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visibility: public
---

# Data Science

**repo:** [krzjoa/awesome-python-data-science](https://github.com/krzjoa/awesome-python-data-science)  
**category:** [[programming-languages|Programming Languages]]

---

Awesome Python Data Science
</h1>
<div align="center"><a href="https://github.com/sindresorhus/awesome">
<img src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg" alt="Awesome" border="0">
</a>
</div>
</br>

> Probably the best curated list of data science software in Python

## Contents
- [Contents](#contents)
- [Machine Learning](#machine-learning)
	- [General Purpose Machine Learning](#general-purpose-machine-learning)
  	- [Gradient Boosting](#gradient-boosting)
	- [Ensemble Methods](#ensemble-methods)
	- [Imbalanced Datasets](#imbalanced-datasets)
	- [Kernel Methods](#kernel-methods)
- [Deep Learning](#deep-learning)
	- [PyTorch](#pytorch)
	- [TensorFlow](#tensorflow)
    - [Keras](#keras)
 	- [JAX](#jax)
	- [Others](#others)
- [Automated Machine Learning](#automated-machine-learning)
- [Natural Language Processing](#natural-language-processing)
- [Computer Audition](#computer-audition)
- [Computer Vision](#computer-vision)
- [Time Series](#time-series)
- [Reinforcement Learning](#reinforcement-learning)
- [Graph Machine Learning](#graph-machine-learning)
- [Graph Manipulation](#graph-manipulation)
- [Learning-to-Rank & Recommender Systems](#[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-to-rank-&-recommender-systems)
- [Probabilistic Graphical Models](#probabilistic-graphical-models)
- [Probabilistic Methods](#probabilistic-methods)
- [Model Explanation](#model-explanation)
- [Optimization](#optimization)
- [Genetic Programming](#genetic-programming)
- [Feature Engineering](#feature-engineering)
	- [General](#general)
	- [Feature Selection](#feature-selection)
- [Visualization](#visualization)
	- [General Purposes](#general-purposes)
	- [Interactive plots](#interactive-plots)
	- [Map](#map)
	- [Automatic Plotting](#automatic-plotting)
	- [NLP](#nlp)
- [Data Manipulation](#data-manipulation)
	- [Data Frames](#data-frames)
	- [Pipelines](#pipelines)
	- [Data-centric AI](#data-centric-ai)
	- [Synthetic Data](#synthetic-data)
- [Deployment](#deployment)
- [Statistics](#statistics)
- [Distributed Computing](#distributed-computing)
- [Experimentation](#experimentation)
- [Data Validation](#data-validation)
- [Evaluation](#evaluation)
- [Computations](#computations)
- [Web Scraping](#web-scraping)
- [Spatial Analysis](#spatial-analysis)
- [Quantum Computing](#quantum-computing)
- [Conversion](#conversion)
- [Contributing](#contributing)
- [License](#license)

## Machine Learning

### General Purpose Machine Learning
* [SciPy](https://scipy.org/) - Fundamental [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) for [scientific computing](/@harrisonqian/awesome/wiki/miscellaneous/scientific-computing) in [Python](/@harrisonqian/awesome/wiki/programming-languages/python)
* [scikit-learn](http://scikit-learn.org/stable/) - [Machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) in [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) library in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).  <img height="20" src="img/R_big.png" alt="R inspired lib">
* [Shogun](https://github.com/shogun-toolbox/shogun) - [Machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) toolbox.
* [xLearn](https://github.com/aksnzhy/xlearn) - High Performance, Easy-to-use, and Scalable [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Package.
* [cuML](https://github.com/rapidsai/cuml) - RAPIDS [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
* [modAL](https://github.com/cosmic-cortex/modAL) - Modular active [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) framework for Python3. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [Sparkit-learn](https://github.com/lensacom/sparkit-learn) - PySpark + scikit-learn = Sparkit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/spark_big.png" alt="[Apache Spark](/@harrisonqian/awesome/wiki/big-data/apache-spark) based">
* [mlpack](https://github.com/mlpack/mlpack) - A scalable C++ [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) library (Python bindings).
* [dlib](https://github.com/davisking/dlib) - Toolkit for making real-world [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) and data analysis applications in C++ (Python bindings).
* [MLxtend](https://github.com/rasbt/mlxtend) - Extension and helper modules for [Python](/@harrisonqian/awesome/wiki/programming-languages/python)'s data analysis and [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) libraries. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [hyperlearn](https://github.com/danielhanchen/hyperlearn) - 50%+ Faster, 50%+ [less](/@harrisonqian/awesome/wiki/front-end-development/less) RAM usage, GPU support re-written Sklearn, Statsmodels. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [Reproducible Experiment Platform (REP)](https://github.com/yandex/rep) - [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) toolbox for Humans. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [scikit-multilearn](https://github.com/scikit-multilearn/scikit-multilearn) - Multi-label classification for [python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [seqlearn](https://github.com/larsmans/seqlearn) - Sequence classification toolkit for [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [pystruct](https://github.com/pystruct/pystruct) - Simple structured [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) framework for [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys) - Highly interpretable classifiers for scikit learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [RuleFit](https://github.com/christophM/rulefit) - Implementation of the rulefit. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [metric-learn](https://github.com/all-umass/metric-learn) - Metric [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) in [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [pyGAM](https://github.com/dswah/pyGAM) - Generalized Additive Models in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [causalml](https://github.com/uber/causalml) - Uplift modeling and causal inference with [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms). <img height="20" src="img/sklearn_big.png" alt="sklearn">

### Gradient Boosting
* [XGBoost](https://github.com/dmlc/xgboost) - Scalable, Portable, and Distributed Gradient Boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
* [LightGBM](https://github.com/Microsoft/LightGBM) - A fast, distributed, high-performance gradient boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
* [CatBoost](https://github.com/catboost/catboost) - An open-source gradient boosting on decision trees library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
* [ThunderGBM](https://github.com/Xtra-Computing/thundergbm) - Fast GBDTs and Random Forests on GPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
* [NGBoost](https://github.com/stanfordmlgroup/ngboost) - Natural Gradient Boosting for Probabilistic Prediction.
* [TensorFlow Decision Forests](https://github.com/tensorflow/decision-forests) - A collection of state-of-the-art [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) for the training, serving and interpretation of Decision Forest models in Keras. <img height="20" src="img/keras_big.png" alt="keras"> <img height="20" src="img/tf_big2.png" alt="[TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow)">

### Ensemble Methods
* [ML-Ensemble](http://ml-ensemble.com/) - High performance ensemble [learning](/@harrisonqian/awesome/wiki/programming-languages/learning). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [Stacking](https://github.com/ikki407/stacking) - Simple and useful stacking library written in [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [stacked_generalization](https://github.com/fukatani/stacked_generalization) - Library for [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) stacking generalization. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [vecstack](https://github.com/vecxoz/vecstack) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) package for stacking (machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) technique). <img height="20" src="img/sklearn_big.png" alt="sklearn">

### Imbalanced Datasets
* [imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn) - Module to perform under-sampling and over-sampling with various techniques. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [imbalanced-algorithms](https://github.com/dialnd/imbalanced-algorithms) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python)-based implementations of [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) for [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) on imbalanced data. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/tf_big2.png" alt="sklearn">

### Kernel Methods
* [pyFM](https://github.com/coreylynch/pyFM) - Factorization machines in [python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [fastFM](https://github.com/ibayer/fastFM) - A library for Factorization Machines. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [tffm](https://github.com/geffy/tffm) - [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) implementation of an arbitrary order Factorization Machine. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/tf_big2.png" alt="sklearn">
* [liquidSVM](https://github.com/liquidSVM/liquidSVM) - An implementation of SVMs.
* [scikit-rvm](https://github.com/JamesRitchie/scikit-rvm) - Relevance Vector Machine implementation using the scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [ThunderSVM](https://github.com/Xtra-Computing/thundersvm) - A fast SVM Library on GPUs and CPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">

## Deep Learning

### PyTorch
* [PyTorch](https://github.com/pytorch/pytorch) - Tensors and Dynamic neural networks in [Python](/@harrisonqian/awesome/wiki/programming-languages/python) with strong GPU acceleration. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [pytorch-lightning](https://github.com/Lightning-AI/lightning) - PyTorch Lightning is just organized PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [ignite](https://github.com/pytorch/ignite) - High-level library to help with training neural networks in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [skorch](https://github.com/dnouri/skorch) - A scikit-learn compatible neural network library that wraps PyTorch. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [Catalyst](https://github.com/catalyst-team/catalyst) - High-level utils for PyTorch DL & RL research. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [ChemicalX](https://github.com/AstraZeneca/chemicalx) - A PyTorch-based [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) library for drug pair scoring. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

### TensorFlow
* [TensorFlow](https://github.com/tensorflow/tensorflow) - Computation using data flow graphs for scalable [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) by Google. <img height="20" src="img/tf_big2.png" alt="sklearn">
* [TensorLayer](https://github.com/zsdonghao/tensorlayer) - [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) and Reinforcement [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Library for Researcher and Engineer. <img height="20" src="img/tf_big2.png" alt="sklearn">
* [TFLearn](https://github.com/tflearn/tflearn) - [Deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) library featuring a higher-level API for [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow). <img height="20" src="img/tf_big2.png" alt="sklearn">
* [Sonnet](https://github.com/deepmind/sonnet) - [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow)-based neural network library. <img height="20" src="img/tf_big2.png" alt="sklearn">
* [tensorpack](https://github.com/ppwwyyxx/tensorpack) - A Neural Net Training Interface on [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow). <img height="20" src="img/tf_big2.png" alt="sklearn">
* [tfdeploy](https://github.com/riga/tfdeploy) - Deploy [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) graphs for fast evaluation and export to [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow)-[less](/@harrisonqian/awesome/wiki/front-end-development/less) environments running numpy. <img height="20" src="img/tf_big2.png" alt="sklearn">
* [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) - [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) ROCm port. <img height="20" src="img/tf_big2.png" alt="sklearn"> <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
* [TensorFlow Fold](https://github.com/tensorflow/fold) - [Deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) with dynamic computation graphs in [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow). <img height="20" src="img/tf_big2.png" alt="sklearn">
* [TensorLight](https://github.com/bsautermeister/tensorlight) - A high-level framework for [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow). <img height="20" src="img/tf_big2.png" alt="sklearn">
* [Mesh TensorFlow](https://github.com/tensorflow/mesh) - Model Parallelism Made Easier. <img height="20" src="img/tf_big2.png" alt="sklearn">
* [Ludwig](https://github.com/uber/ludwig) - A toolbox that allows one to train and test [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) models without the need to write code. <img height="20" src="img/tf_big2.png" alt="sklearn">

### JAX
* [JAX](https://github.com/google/jax) - Composable transformations of [Python](/@harrisonqian/awesome/wiki/programming-languages/python)+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
* [FLAX](https://github.com/google/flax) - A neural network library for JAX that is designed for flexibility.
* [Optax](https://github.com/google-deepmind/optax) - A gradient processing and optimization library for JAX.

### Keras
* [Keras](https://keras.io) - A high-level neural networks API running on top of [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow).  <img height="20" src="img/keras_big.png" alt="Keras compatible">
* [keras-contrib](https://github.com/keras-team/keras-contrib) - Keras community contributions. <img height="20" src="img/keras_big.png" alt="Keras compatible">
* [Hyperas](https://github.com/maxpumperla/hyperas) - Keras + Hyperopt: A straightforward wrapper for a convenient hyperparameter. <img height="20" src="img/keras_big.png" alt="Keras compatible">
* [Elephas](https://github.com/maxpumperla/elephas) - Distributed [Deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) with Keras & Spark. <img height="20" src="img/keras_big.png" alt="Keras compatible">
* [qkeras](https://github.com/google/qkeras) - A quantization [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) library. <img height="20" src="img/keras_big.png" alt="Keras compatible">

### Others
* [transformers](https://github.com/huggingface/transformers) - State-of-the-art [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) for Pytorch, [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow), and JAX. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"> <img height="20" src="img/tf_big2.png" alt="sklearn">
* [Tangent](https://github.com/google/tangent) - Source-to-Source Debuggable Derivatives in Pure [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [autograd](https://github.com/HIPS/autograd) - Efficiently computes derivatives of numpy code.
* [Caffe](https://github.com/BVLC/caffe) - A fast open framework for [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning).
* [nnabla](https://github.com/sony/nnabla) - Neural Network Libraries by Sony.

## Automated Machine Learning
* [auto-sklearn](https://github.com/automl/auto-sklearn) - An AutoML toolkit and a drop-in replacement for a scikit-learn estimator. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) - Automatic architecture search and hyperparameter optimization for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [AutoKeras](https://github.com/keras-team/autokeras) - AutoML library for [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning). <img height="20" src="img/keras_big.png" alt="Keras compatible">
* [AutoGluon](https://github.com/awslabs/autogluon) - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
* [TPOT](https://github.com/rhiever/tpot) - AutoML tool that optimizes [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) pipelines using genetic programming. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) [python](/@harrisonqian/awesome/wiki/programming-languages/python) library.

## Natural Language Processing
* [torchtext](https://github.com/pytorch/text) - Data loaders and abstractions for text and NLP. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [KerasNLP](https://github.com/keras-team/keras-nlp) - Modular Natural Language Processing workflows with Keras. <img height="20" src="img/keras_big.png" alt="Keras based/compatible">
* [spaCy](https://spacy.io/) - Industrial-Strength Natural Language Processing.
* [NLTK](https://github.com/nltk/nltk) -  Modules, data sets, and [tutorials](/@harrisonqian/awesome/wiki/computer-science/tutorials) supporting research and development in Natural Language Processing.
* [CLTK](https://github.com/cltk/cltk) - The Classical Language Toolkik.
* [gensim](https://radimrehurek.com/gensim/) - Topic Modelling for Humans.
* [pyMorfologik](https://github.com/dmirecki/pyMorfologik) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) binding for <a href="https://[github](/@harrisonqian/awesome/wiki/development-environment/github).com/morfologik/morfologik-stemming">Morfologik</a>.
* [skift](https://github.com/shaypal5/skift) - Scikit-learn wrappers for [Python](/@harrisonqian/awesome/wiki/programming-languages/python) fastText. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [Phonemizer](https://github.com/bootphon/phonemizer) - Simple text-to-phonemes converter for multiple languages.
* [flair](https://github.com/zalandoresearch/flair) - Very simple framework for state-of-the-art NLP.

## Computer Audition
* [torchaudio](https://github.com/pytorch/audio) - An audio library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [librosa](https://github.com/librosa/librosa) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) library for audio and [music](/@harrisonqian/awesome/wiki/media/music) analysis.
* [Yaafe](https://github.com/Yaafe/Yaafe) - Audio features extraction.
* [aubio](https://github.com/aubio/aubio) - A library for audio and [music](/@harrisonqian/awesome/wiki/media/music) analysis.
* [Essentia](https://github.com/MTG/essentia) - Library for audio and [music](/@harrisonqian/awesome/wiki/media/music) analysis, description, and synthesis.
* [LibXtract](https://github.com/jamiebullock/LibXtract) - A simple, portable, lightweight library of audio feature extraction functions.
* [Marsyas](https://github.com/marsyas/marsyas) - [Music](/@harrisonqian/awesome/wiki/media/music) Analysis, Retrieval, and Synthesis for Audio Signals.
* [muda](https://github.com/bmcfee/muda) - A library for augmenting annotated audio data.
* [madmom](https://github.com/CPJKU/madmom) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) audio and [music](/@harrisonqian/awesome/wiki/media/music) signal processing library.

## Computer Vision
* [torchvision](https://github.com/pytorch/vision) - [Datasets](/@harrisonqian/awesome/wiki/miscellaneous/datasets), Transforms, and Models specific to [Computer Vision](/@harrisonqian/awesome/wiki/computer-science/computer-vision). <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [PyTorch3D](https://github.com/facebookresearch/pytorch3d) - PyTorch3D is FAIR's library of reusable components for [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) with 3D data. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [KerasCV](https://github.com/keras-team/keras-cv) - Industry-strength [Computer Vision](/@harrisonqian/awesome/wiki/computer-science/computer-vision) workflows with Keras. <img height="20" src="img/keras_big.png" alt="MXNet based">
* [OpenCV](https://github.com/opencv/opencv) - Open Source [Computer Vision](/@harrisonqian/awesome/wiki/computer-science/computer-vision) Library.
* [Decord](https://github.com/dmlc/decord) - An efficient video loader for [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) with smart shuffling that's super easy to digest.
* [MMEngine](https://github.com/open-mmlab/mmengine) - OpenMMLab Foundational Library for Training [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) Models. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [scikit-image](https://github.com/scikit-image/scikit-image) - Image Processing SciKit (Toolbox for SciPy).
* [imgaug](https://github.com/aleju/imgaug) - Image augmentation for [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) experiments.
* [imgaug_extension](https://github.com/cadenai/imgaug_extension) - Additional augmentations for imgaug.
* [Augmentor](https://github.com/mdbloice/Augmentor) - Image augmentation library in [Python](/@harrisonqian/awesome/wiki/programming-languages/python) for [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning).
* [albumentations](https://github.com/albu/albumentations) - Fast image augmentation library and easy-to-use wrapper around other libraries.
* [LAVIS](https://github.com/salesforce/LAVIS) - A One-stop Library for Language-Vision Intelligence.

## Time Series
* [sktime](https://github.com/alan-turing-institute/sktime) - A unified framework for [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) with time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [skforecast](https://github.com/JoaquinAmatRodrigo/skforecast) - Time series forecasting with [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) models
* [darts](https://github.com/unit8co/darts) - A [python](/@harrisonqian/awesome/wiki/programming-languages/python) library for easy manipulation and forecasting of time series.
* [statsforecast](https://github.com/Nixtla/statsforecast) - Lightning fast forecasting with statistical and econometric models.
* [mlforecast](https://github.com/Nixtla/mlforecast) - Scalable [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning)-based time series forecasting.
* [neuralforecast](https://github.com/Nixtla/neuralforecast) - Scalable [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning)-based time series forecasting.
* [tslearn](https://github.com/rtavenar/tslearn) - [Machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) toolkit dedicated to time-series data. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [tick](https://github.com/X-DataInitiative/tick) - Module for statistical [learning](/@harrisonqian/awesome/wiki/programming-languages/learning), with a particular emphasis on time-dependent modeling.  <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [greykite](https://github.com/linkedin/greykite) - A flexible, intuitive, and fast forecasting library next.
* [Prophet](https://github.com/facebook/prophet) - Automatic Forecasting Procedure.
* [PyFlux](https://github.com/RJT1990/pyflux) - Open source time series library for [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [bayesloop](https://github.com/christophmark/bayesloop) - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
* [luminol](https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library.
* [dateutil](https://dateutil.readthedocs.io/en/stable/) - Powerful extensions to the standard datetime module
* [maya](https://github.com/timofurrer/maya) - makes it very easy to parse a string and for changing timezones
* [Chaos Genius](https://github.com/chaos-genius/chaos_genius) - ML powered [analytics](/@harrisonqian/awesome/wiki/miscellaneous/analytics) engine for outlier/anomaly detection and root cause analysis

## Reinforcement Learning
* [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) environments, with popular reference environments and related utilities (formerly [Gym](https://github.com/openai/gym)).
* [PettingZoo](https://github.com/Farama-Foundation/PettingZoo) - An API standard for multi-agent reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) environments, with popular reference environments and related utilities.
* [MAgent2](https://github.com/Farama-Foundation/MAgent2) - An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments.
* [Stable Baselines3](https://github.com/DLR-RM/stable-baselines3) - A set of improved implementations of reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) based on OpenAI Baselines.
* [Shimmy](https://github.com/Farama-Foundation/Shimmy) - An API conversion tool for popular external reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) environments.
* [EnvPool](https://github.com/sail-sg/envpool) - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
* [RLlib](https://ray.readthedocs.io/en/latest/rllib.html) - Scalable Reinforcement [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning).
* [Tianshou](https://github.com/thu-ml/tianshou/#comprehensive-functionality) - An elegant PyTorch deep reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [Acme](https://github.com/google-deepmind/acme) - A library of reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) components and agents.
* [Catalyst-RL](https://github.com/catalyst-team/catalyst-rl) - PyTorch framework for RL research. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [d3rlpy](https://github.com/takuseno/d3rlpy) - An offline deep reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library.
* [DI-engine](https://github.com/opendilab/DI-engine) - OpenDILab Decision AI Engine. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [TF-Agents](https://github.com/tensorflow/agents) - A library for Reinforcement [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) in [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow). <img height="20" src="img/tf_big2.png" alt="TensorFlow">
* [TensorForce](https://github.com/reinforceio/tensorforce) - A [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) library for applied reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning). <img height="20" src="img/tf_big2.png" alt="TensorFlow">
* [TRFL](https://github.com/deepmind/trfl) - [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Reinforcement [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning). <img height="20" src="img/tf_big2.png" alt="sklearn">
* [Dopamine](https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms).
* [keras-rl](https://github.com/keras-rl/keras-rl) - Deep Reinforcement [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) for Keras. <img height="20" src="img/keras_big.png" alt="Keras compatible">
* [garage](https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) research.
* [Horizon](https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning).
* [rlpyt](https://github.com/astooke/rlpyt) - Reinforcement [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [cleanrl](https://github.com/vwxyzjn/cleanrl) - High-quality single file implementation of Deep Reinforcement [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG).
* [Machin](https://github.com/iffiX/machin) -  A reinforcement library designed for pytorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [SKRL](https://github.com/Toni-SM/skrl) - Modular reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/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">
* [Imitation](https://github.com/HumanCompatibleAI/imitation) - Clean PyTorch implementations of imitation and reward [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms). <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

## Graph Machine Learning
* [pytorch_geometric](https://github.com/rusty1s/pytorch_geometric) - Geometric [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) Extension Library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [pytorch_geometric_temporal](https://github.com/benedekrozemberczki/pytorch_geometric_temporal) - Temporal Extension Library for PyTorch Geometric. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [PyTorch Geometric Signed Directed](https://github.com/SherylHYX/pytorch_geometric_signed_directed) -  A signed/directed graph neural network extension library for PyTorch Geometric. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [dgl](https://github.com/dmlc/dgl) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) package built to ease [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) on graph, on top of existing DL [frameworks](/@harrisonqian/awesome/wiki/front-end-development/frameworks). <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"> <img height="20" src="img/tf_big2.png" alt="[TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow)"> <img height="20" src="img/mxnet_big.png" alt="MXNet based">
* [GRAPE](https://github.com/AnacletoLAB/grape/tree/main) - GRAPE is a Rust/[Python](/@harrisonqian/awesome/wiki/programming-languages/python) Graph Representation [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library for Predictions and Evaluations
* [Spektral](https://github.com/danielegrattarola/spektral) - [Deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) on graphs. <img height="20" src="img/keras_big.png" alt="Keras compatible">
* [StellarGraph](https://github.com/stellargraph/stellargraph) - [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) on Graphs. <img height="20" src="img/tf_big2.png" alt="[TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow)">  <img height="20" src="img/keras_big.png" alt="Keras compatible">
* [Graph Nets](https://github.com/google-deepmind/graph_nets) - Build Graph Nets in [Tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow). <img height="20" src="img/tf_big2.png" alt="TensorFlow">
* [TensorFlow GNN](https://github.com/tensorflow/gnn) - A library to build Graph Neural Networks on the [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) platform. <img height="20" src="img/tf_big2.png" alt="TensorFlow">
* [Auto Graph Learning](https://github.com/THUMNLab/AutoGL) -An autoML framework & toolkit for [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) on graphs.
* [PyTorch-BigGraph](https://github.com/facebookresearch/PyTorch-BigGraph) - Generate embeddings from large-scale graph-structured data. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [Auto Graph Learning](https://github.com/THUMNLab/AutoGL) - An autoML framework & toolkit for [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) on graphs.
* [Karate Club](https://github.com/benedekrozemberczki/karateclub) - An unsupervised [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) library for graph-structured data.
* [Little Ball of Fur](https://github.com/benedekrozemberczki/littleballoffur) - A library for sampling graph structured data.
* [GreatX](https://github.com/EdisonLeeeee/GreatX) - A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG). <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [Jraph](https://github.com/google-deepmind/jraph) - A Graph Neural Network Library in Jax.
* [TRL](https://github.com/huggingface/trl) - Train transformer language models with reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning).
* [Cleora](https://github.com/BaseModelAI/cleora) - The Graph Embedding Engine.

## Graph Manipulation
* [Networkx](https://github.com/networkx/networkx) - [Network Analysis](/@harrisonqian/awesome/wiki/big-data/network-analysis) in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [Rustworkx](https://github.com/Qiskit/rustworkx) - A high performance [Python](/@harrisonqian/awesome/wiki/programming-languages/python) graph library implemented in [Rust](/@harrisonqian/awesome/wiki/programming-languages/rust).
* [graph-tool](https://graph-tool.skewed.de/) - an efficient [Python](/@harrisonqian/awesome/wiki/programming-languages/python) module for manipulation and statistical analysis of graphs (a.k.a. networks).
* [igraph](https://github.com/igraph/python-igraph) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) interface for igraph.

## Learning-to-Rank & Recommender Systems
* [LightFM](https://github.com/lyst/lightfm) - A [Python](/@harrisonqian/awesome/wiki/programming-languages/python) implementation of LightFM, a hybrid recommendation algorithm.
* [Spotlight](https://maciejkula.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/spotlight/) - Deep recommender models using PyTorch.
* [Surprise](https://github.com/NicolasHug/Surprise) - A [Python](/@harrisonqian/awesome/wiki/programming-languages/python) scikit for building and analyzing recommender systems.
* [RecBole](https://github.com/RUCAIBox/RecBole) - A unified, comprehensive and efficient recommendation library. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [allRank](https://github.com/allegro/allRank) - allRank is a framework for training [learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-to-rank neural models based on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [TensorFlow Recommenders](https://github.com/tensorflow/recommenders) - A library for building recommender system models using [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow). <img height="20" src="img/tf_big2.png" alt="TensorFlow"> <img height="20" src="img/keras_big.png" alt="Keras compatible">
* [TensorFlow Ranking](https://github.com/tensorflow/ranking) - [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) to Rank in [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow). <img height="20" src="img/tf_big2.png" alt="TensorFlow"> 

## Probabilistic Graphical Models
* [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [pgmpy](https://github.com/pgmpy/pgmpy) - A [python](/@harrisonqian/awesome/wiki/programming-languages/python) library for working with Probabilistic Graphical Models.
* [pyAgrum](https://agrum.gitlab.io/) - A GRaphical Universal Modeler.

## Probabilistic Methods
* [pyro](https://github.com/uber/pyro) - A flexible, scalable deep probabilistic programming library built on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [PyMC](https://github.com/pymc-devs/pymc) - Bayesian Stochastic Modelling in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [ZhuSuan](http://zhusuan.readthedocs.io/en/latest/) - Bayesian [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning). <img height="20" src="img/tf_big2.png" alt="sklearn">
* [GPflow](http://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow). <img height="20" src="img/tf_big2.png" alt="sklearn">
* [InferPy](https://github.com/PGM-Lab/InferPy) - Deep Probabilistic Modelling Made Easy.  <img height="20" src="img/tf_big2.png" alt="sklearn">
* [PyStan](https://github.com/stan-dev/pystan) - Bayesian inference using the No-U-Turn sampler (Python interface).
* [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) package for Bayesian [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) with scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [skpro](https://github.com/alan-turing-institute/skpro) - Supervised domain-agnostic prediction framework for probabilistic modelling by [The Alan Turing Institute](https://www.turing.ac.uk/). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [PyVarInf](https://github.com/ctallec/pyvarinf) - Bayesian [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) methods with Variational Inference for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [emcee](https://github.com/dfm/emcee) - The [Python](/@harrisonqian/awesome/wiki/programming-languages/python) ensemble sampling toolkit for affine-invariant MCMC.
* [hsmmlearn](https://github.com/jvkersch/hsmmlearn) - A library for hidden semi-Markov models with explicit durations.
* [pyhsmm](https://github.com/mattjj/pyhsmm) - Bayesian inference in HSMMs and HMMs.
* [GPyTorch](https://github.com/cornellius-gp/gpytorch) - A highly efficient and modular implementation of Gaussian Processes in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite) - A scikit-learn-inspired API for CRFsuite. <img height="20" src="img/sklearn_big.png" alt="sklearn">

## Model Explanation
* [dalex](https://github.com/ModelOriented/DALEX) - moDel Agnostic Language for Exploration and explanation. <img height="20" src="img/sklearn_big.png" alt="sklearn"><img height="20" src="img/R_big.png" alt="R inspired/ported lib">
* [Shapley](https://github.com/benedekrozemberczki/shapley) - A data-driven framework to quantify the value of classifiers in a [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) ensemble.
* [Alibi](https://github.com/SeldonIO/alibi) - [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) for monitoring and explaining [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) models.
* [anchor](https://github.com/marcotcr/anchor) - Code for "High-Precision Model-Agnostic Explanations" paper.
* [aequitas](https://github.com/dssg/aequitas) - Bias and Fairness Audit Toolkit.
* [Contrastive Explanation](https://github.com/MarcelRobeer/ContrastiveExplanation) - Contrastive Explanation (Foil Trees). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [yellowbrick](https://github.com/DistrictDataLabs/yellowbrick) - Visual analysis and diagnostic tools to facilitate [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) model selection. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [scikit-plot](https://github.com/reiinakano/scikit-plot) - An intuitive library to add plotting functionality to scikit-learn objects. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [shap](https://github.com/slundberg/shap) - A unified approach to explain the output of any [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) model. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [InterpretML](https://github.com/interpretml/interpret) - InterpretML implements the Explainable Boosting Machine (EBM), a modern, fully interpretable [machine learning](/@harrisonqian/awesome/wiki/computer-science/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">
* [ELI5](https://github.com/TeamHG-Memex/eli5) - A library for debugging/inspecting [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) classifiers and explaining their predictions.
* [Lime](https://github.com/marcotcr/lime) - Explaining the predictions of any [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) classifier. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [FairML](https://github.com/adebayoj/fairml) - FairML is a [python](/@harrisonqian/awesome/wiki/programming-languages/python) toolbox auditing the [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) models for bias. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [L2X](https://github.com/Jianbo-Lab/L2X) - Code for replicating the experiments in the paper *[Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) to Explain: An Information-Theoretic Perspective on Model Interpretation*.
* [PDPbox](https://github.com/SauceCat/PDPbox) - Partial dependence plot toolbox.
* [PyCEbox](https://github.com/AustinRochford/PyCEbox) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) Individual Conditional Expectation Plot Toolbox.
* [Skater](https://github.com/datascienceinc/Skater) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) Library for Model Interpretation.
* [model-analysis](https://github.com/tensorflow/model-analysis) - Model analysis tools for [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow). <img height="20" src="img/tf_big2.png" alt="sklearn">
* [themis-ml](https://github.com/cosmicBboy/themis-ml) - A library that implements fairness-aware [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [treeinterpreter](https://github.com/andosa/treeinterpreter) - Interpreting scikit-learn's decision tree and random forest predictions. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [AI Explainability 360](https://github.com/IBM/AIX360) - Interpretability and explainability of data and [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) models.
* [Auralisation](https://github.com/keunwoochoi/Auralisation) - Auralisation of learned features in CNN (for audio).
* [CapsNet-Visualization](https://github.com/bourdakos1/CapsNet-Visualization) - A visualization of the CapsNet layers to better understand how it works.
* [lucid](https://github.com/tensorflow/lucid) - A collection of infrastructure and tools for research in neural network interpretability.
* [Netron](https://github.com/lutzroeder/Netron) - Visualizer for [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) and [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) models (no [Python](/@harrisonqian/awesome/wiki/programming-languages/python) code, but visualizes models from most Python Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) frameworks).
* [FlashLight](https://github.com/dlguys/flashlight) - Visualization Tool for your NeuralNetwork.
* [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch) - Tensorboard for PyTorch (and chainer, mxnet, numpy, ...).

## Genetic Programming
* [gplearn](https://github.com/trevorstephens/gplearn) - Genetic Programming in [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [PyGAD](https://github.com/ahmedfgad/GeneticAlgorithmPython) - Genetic Algorithm in [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"> <img height="20" src="img/keras_big.png" alt="keras">
* [DEAP](https://github.com/DEAP/deap) - Distributed Evolutionary [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [karoo_gp](https://github.com/kstaats/karoo_gp) - A Genetic Programming platform for [Python](/@harrisonqian/awesome/wiki/programming-languages/python) with GPU support. <img height="20" src="img/tf_big2.png" alt="sklearn">
* [monkeys](https://github.com/hchasestevens/monkeys) - A strongly-typed genetic programming framework for [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [sklearn-genetic](https://github.com/manuel-calzolari/sklearn-genetic) - Genetic feature selection module for scikit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">

<a name="opt"></a>
## Optimization
* [Optuna](https://github.com/optuna/optuna) - A hyperparameter optimization framework.
* [pymoo](https://github.com/anyoptimization/pymoo) - Multi-objective Optimization in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [pycma](https://github.com/CMA-ES/pycma?tab=[readme](/@harrisonqian/awesome/wiki/miscellaneous/readme)-ov-file) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) implementation of CMA-ES.
* [Spearmint](https://github.com/HIPS/Spearmint) - Bayesian optimization.
* [BoTorch](https://github.com/pytorch/botorch) - Bayesian optimization in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [scikit-opt](https://github.com/guofei9987/scikit-opt) - Heuristic [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) for optimization.
* [sklearn-genetic-opt](https://github.com/rodrigo-arenas/Sklearn-genetic-opt) - Hyperparameters tuning and feature selection using evolutionary [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [SMAC3](https://github.com/automl/SMAC3) - Sequential Model-based Algorithm Configuration.
* [Optunity](https://github.com/claesenm/optunity) - Is a library containing various optimizers for hyperparameter tuning.
* [hyperopt](https://github.com/hyperopt/hyperopt) - Distributed Asynchronous Hyperparameter Optimization in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [hyperopt-sklearn](https://github.com/hyperopt/hyperopt-sklearn) - [Hyper](/@harrisonqian/awesome/wiki/development-environment/hyper)-parameter optimization for sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [sklearn-deap](https://github.com/rsteca/sklearn-deap) - Use evolutionary [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) instead of gridsearch in scikit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [sigopt_sklearn](https://github.com/sigopt/sigopt_sklearn) - SigOpt wrappers for scikit-learn methods. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [Bayesian Optimization](https://github.com/fmfn/BayesianOptimization) - A [Python](/@harrisonqian/awesome/wiki/programming-languages/python) implementation of global optimization with gaussian processes.
* [SafeOpt](https://github.com/befelix/SafeOpt) - Safe Bayesian Optimization.
* [scikit-optimize](https://github.com/scikit-optimize/scikit-optimize) - Sequential model-based optimization with a `scipy.optimize` interface.
* [Solid](https://github.com/100/Solid) - A comprehensive gradient-free optimization framework written in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [PySwarms](https://github.com/ljvmiranda921/pyswarms) - A research toolkit for particle swarm optimization in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [Platypus](https://github.com/Project-Platypus/Platypus) - A Free and Open Source [Python](/@harrisonqian/awesome/wiki/programming-languages/python) Library for Multiobjective Optimization.
* [GPflowOpt](https://github.com/GPflow/GPflowOpt) - Bayesian Optimization using GPflow. <img height="20" src="img/tf_big2.png" alt="sklearn">
* [POT](https://github.com/rflamary/POT) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) Optimal Transport library.
* [Talos](https://github.com/autonomio/talos) - Hyperparameter Optimization for Keras Models.
* [nlopt](https://github.com/stevengj/nlopt) - Library for nonlinear optimization (global and local, constrained or unconstrained).
* [OR-Tools](https://developers.google.com/optimization) - 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.

## Feature Engineering

### General
* [Featuretools](https://github.com/Featuretools/featuretools) - Automated feature engineering.
* [Feature Engine](https://github.com/feature-engine/feature_engine) - Feature engineering package with sklearn-like functionality. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [OpenFE](https://github.com/IIIS-Li-Group/OpenFE) - Automated feature generation with expert-level performance.
* [skl-groups](https://github.com/dougalsutherland/skl-groups) - A scikit-learn addon to operate on set/"group"-based features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [Feature Forge](https://github.com/machinalis/featureforge) - A set of tools for creating and [testing](/@harrisonqian/awesome/wiki/testing/testing) [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [few](https://github.com/lacava/few) - A feature engineering wrapper for sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [scikit-mdr](https://github.com/EpistasisLab/scikit-mdr) - A sklearn-compatible [Python](/@harrisonqian/awesome/wiki/programming-languages/python) implementation of Multifactor Dimensionality Reduction (MDR) for feature construction. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [dirty_cat](https://github.com/dirty-cat/dirty_cat) - [Machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) on dirty tabular data (especially: string-based variables for classifcation and regression). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [NitroFE](https://github.com/NITRO-AI/NitroFE) - Moving window features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [sk-transformer](https://github.com/chrislemke/sk-transformers) - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps <img height="20" src="img/pandas_big.png" alt="pandas compatible">

### Feature Selection
* [scikit-feature](https://github.com/jundongl/scikit-feature) - Feature selection repository in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [boruta_py](https://github.com/scikit-learn-contrib/boruta_py) - Implementations of the Boruta all-relevant feature selection method. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [BoostARoota](https://github.com/chasedehan/BoostARoota) - A fast xgboost feature selection algorithm. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate) - A scikit-learn-compatible [Python](/@harrisonqian/awesome/wiki/programming-languages/python) implementation of ReBATE, a suite of Relief-based feature selection [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) for [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [zoofs](https://github.com/jaswinder9051998/zoofs) - A feature selection library based on evolutionary [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms).

## Visualization
### General Purposes
* [Matplotlib](https://github.com/matplotlib/matplotlib) - Plotting with [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [seaborn](https://github.com/mwaskom/seaborn) - Statistical [data visualization](/@harrisonqian/awesome/wiki/miscellaneous/data-visualization) using matplotlib.
* [prettyplotlib](https://github.com/olgabot/prettyplotlib) - Painlessly create beautiful matplotlib plots.
* [python-ternary](https://github.com/marcharper/python-ternary) - Ternary plotting library for [Python](/@harrisonqian/awesome/wiki/programming-languages/python) with matplotlib.
* [missingno](https://github.com/ResidentMario/missingno) - Missing [data visualization](/@harrisonqian/awesome/wiki/miscellaneous/data-visualization) module for [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [chartify](https://github.com/spotify/chartify/) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) library that makes it easy for data scientists to create charts.
* [physt](https://github.com/janpipek/physt) - Improved histograms.
### Interactive plots
* [animatplot](https://github.com/t-makaro/animatplot) - A [python](/@harrisonqian/awesome/wiki/programming-languages/python) package for animating plots built on matplotlib.
* [plotly](https://plot.ly/python/) - A [Python](/@harrisonqian/awesome/wiki/programming-languages/python) library that makes interactive and publication-quality graphs.
* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [Altair](https://altair-viz.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/) - Declarative statistical visualization library for [Python](/@harrisonqian/awesome/wiki/programming-languages/python). Can easily do many data transformation within the code to create graph
* [bqplot](https://github.com/bqplot/bqplot) - Plotting library for IPython/[Jupyter](/@harrisonqian/awesome/wiki/miscellaneous/jupyter) notebooks
* [pyecharts](https://github.com/pyecharts/pyecharts) - Migrated from [Echarts](https://github.com/apache/echarts), a [charting](/@harrisonqian/awesome/wiki/front-end-development/charting) and visualization library, to [Python](/@harrisonqian/awesome/wiki/programming-languages/python)'s interactive visual drawing library.<img height="20" src="img/pyecharts.png" alt="pyecharts"> <img height="20" src="img/echarts.png" alt="echarts">
### Map
* [folium](https://python-visualization.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/folium/quickstart.html#Getting-Started) - Makes it easy to visualize data on an interactive open street map
* [geemap](https://github.com/giswqs/geemap) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) package for interactive mapping with Google [Earth](/@harrisonqian/awesome/wiki/miscellaneous/earth) Engine (GEE)
### Automatic Plotting
* [HoloViews](https://github.com/ioam/holoviews) - Stop plotting your data - annotate your data and let it visualize itself.
* [AutoViz](https://github.com/AutoViML/AutoViz): Visualize data automatically with 1 line of code (ideal for machine learning)
* [SweetViz](https://github.com/fbdesignpro/sweetviz): Visualize and compare [datasets](/@harrisonqian/awesome/wiki/miscellaneous/datasets), target values and associations, with one line of code.

### NLP
* [pyLDAvis](https://github.com/bmabey/pyLDAvis): Visualize interactive topic model

## Deployment
* [fastapi](https://fastapi.tiangolo.com/) - Modern, fast (high-performance), a web framework for building APIs with [Python](/@harrisonqian/awesome/wiki/programming-languages/python)
* [streamlit](https://www.streamlit.io/) - Make it easy to deploy the [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) model
* [streamsync](https://github.com/streamsync-cloud/streamsync) - No-code in the front, [Python](/@harrisonqian/awesome/wiki/programming-languages/python) in the back. An open-source framework for creating data [apps](/@harrisonqian/awesome/wiki/platforms/apps).
* [gradio](https://github.com/gradio-app/gradio) - Create UIs for your [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) model in [Python](/@harrisonqian/awesome/wiki/programming-languages/python) in 3 minutes.
* [Vizro](https://github.com/mckinsey/vizro) - A toolkit for creating modular [data visualization](/@harrisonqian/awesome/wiki/miscellaneous/data-visualization) applications.
* [datapane](https://datapane.com/) - A collection of APIs to turn scripts and notebooks into interactive reports.
* [binder](https://mybinder.org/) - Enable sharing and execute [Jupyter](/@harrisonqian/awesome/wiki/miscellaneous/jupyter) Notebooks
* [Deepnote](https://github.com/deepnote/deepnote) - Deepnote is a drop-in replacement for [Jupyter](/@harrisonqian/awesome/wiki/miscellaneous/jupyter) with an AI-first design, sleek UI, new blocks, and native data integrations. Use [Python](/@harrisonqian/awesome/wiki/programming-languages/python), R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data [apps](/@harrisonqian/awesome/wiki/platforms/apps).

## Statistics
* [pandas_summary](https://github.com/mouradmourafiq/pandas-summary) - Extension to pandas dataframes describe function. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
* [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
* [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [stockstats](https://github.com/jealous/stockstats) - Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.
* [weightedcalcs](https://github.com/jsvine/weightedcalcs) - A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.
* [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) - Pairwise Multiple Comparisons Post-hoc Tests.
* [Alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive (alpha) stock factors.

## Data Manipulation

### Data Frames
* [pandas](https://pandas.pydata.org/pandas-docs/stable/) - Powerful [Python](/@harrisonqian/awesome/wiki/programming-languages/python) data analysis toolkit.
* [polars](https://github.com/pola-rs/polars) - A fast multi-threaded, hybrid-out-of-[core](/@harrisonqian/awesome/wiki/platforms/core) DataFrame library.
* [Arctic](https://github.com/manahl/arctic) - High-performance datastore for time series and tick data.
* [datatable](https://github.com/h2oai/datatable) - Data.table for [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
* [pandas_profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects
* [cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library. <img height="20" src="img/pandas_big.png" alt="pandas compatible"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
* [blaze](https://github.com/blaze/blaze) - NumPy and pandas interface to [Big Data](/@harrisonqian/awesome/wiki/big-data/big-data). <img height="20" src="img/pandas_big.png" alt="pandas compatible">
* [pandasql](https://github.com/yhat/pandasql) -  Allows you to query pandas DataFrames using SQL syntax. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
* [pandas-gbq](https://github.com/pydata/pandas-gbq) - pandas Google Big Query. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
* [xpandas](https://github.com/alan-turing-institute/xpandas) - Universal 1d/2d data [containers](/@harrisonqian/awesome/wiki/platforms/containers) with Transformers .functionality for data analysis by [The Alan Turing Institute](https://www.turing.ac.uk/).
* [pysparkling](https://github.com/svenkreiss/pysparkling) - A pure [Python](/@harrisonqian/awesome/wiki/programming-languages/python) implementation of [Apache Spark](/@harrisonqian/awesome/wiki/big-data/apache-spark)'s RDD and DStream interfaces. <img height="20" src="img/spark_big.png" alt="[Apache Spark](/@harrisonqian/awesome/wiki/big-data/apache-spark) based">
* [modin](https://github.com/modin-project/modin) - Speed up your pandas workflows by changing a single line of code. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
* [swifter](https://github.com/jmcarpenter2/swifter) - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
* [pandas-log](https://github.com/eyaltrabelsi/pandas-log) - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
* [vaex](https://github.com/vaexio/vaex) - Out-of-[Core](/@harrisonqian/awesome/wiki/platforms/core) DataFrames for [Python](/@harrisonqian/awesome/wiki/programming-languages/python), ML, visualize and explore big tabular data at a billion rows per second.
* [xarray](https://github.com/pydata/xarray) - 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](/@harrisonqian/awesome/wiki/front-end-development/less) error-prone indexing routines.

### Pipelines
* [pdpipe](https://github.com/shaypal5/pdpipe) - Sasy pipelines for pandas DataFrames.
* [SSPipe](https://sspipe.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) pipe (|) operator with support for DataFrames and Numpy, and Pytorch.
* [pandas-ply](https://github.com/coursera/pandas-ply) - Functional data manipulation for pandas. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
* [Dplython](https://github.com/dodger487/dplython) - Dplyr for [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
* [sklearn-pandas](https://github.com/scikit-learn-contrib/sklearn-pandas) - pandas [integration](/@harrisonqian/awesome/wiki/platforms/integration) with sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pandas_big.png" alt="pandas compatible">
* [Dataset](https://github.com/analysiscenter/dataset) - Helps you conveniently work with random or sequential batches of your data and define data processing.
* [pyjanitor](https://github.com/ericmjl/pyjanitor) - Clean APIs for data cleaning. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
* [meza](https://github.com/reubano/meza) - A [Python](/@harrisonqian/awesome/wiki/programming-languages/python) toolkit for processing tabular data.
* [Prodmodel](https://github.com/prodmodel/prodmodel) - Build system for data science pipelines.
* [dopanda](https://github.com/dovpanda-dev/dovpanda) -  Hints and [tips](/@harrisonqian/awesome/wiki/programming-languages/tips) for using pandas in an analysis environment. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
* [Hamilton](https://github.com/DAGWorks-Inc/hamilton) - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated [Python](/@harrisonqian/awesome/wiki/programming-languages/python) functions.

### Data-centric AI
* [cleanlab](https://github.com/cleanlab/cleanlab) - The standard data-centric AI package for data quality and [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) with messy, real-world data and labels.
* [snorkel](https://github.com/snorkel-team/snorkel) - A system for quickly generating training data with weak supervision.
* [dataprep](https://github.com/sfu-db/dataprep) - Collect, clean, and visualize your data in [Python](/@harrisonqian/awesome/wiki/programming-languages/python) with a few lines of code.

### Synthetic Data

* [ydata-synthetic](https://github.com/ydataai/ydata-synthetic) - A package to generate synthetic tabular and time-series data leveraging the state-of-the-art generative models. <img height="20" src="img/pandas_big.png" alt="pandas compatible">

## Distributed Computing
* [Horovod](https://github.com/uber/horovod) - Distributed training framework for [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow), Keras, PyTorch, and Apache MXNet. <img height="20" src="img/tf_big2.png" alt="sklearn">
* [PySpark](https://spark.apache.org/docs/0.9.0/python-programming-guide.html) - Exposes the Spark programming model to [Python](/@harrisonqian/awesome/wiki/programming-languages/python). <img height="20" src="img/spark_big.png" alt="[Apache Spark](/@harrisonqian/awesome/wiki/big-data/apache-spark) based">
* [Veles](https://github.com/Samsung/veles) - Distributed [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) platform.
* [Jubatus](https://github.com/jubatus/jubatus) - Framework and Library for Distributed Online [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning).
* [DMTK](https://github.com/Microsoft/DMTK) - Microsoft Distributed [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Toolkit.
* [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed [Deep LEarning](/@harrisonqian/awesome/wiki/computer-science/deep-learning).
* [dask-ml](https://github.com/dask/dask-ml) - Distributed and parallel [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning). <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [Distributed](https://github.com/dask/distributed) - Distributed computation in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).

## Experimentation
* [mlflow](https://github.com/mlflow/mlflow) - Open source platform for the [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) lifecycle.
* [Neptune](https://neptune.ai) - A lightweight ML experiment tracking, results visualization, and management tool.
* [dvc](https://github.com/iterative/dvc) - Data Version Control | Git for Data & Models | ML Experiments Management.
* [envd](https://github.com/tensorchord/envd) - 🏕️ [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) development environment for data science and AI/ML engineering teams.
* [Sacred](https://github.com/IDSIA/sacred) - A tool to help you configure, organize, log, and reproduce experiments.
* [Ax](https://github.com/facebook/Ax) - Adaptive Experimentation Platform. <img height="20" src="img/sklearn_big.png" alt="sklearn">

## Data Validation
* [great_expectations](https://github.com/great-expectations/great_expectations) - Always know what to expect from your data.
* [pandera](https://github.com/unionai-oss/pandera) - A lightweight, flexible, and expressive statistical data [testing](/@harrisonqian/awesome/wiki/testing/testing) library.
* [deepchecks](https://github.com/deepchecks/deepchecks) - Validation & [testing](/@harrisonqian/awesome/wiki/testing/testing) of ML models and data during model development, deployment, and production. <img height="20" src="img/sklearn_big.png" alt="sklearn">

---
*truncated — [full list on GitHub](https://github.com/krzjoa/awesome-python-data-science)*