Open Source Python Libraries - Page 19
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Python Libraries
View 75 business solutionsBrowse free open source Python Libraries and projects below. Use the toggles on the left to filter open source Python Libraries by OS, license, language, programming language, and project status.
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Zipline
Zipline, a Pythonic algorithmic trading library
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python package. For a development installation (used to develop Zipline itself), create and activate a virtualenv, then run the etc/dev-install script. Please note that Zipline is not a community-led project. Zipline is maintained by the Quantopian engineering team, and we are quite small and often busy. -
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ArmaNpy is a set of SWIG interface files which allows for generating Python bindings to C++ code which uses the Armadillo matrix library.
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asyncpg
A fast PostgreSQL Database Client Library for Python/asyncio
asyncpg is a high-performance PostgreSQL client library designed for Python's asyncio framework. It offers a clean and efficient implementation of the PostgreSQL server binary protocol, enabling developers to execute database operations asynchronously. This approach allows for scalable and responsive applications that can handle numerous concurrent database connections. -
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aws-devops-zero-to-hero
AWS zero to hero repo for devops engineers to learn AWS in 30 Days
aws-devops-zero-to-hero is a 30-day AWS learning roadmap aimed squarely at DevOps engineers who want both conceptual understanding and hands-on projects. The README is structured as a day-by-day syllabus, starting with “Day 1: Introduction to AWS” and moving through IAM, EC2, VPC networking, security, DNS (Route 53), storage (S3), and many other core services. Each day mixes explanation with at least one concrete project or lab, such as deploying applications on EC2, designing secure VPCs, setting up CI/CD pipelines, or configuring CloudWatch monitoring. Later in the curriculum, you move into topics like CloudFormation, CodeCommit/CodePipeline/CodeBuild/CodeDeploy, Terraform on AWS, CloudTrail and Config for compliance, Elastic Load Balancing, and cloud migration strategies. A full day is dedicated to “500 AWS interview questions and answers,” which makes this repo double as an interview prep resource as well as a skills builder. -
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basVec2DLibs
Python 2D Vector libraries for Pygame
2D Vector Libraries that I have developed. Allows creation of vector models, will check for collisions and can track gravity for the model. Allows models to have optional components. See screenshots. Requires Pygame as a dependency -
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bidict
The bidirectional mapping library for Python
Depended on by Google, Venmo, CERN, Baidu, Tencent, and teams across the world since 2009. Familiar, Pythonic APIs that are carefully designed for safety, simplicity, flexibility, and ergonomics. Lightweight, with no runtime dependencies outside Python's standard library. Implemented in concise, well-factored, fully type-hinted Python code that is optimized for running efficiently as well as for long-term maintenance and stability. Extensively documented. 100% test coverage running continuously across all supported Python versions. Enterprise-level support for bidict can be obtained via the Tidelift subscription. One of the best things about bidict is that it touches a surprising number of interesting Python corners, especially given its small size and scope. Choose a tier and GitHub handles everything else. Your GitHub sponsorship will automatically go on the same bill you already have set up with GitHub, so after the one-click signup, there’s nothing else to do. -
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BinScan is a graphical tool to analyze the library dependencies of binaries. It integrates both tools "ldd" and "nm" into a single GUI frontend. it displays the library dependencies of executables and the symbols it references in shared libraries
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bitstring is a pure Python module designed to help make the creation and analysis of binary data as painless as possible. The latest releases support Python 2.6 and 3.x only. For Python 2.4 and 2.5, plus lots more see the googlecode homepage.
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bluetroller
A library and interface for controlling bluetooth LE devices
bluetroller is a library and interface for controlling all kinds of bluetooth LE devices. A vast number of devices can be controlled via Bluetooth LE, including fitness trackers, lighting, camera sliders, gimbals and many more. Right now these devices can only be controlled via phone apps which are frequently buggy, unmaintained and will stop working after some future phone update. This project aims to grow to become an exhaustive library of these devices. -
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borb
borb is a library for reading, creating and manipulating PDF files
borb is a library for creating and manipulating PDF files in python. borb is a pure python library to read, write, and manipulate PDF documents. It represents a PDF document as a JSON-like data structure of nested lists, dictionaries and primitives (numbers, string, booleans, etc) This is currently a one-man project, so the focus will always be to support those use-cases that are more common in favor of those that are rare. -
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captcha_break
Identification codes
This project will use Keras to build a deep convolutional neural network to identify the captcha verification code. It is recommended to use a graphics card to run the project. The following visualization codes are jupyter notebookall done in . If you want to write a python script, you can run it normally with a little modification. Of course, you can also remove these visualization codes. captcha is a library written in python to generate verification codes. It supports image verification codes and voice verification codes. We use its function of generating image verification codes. First, we set our verification code format to numbers and capital letters, and generate a string of verification codes. It is well known that tensorflow occupies all video memory by default, which is not conducive to us conducting multiple experiments at the same time, so we can use the following code when tensorflow uses the video memory it needs instead of directly occupying all video memory. -
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claude-code-transcripts
Tools for publishing transcripts for Claude Code sessions
claude-code-transcripts is a command-line utility that takes session files exported from Claude Code (in JSON or JSONL format) and turns them into clean, navigable HTML transcripts that can be viewed in any modern web browser. It is designed to make the often dense and verbose outputs from AI coding sessions easier to read, share, and archive by breaking conversations into paginated, annotated pages with navigable timelines of prompts and responses. Users can run this tool locally or fetch sessions from the Claude API, giving flexibility for individual workflows or team documentation practices. The generated HTML includes interactive navigation and can optionally be published to GitHub Gists for sharing with collaborators or embedding in other documentation. It also supports including the raw session JSON alongside the transcript for forensic or archival purposes. -
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cloud-file-system-sdk
CloudFile File System SDK
EaseFilter Cloud File System is a Windows virtual file system which was developed with the file system filter driver. The Cloud File System integrates the cloud storage into the on-premise storage, it lets on-premise application access the cloud files transparently, just as they would access on-premise regular files. With the Cloud File SDK, the developers can implement the cloud disaster recovery (Cloud DR) solution, seamlessly integrate your existing applications to the cloud environment without affecting the original data and programs, without any modification of your existing applications. It can help the small to medium size companies to implement the Cloud DR with the lowest cost and minimal disruption to normal operation. -
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cnn-benchmarks
Benchmarks for popular CNN models
The cnn-benchmarks project is a collection of benchmarking scripts designed to evaluate the performance of convolutional neural networks across different hardware and configurations. It provides standardized implementations of popular CNN architectures, enabling developers to measure training speed, memory usage, and computational efficiency. The project focuses on reproducibility, allowing consistent comparisons between models and environments. It is particularly useful for testing GPUs and optimizing deep learning workloads, as it highlights bottlenecks and performance differences across setups. The repository includes scripts for running benchmarks on various architectures and datasets, making it easy to gather comparative metrics. By simplifying performance evaluation, it helps developers make informed decisions about model design and hardware selection. Overall, cnn-benchmarks is a practical tool for performance analysis in deep learning workflows. -
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cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
The cnn-text-classification-tf repository by Denny Britz is a well-known educational implementation of convolutional neural networks for text classification using TensorFlow, aimed at helping developers and researchers understand how CNNs can be applied to natural language processing tasks. Based loosely on Kim’s influential paper on CNNs for sentence classification, this codebase demonstrates how to preprocess text data, convert words into learned embeddings, and apply multiple convolution filters to extract n-gram features that are then pooled and fed into a classifier. The project includes scripts for training, evaluation, and data handling, making it easy to run experiments on datasets such as movie reviews or other labeled text collections. By breaking down the model into understandable components, it serves as a practical reference for students and practitioners learning how deep learning models handle text beyond traditional bag-of-words approaches. -
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colleague-skill
Transform a cold separation into a warm Skill
colleague-skill is a specialized agent skill designed to simulate a collaborative teammate within AI-driven workflows, enabling agents to behave more like human colleagues in problem-solving scenarios. The project focuses on enhancing interaction quality by introducing role-based behavior, contextual awareness, and cooperative task execution. It allows agents to provide suggestions, feedback, and alternative approaches, mimicking real-world collaboration dynamics. The system likely integrates with broader agent frameworks, enabling seamless inclusion in multi-agent environments. It emphasizes communication and coordination, ensuring that agents can contribute meaningfully to shared objectives. This makes it particularly useful for complex projects where multiple perspectives or iterative refinement are needed. -
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context_menu
A Python library to create and deploy cross-platform native context
A Python library to create and deploy cross-platform native context. context_menu was created as due to the lack of an intuitive and easy to use cross-platform context menu library. The library allows you to create your own context menu entries and control their behavior seamlessly in native Python code. It's fully documented and used by over 80,000 developers worldwide. -
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Fast cython implementation of trie data structure for Python. Development is inactive, but moved to: http://github.com/martinkozak/cytrie.
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cybcon89
Crawl and ouput WAS configuration
The project moved to Bitbucket: GIT https://bitbucket.org/Cybcon/websphere-as-configcrawler/src/master/ Please checkout the Bitbucket GIT repo for updates after v0.644. -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. The collection is suitable for self-paced study, quick reference, or as teaching materials in workshops. By combining narrative explanations with executable code, it shortens the path from theory to working prototypes. -
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databooks
A CLI tool to reduce the friction between data scientists
databooks is a package to ease the collaboration between data scientists using Jupyter notebooks, by reducing the number of git conflicts between different notebooks and resolution of git conflicts when encountered. Simply specify the paths for notebook files to remove metadata. By doing so, we can already avoid many of the conflicts. Specify the paths for notebook files with conflicts to be fixed. Then, databooks finds the source notebooks that caused the conflicts and compares them (so no JSON manipulation!) Specify paths of notebooks to be checked, an expression or recipe of what you'd like to enforce. databooks will run your checks and raise errors if any notebook does not comply with the desired metadata values. This advanced feature allows users to enforce cell tags, sequential cell execution, maximum number of cells, among many other things. -
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django-import-export
Django application and library for importing and exporting data
django-import-export is a Django application and library for importing and exporting data with included admin integration. Support multiple formats (Excel, CSV, JSON, and everything else that tablib supports) Admin integration for importing. Preview import changes. Admin integration for exporting. Export data respecting admin filters. By default all records will be imported, even if no changes are detected. This can be changed setting the skip_unchanged option. Also, the report_skipped option controls whether skipped records appear in the import Result object, and if using the admin whether skipped records will show in the import preview page. Not all data can be easily extracted from an object/model attribute. In order to turn complicated data model into a (generally simpler) processed data structure on export, dehydrate_<fieldname> method should be defined. -
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django-split-settings
Organize Django settings into multiple files and directories
Organize Django settings into multiple files and directories. Easily override and modify settings. Use wildcards in settings file paths and mark settings files as optional. Managing Django’s settings might be tricky. There are severals issues which are encountered by any Django developer along the way. First one is caused by the default project structure. Django clearly offers us a single settings.py file. It seams reasonable at the first glance. And it is actually easy to use just after the start. But when it comes to the real-world it only causes misunderstanding and frustration. At some point, you will need to put some kind of personal settings in the main file: certificate paths, your username or password, database connection, etc. But putting your user-specific values inside the common settings is a bad practice. Other developers would have other settings, and it would just not work for all of you.
