Data Science

Tools for adaptive and parallel samping of mathematical functions.

Fast NumPy array functions written in C.

NumPy-like API accelerated with CUDA.

Parallel computing with task scheduling.

Solve automatic numerical differentiation problems in one or more variables.

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.

The fundamental package needed for scientific computing with Python.

Add built-in support for quaternions to numpy.

Python library for multilinear algebra and tensor factorizations.

A library for audio and music analysis.

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

Python library for audio and music analysis.

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

Python audio and music signal processing library.

Music Analysis, Retrieval, and Synthesis for Audio Signals.

A library for augmenting annotated audio data.

An audio library for PyTorch.

Audio features extraction.

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

Image augmentation library in Python for machine learning.

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

Image augmentation for machine learning experiments.

Additional augmentations for imgaug.

Industry-strength Computer Vision workflows with Keras.

A One-stop Library for Language-Vision Intelligence.

OpenMMLab Foundational Library for Training Deep Learning Models.

Open Source Computer Vision Library.

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data.

Image Processing SciKit (Toolbox for SciPy).

Datasets, Transforms, and Models specific to Computer Vision.

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

Open Neural Network Exchange.

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

Universal model exchange and serialization format for decision tree forests.

High-performance datastore for time series and tick data.

NumPy and pandas interface to Big Data.

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

GPU DataFrame Library.

Collect, clean, and visualize your data in Python with a few lines of code.

Helps you conveniently work with random or sequential batches of your data and define data processing.

Data.table for Python.

Hints and tips for using pandas in an analysis environment.

Dplyr for Python.

A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.

A Python toolkit for processing tabular data.

Speed up your pandas workflows by changing a single line of code.

Powerful Python data analysis toolkit.

pandas Google Big Query.

A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.

Functional data manipulation for pandas.

Allows you to query pandas DataFrames using SQL syntax.

Create HTML profiling reports from pandas DataFrame objects

Sasy pipelines for pandas DataFrames.

A fast multi-threaded, hybrid-out-of-core DataFrame library.

Build system for data science pipelines.

Clean APIs for data cleaning.

A pure Python implementation of Apache Spark's RDD and DStream interfaces.

pandas integration with sklearn.

A system for quickly generating training data with weak supervision.

Python pipe (|) operator with support for DataFrames and Numpy, and Pytorch.

A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.

Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.

Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less error-prone indexing routines.

Universal 1d/2d data containers with Transformers .functionality for data analysis by The Alan Turing Institute.

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

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

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

Evaluate and monitor ML models from validation to production.

Always know what to expect from your data.

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

Library for exploring and validating machine learning data.

Efficiently computes derivatives of numpy code.

A fast open framework for deep learning.

High-level utils for PyTorch DL & RL research.

A PyTorch-based deep learning library for drug pair scoring.

Distributed Deep learning with Keras & Spark.

A neural network library for JAX that is designed for flexibility.

Keras + Hyperopt: A straightforward wrapper for a convenient hyperparameter.

High-level library to help with training neural networks in PyTorch.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.

A high-level neural networks API running on top of TensorFlow.

Keras community contributions.

A toolbox that allows one to train and test deep learning models without the need to write code.

Model Parallelism Made Easier.

Neural Network Libraries by Sony.

A gradient processing and optimization library for JAX.

A platform that helps you build, manage and monitor deep learning models.

Tensors and Dynamic neural networks in Python with strong GPU acceleration.

PyTorch Lightning is just organized PyTorch.

A quantization deep learning library.

A scikit-learn compatible neural network library that wraps PyTorch.

TensorFlow-based neural network library.

Source-to-Source Debuggable Derivatives in Pure Python.

Computation using data flow graphs for scalable machine learning by Google.

Deep learning with dynamic computation graphs in TensorFlow.

TensorFlow ROCm port.

Deep Learning and Reinforcement Learning Library for Researcher and Engineer.

A high-level framework for TensorFlow.

A Neural Net Training Interface on TensorFlow.

Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy.

Deep learning library featuring a higher-level API for TensorFlow.

State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Enable sharing and execute Jupyter Notebooks

A collection of APIs to turn scripts and notebooks into interactive reports.

Modern, fast (high-performance), a web framework for building APIs with Python

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

Make it easy to deploy the machine learning model

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

A toolkit for creating modular data visualization applications.

Distributed and parallel machine learning.

Distributed computation in Python.

Microsoft Distributed Machine Learning Toolkit.

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

Framework and Library for Distributed Online Machine Learning.

PArallel Distributed Deep LEarning.

Exposes the Spark programming model to Python.

Distributed machine learning platform.

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

Machine learning evaluation metric.

Library of useful metrics and plots for evaluating recommender systems.

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

Adaptive Experimentation Platform.

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

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

Open source platform for the machine learning lifecycle.

A lightweight ML experiment tracking, results visualization, and management tool.

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

A fast xgboost feature selection algorithm.

Implementations of the Boruta all-relevant feature selection method.

Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression).

Feature engineering package with sklearn-like functionality.

A set of tools for creating and testing machine learning features.

Automated feature engineering.

A feature engineering wrapper for sklearn.

Moving window features.

Automated feature generation with expert-level performance.

Feature selection repository in Python.

A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.

A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.

A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps

A scikit-learn addon to operate on set/"group"-based features.

Automatic extraction of relevant features from time series.

A feature selection library based on evolutionary algorithms.

Distributed Evolutionary Algorithms in Python.

Genetic Programming in Python.

A Genetic Programming platform for Python with GPU support.

A strongly-typed genetic programming framework for Python.

Genetic Algorithm in Python.

Genetic feature selection module for scikit-learn.

An autoML framework & toolkit for machine learning on graphs.

An autoML framework & toolkit for machine learning on graphs.

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

Build Graph Nets in Tensorflow.

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

A Graph Neural Network Library in Jax.

An unsupervised machine learning library for graph-structured data.

A library for sampling graph structured data.

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

Generate embeddings from large-scale graph-structured data.

Geometric Deep Learning Extension Library for PyTorch.

Temporal Extension Library for PyTorch Geometric.

Deep learning on graphs.

Machine Learning on Graphs.

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

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

A Python implementation of LightFM, a hybrid recommendation algorithm.

A unified, comprehensive and efficient recommendation library.

Deep recommender models using PyTorch.

A Python scikit for building and analyzing recommender systems.

Learning to Rank in TensorFlow.

A library for building recommender system models using TensorFlow.

An open-source gradient boosting on decision trees library.

Uplift modeling and causal inference with machine learning algorithms.

RAPIDS Machine Learning Library.

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

A library for Factorization Machines.

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

Python-based implementations of algorithms for learning on imbalanced data.

Module to perform under-sampling and over-sampling with various techniques.

A fast, distributed, high-performance gradient boosting.

An implementation of SVMs.

Metric learning algorithms in Python.

High performance ensemble learning.

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

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

Modular active learning framework for Python3.

Natural Gradient Boosting for Probabilistic Prediction.

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

Factorization machines in python.

Generalized Additive Models in Python.

Simple structured learning framework for Python.

Machine Learning toolbox for Humans.

Python Wrapper of Regularized Greedy Forest.

A forest of random projection trees.

Implementation of the rulefit.

Machine learning in Python.

Multi-label classification for python.

Relevance Vector Machine implementation using the scikit-learn API.

Sequence classification toolkit for Python.

Machine learning toolbox.

Highly interpretable classifiers for scikit learn.

Wrapper of the Random Bits Forest program written by (Wang et al., 2016).

PySpark + scikit-learn = Sparkit-learn.

Library for machine learning stacking generalization.

Simple and useful stacking library written in Python.

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

TensorFlow implementation of an arbitrary order Factorization Machine.

Fast GBDTs and Random Forests on GPUs.

A fast SVM Library on GPUs and CPUs.

Python package for stacking (machine learning technique).

Scalable, Portable, and Distributed Gradient Boosting.

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

Bias and Fairness Audit Toolkit.

Interpretability and explainability of data and machine learning models.

Algorithms for monitoring and explaining machine learning models.

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

Auralisation of learned features in CNN (for audio).

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

Contrastive Explanation (Foil Trees).

moDel Agnostic Language for Exploration and explanation.

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

FairML is a python toolbox auditing the machine learning models for bias.

Visualization Tool for your NeuralNetwork.

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

Explaining the predictions of any machine learning classifier.

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

Model analysis tools for TensorFlow.

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

Partial dependence plot toolbox.

Python Individual Conditional Expectation Plot Toolbox.

An intuitive library to add plotting functionality to scikit-learn objects.

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

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

Python Library for Model Interpretation.

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

A library that implements fairness-aware machine learning algorithms.

Interpreting scikit-learn's decision tree and random forest predictions.

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

The Classical Language Toolkik.

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

Topic Modelling for Humans.

Modular Natural Language Processing workflows with Keras.

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

Simple text-to-phonemes converter for multiple languages.

Python binding for Morfologik.

Scikit-learn wrappers for Python fastText.

Industrial-Strength Natural Language Processing.

Data loaders and abstractions for text and NLP.

A Python implementation of global optimization with gaussian processes.

Bayesian optimization in PyTorch.

Bayesian Optimization using GPflow.

Distributed Asynchronous Hyperparameter Optimization in Python.

Hyper-parameter optimization for sklearn.

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

A hyperparameter optimization framework.

Is a library containing various optimizers for hyperparameter tuning.

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.

A Free and Open Source Python Library for Multiobjective Optimization.

Python Optimal Transport library.

Python implementation of CMA-ES.

Multi-objective Optimization in Python.

A research toolkit for particle swarm optimization in Python.

Safe Bayesian Optimization.

Heuristic Algorithms for optimization.

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

SigOpt wrappers for scikit-learn methods.

Use evolutionary algorithms instead of gridsearch in scikit-learn.

Hyperparameters tuning and feature selection using evolutionary algorithms.

Sequential Model-based Algorithm Configuration.

A comprehensive gradient-free optimization framework written in Python.

Bayesian optimization.

Hyperparameter Optimization for Keras Models.

A python library for working with Probabilistic Graphical Models.

Probabilistic and graphical models for Python.

A GRaphical Universal Modeler.

The Python ensemble sampling toolkit for affine-invariant MCMC.

Gaussian processes in TensorFlow.

A highly efficient and modular implementation of Gaussian Processes in PyTorch.

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

Deep Probabilistic Modelling Made Easy.

Bayesian inference in HSMMs and HMMs.

Bayesian Stochastic Modelling in Python.

A flexible, scalable deep probabilistic programming library built on PyTorch.

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

Bayesian Deep Learning methods with Variational Inference for PyTorch.

Python package for Bayesian Machine Learning with scikit-learn API.

A scikit-learn-inspired API for CRFsuite.

Supervised domain-agnostic prediction framework for probabilistic modelling by The Alan Turing Institute.

Bayesian Deep Learning.

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.

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

A Python Toolkit for Quantum Machine Learning.

A library of reinforcement learning components and agents.

PyTorch framework for RL research.

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

An offline deep reinforcement learning library.

OpenDILab Decision AI Engine.

A research framework for fast prototyping of reinforcement learning algorithms.

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

A toolkit for reproducible reinforcement learning research.

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

A platform for Applied Reinforcement Learning.

Clean PyTorch implementations of imitation and reward learning algorithms.

Deep Reinforcement Learning for Keras.

A reinforcement library designed for pytorch.

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

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

Scalable Reinforcement Learning.

Reinforcement Learning in PyTorch.

An API conversion tool for popular external reinforcement learning environments.

Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym.

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

A TensorFlow library for applied reinforcement learning.

A library for Reinforcement Learning in TensorFlow.

An elegant PyTorch deep reinforcement learning library.

TensorFlow Reinforcement Learning.

Python tools for geographic data.

Python Spatial Analysis Library.

Performance analysis of predictive (alpha) stock factors.

Create HTML profiling reports from pandas DataFrame objects.

Extension to pandas dataframes describe function.

Pairwise Multiple Comparisons Post-hoc Tests.

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.

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

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

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

Powerful extensions to the standard datetime module

A flexible, intuitive, and fast forecasting library next.

Anomaly Detection and Correlation library.

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

Scalable machine learning-based time series forecasting.

Scalable machine learning-based time series forecasting.

Automatic Forecasting Procedure.

Open source time series library for Python.

Time series forecasting with machine learning models

A unified framework for machine learning with time series.

Lightning fast forecasting with statistical and econometric models.

Module for statistical learning, with a particular emphasis on time-dependent modeling.

Machine learning toolkit dedicated to time-series data.

Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph

A python package for animating plots built on matplotlib.

: Visualize data automatically with 1 line of code (ideal for machine learning)

Interactive Web Plotting for Python.