Open Source Python Libraries - Page 17
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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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Professional Programming
A collection of learning resources for curious software engineers
Professional Programming is a long-running, curated collection of learning resources aimed at helping software engineers grow into well-rounded professionals. It goes far beyond basic “learn to code” material and covers topics like system design, debugging, testing, performance, security, architecture, and software craftsmanship. The list is organized by themes such as coding, design, operations, communication, and career, making it easy to dive into specific aspects of engineering practice. Each resource is hand-picked by the maintainer, focusing on timeless, high-signal articles, talks, and books rather than trendy or shallow content. Because it has been maintained for many years, it also acts as a kind of “canon” of articles that many engineers reference throughout their careers. The repository is especially helpful for self-taught developers or those transitioning from junior to senior roles who want a structured reading roadmap instead of random blog posts. -
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Professional Services
Common solutions and tools developed by Google Cloud
Professional Services repository is a collection of real-world solutions, tools, and reference implementations developed by Google Cloud’s Professional Services team to address common enterprise challenges. Unlike simple sample repositories, it focuses on production-oriented use cases such as data pipelines, machine learning workflows, infrastructure automation, and security management. The repository contains a wide variety of projects, including tools for validating data migrations, generating large datasets for testing, building analytics dashboards, and automating policy enforcement in cloud environments. These solutions are intended to serve as blueprints that organizations can adapt and extend for their own needs rather than turnkey products. It also includes reusable utilities, helper scripts, and example architectures that demonstrate best practices for building scalable and maintainable systems on Google Cloud. -
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Prompt Engineering Interactive Tutorial
Anthropic's Interactive Prompt Engineering Tutorial
Prompt-eng-interactive-tutorial is a comprehensive, hands-on tutorial that teaches the craft of prompt engineering with Claude through guided, executable lessons. It starts with the anatomy of a good prompt and moves into techniques that deliver the “80/20” gains—separating instructions from data, specifying schemas, and setting evaluation criteria. The course leans heavily on realistic failure modes (ambiguity, hallucination, brittle instructions) and shows how to iteratively debug prompts the way you would debug code. Lessons include building prompts from scratch for common tasks like extraction, classification, transformation, and step-by-step reasoning, with checkpoints that let you compare your outputs against solid baselines. You’ll also practice advanced patterns such as tool use, constrained generation, and response validation so outputs are trustworthy and machine-consumable. -
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ProximityForest
Efficient Approximate Nearest Neighbors for General Metric Spaces
A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer Vision and Pattern Recognition, 2012. This source code is provided without warranty and is available under the GPL license. More commercially-friendly licenses may be available. Please contact Stephen O'Hara for license options. Please view the wiki on this site for installation instructions and examples on reproducing the results of the papers. -
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Prudence is an open source container and framework for scalable web frontends and network services, based on proven REST principles. It comes in several flavors: Python, Ruby, Clojure, JavaScript, PHP and Groovy.
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A python library for parsing and building of data structures (binary or textual). It is based on the concept of defining data structures in a declarative manner, where complex structures are composed of a hierarchy of simpler ones.
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PureEnergy is an organized file manager for SuperWikia Super Controller reactors which provide fluent access of each reactor's Sadeic files and other components of the native Super Controller Codec partitions. PureEnergy features IDE & SDK utilities.
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PyAnnotation is a Python Library to access and manipulate linguistically annotated corpus files. Supported file formats are Kura XML, Elan XML and Toolbox files. A Corpus Reader API is provided to support statistical analysis within the NLTK.
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PyCNN
Image Processing with Cellular Neural Networks in Python
Image Processing with Cellular Neural Networks in Python. Cellular Neural Networks (CNN) are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors. -
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PyDoodles
Assorted Mini-Modules for Python
PyDoodles is a set of assorted "mini-modules" for the Python programming language. It's a collection of small ideas combined to create reusable code that is bound to come in handy for someone somewhere. -
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PyG2Plot
Python3 binding Plotting Library
PyG2Plot is a Python 3 binding for AntV G2Plot, an interactive and responsive charting library. It lets Python users create statistical charts through a small amount of code while relying on G2Plot’s grammar-of-graphics foundation. The library is inspired by pyecharts and is designed to make web-based charts available from Python workflows. Users create a Plot instance, set chart options, and render the result as an HTML file, HTML string, notebook preview, or JupyterLab output. It also supports JavaScript callbacks through a JS helper, which makes advanced customization possible when chart behavior needs JavaScript logic. Overall, it is a useful visualization bridge for Python users who want AntV-style charts in scripts, notebooks, or web outputs. -
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PyGCF
Process GURPS Character Assistant GCF files to Python structures
The GURPS Character Assistant program stores its core data as GCF files. These files contain a descriptions of entities and relationships, commands for manipulating them and rules on how to satisfy constraints. Unfortunately, said program is only available for Windows, doesn't support any kind of automation for generation of NPCs, doesn't function under WINE, and is very, very slow. This module is intended to provide a basis for rectifying those problems. -
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PyLibrary
Libraries for Python developers.
Development in Python (be it website or an App development or implementation of an automation framework) always involves certain operations like handling db queries, operations on web, development of data structures, windows operations (handing services, registries), logging and many more... What, if you have these libraries handy with you all the time? Just import and start using them.. In comes PyLibrary.. PyLibrary is a collection of infrastructure libraries that aid faster development in Python. These libraries are well documented, provide robust error handling and are frequently updated/tested for improvements. Chetan Giridhar -
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PyMW is a Python module for parallel master-worker computing in a variety of environments. With the PyMW module, users can write a single program that scales from multicore machines to global computing platforms.
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PyPattyrn
A simple library for implementing common design patterns
PyPattyrn is a Python package aiming to make it easier and faster to implement design patterns into your own projects. Design patterns by nature cannot be directly translated into code as they are just a description of how to solve a particular problem. However, many of the common design patterns have boilerplate code that is common throughout all implementations of the pattern. This package captures that common code and makes it easy to use so that you don't have to write it yourself in all your projects. -
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PyProperties
Provides support for properties files in Python 3.x
pyproperties provides support for properties files in Python. Being written entirely from scratch it is not in any way derived from java.util.Properties. There are projects which try to mimic j.u.P. This is not one of them. It can read, parse and store properties files but also provides some more advanced functionality like manipulating comments and type-guessing. -
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PyQDbf
PySide QDbf
PyQDbf PySide - QDbf Binding LGPL3 QDbf is Qt - DBF files https://github.com/IvanPinezhaninov/qdbf QDbf: Read or Write Dbf files, but not create new table PySide is Python Binding for Qt Libraries -
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PyQtSerialPort
PySide QtSerialPort
PySide QtSerialPort Binding Shiboken LGPL3 QtSerialPort is officially part of Qt (http://www.qt.io). info link: http://wiki.qt.io/QtSerialPort -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. The new version of the code has not been fully tested, it has been tested under GPU and python3. But in theory there shouldn't be too many problems on python2 and CPU. The basic part (the first five chapters) explains the content of PyTorch. This part introduces the main modules in PyTorch and some tools commonly used in deep learning. For this part of the content, Jupyter Notebook is used as a teaching tool here, and readers can modify and run with notebooks and repeat experiments. -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of functionality of PyTorch Geometric to other packages, which needs to be additionally installed. These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations. -
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PyTorch Natural Language Processing
Basic Utilities for PyTorch Natural Language Processing (NLP)
PyTorch-NLP is a library for Natural Language Processing (NLP) in Python. It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. It’s open-source software, released under the BSD3 license. With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. Now you've setup your pipeline, you may want to ensure that some functions run deterministically. Wrap any code that's random, with fork_rng and you'll be good to go. Now that you've computed your vocabulary, you may want to make use of pre-trained word vectors to set your embeddings. -
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PyTorchVideo
A deep learning library for video understanding research
PyTorchVideo is a deep learning library for video understanding, providing modular components and pretrained models for tasks like action recognition, video classification, detection, and self-supervised learning. It is tightly integrated with PyTorch and PyTorch Lightning, offering flexible APIs for building and training spatiotemporal networks. The library includes efficient implementations of state-of-the-art architectures such as SlowFast, X3D, and MViT, optimized for both research prototyping and production inference. It supports video I/O pipelines, data augmentation, distributed training, and mixed precision computation for large-scale experiments. PyTorchVideo also connects seamlessly with other Meta AI tools such as Detectron2 and PyTorch3D for multimodal video analysis. Designed to accelerate research and deployment, it serves as a unified framework for reproducible, high-performance video AI development. -
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PyUPC-EAN
A barcode library/module for python.
PyUPC-EAN is a barcode library/module for Python. It supports the barcode formats upc-e, upc-a, ean-13, ean-8, ean-2, ean-5, itf14, codabar, code11, code39, code93, and msi.
