pyenv

Simple Python version management (by pyenv)

Pyenv Alternatives

Similar projects and alternatives to pyenv

  • Poetry

    pyenv VS Poetry

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  • asdf

    pyenv VS asdf

    Extendable version manager with support for Ruby, Node.js, Elixir, Erlang & more

  • Scout APM

    Truly a developer’s best friend. Scout APM is great for developers who want to find and fix performance issues in their applications. With Scout, we'll take care of the bugs so you can focus on building great things 🚀.

  • pyenv-virtualenv

    pyenv VS pyenv-virtualenv

    a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv)

  • HomeBrew

    pyenv VS HomeBrew

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  • pyenv-win

    pyenv VS pyenv-win

    pyenv for Windows. pyenv is a simple python version management tool. It lets you easily switch between multiple versions of Python. It's simple, unobtrusive, and follows the UNIX tradition of single-purpose tools that do one thing well.

  • SDKMan

    pyenv VS SDKMan

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  • direnv

    pyenv VS direnv

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  • Zigi

    Delete the most useless function ever: context switching.. Zigi monitors Jira and GitHub updates, pings you when PRs need approval and lets you take fast actions - all directly from Slack! Plus it reduces cycle time by up to 75%.

  • miniforge

    pyenv VS miniforge

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  • Pipenv

    pyenv VS Pipenv

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  • virtualenv

    pyenv VS virtualenv

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  • ohmyzsh

    pyenv VS ohmyzsh

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  • nvm

    pyenv VS nvm

    Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions

  • Pew

    pyenv VS Pew

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  • Visual Studio Code

    pyenv VS Visual Studio Code

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    pyenv VS CPython

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    pyenv VS volta

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  • pip-tools

    pyenv VS pip-tools

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  • RVM

    pyenv VS RVM

    Ruby enVironment Manager (RVM)

  • Odin

    pyenv VS Odin

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  • Ansible

    pyenv VS Ansible

    Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.

  • SonarQube

    Static code analysis for 29 languages.. Your projects are multi-language. So is SonarQube analysis. Find Bugs, Vulnerabilities, Security Hotspots, and Code Smells so you can release quality code every time. Get started analyzing your projects today for free.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better pyenv alternative or higher similarity.

pyenv reviews and mentions

Posts with mentions or reviews of pyenv. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-11-22.
  • Vent: I'm tired of the 1001 libraries of virtual environments.
    8 projects | reddit.com/r/Python | 22 Nov 2022
    pyenv - Simple Python Version Management
    8 projects | reddit.com/r/Python | 22 Nov 2022
  • Python 3.12.0 is to remove long-deprecated items
    13 projects | news.ycombinator.com | 16 Nov 2022
    A hidden gem in pyenv is its 'python-build' plugin, which just lets you build and install any Python version:

      git clone https://github.com/pyenv/pyenv.git
    13 projects | news.ycombinator.com | 16 Nov 2022
  • Ask HN: Programming Without a Build System?
    15 projects | news.ycombinator.com | 12 Nov 2022
    > trying to build a lifeboat for Twitter, Python works, but then modules require builds that break.

    > Alternatively, any good resources for the above?

    There are many, _unbelievably many_ writeups and tools for Python building and packaging. Some of them are really neat! But paralysis of choice is real. So is the reality that many of the new/fully integrated/cutting edge tools, however superior they may be, just won't get long term support to catch on and stay relevant.

    When getting started with Python, I very personally like to choose from a few simple options (others are likely to pipe up with their own, and that's great; mine aren't The One Right Way, just some fairly cold/mainstream takes).

    1. First pick what stack you'll be using to develop and test software. In Python this is sadly often going to be different from the stack you'll use to deploy/run it in production, but here we are. There are two sub-choices to be made here:

    1.a. How will you be running the _python interpreter_ in dev/test? "I just want to use the Python that came with my laptop" is fine to a point, but breaks down a lot sooner than folks expect (again, the reasons for this are variously reasonable and stupid, but here we are). Personally, I like pyenv (https://github.com/pyenv/pyenv) here. It's a simple tool that builds interpreters on your system and provides shell aliases to adjust pathing so they can optionally be used. At the opposite extreme from pyenv, some folks choose Python-in-Docker here (pros: reproducible, makes deployment environments very consistent with dev; cons: IDE/quick build-and-run automations get tricker). There are some other tools that wrap/automate the same stuff that pyenv does.

    1.b. How will you be isolating your project's dependencies? "I want to install dependencies globally" breaks down (or worse, breaks your laptop!) pretty quickly, yes it's a bummer. There are three options here: if you really eschew automations/wrappers/thick tools in general, you can do this yourself (i.e. via "pip install --local", optionally in a dedicated development workstation user account); you can use venv (https://docs.python.org/3/library/venv.html stdlib version of virtualenv, yes the names suck and confusing, here we are etc. etc.), which is widely standardized upon and manually use "pip install" while inside your virtualenv, and you can optionally integrate your virtualenv with pyenv so "inside your virtualenv" is easy to achieve via pyenv-virtualenv (https://github.com/pyenv/pyenv-virtualenv); or you can say "hell with this, I want maximum convenience via a wrapper that manages my whole project" and use Poetry (https://python-poetry.org/). There's no right point on that spectrum, it's up to you to decide where you fall on the "I want an integrated experience and to start prototyping quickly" versus "I want to reduce customizations/wrappers/tooling layers" spectrum.

    2. Then, pick how you'll be developing said software: what frameworks or tools you'll be using. A Twitter lifeboat sounds like a webapp, so you'll likely want a web framework. Python has a spectrum of those of varying "thickness"/batteries-included-ness. At the minimum of thickness are tools like Flask (https://flask.palletsprojects.com/en/2.2.x/) and Sanic (like Flask, but with a bias towards performance at the cost of using async and some newer Python programming techniques which tend, in Python, to be harder than the traditional Flask approach: https://sanic.dev). At the maximum of thickness are things like Django/Pyramid. With the minimally-thick frameworks you'll end up plugging together other libraries for things like e.g. database access or web content serving/templating, with the maximally-thick approach that is included but opinionated. Same as before: no right answers, but be clear on the axis (or axes) along with you're choosing.

    3. Choose how you'll be deploying/running the software, maybe after prototyping for awhile. This isn't "lock yourself into a cloud provider/hosting platform", but rather a choice about what tools you use with the hosting environment. Docker is pretty uncontentious here, if you want a generic way to run your Python app on many environments. So is "configure Linux instances to run equivalent Python/package versions to your dev/test environment". If you choose the latter, be aware that (and this is very important/often not discussed) many tools that the Python community suggests for local development or testing are very unsuitable for managing production environments (e.g. a tool based around shell state mutation is going to be extremely inconvenient to productionize).

    Yeah, that's a lot of choices, but in general there are some pretty obvious/uncontentious paths there. Pyenv-for-interpreters/Poetry-for-packaging-and-project-management/Flask-for-web-serving/Docker-for-production is not going to surprise anyone or break any assumptions. Docker/raw-venv/Django is going to be just as easy to Google your way through.

    Again, no one obvious right way (ha!) but plenty of valid options!

    Not sure if that's what you were after. If you want a "just show me how to get started"-type writeup rather than an overview on the choices involved, I'm sure folks here or some quick googling will turn up many!

  • Poor performance using loop:
    2 projects | reddit.com/r/ansible | 10 Nov 2022
  • Frustrated with a mess on my Mac((
    2 projects | reddit.com/r/learnpython | 8 Nov 2022
    You have 10 versions of python, and you installed every one manually by downloading from python.org - is it correct? Check out pyenv.
  • macOS Dev Setup
    23 projects | dev.to | 6 Nov 2022
    Before installing a new Python version, the pyenv wiki recommends having a few dependencies available:
  • Chatting with Sebastian Witowski - Part I: Code Standards, Tooling, and Working in Teams
    6 projects | dev.to | 31 Oct 2022
    So what I learned some time ago is to strike a balance. And the talk that you mentioned shows three tools that I use almost on all my projects: pyenv, virtualenvwrapper, and pipx. They have been working for years. They will still work for years in the future. I've also been using pip-tools since I don't remember when and I think it will still be supported in the future.
  • Pyenv and Virtualenvs Quick-start
    3 projects | dev.to | 28 Oct 2022
    For this, I will use pyenv and the pyenv-virtualenv tools.
  • A note from our sponsor - InfluxDB
    www.influxdata.com | 28 Nov 2022
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Stats

Basic pyenv repo stats
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29,610
8.8
9 days ago
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