coveo-python-oss
pipx
coveo-python-oss | pipx | |
---|---|---|
2 | 38 | |
14 | 8,913 | |
- | 3.7% | |
7.3 | 9.3 | |
12 days ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
coveo-python-oss
-
Python projects with best practices on Github?
You can take look at https://github.com/coveooss/coveo-python-oss for a monorepo that uses stew to test and ship several libraries to pypi.org.
-
How do you deploy Python applications?
Here's a fully automated repository, take a look at the example library first, and take a look at the github actions too! Bonus, if you read closely you'll also obtain OOTB mypy, black, pytest runners with no additional boilerplate! 😉
pipx
-
Keep your AWS CLI config fresh with Cog
Use pipx to install Cog and my aws-sso-config-builder tool in the same environment:
- pipx
-
Implementing Quality Checks In Your Git Workflow With Hooks and pre-commit
Given how useful pre-commit is across projects I generally recommend installing via pip install --user, making it part of a tooling virtual environment, or using pipx:
-
pipx VS instld - a user suggested alternative
2 projects | 9 Dec 2023
- Pipx – Install and Run Python Applications in Isolated Environments
-
Packaging a self contained CLI application for any environment?
I would recommend going with PipX. You toss in a setup.py file, put your project on github, and then anyone on any OS can pipx install your project. It's a glorious thing. The only thing they need is 1) some supported version of Python installed, 2) pipx installed. They can even get updates by calling pipx upgrade.
-
Some confusion with system version and pyenv
See https://github.com/pypa/pipx/issues/278
-
List of software package management systems
Good overview. There are quite a few on there I was not aware of. That said, I am not sure the organizational schema makes a tone of sense. I would assume most users that come across this would be looking for a package manager for a specific platform and then weighing the options of binary/source/etc., instead of the other way around.
Also, pipx (https://github.com/pypa/pipx) would be a good addition to the list. I'd add it but I'm not sure where it would go. Maybe every section? It's cross platform and handles both binary and source based app distributions.
-
After using Python for over 2 years I am still really confused about all of the installation stuff and virtual environments
Pip is pretty simple and useful for me - you have your own environment for every script/program, requirements.txt is simple to understand too... It's kinda good solution for regular users... For more complex projects we have Poetry, PipX, that was inspired by NPM(x), I think...
-
Apple Unveils MacBook Pro Featuring M2 Pro and M2 Max
What benefit would joining your cult bestow upon me that brew does not already?
My brew list is intentionally very short and my faffing about desire is limited.
Generally I use brew to pull in asdf (https://github.com/asdf-vm/asdf) to install programming languages/tooling, it works flawlessly.
I use Pipx (https://github.com/pypa/pipx) to install python thingies (such as yt-dlp) as a cli. Go and Rust handle binaries in their languages beautifully and without issues.
What are some alternatives?
system-design-primer - Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Poetry - Python packaging and dependency management made easy
ubelt - A Python utility library with a stdlib like feel and extra batteries. Paths, Progress, Dicts, Downloads, Caching, Hashing: ubelt makes it easy!
opstrat - Option visualization python package
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Lean and Mean Docker containers - Slim(toolkit): Don't change anything in your container image and minify it by up to 30x (and for compiled languages even more) making it secure too! (free and open source)
fastapi-memory-leak
dust - A more intuitive version of du in rust
smartsheet-python-sdk - Library that uses Python to connect to Smartsheet services (using API 2.0).
private-pypi - private pypi server
Pyjion
translate-shell - :speech_balloon: Command-line translator using Google Translate, Bing Translator, Yandex.Translate, etc.