1325 lines
68 KiB
HTML
1325 lines
68 KiB
HTML
<div data-align="center">
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<pre><code><a href="https://krzjoa.github.io/awesome-python-data-science/"><img width="250" height="250" src="img/py-datascience.png" alt="pyds"></a>
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<br>
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<br>
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<br></code></pre>
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</div>
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<h1 align="center">
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Awesome Python Data Science
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</h1>
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<div data-align="center">
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<p><a href="https://github.com/sindresorhus/awesome">
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<img src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg" alt="Awesome" border="0">
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</a></p>
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</div>
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<p></br></p>
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<blockquote>
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<p>Probably the best curated list of data science software in Python</p>
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</blockquote>
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<h2 id="contents">Contents</h2>
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<ul>
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<li><a href="#contents">Contents</a></li>
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<li><a href="#machine-learning">Machine Learning</a>
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<ul>
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<li><a href="#general-purpose-machine-learning">General Purpose Machine
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Learning</a></li>
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<li><a href="#gradient-boosting">Gradient Boosting</a></li>
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<li><a href="#ensemble-methods">Ensemble Methods</a></li>
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<li><a href="#imbalanced-datasets">Imbalanced Datasets</a></li>
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<li><a href="#random-forests">Random Forests</a></li>
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<li><a href="#kernel-methods">Kernel Methods</a></li>
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</ul></li>
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<li><a href="#deep-learning">Deep Learning</a>
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<ul>
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<li><a href="#pytorch">PyTorch</a></li>
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<li><a href="#tensorflow">TensorFlow</a></li>
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||
<li><a href="#jax">JAX</a></li>
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<li><a href="#others">Others</a></li>
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</ul></li>
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<li><a href="#automated-machine-learning">Automated Machine
|
||
Learning</a></li>
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||
<li><a href="#natural-language-processing">Natural Language
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Processing</a></li>
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||
<li><a href="#computer-audition">Computer Audition</a></li>
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||
<li><a href="#computer-vision">Computer Vision</a></li>
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||
<li><a href="#time-series">Time Series</a></li>
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||
<li><a href="#reinforcement-learning">Reinforcement Learning</a></li>
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||
<li><a href="#graph-machine-learning">Graph Machine Learning</a></li>
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||
<li><a
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href="#learning-to-rank-&-recommender-systems">Learning-to-Rank
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||
& Recommender Systems</a></li>
|
||
<li><a href="#probabilistic-graphical-models">Probabilistic Graphical
|
||
Models</a></li>
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||
<li><a href="#probabilistic-methods">Probabilistic Methods</a></li>
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||
<li><a href="#model-explanation">Model Explanation</a></li>
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||
<li><a href="#optimization">Optimization</a></li>
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||
<li><a href="#genetic-programming">Genetic Programming</a></li>
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||
<li><a href="#feature-engineering">Feature Engineering</a>
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<ul>
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<li><a href="#general">General</a></li>
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||
<li><a href="#feature-selection">Feature Selection</a></li>
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</ul></li>
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||
<li><a href="#visualization">Visualization</a>
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<ul>
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<li><a href="#general-purposes">General Purposes</a></li>
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<li><a href="#interactive-plots">Interactive plots</a></li>
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<li><a href="#map">Map</a></li>
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<li><a href="#automatic-plotting">Automatic Plotting</a></li>
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<li><a href="#nlp">NLP</a></li>
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</ul></li>
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<li><a href="#data-manipulation">Data Manipulation</a>
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<ul>
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<li><a href="#data-frames">Data Frames</a></li>
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<li><a href="#pipelines">Pipelines</a></li>
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<li><a href="#data-centric-ai">Data-centric AI</a></li>
|
||
<li><a href="#synthetic-data">Synthetic Data</a></li>
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</ul></li>
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<li><a href="#deployment">Deployment</a></li>
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||
<li><a href="#statistics">Statistics</a></li>
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||
<li><a href="#distributed-computing">Distributed Computing</a></li>
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||
<li><a href="#experimentation">Experimentation</a></li>
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<li><a href="#data-validation">Data Validation</a></li>
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||
<li><a href="#evaluation">Evaluation</a></li>
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||
<li><a href="#computations">Computations</a></li>
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||
<li><a href="#web-scraping">Web Scraping</a></li>
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||
<li><a href="#spatial-analysis">Spatial Analysis</a></li>
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||
<li><a href="#quantum-computing">Quantum Computing</a></li>
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<li><a href="#conversion">Conversion</a></li>
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<li><a href="#contributing">Contributing</a></li>
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||
<li><a href="#license">License</a></li>
|
||
</ul>
|
||
<h2 id="machine-learning">Machine Learning</h2>
|
||
<h3 id="general-purpose-machine-learning">General Purpose Machine
|
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Learning</h3>
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<ul>
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<li><a href="http://scikit-learn.org/stable/">scikit-learn</a> - Machine
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learning in Python.
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
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<li><a href="https://github.com/pycaret/pycaret">PyCaret</a> - An
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open-source, low-code machine learning library in Python.
|
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<img height="20" src="img/R_big.png" alt="R inspired lib"></li>
|
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<li><a href="https://github.com/shogun-toolbox/shogun">Shogun</a> -
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Machine learning toolbox.</li>
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<li><a href="https://github.com/aksnzhy/xlearn">xLearn</a> - High
|
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Performance, Easy-to-use, and Scalable Machine Learning Package.</li>
|
||
<li><a href="https://github.com/rapidsai/cuml">cuML</a> - RAPIDS Machine
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Learning Library.
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<img height="20" src="img/sklearn_big.png" alt="sklearn">
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<img height="20" src="img/gpu_big.png" alt="GPU accelerated"></li>
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<li><a href="https://github.com/cosmic-cortex/modAL">modAL</a> - Modular
|
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active learning framework for Python3.
|
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
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href="https://github.com/lensacom/sparkit-learn">Sparkit-learn</a> -
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PySpark + scikit-learn = Sparkit-learn.
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||
<img height="20" src="img/sklearn_big.png" alt="sklearn">
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<img height="20" src="img/spark_big.png" alt="Apache Spark based"></li>
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<li><a href="https://github.com/mlpack/mlpack">mlpack</a> - A scalable
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C++ machine learning library (Python bindings).</li>
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<li><a href="https://github.com/davisking/dlib">dlib</a> - Toolkit for
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making real-world machine learning and data analysis applications in C++
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(Python bindings).</li>
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<li><a href="https://github.com/rasbt/mlxtend">MLxtend</a> - Extension
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||
and helper modules for Python’s data analysis and machine learning
|
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libraries.
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
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<li><a href="https://github.com/danielhanchen/hyperlearn">hyperlearn</a>
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- 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn,
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Statsmodels. <img height="20" src="img/sklearn_big.png" alt="sklearn">
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<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
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<li><a href="https://github.com/yandex/rep">Reproducible Experiment
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Platform (REP)</a> - Machine Learning toolbox for Humans.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
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<li><a
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href="https://github.com/scikit-multilearn/scikit-multilearn">scikit-multilearn</a>
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- Multi-label classification for python.
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
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<li><a href="https://github.com/larsmans/seqlearn">seqlearn</a> -
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Sequence classification toolkit for Python.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
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||
<li><a href="https://github.com/pystruct/pystruct">pystruct</a> - Simple
|
||
structured learning framework for Python.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
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href="https://github.com/tmadl/sklearn-expertsys">sklearn-expertsys</a>
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- Highly interpretable classifiers for scikit learn.
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
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<li><a href="https://github.com/christophM/rulefit">RuleFit</a> -
|
||
Implementation of the rulefit.
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
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<li><a href="https://github.com/all-umass/metric-learn">metric-learn</a>
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||
- Metric learning algorithms in Python.
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||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
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<li><a href="https://github.com/dswah/pyGAM">pyGAM</a> - Generalized
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Additive Models in Python.</li>
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<li><a href="https://github.com/uber/causalml">causalml</a> - Uplift
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modeling and causal inference with machine learning algorithms.
|
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
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</ul>
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<h3 id="gradient-boosting">Gradient Boosting</h3>
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||
<ul>
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<li><a href="https://github.com/dmlc/xgboost">XGBoost</a> - Scalable,
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||
Portable, and Distributed Gradient Boosting.
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||
<img height="20" src="img/sklearn_big.png" alt="sklearn">
|
||
<img height="20" src="img/gpu_big.png" alt="GPU accelerated"></li>
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||
<li><a href="https://github.com/Microsoft/LightGBM">LightGBM</a> - A
|
||
fast, distributed, high-performance gradient boosting.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn">
|
||
<img height="20" src="img/gpu_big.png" alt="GPU accelerated"></li>
|
||
<li><a href="https://github.com/catboost/catboost">CatBoost</a> - An
|
||
open-source gradient boosting on decision trees library.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn">
|
||
<img height="20" src="img/gpu_big.png" alt="GPU accelerated"></li>
|
||
<li><a
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||
href="https://github.com/Xtra-Computing/thundergbm">ThunderGBM</a> -
|
||
Fast GBDTs and Random Forests on GPUs.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn">
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||
<img height="20" src="img/gpu_big.png" alt="GPU accelerated"></li>
|
||
<li><a href="https://github.com/stanfordmlgroup/ngboost">NGBoost</a> -
|
||
Natural Gradient Boosting for Probabilistic Prediction.</li>
|
||
<li><a href="https://github.com/tensorflow/decision-forests">TensorFlow
|
||
Decision Forests</a> - A collection of state-of-the-art algorithms for
|
||
the training, serving and interpretation of Decision Forest models in
|
||
Keras. <img height="20" src="img/keras_big.png" alt="keras">
|
||
<img height="20" src="img/tf_big2.png" alt="TensorFlow"></li>
|
||
</ul>
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<h3 id="ensemble-methods">Ensemble Methods</h3>
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<ul>
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<li><a href="http://ml-ensemble.com/">ML-Ensemble</a> - High performance
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ensemble learning.
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
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<li><a href="https://github.com/ikki407/stacking">Stacking</a> - Simple
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and useful stacking library written in Python.
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
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||
href="https://github.com/fukatani/stacked_generalization">stacked_generalization</a>
|
||
- Library for machine learning stacking generalization.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/vecxoz/vecstack">vecstack</a> - Python
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||
package for stacking (machine learning technique).
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
</ul>
|
||
<h3 id="imbalanced-datasets">Imbalanced Datasets</h3>
|
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<ul>
|
||
<li><a
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href="https://github.com/scikit-learn-contrib/imbalanced-learn">imbalanced-learn</a>
|
||
- Module to perform under-sampling and over-sampling with various
|
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techniques.
|
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
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href="https://github.com/dialnd/imbalanced-algorithms">imbalanced-algorithms</a>
|
||
- Python-based implementations of algorithms for learning on imbalanced
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data. <img height="20" src="img/sklearn_big.png" alt="sklearn">
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<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
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</ul>
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<h3 id="random-forests">Random Forests</h3>
|
||
<ul>
|
||
<li><a href="https://github.com/lyst/rpforest">rpforest</a> - A forest
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of random projection trees.
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
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href="https://github.com/tmadl/sklearn-random-bits-forest">sklearn-random-bits-forest</a>
|
||
- Wrapper of the Random Bits Forest program written by (Wang et al.,
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2016).<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
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<li><a href="https://github.com/fukatani/rgf_python">rgf_python</a> -
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Python Wrapper of Regularized Greedy Forest.
|
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<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
</ul>
|
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<h3 id="kernel-methods">Kernel Methods</h3>
|
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<ul>
|
||
<li><a href="https://github.com/coreylynch/pyFM">pyFM</a> -
|
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Factorization machines in python.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/ibayer/fastFM">fastFM</a> - A library
|
||
for Factorization Machines.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
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<li><a href="https://github.com/geffy/tffm">tffm</a> - TensorFlow
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implementation of an arbitrary order Factorization Machine.
|
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<img height="20" src="img/sklearn_big.png" alt="sklearn">
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/liquidSVM/liquidSVM">liquidSVM</a> - An
|
||
implementation of SVMs.</li>
|
||
<li><a href="https://github.com/JamesRitchie/scikit-rvm">scikit-rvm</a>
|
||
- Relevance Vector Machine implementation using the scikit-learn API.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
|
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href="https://github.com/Xtra-Computing/thundersvm">ThunderSVM</a> - A
|
||
fast SVM Library on GPUs and CPUs.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn">
|
||
<img height="20" src="img/gpu_big.png" alt="GPU accelerated"></li>
|
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</ul>
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<h2 id="deep-learning">Deep Learning</h2>
|
||
<h3 id="pytorch">PyTorch</h3>
|
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<ul>
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<li><a href="https://github.com/pytorch/pytorch">PyTorch</a> - Tensors
|
||
and Dynamic neural networks in Python with strong GPU acceleration.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a
|
||
href="https://github.com/Lightning-AI/lightning">pytorch-lightning</a> -
|
||
PyTorch Lightning is just organized PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/pytorch/ignite">ignite</a> - High-level
|
||
library to help with training neural networks in PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/dnouri/skorch">skorch</a> - A
|
||
scikit-learn compatible neural network library that wraps PyTorch.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn">
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/catalyst-team/catalyst">Catalyst</a> -
|
||
High-level utils for PyTorch DL & RL research.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/AstraZeneca/chemicalx">ChemicalX</a> - A
|
||
PyTorch-based deep learning library for drug pair scoring.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
</ul>
|
||
<h3 id="tensorflow">TensorFlow</h3>
|
||
<ul>
|
||
<li><a href="https://github.com/tensorflow/tensorflow">TensorFlow</a> -
|
||
Computation using data flow graphs for scalable machine learning by
|
||
Google. <img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/zsdonghao/tensorlayer">TensorLayer</a> -
|
||
Deep Learning and Reinforcement Learning Library for Researcher and
|
||
Engineer. <img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/tflearn/tflearn">TFLearn</a> - Deep
|
||
learning library featuring a higher-level API for TensorFlow.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/deepmind/sonnet">Sonnet</a> -
|
||
TensorFlow-based neural network library.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/ppwwyyxx/tensorpack">tensorpack</a> - A
|
||
Neural Net Training Interface on TensorFlow.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/polyaxon/polyaxon">Polyaxon</a> - A
|
||
platform that helps you build, manage and monitor deep learning models.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/riga/tfdeploy">tfdeploy</a> - Deploy
|
||
TensorFlow graphs for fast evaluation and export to TensorFlow-less
|
||
environments running numpy.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a
|
||
href="https://github.com/ROCmSoftwarePlatform/tensorflow-upstream">tensorflow-upstream</a>
|
||
- TensorFlow ROCm port.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn">
|
||
<img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU"></li>
|
||
<li><a href="https://github.com/tensorflow/fold">TensorFlow Fold</a> -
|
||
Deep learning with dynamic computation graphs in TensorFlow.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a
|
||
href="https://github.com/bsautermeister/tensorlight">TensorLight</a> - A
|
||
high-level framework for TensorFlow.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/tensorflow/mesh">Mesh TensorFlow</a> -
|
||
Model Parallelism Made Easier.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/uber/ludwig">Ludwig</a> - A toolbox that
|
||
allows one to train and test deep learning models without the need to
|
||
write code. <img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://keras.io">Keras</a> - A high-level neural networks
|
||
API running on top of TensorFlow.
|
||
<img height="20" src="img/keras_big.png" alt="Keras compatible"></li>
|
||
<li><a
|
||
href="https://github.com/keras-team/keras-contrib">keras-contrib</a> -
|
||
Keras community contributions.
|
||
<img height="20" src="img/keras_big.png" alt="Keras compatible"></li>
|
||
<li><a href="https://github.com/maxpumperla/hyperas">Hyperas</a> - Keras
|
||
+ Hyperopt: A straightforward wrapper for a convenient hyperparameter.
|
||
<img height="20" src="img/keras_big.png" alt="Keras compatible"></li>
|
||
<li><a href="https://github.com/maxpumperla/elephas">Elephas</a> -
|
||
Distributed Deep learning with Keras & Spark.
|
||
<img height="20" src="img/keras_big.png" alt="Keras compatible"></li>
|
||
<li><a href="https://github.com/google/qkeras">qkeras</a> - A
|
||
quantization deep learning library.
|
||
<img height="20" src="img/keras_big.png" alt="Keras compatible"></li>
|
||
</ul>
|
||
<h3 id="jax">JAX</h3>
|
||
<ul>
|
||
<li><a href="https://github.com/google/jax">JAX</a> - Composable
|
||
transformations of Python+NumPy programs: differentiate, vectorize, JIT
|
||
to GPU/TPU, and more.</li>
|
||
<li><a href="https://github.com/google/flax">FLAX</a> - A neural network
|
||
library for JAX that is designed for flexibility.</li>
|
||
<li><a href="https://github.com/google-deepmind/optax">Optax</a> - A
|
||
gradient processing and optimization library for JAX.</li>
|
||
</ul>
|
||
<h3 id="others">Others</h3>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/huggingface/transformers">transformers</a> -
|
||
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/google/tangent">Tangent</a> -
|
||
Source-to-Source Debuggable Derivatives in Pure Python.</li>
|
||
<li><a href="https://github.com/HIPS/autograd">autograd</a> -
|
||
Efficiently computes derivatives of numpy code.</li>
|
||
<li><a href="https://github.com/BVLC/caffe">Caffe</a> - A fast open
|
||
framework for deep learning.</li>
|
||
<li><a href="https://github.com/sony/nnabla">nnabla</a> - Neural Network
|
||
Libraries by Sony.</li>
|
||
</ul>
|
||
<h2 id="automated-machine-learning">Automated Machine Learning</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/automl/auto-sklearn">auto-sklearn</a> -
|
||
An AutoML toolkit and a drop-in replacement for a scikit-learn
|
||
estimator.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/automl/Auto-PyTorch">Auto-PyTorch</a> -
|
||
Automatic architecture search and hyperparameter optimization for
|
||
PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/keras-team/autokeras">AutoKeras</a> -
|
||
AutoML library for deep learning.
|
||
<img height="20" src="img/keras_big.png" alt="Keras compatible"></li>
|
||
<li><a href="https://github.com/awslabs/autogluon">AutoGluon</a> -
|
||
AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.</li>
|
||
<li><a href="https://github.com/rhiever/tpot">TPOT</a> - AutoML tool
|
||
that optimizes machine learning pipelines using genetic programming.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/AxeldeRomblay/MLBox">MLBox</a> - A
|
||
powerful Automated Machine Learning python library.</li>
|
||
</ul>
|
||
<h2 id="natural-language-processing">Natural Language Processing</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/pytorch/text">torchtext</a> - Data
|
||
loaders and abstractions for text and NLP.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/keras-team/keras-nlp">KerasNLP</a> -
|
||
Modular Natural Language Processing workflows with Keras.
|
||
<img height="20" src="img/keras_big.png" alt="Keras based/compatible"></li>
|
||
<li><a href="https://spacy.io/">spaCy</a> - Industrial-Strength Natural
|
||
Language Processing.</li>
|
||
<li><a href="https://github.com/nltk/nltk">NLTK</a> - Modules, data
|
||
sets, and tutorials supporting research and development in Natural
|
||
Language Processing.</li>
|
||
<li><a href="https://github.com/cltk/cltk">CLTK</a> - The Classical
|
||
Language Toolkik.</li>
|
||
<li><a href="https://radimrehurek.com/gensim/">gensim</a> - Topic
|
||
Modelling for Humans.</li>
|
||
<li><a href="https://github.com/dmirecki/pyMorfologik">pyMorfologik</a>
|
||
- Python binding for
|
||
<a href="https://github.com/morfologik/morfologik-stemming">Morfologik</a>.</li>
|
||
<li><a href="https://github.com/shaypal5/skift">skift</a> - Scikit-learn
|
||
wrappers for Python fastText.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/bootphon/phonemizer">Phonemizer</a> -
|
||
Simple text-to-phonemes converter for multiple languages.</li>
|
||
<li><a href="https://github.com/zalandoresearch/flair">flair</a> - Very
|
||
simple framework for state-of-the-art NLP.</li>
|
||
</ul>
|
||
<h2 id="computer-audition">Computer Audition</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/pytorch/audio">torchaudio</a> - An audio
|
||
library for PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/librosa/librosa">librosa</a> - Python
|
||
library for audio and music analysis.</li>
|
||
<li><a href="https://github.com/Yaafe/Yaafe">Yaafe</a> - Audio features
|
||
extraction.</li>
|
||
<li><a href="https://github.com/aubio/aubio">aubio</a> - A library for
|
||
audio and music analysis.</li>
|
||
<li><a href="https://github.com/MTG/essentia">Essentia</a> - Library for
|
||
audio and music analysis, description, and synthesis.</li>
|
||
<li><a href="https://github.com/jamiebullock/LibXtract">LibXtract</a> -
|
||
A simple, portable, lightweight library of audio feature extraction
|
||
functions.</li>
|
||
<li><a href="https://github.com/marsyas/marsyas">Marsyas</a> - Music
|
||
Analysis, Retrieval, and Synthesis for Audio Signals.</li>
|
||
<li><a href="https://github.com/bmcfee/muda">muda</a> - A library for
|
||
augmenting annotated audio data.</li>
|
||
<li><a href="https://github.com/CPJKU/madmom">madmom</a> - Python audio
|
||
and music signal processing library.</li>
|
||
</ul>
|
||
<h2 id="computer-vision">Computer Vision</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/pytorch/vision">torchvision</a> -
|
||
Datasets, Transforms, and Models specific to Computer Vision.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a
|
||
href="https://github.com/facebookresearch/pytorch3d">PyTorch3D</a> -
|
||
PyTorch3D is FAIR’s library of reusable components for deep learning
|
||
with 3D data.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/keras-team/keras-cv">KerasCV</a> -
|
||
Industry-strength Computer Vision workflows with Keras.
|
||
<img height="20" src="img/keras_big.png" alt="MXNet based"></li>
|
||
<li><a href="https://github.com/opencv/opencv">OpenCV</a> - Open Source
|
||
Computer Vision Library.</li>
|
||
<li><a href="https://github.com/dmlc/decord">Decord</a> - An efficient
|
||
video loader for deep learning with smart shuffling that’s super easy to
|
||
digest.</li>
|
||
<li><a href="https://github.com/open-mmlab/mmengine">MMEngine</a> -
|
||
OpenMMLab Foundational Library for Training Deep Learning Models.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a
|
||
href="https://github.com/scikit-image/scikit-image">scikit-image</a> -
|
||
Image Processing SciKit (Toolbox for SciPy).</li>
|
||
<li><a href="https://github.com/aleju/imgaug">imgaug</a> - Image
|
||
augmentation for machine learning experiments.</li>
|
||
<li><a
|
||
href="https://github.com/cadenai/imgaug_extension">imgaug_extension</a>
|
||
- Additional augmentations for imgaug.</li>
|
||
<li><a href="https://github.com/mdbloice/Augmentor">Augmentor</a> -
|
||
Image augmentation library in Python for machine learning.</li>
|
||
<li><a href="https://github.com/albu/albumentations">albumentations</a>
|
||
- Fast image augmentation library and easy-to-use wrapper around other
|
||
libraries.</li>
|
||
<li><a href="https://github.com/salesforce/LAVIS">LAVIS</a> - A One-stop
|
||
Library for Language-Vision Intelligence.</li>
|
||
</ul>
|
||
<h2 id="time-series">Time Series</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/alan-turing-institute/sktime">sktime</a>
|
||
- A unified framework for machine learning with time series.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
|
||
href="https://github.com/JoaquinAmatRodrigo/skforecast">skforecast</a> -
|
||
Time series forecasting with machine learning models</li>
|
||
<li><a href="https://github.com/unit8co/darts">darts</a> - A python
|
||
library for easy manipulation and forecasting of time series.</li>
|
||
<li><a href="https://github.com/Nixtla/statsforecast">statsforecast</a>
|
||
- Lightning fast forecasting with statistical and econometric
|
||
models.</li>
|
||
<li><a href="https://github.com/Nixtla/mlforecast">mlforecast</a> -
|
||
Scalable machine learning-based time series forecasting.</li>
|
||
<li><a
|
||
href="https://github.com/Nixtla/neuralforecast">neuralforecast</a> -
|
||
Scalable machine learning-based time series forecasting.</li>
|
||
<li><a href="https://github.com/rtavenar/tslearn">tslearn</a> - Machine
|
||
learning toolkit dedicated to time-series data.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/X-DataInitiative/tick">tick</a> - Module
|
||
for statistical learning, with a particular emphasis on time-dependent
|
||
modeling. <img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/linkedin/greykite">greykite</a> - A
|
||
flexible, intuitive, and fast forecasting library next.</li>
|
||
<li><a href="https://github.com/facebook/prophet">Prophet</a> -
|
||
Automatic Forecasting Procedure.</li>
|
||
<li><a href="https://github.com/RJT1990/pyflux">PyFlux</a> - Open source
|
||
time series library for Python.</li>
|
||
<li><a href="https://github.com/christophmark/bayesloop">bayesloop</a> -
|
||
Probabilistic programming framework that facilitates objective model
|
||
selection for time-varying parameter models.</li>
|
||
<li><a href="https://github.com/linkedin/luminol">luminol</a> - Anomaly
|
||
Detection and Correlation library.</li>
|
||
<li><a href="https://dateutil.readthedocs.io/en/stable/">dateutil</a> -
|
||
Powerful extensions to the standard datetime module</li>
|
||
<li><a href="https://github.com/timofurrer/maya">maya</a> - makes it
|
||
very easy to parse a string and for changing timezones</li>
|
||
<li><a href="https://github.com/chaos-genius/chaos_genius">Chaos
|
||
Genius</a> - ML powered analytics engine for outlier/anomaly detection
|
||
and root cause analysis</li>
|
||
</ul>
|
||
<h2 id="reinforcement-learning">Reinforcement Learning</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/Farama-Foundation/Gymnasium">Gymnasium</a> - An
|
||
API standard for single-agent reinforcement learning environments, with
|
||
popular reference environments and related utilities (formerly <a
|
||
href="https://github.com/openai/gym">Gym</a>).</li>
|
||
<li><a
|
||
href="https://github.com/Farama-Foundation/PettingZoo">PettingZoo</a> -
|
||
An API standard for multi-agent reinforcement learning environments,
|
||
with popular reference environments and related utilities.</li>
|
||
<li><a href="https://github.com/Farama-Foundation/MAgent2">MAgent2</a> -
|
||
An engine for high performance multi-agent environments with very large
|
||
numbers of agents, along with a set of reference environments.</li>
|
||
<li><a href="https://github.com/DLR-RM/stable-baselines3">Stable
|
||
Baselines3</a> - A set of improved implementations of reinforcement
|
||
learning algorithms based on OpenAI Baselines.</li>
|
||
<li><a href="https://github.com/Farama-Foundation/Shimmy">Shimmy</a> -
|
||
An API conversion tool for popular external reinforcement learning
|
||
environments.</li>
|
||
<li><a href="https://github.com/sail-sg/envpool">EnvPool</a> - C++-based
|
||
high-performance parallel environment execution engine (vectorized env)
|
||
for general RL environments.</li>
|
||
<li><a href="https://ray.readthedocs.io/en/latest/rllib.html">RLlib</a>
|
||
- Scalable Reinforcement Learning.</li>
|
||
<li><a
|
||
href="https://github.com/thu-ml/tianshou/#comprehensive-functionality">Tianshou</a>
|
||
- An elegant PyTorch deep reinforcement learning library.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/google-deepmind/acme">Acme</a> - A
|
||
library of reinforcement learning components and agents.</li>
|
||
<li><a
|
||
href="https://github.com/catalyst-team/catalyst-rl">Catalyst-RL</a> -
|
||
PyTorch framework for RL research.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/takuseno/d3rlpy">d3rlpy</a> - An offline
|
||
deep reinforcement learning library.</li>
|
||
<li><a href="https://github.com/opendilab/DI-engine">DI-engine</a> -
|
||
OpenDILab Decision AI Engine.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/tensorflow/agents">TF-Agents</a> - A
|
||
library for Reinforcement Learning in TensorFlow.
|
||
<img height="20" src="img/tf_big2.png" alt="TensorFlow"></li>
|
||
<li><a href="https://github.com/reinforceio/tensorforce">TensorForce</a>
|
||
- A TensorFlow library for applied reinforcement learning.
|
||
<img height="20" src="img/tf_big2.png" alt="TensorFlow"></li>
|
||
<li><a href="https://github.com/deepmind/trfl">TRFL</a> - TensorFlow
|
||
Reinforcement Learning.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/google/dopamine">Dopamine</a> - A
|
||
research framework for fast prototyping of reinforcement learning
|
||
algorithms.</li>
|
||
<li><a href="https://github.com/keras-rl/keras-rl">keras-rl</a> - Deep
|
||
Reinforcement Learning for Keras.
|
||
<img height="20" src="img/keras_big.png" alt="Keras compatible"></li>
|
||
<li><a href="https://github.com/rlworkgroup/garage">garage</a> - A
|
||
toolkit for reproducible reinforcement learning research.</li>
|
||
<li><a href="https://github.com/facebookresearch/Horizon">Horizon</a> -
|
||
A platform for Applied Reinforcement Learning.</li>
|
||
<li><a href="https://github.com/astooke/rlpyt">rlpyt</a> - Reinforcement
|
||
Learning in PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/vwxyzjn/cleanrl">cleanrl</a> -
|
||
High-quality single file implementation of Deep Reinforcement Learning
|
||
algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3,
|
||
SAC, PPG).</li>
|
||
<li><a href="https://github.com/iffiX/machin">Machin</a> - A
|
||
reinforcement library designed for pytorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/Toni-SM/skrl">SKRL</a> - Modular
|
||
reinforcement learning library (on PyTorch and JAX) with support for
|
||
NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a
|
||
href="https://github.com/HumanCompatibleAI/imitation">Imitation</a> -
|
||
Clean PyTorch implementations of imitation and reward learning
|
||
algorithms.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
</ul>
|
||
<h2 id="graph-machine-learning">Graph Machine Learning</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/rusty1s/pytorch_geometric">pytorch_geometric</a>
|
||
- Geometric Deep Learning Extension Library for PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a
|
||
href="https://github.com/benedekrozemberczki/pytorch_geometric_temporal">pytorch_geometric_temporal</a>
|
||
- Temporal Extension Library for PyTorch Geometric.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a
|
||
href="https://github.com/SherylHYX/pytorch_geometric_signed_directed">PyTorch
|
||
Geometric Signed Directed</a> - A signed/directed graph neural network
|
||
extension library for PyTorch Geometric.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/dmlc/dgl">dgl</a> - Python package built
|
||
to ease deep learning on graph, on top of existing DL frameworks.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
|
||
<img height="20" src="img/tf_big2.png" alt="TensorFlow">
|
||
<img height="20" src="img/mxnet_big.png" alt="MXNet based"></li>
|
||
<li><a href="https://github.com/danielegrattarola/spektral">Spektral</a>
|
||
- Deep learning on graphs.
|
||
<img height="20" src="img/keras_big.png" alt="Keras compatible"></li>
|
||
<li><a
|
||
href="https://github.com/stellargraph/stellargraph">StellarGraph</a> -
|
||
Machine Learning on Graphs.
|
||
<img height="20" src="img/tf_big2.png" alt="TensorFlow">
|
||
<img height="20" src="img/keras_big.png" alt="Keras compatible"></li>
|
||
<li><a href="https://github.com/google-deepmind/graph_nets">Graph
|
||
Nets</a> - Build Graph Nets in Tensorflow.
|
||
<img height="20" src="img/tf_big2.png" alt="TensorFlow"></li>
|
||
<li><a href="https://github.com/tensorflow/gnn">TensorFlow GNN</a> - A
|
||
library to build Graph Neural Networks on the TensorFlow platform.
|
||
<img height="20" src="img/tf_big2.png" alt="TensorFlow"></li>
|
||
<li><a href="https://github.com/THUMNLab/AutoGL">Auto Graph Learning</a>
|
||
-An autoML framework & toolkit for machine learning on graphs.</li>
|
||
<li><a
|
||
href="https://github.com/facebookresearch/PyTorch-BigGraph">PyTorch-BigGraph</a>
|
||
- Generate embeddings from large-scale graph-structured data.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/THUMNLab/AutoGL">Auto Graph Learning</a>
|
||
- An autoML framework & toolkit for machine learning on graphs.</li>
|
||
<li><a href="https://github.com/benedekrozemberczki/karateclub">Karate
|
||
Club</a> - An unsupervised machine learning library for graph-structured
|
||
data.</li>
|
||
<li><a
|
||
href="https://github.com/benedekrozemberczki/littleballoffur">Little
|
||
Ball of Fur</a> - A library for sampling graph structured data.</li>
|
||
<li><a href="https://github.com/EdisonLeeeee/GreatX">GreatX</a> - A
|
||
graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/google-deepmind/jraph">Jraph</a> - A
|
||
Graph Neural Network Library in Jax.</li>
|
||
</ul>
|
||
<h2 id="learning-to-rank-recommender-systems">Learning-to-Rank &
|
||
Recommender Systems</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/lyst/lightfm">LightFM</a> - A Python
|
||
implementation of LightFM, a hybrid recommendation algorithm.</li>
|
||
<li><a href="https://maciejkula.github.io/spotlight/">Spotlight</a> -
|
||
Deep recommender models using PyTorch.</li>
|
||
<li><a href="https://github.com/NicolasHug/Surprise">Surprise</a> - A
|
||
Python scikit for building and analyzing recommender systems.</li>
|
||
<li><a href="https://github.com/RUCAIBox/RecBole">RecBole</a> - A
|
||
unified, comprehensive and efficient recommendation library.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/allegro/allRank">allRank</a> - allRank
|
||
is a framework for training learning-to-rank neural models based on
|
||
PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/tensorflow/recommenders">TensorFlow
|
||
Recommenders</a> - A library for building recommender system models
|
||
using TensorFlow.
|
||
<img height="20" src="img/tf_big2.png" alt="TensorFlow">
|
||
<img height="20" src="img/keras_big.png" alt="Keras compatible"></li>
|
||
<li><a href="https://github.com/tensorflow/ranking">TensorFlow
|
||
Ranking</a> - Learning to Rank in TensorFlow.
|
||
<img height="20" src="img/tf_big2.png" alt="TensorFlow"></li>
|
||
</ul>
|
||
<h2 id="probabilistic-graphical-models">Probabilistic Graphical
|
||
Models</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/jmschrei/pomegranate">pomegranate</a> -
|
||
Probabilistic and graphical models for Python.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/pgmpy/pgmpy">pgmpy</a> - A python
|
||
library for working with Probabilistic Graphical Models.</li>
|
||
<li><a href="https://agrum.gitlab.io/">pyAgrum</a> - A GRaphical
|
||
Universal Modeler.</li>
|
||
</ul>
|
||
<h2 id="probabilistic-methods">Probabilistic Methods</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/uber/pyro">pyro</a> - A flexible,
|
||
scalable deep probabilistic programming library built on PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/pymc-devs/pymc">PyMC</a> - Bayesian
|
||
Stochastic Modelling in Python.</li>
|
||
<li><a href="http://zhusuan.readthedocs.io/en/latest/">ZhuSuan</a> -
|
||
Bayesian Deep Learning.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a
|
||
href="http://gpflow.readthedocs.io/en/latest/?badge=latest">GPflow</a> -
|
||
Gaussian processes in TensorFlow.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/PGM-Lab/InferPy">InferPy</a> - Deep
|
||
Probabilistic Modelling Made Easy.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/stan-dev/pystan">PyStan</a> - Bayesian
|
||
inference using the No-U-Turn sampler (Python interface).</li>
|
||
<li><a
|
||
href="https://github.com/AmazaspShumik/sklearn-bayes">sklearn-bayes</a>
|
||
- Python package for Bayesian Machine Learning with scikit-learn API.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/alan-turing-institute/skpro">skpro</a> -
|
||
Supervised domain-agnostic prediction framework for probabilistic
|
||
modelling by <a href="https://www.turing.ac.uk/">The Alan Turing
|
||
Institute</a>.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/ctallec/pyvarinf">PyVarInf</a> -
|
||
Bayesian Deep Learning methods with Variational Inference for PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a href="https://github.com/dfm/emcee">emcee</a> - The Python
|
||
ensemble sampling toolkit for affine-invariant MCMC.</li>
|
||
<li><a href="https://github.com/jvkersch/hsmmlearn">hsmmlearn</a> - A
|
||
library for hidden semi-Markov models with explicit durations.</li>
|
||
<li><a href="https://github.com/mattjj/pyhsmm">pyhsmm</a> - Bayesian
|
||
inference in HSMMs and HMMs.</li>
|
||
<li><a href="https://github.com/cornellius-gp/gpytorch">GPyTorch</a> - A
|
||
highly efficient and modular implementation of Gaussian Processes in
|
||
PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"></li>
|
||
<li><a
|
||
href="https://github.com/TeamHG-Memex/sklearn-crfsuite">sklearn-crfsuite</a>
|
||
- A scikit-learn-inspired API for CRFsuite.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
</ul>
|
||
<h2 id="model-explanation">Model Explanation</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/ModelOriented/DALEX">dalex</a> - moDel
|
||
Agnostic Language for Exploration and explanation.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"><img height="20" src="img/R_big.png" alt="R inspired/ported lib"></li>
|
||
<li><a href="https://github.com/benedekrozemberczki/shapley">Shapley</a>
|
||
- A data-driven framework to quantify the value of classifiers in a
|
||
machine learning ensemble.</li>
|
||
<li><a href="https://github.com/SeldonIO/alibi">Alibi</a> - Algorithms
|
||
for monitoring and explaining machine learning models.</li>
|
||
<li><a href="https://github.com/marcotcr/anchor">anchor</a> - Code for
|
||
“High-Precision Model-Agnostic Explanations” paper.</li>
|
||
<li><a href="https://github.com/dssg/aequitas">aequitas</a> - Bias and
|
||
Fairness Audit Toolkit.</li>
|
||
<li><a
|
||
href="https://github.com/MarcelRobeer/ContrastiveExplanation">Contrastive
|
||
Explanation</a> - Contrastive Explanation (Foil Trees).
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
|
||
href="https://github.com/DistrictDataLabs/yellowbrick">yellowbrick</a> -
|
||
Visual analysis and diagnostic tools to facilitate machine learning
|
||
model selection.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/reiinakano/scikit-plot">scikit-plot</a>
|
||
- An intuitive library to add plotting functionality to scikit-learn
|
||
objects. <img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/slundberg/shap">shap</a> - A unified
|
||
approach to explain the output of any machine learning model.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/TeamHG-Memex/eli5">ELI5</a> - A library
|
||
for debugging/inspecting machine learning classifiers and explaining
|
||
their predictions.</li>
|
||
<li><a href="https://github.com/marcotcr/lime">Lime</a> - Explaining the
|
||
predictions of any machine learning classifier.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/adebayoj/fairml">FairML</a> - FairML is
|
||
a python toolbox auditing the machine learning models for bias.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/Jianbo-Lab/L2X">L2X</a> - Code for
|
||
replicating the experiments in the paper <em>Learning to Explain: An
|
||
Information-Theoretic Perspective on Model Interpretation</em>.</li>
|
||
<li><a href="https://github.com/SauceCat/PDPbox">PDPbox</a> - Partial
|
||
dependence plot toolbox.</li>
|
||
<li><a href="https://github.com/AustinRochford/PyCEbox">PyCEbox</a> -
|
||
Python Individual Conditional Expectation Plot Toolbox.</li>
|
||
<li><a href="https://github.com/datascienceinc/Skater">Skater</a> -
|
||
Python Library for Model Interpretation.</li>
|
||
<li><a
|
||
href="https://github.com/tensorflow/model-analysis">model-analysis</a> -
|
||
Model analysis tools for TensorFlow.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/cosmicBboy/themis-ml">themis-ml</a> - A
|
||
library that implements fairness-aware machine learning algorithms.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
|
||
href="https://github.com/andosa/treeinterpreter">treeinterpreter</a> -
|
||
Interpreting scikit-learn’s decision tree and random forest predictions.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/IBM/AIX360">AI Explainability 360</a> -
|
||
Interpretability and explainability of data and machine learning
|
||
models.</li>
|
||
<li><a
|
||
href="https://github.com/keunwoochoi/Auralisation">Auralisation</a> -
|
||
Auralisation of learned features in CNN (for audio).</li>
|
||
<li><a
|
||
href="https://github.com/bourdakos1/CapsNet-Visualization">CapsNet-Visualization</a>
|
||
- A visualization of the CapsNet layers to better understand how it
|
||
works.</li>
|
||
<li><a href="https://github.com/tensorflow/lucid">lucid</a> - A
|
||
collection of infrastructure and tools for research in neural network
|
||
interpretability.</li>
|
||
<li><a href="https://github.com/lutzroeder/Netron">Netron</a> -
|
||
Visualizer for deep learning and machine learning models (no Python
|
||
code, but visualizes models from most Python Deep Learning
|
||
frameworks).</li>
|
||
<li><a href="https://github.com/dlguys/flashlight">FlashLight</a> -
|
||
Visualization Tool for your NeuralNetwork.</li>
|
||
<li><a
|
||
href="https://github.com/lanpa/tensorboard-pytorch">tensorboard-pytorch</a>
|
||
- Tensorboard for PyTorch (and chainer, mxnet, numpy, …).</li>
|
||
</ul>
|
||
<h2 id="genetic-programming">Genetic Programming</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/trevorstephens/gplearn">gplearn</a> -
|
||
Genetic Programming in Python.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
|
||
href="https://github.com/ahmedfgad/GeneticAlgorithmPython">PyGAD</a> -
|
||
Genetic Algorithm in Python.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
|
||
<img height="20" src="img/keras_big.png" alt="keras"></li>
|
||
<li><a href="https://github.com/DEAP/deap">DEAP</a> - Distributed
|
||
Evolutionary Algorithms in Python.</li>
|
||
<li><a href="https://github.com/kstaats/karoo_gp">karoo_gp</a> - A
|
||
Genetic Programming platform for Python with GPU support.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/hchasestevens/monkeys">monkeys</a> - A
|
||
strongly-typed genetic programming framework for Python.</li>
|
||
<li><a
|
||
href="https://github.com/manuel-calzolari/sklearn-genetic">sklearn-genetic</a>
|
||
- Genetic feature selection module for scikit-learn.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
</ul>
|
||
<p><a name="opt"></a> ## Optimization * <a
|
||
href="https://github.com/optuna/optuna">Optuna</a> - A hyperparameter
|
||
optimization framework. * <a
|
||
href="https://github.com/anyoptimization/pymoo">pymoo</a> -
|
||
Multi-objective Optimization in Python. * <a
|
||
href="https://github.com/CMA-ES/pycma?tab=readme-ov-file">pycma</a> -
|
||
Python implementation of CMA-ES. * <a
|
||
href="https://github.com/HIPS/Spearmint">Spearmint</a> - Bayesian
|
||
optimization. * <a href="https://github.com/pytorch/botorch">BoTorch</a>
|
||
- Bayesian optimization in PyTorch.
|
||
<img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
|
||
* <a href="https://github.com/guofei9987/scikit-opt">scikit-opt</a> -
|
||
Heuristic Algorithms for optimization. * <a
|
||
href="https://github.com/rodrigo-arenas/Sklearn-genetic-opt">sklearn-genetic-opt</a>
|
||
- Hyperparameters tuning and feature selection using evolutionary
|
||
algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn"> *
|
||
<a href="https://github.com/automl/SMAC3">SMAC3</a> - Sequential
|
||
Model-based Algorithm Configuration. * <a
|
||
href="https://github.com/claesenm/optunity">Optunity</a> - Is a library
|
||
containing various optimizers for hyperparameter tuning. * <a
|
||
href="https://github.com/hyperopt/hyperopt">hyperopt</a> - Distributed
|
||
Asynchronous Hyperparameter Optimization in Python. * <a
|
||
href="https://github.com/hyperopt/hyperopt-sklearn">hyperopt-sklearn</a>
|
||
- Hyper-parameter optimization for sklearn.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"> * <a
|
||
href="https://github.com/rsteca/sklearn-deap">sklearn-deap</a> - Use
|
||
evolutionary algorithms instead of gridsearch in scikit-learn.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"> * <a
|
||
href="https://github.com/sigopt/sigopt_sklearn">sigopt_sklearn</a> -
|
||
SigOpt wrappers for scikit-learn methods.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"> * <a
|
||
href="https://github.com/fmfn/BayesianOptimization">Bayesian
|
||
Optimization</a> - A Python implementation of global optimization with
|
||
gaussian processes. * <a
|
||
href="https://github.com/befelix/SafeOpt">SafeOpt</a> - Safe Bayesian
|
||
Optimization. * <a
|
||
href="https://github.com/scikit-optimize/scikit-optimize">scikit-optimize</a>
|
||
- Sequential model-based optimization with a <code>scipy.optimize</code>
|
||
interface. * <a href="https://github.com/100/Solid">Solid</a> - A
|
||
comprehensive gradient-free optimization framework written in Python. *
|
||
<a href="https://github.com/ljvmiranda921/pyswarms">PySwarms</a> - A
|
||
research toolkit for particle swarm optimization in Python. * <a
|
||
href="https://github.com/Project-Platypus/Platypus">Platypus</a> - A
|
||
Free and Open Source Python Library for Multiobjective Optimization. *
|
||
<a href="https://github.com/GPflow/GPflowOpt">GPflowOpt</a> - Bayesian
|
||
Optimization using GPflow.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"> * <a
|
||
href="https://github.com/rflamary/POT">POT</a> - Python Optimal
|
||
Transport library. * <a
|
||
href="https://github.com/autonomio/talos">Talos</a> - Hyperparameter
|
||
Optimization for Keras Models. * <a
|
||
href="https://github.com/stevengj/nlopt">nlopt</a> - Library for
|
||
nonlinear optimization (global and local, constrained or unconstrained).
|
||
* <a href="https://developers.google.com/optimization">OR-Tools</a> - 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.</p>
|
||
<h2 id="feature-engineering">Feature Engineering</h2>
|
||
<h3 id="general">General</h3>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/Featuretools/featuretools">Featuretools</a> -
|
||
Automated feature engineering.</li>
|
||
<li><a href="https://github.com/feature-engine/feature_engine">Feature
|
||
Engine</a> - Feature engineering package with sklearn-like
|
||
functionality.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/IIIS-Li-Group/OpenFE">OpenFE</a> -
|
||
Automated feature generation with expert-level performance.</li>
|
||
<li><a
|
||
href="https://github.com/dougalsutherland/skl-groups">skl-groups</a> - A
|
||
scikit-learn addon to operate on set/“group”-based features.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/machinalis/featureforge">Feature
|
||
Forge</a> - A set of tools for creating and testing machine learning
|
||
features. <img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/lacava/few">few</a> - A feature
|
||
engineering wrapper for sklearn.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/EpistasisLab/scikit-mdr">scikit-mdr</a>
|
||
- A sklearn-compatible Python implementation of Multifactor
|
||
Dimensionality Reduction (MDR) for feature construction.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/blue-yonder/tsfresh">tsfresh</a> -
|
||
Automatic extraction of relevant features from time series.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/dirty-cat/dirty_cat">dirty_cat</a> -
|
||
Machine learning on dirty tabular data (especially: string-based
|
||
variables for classifcation and regression).
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/NITRO-AI/NitroFE">NitroFE</a> - Moving
|
||
window features.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
|
||
href="https://github.com/chrislemke/sk-transformers">sk-transformer</a>
|
||
- A collection of various pandas & scikit-learn compatible
|
||
transformers for all kinds of preprocessing and feature engineering
|
||
steps
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
</ul>
|
||
<h3 id="feature-selection">Feature Selection</h3>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/jundongl/scikit-feature">scikit-feature</a> -
|
||
Feature selection repository in Python.</li>
|
||
<li><a
|
||
href="https://github.com/scikit-learn-contrib/boruta_py">boruta_py</a> -
|
||
Implementations of the Boruta all-relevant feature selection method.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/chasedehan/BoostARoota">BoostARoota</a>
|
||
- A fast xgboost feature selection algorithm.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a
|
||
href="https://github.com/EpistasisLab/scikit-rebate">scikit-rebate</a> -
|
||
A scikit-learn-compatible Python implementation of ReBATE, a suite of
|
||
Relief-based feature selection algorithms for Machine Learning.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/jaswinder9051998/zoofs">zoofs</a> - A
|
||
feature selection library based on evolutionary algorithms.</li>
|
||
</ul>
|
||
<h2 id="visualization">Visualization</h2>
|
||
<h3 id="general-purposes">General Purposes</h3>
|
||
<ul>
|
||
<li><a href="https://github.com/matplotlib/matplotlib">Matplotlib</a> -
|
||
Plotting with Python.</li>
|
||
<li><a href="https://github.com/mwaskom/seaborn">seaborn</a> -
|
||
Statistical data visualization using matplotlib.</li>
|
||
<li><a href="https://github.com/olgabot/prettyplotlib">prettyplotlib</a>
|
||
- Painlessly create beautiful matplotlib plots.</li>
|
||
<li><a
|
||
href="https://github.com/marcharper/python-ternary">python-ternary</a> -
|
||
Ternary plotting library for Python with matplotlib.</li>
|
||
<li><a href="https://github.com/ResidentMario/missingno">missingno</a> -
|
||
Missing data visualization module for Python.</li>
|
||
<li><a href="https://github.com/spotify/chartify/">chartify</a> - Python
|
||
library that makes it easy for data scientists to create charts.</li>
|
||
<li><a href="https://github.com/janpipek/physt">physt</a> - Improved
|
||
histograms. ### Interactive plots</li>
|
||
<li><a href="https://github.com/t-makaro/animatplot">animatplot</a> - A
|
||
python package for animating plots built on matplotlib.</li>
|
||
<li><a href="https://plot.ly/python/">plotly</a> - A Python library that
|
||
makes interactive and publication-quality graphs.</li>
|
||
<li><a href="https://github.com/bokeh/bokeh">Bokeh</a> - Interactive Web
|
||
Plotting for Python.</li>
|
||
<li><a href="https://altair-viz.github.io/">Altair</a> - Declarative
|
||
statistical visualization library for Python. Can easily do many data
|
||
transformation within the code to create graph</li>
|
||
<li><a href="https://github.com/bqplot/bqplot">bqplot</a> - Plotting
|
||
library for IPython/Jupyter notebooks</li>
|
||
<li><a href="https://github.com/pyecharts/pyecharts">pyecharts</a> -
|
||
Migrated from <a href="https://github.com/apache/echarts">Echarts</a>, a
|
||
charting and visualization library, to Python’s interactive visual
|
||
drawing
|
||
library.<img height="20" src="img/pyecharts.png" alt="pyecharts">
|
||
<img height="20" src="img/echarts.png" alt="echarts"> ### Map</li>
|
||
<li><a
|
||
href="https://python-visualization.github.io/folium/quickstart.html#Getting-Started">folium</a>
|
||
- Makes it easy to visualize data on an interactive open street map</li>
|
||
<li><a href="https://github.com/giswqs/geemap">geemap</a> - Python
|
||
package for interactive mapping with Google Earth Engine (GEE) ###
|
||
Automatic Plotting</li>
|
||
<li><a href="https://github.com/ioam/holoviews">HoloViews</a> - Stop
|
||
plotting your data - annotate your data and let it visualize
|
||
itself.</li>
|
||
<li><a href="https://github.com/AutoViML/AutoViz">AutoViz</a>: Visualize
|
||
data automatically with 1 line of code (ideal for machine learning)</li>
|
||
<li><a href="https://github.com/fbdesignpro/sweetviz">SweetViz</a>:
|
||
Visualize and compare datasets, target values and associations, with one
|
||
line of code.</li>
|
||
</ul>
|
||
<h3 id="nlp">NLP</h3>
|
||
<ul>
|
||
<li><a href="https://github.com/bmabey/pyLDAvis">pyLDAvis</a>: Visualize
|
||
interactive topic model</li>
|
||
</ul>
|
||
<h2 id="deployment">Deployment</h2>
|
||
<ul>
|
||
<li><a href="https://fastapi.tiangolo.com/">fastapi</a> - Modern, fast
|
||
(high-performance), a web framework for building APIs with Python</li>
|
||
<li><a href="https://www.streamlit.io/">streamlit</a> - Make it easy to
|
||
deploy the machine learning model</li>
|
||
<li><a
|
||
href="https://github.com/streamsync-cloud/streamsync">streamsync</a> -
|
||
No-code in the front, Python in the back. An open-source framework for
|
||
creating data apps.</li>
|
||
<li><a href="https://github.com/gradio-app/gradio">gradio</a> - Create
|
||
UIs for your machine learning model in Python in 3 minutes.</li>
|
||
<li><a href="https://github.com/mckinsey/vizro">Vizro</a> - A toolkit
|
||
for creating modular data visualization applications.</li>
|
||
<li><a href="https://datapane.com/">datapane</a> - A collection of APIs
|
||
to turn scripts and notebooks into interactive reports.</li>
|
||
<li><a href="https://mybinder.org/">binder</a> - Enable sharing and
|
||
execute Jupyter Notebooks</li>
|
||
</ul>
|
||
<h2 id="statistics">Statistics</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/mouradmourafiq/pandas-summary">pandas_summary</a>
|
||
- Extension to pandas dataframes describe function.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a
|
||
href="https://github.com/pandas-profiling/pandas-profiling">Pandas
|
||
Profiling</a> - Create HTML profiling reports from pandas DataFrame
|
||
objects.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a href="https://github.com/statsmodels/statsmodels">statsmodels</a>
|
||
- Statistical modeling and econometrics in Python.</li>
|
||
<li><a href="https://github.com/jealous/stockstats">stockstats</a> -
|
||
Supply a wrapper <code>StockDataFrame</code> based on the
|
||
<code>pandas.DataFrame</code> with inline stock statistics/indicators
|
||
support.</li>
|
||
<li><a href="https://github.com/jsvine/weightedcalcs">weightedcalcs</a>
|
||
- A pandas-based utility to calculate weighted means, medians,
|
||
distributions, standard deviations, and more.</li>
|
||
<li><a
|
||
href="https://github.com/maximtrp/scikit-posthocs">scikit-posthocs</a> -
|
||
Pairwise Multiple Comparisons Post-hoc Tests.</li>
|
||
<li><a href="https://github.com/quantopian/alphalens">Alphalens</a> -
|
||
Performance analysis of predictive (alpha) stock factors.</li>
|
||
</ul>
|
||
<h2 id="data-manipulation">Data Manipulation</h2>
|
||
<h3 id="data-frames">Data Frames</h3>
|
||
<ul>
|
||
<li><a href="https://pandas.pydata.org/pandas-docs/stable/">pandas</a> -
|
||
Powerful Python data analysis toolkit.</li>
|
||
<li><a href="https://github.com/pola-rs/polars">polars</a> - A fast
|
||
multi-threaded, hybrid-out-of-core DataFrame library.</li>
|
||
<li><a href="https://github.com/manahl/arctic">Arctic</a> -
|
||
High-performance datastore for time series and tick data.</li>
|
||
<li><a href="https://github.com/h2oai/datatable">datatable</a> -
|
||
Data.table for Python.
|
||
<img height="20" src="img/R_big.png" alt="R inspired/ported lib"></li>
|
||
<li><a
|
||
href="https://github.com/pandas-profiling/pandas-profiling">pandas_profiling</a>
|
||
- Create HTML profiling reports from pandas DataFrame objects</li>
|
||
<li><a href="https://github.com/rapidsai/cudf">cuDF</a> - GPU DataFrame
|
||
Library.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible">
|
||
<img height="20" src="img/gpu_big.png" alt="GPU accelerated"></li>
|
||
<li><a href="https://github.com/blaze/blaze">blaze</a> - NumPy and
|
||
pandas interface to Big Data.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a href="https://github.com/yhat/pandasql">pandasql</a> - Allows you
|
||
to query pandas DataFrames using SQL syntax.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a href="https://github.com/pydata/pandas-gbq">pandas-gbq</a> -
|
||
pandas Google Big Query.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a
|
||
href="https://github.com/alan-turing-institute/xpandas">xpandas</a> -
|
||
Universal 1d/2d data containers with Transformers .functionality for
|
||
data analysis by <a href="https://www.turing.ac.uk/">The Alan Turing
|
||
Institute</a>.</li>
|
||
<li><a href="https://github.com/svenkreiss/pysparkling">pysparkling</a>
|
||
- A pure Python implementation of Apache Spark’s RDD and DStream
|
||
interfaces.
|
||
<img height="20" src="img/spark_big.png" alt="Apache Spark based"></li>
|
||
<li><a href="https://github.com/modin-project/modin">modin</a> - Speed
|
||
up your pandas workflows by changing a single line of code.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a href="https://github.com/jmcarpenter2/swifter">swifter</a> - A
|
||
package that efficiently applies any function to a pandas dataframe or
|
||
series in the fastest available manner.</li>
|
||
<li><a href="https://github.com/eyaltrabelsi/pandas-log">pandas-log</a>
|
||
- A package that allows providing feedback about basic pandas operations
|
||
and finds both business logic and performance issues.</li>
|
||
<li><a href="https://github.com/vaexio/vaex">vaex</a> - Out-of-Core
|
||
DataFrames for Python, ML, visualize and explore big tabular data at a
|
||
billion rows per second.</li>
|
||
<li><a href="https://github.com/pydata/xarray">xarray</a> - 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.</li>
|
||
</ul>
|
||
<h3 id="pipelines">Pipelines</h3>
|
||
<ul>
|
||
<li><a href="https://github.com/shaypal5/pdpipe">pdpipe</a> - Sasy
|
||
pipelines for pandas DataFrames.</li>
|
||
<li><a href="https://sspipe.github.io/">SSPipe</a> - Python pipe (|)
|
||
operator with support for DataFrames and Numpy, and Pytorch.</li>
|
||
<li><a href="https://github.com/coursera/pandas-ply">pandas-ply</a> -
|
||
Functional data manipulation for pandas.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a href="https://github.com/dodger487/dplython">Dplython</a> - Dplyr
|
||
for Python.
|
||
<img height="20" src="img/R_big.png" alt="R inspired/ported lib"></li>
|
||
<li><a
|
||
href="https://github.com/scikit-learn-contrib/sklearn-pandas">sklearn-pandas</a>
|
||
- pandas integration with sklearn.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn">
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a href="https://github.com/analysiscenter/dataset">Dataset</a> -
|
||
Helps you conveniently work with random or sequential batches of your
|
||
data and define data processing.</li>
|
||
<li><a href="https://github.com/ericmjl/pyjanitor">pyjanitor</a> - Clean
|
||
APIs for data cleaning.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a href="https://github.com/reubano/meza">meza</a> - A Python
|
||
toolkit for processing tabular data.</li>
|
||
<li><a href="https://github.com/prodmodel/prodmodel">Prodmodel</a> -
|
||
Build system for data science pipelines.</li>
|
||
<li><a href="https://github.com/dovpanda-dev/dovpanda">dopanda</a> -
|
||
Hints and tips for using pandas in an analysis environment.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a href="https://github.com/DAGWorks-Inc/hamilton">Hamilton</a> - A
|
||
microframework for dataframe generation that applies Directed Acyclic
|
||
Graphs specified by a flow of lazily evaluated Python functions.</li>
|
||
</ul>
|
||
<h3 id="data-centric-ai">Data-centric AI</h3>
|
||
<ul>
|
||
<li><a href="https://github.com/cleanlab/cleanlab">cleanlab</a> - The
|
||
standard data-centric AI package for data quality and machine learning
|
||
with messy, real-world data and labels.</li>
|
||
<li><a href="https://github.com/snorkel-team/snorkel">snorkel</a> - A
|
||
system for quickly generating training data with weak supervision.</li>
|
||
<li><a href="https://github.com/sfu-db/dataprep">dataprep</a> - Collect,
|
||
clean, and visualize your data in Python with a few lines of code.</li>
|
||
</ul>
|
||
<h3 id="synthetic-data">Synthetic Data</h3>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/ydataai/ydata-synthetic">ydata-synthetic</a> -
|
||
A package to generate synthetic tabular and time-series data leveraging
|
||
the state-of-the-art generative models.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
</ul>
|
||
<h2 id="distributed-computing">Distributed Computing</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/uber/horovod">Horovod</a> - Distributed
|
||
training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
|
||
<img height="20" src="img/tf_big2.png" alt="sklearn"></li>
|
||
<li><a
|
||
href="https://spark.apache.org/docs/0.9.0/python-programming-guide.html">PySpark</a>
|
||
- Exposes the Spark programming model to Python.
|
||
<img height="20" src="img/spark_big.png" alt="Apache Spark based"></li>
|
||
<li><a href="https://github.com/Samsung/veles">Veles</a> - Distributed
|
||
machine learning platform.</li>
|
||
<li><a href="https://github.com/jubatus/jubatus">Jubatus</a> - Framework
|
||
and Library for Distributed Online Machine Learning.</li>
|
||
<li><a href="https://github.com/Microsoft/DMTK">DMTK</a> - Microsoft
|
||
Distributed Machine Learning Toolkit.</li>
|
||
<li><a href="https://github.com/PaddlePaddle/Paddle">PaddlePaddle</a> -
|
||
PArallel Distributed Deep LEarning.</li>
|
||
<li><a href="https://github.com/dask/dask-ml">dask-ml</a> - Distributed
|
||
and parallel machine learning.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/dask/distributed">Distributed</a> -
|
||
Distributed computation in Python.</li>
|
||
</ul>
|
||
<h2 id="experimentation">Experimentation</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/mlflow/mlflow">mlflow</a> - Open source
|
||
platform for the machine learning lifecycle.</li>
|
||
<li><a href="https://neptune.ai">Neptune</a> - A lightweight ML
|
||
experiment tracking, results visualization, and management tool.</li>
|
||
<li><a href="https://github.com/iterative/dvc">dvc</a> - Data Version
|
||
Control | Git for Data & Models | ML Experiments Management.</li>
|
||
<li><a href="https://github.com/tensorchord/envd">envd</a> - 🏕️ machine
|
||
learning development environment for data science and AI/ML engineering
|
||
teams.</li>
|
||
<li><a href="https://github.com/IDSIA/sacred">Sacred</a> - A tool to
|
||
help you configure, organize, log, and reproduce experiments.</li>
|
||
<li><a href="https://github.com/facebook/Ax">Ax</a> - Adaptive
|
||
Experimentation Platform.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
</ul>
|
||
<h2 id="data-validation">Data Validation</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/great-expectations/great_expectations">great_expectations</a>
|
||
- Always know what to expect from your data.</li>
|
||
<li><a href="https://github.com/unionai-oss/pandera">pandera</a> - A
|
||
lightweight, flexible, and expressive statistical data testing
|
||
library.</li>
|
||
<li><a href="https://github.com/deepchecks/deepchecks">deepchecks</a> -
|
||
Validation & testing of ML models and data during model development,
|
||
deployment, and production.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/evidentlyai/evidently">evidently</a> -
|
||
Evaluate and monitor ML models from validation to production.</li>
|
||
<li><a href="https://github.com/tensorflow/data-validation">TensorFlow
|
||
Data Validation</a> - Library for exploring and validating machine
|
||
learning data.</li>
|
||
<li><a href="https://github.com/capitalone/datacompy">DataComPy</a>- A
|
||
library to compare Pandas, Polars, and Spark data frames. It provides
|
||
stats and lets users adjust for match accuracy.</li>
|
||
</ul>
|
||
<h2 id="evaluation">Evaluation</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/statisticianinstilettos/recmetrics">recmetrics</a>
|
||
- Library of useful metrics and plots for evaluating recommender
|
||
systems.</li>
|
||
<li><a href="https://github.com/benhamner/Metrics">Metrics</a> - Machine
|
||
learning evaluation metric.</li>
|
||
<li><a
|
||
href="https://github.com/edublancas/sklearn-evaluation">sklearn-evaluation</a>
|
||
- Model evaluation made easy: plots, tables, and markdown reports.
|
||
<img height="20" src="img/sklearn_big.png" alt="sklearn"></li>
|
||
<li><a href="https://github.com/IBM/AIF360">AI Fairness 360</a> -
|
||
Fairness metrics for datasets and ML models, explanations, and
|
||
algorithms to mitigate bias in datasets and models.</li>
|
||
</ul>
|
||
<h2 id="computations">Computations</h2>
|
||
<ul>
|
||
<li><a href="http://www.numpy.org/">numpy</a> - The fundamental package
|
||
needed for scientific computing with Python.</li>
|
||
<li><a href="https://github.com/dask/dask">Dask</a> - Parallel computing
|
||
with task scheduling.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a href="https://github.com/kwgoodman/bottleneck">bottleneck</a> -
|
||
Fast NumPy array functions written in C.</li>
|
||
<li><a href="https://github.com/cupy/cupy">CuPy</a> - NumPy-like API
|
||
accelerated with CUDA.</li>
|
||
<li><a href="https://github.com/mnick/scikit-tensor">scikit-tensor</a> -
|
||
Python library for multilinear algebra and tensor factorizations.</li>
|
||
<li><a href="https://github.com/pbrod/numdifftools">numdifftools</a> -
|
||
Solve automatic numerical differentiation problems in one or more
|
||
variables.</li>
|
||
<li><a href="https://github.com/moble/quaternion">quaternion</a> - Add
|
||
built-in support for quaternions to numpy.</li>
|
||
<li><a href="https://github.com/python-adaptive/adaptive">adaptive</a> -
|
||
Tools for adaptive and parallel samping of mathematical functions.</li>
|
||
<li><a href="https://github.com/pydata/numexpr">NumExpr</a> - 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.</li>
|
||
</ul>
|
||
<h2 id="web-scraping">Web Scraping</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://www.crummy.com/software/BeautifulSoup/bs4/doc/">BeautifulSoup</a>:
|
||
The easiest library to scrape static websites for beginners</li>
|
||
<li><a href="https://scrapy.org/">Scrapy</a>: Fast and extensible
|
||
scraping library. Can write rules and create customized scraper without
|
||
touching the core</li>
|
||
<li><a
|
||
href="https://selenium-python.readthedocs.io/installation.html#introduction">Selenium</a>:
|
||
Use Selenium Python API to access all functionalities of Selenium
|
||
WebDriver in an intuitive way like a real user.</li>
|
||
<li><a href="https://github.com/clips/pattern">Pattern</a>: High level
|
||
scraping for well-establish websites such as Google, Twitter, and
|
||
Wikipedia. Also has NLP, machine learning algorithms, and
|
||
visualization</li>
|
||
<li><a
|
||
href="https://github.com/taspinar/twitterscraper">twitterscraper</a>:
|
||
Efficient library to scrape Twitter</li>
|
||
</ul>
|
||
<h2 id="spatial-analysis">Spatial Analysis</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/geopandas/geopandas">GeoPandas</a> -
|
||
Python tools for geographic data.
|
||
<img height="20" src="img/pandas_big.png" alt="pandas compatible"></li>
|
||
<li><a href="https://github.com/pysal/pysal">PySal</a> - Python Spatial
|
||
Analysis Library.</li>
|
||
</ul>
|
||
<h2 id="quantum-computing">Quantum Computing</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/Qiskit/qiskit">qiskit</a> - Qiskit is an
|
||
open-source SDK for working with quantum computers at the level of
|
||
circuits, algorithms, and application modules.</li>
|
||
<li><a href="https://github.com/quantumlib/Cirq">cirq</a> - A python
|
||
framework for creating, editing, and invoking Noisy Intermediate Scale
|
||
Quantum (NISQ) circuits.</li>
|
||
<li><a href="https://github.com/XanaduAI/pennylane">PennyLane</a> -
|
||
Quantum machine learning, automatic differentiation, and optimization of
|
||
hybrid quantum-classical computations.</li>
|
||
<li><a href="https://github.com/qmlcode/qml">QML</a> - A Python Toolkit
|
||
for Quantum Machine Learning.</li>
|
||
</ul>
|
||
<h2 id="conversion">Conversion</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/nok/sklearn-porter">sklearn-porter</a> -
|
||
Transpile trained scikit-learn estimators to C, Java, JavaScript, and
|
||
others.</li>
|
||
<li><a href="https://github.com/onnx/onnx">ONNX</a> - Open Neural
|
||
Network Exchange.</li>
|
||
<li><a href="https://github.com/Microsoft/MMdnn">MMdnn</a> - A set of
|
||
tools to help users inter-operate among different deep learning
|
||
frameworks.</li>
|
||
<li><a href="https://github.com/dmlc/treelite">treelite</a> - Universal
|
||
model exchange and serialization format for decision tree forests.</li>
|
||
</ul>
|
||
<h2 id="contributing">Contributing</h2>
|
||
<p>Contributions are welcome! :sunglasses: </br> Read the
|
||
<a href=https://github.com/krzjoa/awesome-python-datascience/blob/master/CONTRIBUTING.md>contribution
|
||
guideline</a>.</p>
|
||
<h2 id="license">License</h2>
|
||
<p>This work is licensed under the Creative Commons Attribution 4.0
|
||
International License - <a
|
||
href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></p>
|
||
<p><a
|
||
href="https://github.com/krzjoa/awesome-python-data-science">pythondatascience.md
|
||
Github</a></p>
|