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https://github.com/jacob98415/awesome-python-data-science
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https://github.com/jacob98415/awesome-python-data-science
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- Host: GitHub
- URL: https://github.com/jacob98415/awesome-python-data-science
- Owner: jacob98415
- License: cc-by-4.0
- Created: 2023-06-05T16:14:27.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-06-05T17:13:00.000Z (over 1 year ago)
- Last Synced: 2024-05-22T01:00:57.021Z (6 months ago)
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- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
Awesome Python Data Science
> Probably the best-curated list of data science software in Python
## Contents
- [Contents](#contents)
- [Machine Learning](#machine-learning)
- [General Purpose Machine Learning](#general-purpose-machine-learning)
- [Automated Machine Learning](#automated-machine-learning)
- [Ensemble Methods](#ensemble-methods)
- [Imbalanced Datasets](#imbalanced-datasets)
- [Random Forests](#random-forests)
- [Extreme Learning Machine](#extreme-learning-machine)
- [Kernel Methods](#kernel-methods)
- [Gradient Boosting](#gradient-boosting)
- [Deep Learning](#deep-learning)
- [PyTorch](#pytorch)
- [TensorFlow](#tensorflow)
- [MXNet](#mxnet)
- [Others](#others)
- [Web Scraping](#web-scraping)
- [Data Manipulation](#data-manipulation)
- [Data Frames](#data-frames)
- [Pipelines](#pipelines)
- [Data-centric AI](#data-centric-ai)
- [Synthetic Data](#synthetic-data)
- [Feature Engineering](#feature-engineering)
- [General](#general)
- [Feature Selection](#feature-selection)
- [Visualization](#visualization)
- [General Purposes](#general-purposes)
- [Interactive plots](#interactive-plots)
- [Map](#map)
- [Automatic Plotting](#automatic-plotting)
- [NLP](#nlp)
- [Deployment](#deployment)
- [Model Explanation](#model-explanation)
- [Reinforcement Learning](#reinforcement-learning)
- [Probabilistic Methods](#probabilistic-methods)
- [Genetic Programming](#genetic-programming)
- [Optimization](#optimization)
- [Time Series](#time-series)
- [Natural Language Processing](#natural-language-processing)
- [Computer Audition](#computer-audition)
- [Computer Vision](#computer-vision)
- [Statistics](#statistics)
- [Distributed Computing](#distributed-computing)
- [Experimentation](#experimentation)
- [Data Validation](#data-validation)
- [Evaluation](#evaluation)
- [Computations](#computations)
- [Spatial Analysis](#spatial-analysis)
- [Quantum Computing](#quantum-computing)
- [Conversion](#conversion)
- [Related Resources](#related-resources)
- [Contributing](#contributing)
- [License](#license)
## Machine Learning
### General Purpose Machine Learning
* [scikit-learn](http://scikit-learn.org/stable/) - Machine learning in Python. 
* [Shogun](http://www.shogun-toolbox.org/) - Machine learning toolbox.
* [xLearn](https://github.com/aksnzhy/xlearn) - High Performance, Easy-to-use, and Scalable Machine Learning Package.
* [cuML](https://github.com/rapidsai/cuml) - RAPIDS Machine Learning Library. 
* [modAL](https://github.com/cosmic-cortex/modAL) - Modular active learning framework for Python3.
* [Sparkit-learn](https://github.com/lensacom/sparkit-learn) - PySpark + scikit-learn = Sparkit-learn. 
* [mlpack](https://github.com/mlpack/mlpack) - A scalable C++ machine learning library (Python bindings).
* [dlib](https://github.com/davisking/dlib) - Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).
* [MLxtend](https://github.com/rasbt/mlxtend) - Extension and helper modules for Python's data analysis and machine learning libraries.
* [hyperlearn](https://github.com/danielhanchen/hyperlearn) - 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels. 
* [Reproducible Experiment Platform (REP)](https://github.com/yandex/rep) - Machine Learning toolbox for Humans.
* [scikit-multilearn](https://github.com/scikit-multilearn/scikit-multilearn) - Multi-label classification for python.
* [seqlearn](https://github.com/larsmans/seqlearn) - Sequence classification toolkit for Python.
* [pystruct](https://github.com/pystruct/pystruct) - Simple structured learning framework for Python.
* [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys) - Highly interpretable classifiers for scikit learn.
* [RuleFit](https://github.com/christophM/rulefit) - Implementation of the rulefit.
* [metric-learn](https://github.com/all-umass/metric-learn) - Metric learning algorithms in Python.
* [pyGAM](https://github.com/dswah/pyGAM) - Generalized Additive Models in Python.
* [Karate Club](https://github.com/benedekrozemberczki/karateclub) - An unsupervised machine learning library for graph-structured data.
* [Little Ball of Fur](https://github.com/benedekrozemberczki/littleballoffur) - A library for sampling graph structured data.
* [causalml](https://github.com/uber/causalml) - Uplift modeling and causal inference with machine learning algorithms.
### Automated Machine Learning
* [TPOT](https://github.com/rhiever/tpot) - Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
* [auto-sklearn](https://github.com/automl/auto-sklearn) - An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
* [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library.
* [AutoGluon](https://github.com/awslabs/autogluon) - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
### Ensemble Methods
* [ML-Ensemble](http://ml-ensemble.com/) - High performance ensemble learning.
* [Stacking](https://github.com/ikki407/stacking) - Simple and useful stacking library written in Python.
* [stacked_generalization](https://github.com/fukatani/stacked_generalization) - Library for machine learning stacking generalization.
* [vecstack](https://github.com/vecxoz/vecstack) - Python package for stacking (machine learning technique).
### Imbalanced Datasets
* [imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn) - Module to perform under-sampling and over-sampling with various techniques.
* [imbalanced-algorithms](https://github.com/dialnd/imbalanced-algorithms) - Python-based implementations of algorithms for learning on imbalanced data. 
### Random Forests
* [rpforest](https://github.com/lyst/rpforest) - A forest of random projection trees.
* [sklearn-random-bits-forest](https://github.com/tmadl/sklearn-random-bits-forest) - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).
* [rgf_python](https://github.com/fukatani/rgf_python) - Python Wrapper of Regularized Greedy Forest.
### Extreme Learning Machine
* [Python-ELM](https://github.com/dclambert/Python-ELM) - Extreme Learning Machine implementation in Python.
* [Python Extreme Learning Machine (ELM)](https://github.com/acba/elm) - A machine learning technique used for classification/regression tasks.
* [hpelm](https://github.com/akusok/hpelm) - High-performance implementation of Extreme Learning Machines (fast randomized neural networks).
### Kernel Methods
* [pyFM](https://github.com/coreylynch/pyFM) - Factorization machines in python.
* [fastFM](https://github.com/ibayer/fastFM) - A library for Factorization Machines.
* [tffm](https://github.com/geffy/tffm) - TensorFlow implementation of an arbitrary order Factorization Machine.
* [liquidSVM](https://github.com/liquidSVM/liquidSVM) - An implementation of SVMs.
* [scikit-rvm](https://github.com/JamesRitchie/scikit-rvm) - Relevance Vector Machine implementation using the scikit-learn API.
* [ThunderSVM](https://github.com/Xtra-Computing/thundersvm) - A fast SVM Library on GPUs and CPUs.
### Gradient Boosting
* [XGBoost](https://github.com/dmlc/xgboost) - Scalable, Portable, and Distributed Gradient Boosting.
* [LightGBM](https://github.com/Microsoft/LightGBM) - A fast, distributed, high-performance gradient boosting.
* [CatBoost](https://github.com/catboost/catboost) - An open-source gradient boosting on decision trees library.
* [ThunderGBM](https://github.com/Xtra-Computing/thundergbm) - Fast GBDTs and Random Forests on GPUs.
## Deep Learning
### PyTorch
* [PyTorch](https://github.com/pytorch/pytorch) - Tensors and Dynamic neural networks in Python with strong GPU acceleration.
* [pytorch-lightning](https://github.com/Lightning-AI/lightning) - PyTorch Lightning is just organized PyTorch.
* [torchvision](https://github.com/pytorch/vision) - Datasets, Transforms, and Models specific to Computer Vision.
* [torchtext](https://github.com/pytorch/text) - Data loaders and abstractions for text and NLP.
* [torchaudio](https://github.com/pytorch/audio) - An audio library for PyTorch.
* [ignite](https://github.com/pytorch/ignite) - High-level library to help with training neural networks in PyTorch.
* [skorch](https://github.com/dnouri/skorch) - A scikit-learn compatible neural network library that wraps PyTorch.
* [pytorch_geometric](https://github.com/rusty1s/pytorch_geometric) - Geometric Deep Learning Extension Library for PyTorch.
* [Catalyst](https://github.com/catalyst-team/catalyst) - High-level utils for PyTorch DL & RL research.
* [pytorch_geometric_temporal](https://github.com/benedekrozemberczki/pytorch_geometric_temporal) - Temporal Extension Library for PyTorch Geometric.
* [ChemicalX](https://github.com/AstraZeneca/chemicalx) - A PyTorch-based deep learning library for drug pair scoring.
### TensorFlow
* [TensorFlow](https://github.com/tensorflow/tensorflow) - Computation using data flow graphs for scalable machine learning by Google.
* [TensorLayer](https://github.com/zsdonghao/tensorlayer) - Deep Learning and Reinforcement Learning Library for Researcher and Engineer.
* [TFLearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow.
* [Sonnet](https://github.com/deepmind/sonnet) - TensorFlow-based neural network library.
* [tensorpack](https://github.com/ppwwyyxx/tensorpack) - A Neural Net Training Interface on TensorFlow.
* [Polyaxon](https://github.com/polyaxon/polyaxon) - A platform that helps you build, manage and monitor deep learning models.
* [NeuPy](https://github.com/itdxer/neupy) - NeuPy is a Python library for Artificial Neural Networks and Deep Learning (previously: 
).
* [tfdeploy](https://github.com/riga/tfdeploy) - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy.
* [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) - TensorFlow ROCm port. 
* [TensorFlow Fold](https://github.com/tensorflow/fold) - Deep learning with dynamic computation graphs in TensorFlow.
* [tensorlm](https://github.com/batzner/tensorlm) - Wrapper library for text generation/language models at char and word level with RNN.
* [TensorLight](https://github.com/bsautermeister/tensorlight) - A high-level framework for TensorFlow.
* [Mesh TensorFlow](https://github.com/tensorflow/mesh) - Model Parallelism Made Easier.
* [Ludwig](https://github.com/uber/ludwig) - A toolbox that allows one to train and test deep learning models without the need to write code.
* [Keras](https://keras.io) - A high-level neural networks API running on top of TensorFlow. 
* [keras-contrib](https://github.com/keras-team/keras-contrib) - Keras community contributions.
* [Hyperas](https://github.com/maxpumperla/hyperas) - Keras + Hyperopt: A straightforward wrapper for a convenient hyperparameter.
* [Elephas](https://github.com/maxpumperla/elephas) - Distributed Deep learning with Keras & Spark.
* [Hera](https://github.com/keplr-io/hera) - Train/evaluate a Keras model, and get metrics streamed to a dashboard in your browser.
* [Spektral](https://github.com/danielegrattarola/spektral) - Deep learning on graphs.
* [qkeras](https://github.com/google/qkeras) - A quantization deep learning library.
### MXNet
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler. 
* [Gluon](https://github.com/gluon-api/gluon-api) - A clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet).
* [MXbox](https://github.com/Lyken17/mxbox) - Simple, efficient, and flexible vision toolbox for the mxnet framework.
* [gluon-cv](https://github.com/dmlc/gluon-cv) - Provides implementations of the state-of-the-art deep learning models in computer vision.
* [gluon-nlp](https://github.com/dmlc/gluon-nlp) - NLP made easy.
* [Xfer](https://github.com/amzn/xfer) - Transfer Learning library for Deep Neural Networks.
* [MXNet](https://github.com/ROCmSoftwarePlatform/mxnet) - HIP Port of MXNet.
### Others
* [jax](https://github.com/google/jax) - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
* [Tangent](https://github.com/google/tangent) - Source-to-Source Debuggable Derivatives in Pure Python.
* [autograd](https://github.com/HIPS/autograd) - Efficiently computes derivatives of numpy code.
* [Myia](https://github.com/mila-udem/myia) - Deep Learning framework (pre-alpha).
* [nnabla](https://github.com/sony/nnabla) - Neural Network Libraries by Sony.
* [Caffe](https://github.com/BVLC/caffe) - A fast open framework for deep learning.
* [hipCaffe](https://github.com/ROCmSoftwarePlatform/hipCaffe) - The HIP port of Caffe.
**[DISCONTINUED PROJECTS](https://github.com/krzjoa/awesome-python-data-science/blob/master/other/deprecated.md#deep-learning)**
## Web Scraping
* [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/): The easiest library to scrape static websites for beginners
* [Scrapy](https://scrapy.org/): Fast and extensible scraping library. Can write rules and create customized scraper without touching the core
* [Selenium](https://selenium-python.readthedocs.io/installation.html#introduction): Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.
* [Pattern](https://github.com/clips/pattern): High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization
* [twitterscraper](https://github.com/taspinar/twitterscraper): Efficient library to scrape Twitter
## Data Manipulation
### Data Frames
* [pandas](https://pandas.pydata.org/pandas-docs/stable/) - Powerful Python data analysis toolkit.
* [pandas_profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects
* [cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library. 
* [blaze](https://github.com/blaze/blaze) - NumPy and pandas interface to Big Data.
* [pandasql](https://github.com/yhat/pandasql) - Allows you to query pandas DataFrames using SQL syntax.
* [pandas-gbq](https://github.com/pydata/pandas-gbq) - pandas Google Big Query.
* [xpandas](https://github.com/alan-turing-institute/xpandas) - Universal 1d/2d data containers with Transformers .functionality for data analysis by [The Alan Turing Institute](https://www.turing.ac.uk/).
* [pysparkling](https://github.com/svenkreiss/pysparkling) - A pure Python implementation of Apache Spark's RDD and DStream interfaces.
* [Arctic](https://github.com/manahl/arctic) - High-performance datastore for time series and tick data.
* [datatable](https://github.com/h2oai/datatable) - Data.table for Python. 
* [koalas](https://github.com/databricks/koalas) - pandas API on Apache Spark.
* [modin](https://github.com/modin-project/modin) - Speed up your pandas workflows by changing a single line of code.
* [swifter](https://github.com/jmcarpenter2/swifter) - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
* [pandas_flavor](https://github.com/Zsailer/pandas_flavor) - A package that allows writing your own flavor of Pandas easily.
* [pandas-log](https://github.com/eyaltrabelsi/pandas-log) - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
* [vaex](https://github.com/vaexio/vaex) - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
* [xarray](https://github.com/pydata/xarray) - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less error-prone indexing routines.
* [sk-transformer](https://github.com/chrislemke/sk-transformers) - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps
* [polars](https://github.com/pola-rs/polars) - A fast multi-threaded, hybrid-out-of-core DataFrame library.
### Pipelines
* [pdpipe](https://github.com/shaypal5/pdpipe) - Sasy pipelines for pandas DataFrames.
* [SSPipe](https://sspipe.github.io/) - Python pipe (|) operator with support for DataFrames and Numpy, and Pytorch.
* [pandas-ply](https://github.com/coursera/pandas-ply) - Functional data manipulation for pandas.
* [Dplython](https://github.com/dodger487/dplython) - Dplyr for Python.
* [sklearn-pandas](https://github.com/scikit-learn-contrib/sklearn-pandas) - pandas integration with sklearn.
* [Dataset](https://github.com/analysiscenter/dataset) - Helps you conveniently work with random or sequential batches of your data and define data processing.
* [pyjanitor](https://github.com/ericmjl/pyjanitor) - Clean APIs for data cleaning.
* [meza](https://github.com/reubano/meza) - A Python toolkit for processing tabular data.
* [Prodmodel](https://github.com/prodmodel/prodmodel) - Build system for data science pipelines.
* [dopanda](https://github.com/dovpanda-dev/dovpanda) - Hints and tips for using pandas in an analysis environment.
* [Hamilton](https://github.com/stitchfix/hamilton) - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.
### Data-centric AI
* [cleanlab](https://github.com/cleanlab/cleanlab) - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
* [snorkel](https://github.com/snorkel-team/snorkel) - A system for quickly generating training data with weak supervision.
* [dataprep](https://github.com/sfu-db/dataprep) - Collect, clean, and visualize your data in Python with a few lines of code.
### Synthetic Data
* [ydata-synthetic](https://github.com/ydataai/ydata-synthetic) - A package to generate synthetic tabular and time-series data leveraging the state-of-the-art generative models.
## Feature Engineering
### General
* [Featuretools](https://github.com/Featuretools/featuretools) - Automated feature engineering.
* [Feature Engine](https://github.com/feature-engine/feature_engine) - Feature engineering package with sklearn-like functionality.
* [skl-groups](https://github.com/dougalsutherland/skl-groups) - A scikit-learn addon to operate on set/"group"-based features.
* [Feature Forge](https://github.com/machinalis/featureforge) - A set of tools for creating and testing machine learning features.
* [few](https://github.com/lacava/few) - A feature engineering wrapper for sklearn.
* [scikit-mdr](https://github.com/EpistasisLab/scikit-mdr) - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.
* [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series.
* [dirty_cat](https://github.com/dirty-cat/dirty_cat) - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression).
* [NitroFE](https://github.com/NITRO-AI/NitroFE) - Moving window features.
### Feature Selection
* [scikit-feature](https://github.com/jundongl/scikit-feature) - Feature selection repository in Python.
* [boruta_py](https://github.com/scikit-learn-contrib/boruta_py) - Implementations of the Boruta all-relevant feature selection method.
* [BoostARoota](https://github.com/chasedehan/BoostARoota) - A fast xgboost feature selection algorithm.
* [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate) - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
* [zoofs](https://github.com/jaswinder9051998/zoofs) - A feature selection library based on evolutionary algorithms.
## Visualization
### General Purposes
* [Matplotlib](https://github.com/matplotlib/matplotlib) - Plotting with Python.
* [seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization using matplotlib.
* [prettyplotlib](https://github.com/olgabot/prettyplotlib) - Painlessly create beautiful matplotlib plots.
* [python-ternary](https://github.com/marcharper/python-ternary) - Ternary plotting library for Python with matplotlib.
* [missingno](https://github.com/ResidentMario/missingno) - Missing data visualization module for Python.
* [chartify](https://github.com/spotify/chartify/) - Python library that makes it easy for data scientists to create charts.
* [physt](https://github.com/janpipek/physt) - Improved histograms.
### Interactive plots
* [animatplot](https://github.com/t-makaro/animatplot) - A python package for animating plots built on matplotlib.
* [plotly](https://plot.ly/python/) - A Python library that makes interactive and publication-quality graphs.
* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
* [Altair](https://altair-viz.github.io/) - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
* [bqplot](https://github.com/bqplot/bqplot) - Plotting library for IPython/Jupyter notebooks
* [pyecharts](https://github.com/pyecharts/pyecharts) - Migrated from [Echarts](https://github.com/apache/echarts), a charting and visualization library, to Python's interactive visual drawing library.
### Map
* [folium](https://python-visualization.github.io/folium/quickstart.html#Getting-Started) - Makes it easy to visualize data on an interactive open street map
* [geemap](https://github.com/giswqs/geemap) - Python package for interactive mapping with Google Earth Engine (GEE)
### Automatic Plotting
* [HoloViews](https://github.com/ioam/holoviews) - Stop plotting your data - annotate your data and let it visualize itself.
* [AutoViz](https://github.com/AutoViML/AutoViz): Visualize data automatically with 1 line of code (ideal for machine learning)
* [SweetViz](https://github.com/fbdesignpro/sweetviz): Visualize and compare datasets, target values and associations, with one line of code.
### NLP
* [pyLDAvis](https://github.com/bmabey/pyLDAvis): Visualize interactive topic model
## Deployment
* [fastapi](https://fastapi.tiangolo.com/) - Modern, fast (high-performance), a web framework for building APIs with Python
* [streamlit](https://www.streamlit.io/) - Make it easy to deploy the machine learning model
* [gradio](https://github.com/gradio-app/gradio) - Create UIs for your machine learning model in Python in 3 minutes.
* [datapane](https://datapane.com/) - A collection of APIs to turn scripts and notebooks into interactive reports.
* [binder](https://mybinder.org/) - Enable sharing and execute Jupyter Notebooks
## Model Explanation
* [dalex](https://github.com/ModelOriented/DALEX) - moDel Agnostic Language for Exploration and explanation.
* [Shapley](https://github.com/benedekrozemberczki/shapley) - A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
* [Alibi](https://github.com/SeldonIO/alibi) - Algorithms for monitoring and explaining machine learning models.
* [anchor](https://github.com/marcotcr/anchor) - Code for "High-Precision Model-Agnostic Explanations" paper.
* [aequitas](https://github.com/dssg/aequitas) - Bias and Fairness Audit Toolkit.
* [Contrastive Explanation](https://github.com/MarcelRobeer/ContrastiveExplanation) - Contrastive Explanation (Foil Trees).
* [yellowbrick](https://github.com/DistrictDataLabs/yellowbrick) - Visual analysis and diagnostic tools to facilitate machine learning model selection.
* [scikit-plot](https://github.com/reiinakano/scikit-plot) - An intuitive library to add plotting functionality to scikit-learn objects.
* [shap](https://github.com/slundberg/shap) - A unified approach to explain the output of any machine learning model.
* [ELI5](https://github.com/TeamHG-Memex/eli5) - A library for debugging/inspecting machine learning classifiers and explaining their predictions.
* [Lime](https://github.com/marcotcr/lime) - Explaining the predictions of any machine learning classifier.
* [FairML](https://github.com/adebayoj/fairml) - FairML is a python toolbox auditing the machine learning models for bias.
* [L2X](https://github.com/Jianbo-Lab/L2X) - Code for replicating the experiments in the paper *Learning to Explain: An Information-Theoretic Perspective on Model Interpretation*.
* [PDPbox](https://github.com/SauceCat/PDPbox) - Partial dependence plot toolbox.
* [PyCEbox](https://github.com/AustinRochford/PyCEbox) - Python Individual Conditional Expectation Plot Toolbox.
* [Skater](https://github.com/datascienceinc/Skater) - Python Library for Model Interpretation.
* [model-analysis](https://github.com/tensorflow/model-analysis) - Model analysis tools for TensorFlow.
* [themis-ml](https://github.com/cosmicBboy/themis-ml) - A library that implements fairness-aware machine learning algorithms.
* [treeinterpreter](https://github.com/andosa/treeinterpreter) - Interpreting scikit-learn's decision tree and random forest predictions.
* [AI Explainability 360](https://github.com/IBM/AIX360) - Interpretability and explainability of data and machine learning models.
* [Auralisation](https://github.com/keunwoochoi/Auralisation) - Auralisation of learned features in CNN (for audio).
* [CapsNet-Visualization](https://github.com/bourdakos1/CapsNet-Visualization) - A visualization of the CapsNet layers to better understand how it works.
* [lucid](https://github.com/tensorflow/lucid) - A collection of infrastructure and tools for research in neural network interpretability.
* [Netron](https://github.com/lutzroeder/Netron) - Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).
* [FlashLight](https://github.com/dlguys/flashlight) - Visualization Tool for your NeuralNetwork.
* [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch) - Tensorboard for PyTorch (and chainer, mxnet, numpy, ...).
* [mxboard](https://github.com/awslabs/mxboard) - Logging MXNet data for visualization in TensorBoard.
## Reinforcement Learning
* [OpenAI Gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.
* [Coach](https://github.com/NervanaSystems/coach) - Easy experimentation with state-of-the-art Reinforcement Learning algorithms.
* [garage](https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.
* [OpenAI Baselines](https://github.com/openai/baselines) - High-quality implementations of reinforcement learning algorithms.
* [Stable Baselines](https://github.com/hill-a/stable-baselines) - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
* [RLlib](https://ray.readthedocs.io/en/latest/rllib.html) - Scalable Reinforcement Learning.
* [Horizon](https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement Learning.
* [TF-Agents](https://github.com/tensorflow/agents) - A library for Reinforcement Learning in TensorFlow.
* [TensorForce](https://github.com/reinforceio/tensorforce) - A TensorFlow library for applied reinforcement learning.
* [TRFL](https://github.com/deepmind/trfl) - TensorFlow Reinforcement Learning.
* [Dopamine](https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement learning algorithms.
* [keras-rl](https://github.com/keras-rl/keras-rl) - Deep Reinforcement Learning for Keras.
* [ChainerRL](https://github.com/chainer/chainerrl) - A deep reinforcement learning library built on top of Chainer.
## Probabilistic Methods
* [pyro](https://github.com/uber/pyro) - A flexible, scalable deep probabilistic programming library built on PyTorch.
* [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python.
* [ZhuSuan](http://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning.
* [PyMC](https://github.com/pymc-devs/pymc) - Bayesian Stochastic Modelling in Python.
* [InferPy](https://github.com/PGM-Lab/InferPy) - Deep Probabilistic Modelling Made Easy.
* [GPflow](http://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in TensorFlow.
* [PyStan](https://github.com/stan-dev/pystan) - Bayesian inference using the No-U-Turn sampler (Python interface).
* [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes) - Python package for Bayesian Machine Learning with scikit-learn API.
* [pgmpy](https://github.com/pgmpy/pgmpy) - A python library for working with Probabilistic Graphical Models.
* [skpro](https://github.com/alan-turing-institute/skpro) - Supervised domain-agnostic prediction framework for probabilistic modelling by [The Alan Turing Institute](https://www.turing.ac.uk/).
* [PtStat](https://github.com/stepelu/ptstat) - Probabilistic Programming and Statistical Inference in PyTorch.
* [PyVarInf](https://github.com/ctallec/pyvarinf) - Bayesian Deep Learning methods with Variational Inference for PyTorch.
* [emcee](https://github.com/dfm/emcee) - The Python ensemble sampling toolkit for affine-invariant MCMC.
* [hsmmlearn](https://github.com/jvkersch/hsmmlearn) - A library for hidden semi-Markov models with explicit durations.
* [pyhsmm](https://github.com/mattjj/pyhsmm) - Bayesian inference in HSMMs and HMMs.
* [GPyTorch](https://github.com/cornellius-gp/gpytorch) - A highly efficient and modular implementation of Gaussian Processes in PyTorch.
* [MXFusion](https://github.com/amzn/MXFusion) - Modular Probabilistic Programming on MXNet.
* [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite) - A scikit-learn-inspired API for CRFsuite.
## Genetic Programming
* [gplearn](https://github.com/trevorstephens/gplearn) - Genetic Programming in Python.
* [DEAP](https://github.com/DEAP/deap) - Distributed Evolutionary Algorithms in Python.
* [karoo_gp](https://github.com/kstaats/karoo_gp) - A Genetic Programming platform for Python with GPU support.
* [monkeys](https://github.com/hchasestevens/monkeys) - A strongly-typed genetic programming framework for Python.
* [sklearn-genetic](https://github.com/manuel-calzolari/sklearn-genetic) - Genetic feature selection module for scikit-learn.
## Optimization
* [Optuna](https://github.com/optuna/optuna) - A hyperparameter optimization framework.
* [Spearmint](https://github.com/HIPS/Spearmint) - Bayesian optimization.
* [BoTorch](https://github.com/pytorch/botorch) - Bayesian optimization in PyTorch.
* [scikit-opt](https://github.com/guofei9987/scikit-opt) - Heuristic Algorithms for optimization.
* [sklearn-genetic-opt](https://github.com/rodrigo-arenas/Sklearn-genetic-opt) - Hyperparameters tuning and feature selection using evolutionary algorithms.
* [SMAC3](https://github.com/automl/SMAC3) - Sequential Model-based Algorithm Configuration.
* [Optunity](https://github.com/claesenm/optunity) - Is a library containing various optimizers for hyperparameter tuning.
* [hyperopt](https://github.com/hyperopt/hyperopt) - Distributed Asynchronous Hyperparameter Optimization in Python.
* [hyperopt-sklearn](https://github.com/hyperopt/hyperopt-sklearn) - Hyper-parameter optimization for sklearn.
* [sklearn-deap](https://github.com/rsteca/sklearn-deap) - Use evolutionary algorithms instead of gridsearch in scikit-learn.
* [sigopt_sklearn](https://github.com/sigopt/sigopt_sklearn) - SigOpt wrappers for scikit-learn methods.
* [Bayesian Optimization](https://github.com/fmfn/BayesianOptimization) - A Python implementation of global optimization with gaussian processes.
* [SafeOpt](https://github.com/befelix/SafeOpt) - Safe Bayesian Optimization.
* [scikit-optimize](https://github.com/scikit-optimize/scikit-optimize) - Sequential model-based optimization with a `scipy.optimize` interface.
* [Solid](https://github.com/100/Solid) - A comprehensive gradient-free optimization framework written in Python.
* [PySwarms](https://github.com/ljvmiranda921/pyswarms) - A research toolkit for particle swarm optimization in Python.
* [Platypus](https://github.com/Project-Platypus/Platypus) - A Free and Open Source Python Library for Multiobjective Optimization.
* [GPflowOpt](https://github.com/GPflow/GPflowOpt) - Bayesian Optimization using GPflow.
* [POT](https://github.com/rflamary/POT) - Python Optimal Transport library.
* [Talos](https://github.com/autonomio/talos) - Hyperparameter Optimization for Keras Models.
* [nlopt](https://github.com/stevengj/nlopt) - Library for nonlinear optimization (global and local, constrained or unconstrained).
* [OR-Tools](https://developers.google.com/optimization) - An open-source software suite for optimization by Google; provides a unified programming interface to a half dozen solvers: SCIP, GLPK, GLOP, CP-SAT, CPLEX, and Gurobi.
## Time Series
* [sktime](https://github.com/alan-turing-institute/sktime) - A unified framework for machine learning with time series.
* [darts](https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series.
* [statsforecast](https://github.com/Nixtla/statsforecast) - Lightning fast forecasting with statistical and econometric models.
* [mlforecast](https://github.com/Nixtla/mlforecast) - Scalable machine learning-based time series forecasting.
* [neuralforecast](https://github.com/Nixtla/neuralforecast) - Scalable machine learning-based time series forecasting.
* [tslearn](https://github.com/rtavenar/tslearn) - Machine learning toolkit dedicated to time-series data.
* [tick](https://github.com/X-DataInitiative/tick) - Module for statistical learning, with a particular emphasis on time-dependent modeling.
* [greykite](https://github.com/linkedin/greykite) - A flexible, intuitive, and fast forecasting library next.
* [Prophet](https://github.com/facebook/prophet) - Automatic Forecasting Procedure.
* [PyFlux](https://github.com/RJT1990/pyflux) - Open source time series library for Python.
* [bayesloop](https://github.com/christophmark/bayesloop) - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
* [luminol](https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library.
* [dateutil](https://dateutil.readthedocs.io/en/stable/) - Powerful extensions to the standard datetime module
* [maya](https://github.com/timofurrer/maya) - makes it very easy to parse a string and for changing timezones
* [Chaos Genius](https://github.com/chaos-genius/chaos_genius) - ML powered analytics engine for outlier/anomaly detection and root cause analysis
## Natural Language Processing
* [spaCy](https://spacy.io/) - Industrial-Strength Natural Language Processing.
* [NLTK](https://github.com/nltk/nltk) - Modules, data sets, and tutorials supporting research and development in Natural Language Processing.
* [CLTK](https://github.com/cltk/cltk) - The Classical Language Toolkik.
* [gensim](https://radimrehurek.com/gensim/) - Topic Modelling for Humans.
* [pyMorfologik](https://github.com/dmirecki/pyMorfologik) - Python binding for Morfologik.
* [skift](https://github.com/shaypal5/skift) - Scikit-learn wrappers for Python fastText.
* [Phonemizer](https://github.com/bootphon/phonemizer) - Simple text-to-phonemes converter for multiple languages.
* [flair](https://github.com/zalandoresearch/flair) - Very simple framework for state-of-the-art NLP.
## Computer Audition
* [librosa](https://github.com/librosa/librosa) - Python library for audio and music analysis.
* [Yaafe](https://github.com/Yaafe/Yaafe) - Audio features extraction.
* [aubio](https://github.com/aubio/aubio) - A library for audio and music analysis.
* [Essentia](https://github.com/MTG/essentia) - Library for audio and music analysis, description, and synthesis.
* [LibXtract](https://github.com/jamiebullock/LibXtract) - A simple, portable, lightweight library of audio feature extraction functions.
* [Marsyas](https://github.com/marsyas/marsyas) - Music Analysis, Retrieval, and Synthesis for Audio Signals.
* [muda](https://github.com/bmcfee/muda) - A library for augmenting annotated audio data.
* [madmom](https://github.com/CPJKU/madmom) - Python audio and music signal processing library.
## Computer Vision
* [OpenCV](https://github.com/opencv/opencv) - Open Source Computer Vision Library.
* [scikit-image](https://github.com/scikit-image/scikit-image) - Image Processing SciKit (Toolbox for SciPy).
* [imgaug](https://github.com/aleju/imgaug) - Image augmentation for machine learning experiments.
* [imgaug_extension](https://github.com/cadenai/imgaug_extension) - Additional augmentations for imgaug.
* [Augmentor](https://github.com/mdbloice/Augmentor) - Image augmentation library in Python for machine learning.
* [albumentations](https://github.com/albu/albumentations) - Fast image augmentation library and easy-to-use wrapper around other libraries.
## Statistics
* [pandas_summary](https://github.com/mouradmourafiq/pandas-summary) - Extension to pandas dataframes describe function.
* [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects.
* [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.
* [stockstats](https://github.com/jealous/stockstats) - Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.
* [weightedcalcs](https://github.com/jsvine/weightedcalcs) - A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.
* [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) - Pairwise Multiple Comparisons Post-hoc Tests.
* [Alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive (alpha) stock factors.
## Distributed Computing
* [Horovod](https://github.com/uber/horovod) - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
* [PySpark](https://spark.apache.org/docs/0.9.0/python-programming-guide.html) - Exposes the Spark programming model to Python.
* [Veles](https://github.com/Samsung/veles) - Distributed machine learning platform.
* [Jubatus](https://github.com/jubatus/jubatus) - Framework and Library for Distributed Online Machine Learning.
* [DMTK](https://github.com/Microsoft/DMTK) - Microsoft Distributed Machine Learning Toolkit.
* [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning.
* [dask-ml](https://github.com/dask/dask-ml) - Distributed and parallel machine learning.
* [Distributed](https://github.com/dask/distributed) - Distributed computation in Python.
## Experimentation
* [mlflow](https://github.com/mlflow/mlflow) - Open source platform for the machine learning lifecycle.
* [Neptune](https://neptune.ai) - A lightweight ML experiment tracking, results visualization, and management tool.
* [dvc](https://github.com/iterative/dvc) - Data Version Control | Git for Data & Models | ML Experiments Management.
* [envd](https://github.com/tensorchord/envd) - 🏕️ machine learning development environment for data science and AI/ML engineering teams.
* [Sacred](https://github.com/IDSIA/sacred) - A tool to help you configure, organize, log, and reproduce experiments.
* [Ax](https://github.com/facebook/Ax) - Adaptive Experimentation Platform.
## Data Validation
* [great_expectations](https://github.com/great-expectations/great_expectations) - Always know what to expect from your data.
* [pandera](https://github.com/unionai-oss/pandera) - A lightweight, flexible, and expressive statistical data testing library.
* [deepchecks](https://github.com/deepchecks/deepchecks) - Validation & testing of ML models and data during model development, deployment, and production.
* [evidently](https://github.com/evidentlyai/evidently) - Evaluate and monitor ML models from validation to production.
* [TensorFlow Data Validation](https://github.com/tensorflow/data-validation) - Library for exploring and validating machine learning data.
## Evaluation
* [recmetrics](https://github.com/statisticianinstilettos/recmetrics) - Library of useful metrics and plots for evaluating recommender systems.
* [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metric.
* [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation) - Model evaluation made easy: plots, tables, and markdown reports.
* [AI Fairness 360](https://github.com/IBM/AIF360) - Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.
## Computations
* [numpy](http://www.numpy.org/) - The fundamental package needed for scientific computing with Python.
* [Dask](https://github.com/dask/dask) - Parallel computing with task scheduling.
* [bottleneck](https://github.com/kwgoodman/bottleneck) - Fast NumPy array functions written in C.
* [CuPy](https://github.com/cupy/cupy) - NumPy-like API accelerated with CUDA.
* [scikit-tensor](https://github.com/mnick/scikit-tensor) - Python library for multilinear algebra and tensor factorizations.
* [numdifftools](https://github.com/pbrod/numdifftools) - Solve automatic numerical differentiation problems in one or more variables.
* [quaternion](https://github.com/moble/quaternion) - Add built-in support for quaternions to numpy.
* [adaptive](https://github.com/python-adaptive/adaptive) - Tools for adaptive and parallel samping of mathematical functions.
* [NumExpr](https://github.com/pydata/numexpr) - A fast numerical expression evaluator for NumPy that comes with an integrated computing virtual machine to speed calculations up by avoiding memory allocation for intermediate results.
## Spatial Analysis
* [GeoPandas](https://github.com/geopandas/geopandas) - Python tools for geographic data.
* [PySal](https://github.com/pysal/pysal) - Python Spatial Analysis Library.
## Quantum Computing
* [qiskit](https://github.com/Qiskit/qiskit) - Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.
* [cirq](https://github.com/quantumlib/Cirq) - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
* [PennyLane](https://github.com/XanaduAI/pennylane) - Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
* [QML](https://github.com/qmlcode/qml) - A Python Toolkit for Quantum Machine Learning.
## Conversion
* [sklearn-porter](https://github.com/nok/sklearn-porter) - Transpile trained scikit-learn estimators to C, Java, JavaScript, and others.
* [ONNX](https://github.com/onnx/onnx) - Open Neural Network Exchange.
* [MMdnn](https://github.com/Microsoft/MMdnn) - A set of tools to help users inter-operate among different deep learning frameworks.
## Contributing
Contributions are welcome! :sunglasses:
Read the contribution guideline.
## License
This work is licensed under the Creative Commons Attribution 4.0 International License - [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)