Open Source Python Libraries - Page 6
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Python Libraries
View 74 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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Python Fire
Automatically generate CLIs from absolutely any Python object
Python Fire is a library that automatically generates command line interfaces (CLIs) from absolutely any Python object. It’s a really simple and easy way to create CLI in Python, and can also enable you to explore existing code or turn other people’s code into a CLI. Python Fire lets you call Fire on any Python object: be it functions, classes, objects, modules, lists-- you name it! It will help you develop as well as debug Python code, and make transitioning between Bash and Python a whole lot easier. -
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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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Python Progressbar
Progressbar 2 - A progress bar for Python 2 and Python 3
A text progress bar is typically used to display the progress of a long-running operation, providing a visual cue that processing is underway. The progressbar is based on the old Python progressbar package that was published on the now-defunct Google Code. Since that project was completely abandoned by its developer and the developer did not respond to my email, I decided to fork the package. This package is still backward compatible with the original progressbar package so you can safely use it as a drop-in replacement for existing projects. The ProgressBar class manages the current progress, and the format of the line is given by a number of widgets. A widget is an object that may display differently depending on the state of the progress bar. -
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SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
SageMaker Hugging Face Inference Toolkit is an open-source library for serving Transformers models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging Face Inference Toolkit implements various additional environment variables to simplify your deployment experience. The Hugging Face Inference Toolkit allows user to override the default methods of the HuggingFaceHandlerService. SageMaker Hugging Face Inference Toolkit is licensed under the Apache 2.0 License. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
Shumai is an experimental differentiable tensor library for TypeScript and JavaScript, developed by Facebook Research. It provides a high-performance framework for numerical computing and machine learning within modern JavaScript runtimes. Built on Bun and Flashlight, with ArrayFire as its numerical backend, Shumai brings GPU-accelerated tensor operations, automatic differentiation, and scientific computing tools directly to JavaScript developers. It allows seamless integration of machine learning, deep learning, and custom differentiable programs into web-based or server-side environments without relying on Python frameworks. The library supports matrix operations, gradient computation, and tensor conversions with intuitive APIs and near-native speed, thanks to Bun’s low-overhead FFI bindings. It can automatically leverage GPU acceleration on Linux (via CUDA) and CPU computation on macOS. -
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Solid Python
A comprehensive gradient-free optimization framework written in Python
Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients, and allows for very rapid development using them. -
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Sonnet
TensorFlow-based neural network library
Sonnet is a neural network library built on top of TensorFlow designed to provide simple, composable abstractions for machine learning research. Sonnet can be used to build neural networks for various purposes, including different types of learning. Sonnet’s programming model revolves around a single concept: modules. These modules can hold references to parameters, other modules and methods that apply some function on the user input. There are a number of predefined modules that already ship with Sonnet, making it quite powerful and yet simple at the same time. Users are also encouraged to build their own modules. Sonnet is designed to be extremely unopinionated about your use of modules. It is simple to understand, and offers clear and focused code. -
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Stanza
Stanford NLP Python library for many human languages
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
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Stock prediction deep neural learning
Predicting stock prices using a TensorFlow LSTM
Predicting stock prices can be a challenging task as it often does not follow any specific pattern. However, deep neural learning can be used to identify patterns through machine learning. One of the most effective techniques for series forecasting is using LSTM (long short-term memory) networks, which are a type of recurrent neural network (RNN) capable of remembering information over a long period of time. This makes them extremely useful for predicting stock prices. Predicting stock prices is a complex task, as it is influenced by various factors such as market trends, political events, and economic indicators. The fluctuations in stock prices are driven by the forces of supply and demand, which can be unpredictable at times. To identify patterns and trends in stock prices, deep learning techniques can be used for machine learning. Long short-term memory (LSTM) is a type of recurrent neural network (RNN) that is specifically designed for sequence modeling and prediction. -
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and compare the results. Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. T2T was developed by researchers and engineers in the Google Brain team and a community of users. It is now deprecated, we keep it running and welcome bug-fixes, but encourage users to use the successor library Trax. -
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The Data Engineering Handbook
Links to everything you'd ever want to learn about data engineering
The Data Engineering Handbook is a comprehensive, community-curated repository that aggregates essential learning resources for anyone interested in becoming a professional data engineer. Rather than being a code project itself, it’s a learning handbook that links to books, articles, tutorials, community groups, boot camps, and real-world project examples that collectively form a roadmap to mastering data engineering skills. It includes beginner and intermediate boot camps, interview guides, data cleaning and transformation resources, and curated lists of newsletters and industry communities, making it useful both for self-study and technical interview preparation. The repository is actively maintained and widely starred, reflecting its role as a go-to reference for newcomers and experienced practitioners alike. -
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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. -
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earthengine-py-notebooks
A collection of 360+ Jupyter Python notebook examples
earthengine-py-notebooks is a comprehensive collection of hundreds of Jupyter Python notebooks that serve as examples and tutorials for using the Google Earth Engine Python API. These notebooks are organized into thematic areas such as image processing, machine learning, visualization, filtering, and asset management, exposing users to real geospatial analysis tasks. The repository makes it easier to explore Earth Engine’s large geospatial data catalog, interactively display map layers, and generate visual insights without the need for external GIS software by leveraging interactive widgets and mapping libraries. Many of the notebooks integrate with tools like folium, ipyleaflet, and geemap to bridge Earth Engine data with Python’s rich ecosystem for plotting and analysis. Users can quickly adapt the examples for their own remote sensing, environmental monitoring, or spatial data science projects, and can run the code in environments like Google Colab. -
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libtmux
Python API / wrapper for tmux
libtmux is a typed Python library that provides a wrapper for interacting programmatically with tmux, a terminal multiplexer. You can use it to manage tmux servers, sessions, windows, and panes. Additionally, libtmux powers tmuxp, a tmux workspace manager. libtmux builds upon tmux’s target and formats to create an object mapping to traverse, inspect and interact with live tmux sessions. -
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n8n Workflows
All of the workflows of n8n i could find (also from the site itself)
This repository aggregates a well-organized collection of community-submitted automation workflows built for n8n, a fair-code, self-hostable automation tool. It serves as an index for discovering ready-made flows to automate tasks across multiple services and platforms. 2,057 workflows with meaningful, searchable names. 365 unique integrations across popular platforms. 29,445 total nodes with professional categorization. Quality assurance - All workflows analyzed and categorized. -
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py2many
Transpiler of Python to many other languages
Python is popular, and easy to program in, but it has poor runtime performance. We can fix that by transpiring a subset of the language into a more performant, statically typed language. A second benefit is security. Writing security-sensitive code in a low-level language like C is error-prone and could lead to privilege escalation. Specialized languages such as wuffs exist to address this use case. py2many can be a more general-purpose solution to the problem where you can verify the source via unit tests before you transpile. Swift and Kotlin dominate the mobile app development workflow. However, there is no one solution that works well for lower level libraries where there is desire to share code between platforms. Kotlin Mobile Multiplatform (KMM) is a player in this place, but it hasn't really caught on. py2many provides an alternative. -
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pyglet
pyglet is a cross-platform windowing and multimedia library for Python
Pyglet is a cross-platform windowing and multimedia library for Python, intended for developing games and other visually rich applications. It supports windowing, input event handling, OpenGL graphics, loading images and videos, and playing sounds and music. -
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pyimgui
Cython-based Python bindings for dear imgui
pyimgui is a set of Cython-based Python bindings for the popular Dear ImGui library, enabling developers to create fast and flexible graphical user interfaces in Python applications. It facilitates the integration of Dear ImGui's immediate-mode GUI paradigm into Python projects, allowing for the rapid development of tools and applications with complex user interfaces. -
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urllib3
Python HTTP library with thread-safe connection pooling
urllib3 is a powerful, user-friendly HTTP client for Python. Much of the Python ecosystem already uses urllib3 and you should too. Thread safety, connection pooling. Client-side TLS/SSL verification. File uploads with multipart encoding. Helpers for retrying requests and dealing with HTTP redirects. Support for gzip, deflate, brotli, and zstd encoding. Proxy support for HTTP and SOCKS. 100% test coverage. Professional support for urllib3 is available as part of the Tidelift Subscription. Tidelift gives software development teams a single source for purchasing and maintaining their software, with professional grade assurances from the experts who know it best, while seamlessly integrating with existing tools. -
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uvloop
Ultra fast asyncio event loop
uvloop is an ultra-fast, drop-in replacement of the built-in asyncio event loop. Together with asyncio and the power of async/await in Python 3.5, uvloop makes it easier than ever to write high-performance Python networking code. uvloop makes asyncio incredibly fast-- 2 to 4 times faster than nodejs, or any other Python asynchronous framework. The performance of asyncio when it is uvloop-based is almost comparable to that of Go programs. uvloop is written in Cython and is built on top of libuv, a high performance, fast and stable multiplatform asynchronous I/O library used by nodejs. -
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vim-jukit
Jupyter-Notebook inspired Neovim/Vim Plugin
REPL plugin and Jupyter-Notebook alternative for (Neo)Vim. This plugin is aimed at users in search for a REPL plugin with lots of additional features. -
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gditools
A Python program/library aimed at GD-ROM image files.
This Python program/library is designed to handle GD-ROM image (GDI) files. It can be used to list files, extract data, generate sorttxt file, extract bootstrap (IP.BIN) file and more. This project can be used in standalone mode, in interactive mode or as a library in another Python program (check the 'addons' folder to learn how). For your convenience, you can use the gditools.py GUI program supplied in the Files section (optional). To use this project you must install the Python 2.7.x branch release binaries. See the README.TXT file for more informations. -
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FSP - File Service Protocol Suite
UDP File transfer protocol
FSP - File Service Protocol. FSP is lightweight UDP based protocol for transferring files. It is designed for anonymous transfers over unreliable networks.
