Install a specific version

Set global default

Set version for current project only

Activate it

Install packages

Deactivate

Create a virtual environment

Activate it

Install dependencies Every Python developer eventually hits the same wall: one project needs Python 3.9, another requires 3.11, and the new one won't run on anything below 3.12. The instinct is to upgrade globally — which promptly breaks something else.

There's a better way. Two tools worth knowing: pyenv and conda. They solve the same problem differently, and knowing which to reach for matters. pyenv — Lightweight, Automatic Version Switchingpyenv lets you install and switch between Python versions at the system, user, or project level. Critically, it never touches your system Python.Installing pyenvLinux / Mac:bashcurl https://pyenv.run | bashAdd this to your ~/.bashrc or ~/.zshrc:bashexport PYENV_ROOT="$HOME/.pyenv"export PATH="$PYENV_ROOT/bin:$PATH"eval "$(pyenv init -)"Windows — use pyenv-win:powershellInvoke-WebRequest -UseBasicParsing -Uri "https://raw.githubusercontent.com/pyenv-win/pyenv-win/master/pyenv-win/install-pyenv-win.ps1" -OutFile "./install-pyenv-win.ps1"; &"./install-pyenv-win.ps1"Basic Usagebash# See available versionspyenv install --list pyenv local 3.10.14pyenv local creates a .python-version file in your project directory. Every time you cd into that folder, pyenv switches automatically — no manual toggling.Per-Project Workflowbashcd my-projectpyenv local 3.10.14python --version # Python 3.10.14 cd ../other-projectpyenv local 3.12.0python --version # Python 3.12.0Clean and automatic. conda — Full Environment Managementconda handles Python versions and packages together. It's heavier than pyenv but earns its weight when dependencies get complex — particularly in data science and ML.Installing condaDownload Miniconda — the minimal install — for Windows, Linux, or Mac.Basic Usagebash# Create an environment with a specific Python versionconda create -n myenv python=3.10 conda install numpy pandas conda deactivatePer-Project Workflowbashcd my-projectconda activate project-39 # Python 3.9 environment cd ../other-projectconda activate project-312 # Python 3.12 environmentUnlike pyenv, switching isn't automatic — you activate manually. Small trade-off for what you get in return. pyenv vs conda — Which One?pyenvcondaPython version switchingAutomatic (per directory)Manual activationPackage managementNo — use pip + venvYes, built-inBest forGeneral / backend / web devData science / MLWindows supportVia pyenv-win (limited)ExcellentOverheadLightweightHeavierUse pyenv if you want lightweight, automatic version switching per project.Use conda if you're in data science, need environment and package management together, or are primarily on Windows. The Ideal Setup: pyenv + venv TogetherFor most developers, this combination covers everything:bash# Set Python version with pyenvpyenv local 3.11.9 source .venv/bin/activate # Linux/Mac.venv\Scripts\activate # Windows pip install -r requirements.txtpyenv handles the version. venv handles dependency isolation. Best of both. Common Issues Worth Knowingpyenv: command not found after installYour shell config wasn't reloaded. Run source ~/.bashrc (or ~/.zshrc) after editing it, or close and reopen your terminal.conda: environment not activating in scriptsRun conda init once after installing, then restart your terminal. This adds the necessary hooks to your shell profile.python --version still shows system Python after pyenv localMake sure the pyenv init lines are actually in your shell config and that you've reloaded it. Running pyenv versions should list your installed versions. Quick Referencebash# pyenvpyenv install 3.11.9pyenv local 3.11.9 # project-levelpyenv global 3.11.9 # system-levelpyenv versions # list installed versions conda create -n myenv python=3.11conda activate myenvconda deactivateconda env list # list all environments Managing Python versions correctly is one of those things that saves you hours of debugging later. Set it up once, stop thinking about it.If you're running into an issue not covered here, drop it in the comments. Templates let you quickly answer FAQs or store snippets for re-use. Excellent guide on a common pitfall; the comparison between pyenv and virtual environments really clarifies when to use each for a stable workflow. Your practical approach to isolating dependencies is a must-read for anyone looking to keep their local development environment clean and conflict-free. as well , this person and/or - Email [email protected]- Location Ahmedabad, India- Education B.Tech In Computer Science From JEC , Jaipur- Pronouns He- Work Senior DevSecOps Engineer- Joined May 24, 2024