Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

awesome-python-data-science

From gitlab
https://github.com/jacob98415/awesome-python-data-science

  • scikit-learn - Machine learning in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • Shogun - Machine learning toolbox.
  • xLearn - High Performance, Easy-to-use, and Scalable Machine Learning Package.
  • cuML - RAPIDS Machine Learning Library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
  • modAL - Modular active learning framework for Python3. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • Sparkit-learn - PySpark + scikit-learn = Sparkit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/spark_big.png" alt="Apache Spark based">
  • mlpack - A scalable C++ machine learning library (Python bindings).
  • dlib - Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).
  • MLxtend - Extension and helper modules for Python's data analysis and machine learning libraries. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • hyperlearn - 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • Reproducible Experiment Platform (REP) - Machine Learning toolbox for Humans. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • scikit-multilearn - Multi-label classification for python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • seqlearn - Sequence classification toolkit for Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • pystruct - Simple structured learning framework for Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • sklearn-expertsys - Highly interpretable classifiers for scikit learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • RuleFit - Implementation of the rulefit. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • metric-learn - Metric learning algorithms in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • pyGAM - Generalized Additive Models in Python.
  • Karate Club - An unsupervised machine learning library for graph-structured data.
  • Little Ball of Fur - A library for sampling graph structured data.
  • causalml - Uplift modeling and causal inference with machine learning algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • TPOT - Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • auto-sklearn - An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • MLBox - A powerful Automated Machine Learning python library.
  • AutoGluon - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
  • ML-Ensemble - High performance ensemble learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • Stacking - Simple and useful stacking library written in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • stacked_generalization - Library for machine learning stacking generalization. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • vecstack - Python package for stacking (machine learning technique). <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • imbalanced-learn - Module to perform under-sampling and over-sampling with various techniques. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • imbalanced-algorithms - Python-based implementations of algorithms for learning on imbalanced data. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/tf_big2.png" alt="sklearn">
  • rpforest - A forest of random projection trees. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • sklearn-random-bits-forest - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).<img height="20" src="img/sklearn_big.png" alt="sklearn">
  • rgf_python - Python Wrapper of Regularized Greedy Forest. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • Python-ELM - Extreme Learning Machine implementation in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • Python Extreme Learning Machine (ELM) - A machine learning technique used for classification/regression tasks.
  • hpelm - High-performance implementation of Extreme Learning Machines (fast randomized neural networks). <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
  • pyFM - Factorization machines in python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • fastFM - A library for Factorization Machines. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • tffm - TensorFlow implementation of an arbitrary order Factorization Machine. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/tf_big2.png" alt="sklearn">
  • liquidSVM - An implementation of SVMs.
  • scikit-rvm - Relevance Vector Machine implementation using the scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • ThunderSVM - 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">
  • XGBoost - Scalable, Portable, and Distributed Gradient Boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
  • LightGBM - 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">
  • CatBoost - 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">
  • ThunderGBM - Fast GBDTs and Random Forests on GPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
  • PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • pytorch-lightning - PyTorch Lightning is just organized PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • torchvision - Datasets, Transforms, and Models specific to Computer Vision. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • torchtext - Data loaders and abstractions for text and NLP. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • torchaudio - An audio library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • ignite - High-level library to help with training neural networks in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • skorch - 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">
  • pytorch_geometric - Geometric Deep Learning Extension Library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • Catalyst - High-level utils for PyTorch DL & RL research. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • pytorch_geometric_temporal - Temporal Extension Library for PyTorch Geometric. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • ChemicalX - A PyTorch-based deep learning library for drug pair scoring. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • TensorFlow - Computation using data flow graphs for scalable machine learning by Google. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • TensorLayer - Deep Learning and Reinforcement Learning Library for Researcher and Engineer. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • TFLearn - Deep learning library featuring a higher-level API for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • Sonnet - TensorFlow-based neural network library. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • tensorpack - A Neural Net Training Interface on TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • Polyaxon - A platform that helps you build, manage and monitor deep learning models. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • NeuPy - NeuPy is a Python library for Artificial Neural Networks and Deep Learning (previously: <img height="20" src="img/theano_big.png" alt="Theano compatible">). <img height="20" src="img/tf_big2.png" alt="sklearn">
  • tfdeploy - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • tensorflow-upstream - 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">
  • TensorFlow Fold - Deep learning with dynamic computation graphs in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • tensorlm - Wrapper library for text generation/language models at char and word level with RNN. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • TensorLight - A high-level framework for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • Mesh TensorFlow - Model Parallelism Made Easier. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • Ludwig - 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">
  • Keras - A high-level neural networks API running on top of TensorFlow. <img height="20" src="img/keras_big.png" alt="Keras compatible">
  • keras-contrib - Keras community contributions. <img height="20" src="img/keras_big.png" alt="Keras compatible">
  • Hyperas - Keras + Hyperopt: A straightforward wrapper for a convenient hyperparameter. <img height="20" src="img/keras_big.png" alt="Keras compatible">
  • Elephas - Distributed Deep learning with Keras & Spark. <img height="20" src="img/keras_big.png" alt="Keras compatible">
  • Hera - Train/evaluate a Keras model, and get metrics streamed to a dashboard in your browser. <img height="20" src="img/keras_big.png" alt="Keras compatible">
  • Spektral - Deep learning on graphs. <img height="20" src="img/keras_big.png" alt="Keras compatible">
  • qkeras - A quantization deep learning library. <img height="20" src="img/keras_big.png" alt="Keras compatible">
  • MXNet - HIP Port of MXNet. <img height="20" src="img/mxnet_big.png" alt="MXNet based"> <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
  • Gluon - A clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet). <img height="20" src="img/mxnet_big.png" alt="MXNet based">
  • MXbox - Simple, efficient, and flexible vision toolbox for the mxnet framework. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
  • gluon-cv - Provides implementations of the state-of-the-art deep learning models in computer vision. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
  • gluon-nlp - NLP made easy. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
  • Xfer - Transfer Learning library for Deep Neural Networks. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
  • MXNet - HIP Port of MXNet. <img height="20" src="img/mxnet_big.png" alt="MXNet based"> <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
  • jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
  • Tangent - Source-to-Source Debuggable Derivatives in Pure Python.
  • autograd - Efficiently computes derivatives of numpy code.
  • Myia - Deep Learning framework (pre-alpha).
  • nnabla - Neural Network Libraries by Sony.
  • Caffe - A fast open framework for deep learning.
  • hipCaffe - The HIP port of Caffe. <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
  • DISCONTINUED PROJECTS
  • BeautifulSoup
  • Scrapy
  • Selenium
  • Pattern - establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization
  • twitterscraper
  • pandas - Powerful Python data analysis toolkit.
  • pandas_profiling - Create HTML profiling reports from pandas DataFrame objects
  • cuDF - 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">
  • blaze - NumPy and pandas interface to Big Data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • pandasql - Allows you to query pandas DataFrames using SQL syntax. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • pandas-gbq - pandas Google Big Query. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • xpandas - Universal 1d/2d data containers with Transformers .functionality for data analysis by [The Alan Turing Institute](https://www.turing.ac.uk/).
  • pysparkling - A pure Python implementation of Apache Spark's RDD and DStream interfaces. <img height="20" src="img/spark_big.png" alt="Apache Spark based">
  • Arctic - High-performance datastore for time series and tick data.
  • datatable - Data.table for Python. <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
  • koalas - pandas API on Apache Spark. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • modin - Speed up your pandas workflows by changing a single line of code. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • swifter - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
  • pandas_flavor - A package that allows writing your own flavor of Pandas easily.
  • pandas-log - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
  • vaex - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
  • xarray - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less error-prone indexing routines.
  • sk-transformer - 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">
  • polars - A fast multi-threaded, hybrid-out-of-core DataFrame library.
  • pdpipe - Sasy pipelines for pandas DataFrames.
  • SSPipe - Python pipe (|) operator with support for DataFrames and Numpy, and Pytorch.
  • pandas-ply - Functional data manipulation for pandas. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • Dplython - Dplyr for Python. <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
  • sklearn-pandas - 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">
  • Dataset - Helps you conveniently work with random or sequential batches of your data and define data processing.
  • pyjanitor - Clean APIs for data cleaning. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • meza - A Python toolkit for processing tabular data.
  • Prodmodel - Build system for data science pipelines.
  • dopanda - Hints and tips for using pandas in an analysis environment. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • Hamilton - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.
  • cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
  • snorkel - A system for quickly generating training data with weak supervision.
  • dataprep - Collect, clean, and visualize your data in Python with a few lines of code.
  • ydata-synthetic - 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">
  • Featuretools - Automated feature engineering.
  • Feature Engine - Feature engineering package with sklearn-like functionality. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • skl-groups - A scikit-learn addon to operate on set/"group"-based features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • Feature Forge - A set of tools for creating and testing machine learning features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • few - A feature engineering wrapper for sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • scikit-mdr - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • tsfresh - Automatic extraction of relevant features from time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • dirty_cat - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression). <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • NitroFE - Moving window features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • scikit-feature - Feature selection repository in Python.
  • boruta_py - Implementations of the Boruta all-relevant feature selection method. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • BoostARoota - A fast xgboost feature selection algorithm. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • scikit-rebate - 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">
  • zoofs - A feature selection library based on evolutionary algorithms.
  • Matplotlib - Plotting with Python.
  • seaborn - Statistical data visualization using matplotlib.
  • prettyplotlib - Painlessly create beautiful matplotlib plots.
  • python-ternary - Ternary plotting library for Python with matplotlib.
  • missingno - Missing data visualization module for Python.
  • chartify - Python library that makes it easy for data scientists to create charts.
  • physt - Improved histograms.
  • animatplot - A python package for animating plots built on matplotlib.
  • plotly - A Python library that makes interactive and publication-quality graphs.
  • Bokeh - Interactive Web Plotting for Python.
  • Altair - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
  • bqplot - Plotting library for IPython/Jupyter notebooks
  • pyecharts - Migrated from [Echarts](https://github.com/apache/echarts), 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">
  • folium - Makes it easy to visualize data on an interactive open street map
  • geemap - Python package for interactive mapping with Google Earth Engine (GEE)
  • HoloViews - Stop plotting your data - annotate your data and let it visualize itself.
  • AutoViz
  • SweetViz
  • pyLDAvis
  • fastapi - Modern, fast (high-performance), a web framework for building APIs with Python
  • streamlit - Make it easy to deploy the machine learning model
  • gradio - Create UIs for your machine learning model in Python in 3 minutes.
  • datapane - A collection of APIs to turn scripts and notebooks into interactive reports.
  • binder - Enable sharing and execute Jupyter Notebooks
  • dalex - 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">
  • Shapley - A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
  • Alibi - Algorithms for monitoring and explaining machine learning models.
  • anchor - Code for "High-Precision Model-Agnostic Explanations" paper.
  • aequitas - Bias and Fairness Audit Toolkit.
  • Contrastive Explanation - Contrastive Explanation (Foil Trees). <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • yellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • scikit-plot - An intuitive library to add plotting functionality to scikit-learn objects. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • shap - A unified approach to explain the output of any machine learning model. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • ELI5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions.
  • Lime - Explaining the predictions of any machine learning classifier. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • FairML - FairML is a python toolbox auditing the machine learning models for bias. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • L2X - Code for replicating the experiments in the paper *Learning to Explain: An Information-Theoretic Perspective on Model Interpretation*.
  • PDPbox - Partial dependence plot toolbox.
  • PyCEbox - Python Individual Conditional Expectation Plot Toolbox.
  • Skater - Python Library for Model Interpretation.
  • model-analysis - Model analysis tools for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • themis-ml - A library that implements fairness-aware machine learning algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • treeinterpreter - Interpreting scikit-learn's decision tree and random forest predictions. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • AI Explainability 360 - Interpretability and explainability of data and machine learning models.
  • Auralisation - Auralisation of learned features in CNN (for audio).
  • CapsNet-Visualization - A visualization of the CapsNet layers to better understand how it works.
  • lucid - A collection of infrastructure and tools for research in neural network interpretability.
  • Netron - Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).
  • FlashLight - Visualization Tool for your NeuralNetwork.
  • tensorboard-pytorch - Tensorboard for PyTorch (and chainer, mxnet, numpy, ...).
  • mxboard - Logging MXNet data for visualization in TensorBoard. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
  • OpenAI Gym - A toolkit for developing and comparing reinforcement learning algorithms.
  • Coach - Easy experimentation with state-of-the-art Reinforcement Learning algorithms.
  • garage - A toolkit for reproducible reinforcement learning research.
  • OpenAI Baselines - High-quality implementations of reinforcement learning algorithms.
  • Stable Baselines - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
  • RLlib - Scalable Reinforcement Learning.
  • Horizon - A platform for Applied Reinforcement Learning.
  • TF-Agents - A library for Reinforcement Learning in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • TensorForce - A TensorFlow library for applied reinforcement learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • TRFL - TensorFlow Reinforcement Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • Dopamine - A research framework for fast prototyping of reinforcement learning algorithms.
  • keras-rl - Deep Reinforcement Learning for Keras. <img height="20" src="img/keras_big.png" alt="Keras compatible">
  • ChainerRL - A deep reinforcement learning library built on top of Chainer.
  • pyro - A flexible, scalable deep probabilistic programming library built on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • pomegranate - Probabilistic and graphical models for Python. <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
  • ZhuSuan - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • PyMC - Bayesian Stochastic Modelling in Python.
  • InferPy - Deep Probabilistic Modelling Made Easy. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • GPflow - Gaussian processes in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • PyStan - Bayesian inference using the No-U-Turn sampler (Python interface).
  • sklearn-bayes - Python package for Bayesian Machine Learning with scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • pgmpy - A python library for working with Probabilistic Graphical Models.
  • skpro - Supervised domain-agnostic prediction framework for probabilistic modelling by [The Alan Turing Institute](https://www.turing.ac.uk/). <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • PtStat - Probabilistic Programming and Statistical Inference in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • PyVarInf - Bayesian Deep Learning methods with Variational Inference for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • emcee - The Python ensemble sampling toolkit for affine-invariant MCMC.
  • hsmmlearn - A library for hidden semi-Markov models with explicit durations.
  • pyhsmm - Bayesian inference in HSMMs and HMMs.
  • GPyTorch - A highly efficient and modular implementation of Gaussian Processes in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • MXFusion - Modular Probabilistic Programming on MXNet. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
  • sklearn-crfsuite - A scikit-learn-inspired API for CRFsuite. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • gplearn - Genetic Programming in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • DEAP - Distributed Evolutionary Algorithms in Python.
  • karoo_gp - A Genetic Programming platform for Python with GPU support. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • monkeys - A strongly-typed genetic programming framework for Python.
  • sklearn-genetic - Genetic feature selection module for scikit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • Optuna - A hyperparameter optimization framework.
  • Spearmint - Bayesian optimization.
  • BoTorch - Bayesian optimization in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
  • scikit-opt - Heuristic Algorithms for optimization.
  • sklearn-genetic-opt - Hyperparameters tuning and feature selection using evolutionary algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • SMAC3 - Sequential Model-based Algorithm Configuration.
  • Optunity - Is a library containing various optimizers for hyperparameter tuning.
  • hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python.
  • hyperopt-sklearn - Hyper-parameter optimization for sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • sklearn-deap - Use evolutionary algorithms instead of gridsearch in scikit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • sigopt_sklearn - SigOpt wrappers for scikit-learn methods. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • Bayesian Optimization - A Python implementation of global optimization with gaussian processes.
  • SafeOpt - Safe Bayesian Optimization.
  • scikit-optimize - Sequential model-based optimization with a `scipy.optimize` interface.
  • Solid - A comprehensive gradient-free optimization framework written in Python.
  • PySwarms - A research toolkit for particle swarm optimization in Python.
  • Platypus - A Free and Open Source Python Library for Multiobjective Optimization.
  • GPflowOpt - Bayesian Optimization using GPflow. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • POT - Python Optimal Transport library.
  • Talos - Hyperparameter Optimization for Keras Models.
  • nlopt - Library for nonlinear optimization (global and local, constrained or unconstrained).
  • OR-Tools - An open-source software suite for optimization by Google; provides a unified programming interface to a half dozen solvers: SCIP, GLPK, GLOP, CP-SAT, CPLEX, and Gurobi.
  • sktime - A unified framework for machine learning with time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • darts - A python library for easy manipulation and forecasting of time series.
  • statsforecast - Lightning fast forecasting with statistical and econometric models.
  • mlforecast - Scalable machine learning-based time series forecasting.
  • neuralforecast - Scalable machine learning-based time series forecasting.
  • tslearn - Machine learning toolkit dedicated to time-series data. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • tick - Module for statistical learning, with a particular emphasis on time-dependent modeling. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • greykite - A flexible, intuitive, and fast forecasting library next.
  • Prophet - Automatic Forecasting Procedure.
  • PyFlux - Open source time series library for Python.
  • bayesloop - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
  • luminol - Anomaly Detection and Correlation library.
  • dateutil - Powerful extensions to the standard datetime module
  • maya - makes it very easy to parse a string and for changing timezones
  • Chaos Genius - ML powered analytics engine for outlier/anomaly detection and root cause analysis
  • spaCy - Industrial-Strength Natural Language Processing.
  • NLTK - Modules, data sets, and tutorials supporting research and development in Natural Language Processing.
  • CLTK - The Classical Language Toolkik.
  • gensim - Topic Modelling for Humans.
  • pyMorfologik - Python binding for <a href="https://github.com/morfologik/morfologik-stemming">Morfologik</a>.
  • skift - Scikit-learn wrappers for Python fastText. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • Phonemizer - Simple text-to-phonemes converter for multiple languages.
  • flair - Very simple framework for state-of-the-art NLP.
  • librosa - Python library for audio and music analysis.
  • Yaafe - Audio features extraction.
  • aubio - A library for audio and music analysis.
  • Essentia - Library for audio and music analysis, description, and synthesis.
  • LibXtract - A simple, portable, lightweight library of audio feature extraction functions.
  • Marsyas - Music Analysis, Retrieval, and Synthesis for Audio Signals.
  • muda - A library for augmenting annotated audio data.
  • madmom - Python audio and music signal processing library.
  • OpenCV - Open Source Computer Vision Library.
  • scikit-image - Image Processing SciKit (Toolbox for SciPy).
  • imgaug - Image augmentation for machine learning experiments.
  • imgaug_extension - Additional augmentations for imgaug.
  • Augmentor - Image augmentation library in Python for machine learning.
  • albumentations - Fast image augmentation library and easy-to-use wrapper around other libraries.
  • pandas_summary - Extension to pandas dataframes describe function. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • Pandas Profiling - Create HTML profiling reports from pandas DataFrame objects. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • statsmodels - Statistical modeling and econometrics in Python.
  • stockstats - Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.
  • weightedcalcs - A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.
  • scikit-posthocs - Pairwise Multiple Comparisons Post-hoc Tests.
  • Alphalens - Performance analysis of predictive (alpha) stock factors.
  • Horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. <img height="20" src="img/tf_big2.png" alt="sklearn">
  • PySpark - Exposes the Spark programming model to Python. <img height="20" src="img/spark_big.png" alt="Apache Spark based">
  • Veles - Distributed machine learning platform.
  • Jubatus - Framework and Library for Distributed Online Machine Learning.
  • DMTK - Microsoft Distributed Machine Learning Toolkit.
  • PaddlePaddle - PArallel Distributed Deep LEarning.
  • dask-ml - Distributed and parallel machine learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • Distributed - Distributed computation in Python.
  • mlflow - Open source platform for the machine learning lifecycle.
  • Neptune - A lightweight ML experiment tracking, results visualization, and management tool.
  • dvc - Data Version Control | Git for Data & Models | ML Experiments Management.
  • envd - 🏕️ machine learning development environment for data science and AI/ML engineering teams.
  • Sacred - A tool to help you configure, organize, log, and reproduce experiments.
  • Ax - Adaptive Experimentation Platform. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • great_expectations - Always know what to expect from your data.
  • pandera - A lightweight, flexible, and expressive statistical data testing library.
  • deepchecks - Validation & testing of ML models and data during model development, deployment, and production. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • evidently - Evaluate and monitor ML models from validation to production.
  • TensorFlow Data Validation - Library for exploring and validating machine learning data.
  • recmetrics - Library of useful metrics and plots for evaluating recommender systems.
  • Metrics - Machine learning evaluation metric.
  • sklearn-evaluation - Model evaluation made easy: plots, tables, and markdown reports. <img height="20" src="img/sklearn_big.png" alt="sklearn">
  • AI Fairness 360 - Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.
  • numpy - The fundamental package needed for scientific computing with Python.
  • Dask - Parallel computing with task scheduling. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • bottleneck - Fast NumPy array functions written in C.
  • CuPy - NumPy-like API accelerated with CUDA.
  • scikit-tensor - Python library for multilinear algebra and tensor factorizations.
  • numdifftools - Solve automatic numerical differentiation problems in one or more variables.
  • quaternion - Add built-in support for quaternions to numpy.
  • adaptive - Tools for adaptive and parallel samping of mathematical functions.
  • NumExpr - A fast numerical expression evaluator for NumPy that comes with an integrated computing virtual machine to speed calculations up by avoiding memory allocation for intermediate results.
  • GeoPandas - Python tools for geographic data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
  • PySal - Python Spatial Analysis Library.
  • qiskit - Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.
  • cirq - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
  • PennyLane - Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
  • QML - A Python Toolkit for Quantum Machine Learning.
  • sklearn-porter - Transpile trained scikit-learn estimators to C, Java, JavaScript, and others.
  • ONNX - Open Neural Network Exchange.
  • MMdnn - A set of tools to help users inter-operate among different deep learning frameworks.
  • CC BY 4.0
Programming Languages
Python 205 Jupyter Notebook 26 C++ 24 JavaScript 3 C 2 HTML 1 Rust 1 Go 1 TypeScript 1
Keywords
machine-learning 101 python 89 deep-learning 45 data-science 43 tensorflow 21 pytorch 19 scikit-learn 18 pandas 18 neural-network 13 keras 13 statistics 11 visualization 11 ml 10 ai 10 time-series 10 optimization 10 mxnet 9 hyperparameter-optimization 9 numpy 9 data-analysis 9 data-visualization 7 gpu 7 computer-vision 7 c-plus-plus 7 forecasting 7 reinforcement-learning 7 xgboost 7 cuda 6 dask 6 dataframe 6 artificial-intelligence 6 distributed 6 feature-selection 6 nlp 6 automl 6 interpretability 6 machine-learning-algorithms 5 neural-networks 5 machinelearning 5 fairness 5 lightgbm 5 pandas-dataframe 5 jupyter 5 plotting 5 r 5 mlops 5 distributed-computing 4 algorithm 4 reproducibility 4 pydata 4