Python Virtual Environments — venv vs conda vs pyenv (Complete Setup Guide)

Why Virtual Environments Matter

Python virtual environments solve a fundamental problem: different projects need different package versions. Without isolation, installing package A for one project can break package B in another. A virtual environment creates a self-contained directory with its own Python interpreter and installed packages.

Creating a Virtual Environment with venv

The venv module is built into Python 3.3+ and requires no installation:

# Create a virtual environment named 'venv'
python3 -m venv venv

# Activate it
source venv/bin/activate        # Linux/macOS
venv\Scripts\activate          # Windows PowerShell

# Verify it's active (shows path to venv python)
which python

# Install packages
pip install requests flask

# Save dependencies
pip freeze > requirements.txt

# Deactivate when done
deactivate

Recreating from requirements.txt

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Using pyenv for Python Version Management

pyenv lets you switch between Python versions globally or per-project:

# Install pyenv (macOS)
brew install pyenv

# Install a Python version
pyenv install 3.12.0

# Set version for current directory
pyenv local 3.12.0

# Verify
python --version  # Python 3.12.0

conda for Data Science Projects

conda manages both Python and non-Python dependencies (C libraries, CUDA, etc.):

# Create conda environment
conda create -n myproject python=3.11

# Activate
conda activate myproject

# Install packages
conda install numpy pandas scikit-learn
pip install anything-else

# Export environment
conda env export > environment.yml

# Recreate on another machine
conda env create -f environment.yml

When to Use Which Tool

• venv: Standard Python projects, web apps, scripting. Built-in, fast, lightweight.
• pyenv: Managing multiple Python versions on the same machine.
• conda: Data science, ML projects that require C/CUDA libraries. Slower but handles binary dependencies.
• uv: Modern replacement for pip+venv, 10-100x faster, drop-in compatible.

Frequently Asked Questions

Should I commit my venv directory?

No. Add venv/ to .gitignore. Commit requirements.txt or pyproject.toml instead.

Why does 'python' vs 'python3' matter?

On many Linux systems, python refers to Python 2 while python3 refers to Python 3. Inside an activated virtual environment, both point to the environment's interpreter.

How do I use a virtual environment in VS Code?

Press Ctrl+Shift+P → "Python: Select Interpreter" → choose the interpreter inside your venv folder.