Creating a Python Virtual Environment (venv): A Step-by-Step Guide

Managing Python dependencies can quickly become overwhelming when juggling multiple projects. Different libraries and versions often lead to compatibility issues—this is where virtual environments come in handy. In this blog, you’ll learn how to create and manage isolated python virtual environment (venv) using the built-in venv module.

Whether you're working on a data science project, web app, or automation script, this guide will help you keep your development setup clean and conflict-free.

What Is a Virtual Environment in Python?

A virtual environment is a self-contained directory that includes its own Python interpreter and a local pip installation, completely isolated from the global Python setup on your system. This means every project can operate in its own dedicated environment with its own dependencies, ensuring clean separation and better project management.

This is especially important when:

• You need to work with different versions of the same library across multiple projects

• You're contributing to a project that requires specific package versions

• You want to avoid breaking existing code by installing new packages globally

• You plan to distribute your project and want to document dependencies cleanly

Using virtual environments allows you to:

• Use different library versions for each project

• Prevent package conflicts and version mismatches

• Easily share and recreate project environments using a requirements.txt file

• Experiment safely with packages and roll back without affecting your global Python setup

Whether you're developing a machine learning model, building a web application, or automating tasks with scripts, virtual environments provide a controlled and reproducible workspace.

Prerequisites

• Python 3.3 or later installed

• A terminal or command prompt

• Basic command line knowledge

  
  

You can check your Python version with:

python3 --version

Output:
Python 3.13.3

How to Create a Python Virtual Environment (venv)

Creating a virtual environment in Python allows you to isolate project-specific dependencies and avoid conflicts with global packages. In this quick guide, you’ll learn how to use Python’s built-in venv module to set up a clean, manageable development environment for any Python project.

Step 1: Navigate to Your Project Directory

Open your terminal and move into the folder where you want to set up the project:

cd path/to/your/project

Step 2: Create the Virtual Environment

python3 -m venv env

Here, env is the name of your virtual environment folder. You can name it anything you like, but env or .venv are common conventions.

This command creates:

• A bin/ or Scripts/ directory with a local Python interpreter

• A lib/ directory with site-packages

• A pyvenv.cfg file to track the environment

Step 3: Activating the Virtual Environment

On macOS/Linux:

source env/bin/activate

Output:

(env) Samul@Samul-Mac-mini python-env % 

On Windows (CMD):

env\Scripts\activate

On Windows PowerShell:

.\env\Scripts\Activate.ps1

Once activated, your shell prompt should be prefixed with (env)—indicating that you're working inside the virtual environment.

Step 4: Installing Packages Inside the Environment

Once inside your venv, use pip like usual. For example:

pip install numpy pandas flask

Output:

...
Collecting werkzeug>=3.1.0 (from flask)
  Downloading werkzeug-3.1.3-py3-none-any.whl.metadata (3.7 kB)
Collecting six>=1.5 (from python-dateutil>=2.8.2->pandas)
  Downloading six-1.17.0-py2.py3-none-any.whl.metadata (1.7 kB)
Downloading numpy-2.3.1-cp313-cp313-macosx_14_0_arm64.whl (5.1 MB)
   ━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━ 2.4/5.1 MB 102.0 kB/s eta 0:00:27
...

To list installed packages:

pip list

Output:

Package         Version
--------------- -----------
blinker         1.9.0
click           8.2.1
Flask           3.1.1
itsdangerous    2.2.0
Jinja2          3.1.6
MarkupSafe      3.0.2
numpy           2.3.1
pandas          2.3.1
pip             25.0.1
python-dateutil 2.9.0.post0
pytz            2025.2
six             1.17.0
tzdata          2025.2
Werkzeug        3.1.3

To freeze dependencies to a file:

pip freeze > requirements.txt

To install packages from that file later:

pip install -r requirements.txt

Step 5: Deactivating the Virtual Environment

To exit the virtual environment and return to your system Python:

deactivate

Optional: Ignore env/ in Git Projects

Add this line to your .gitignore file so your environment folder isn't tracked in version control:

env/

This keeps your Git repositories clean and avoids pushing unnecessary binaries.

Why Use venv Instead of Global Python?

Scenario	Without venv	With venv
Package conflict	High risk	Isolated per project
Dependency sharing	Manual	Easy with requirements.txt
Clean uninstall	Tedious	Just delete the env/ folder
Team collaboration	Risky	Safe and predictable

Few things to consider:

• You can create multiple environments for different Python versions by using pyenv or tools like conda.

• Use .venv instead of env to keep it hidden from most file explorers and IDEs.

• Python IDEs like VS Code, PyCharm, and JupyterLab automatically detect virtual environments in your project.

Conclusion

Using a virtual environment in Python is a recommended best practice for all types of projects—from small automation scripts to full-scale production applications. With just a few simple commands, you can isolate project dependencies, maintain a clean workspace, and avoid version conflicts that often arise from sharing libraries across projects.

Whether you're building web applications with Flask, Django, or FastAPI, or diving into data science with Pandas, NumPy, or Scikit-learn, setting up a virtual environment is the essential first step toward a more stable, reproducible, and scalable Python development workflow.