pyinfra is a python-native, agentless automation tool that runs commands over ssh — concurrently, idempotently, and 6× faster than ansible.
Ortho is a python based app that renders orthographic templates from existing 3d models, akin to a ‘blueprint creator’. It’s free and requires only python and a browser. No coding or commands, just run the launcher, then drag and drop your file or zip,then configure in the browser. Runs on PC and Mac, pretty much fool proof.
No more googling for low res watermarked inaccurate paywalled. Templates. Not AI.
Signal messaging library for Python using Presage (Rust).
YS (aka YAMLScript) is a new YAML loader for 15 (and counting) programming languages:
C#, Clojure, Crystal, Go, Haskell, Java, Julia, Lua, NodeJS, Perl, PHP, Python, Raku, Ruby and Rust.
Try using YS in place of your current YAML loader!
- It's as easy to use as your current YAML loader
- Loads your existing YAML files properly
- It works the same way in every programming language
- Same API, same features, same bugs, same bug fixes
- YS has optional functional programming features
- File imports, string interpolation, standard library, etc
- Everything a compiled programming language has
How can YS offer all this?
83 languages for you to master
Become fluent in your chosen programming languages by completing these tracks created by our awesome team of contributors
Conjure is an interactive environment for evaluating code within your running program.
The core features of Conjure are language agnostic (although it’s targeted at Lisps for now), with each language client providing their own extra tools. Here’s the currently supported languages, contributions and 3rd party plugins that add clients are highly encouraged! You can find a comparison table for all clients and supported features in the wiki.
Typer is a library for building CLI applications that users will love using and developers will love creating. Based on Python type hints.
It's also a command line tool to run scripts, automatically converting them to CLI applications.
The key features are:
- Intuitive to write: Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs.
- Easy to use: It's easy to use for the final users. Automatic help, and automatic completion for all shells.
- Short: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.
- Start simple: The simplest example adds only 2 lines of code to your app: 1 import, 1 function call.
- Grow large: Grow in complexity as much as you want, create arbitrarily complex trees of commands and groups of subcommands, with options and arguments.
- Run scripts: Typer includes a typer command/program that you can use to run scripts, automatically converting them to CLIs, even if they don't use Typer internally.
Ansible Runner is a tool and python library that helps when interfacing with Ansible directly or as part of another system whether that be through a container image interface, as a standalone tool, or as a Python module that can be imported. The goal is to provide a stable and consistent interface abstraction to Ansible. This allows Ansible to be embedded into other systems that don’t want to manage the complexities of the interface on their own (such as CI/CD platforms, Jenkins, or other automated tooling).
Topics
An extremely fast Python package and project manager, written in Rust.
Shows a bar chart with benchmark results.
Installing Trio's dependencies with a warm cache.
Highlights
🚀 A single tool to replace pip, pip-tools, pipx, poetry, pyenv, twine, virtualenv, and more.
⚡️ 10-100x faster than pip.
🗂️ Provides comprehensive project management, with a universal lockfile.
❇️ Runs scripts, with support for inline dependency metadata.
🐍 Installs and manages Python versions.
🛠️ Runs and installs tools published as Python packages.
🔩 Includes a pip-compatible interface for a performance boost with a familiar CLI.
🏢 Supports Cargo-style workspaces for scalable projects.
💾 Disk-space efficient, with a global cache for dependency deduplication.
⏬ Installable without Rust or Python via curl or pip.
🖥️ Supports macOS, Linux, and Windows.
A environment management tool for Ansible content development that provides isolated workspaces for development. ansible-dev-environment (ade) manages virtual environments, collection installation and removal, and Python dependency resolution to ensure consistent, reproducible development environments.
Overview
ansible-dev-environment (ade) provides comprehensive collection development environment management by:
- Creating isolated virtual environments
- Installing and removing collections with full dependency tracking
- Resolving and installing collection Python dependencies from requirements.txt and test-requirements.txt
- Installing collections in editable mode with symlinks for active development
- Managing development dependencies like ansible-dev-tools
- Providing configurable workspace isolation
While ansible-galaxy efficiently manages collection installation and dependencies, it does not handle Python package dependencies that collections may require. ade complements this by ensuring all Python requirements are properly managed within isolated environments.
Collections are installed into Python's site-packages directory, making them discoverable by both Ansible and Python tooling including pytest.
Sometimes tests need to invoke functionality which depends on global settings or which invokes code which cannot be easily tested such as network access. The monkeypatch fixture helps you to safely set/delete an attribute, dictionary item or environment variable, or to modify sys.path for importing.
The monkeypatch fixture provides these helper methods for safely patching and mocking functionality in tests:
- monkeypatch.setattr(obj, name, value, raising=True)
- monkeypatch.delattr(obj, name, raising=True)
- monkeypatch.setitem(mapping, name, value)
- monkeypatch.delitem(obj, name, raising=True)
- monkeypatch.setenv(name, value, prepend=None)
- monkeypatch.delenv(name, raising=True)
- monkeypatch.syspath_prepend(path)
- monkeypatch.chdir(path)
- monkeypatch.context()
All modifications will be undone after the requesting test function or fixture has finished. The raising parameter determines if a KeyError or AttributeError will be raised if the target of the set/deletion operation does not exist.
Continuous profiling for analysis of CPU, memory usage over time, and down to the line number. Saving infrastructure cost, improving performance, and increasing reliability.
Parca supports profiling for Python versions from 2.7 to 3.11, with ongoing work for 3.12 and full support anticipated for 3.13.
The project’s modular design allows quick adaptation to new Python runtime changes.
-- Profiling Python with eBPF: A New Frontier in Performance Analysis | kakkoyun
Discover how eBPF and Parca are transforming Python profiling, enabling continuous, efficient, and non-intrusive performance analysis directly in production.
Structured logging means that you don’t write hard-to-parse and hard-to-keep-consistent prose in your log entries. Instead, you log events that happen in a context of key-value pairs.
- hynek/structlog: Simple, powerful, and fast logging for Python.
- A Comprehensive Guide to Python Logging with Structlog | Better Stack Community
Abstract Syntax Trees, ASTs, are a powerful feature of Python. You can write programs that inspect and modify Python code, after the syntax has been parsed, but before it gets compiled to byte code. That opens up a world of possibilities for introspection, testing, and mischief.
The official documentation for the ast module used to be rather brief. A large part of the material from Green Tree Snakes, describing all of the AST node classes, has now been merged into the ast module docs. The remainder here aims to serve as a field guide (or forest guide?) to working with ASTs in practice.
Aruba Central Python Package Index SDK
Aruba Central is an unified cloud-based network management and configuration platform for campus, branch, remote and data center networks. There are various needs for automation and programmability like automating repetitive tasks, configuring multiple devices, monitoring and more. This python package is to programmatically interact with Aruba Central via REST APIs.
Ics.py is a pythonic iCalendar (rfc5545) library. Its goals are to read and write ics data in a developer-friendly way.
It is written in Python 3 (3.6, 3.7 and 3.8 supported) and is Apache2 Licensed.
The iCalendar specification is complicated, you don’t like RFCs but you want/have to use the ics format and you love pythonic APIs? ics.py is for you!
This article shows how to install Python on Red Hat Enterprise Linux (RHEL), including both versions 3 and 2 of Python along with the pip, vent, virtualenv, and pipenv utilities. The article starts with RHEL 9 and continues with RHEL 8 and RHEL 7, including plenty of tips useful in all versions. The article also covers virtual environments, which help you ensure that your applications have the correct versions of Perl and related modules.
jsonschema is an implementation of the JSON Schema specification for Python.