in Python.

Machine learning toolbox.

High Performance, Easy-to-use, and Scalable Machine Learning Package.

RAPIDS Machine Learning Library.

Modular active learning framework for Python3.

PySpark + scikit-learn = Sparkit-learn.

A scalable C++ machine learning library (Python bindings).

Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).

Extension and helper modules for Python's data analysis and machine learning libraries.

50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels.

Machine Learning toolbox for Humans.

Multi-label classification for python.

Sequence classification toolkit for Python.

Simple structured learning framework for Python.

Highly interpretable classifiers for scikit learn.

Implementation of the rulefit.

Metric learning algorithms in Python.

Generalized Additive Models in Python.

Uplift modeling and causal inference with machine learning algorithms.

Scalable, Portable, and Distributed Gradient Boosting.

A fast, distributed, high-performance gradient boosting.

An open-source gradient boosting on decision trees library.

Fast GBDTs and Random Forests on GPUs.

Natural Gradient Boosting for Probabilistic Prediction.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

An AutoML toolkit and a drop-in replacement for a scikit-learn estimator.

Automatic architecture search and hyperparameter optimization for PyTorch.

AutoML library for deep learning.

AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.

AutoML tool that optimizes machine learning pipelines using genetic programming.

A powerful Automated Machine Learning python library.

Data loaders and abstractions for text and NLP. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

Modular Natural Language Processing workflows with Keras. <img height="20" src="img/keras_big.png" alt="Keras based/compatible">

Modules, data sets, and tutorials supporting research and development in Natural Language Processing.

The Classical Language Toolkik.

Python binding for <a href="https://github.com/morfologik/morfologik-stemming">Morfologik</a>.

Scikit-learn wrappers for Python fastText. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Simple text-to-phonemes converter for multiple languages.

Very simple framework for state-of-the-art NLP.

An audio library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

Python library for audio and music analysis.

Audio features extraction.

A library for audio and music analysis.

Library for audio and music analysis, description, and synthesis.

A simple, portable, lightweight library of audio feature extraction functions.

Music Analysis, Retrieval, and Synthesis for Audio Signals.

A library for augmenting annotated audio data.

Python audio and music signal processing library.

Datasets, Transforms, and Models specific to Computer Vision. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

Industry-strength Computer Vision workflows with Keras. <img height="20" src="img/keras_big.png" alt="MXNet based">

Open Source Computer Vision Library.

An efficient video loader for deep learning with smart shuffling that's super easy to digest.

OpenMMLab Foundational Library for Training Deep Learning Models. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

Image Processing SciKit (Toolbox for SciPy).

Image augmentation for machine learning experiments.

Additional augmentations for imgaug.

Image augmentation library in Python for machine learning.

Fast image augmentation library and easy-to-use wrapper around other libraries.

A One-stop Library for Language-Vision Intelligence.

A unified framework for machine learning with time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Time series forecasting with machine learning models

A python library for easy manipulation and forecasting of time series.

Lightning fast forecasting with statistical and econometric models.

Scalable machine learning-based time series forecasting.

Scalable machine learning-based time series forecasting.

Machine learning toolkit dedicated to time-series data. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Module for statistical learning, with a particular emphasis on time-dependent modeling. <img height="20" src="img/sklearn_big.png" alt="sklearn">

A flexible, intuitive, and fast forecasting library next.

Automatic Forecasting Procedure.

Open source time series library for Python.

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

Anomaly Detection and Correlation library.

makes it very easy to parse a string and for changing timezones

ML powered analytics engine for outlier/anomaly detection and root cause analysis

An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym).

An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities.

An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments.

A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.

An API conversion tool for popular external reinforcement learning environments.

C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.

An elegant PyTorch deep reinforcement learning library. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

A library of reinforcement learning components and agents.

PyTorch framework for RL research. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

An offline deep reinforcement learning library.

OpenDILab Decision AI Engine. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

A library for Reinforcement Learning in TensorFlow. <img height="20" src="img/tf_big2.png" alt="TensorFlow">

A TensorFlow library for applied reinforcement learning. <img height="20" src="img/tf_big2.png" alt="TensorFlow">

TensorFlow Reinforcement Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">

A research framework for fast prototyping of reinforcement learning algorithms.

Deep Reinforcement Learning for Keras. <img height="20" src="img/keras_big.png" alt="Keras compatible">

A toolkit for reproducible reinforcement learning research.

A platform for Applied Reinforcement Learning.

Reinforcement Learning in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG).

A reinforcement library designed for pytorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

Clean PyTorch implementations of imitation and reward learning algorithms. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

Geometric Deep Learning Extension Library for PyTorch.

Temporal Extension Library for PyTorch Geometric.

A signed/directed graph neural network extension library for PyTorch Geometric.

Python package built to ease deep learning on graph, on top of existing DL frameworks.

GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations

Deep learning on graphs.

Machine Learning on Graphs.

Build Graph Nets in Tensorflow.

A library to build Graph Neural Networks on the TensorFlow platform.

An autoML framework & toolkit for machine learning on graphs.

Generate embeddings from large-scale graph-structured data.

An unsupervised machine learning library for graph-structured data.

A library for sampling graph structured data.

A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).

A Graph Neural Network Library in Jax.

Train transformer language models with reinforcement learning.

The Graph Embedding Engine.

Network Analysis in Python.

A high performance Python graph library implemented in Rust.

Python interface for igraph.

A Python implementation of LightFM, a hybrid recommendation algorithm.

Deep recommender models using PyTorch.

A Python scikit for building and analyzing recommender systems.

A unified, comprehensive and efficient recommendation library.

allRank is a framework for training learning-to-rank neural models based on PyTorch.

A library for building recommender system models using TensorFlow.

Learning to Rank in TensorFlow.

Probabilistic and graphical models for Python. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

A python library for working with Probabilistic Graphical Models.

A flexible, scalable deep probabilistic programming library built on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

Bayesian Stochastic Modelling in Python.

Deep Probabilistic Modelling Made Easy. <img height="20" src="img/tf_big2.png" alt="sklearn">

Bayesian inference using the No-U-Turn sampler (Python interface).

Python package for Bayesian Machine Learning with scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Supervised domain-agnostic prediction framework for probabilistic modelling by The Alan Turing Institute. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Bayesian Deep Learning methods with Variational Inference for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

The Python ensemble sampling toolkit for affine-invariant MCMC.

A library for hidden semi-Markov models with explicit durations.

Bayesian inference in HSMMs and HMMs.

A highly efficient and modular implementation of Gaussian Processes in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

A scikit-learn-inspired API for CRFsuite. <img height="20" src="img/sklearn_big.png" alt="sklearn">

moDel Agnostic Language for Exploration and explanation. <img height="20" src="img/sklearnbig.png" alt="sklearn"><img height="20" src="img/Rbig.png" alt="R inspired/ported lib">

A data-driven framework to quantify the value of classifiers in a machine learning ensemble.

Algorithms for monitoring and explaining machine learning models.

Code for "High-Precision Model-Agnostic Explanations" paper.

Bias and Fairness Audit Toolkit.

Contrastive Explanation (Foil Trees). <img height="20" src="img/sklearn_big.png" alt="sklearn">

Visual analysis and diagnostic tools to facilitate machine learning model selection. <img height="20" src="img/sklearn_big.png" alt="sklearn">

An intuitive library to add plotting functionality to scikit-learn objects. <img height="20" src="img/sklearn_big.png" alt="sklearn">

A unified approach to explain the output of any machine learning model. <img height="20" src="img/sklearn_big.png" alt="sklearn">

InterpretML implements the Explainable Boosting Machine (EBM), a modern, fully interpretable machine learning model based on Generalized Additive Models (GAMs). This open-source package also provides visualization tools for EBMs, other glass-box models, and black-box explanations. <img height="20" src="img/sklearn_big.png" alt="sklearn">

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

Explaining the predictions of any machine learning classifier. <img height="20" src="img/sklearn_big.png" alt="sklearn">

FairML is a python toolbox auditing the machine learning models for bias. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.

Partial dependence plot toolbox.

Python Individual Conditional Expectation Plot Toolbox.

Python Library for Model Interpretation.

Model analysis tools for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">

A library that implements fairness-aware machine learning algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Interpreting scikit-learn's decision tree and random forest predictions. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Interpretability and explainability of data and machine learning models.

Auralisation of learned features in CNN (for audio).

A visualization of the CapsNet layers to better understand how it works.

A collection of infrastructure and tools for research in neural network interpretability.

Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).

Visualization Tool for your NeuralNetwork.

Tensorboard for PyTorch (and chainer, mxnet, numpy, ...).

Genetic Programming in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Genetic Algorithm in Python. <img height="20" src="img/pytorchbig2.png" alt="PyTorch based/compatible"> <img height="20" src="img/kerasbig.png" alt="keras">

Distributed Evolutionary Algorithms in Python.

A Genetic Programming platform for Python with GPU support. <img height="20" src="img/tf_big2.png" alt="sklearn">

A strongly-typed genetic programming framework for Python.

Genetic feature selection module for scikit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">

A hyperparameter optimization framework.

Multi-objective Optimization in Python.

Python implementation of CMA-ES.

Bayesian optimization.

Bayesian optimization in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

Heuristic Algorithms for optimization.

Hyperparameters tuning and feature selection using evolutionary algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Sequential Model-based Algorithm Configuration.

Is a library containing various optimizers for hyperparameter tuning.

Distributed Asynchronous Hyperparameter Optimization in Python.

Hyper-parameter optimization for sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn">

Use evolutionary algorithms instead of gridsearch in scikit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">

SigOpt wrappers for scikit-learn methods. <img height="20" src="img/sklearn_big.png" alt="sklearn">

A Python implementation of global optimization with gaussian processes.

Safe Bayesian Optimization.

Sequential model-based optimization with a scipy.optimize interface.

A comprehensive gradient-free optimization framework written in Python.

A research toolkit for particle swarm optimization in Python.

A Free and Open Source Python Library for Multiobjective Optimization.

Bayesian Optimization using GPflow. <img height="20" src="img/tf_big2.png" alt="sklearn">

Python Optimal Transport library.

Hyperparameter Optimization for Keras Models.

Library for nonlinear optimization (global and local, constrained or unconstrained).

An open-source software suite for optimization by Google; provides a unified programming interface to a half dozen solvers: SCIP, GLPK, GLOP, CP-SAT, CPLEX, and Gurobi.

No-code in the front, Python in the back. An open-source framework for creating data apps.

Create UIs for your machine learning model in Python in 3 minutes.

A toolkit for creating modular data visualization applications.

Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps.

Create UIs for your machine learning model in Python in 3 minutes.

Extension to pandas dataframes describe function. <img height="20" src="img/pandas_big.png" alt="pandas compatible">

Statistical modeling and econometrics in Python.

Supply a wrapper `StockDataFrame based on the pandas.DataFrame` with inline stock statistics/indicators support.

A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.

Pairwise Multiple Comparisons Post-hoc Tests.

Performance analysis of predictive (alpha) stock factors.

A package to generate synthetic tabular and time-series data leveraging the state-of-the-art generative models.

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

Distributed machine learning platform.

Framework and Library for Distributed Online Machine Learning.

Microsoft Distributed Machine Learning Toolkit.

PArallel Distributed Deep LEarning.

Distributed and parallel machine learning.

Distributed computation in Python.

Open source platform for the machine learning lifecycle.

Data Version Control | Git for Data & Models | ML Experiments Management.

️ machine learning development environment for data science and AI/ML engineering teams.

A tool to help you configure, organize, log, and reproduce experiments.

Adaptive Experimentation Platform.

Always know what to expect from your data.

A lightweight, flexible, and expressive statistical data testing library.

Validation & testing of ML models and data during model development, deployment, and production.

Evaluate and monitor ML models from validation to production.

Library for exploring and validating machine learning data.

A library to compare Pandas, Polars, and Spark data frames. It provides stats and lets users adjust for match accuracy.

Library of useful metrics and plots for evaluating recommender systems.

Machine learning evaluation metric.

Model evaluation made easy: plots, tables, and markdown reports.

Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.

Algorithms for outlier, adversarial and drift detection.

Parallel computing with task scheduling.

Fast NumPy array functions written in C.

NumPy-like API accelerated with CUDA.

Python library for multilinear algebra and tensor factorizations.

Solve automatic numerical differentiation problems in one or more variables.

Add built-in support for quaternions to numpy.

Tools for adaptive and parallel samping of mathematical functions.

A fast numerical expression evaluator for NumPy that comes with an integrated computing virtual machine to speed calculations up by avoiding memory allocation for intermediate results.

High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization

Efficient library to scrape Twitter

Python tools for geographic data.

Python Spatial Analysis Library.

Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.

A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.

Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.

A Python Toolkit for Quantum Machine Learning.

Transpile trained scikit-learn estimators to C, Java, JavaScript, and others.

Open Neural Network Exchange.

A set of tools to help users inter-operate among different deep learning frameworks.

Universal model exchange and serialization format for decision tree forests.