Virtual Envs & Dependency Management

What is a virtual environment? Well, this is simply a self-contained location that enables you to maintain separate and isolated environments for your Python projects. This isolation lets you manage dependencies, versions, and packages without conflicts across different projects. When you work on multiple Python projects, it's likely that some of them depend on different versions of external libraries or even Python itself. Without virtual environments, if you were to install every dependency globally, you might run into issues where one project needs a different version of a library than another project. This can lead to compatibility issues. Virtual environments solve this issue by allowing each project to have its own set of installed packages, independent of what's installed globally or in other environments. So now, let's look at how you create virtual environments. So to begin, we need to open up a terminal or a command prompt. If you're on Mac or Linux, open up your terminal. If you're on Windows, open up command prompt or a PowerShell instance. From here, we're going to navigate into the directory where we want to create a new virtual environment. It's worth noting that virtual environments are simply directories on your system, so you want to put them in a place that's logical, like the location of a Python project that requires this environment. In my case, you can see that I am in the example directory, which is on my desktop. So simply go to a directory where you want to create your virtual environment. From here, we're going to create a new environment using the built-in venv command or v-e-n-v. Now, it's worth noting that there are other tools that can do this, like Conda, virtualenv, Pipenv, and Poetry, but venv is built into Python, and it's the most common one. Now, the commands for creating and activating the virtual environment are slightly different for Mac or Windows, but I'll run through both sets. So if you're on Windows, you're going to use this command, which is python -m venv, and then env. Now, the env part here at the end is the name of the environment. You can name this anything that you like, but it's convention to name it env. Now, if you're on Mac or Linux, you'll simply change the command to be python3. So in my case, I'm on Mac, so I'm going to run python3 -m venv env. Now at this point, it will create a new directory for us. So if we type ls, you'll see that we have the environment env in this directory. Now, what we need to do is activate the virtual environment, and to do that, again, the command will be slightly different depending if you're on Mac or Windows. If you're on Windows, you're going to run the following command. Now, the command is this: the name of your environment, which in this case is env, \Scripts\activate.bat. Now, for some reason that doesn't work, you can try just \activate, so remove that .bat extension. Now, if you are on Mac or Linux, the command is a little bit different. This is going to be source env/bin/activate, like that. And once you've activated the environment, you should see that you get a prefix with the name of the environment appearing before the normal line in your terminal. So whether you're on Mac, Windows, or Linux, you'll know this is working once you see the parenthesized version of your environment name before your main terminal line. Now that we're here, I'll show you how we deactivate the environment, so to go back into our global Python installation. To do that, you simply type deactivate. The command will be the same whether you're on Mac, Windows, or Linux. Then we can reactivate by using the source command again or that activate script that I showed you on Windows. So now that the environment is created and activated, we can go ahead and install some packages into it. Now to do this, we're going to use the pip command, which stands for the Preferred Installer Program or the recursive acronym, which is Pip Installs Packages. Now if we want to view the packages that are in the current environment, we can start by typing pip list. Now, it will show us that we can actually upgrade pip if we'd like to, we don't need to do that right now, but we can see that we just have two packages: pip and setuptools. The reason we don't see any other ones is because we're in the virtual environment now, not in our global version of Python. If we were to deactivate this and run the command again, we'd see any packages in our global environment, but here we're in the virtual one. Now that we've done that, we can install a package. So one some of us have probably used before is the requests package. So let's go pip install requests. When we do that, it should install everything, and if we type pip list, you'll see now that we get all of the dependencies for requests, as well as the requests package itself. You'll notice now if I deactivate the environment, and I type pip list—it's actually needs to be pip3 here—I'll get a huge, long list of a bunch of other packages that are installed globally, whereas if I go back into the environment here, and I type pip list, we just see the ones that are a part of this environment. So now at this point, let's imagine we've done some intense Python development and we're ready to push this to GitHub or share our project with someone else. Now in order for them to run this code, they'll likely need the same requirements as us. Now rather than exporting our entire environment, we can simply save a list of all of the dependencies we used into a requirements.txt file. Now to do that, we're going to use the following command, which is pip freeze, we're then going to use the greater-than sign, and then we're going to type requirements.txt, if I spell that correctly. And what this will do is take the output of pip freeze, which gives us all of the different packages and their version, and save it in this file. So when I do that, and I type ls, you'll see that now we get a requirements.txt file. If we have a look at requirements.txt, it has the output of pip list that we looked at before. Now let's get out of this here, and let's see how we could use the requirements.txt file to actually install these various packages. So let's imagine we're someone else, we've now got access to this code, we want to start working in it, and we have a requirements.txt file. Well, first we'll make sure that we're out of this environment, and we'll probably end up creating a new one. So we'll start again, and we'll type the command. So in this case, python3 -m venv, we'll just call this my_env. We'll create a new environment, we will then activate it, so this will be source, and then my_env/bin/activate. Again, on Windows this is going to be a different command for activation. Now we're inside of this environment. This is a new environment, and you'll notice that now we have env, my_env, and requirements.txt. If I pip list inside of here, you'll see that we don't have those requirements, so the requests module that we need, but we can install them using this requirements.txt file. To do that, we very simply just type pip install -r, and then requirements.txt. The -r stands for install from a file, so we specify the file name, which is requirements.txt, and now what pip will do is read this file, find all of the requirements, and install it in this environment. So when I hit Enter here, it's going to go ahead and install that, and now if we type pip list, you'll see that we get a list of all of the different packages that were from our requirements.txt file. So this is very common practice: you'll actually download a Python project, you'll see a requirements.txt file, what's expected from that point forward is you're going to create a new virtual environment, you're going to activate it, you're then going to install all of the different packages using the requirements.txt file. Again, that command was pip install -r requirements.txt. Now at this point, you've learned how to create an environment, activate it, install individual packages, install packages from a file, and save your packages or dependencies into a requirements.txt file. You pretty much know everything you need to about virtual environments, but I'll share with you a few last helpful tips that you'll probably want to know. So first of all, it's common practice to create a virtual environment folder inside of the directory where your Python files exist. So for example, maybe I'll make a new directory called project, and this is where I'm going to have all of my code. So I'll cd into project, and maybe I have a Python file, so I can say maybe just touch main.py. Now what I would do is create my virtual environment inside of this directory, so it's alongside my Python files and all of my source code. Now it's common practice that we call this env or venv. You can call it whatever you want, but typically this is what it will be named. Now once we create that and activate the environment, we want to make sure we don't save any of our source code inside of that directory. So I'm not going to do it right now, but let's say that we have this venv folder, right, so we have main.py and venv where our virtual environment is, we're not going to put any of our custom Python code inside of here. It's just going to sit inside of the main directory. This folder is only used for storing the things related to our virtual environment. That's about it. If you're finished with the virtual environment and for some reason you don't need it anymore, you can simply delete this folder and that's all you have to do. So if you're on Mac, you can type something like rm -r venv, that's going to delete the directory. You also can just simply find it and delete it, so right-click it, move it to trash, whatever you want. Now that's it, that's all I have for you. If you enjoy my teaching style and you want to become a developer, then consider checking out my software development course from the link in the description. Leave a like on this video, subscribe to the channel, and I will see you in the next one.