How to Create Virtual Environment in Python?

A virtual environment is an isolated environment for python projects. We can consider it to be like specific containers for specific projects.

We can have a global version installed, but from project to project, it may differ, or to make code work, we must have a specific version of python installed. To achieve these, we need virtual environments.

Why do We Need a Virtual Environment?

Python itself could be better at dependency management. If you’re not specific, then pip will place all the external packages you install in a folder called site-packages/ in your base Python installation. This may lead to several issues if all of your external packages land in the same folder.

We need a virtual environment for the following:

To Avoid System Pollution

If you install packages to your operating system’s global Python, these packages will mix with the system-relevant packages. This mix-up could have unexpected side effects on tasks crucial to your operating system’s normal behavior.

To Sidestep Dependency Conflicts

One of your projects might require a different version of an external library than another one. If you have only one place to install packages, you can’t work with two different versions of the same library. This is one of the most common reasons for the recommendation to use a Python virtual environment.

Let's take an example where we are working on two projects named ProjectA and ProjectB. These projects use a different version of python. Let's say ProjectA uses V2.3 and ProjectB uses V3.0.

As it would be installed globally, there is no way to differentiate between them. If we installed them in our virtual environment, each project would use its specified version.

Transform Your Career

Choose from our industry-leading programs designed for career success

NSDC Certified

Modern Software and AI Engineering Program

Master full-stack development with AI integration

12 MonthsDuration

AI-LedCurriculum

Career SupportSupport

+1000 more

Go to Program

NSDC Certified

Modern Data Science and ML with specialisation in AI

Advanced data science techniques with AI specialization

12 MonthsDuration

AI-LedCurriculum

Career SupportSupport

+1000 more

Go to Program

NSDC Certified

Advanced AIML with Specialisation in Agentic AI

Deep dive into AIML with focus on Agentic systems

12 MonthsDuration

AI-LedCurriculum

Career SupportSupport

+1000 more

Go to Program

NSDC Certified

DevOps, Cloud & AI Platform Engineering

Build and manage AI-powered cloud infrastructure

12 MonthsDuration

AI-LedCurriculum

Career SupportSupport

+1000 more

Go to Program

NSDC Certified

AI Engineering Advanced Certification by IIT-Roorkee

Premier AI engineering certification from IIT-Roorkee

3 MonthsDuration

AI-LedCurriculum

Career SupportSupport

Go to Program

How does a Virtual Environment Work?

When we create a virtual environment, a module named virtualenv creates isolated python environments. This module creates folders and sub-folders that contain all the necessary executables to use the packages of a different version that a project may need.

Creating a Virtual Environment

To create a virtual Environment, we have first to install a module named virtualenv.To install virtualenv, we have to run the pip command :

This installs the required module to create the virtual environment. We can check if the installation was good and if we are ready to work with the virtual environment.

Test your installation:

You can create a virtualenv using the following command:

After running this command, a directory named my_name will be created. This is the directory that contains all the necessary executables to use the packages that a Python project would need. This is where Python packages will be installed.

Sometimes we may need to specify the Python interpreter of our choice, for example, Python 3, it could be done using the following command:

or to create one with Python 2.7 we can use the following command:

Now, after creating a virtual environment, you need to activate it to make sure the packages are installed in the Virtual Environment. Remember to activate the relevant virtual environment every time you work on the project. This can be done using the following command:

Once the virtual environment is activated, the name of your virtual environment will appear on the left side of the terminal. This will let you know that the virtual environment is currently active.

Now you can install dependencies related to the project in this virtual environment. For example, if you are using Fastapi $0.77.1$ for a project, you can install it like you install other packages.

Using this command would place the Fastapi $0.77.1$ package in the virtualenv_name folder and will be isolated from the complete system. This makes only the specific project access the package fastapi for execution.

We can also deactivate the virtual environment and switch to the global environment by the following command:

Managing Packages with pip

There is a bunch of option for managing the packages with the pip command.

Installing from Version Control Systems

pip can install packages directly from their version control system. For example, we can install directly from a git repository:

Upgrading Packages

We can also upgrade packages in place using --upgrade flag. For example, we want to install the latest version of fastapi and all its dependencies.

Using Requirements Files

With a huge project, we tend to need many different packages. Instead of installing packages individually, pip allows you to declare all dependencies in a Requirements File. For example, you could create a requirements.txt file containing:

fastapi==0.77.1 google-auth==1.1.0 And tell pip to install all of the packages in this file using the -r flag:

Freezing Dependencies

Pip can export a list of all installed packages and their versions using the freeze command:

Which will output a list of package specifiers such as:

google-auth==1.1.1
ianyio==3.6.1
fastapi==0.77.1
idna==3.3
pydantic==1.9.1
sniffio==1.2.0
starlette==0.19.1       
typing_extensions==4.2.0

Read More:

• Packages in Python.