From 5f45e952eb78408cfa73c7b47ac847be9429bcc4 Mon Sep 17 00:00:00 2001
From: bijay-odyssey <bijaybeezoe@gmail.com>
Date: Fri, 28 Aug 2026 16:23:37 +0545
Subject: [PATCH] Add edaprep to Feature Engineering

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-<div align="center">
-    <a href="https://krzjoa.github.io/awesome-python-data-science/"><img width="250" height="250" src="img/py-datascience.png" alt="pyds"></a>
-    <br>
-    <br>
-    <br>
-</div>
-
-<h1 align="center">
-    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-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)
-
-- [TabGAN](https://github.com/Diyago/Tabular-data-generation) - Synthetic tabular data generation using GANs, Diffusion Models, and LLMs. <img height="16" width="16" src="https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png" alt="sklearn">
-- [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 for scientific computing in Python
-* [scikit-learn](https://scikit-learn.org/stable/) - Machine learning in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python.  <img height="20" src="img/R_big.png" alt="R inspired lib">
-* [Shogun](https://github.com/shogun-toolbox/shogun) - Machine learning toolbox.
-* [xLearn](https://github.com/aksnzhy/xlearn) - High Performance, Easy-to-use, and Scalable Machine Learning Package.
-* [cuML](https://github.com/rapidsai/cuml) - RAPIDS 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 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 based">
-* [mlpack](https://github.com/mlpack/mlpack) - A scalable C++ machine learning library (Python bindings).
-* [dlib](https://github.com/davisking/dlib) - Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).
-* [MLxtend](https://github.com/rasbt/mlxtend) - Extension and helper modules for Python's data analysis and machine learning libraries. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [hyperlearn](https://github.com/danielhanchen/hyperlearn) - 50%+ Faster, 50%+ 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 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. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [seqlearn](https://github.com/larsmans/seqlearn) - Sequence classification toolkit for Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [pystruct](https://github.com/pystruct/pystruct) - Simple structured learning framework for 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 algorithms in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [pyGAM](https://github.com/dswah/pyGAM) - Generalized Additive Models in Python.
-* [causalml](https://github.com/uber/causalml) - Uplift modeling and causal inference with machine learning 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 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">
-
-### Ensemble Methods
-* [ML-Ensemble](http://ml-ensemble.com/) - High performance ensemble 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. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [stacked_generalization](https://github.com/fukatani/stacked_generalization) - Library for machine learning stacking generalization. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [vecstack](https://github.com/vecxoz/vecstack) - Python package for stacking (machine 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-based implementations of algorithms for 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. <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 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 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 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 by Google. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [TensorLayer](https://github.com/zsdonghao/tensorlayer) - Deep Learning and Reinforcement Learning Library for Researcher and Engineer. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [TFLearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [Sonnet](https://github.com/deepmind/sonnet) - 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. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [tfdeploy](https://github.com/riga/tfdeploy) - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) - 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 with dynamic computation graphs in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [TensorLight](https://github.com/bsautermeister/tensorlight) - A high-level framework for 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 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+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.  <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 with Keras & Spark. <img height="20" src="img/keras_big.png" alt="Keras compatible">
-* [qkeras](https://github.com/google/qkeras) - A quantization 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 for Pytorch, 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.
-* [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.
-* [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. <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 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 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 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 binding for <a href="https://github.com/morfologik/morfologik-stemming">Morfologik</a>.
-* [skift](https://github.com/shaypal5/skift) - Scikit-learn wrappers for 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 library for audio and music analysis.
-* [Yaafe](https://github.com/Yaafe/Yaafe) - Audio features extraction.
-* [aubio](https://github.com/aubio/aubio) - A library for audio and music analysis.
-* [Essentia](https://github.com/MTG/essentia) - Library for audio and 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 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 audio and music signal processing library.
-
-## Computer Vision
-* [torchvision](https://github.com/pytorch/vision) - Datasets, Transforms, and Models specific to 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 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 workflows with Keras. <img height="20" src="img/keras_big.png" alt="MXNet based">
-* [OpenCV](https://github.com/opencv/opencv) - Open Source Computer Vision Library.
-* [Decord](https://github.com/dmlc/decord) - An efficient video loader for 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 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 experiments.
-* [imgaug_extension](https://github.com/cadenai/imgaug_extension) - Additional augmentations for imgaug.
-* [Augmentor](https://github.com/mdbloice/Augmentor) - Image augmentation library in Python for 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 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 models
-* [darts](https://github.com/unit8co/darts) - A 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-based time series forecasting.
-* [neuralforecast](https://github.com/Nixtla/neuralforecast) - Scalable machine learning-based time series forecasting.
-* [tslearn](https://github.com/rtavenar/tslearn) - 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, 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.
-* [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 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 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 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 algorithms based on OpenAI Baselines.
-* [Shimmy](https://github.com/Farama-Foundation/Shimmy) - An API conversion tool for popular external reinforcement 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.
-* [Tianshou](https://github.com/thu-ml/tianshou/#comprehensive-functionality) - An elegant PyTorch deep reinforcement 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 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 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 in TensorFlow. <img height="20" src="img/tf_big2.png" alt="TensorFlow">
-* [TensorForce](https://github.com/reinforceio/tensorforce) - A TensorFlow library for applied reinforcement learning. <img height="20" src="img/tf_big2.png" alt="TensorFlow">
-* [TRFL](https://github.com/deepmind/trfl) - TensorFlow Reinforcement 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 algorithms.
-* [keras-rl](https://github.com/keras-rl/keras-rl) - Deep Reinforcement 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 research.
-* [Horizon](https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement Learning.
-* [rlpyt](https://github.com/astooke/rlpyt) - Reinforcement 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 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 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 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 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 package built to ease deep learning on graph, on top of existing DL frameworks. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"> <img height="20" src="img/tf_big2.png" alt="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 Graph Representation Learning library for Predictions and Evaluations
-* [Spektral](https://github.com/danielegrattarola/spektral) - Deep learning on graphs. <img height="20" src="img/keras_big.png" alt="Keras compatible">
-* [StellarGraph](https://github.com/stellargraph/stellargraph) - Machine Learning on Graphs. <img height="20" src="img/tf_big2.png" alt="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. <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 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 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 on graphs.
-* [Karate Club](https://github.com/benedekrozemberczki/karateclub) - An unsupervised 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.
-* [Cleora](https://github.com/BaseModelAI/cleora) - The Graph Embedding Engine.
-
-## Graph Manipulation
-* [Networkx](https://github.com/networkx/networkx) - Network Analysis in Python.
-* [Rustworkx](https://github.com/Qiskit/rustworkx) - A high performance Python graph library implemented in Rust.
-* [graph-tool](https://graph-tool.skewed.de/) - an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks).
-* [igraph](https://github.com/igraph/python-igraph) - Python interface for igraph.
-
-## Learning-to-Rank & Recommender Systems
-* [LightFM](https://github.com/lyst/lightfm) - A Python implementation of LightFM, a hybrid recommendation algorithm.
-* [Spotlight](https://maciejkula.github.io/spotlight/) - Deep recommender models using PyTorch.
-* [Surprise](https://github.com/NicolasHug/Surprise) - A 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-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. <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 to Rank in 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. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [pgmpy](https://github.com/pgmpy/pgmpy) - A 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.
-* [ZhuSuan](https://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [GPflow](https://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in 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 package for Bayesian 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 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 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 ensemble.
-* [Alibi](https://github.com/SeldonIO/alibi) - Algorithms for monitoring and explaining 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 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 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 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 classifiers and explaining their predictions.
-* [Lime](https://github.com/marcotcr/lime) - Explaining the predictions of any machine learning classifier. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [FairML](https://github.com/adebayoj/fairml) - FairML is a python toolbox auditing the 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 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 Individual Conditional Expectation Plot Toolbox.
-* [Skater](https://github.com/datascienceinc/Skater) - Python Library for Model Interpretation.
-* [model-analysis](https://github.com/tensorflow/model-analysis) - Model analysis tools for 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 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 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 and machine learning models (no Python code, but visualizes models from most Python Deep 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. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [PyGAD](https://github.com/ahmedfgad/GeneticAlgorithmPython) - Genetic Algorithm in 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 in Python.
-* [karoo_gp](https://github.com/kstaats/karoo_gp) - A Genetic Programming platform for 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.
-* [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.
-* [pycma](https://github.com/CMA-ES/pycma?tab=readme-ov-file) - 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 for optimization.
-* [sklearn-genetic-opt](https://github.com/rodrigo-arenas/Sklearn-genetic-opt) - Hyperparameters tuning and feature selection using evolutionary 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.
-* [hyperopt-sklearn](https://github.com/hyperopt/hyperopt-sklearn) - 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 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 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.
-* [PySwarms](https://github.com/ljvmiranda921/pyswarms) - A research toolkit for particle swarm optimization in Python.
-* [Platypus](https://github.com/Project-Platypus/Platypus) - A Free and Open Source 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 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 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 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 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">
-* [tubular](https://github.com/azukds/tubular) - Collection of scikit-learn compatible transformers written in [narwhals]( https://github.com/narwhals-dev/narwhals), which can accept either polars/pandas inputs and utilise the chosen library under the hood. <img height="20" src="img/sklearn_big.png" alt="sklearn"><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.
-* [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 implementation of ReBATE, a suite of Relief-based feature selection algorithms for 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.
-
-## Visualization
-### General Purposes
-* [Matplotlib](https://github.com/matplotlib/matplotlib) - Plotting with Python.
-* [seaborn](https://github.com/mwaskom/seaborn) - Statistical 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 with matplotlib.
-* [missingno](https://github.com/ResidentMario/missingno) - Missing data visualization module for Python.
-* [chartify](https://github.com/spotify/chartify/) - 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 package for animating plots built on matplotlib.
-* [plotly](https://plot.ly/python/) - A Python library that makes interactive and publication-quality graphs.
-* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
-* [Altair](https://altair-viz.github.io/) - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
-* [bqplot](https://github.com/bqplot/bqplot) - Plotting library for IPython/Jupyter notebooks
-* [pyecharts](https://github.com/pyecharts/pyecharts) - Migrated from [Echarts](https://github.com/apache/echarts), a charting and visualization library, to 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.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 package for interactive mapping with Google 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, 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
-* [streamlit](https://www.streamlit.io/) - Make it easy to deploy the machine learning model
-* [streamsync](https://github.com/streamsync-cloud/streamsync) - No-code in the front, Python in the back. An open-source framework for creating data apps.
-* [gradio](https://github.com/gradio-app/gradio) - Create UIs for your machine learning model in Python in 3 minutes.
-* [Vizro](https://github.com/mckinsey/vizro) - A toolkit for creating modular 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 Notebooks
-* [Deepnote](https://github.com/deepnote/deepnote) - Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps.
-
-
-## 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.
-* [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 data analysis toolkit.
-* [polars](https://github.com/pola-rs/polars) - A fast multi-threaded, hybrid-out-of-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. <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. <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 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 implementation of Apache Spark's RDD and DStream interfaces. <img height="20" src="img/spark_big.png" alt="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 DataFrames for 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 error-prone indexing routines.
-
-### Pipelines
-* [pdpipe](https://github.com/shaypal5/pdpipe) - Sasy pipelines for pandas DataFrames.
-* [SSPipe](https://sspipe.github.io/) - 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. <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 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 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 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 functions.
-
-### Data-centric AI
-* [cleanlab](https://github.com/cleanlab/cleanlab) - The standard data-centric AI package for data quality and 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 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, 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. <img height="20" src="img/spark_big.png" alt="Apache Spark based">
-* [Veles](https://github.com/Samsung/veles) - Distributed machine learning platform.
-* [Jubatus](https://github.com/jubatus/jubatus) - Framework and Library for Distributed Online Machine Learning.
-* [DMTK](https://github.com/Microsoft/DMTK) - Microsoft Distributed Machine Learning Toolkit.
-* [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning.
-* [dask-ml](https://github.com/dask/dask-ml) - Distributed and parallel machine learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [Distributed](https://github.com/dask/distributed) - Distributed computation in Python.
-
-## Experimentation
-* [mlflow](https://github.com/mlflow/mlflow) - Open source platform for the 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 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 library.
-* [deepchecks](https://github.com/deepchecks/deepchecks) - Validation & testing of ML models and data during model development, deployment, and production. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [evidently](https://github.com/evidentlyai/evidently) - Evaluate and monitor ML models from validation to production.
-* [TensorFlow Data Validation](https://github.com/tensorflow/data-validation) - Library for exploring and validating machine learning data.
-* [DataComPy](https://github.com/capitalone/datacompy)- A library to compare Pandas, Polars, and Spark data frames. It provides stats and lets users adjust for match accuracy.
-
-## Evaluation
-* [recmetrics](https://github.com/statisticianinstilettos/recmetrics) - Library of useful metrics and plots for evaluating recommender systems.
-* [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metric.
-* [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation) - Model evaluation made easy: plots, tables, and markdown reports. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [AI Fairness 360](https://github.com/IBM/AIF360) - Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.
-* [alibi-detect](https://github.com/SeldonIO/alibi-detect) - Algorithms for outlier, adversarial and drift detection.<img height="20" src="img/alibi-detect.png" alt="sklearn">
-
-## Computations
-* [NumPy](https://numpy.org/) - The fundamental package for scientific computing with Python
-* [Dask](https://github.com/dask/dask) - Parallel computing with task scheduling. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [bottleneck](https://github.com/kwgoodman/bottleneck) - Fast NumPy array functions written in C.
-* [CuPy](https://github.com/cupy/cupy) - NumPy-like API accelerated with CUDA.
-* [scikit-tensor](https://github.com/mnick/scikit-tensor) - Python library for multilinear algebra and tensor factorizations.
-* [numdifftools](https://github.com/pbrod/numdifftools) - Solve automatic numerical differentiation problems in one or more variables.
-* [quaternion](https://github.com/moble/quaternion) - Add built-in support for quaternions to numpy.
-* [adaptive](https://github.com/python-adaptive/adaptive) - Tools for adaptive and parallel samping of mathematical functions.
-* [NumExpr](https://github.com/pydata/numexpr) - 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.
-
-## Web Scraping
-* [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/): The easiest library to scrape static websites for beginners
-* [Scrapy](https://scrapy.org/): Fast and extensible scraping library. Can write rules and create customized scraper without touching the core
-* [Selenium](https://selenium-python.readthedocs.io/installation.html#introduction): Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.
-* [Pattern](https://github.com/clips/pattern): High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization
-* [twitterscraper](https://github.com/taspinar/twitterscraper): Efficient library to scrape Twitter
-
-## Spatial Analysis
-* [GeoPandas](https://github.com/geopandas/geopandas) - Python tools for geographic data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [PySal](https://github.com/pysal/pysal) - Python Spatial Analysis Library.
-
-## Quantum Computing
-* [qiskit](https://github.com/Qiskit/qiskit) - Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.
-* [cirq](https://github.com/quantumlib/Cirq) - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
-* [PennyLane](https://github.com/XanaduAI/pennylane) - Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
-* [QML](https://github.com/qmlcode/qml) - A Python Toolkit for Quantum Machine Learning.
-
-## Conversion
-* [sklearn-porter](https://github.com/nok/sklearn-porter) - Transpile trained scikit-learn estimators to C, Java, JavaScript, and others.
-* [ONNX](https://github.com/onnx/onnx) - Open Neural Network Exchange.
-* [MMdnn](https://github.com/Microsoft/MMdnn) -  A set of tools to help users inter-operate among different deep learning frameworks.
-* [treelite](https://github.com/dmlc/treelite) - Universal model exchange and serialization format for decision tree forests.
-
-## Contributing
-Contributions are welcome! :sunglasses: </br>
-Read the <a href=https://github.com/krzjoa/awesome-python-datascience/blob/master/CONTRIBUTING.md>contribution guideline</a>.
-
-## License
-This work is licensed under the Creative Commons Attribution 4.0 International License - [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+<div align="center">
+    <a href="https://krzjoa.github.io/awesome-python-data-science/"><img width="250" height="250" src="img/py-datascience.png" alt="pyds"></a>
+    <br>
+    <br>
+    <br>
+</div>
+
+<h1 align="center">
+    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-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)
+
+- [TabGAN](https://github.com/Diyago/Tabular-data-generation) - Synthetic tabular data generation using GANs, Diffusion Models, and LLMs. <img height="16" width="16" src="https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png" alt="sklearn">
+- [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 for scientific computing in Python
+* [scikit-learn](https://scikit-learn.org/stable/) - Machine learning in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python.  <img height="20" src="img/R_big.png" alt="R inspired lib">
+* [Shogun](https://github.com/shogun-toolbox/shogun) - Machine learning toolbox.
+* [xLearn](https://github.com/aksnzhy/xlearn) - High Performance, Easy-to-use, and Scalable Machine Learning Package.
+* [cuML](https://github.com/rapidsai/cuml) - RAPIDS 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 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 based">
+* [mlpack](https://github.com/mlpack/mlpack) - A scalable C++ machine learning library (Python bindings).
+* [dlib](https://github.com/davisking/dlib) - Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).
+* [MLxtend](https://github.com/rasbt/mlxtend) - Extension and helper modules for Python's data analysis and machine learning libraries. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [hyperlearn](https://github.com/danielhanchen/hyperlearn) - 50%+ Faster, 50%+ 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 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. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [seqlearn](https://github.com/larsmans/seqlearn) - Sequence classification toolkit for Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [pystruct](https://github.com/pystruct/pystruct) - Simple structured learning framework for 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 algorithms in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [pyGAM](https://github.com/dswah/pyGAM) - Generalized Additive Models in Python.
+* [causalml](https://github.com/uber/causalml) - Uplift modeling and causal inference with machine learning 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 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">
+
+### Ensemble Methods
+* [ML-Ensemble](http://ml-ensemble.com/) - High performance ensemble 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. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [stacked_generalization](https://github.com/fukatani/stacked_generalization) - Library for machine learning stacking generalization. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [vecstack](https://github.com/vecxoz/vecstack) - Python package for stacking (machine 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-based implementations of algorithms for 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. <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 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 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 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 by Google. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [TensorLayer](https://github.com/zsdonghao/tensorlayer) - Deep Learning and Reinforcement Learning Library for Researcher and Engineer. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [TFLearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [Sonnet](https://github.com/deepmind/sonnet) - 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. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [tfdeploy](https://github.com/riga/tfdeploy) - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) - 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 with dynamic computation graphs in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [TensorLight](https://github.com/bsautermeister/tensorlight) - A high-level framework for 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 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+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.  <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 with Keras & Spark. <img height="20" src="img/keras_big.png" alt="Keras compatible">
+* [qkeras](https://github.com/google/qkeras) - A quantization 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 for Pytorch, 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.
+* [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.
+* [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. <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 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 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 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 binding for <a href="https://github.com/morfologik/morfologik-stemming">Morfologik</a>.
+* [skift](https://github.com/shaypal5/skift) - Scikit-learn wrappers for 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 library for audio and music analysis.
+* [Yaafe](https://github.com/Yaafe/Yaafe) - Audio features extraction.
+* [aubio](https://github.com/aubio/aubio) - A library for audio and music analysis.
+* [Essentia](https://github.com/MTG/essentia) - Library for audio and 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 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 audio and music signal processing library.
+
+## Computer Vision
+* [torchvision](https://github.com/pytorch/vision) - Datasets, Transforms, and Models specific to 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 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 workflows with Keras. <img height="20" src="img/keras_big.png" alt="MXNet based">
+* [OpenCV](https://github.com/opencv/opencv) - Open Source Computer Vision Library.
+* [Decord](https://github.com/dmlc/decord) - An efficient video loader for 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 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 experiments.
+* [imgaug_extension](https://github.com/cadenai/imgaug_extension) - Additional augmentations for imgaug.
+* [Augmentor](https://github.com/mdbloice/Augmentor) - Image augmentation library in Python for 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 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 models
+* [darts](https://github.com/unit8co/darts) - A 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-based time series forecasting.
+* [neuralforecast](https://github.com/Nixtla/neuralforecast) - Scalable machine learning-based time series forecasting.
+* [tslearn](https://github.com/rtavenar/tslearn) - 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, 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.
+* [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 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 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 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 algorithms based on OpenAI Baselines.
+* [Shimmy](https://github.com/Farama-Foundation/Shimmy) - An API conversion tool for popular external reinforcement 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.
+* [Tianshou](https://github.com/thu-ml/tianshou/#comprehensive-functionality) - An elegant PyTorch deep reinforcement 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 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 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 in TensorFlow. <img height="20" src="img/tf_big2.png" alt="TensorFlow">
+* [TensorForce](https://github.com/reinforceio/tensorforce) - A TensorFlow library for applied reinforcement learning. <img height="20" src="img/tf_big2.png" alt="TensorFlow">
+* [TRFL](https://github.com/deepmind/trfl) - TensorFlow Reinforcement 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 algorithms.
+* [keras-rl](https://github.com/keras-rl/keras-rl) - Deep Reinforcement 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 research.
+* [Horizon](https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement Learning.
+* [rlpyt](https://github.com/astooke/rlpyt) - Reinforcement 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 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 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 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 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 package built to ease deep learning on graph, on top of existing DL frameworks. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"> <img height="20" src="img/tf_big2.png" alt="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 Graph Representation Learning library for Predictions and Evaluations
+* [Spektral](https://github.com/danielegrattarola/spektral) - Deep learning on graphs. <img height="20" src="img/keras_big.png" alt="Keras compatible">
+* [StellarGraph](https://github.com/stellargraph/stellargraph) - Machine Learning on Graphs. <img height="20" src="img/tf_big2.png" alt="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. <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 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 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 on graphs.
+* [Karate Club](https://github.com/benedekrozemberczki/karateclub) - An unsupervised 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.
+* [Cleora](https://github.com/BaseModelAI/cleora) - The Graph Embedding Engine.
+
+## Graph Manipulation
+* [Networkx](https://github.com/networkx/networkx) - Network Analysis in Python.
+* [Rustworkx](https://github.com/Qiskit/rustworkx) - A high performance Python graph library implemented in Rust.
+* [graph-tool](https://graph-tool.skewed.de/) - an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks).
+* [igraph](https://github.com/igraph/python-igraph) - Python interface for igraph.
+
+## Learning-to-Rank & Recommender Systems
+* [LightFM](https://github.com/lyst/lightfm) - A Python implementation of LightFM, a hybrid recommendation algorithm.
+* [Spotlight](https://maciejkula.github.io/spotlight/) - Deep recommender models using PyTorch.
+* [Surprise](https://github.com/NicolasHug/Surprise) - A 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-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. <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 to Rank in 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. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
+* [pgmpy](https://github.com/pgmpy/pgmpy) - A 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.
+* [ZhuSuan](https://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [GPflow](https://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in 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 package for Bayesian 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 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 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 ensemble.
+* [Alibi](https://github.com/SeldonIO/alibi) - Algorithms for monitoring and explaining 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 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 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 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 classifiers and explaining their predictions.
+* [Lime](https://github.com/marcotcr/lime) - Explaining the predictions of any machine learning classifier. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [FairML](https://github.com/adebayoj/fairml) - FairML is a python toolbox auditing the 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 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 Individual Conditional Expectation Plot Toolbox.
+* [Skater](https://github.com/datascienceinc/Skater) - Python Library for Model Interpretation.
+* [model-analysis](https://github.com/tensorflow/model-analysis) - Model analysis tools for 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 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 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 and machine learning models (no Python code, but visualizes models from most Python Deep 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. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [PyGAD](https://github.com/ahmedfgad/GeneticAlgorithmPython) - Genetic Algorithm in 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 in Python.
+* [karoo_gp](https://github.com/kstaats/karoo_gp) - A Genetic Programming platform for 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.
+* [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.
+* [pycma](https://github.com/CMA-ES/pycma?tab=readme-ov-file) - 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 for optimization.
+* [sklearn-genetic-opt](https://github.com/rodrigo-arenas/Sklearn-genetic-opt) - Hyperparameters tuning and feature selection using evolutionary 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.
+* [hyperopt-sklearn](https://github.com/hyperopt/hyperopt-sklearn) - 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 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 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.
+* [PySwarms](https://github.com/ljvmiranda921/pyswarms) - A research toolkit for particle swarm optimization in Python.
+* [Platypus](https://github.com/Project-Platypus/Platypus) - A Free and Open Source 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 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 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 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 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">
+* [tubular](https://github.com/azukds/tubular) - Collection of scikit-learn compatible transformers written in [narwhals]( https://github.com/narwhals-dev/narwhals), which can accept either polars/pandas inputs and utilise the chosen library under the hood. <img height="20" src="img/sklearn_big.png" alt="sklearn"><img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [edaprep](https://github.com/bijay-odyssey/edaprep) - Leakage-safe EDA and preprocessing with a planner that explains every automatic decision and names the measurement behind it. <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.
+* [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 implementation of ReBATE, a suite of Relief-based feature selection algorithms for 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.
+
+## Visualization
+### General Purposes
+* [Matplotlib](https://github.com/matplotlib/matplotlib) - Plotting with Python.
+* [seaborn](https://github.com/mwaskom/seaborn) - Statistical 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 with matplotlib.
+* [missingno](https://github.com/ResidentMario/missingno) - Missing data visualization module for Python.
+* [chartify](https://github.com/spotify/chartify/) - 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 package for animating plots built on matplotlib.
+* [plotly](https://plot.ly/python/) - A Python library that makes interactive and publication-quality graphs.
+* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
+* [Altair](https://altair-viz.github.io/) - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
+* [bqplot](https://github.com/bqplot/bqplot) - Plotting library for IPython/Jupyter notebooks
+* [pyecharts](https://github.com/pyecharts/pyecharts) - Migrated from [Echarts](https://github.com/apache/echarts), a charting and visualization library, to 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.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 package for interactive mapping with Google 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, 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
+* [streamlit](https://www.streamlit.io/) - Make it easy to deploy the machine learning model
+* [streamsync](https://github.com/streamsync-cloud/streamsync) - No-code in the front, Python in the back. An open-source framework for creating data apps.
+* [gradio](https://github.com/gradio-app/gradio) - Create UIs for your machine learning model in Python in 3 minutes.
+* [Vizro](https://github.com/mckinsey/vizro) - A toolkit for creating modular 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 Notebooks
+* [Deepnote](https://github.com/deepnote/deepnote) - Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps.
+
+
+## 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.
+* [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 data analysis toolkit.
+* [polars](https://github.com/pola-rs/polars) - A fast multi-threaded, hybrid-out-of-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. <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. <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 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 implementation of Apache Spark's RDD and DStream interfaces. <img height="20" src="img/spark_big.png" alt="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 DataFrames for 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 error-prone indexing routines.
+
+### Pipelines
+* [pdpipe](https://github.com/shaypal5/pdpipe) - Sasy pipelines for pandas DataFrames.
+* [SSPipe](https://sspipe.github.io/) - 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. <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 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 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 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 functions.
+
+### Data-centric AI
+* [cleanlab](https://github.com/cleanlab/cleanlab) - The standard data-centric AI package for data quality and 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 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, 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. <img height="20" src="img/spark_big.png" alt="Apache Spark based">
+* [Veles](https://github.com/Samsung/veles) - Distributed machine learning platform.
+* [Jubatus](https://github.com/jubatus/jubatus) - Framework and Library for Distributed Online Machine Learning.
+* [DMTK](https://github.com/Microsoft/DMTK) - Microsoft Distributed Machine Learning Toolkit.
+* [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning.
+* [dask-ml](https://github.com/dask/dask-ml) - Distributed and parallel machine learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [Distributed](https://github.com/dask/distributed) - Distributed computation in Python.
+
+## Experimentation
+* [mlflow](https://github.com/mlflow/mlflow) - Open source platform for the 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 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 library.
+* [deepchecks](https://github.com/deepchecks/deepchecks) - Validation & testing of ML models and data during model development, deployment, and production. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [evidently](https://github.com/evidentlyai/evidently) - Evaluate and monitor ML models from validation to production.
+* [TensorFlow Data Validation](https://github.com/tensorflow/data-validation) - Library for exploring and validating machine learning data.
+* [DataComPy](https://github.com/capitalone/datacompy)- A library to compare Pandas, Polars, and Spark data frames. It provides stats and lets users adjust for match accuracy.
+
+## Evaluation
+* [recmetrics](https://github.com/statisticianinstilettos/recmetrics) - Library of useful metrics and plots for evaluating recommender systems.
+* [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metric.
+* [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation) - Model evaluation made easy: plots, tables, and markdown reports. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [AI Fairness 360](https://github.com/IBM/AIF360) - Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.
+* [alibi-detect](https://github.com/SeldonIO/alibi-detect) - Algorithms for outlier, adversarial and drift detection.<img height="20" src="img/alibi-detect.png" alt="sklearn">
+
+## Computations
+* [NumPy](https://numpy.org/) - The fundamental package for scientific computing with Python
+* [Dask](https://github.com/dask/dask) - Parallel computing with task scheduling. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [bottleneck](https://github.com/kwgoodman/bottleneck) - Fast NumPy array functions written in C.
+* [CuPy](https://github.com/cupy/cupy) - NumPy-like API accelerated with CUDA.
+* [scikit-tensor](https://github.com/mnick/scikit-tensor) - Python library for multilinear algebra and tensor factorizations.
+* [numdifftools](https://github.com/pbrod/numdifftools) - Solve automatic numerical differentiation problems in one or more variables.
+* [quaternion](https://github.com/moble/quaternion) - Add built-in support for quaternions to numpy.
+* [adaptive](https://github.com/python-adaptive/adaptive) - Tools for adaptive and parallel samping of mathematical functions.
+* [NumExpr](https://github.com/pydata/numexpr) - 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.
+
+## Web Scraping
+* [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/): The easiest library to scrape static websites for beginners
+* [Scrapy](https://scrapy.org/): Fast and extensible scraping library. Can write rules and create customized scraper without touching the core
+* [Selenium](https://selenium-python.readthedocs.io/installation.html#introduction): Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.
+* [Pattern](https://github.com/clips/pattern): High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization
+* [twitterscraper](https://github.com/taspinar/twitterscraper): Efficient library to scrape Twitter
+
+## Spatial Analysis
+* [GeoPandas](https://github.com/geopandas/geopandas) - Python tools for geographic data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [PySal](https://github.com/pysal/pysal) - Python Spatial Analysis Library.
+
+## Quantum Computing
+* [qiskit](https://github.com/Qiskit/qiskit) - Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.
+* [cirq](https://github.com/quantumlib/Cirq) - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
+* [PennyLane](https://github.com/XanaduAI/pennylane) - Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
+* [QML](https://github.com/qmlcode/qml) - A Python Toolkit for Quantum Machine Learning.
+
+## Conversion
+* [sklearn-porter](https://github.com/nok/sklearn-porter) - Transpile trained scikit-learn estimators to C, Java, JavaScript, and others.
+* [ONNX](https://github.com/onnx/onnx) - Open Neural Network Exchange.
+* [MMdnn](https://github.com/Microsoft/MMdnn) -  A set of tools to help users inter-operate among different deep learning frameworks.
+* [treelite](https://github.com/dmlc/treelite) - Universal model exchange and serialization format for decision tree forests.
+
+## Contributing
+Contributions are welcome! :sunglasses: </br>
+Read the <a href=https://github.com/krzjoa/awesome-python-datascience/blob/master/CONTRIBUTING.md>contribution guideline</a>.
+
+## License
+This work is licensed under the Creative Commons Attribution 4.0 International License - [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)