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Awesome Python Data Science

Probably the best curated list of data science software in Python

Contents

• Contents
• Machine Learning
  • General Purpose Machine Learning
  • Gradient Boosting
  • Ensemble Methods
  • Imbalanced Datasets
  • Random Forests
  • Kernel Methods
• Deep Learning
  • PyTorch
  • TensorFlow
  • JAX
  • Others
• Automated Machine Learning
• Natural Language Processing
• Computer Audition
• Computer Vision
• Time Series
• Reinforcement Learning
• Graph Machine Learning
• Learning-to-Rank & Recommender Systems
• Probabilistic Graphical Models
• Probabilistic Methods
• Model Explanation
• Optimization
• Genetic Programming
• Feature Engineering
  • General
  • Feature Selection
• Visualization
  • General Purposes
  • Interactive plots
  • Map
  • Automatic Plotting
  • NLP
• Data Manipulation
  • Data Frames
  • Pipelines
  • Data-centric AI
  • Synthetic Data
• Deployment
• Statistics
• Distributed Computing
• Experimentation
• Data Validation
• Evaluation
• Computations
• Web Scraping
• Spatial Analysis
• Quantum Computing
• Conversion
• Contributing
• License

Machine Learning

General Purpose Machine Learning

• scikit-learn - Machine learning in Python.
• PyCaret - An open-source, low-code machine learning library in Python.
• Shogun - Machine learning toolbox.
• xLearn - High Performance, Easy-to-use, and Scalable Machine Learning Package.
• cuML - RAPIDS Machine Learning Library.
• modAL - Modular active learning framework for Python3.
• Sparkit-learn - PySpark + scikit-learn = Sparkit-learn.
• mlpack - A scalable C++ machine learning library (Python bindings).
• dlib - Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).
• MLxtend - Extension and helper modules for Python’s data analysis and machine learning libraries.
• hyperlearn - 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels.
• Reproducible Experiment Platform (REP) - Machine Learning toolbox for Humans.
• scikit-multilearn - Multi-label classification for python.
• seqlearn - Sequence classification toolkit for Python.
• pystruct - Simple structured learning framework for Python.
• sklearn-expertsys - Highly interpretable classifiers for scikit learn.
• RuleFit - Implementation of the rulefit.
• metric-learn - Metric learning algorithms in Python.
• pyGAM - Generalized Additive Models in Python.
• causalml - Uplift modeling and causal inference with machine learning algorithms.

Gradient Boosting

• XGBoost - Scalable, Portable, and Distributed Gradient Boosting.
• LightGBM - A fast, distributed, high-performance gradient boosting.
• CatBoost - An open-source gradient boosting on decision trees library.
• ThunderGBM - Fast GBDTs and Random Forests on GPUs.
• NGBoost - Natural Gradient Boosting for Probabilistic Prediction.
• TensorFlow Decision Forests - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

Ensemble Methods

• ML-Ensemble - High performance ensemble learning.
• Stacking - Simple and useful stacking library written in Python.
• stacked_generalization - Library for machine learning stacking generalization.
• vecstack - Python package for stacking (machine learning technique).

Imbalanced Datasets

• imbalanced-learn - Module to perform under-sampling and over-sampling with various techniques.
• imbalanced-algorithms - Python-based implementations of algorithms for learning on imbalanced data.

Random Forests

• rpforest - A forest of random projection trees.
• sklearn-random-bits-forest - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).
• rgf_python - Python Wrapper of Regularized Greedy Forest.

Kernel Methods

• pyFM - Factorization machines in python.
• fastFM - A library for Factorization Machines.
• tffm - TensorFlow implementation of an arbitrary order Factorization Machine.
• liquidSVM - An implementation of SVMs.
• scikit-rvm - Relevance Vector Machine implementation using the scikit-learn API.
• ThunderSVM - A fast SVM Library on GPUs and CPUs.

Deep Learning

PyTorch

• PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration.
• pytorch-lightning - PyTorch Lightning is just organized PyTorch.
• ignite - High-level library to help with training neural networks in PyTorch.
• skorch - A scikit-learn compatible neural network library that wraps PyTorch.
• Catalyst - High-level utils for PyTorch DL & RL research.
• ChemicalX - A PyTorch-based deep learning library for drug pair scoring.

TensorFlow

• TensorFlow - Computation using data flow graphs for scalable machine learning by Google.
• TensorLayer - Deep Learning and Reinforcement Learning Library for Researcher and Engineer.
• TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
• Sonnet - TensorFlow-based neural network library.
• tensorpack - A Neural Net Training Interface on TensorFlow.
• Polyaxon - A platform that helps you build, manage and monitor deep learning models.
• tfdeploy - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy.
• tensorflow-upstream - TensorFlow ROCm port.
• TensorFlow Fold - Deep learning with dynamic computation graphs in TensorFlow.
• TensorLight - A high-level framework for TensorFlow.
• Mesh TensorFlow - Model Parallelism Made Easier.
• Ludwig - A toolbox that allows one to train and test deep learning models without the need to write code.
• Keras - A high-level neural networks API running on top of TensorFlow.
• keras-contrib - Keras community contributions.
• Hyperas - Keras + Hyperopt: A straightforward wrapper for a convenient hyperparameter.
• Elephas - Distributed Deep learning with Keras & Spark.
• qkeras - A quantization deep learning library.

JAX

• JAX - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
• FLAX - A neural network library for JAX that is designed for flexibility.
• Optax - A gradient processing and optimization library for JAX.

Others

• transformers - State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
• Tangent - Source-to-Source Debuggable Derivatives in Pure Python.
• autograd - Efficiently computes derivatives of numpy code.
• Caffe - A fast open framework for deep learning.
• nnabla - Neural Network Libraries by Sony.

Automated Machine Learning

• auto-sklearn - An AutoML toolkit and a drop-in replacement for a scikit-learn estimator.
• Auto-PyTorch - Automatic architecture search and hyperparameter optimization for PyTorch.
• AutoKeras - AutoML library for deep learning.
• AutoGluon - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
• TPOT - AutoML tool that optimizes machine learning pipelines using genetic programming.
• MLBox - A powerful Automated Machine Learning python library.

Natural Language Processing

• torchtext - Data loaders and abstractions for text and NLP.
• KerasNLP - Modular Natural Language Processing workflows with Keras.
• spaCy - Industrial-Strength Natural Language Processing.
• NLTK - Modules, data sets, and tutorials supporting research and development in Natural Language Processing.
• CLTK - The Classical Language Toolkik.
• gensim - Topic Modelling for Humans.
• pyMorfologik - Python binding for Morfologik.
• skift - Scikit-learn wrappers for Python fastText.
• Phonemizer - Simple text-to-phonemes converter for multiple languages.
• flair - Very simple framework for state-of-the-art NLP.

Computer Audition

• torchaudio - An audio library for PyTorch.
• librosa - Python library for audio and music analysis.
• Yaafe - Audio features extraction.
• aubio - A library for audio and music analysis.
• Essentia - Library for audio and music analysis, description, and synthesis.
• LibXtract - A simple, portable, lightweight library of audio feature extraction functions.
• Marsyas - Music Analysis, Retrieval, and Synthesis for Audio Signals.
• muda - A library for augmenting annotated audio data.
• madmom - Python audio and music signal processing library.

Computer Vision

• torchvision - Datasets, Transforms, and Models specific to Computer Vision.
• PyTorch3D - PyTorch3D is FAIR’s library of reusable components for deep learning with 3D data.
• KerasCV - Industry-strength Computer Vision workflows with Keras.
• OpenCV - Open Source Computer Vision Library.
• Decord - An efficient video loader for deep learning with smart shuffling that’s super easy to digest.
• MMEngine - OpenMMLab Foundational Library for Training Deep Learning Models.
• scikit-image - Image Processing SciKit (Toolbox for SciPy).
• imgaug - Image augmentation for machine learning experiments.
• imgaug_extension - Additional augmentations for imgaug.
• Augmentor - Image augmentation library in Python for machine learning.
• albumentations - Fast image augmentation library and easy-to-use wrapper around other libraries.
• LAVIS - A One-stop Library for Language-Vision Intelligence.

Time Series

• sktime - A unified framework for machine learning with time series.
• skforecast - Time series forecasting with machine learning models
• darts - A python library for easy manipulation and forecasting of time series.
• statsforecast - Lightning fast forecasting with statistical and econometric models.
• mlforecast - Scalable machine learning-based time series forecasting.
• neuralforecast - Scalable machine learning-based time series forecasting.
• tslearn - Machine learning toolkit dedicated to time-series data.
• tick - Module for statistical learning, with a particular emphasis on time-dependent modeling.
• greykite - A flexible, intuitive, and fast forecasting library next.
• Prophet - Automatic Forecasting Procedure.
• PyFlux - Open source time series library for Python.
• bayesloop - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
• luminol - Anomaly Detection and Correlation library.
• dateutil - Powerful extensions to the standard datetime module
• maya - makes it very easy to parse a string and for changing timezones
• Chaos Genius - ML powered analytics engine for outlier/anomaly detection and root cause analysis

Reinforcement Learning

• Gymnasium - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym).
• PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities.
• MAgent2 - An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments.
• Stable Baselines3 - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
• Shimmy - An API conversion tool for popular external reinforcement learning environments.
• EnvPool - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
• RLlib - Scalable Reinforcement Learning.
• Tianshou - An elegant PyTorch deep reinforcement learning library.
• Acme - A library of reinforcement learning components and agents.
• Catalyst-RL - PyTorch framework for RL research.
• d3rlpy - An offline deep reinforcement learning library.
• DI-engine - OpenDILab Decision AI Engine.
• TF-Agents - A library for Reinforcement Learning in TensorFlow.
• TensorForce - A TensorFlow library for applied reinforcement learning.
• TRFL - TensorFlow Reinforcement Learning.
• Dopamine - A research framework for fast prototyping of reinforcement learning algorithms.
• keras-rl - Deep Reinforcement Learning for Keras.
• garage - A toolkit for reproducible reinforcement learning research.
• Horizon - A platform for Applied Reinforcement Learning.
• rlpyt - Reinforcement Learning in PyTorch.
• cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG).
• Machin - A reinforcement library designed for pytorch.
• SKRL - Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym.
• Imitation - Clean PyTorch implementations of imitation and reward learning algorithms.

Graph Machine Learning

• pytorch_geometric - Geometric Deep Learning Extension Library for PyTorch.
• pytorch_geometric_temporal - Temporal Extension Library for PyTorch Geometric.
• PyTorch Geometric Signed Directed - A signed/directed graph neural network extension library for PyTorch Geometric.
• dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
• Spektral - Deep learning on graphs.
• StellarGraph - Machine Learning on Graphs.
• Graph Nets - Build Graph Nets in Tensorflow.
• TensorFlow GNN - A library to build Graph Neural Networks on the TensorFlow platform.
• Auto Graph Learning -An autoML framework & toolkit for machine learning on graphs.
• PyTorch-BigGraph - Generate embeddings from large-scale graph-structured data.
• Auto Graph Learning - An autoML framework & toolkit for machine learning on graphs.
• Karate Club - An unsupervised machine learning library for graph-structured data.
• Little Ball of Fur - A library for sampling graph structured data.
• GreatX - A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).
• Jraph - A Graph Neural Network Library in Jax.

Learning-to-Rank & Recommender Systems

• LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
• Spotlight - Deep recommender models using PyTorch.
• Surprise - A Python scikit for building and analyzing recommender systems.
• RecBole - A unified, comprehensive and efficient recommendation library.
• allRank - allRank is a framework for training learning-to-rank neural models based on PyTorch.
• TensorFlow Recommenders - A library for building recommender system models using TensorFlow.
• TensorFlow Ranking - Learning to Rank in TensorFlow.

Probabilistic Graphical Models

• pomegranate - Probabilistic and graphical models for Python.
• pgmpy - A python library for working with Probabilistic Graphical Models.
• pyAgrum - A GRaphical Universal Modeler.

Probabilistic Methods

• pyro - A flexible, scalable deep probabilistic programming library built on PyTorch.
• PyMC - Bayesian Stochastic Modelling in Python.
• ZhuSuan - Bayesian Deep Learning.
• GPflow - Gaussian processes in TensorFlow.
• InferPy - Deep Probabilistic Modelling Made Easy.
• PyStan - Bayesian inference using the No-U-Turn sampler (Python interface).
• sklearn-bayes - Python package for Bayesian Machine Learning with scikit-learn API.
• skpro - Supervised domain-agnostic prediction framework for probabilistic modelling by The Alan Turing Institute.
• PyVarInf - Bayesian Deep Learning methods with Variational Inference for PyTorch.
• emcee - The Python ensemble sampling toolkit for affine-invariant MCMC.
• hsmmlearn - A library for hidden semi-Markov models with explicit durations.
• pyhsmm - Bayesian inference in HSMMs and HMMs.
• GPyTorch - A highly efficient and modular implementation of Gaussian Processes in PyTorch.
• sklearn-crfsuite - A scikit-learn-inspired API for CRFsuite.

Model Explanation

• dalex - moDel Agnostic Language for Exploration and explanation.
• Shapley - A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
• Alibi - Algorithms for monitoring and explaining machine learning models.
• anchor - Code for “High-Precision Model-Agnostic Explanations” paper.
• aequitas - Bias and Fairness Audit Toolkit.
• Contrastive Explanation - Contrastive Explanation (Foil Trees).
• yellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection.
• scikit-plot - An intuitive library to add plotting functionality to scikit-learn objects.
• shap - A unified approach to explain the output of any machine learning model.
• ELI5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions.
• Lime - Explaining the predictions of any machine learning classifier.
• FairML - FairML is a python toolbox auditing the machine learning models for bias.
• L2X - Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.
• PDPbox - Partial dependence plot toolbox.
• PyCEbox - Python Individual Conditional Expectation Plot Toolbox.
• Skater - Python Library for Model Interpretation.
• model-analysis - Model analysis tools for TensorFlow.
• themis-ml - A library that implements fairness-aware machine learning algorithms.
• treeinterpreter - Interpreting scikit-learn’s decision tree and random forest predictions.
• AI Explainability 360 - Interpretability and explainability of data and machine learning models.
• Auralisation - Auralisation of learned features in CNN (for audio).
• CapsNet-Visualization - A visualization of the CapsNet layers to better understand how it works.
• lucid - A collection of infrastructure and tools for research in neural network interpretability.
• Netron - Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).
• FlashLight - Visualization Tool for your NeuralNetwork.
• tensorboard-pytorch - Tensorboard for PyTorch (and chainer, mxnet, numpy, …).

Genetic Programming

• gplearn - Genetic Programming in Python.
• PyGAD - Genetic Algorithm in Python.
• DEAP - Distributed Evolutionary Algorithms in Python.
• karoo_gp - A Genetic Programming platform for Python with GPU support.
• monkeys - A strongly-typed genetic programming framework for Python.
• sklearn-genetic - Genetic feature selection module for scikit-learn.

## Optimization * Optuna - A hyperparameter optimization framework. * pymoo - Multi-objective Optimization in Python. * pycma - Python implementation of CMA-ES. * Spearmint - Bayesian optimization. * BoTorch - Bayesian optimization in PyTorch. * scikit-opt - Heuristic Algorithms for optimization. * sklearn-genetic-opt - Hyperparameters tuning and feature selection using evolutionary algorithms. * SMAC3 - Sequential Model-based Algorithm Configuration. * Optunity - Is a library containing various optimizers for hyperparameter tuning. * hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python. * hyperopt-sklearn - Hyper-parameter optimization for sklearn. * sklearn-deap - Use evolutionary algorithms instead of gridsearch in scikit-learn. * sigopt_sklearn - SigOpt wrappers for scikit-learn methods. * Bayesian Optimization - A Python implementation of global optimization with gaussian processes. * SafeOpt - Safe Bayesian Optimization. * scikit-optimize - Sequential model-based optimization with a scipy.optimize interface. * Solid - A comprehensive gradient-free optimization framework written in Python. * PySwarms - A research toolkit for particle swarm optimization in Python. * Platypus - A Free and Open Source Python Library for Multiobjective Optimization. * GPflowOpt - Bayesian Optimization using GPflow. * POT - Python Optimal Transport library. * Talos - Hyperparameter Optimization for Keras Models. * nlopt - Library for nonlinear optimization (global and local, constrained or unconstrained). * OR-Tools - 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 - Automated feature engineering.
• Feature Engine - Feature engineering package with sklearn-like functionality.
• OpenFE - Automated feature generation with expert-level performance.
• skl-groups - A scikit-learn addon to operate on set/“group”-based features.
• Feature Forge - A set of tools for creating and testing machine learning features.
• few - A feature engineering wrapper for sklearn.
• scikit-mdr - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.
• tsfresh - Automatic extraction of relevant features from time series.
• dirty_cat - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression).
• NitroFE - Moving window features.
• sk-transformer - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps

Feature Selection

• scikit-feature - Feature selection repository in Python.
• boruta_py - Implementations of the Boruta all-relevant feature selection method.
• BoostARoota - A fast xgboost feature selection algorithm.
• scikit-rebate - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
• zoofs - A feature selection library based on evolutionary algorithms.

Visualization

General Purposes

• Matplotlib - Plotting with Python.
• seaborn - Statistical data visualization using matplotlib.
• prettyplotlib - Painlessly create beautiful matplotlib plots.
• python-ternary - Ternary plotting library for Python with matplotlib.
• missingno - Missing data visualization module for Python.
• chartify - Python library that makes it easy for data scientists to create charts.
• physt - Improved histograms. ### Interactive plots
• animatplot - A python package for animating plots built on matplotlib.
• plotly - A Python library that makes interactive and publication-quality graphs.
• Bokeh - Interactive Web Plotting for Python.
• Altair - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
• bqplot - Plotting library for IPython/Jupyter notebooks
• pyecharts - Migrated from Echarts, a charting and visualization library, to Python’s interactive visual drawing library. ### Map
• folium - Makes it easy to visualize data on an interactive open street map
• geemap - Python package for interactive mapping with Google Earth Engine (GEE) ### Automatic Plotting
• HoloViews - Stop plotting your data - annotate your data and let it visualize itself.
• AutoViz: Visualize data automatically with 1 line of code (ideal for machine learning)
• SweetViz: Visualize and compare datasets, target values and associations, with one line of code.

NLP

• pyLDAvis: Visualize interactive topic model

Deployment

• fastapi - Modern, fast (high-performance), a web framework for building APIs with Python
• streamlit - Make it easy to deploy the machine learning model
• streamsync - No-code in the front, Python in the back. An open-source framework for creating data apps.
• gradio - Create UIs for your machine learning model in Python in 3 minutes.
• Vizro - A toolkit for creating modular data visualization applications.
• datapane - A collection of APIs to turn scripts and notebooks into interactive reports.
• binder - Enable sharing and execute Jupyter Notebooks

Statistics

• pandas_summary - Extension to pandas dataframes describe function.
• Pandas Profiling - Create HTML profiling reports from pandas DataFrame objects.
• statsmodels - Statistical modeling and econometrics in Python.
• stockstats - Supply a wrapper StockDataFrame based on the pandas.DataFrame with inline stock statistics/indicators support.
• weightedcalcs - A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.
• scikit-posthocs - Pairwise Multiple Comparisons Post-hoc Tests.
• Alphalens - Performance analysis of predictive (alpha) stock factors.

Data Manipulation

Data Frames

• pandas - Powerful Python data analysis toolkit.
• polars - A fast multi-threaded, hybrid-out-of-core DataFrame library.
• Arctic - High-performance datastore for time series and tick data.
• datatable - Data.table for Python.
• pandas_profiling - Create HTML profiling reports from pandas DataFrame objects
• cuDF - GPU DataFrame Library.
• blaze - NumPy and pandas interface to Big Data.
• pandasql - Allows you to query pandas DataFrames using SQL syntax.
• pandas-gbq - pandas Google Big Query.
• xpandas - Universal 1d/2d data containers with Transformers .functionality for data analysis by The Alan Turing Institute.
• pysparkling - A pure Python implementation of Apache Spark’s RDD and DStream interfaces.
• modin - Speed up your pandas workflows by changing a single line of code.
• swifter - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
• pandas-log - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
• vaex - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
• 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 - Sasy pipelines for pandas DataFrames.
• SSPipe - Python pipe (|) operator with support for DataFrames and Numpy, and Pytorch.
• pandas-ply - Functional data manipulation for pandas.
• Dplython - Dplyr for Python.
• sklearn-pandas - pandas integration with sklearn.
• Dataset - Helps you conveniently work with random or sequential batches of your data and define data processing.
• pyjanitor - Clean APIs for data cleaning.
• meza - A Python toolkit for processing tabular data.
• Prodmodel - Build system for data science pipelines.
• dopanda - Hints and tips for using pandas in an analysis environment.
• Hamilton - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.

Data-centric AI

• cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
• snorkel - A system for quickly generating training data with weak supervision.
• dataprep - Collect, clean, and visualize your data in Python with a few lines of code.

Synthetic Data

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

Distributed Computing

• Horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
• PySpark - Exposes the Spark programming model to Python.
• Veles - Distributed machine learning platform.
• Jubatus - Framework and Library for Distributed Online Machine Learning.
• DMTK - Microsoft Distributed Machine Learning Toolkit.
• PaddlePaddle - PArallel Distributed Deep LEarning.
• dask-ml - Distributed and parallel machine learning.
• Distributed - Distributed computation in Python.

Experimentation

• mlflow - Open source platform for the machine learning lifecycle.
• Neptune - A lightweight ML experiment tracking, results visualization, and management tool.
• dvc - Data Version Control | Git for Data & Models | ML Experiments Management.
• envd - 🏕️ machine learning development environment for data science and AI/ML engineering teams.
• Sacred - A tool to help you configure, organize, log, and reproduce experiments.
• Ax - Adaptive Experimentation Platform.

Data Validation

• great_expectations - Always know what to expect from your data.
• pandera - A lightweight, flexible, and expressive statistical data testing library.
• deepchecks - Validation & testing of ML models and data during model development, deployment, and production.
• evidently - Evaluate and monitor ML models from validation to production.
• TensorFlow Data Validation - Library for exploring and validating machine learning data.
• DataComPy- A library to compare Pandas, Polars, and Spark data frames. It provides stats and lets users adjust for match accuracy.

Evaluation

• recmetrics - Library of useful metrics and plots for evaluating recommender systems.
• Metrics - Machine learning evaluation metric.
• sklearn-evaluation - Model evaluation made easy: plots, tables, and markdown reports.
• AI Fairness 360 - Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.

Computations

• numpy - The fundamental package needed for scientific computing with Python.
• Dask - Parallel computing with task scheduling.
• bottleneck - Fast NumPy array functions written in C.
• CuPy - NumPy-like API accelerated with CUDA.
• scikit-tensor - Python library for multilinear algebra and tensor factorizations.
• numdifftools - Solve automatic numerical differentiation problems in one or more variables.
• quaternion - Add built-in support for quaternions to numpy.
• adaptive - Tools for adaptive and parallel samping of mathematical functions.
• 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: The easiest library to scrape static websites for beginners
• Scrapy: Fast and extensible scraping library. Can write rules and create customized scraper without touching the core
• Selenium: Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.
• Pattern: High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization
• twitterscraper: Efficient library to scrape Twitter

Spatial Analysis

• GeoPandas - Python tools for geographic data.
• PySal - Python Spatial Analysis Library.

Quantum Computing

• qiskit - Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.
• cirq - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
• PennyLane - Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
• QML - A Python Toolkit for Quantum Machine Learning.

Conversion

• sklearn-porter - Transpile trained scikit-learn estimators to C, Java, JavaScript, and others.
• ONNX - Open Neural Network Exchange.
• MMdnn - A set of tools to help users inter-operate among different deep learning frameworks.
• treelite - Universal model exchange and serialization format for decision tree forests.

Contributing

Contributions are welcome! :sunglasses:
Read the contribution guideline.

License

This work is licensed under the Creative Commons Attribution 4.0 International License - CC BY 4.0

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