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Python - Data Science

Awesome Python Data Science

Probably the best curated list of data science software in Python


Machine Learning

General Purpouse Machine Learning

  • scikit-learn - Machine learning 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.
  • Karate Club - An unsupervised machine learning library for graph structured data.
  • Little Ball of Fur - A library for sampling graph structured data.
  • causalml - Uplift modeling and causal inference with machine learning algorithms.
  • Deepchecks - Validation & testing of ML models and data during model development, deployment, and production.

Automated Machine Learning

  • TPOT - Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
  • auto-sklearn - An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
  • MLBox - A powerful Automated Machine Learning python library.

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

Extreme Learning Machine

  • Python-ELM - Extreme Learning Machine implementation in Python.
  • Python Extreme Learning Machine (ELM) - A machine learning technique used for classification/regression tasks.
  • hpelm - High performance implementation of Extreme Learning Machines (fast randomized neural networks).

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.

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.

Deep Learning


  • PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration.
  • torchvision - Datasets, Transforms and Models specific to Computer Vision.
  • torchtext - Data loaders and abstractions for text and NLP.
  • torchaudio - An audio library for PyTorch.
  • ignite - High-level library to help with training neural networks in PyTorch.
  • PyToune - A Keras-like framework and utilities for PyTorch.
  • skorch - A scikit-learn compatible neural network library that wraps pytorch.
  • PyTorchNet - An abstraction to train neural networks.
  • pytorch_geometric - Geometric Deep Learning Extension Library for PyTorch.
  • Catalyst - High-level utils for PyTorch DL & RL research.
  • pytorch_geometric_temporal - Temporal Extension Library for PyTorch Geometric.


  • 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.
  • NeuPy - NeuPy is a Python library for Artificial Neural Networks and Deep Learning (previously:).
  • 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.
  • tensorlm - Wrapper library for text generation / language models at char and word level with RNN.
  • TensorLight - A high-level framework for TensorFlow.
  • Mesh TensorFlow - Model Parallelism Made Easier.
  • Ludwig - A toolbox, that allows 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 very simple wrapper for convenient hyperparameter.
  • Elephas - Distributed Deep learning with Keras & Spark.
  • Hera - Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.
  • Spektral - Deep learning on graphs.
  • qkeras - A quantization deep learning library.


  • MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler.
  • Gluon - A clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet).
  • MXbox - Simple, efficient and flexible vision toolbox for mxnet framework.
  • gluon-cv - Provides implementations of the state-of-the-art deep learning models in computer vision.
  • gluon-nlp - NLP made easy.
  • Xfer - Transfer Learning library for Deep Neural Networks.
  • MXNet - HIP Port of MXNet.


  • Tangent - Source-to-Source Debuggable Derivatives in Pure Python.
  • autograd - Efficiently computes derivatives of numpy code.
  • Myia - Deep Learning framework (pre-alpha).
  • nnabla - Neural Network Libraries by Sony.
  • Caffe - A fast open framework for deep learning.
  • hipCaffe - The HIP port of Caffe.


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 coure
  • 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

Data Manipulation

Data Containers

  • pandas - Powerful Python data analysis toolkit.
  • 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.
  • Arctic - High performance datastore for time series and tick data.
  • datatable - Data.table for Python.
  • koalas - pandas API on Apache Spark.
  • modin - Speed up your pandas workflows by changing a single line of code.
  • swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner.
  • pandas_flavor - A package which allow to write your own flavor of Pandas easily.
  • pandas-log - A package which allow to provide feedback about basic pandas operations and find both buisness logic and performance issues.
  • vaex - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.


  • 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.
  • CircleCi: Automates your software builds, tests, and deployments.

Feature Engineering


  • Featuretools - Automated feature engineering.
  • 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 feature.
  • 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.

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.


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 build 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.


  • 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.


  • pyLDAvis: Visualize interactive topic model


  • datapane - A collection of APIs to turn scripts and notebooks into interactive reports.
  • binder - Enable sharing and execute Jupyter Notebooks
  • fastapi - Modern, fast (high-performance), web framework for building APIs with Python
  • streamlit - Make it easy to deploy machine learning model

Model 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.
  • pyBreakDown - Python implementation of R package breakDown.
  • 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, ...).
  • mxboard - Logging MXNet data for visualization in TensorBoard.

Reinforcement Learning

  • OpenAI Gym - A toolkit for developing and comparing reinforcement learning algorithms.
  • Coach - Easy experimentation with state of the art Reinforcement Learning algorithms.
  • garage - A toolkit for reproducible reinforcement learning research.
  • OpenAI Baselines - High-quality implementations of reinforcement learning algorithms.
  • Stable Baselines - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
  • RLlib - Scalable Reinforcement Learning.
  • Horizon - A platform for Applied Reinforcement Learning.
  • 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.
  • ChainerRL - A deep reinforcement learning library built on top of Chainer.

Probabilistic Methods

  • pomegranate - Probabilistic and graphical models for Python.
  • pyro - A flexible, scalable deep probabilistic programming library built on PyTorch.
  • ZhuSuan - Bayesian Deep Learning.
  • PyMC - Bayesian Stochastic Modelling in Python.
  • PyMC3 - Python package for Bayesian statistical modeling and Probabilistic Machine Learning.
  • sampled - Decorator for reusable models in PyMC3.
  • Edward - A library for probabilistic modeling, inference, and criticism.
  • InferPy - Deep Probabilistic Modelling Made Easy.
  • GPflow - Gaussian processes in TensorFlow.
  • PyStan - Bayesian inference using the No-U-Turn sampler (Python interface).
  • sklearn-bayes - Python package for Bayesian Machine Learning with scikit-learn API.
  • skggm - Estimation of general graphical models.
  • pgmpy - A python library for working with Probabilistic Graphical Models.
  • skpro - Supervised domain-agnostic prediction framework for probabilistic modelling by The Alan Turing Institute.
  • Aboleth - A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation.
  • PtStat - Probabilistic Programming and Statistical Inference in PyTorch.
  • 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.
  • MXFusion - Modular Probabilistic Programming on MXNet.
  • sklearn-crfsuite - A scikit-learn inspired API for CRFsuite.

Genetic Programming

  • gplearn - Genetic Programming 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.


  • Spearmint - Bayesian optimization.
  • BoTorch - Bayesian optimization in PyTorch.
  • scikit-opt - Heuristic Algorithms for optimization.
  • 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).

Time Series

  • sktime - A unified framework for machine learning with time series.
  • tslearn - Machine learning toolkit dedicated to time-series data.
  • tick - Module for statistical learning, with a particular emphasis on time-dependent modelling.
  • 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

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.
  • PSI-Toolkit - A natural language processing toolkit.
  • 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.
  • spaCy - Industrial-Strength Natural Language Processing.

Computer Audition

  • 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

  • OpenCV - Open Source Computer Vision Library.
  • 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.


  • 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.

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.


  • Sacred - A tool to help you configure, organize, log and reproduce experiments.
  • Xcessiv - A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling.
  • Persimmon - A visual dataflow programming language for sklearn.
  • Ax - Adaptive Experimentation Platform.
  • Neptune - A lightweight ML experiment tracking, results visualization and management tool.


  • 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.


  • 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.

Spatial Analysis

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

Quantum Computing

  • PennyLane - Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
  • QML - A Python Toolkit for Quantum Machine Learning.


  • 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.


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

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