Python's venv: A Comparison with Conda for Research Computing

As researchers increasingly embrace Python for everything from data analysis to simulation, the question of environment management becomes not just technical but practical: how do we ensure our code works consistently today, next week, and when shared with collaborators? Let’s explore Python’s built-in venv and compare it with Conda, the longtime favorite in scientific computing circles.

What is venv and How to Use It

venv is Python’s native solution for creating isolated environments. Since Python 3.3, it’s been included in the standard library, eliminating the need for additional installations. While many researchers have traditionally reached for Conda, this built-in tool has matured into a compelling alternative that deserves consideration in your workflow.

# Creating a virtual environment with venv
python -m venv myenv

# Activating it
# On Windows:
myenv\Scripts\activate
# On Unix/MacOS:
source myenv/bin/activate

# Installing packages
pip install numpy scipy matplotlib

# Deactivating when done
deactivate

# Creating requirements file for sharing
pip freeze > requirements.txt

# Installing from requirements
pip install -r requirements.txt

Architectural Differences

The fundamental distinction between venv and Conda can be visualized in their architecture:

When you create a venv, it essentially:

1. Creates a directory structure that mimics a Python installation
2. Installs pip within that environment
3. Points to the system Python interpreter
4. Creates isolation by modifying environment variables

venv creates a lightweight Python environment that points to packages installed via pip, while Conda creates a more comprehensive environment that can include non-Python components.

venv vs. Conda: A Practical Comparison for Researchers

Package Ecosystem

venv:

• Relies on pip and PyPI, where cutting-edge research packages often appear first
• Increasingly handles scientific dependencies well through binary wheels
• Perfect for pure Python implementations common in ML and data science

Conda:

• Excels with complex packages requiring compiled components (think bioinformatics tools)
• Provides curated, tested package collections via channels like Conda-forge
• Handles interdependent libraries that need precise version alignment

Performance Considerations

The performance difference is most noticeable in environment activation, venv activations is a ~0.1-0.3s, while Conda is a ~1-3s.

This difference again stems from venv’s simpler activation process:

This activation speed makes venv particularly appealing for researchers who frequently switch between projects or need to quickly deploy environments.

OS Compatibility

venv on macOS/Linux:

• Embraces Unix philosophy with simple file structures and shell scripts
• Integrates seamlessly with research computing environments and HPC clusters
• Leverages system-level optimizations for scientific libraries

Conda across platforms:

• Provides consistent experience across Windows, macOS, and Linux
• Handles platform-specific binary dependencies transparently
• Simplifies collaboration across different operating systems

Best Practices for venv in Research

To get the most out of venv in a research context:

1. Create environment-specific entries in your Jupyter kernel list:

   python -m ipykernel install --user --name=myproject --display-name="Python (My Project)"
   

2. Use development mode for your own research packages:

   pip install -e .
   

3. Specify exact versions in requirements files:

   pip freeze > requirements.txt
   

4. Consider project-local configurations:

   python -m venv --prompt="myproject" .venv
   

The Researcher’s Decision Framework

Consider your research context when choosing:

• Computational complexity: Are you working with pure Python algorithms or complex compiled libraries?
• Collaboration needs: Will your code run primarily on your machine or across diverse environments?
• Development pace: Do you need the latest packages or value stability above all?

When venv Shines in Research

# A typical ML research workflow with venv
python -m venv research_env
source research_env/bin/activate
pip install torch torchvision matplotlib pandas jupyter
pip install -e .  # Install your research package in development mode

venv excels when you’re developing novel methods, frequently updating dependencies, or working primarily with Python-based tools. Many ML researchers find venv’s simplicity and speed advantageous for rapid experimentation cycles.

When Conda Remains Invaluable

# A bioinformatics workflow with Conda
Conda create -n genomics python=3.11 biopython samtools bedtools blast
Conda activate genomics
Conda install -c bioConda pysam

For fields like bioinformatics, computational chemistry, or geospatial analysis where complex compiled dependencies are common, Conda’s ability to manage non-Python libraries remains unmatched.

Conclusion

The rise of venv doesn’t signal Conda’s obsolescence but rather offers researchers more options. Many scientists now adopt a hybrid approach: venv for rapid development and pure Python projects, Conda for complex interdisciplinary work requiring specialized libraries.

As scientific computing continues to evolve, the ability to choose the right tool for each specific research context becomes increasingly valuable. venv’s simplicity and performance make it worth considering for your next project—especially if you’ve defaulted to Conda out of habit rather than necessity.

Remember that the best environment manager is the one that stays out of your way and lets you focus on what matters most: your research.