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-Awesome Python Data Science
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-▐ Probably the best curated list of data science software in Python
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-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)
-- **Random Forests** (#random-forests)          
-- **Kernel Methods** (#kernel-methods)          
-- Deep Learning (#deep-learning)
-- **PyTorch** (#pytorch)      
-- **TensorFlow** (#tensorflow)
-- **MXNet** (#mxnet)          
- 	- 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)
-- 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)  
-- 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)
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-Machine Learning
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-General Purpose Machine Learning
-⟡ scikit-learn (http://scikit-learn.org/stable/) - Machine learning in Python. 
-⟡ PyCaret (https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python.  
-⟡ 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. 
-⟡ modAL (https://github.com/cosmic-cortex/modAL) - Modular active learning framework for Python3. 
-⟡ Sparkit-learn (https://github.com/lensacom/sparkit-learn) - PySpark + scikit-learn = Sparkit-learn. 
-⟡ 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. 
-⟡ hyperlearn (https://github.com/danielhanchen/hyperlearn) - 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels. 
-⟡ Reproducible Experiment Platform (REP) (https://github.com/yandex/rep) - Machine Learning toolbox for Humans. 
-⟡ scikit-multilearn (https://github.com/scikit-multilearn/scikit-multilearn) - Multi-label classification for python. 
-⟡ seqlearn (https://github.com/larsmans/seqlearn) - Sequence classification toolkit for Python. 
-⟡ pystruct (https://github.com/pystruct/pystruct) - Simple structured learning framework for Python. 
-⟡ sklearn-expertsys (https://github.com/tmadl/sklearn-expertsys) - Highly interpretable classifiers for scikit learn. 
-⟡ RuleFit (https://github.com/christophM/rulefit) - Implementation of the rulefit. 
-⟡ metric-learn (https://github.com/all-umass/metric-learn) - Metric learning algorithms in Python. 
-⟡ 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. 
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-Gradient Boosting
-⟡ XGBoost (https://github.com/dmlc/xgboost) - Scalable, Portable, and Distributed Gradient Boosting. 
-⟡ LightGBM (https://github.com/Microsoft/LightGBM) - A fast, distributed, high-performance gradient boosting. 
-⟡ CatBoost (https://github.com/catboost/catboost) - An open-source gradient boosting on decision trees library. 
-⟡ ThunderGBM (https://github.com/Xtra-Computing/thundergbm) - Fast GBDTs and Random Forests on GPUs. 
-⟡ 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. 
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-Ensemble Methods
-⟡ ML-Ensemble (http://ml-ensemble.com/) - High performance ensemble learning. 
-⟡ Stacking (https://github.com/ikki407/stacking) - Simple and useful stacking library written in Python. 
-⟡ stacked_generalization (https://github.com/fukatani/stacked_generalization) - Library for machine learning stacking generalization. 
-⟡ vecstack (https://github.com/vecxoz/vecstack) - Python package for stacking (machine learning technique). 
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-Imbalanced Datasets
-⟡ imbalanced-learn (https://github.com/scikit-learn-contrib/imbalanced-learn) - Module to perform under-sampling and over-sampling with various techniques. 
-⟡ imbalanced-algorithms (https://github.com/dialnd/imbalanced-algorithms) - Python-based implementations of algorithms for learning on imbalanced data. 
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-Random Forests
-⟡ rpforest (https://github.com/lyst/rpforest) - A forest of random projection trees. 
-⟡ sklearn-random-bits-forest (https://github.com/tmadl/sklearn-random-bits-forest) - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).
-⟡ rgf_python (https://github.com/fukatani/rgf_python) - Python Wrapper of Regularized Greedy Forest. 
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-Kernel Methods
-⟡ pyFM (https://github.com/coreylynch/pyFM) - Factorization machines in python. 
-⟡ fastFM (https://github.com/ibayer/fastFM) - A library for Factorization Machines. 
-⟡ tffm (https://github.com/geffy/tffm) - TensorFlow implementation of an arbitrary order Factorization Machine. 
-⟡ 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. 
-⟡ ThunderSVM (https://github.com/Xtra-Computing/thundersvm) - A fast SVM Library on GPUs and CPUs. 
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-Deep Learning
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-PyTorch
-⟡ PyTorch (https://github.com/pytorch/pytorch) - Tensors and Dynamic neural networks in Python with strong GPU acceleration. 
-⟡ pytorch-lightning (https://github.com/Lightning-AI/lightning) - PyTorch Lightning is just organized PyTorch. 
-⟡ ignite (https://github.com/pytorch/ignite) - High-level library to help with training neural networks in PyTorch. 
-⟡ skorch (https://github.com/dnouri/skorch) - A scikit-learn compatible neural network library that wraps PyTorch. 
-⟡ Catalyst (https://github.com/catalyst-team/catalyst) - High-level utils for PyTorch DL & RL research. 
-⟡ ChemicalX (https://github.com/AstraZeneca/chemicalx) - A PyTorch-based deep learning library for drug pair scoring. 
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-TensorFlow
-⟡ TensorFlow (https://github.com/tensorflow/tensorflow) - Computation using data flow graphs for scalable machine learning by Google. 
-⟡ TensorLayer (https://github.com/zsdonghao/tensorlayer) - Deep Learning and Reinforcement Learning Library for Researcher and Engineer. 
-⟡ TFLearn (https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow. 
-⟡ Sonnet (https://github.com/deepmind/sonnet) - TensorFlow-based neural network library. 
-⟡ tensorpack (https://github.com/ppwwyyxx/tensorpack) - A Neural Net Training Interface on TensorFlow. 
-⟡ Polyaxon (https://github.com/polyaxon/polyaxon) - A platform that helps you build, manage and monitor deep learning models. 
-⟡ tfdeploy (https://github.com/riga/tfdeploy) - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy. 
-⟡ tensorflow-upstream (https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) - TensorFlow ROCm port. 
-⟡ TensorFlow Fold (https://github.com/tensorflow/fold) - Deep learning with dynamic computation graphs in TensorFlow. 
-⟡ TensorLight (https://github.com/bsautermeister/tensorlight) - A high-level framework for TensorFlow. 
-⟡ Mesh TensorFlow (https://github.com/tensorflow/mesh) - Model Parallelism Made Easier. 
-⟡ Ludwig (https://github.com/uber/ludwig) - A toolbox that allows one to train and test deep learning models without the need to write code. 
-⟡ Keras (https://keras.io) - A high-level neural networks API running on top of TensorFlow.  
-⟡ keras-contrib (https://github.com/keras-team/keras-contrib) - Keras community contributions. 
-⟡ Hyperas (https://github.com/maxpumperla/hyperas) - Keras + Hyperopt: A straightforward wrapper for a convenient hyperparameter. 
-⟡ Elephas (https://github.com/maxpumperla/elephas) - Distributed Deep learning with Keras & Spark. 
-⟡ qkeras (https://github.com/google/qkeras) - A quantization deep learning library. 
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-MXNet
-⟡ MXNet (https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler. 
-⟡ Gluon (https://github.com/gluon-api/gluon-api) - A clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet). 
-⟡ Xfer (https://github.com/amzn/xfer) - Transfer Learning library for Deep Neural Networks. 
-⟡ MXNet (https://github.com/ROCmSoftwarePlatform/mxnet) - HIP Port of MXNet. 
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-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.
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-Others
-⟡ transformers (https://github.com/huggingface/transformers) - State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. 
-⟡ 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.
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-Automated Machine Learning
-⟡ auto-sklearn (https://github.com/automl/auto-sklearn) - An AutoML toolkit and a drop-in replacement for a scikit-learn estimator. 
-⟡ Auto-PyTorch (https://github.com/automl/Auto-PyTorch) - Automatic architecture search and hyperparameter optimization for PyTorch. 
-⟡ AutoKeras (https://github.com/keras-team/autokeras) - AutoML library for deep learning. 
-⟡ 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. 
-⟡ MLBox (https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library.
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-Natural Language Processing
-⟡ torchtext (https://github.com/pytorch/text) - Data loaders and abstractions for text and NLP. 
-⟡ gluon-nlp (https://github.com/dmlc/gluon-nlp) - NLP made easy. 
-⟡ KerasNLP (https://github.com/keras-team/keras-nlp) - Modular Natural Language Processing workflows with Keras. 
-⟡ 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 .
-⟡ skift (https://github.com/shaypal5/skift) - Scikit-learn wrappers for Python fastText. 
-⟡ 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.
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-Computer Audition
-⟡ torchaudio (https://github.com/pytorch/audio) - An audio library for PyTorch. 
-⟡ 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.
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-Computer Vision
-⟡ torchvision (https://github.com/pytorch/vision) - Datasets, Transforms, and Models specific to Computer Vision. 
-⟡ PyTorch3D (https://github.com/facebookresearch/pytorch3d) - PyTorch3D is FAIR's library of reusable components for deep learning with 3D data. 
-⟡ gluon-cv (https://github.com/dmlc/gluon-cv) - Provides implementations of the state-of-the-art  deep learning models in computer vision. 
-⟡ KerasCV (https://github.com/keras-team/keras-cv) - Industry-strength Computer Vision workflows with Keras. 
-⟡ 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. 
-⟡ 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.
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-Time Series
-⟡ sktime (https://github.com/alan-turing-institute/sktime) - A unified framework for machine learning with time series. 
-⟡ 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. 
-⟡ tick (https://github.com/X-DataInitiative/tick) - Module for statistical learning, with a particular emphasis on time-dependent modeling.  
-⟡ 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
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-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. 
-⟡ 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. 
-⟡ d3rlpy (https://github.com/takuseno/d3rlpy) - An offline deep reinforcement learning library.
-⟡ DI-engine (https://github.com/opendilab/DI-engine) - OpenDILab Decision AI Engine. 
-⟡ TF-Agents (https://github.com/tensorflow/agents) - A library for Reinforcement Learning in TensorFlow. 
-⟡ TensorForce (https://github.com/reinforceio/tensorforce) - A TensorFlow library for applied reinforcement learning. 
-⟡ TRFL (https://github.com/deepmind/trfl) - TensorFlow Reinforcement Learning. 
-⟡ 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. 
-⟡ 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. 
-⟡ 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. 
-⟡ 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. 
-⟡ Imitation (https://github.com/HumanCompatibleAI/imitation) - Clean PyTorch implementations of imitation and reward learning algorithms. 
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-Graph Machine Learning
-⟡ pytorch_geometric (https://github.com/rusty1s/pytorch_geometric) - Geometric Deep Learning Extension Library for PyTorch. 
-⟡ pytorch_geometric_temporal (https://github.com/benedekrozemberczki/pytorch_geometric_temporal) - Temporal Extension Library for PyTorch Geometric. 
-⟡ PyTorch Geometric Signed Directed (https://github.com/SherylHYX/pytorch_geometric_signed_directed) -  A signed/directed graph neural network extension library for PyTorch Geometric. 
-⟡ dgl (https://github.com/dmlc/dgl) - Python package built to ease deep learning on graph, on top of existing DL frameworks. 
-⟡ Spektral (https://github.com/danielegrattarola/spektral) - Deep learning on graphs. 
-⟡ StellarGraph (https://github.com/stellargraph/stellargraph) - Machine Learning on Graphs. 
-⟡ Graph Nets (https://github.com/google-deepmind/graph_nets) - Build Graph Nets in Tensorflow. 
-⟡ TensorFlow GNN (https://github.com/tensorflow/gnn) - A library to build Graph Neural Networks on the TensorFlow platform. 
-⟡ 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. 
-⟡ 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). 
-⟡ Jraph (https://github.com/google-deepmind/jraph) - A Graph Neural Network Library in Jax.
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-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. 
-⟡ allRank (https://github.com/allegro/allRank) - allRank is a framework for training learning-to-rank neural models based on PyTorch. 
-⟡ TensorFlow Recommenders (https://github.com/tensorflow/recommenders) - A library for building recommender system models using TensorFlow. 
-⟡ TensorFlow Ranking (https://github.com/tensorflow/ranking) - Learning to Rank in TensorFlow.  
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-Probabilistic Graphical Models
-⟡ pomegranate (https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python. 
-⟡ pgmpy (https://github.com/pgmpy/pgmpy) - A python library for working with Probabilistic Graphical Models.
-⟡ pyAgrum (https://agrum.gitlab.io/) - A GRaphical Universal Modeler.
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-Probabilistic Methods
-⟡ pyro (https://github.com/uber/pyro) - A flexible, scalable deep probabilistic programming library built on PyTorch. 
-⟡ PyMC (https://github.com/pymc-devs/pymc) - Bayesian Stochastic Modelling in Python.
-⟡ ZhuSuan (http://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. 
-⟡ GPflow (http://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in TensorFlow. 
-⟡ InferPy (https://github.com/PGM-Lab/InferPy) - Deep Probabilistic Modelling Made Easy.  
-⟡ 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. 
-⟡ 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/). 
-⟡ PyVarInf (https://github.com/ctallec/pyvarinf) - Bayesian Deep Learning methods with Variational Inference for PyTorch. 
-⟡ 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. 
-⟡ sklearn-crfsuite (https://github.com/TeamHG-Memex/sklearn-crfsuite) - A scikit-learn-inspired API for CRFsuite. 
-
-Model Explanation
-
-⟡ dalex (https://github.com/ModelOriented/DALEX) - moDel Agnostic Language for Exploration and explanation. 
-⟡ 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). 
-⟡ yellowbrick (https://github.com/DistrictDataLabs/yellowbrick) - Visual analysis and diagnostic tools to facilitate machine learning model selection. 
-⟡ scikit-plot (https://github.com/reiinakano/scikit-plot) - An intuitive library to add plotting functionality to scikit-learn objects. 
-⟡ shap (https://github.com/slundberg/shap) - A unified approach to explain the output of any machine learning model. 
-⟡ 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. 
-⟡ FairML (https://github.com/adebayoj/fairml) - FairML is a python toolbox auditing the machine learning models for bias. 
-⟡ 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. 
-⟡ themis-ml (https://github.com/cosmicBboy/themis-ml) - A library that implements fairness-aware machine learning algorithms. 
-⟡ treeinterpreter (https://github.com/andosa/treeinterpreter) - Interpreting scikit-learn's decision tree and random forest predictions. 
-⟡ 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, ...).
-⟡ mxboard (https://github.com/awslabs/mxboard) - Logging MXNet data for visualization in TensorBoard. 
-
-Genetic Programming
-⟡ gplearn (https://github.com/trevorstephens/gplearn) - Genetic Programming in Python. 
-⟡ 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. 
-⟡ 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. 
-
-
-Optimization
-⟡ Optuna (https://github.com/optuna/optuna) - A hyperparameter optimization framework.
-⟡ Spearmint (https://github.com/HIPS/Spearmint) - Bayesian optimization.
-⟡ BoTorch (https://github.com/pytorch/botorch) - Bayesian optimization in PyTorch. 
-⟡ 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. 
-⟡ 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. 
-⟡ sklearn-deap (https://github.com/rsteca/sklearn-deap) - Use evolutionary algorithms instead of gridsearch in scikit-learn. 
-⟡ sigopt_sklearn (https://github.com/sigopt/sigopt_sklearn) - SigOpt wrappers for scikit-learn methods. 
-⟡ 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. 
-⟡ 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. 
-⟡ 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. 
-⟡ Feature Forge (https://github.com/machinalis/featureforge) - A set of tools for creating and testing machine learning features. 
-⟡ few (https://github.com/lacava/few) - A feature engineering wrapper for sklearn. 
-⟡ scikit-mdr (https://github.com/EpistasisLab/scikit-mdr) - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction. 
-⟡ tsfresh (https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series. 
-⟡ dirty_cat (https://github.com/dirty-cat/dirty_cat) - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression). 
-⟡ NitroFE (https://github.com/NITRO-AI/NitroFE) - Moving window features. 
-⟡ 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 
-
-
-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. 
-⟡ BoostARoota (https://github.com/chasedehan/BoostARoota) - A fast xgboost feature selection algorithm. 
-⟡ 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. 
-⟡ 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.
-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
-
-
-Statistics
-⟡ pandas_summary (https://github.com/mouradmourafiq/pandas-summary) - Extension to pandas dataframes describe function. 
-⟡ Pandas Profiling (https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects. 
-⟡ 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. 
-⟡ 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. 
-⟡ blaze (https://github.com/blaze/blaze) - NumPy and pandas interface to Big Data. 
-⟡ pandasql (https://github.com/yhat/pandasql) -  Allows you to query pandas DataFrames using SQL syntax. 
-⟡ pandas-gbq (https://github.com/pydata/pandas-gbq) - pandas Google Big Query. 
-⟡ 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. 
-⟡ modin (https://github.com/modin-project/modin) - Speed up your pandas workflows by changing a single line of code. 
-⟡ 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. 
-⟡ Dplython (https://github.com/dodger487/dplython) - Dplyr for Python. 
-⟡ sklearn-pandas (https://github.com/scikit-learn-contrib/sklearn-pandas) - pandas integration with sklearn. 
-⟡ 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. 
-⟡ 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. 
-⟡ 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. 
-
-Distributed Computing
-⟡ Horovod (https://github.com/uber/horovod) - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. 
-⟡ PySpark (https://spark.apache.org/docs/0.9.0/python-programming-guide.html) - Exposes the Spark programming model to Python. 
-⟡ 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. 
-⟡ 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. 
-
-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. 
-⟡ 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.
-
-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. 
-⟡ 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.
-
-Computations
-⟡ numpy (http://www.numpy.org/) - The fundamental package needed for scientific computing with Python.
-⟡ Dask (https://github.com/dask/dask) - Parallel computing with task scheduling. 
-⟡ 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. 
-⟡ 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: 
-Read the .
-
-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/)