What is Python venv?
Python venv, short for virtual environment, is an isolated runtime environment that allows developers to work on a specific project without affecting others. It is a feature built into Python since version 3.3, as a part of the standard library.
A virtual environment is a self-contained directory tree that contains a Python installation for a particular version of Python, along with a number of additional packages. Different applications can then use different virtual environments, thereby avoiding conflicts between different versions of the same package.
Why Use Python venv?
Virtual environments are essential tools for Python developers for several reasons:
- Isolation: A virtual environment helps you keep your project dependencies isolated from each other. This isolation ensures that each of your projects can have its own set of dependencies, which can be helpful when different projects require different versions of the same package.
- Versatility: The ability to switch between different versions of Python is helpful when testing code compatibility across different Python versions.
- Simplicity: Virtual environments make it easy to manage dependencies. This helps simplify the process of sharing your code or deploying it to production.
- Consistency: They ensure that everyone on your team is working with the same set of dependencies, which helps eliminate the “works on my machine” problem.
For an example, we have 2 versions of Python existing in our system:
- Python 3.10 has package
- Python 3.8 has package
In project A, we need Python 3.8 with
Now in folder of project A we can call:
python3.8 main.py, it will execute the file
main.py that we wrote that needs
requests==2.0.0. It is fine now right?
But what if we have another project B that needs Python 3.8 and
In project B, we cannot reuse the global Python 3.8 anymore. If we do
python3.8 main.py then it will use
requests==2.0.0. We cannot use Python 3.10 either… So how do we do it?
How to Use Python venv
Let’s get into the step-by-step guide on how to use Python venv.
Continuing with our example on project B, in the project’s folder:
Step 1: Creating a Virtual Environment
To create a virtual environment, decide upon a directory where you want to place it, and run the venv module as a script with the directory path:
python3.8 -m venv venv
This command will create a directory named
venv if it doesn’t already exist, and also create directories inside it containing a copy of the Python interpreter, the standard library, and various supporting files.
Step 2: Activating the Virtual Environment
Before you can start installing or using packages in your virtual environment, you’ll need to activate it. Activating this environment will put the virtual environment-specific
pip executables into your shell’s
On Unix or MacOS:
After this, we have an output like this:
~/projectB $ python3.8 -m venv venv ~/projectB $ source venv/bin/activate (venv) ~/projectB $ python3.8 -V Python 3.8.15 (venv) ~/projectB $ python -V Python 3.8.15
It’s handy to use
python instead of
python3.8 now, because in your isolated environment, Python 3.8 is the default Python for you!
Step 3: Using the Virtual Environment
Now that you’re in your virtual environment, you can install packages locally without affecting the broader system. For instance, let’s install a package named
pip install requests==2.3.0
Now we can safely run our script in this isolated environment, no more conflict! We can upgrade the requests package and it won’t affect anything else:
pip install requests==2.31.0
Step 4: Deactivating the Virtual Environment
When you are done working in the virtual environment, you can deactivate it:
This puts you back to the system’s default Python interpreter with all its installed libraries.
Step 5: Removing the Virtual Environment
If you’re done with the virtual environment, you can just delete its directory. You probably never have to do this unless you need to reinstall everything from scratch.
rm -rf venv
Step 6: Sharing the Virtual Environment
To share your project along with its dependencies, it is common to create a
requirements.txt file, which lists all of the packages in the current environment.
pip freeze > requirements.txt
requirements.txt file can be committed to version control and shared. Other users can then install these dependencies using:
pip install -r requirements.txt
Python venv is a tool that helps to segregate Python environments for different projects, making dependency management easier, reliable and more predictable. It’s a must-have tool for every Python developer.
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