Python Virtual Environments: venv vs Conda

Managing dependencies is one of the first challenges you face when working on multiple Python projects. Two tools dominate the ecosystem: Python’s built-in venv module and Conda. This post explains when to use each and shows the essential commands.

Why Isolate Environments?

Each project may require different versions of the same library. Without isolation, installing a package for one project can break another. Virtual environments solve this by giving each project its own sandboxed Python installation.

Creating a Virtual Environment with venv

venv is part of the Python standard library — no installation needed.

# Create the environment
python -m venv .venv

# Activate it (Windows)
.venv\Scripts\activate

# Activate it (macOS / Linux)
source .venv/bin/activate

# Install packages
pip install pandas scikit-learn

# Save dependencies
pip freeze > requirements.txt

# Deactivate
deactivate

Use venv when you are working with pure Python packages and pip.

Creating an Environment with Conda

Conda goes further: it manages both Python versions and non-Python dependencies (e.g. GDAL, CUDA libraries).

# Create an environment with a specific Python version
conda create --name myenv python=3.11

# Activate it
conda activate myenv

# Install packages (Conda channel first, then pip for the rest)
conda install pandas scikit-learn
pip install streamlit

# Export the environment
conda env export > environment.yml

# Recreate from file
conda env create -f environment.yml

# Deactivate
conda deactivate

Which One Should You Use?

Practical Tips

• Commit requirements.txt or environment.yml to version control — never commit the environment folder itself.
• Name your environment after the project, not generically (e.g. eucface-analysis, not myenv).
• For Conda, prefer conda-forge as your primary channel for more up-to-date packages.

See also the portfolio pages on Python virtual environments and Conda environments for scripts you can reuse directly.