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, producing easily understood decision rules instead of black box models.
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.

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

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

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.

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

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

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

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.

Others

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.

DISCONTINUED PROJECTS

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.

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

Feature Engineering

General

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.

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 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.

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

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).
gelato – Bayesian dessert for Lasagne.
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.

Optimization

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).

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.

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.

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

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.

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.

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.

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.

Contributing

Contributions are welcome!
Read the contribution guideline.

License

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