Imagine yourself walking into a grocery store for a specific item. However, to your surprise, there is absolutely no organization within the entire store—no way to distinguish between products; no way to tell what product is for what purpose; simply no way to find your item.
You go to the counter and ask the grocer where the specific product is, but all he tells you is to “search for it.”
Now, what do you do? The only option left for you is to find the item you so desperately want on your own by searching every product in the store.
This grocery store is your computer, your Python package bin. All those disorganized products lying on the shelf are the endless torrent of packages you have installed over the years for your random projects.
The next time you start a project or install some software, you will not understand if the version is up to date, if it collides with another package or python version, or if the package exists at all. Such disorganization can cause setbacks, and that not only disrupts your project but takes away the valuable moment that otherwise could have been used for something more productive.
So what is the solution? Python Virtual Environment.
Python Virtual Environments (virtualenvs) helps decouple and isolate versions of Python and their related pip versions. This authorizes end-users to install and manage their own set of software that is independent of those provided by the system.
One of the primary advantages of using Python is its active developer community and availability of many software packages on pypi.org.
What Is A Python Virtual Environment?
A Python Virtual Environment or virtualenvs is nothing but a directory having a specified structure. It consists of a subdirectory, also called bin_subdirectory, that is connected to a Python interpreter and other subdirectories that hold software installed within the virtual environment.
When imploring the Python interpreter by using the way to the bin subdirectory of virtualenv, the Python interpreter perceives the way to use the related software in the virtualenv (as contrasting to any software installed adjacent to the Python interpreter’s actual place). It is this perspective that virtual environments are virtual and not in the way you know it. They are not virtual like a simulated machine.
You can immediately use it when you set up a virtualenv by imploring Python and applying the complete route to the subdirectory_bin. ( for e.g., my_venv/bin/python3 my_program.py).
For ease, when you set up a virtual environment, it gives you an activated script that you can implore and which will allow you to set the subdirectory bin for your virtual environment initially on your route (simultaneously it upgrades your shell cue and informs you that the change is accomplished).
When your virtualenv is set in motion, you are no longer required to use the full trail to the Python interpreter ( for e.g., my_venv/bin/python3 my_program.py).
Important to remember: If you are imploring the Python script with the complete trail to the Python interpreter in the simulated domain, it will run in the simulated domain despite not being there in a collaborative session where you have used the activate command. This is particularly helpful for Bash scripts and cron jobs, where triggering the virtualenv is problematic.
Should You Always Use A Virtual Environment?
With a virtual environment, you have complete control over the environment. You would know the package versions that are required to be updated and what versions are installed. Virtual environments give you a replicable and stable environment.
You have complete control over the versions of Python used, the installed packages, and their scheduled upgrades. In fact, the modern versions of the Python support virtual environments over the boundary. Having said that, it is not the only way to maintain a replicable environment. There are other options like using Conda in the home directory or project space that is also available on the CS system.
If you need to have a say on your updates to new packages of Python, all you need is to create your own Python interpreter and create a virtual world based on the interpreter. By this process, you can disengage the servers from the “System Python” update schedule.
There is, however, an exception to using a virtual environment. Suppose you have a simple program that only uses modules from the Python Standard Library (i.e., no third-party modules that would need a pip install). In such a case, you might contemplate not using a simulated environment.
Python has various modules and versions for different applications. A project may require a third-party library, which is installed. Another project also uses a similar directory for retrieval and storage but doesn’t require third-party software. So, the simulated environment can come into play and create a different secluded environment for both the projects, and each project can store and retrieve packages from their specific environments.
Let us consider another scenario where you are creating a web application using Django. Let’s say you are developing two projects, project A and project B.
If project A uses Django 2.2 and project B uses Django 3.2, they would be accumulated in the same directory with the same name, and then an error may occur. In such cases, virtual environments can be really helpful for you to maintain the dependencies of both environments.
Virtual environments aid in these problems by creating unique, isolated environments where all the packages and versions you install only apply to that particular environment. It is like a private island – but for code. Anything that happens on this island doesn’t affect your mainland, and anything that occurs on the island does not.
Once you are done staying on the island, come back to the mainland. In other words, you can come back to the original environment once you are done working on the projects.
Now, let us know more about the pros and cons of Python Virtual Environment:
Python Virtual Environment: Pros And Cons
- You can use any package of Python you want for a particular environment without having to worry about collisions.
- You can arrange your packages much better and know exactly which packages you need to run your code in case someone else wants to run the code on their machine.
- Your main Python versions directory does not get flooded with unnecessary Python packages.
- Comes in stock with Python, and no extra tools are required.
- Builds a primary virtualenv that works with almost all the tools: requirements.txt supports every domain manager using command pip.
- It acknowledges the software that is installed: builds a domain with the help of everything that had invoked Python to build it, so you are still in the loop of manually controlling the versions of Python.
- No whistles and no bells but only the installable_ pip in the domain
Virtual environments are of great advantage when working on different projects. If your project is not package-dependent, there is no need for isolated versions and packages. Still, if your project requires even a few packages, there is nothing you can do without the help of virtual environments.