Open Source Python Algorithms
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Python Algorithms
View 5753 business solutionsBrowse free open source Python Algorithms and projects below. Use the toggles on the left to filter open source Python Algorithms by OS, license, language, programming language, and project status.
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Clipper
Polygon and line clipping and offsetting library (C++, C#, Delphi)
This library is now obsolete and no longer being maintained. It has been superceded by my Clipper2 library - https://github.com/AngusJohnson/Clipper2.">
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GFPGAN
GFPGAN aims at developing Practical Algorithms
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration. Colab Demo for GFPGAN; (Another Colab Demo for the original paper model) Online demo: Huggingface (return only the cropped face) Online demo: Replicate.ai (may need to sign in, return the whole image). Online demo: Baseten.co (backed by GPU, returns the whole image). We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode. GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration. Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs. -
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ImageAI
A python library built to empower developers
ImageAI is an easy-to-use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find features supported, links to official documentation as well as articles on ImageAI. ImageAI is widely used around the world by professionals, students, research groups and businesses. ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet. ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. -
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YAPF
A formatter for Python files
YAPF is a Python code formatter that automatically rewrites source to match a chosen style, using a clang-format–inspired algorithm to search for the “best” layout under your rules. Instead of relying on a fixed set of heuristics, it explores formatting decisions and chooses the lowest-cost result, aiming to produce code a human would write when following a style guide. You can run it as a command-line tool or call it as a library via FormatCode / FormatFile, making it easy to embed in editors, CI, and custom tooling. Styles are highly configurable: start from presets like pep8, google, yapf, or facebook, then override dozens of options in .style.yapf, setup.cfg, or pyproject.toml. It supports recursive directory formatting, line-range formatting, and diff-only output so you can check or fix just the lines you touched. -
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Free Java Flowchart simulator / interpreter
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Baselines
High-quality implementations of reinforcement learning algorithms
Unlike the other two, openai/baselines is not currently a maintained or prominent repo in the OpenAI organization (and I found no strong reference in OpenAI’s main GitHub). Historically, “baselines” repositories are often used for baseline implementations of reinforcement learning algorithms or reference models (e.g. in the RL domain). If there was an OpenAI “baselines” repo, it might have contained reference implementations for reinforcement learning or model policy baselines to compare new work against. However, I couldn’t locate an active “openai/baselines” in the latest OpenAI repos, so it may have been archived, removed, or merged into other projects. If you meant a different “baselines” (e.g. OpenAI Baselines for reinforcement learning), I can look up that specific one. -
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FuzzyWuzzy
Fuzzy string matching in Python
We’ve made it our mission to pull in event tickets from every corner of the internet, showing you them all on the same screen so you can compare them and get to your game/concert/show as quickly as possible. Of course, a big problem with most corners of the internet is labeling. One of our most consistently frustrating issues is trying to figure out whether two ticket listings are for the same real-life event (that is, without enlisting the help of our army of interns). To pick an example completely at random, Cirque du Soleil has a show running in New York called “Zarkana”. When we scour the web to find tickets for sale, mostly those tickets are identified by a title, date, time, and venue. We’ve built up a library of “fuzzy” string matching routines to help us along. And good news! We’re open sourcing it. The library is called “Fuzzywuzzy”, the code is pure python, and it depends only on the (excellent) difflib python library. -
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Active Learning
Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks. -
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Algorithms in Python
Data Structures and Algorithms in Python
Algorithms in Python is a collection of algorithm and data structure implementations (primarily in Python) meant to serve as both learning material and reference code for engineers. It includes code for graph algorithms, heap data structures, stacks, queues, and more — each implemented cleanly so learners can trace logic and adapt for their problems. The repository is particularly useful for people preparing for competitive programming, job interviews, or building a foundational understanding of algorithmic patterns. Because it’s openly maintained, you can browse through issues, see test cases, and observe coding style in a “learning through code” fashion. It also serves as a playground where you can add problems, measure performance, and compare different algorithmic approaches. For anyone striving to move from “I know the syntax” to “I know how to use the right algorithm at the right time,” this repository is a practical asset. -
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Binarytree
Python library for studying Binary Trees
Binarytree is Python library that lets you generate, visualize, inspect and manipulate binary trees. Skip the tedious work of setting up test data, and dive straight into practicing algorithms. Heaps and BSTs (binary search trees) are also supported. Binarytree supports another representation which is more compact but without the indexing properties. Traverse trees using different algorithms. -
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Elementary Algorithms
Book of elementary algorithms and data structures
This book introduces elementary algorithms and data structure. It includes side-by-side comparison of purely functional realization and their imperative counterpart. From 2020/12, I started re-writing this book. The PDF can be downloaded for preview (EN, 中文). The 1st edition in Chinese (中文) was published in 2017. I recently switched my focus to the Mathematics of programming, the new book is also available in (github). To build the book in PDF format from the sources, you need the following software pre-installed, TeXLive, The book is built with XeLaTeX, a Unicode friendly version of TeX. You need the GNU make tool, in Debian/Ubuntu like Linux, it can be installed through the apt-get command. -
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FATE
An industrial grade federated learning framework
FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. FATE became open-source in February 2019. FATE TSC was established to lead FATE open-source community, with members from major domestic cloud computing and financial service enterprises. FedAI is a community that helps businesses and organizations build AI models effectively and collaboratively, by using data in accordance with user privacy protection, data security, data confidentiality and government regulations. -
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Gym
Toolkit for developing and comparing reinforcement learning algorithms
Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. These environments have a shared interface, allowing you to write general algorithms. -
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Python Outlier Detection
A Python toolbox for scalable outlier detection
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as outlier detection or anomaly detection. PyOD includes more than 30 detection algorithms, from classical LOF (SIGMOD 2000) to the latest COPOD (ICDM 2020) and SUOD (MLSys 2021). Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural network-based models, e.g., AutoEncoders, which are implemented in both PyTorch and Tensorflow. PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. In total, 17 benchmark datasets are used for comparison, which can be downloaded at ODDS. -
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The Algorithms Python
All Algorithms implemented in Python
The Algorithms-Python project is a comprehensive collection of Python implementations for a wide range of algorithms and data structures. It serves primarily as an educational resource for learners and developers who want to understand how algorithms work under the hood. Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. The project covers various domains including mathematics, cryptography, machine learning, sorting, graph theory, and more. With contributions from a large global community, it continually grows and improves through collaboration and peer review. This repository is an ideal reference for students, educators, and developers seeking hands-on experience with algorithmic concepts in Python. -
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TorBot
Dark Web OSINT Tool
Contributions to this project are always welcome. To add a new feature fork the dev branch and give a pull request when your new feature is tested and complete. If its a new module, it should be put inside the modules directory. The branch name should be your new feature name in the format <Feature_featurename_version(optional)>. On Linux platforms, you can make an executable for TorBot by using the install.sh script. You will need to give the script the correct permissions using chmod +x install.sh Now you can run ./install.sh to create the torBot binary. Run ./torBot to execute the program. Crawl custom domains.(Completed). Check if the link is live.(Completed). Built-in Updater.(Completed). TorBot GUI (In progress). Social Media integration.(not Started). -
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The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
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Evolving Objects
This project have been merged within Paradiseo.
See the new project page: https://nojhan.github.io/paradiseo/ (Archived project page: http://eodev.sourceforge.net/) -
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CloudI is an open-source private cloud computing framework for efficient, secure, and internal data processing. CloudI provides scaling for previously unscalable source code with efficient fault-tolerant execution of ATS, C/C++, Erlang/Elixir, Go, Haskell, Java, JavaScript/node.js, OCaml, Perl, PHP, Python, Ruby, or Rust services. The bare essentials for efficient fault-tolerant processing on a cloud!
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FRODO 2
Open-Source Framework for Distributed Constraint Optimization (DCOP)
FRODO is a Java platform to solve Distributed Constraint Satisfaction Problems (DisCSPs) and Optimization Problems (DCOPs). It provides implementations for a variety of algorithms, including DPOP (and its variants), ADOPT, SynchBB, DSA... -
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Cryptography Tools
Classic & Modern Cryptography tools
Cryptography Tools is a project to develop demonstration tools on classic (currently Caesar and Playfair) & modern crypto-systems, including private & public key encryptions, digital signatures, cryptographic hashes and authenticated encryption. -
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ZHSoftware
ZHSoftware
ZH软件&源码库 -
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UIMP(Unified Image Manipulation Program) is an OS independent Image Processing system, which provides over 1000 algorithms and supports web online, console and GUI program.
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Digraph3
A collection of python3 modules for Algorithmic Decision Theory
This collection of Python3 modules provides a large range of implemented decision aiding algorithms useful in the field of outranking digraphs based Multiple Criteria Decision Aid (MCDA), especially best choice, linear ranking and absolute or relative rating algorithms with multiple incommensurable criteria. Technical documentation and tutorials are available under the following link: https://digraph3.readthedocs.io/en/latest/ The tutorials introduce the main objects like digraphs, outranking digraphs and performance tableaux. There is also a tutorial provided on undirected graphs. Some tutorials are problem oriented and show how to compute the winner of an election, how to build a best choice recommendation, or how to linearly rank or rate with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing maximal independent sets (MISs) and kernels in graphs and digraphs.