Open Source Python Libraries - Page 16
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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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OnAccessFileEncryption
EaseFilter OnAcess File Encryption SDK
Transparent file encryption performs real-time I/O encryption and decryption of the files in any block data with 16 bytes. The encryption uses a 256 bits symmetric key to encrypt or decrypt the data with AES encryption algorithm. Auto file encryption protects data "at rest", meaning the transparent data and files encryption. It provides the ability to comply with policies which can be applied by users, processes and file type. This allows only authorized users and processes to access the encrypted files, unauthorized users and processes can’t access the encrypted files. The Auto FileCrypt Tool was developed with EaseFilter Encryption Filter Driver(EEFD) SDK, a file level encryption filter driver. Run the auto file encryption service with administrator permission, add the managed folders as encryption folder, when the files were added to the managed folder, the files will be encrypted automatically, when files were read, the data will be decrypted in memory automatically. -
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Opacus
Training PyTorch models with differential privacy
Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. Vectorized per-sample gradient computation that is 10x faster than micro batching. Supports most types of PyTorch models and can be used with minimal modification to the original neural network. Open source, modular API for differential privacy research. Everyone is welcome to contribute. ML practitioners will find this to be a gentle introduction to training a model with differential privacy as it requires minimal code changes. Differential Privacy researchers will find this easy to experiment and tinker with, allowing them to focus on what matters. -
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That project aims at delivering a reference implementation of a library, estimating and serving average prices paid for air travel products. It is not intended for use by an actual airline, but rather by simulators or other airline-related modules of
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Optimized Storage for temporal Data
open Optimized Storage of time series data
Beta version. Base class for optimized storage of time series data. Uses any kind of relational database. Cross plateform with multiple languages (C++, C#, Java). Conditional storage based on value variation : DeltaValue and DeltaTime params. Get back data without losts. -
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PROTON
High-level python framework that facilitates rapid server-side develop
PROTON is a high-level Python framework that facilitates rapid server-side development with clean & pragmatic design. Thanks for checking it out! PROTON aims at easing server-side development for all Python enthusiasts. Essentially, by running a shell command, developer will auto generate necessary Model, Controller and APIs! All of this with connectivity to Transactional Databases (PROTON supports Postgresql, MySQL & SQL Server),caching (Redis middleware), Auto generated OpenAPI specs & descriptive logging! One command, to get a production ready server-side stack! -
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PageIndex
Document Index for Vectorless, Reasoning-based RAG
PageIndex is an innovative open-source framework that reimagines retrieval-augmented generation (RAG) by eliminating conventional vector similarity search and instead building hierarchical semantic indexes that mirror a document’s natural structure. Rather than chunking text and embedding it into a vector database, PageIndex constructs a tree-structured index — similar to a detailed, AI-enhanced table of contents — that a large language model can traverse to locate the most relevant sections of long documents. This reasoning-driven retrieval aligns more naturally with how humans explore complex texts, improving relevance and traceability, especially in professional domains like financial reports, legal contracts, and technical manuals. The project includes example notebooks, scripts for tree generation and search, and support for multiple document formats including PDF and markdown, with tools designed to preserve context and semantic boundaries. -
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Papis
Powerful and highly extensible command-line based document
Papis is a powerful and highly extensible CLI document and bibliography manager. With Papis, you can search your library for books and papers, add documents and notes, import and export to and from other formats, and much much more. Papis uses a human-readable and easily hackable .yaml file to store each entry's bibliographical data. It strives to be easy to use while providing a wide range of features. And for those who still want more, Papis makes it easy to write scripts that extend its features even further. -
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PennyLane
A cross-platform Python library for differentiable programming
A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy, PyTorch, JAX, or TensorFlow, allowing hybrid CPU-GPU-QPU computations. The same quantum circuit model can be run on different devices. Install plugins to run your computational circuits on more devices, including Strawberry Fields, Amazon Braket, Qiskit and IBM Q, Google Cirq, Rigetti Forest, and the Microsoft QDK. -
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Penzai
A JAX research toolkit to build, edit, & visualize neural networks
Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained inspection and modification after training. Its modular design includes tools for tree manipulation, named axes, and declarative neural network construction. The library integrates tightly with Treescope, an advanced pretty-printer for visualizing deeply nested JAX pytrees and NDArray structures. Penzai’s penzai.nn module provides a compositional, combinator-based API for building neural networks. -
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C Library to manage a pool of event/task in a persistent way to assure that your events/tasks won't be deleted because of a failure. Events/tasks are saved on a FS. if FS is NFS, NFS availability checks are made. (comes with a python binding)
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Petastorm
Petastorm library enables single machine or distributed training
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code. Petastorm is an open-source data access library developed at Uber ATG. This library enables single machine or distributed training and evaluation of deep learning models directly from datasets in Apache Parquet format. Petastorm supports popular Python-based machine learning (ML) frameworks such as Tensorflow, PyTorch, and PySpark. It can also be used from pure Python code. A dataset created using Petastorm is stored in Apache Parquet format. On top of a Parquet schema, petastorm also stores higher-level schema information that makes multidimensional arrays into a native part of a petastorm dataset. Petastorm supports extensible data codecs. These enable a user to use one of the standard data compressions (jpeg, png) or implement her own. -
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Functional Programming for Python. Provides a small mixin to provide 1) type constraints for named tuples 2) pre/postcondition typechecking for functions 3) syntactical sugar to make your code look pretty (accomplished through a PEP 302 import hook)
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Pinject
A pythonic dependency injection library
Pinject is a lightweight dependency-injection library for Python that favors explicit wiring and testability over magic. Instead of global singletons, you declare providers (bindings) that describe how to construct objects, and Pinject resolves the graph by inspecting call signatures. Its container supports constructor injection and fine-grained scoping so you can share expensive resources while keeping tests isolated. The library leans on Python’s introspection to minimize boilerplate, making it natural to adopt in codebases that already rely on type hints or keyword arguments. Because bindings are just Python functions and classes, refactoring remains straightforward and the DI graph is easy to reason about. Pinject is particularly useful for medium-to-large services where configuration, logging, data clients, and business logic need clean separation without resorting to manual plumbing. -
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To install these libraries, you need to have Python installed on your PC, preferably the latest version. This file only works for Windows.
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Playground Cheatsheet for Python
Playground and cheatsheet for learning Python
learn-python is another repository by Oleksii Trekhleb that serves as both a playground and an interactive cheatsheet for learning Python. It contains numerous Python scripts organized by topic (lists, dictionaries, loops, functions, classes, modules, etc.), each with code examples, explanations, test assertions, and links to further readings. The design supports “learn by doing”: you can modify the code, run the tests, see how behavior changes, and thus internalize Python language features, idioms, and good style practices (including linting and PEP8). Because it is organized in bite-sized chunks, it’s ideal for beginners or people refreshing their Python skills who want to revisit syntax and common patterns before moving into larger frameworks or applications. It also supports usage as a reference: if you forgot how a list comprehension works or how decorators behave, you can quickly open the relevant script. -
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Poor Http
WSGI Server, WSGI Connector, Python doc generator
Poor Http Server is standalone wsgi server, which is designed for using python web applications. Unlike other projects, this is not framework, but single server, light wsgi connector, and python doc generator. -
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Portfolio-Generator
Portfolios Made Easy, Generate portfolios in 3 easy steps
A webapp made in Django to generate a portfolio. -
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Prefix Tree
A lighting-fast prefix tree and DAWG implementation in C and python
A lighting-fast prefix tree (trie) and compressed prefix tree (DAWG) implementation. This library can be usefull for longest-prefix matching, code completion, dictionary compression, spell checking. -
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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.
