Python Virtual Environments - Everything You Need

If you’ve ever Googled “how to install Python packages safely” or “why is my project breaking after a new pip install?”, you’ve likely stumbled into the confusing world of Python virtual environments.

Should you use venv? What about virtualenv? And what’s the deal with pyenv and conda — are they the same? 🤯

Don’t worry — you’re not alone. Even experienced developers sometimes mix them up.

In this guide, we’ll break it all down for you in plain English:

• What a virtual environment really is
• Why it’s a game-changer for your Python projects
• And how to choose the right tool (venv, virtualenv, pyenv, or conda) for your workflow

No jargon. No assumptions. Just clean, beginner-friendly explanations with real examples.

Table of Contents#

1. What Is a Python Virtual Environment (and Why Should You Care?)
2. The Big 4: virtualenv vs venv vs pyenv vs conda
3. How to Use Them (with Examples)

• Option 1: virtualenv
• Option 2: venv
• Option 3: pyenv
• Option 4: conda

4. Which One Should You Use?

1. What Is a Python Virtual Environment (and Why Should You Care)?#

The Problem:#

Imagine you’re working on two projects:

• Project A needs Django 2.2
• Project B needs Django 4.0

If everything uses the same global Python, these versions will conflict.

The Solution:#

A virtual environment is like a self-contained Python workspace. Each project can:

• Use its own Python version
• Have its own installed packages
• Be completely isolated from other projects

It’s like giving each project its own sandbox so they don’t step on each other’s toes.

2. The Big 4: virtualenv vs venv vs pyenv vs conda#

3. How to Use Them (with Examples)#

Option 1: virtualenv – The Original Python Virtual Environment Tool#

History:

• Released before Python 3.3
• It was the first popular tool to create isolated Python environments.
• Once Python 3.3+ came out, venv was added to the standard library — inspired by virtualenv.

Key Features:

• Works with Python 2 and Python 3
• Still widely used, especially for legacy compatibility
• Offers more advanced features than venv (like better cross-platform behavior and integration)

When to Use virtualenv:

• You’re working on Python 2 projects (which venv doesn’t support)
• You need more control or compatibility than venv provides
• You’re on an older system or in a setup where venv isn’t available

1. Install it:#

pip install virtualenv

2. Create an environment:#

virtualenv myenv

3. Activate it:#

• Windows:

  myenv\Scripts\activate

• macOS/Linux:

  source myenv/bin/activate

4. Exit:#

deactivate

Option 2: venv (Best for Beginners)#

History:

• Introduced in Python 3.3+ as a built-in way to create virtual environments.
• Designed to replace the older third-party tool virtualenv.

Key Features:

• Comes with Python — no installation needed
• Creates lightweight isolated environments
• Very easy to use
• Doesn’t manage different Python versions — you use the Python you already have

Good for:

• Beginners
• Web/app development
• Projects that don’t need multiple Python versions

1. Create a virtual environment:#

python -m venv myenv

2. Activate it:#

• Windows:

  myenv\Scripts\activate

• macOS/Linux:

  source myenv/bin/activate

3. Install packages:#

pip install requests

4. Deactivate:#

deactivate

Now all packages (like requests) are inside myenv only.

Option 3: pyenv (Manage Multiple Python Versions)#

History:

• Created by the open-source community to solve a common developer pain point: installing and switching between multiple versions of Python.
• Inspired by tools like rbenv (for Ruby).

Key Features:

• Lets you install and switch between many Python versions (2.x, 3.x, even PyPy!)
• Works on macOS/Linux, partial support for Windows via WSL
• Can be extended with plugins like pyenv-virtualenv to create virtual environments

Good for:

• Developers working with many projects or legacy Python versions
• Contributors to open-source libraries needing to test across Python versions
• People who want full control over Python versions

1. Install pyenv (macOS example):#

brew install pyenv

2. Install a Python version:#

pyenv install 3.10.6

3. Set it globally or per-project:#

pyenv global 3.10.6

(Optional) Use with pyenv-virtualenv plugin:#

pyenv virtualenv 3.10.6 myenv
pyenv activate myenv

Great for working on projects that need different Python versions.

Option 4: conda (Popular in Data Science)#

History:

• Developed by Anaconda, Inc., primarily for data scientists and researchers
• Created because tools like pip and venv had trouble managing native dependencies (C/C++ libraries, compiled code)

Key Features:

• Manages both environments and packages
• Can install Python itself (you don’t need to install Python separately)
• Supports installing tricky scientific packages (like numpy, pandas, scikit-learn) with one command
• Cross-platform: works well on Windows, macOS, and Linux

Good for:

• Data science and machine learning workflows
• Projects requiring packages with compiled code or system dependencies
• People who want fast, all-in-one package + environment management

1. Create an environment:#

conda create -n myenv python=3.10

2. Activate it:#

conda activate myenv

3. Install packages:#

conda install numpy pandas

4. Deactivate:#

conda deactivate

Conda environments come with Python and often install scientific libraries faster and more reliably.

4. Which One Should You Use?#