Get started with conda environments 🤝
This article is originally published at https://www.dataschool.io/
In a previous post, I explained the differences between conda, Anaconda, and Miniconda.
I said that you can use conda to manage virtual environments:
If you’re not familiar with virtual environments, they allow you to maintain isolated environments with different packages and versions of those packages.
In this post, I’m going to explain the benefits of virtual environments and how to use virtual environments in conda.
Let’s go! 👇
Why use virtual environments?
A virtual environment is like a “workspace” where you can install a set of packages with specific versions. Each environment is isolated from all other environments, and also isolated from the base environment. (The base environment is created when you install conda.)
So, why use virtual environments at all?
- Different packages can have conflicting requirements for their dependencies, meaning installing one may cause the other to stop working.
- If you put them in separate environments instead, you can switch between the environments as needed, and both will continue to work.
Thus by using environments, you won’t breaking existing projects when you install, update, or remove packages, since each project can have its own environment.
You can also delete environments once you’re done with them, and if you run into problems with an environment, it’s easy to start a new one!
Six commands you need to know
conda environments have a lot of complexity, but there are actually only six commands you need to learn in order to get most of the benefits:
1️⃣ conda create -n myenv jupyter pandas matplotlib scikit-learn
This tells conda to:
- Create an environment named myenv (or whatever name you choose)
- Install jupyter, pandas, matplotlib, and scikit-learn in myenv (or whatever packages you need installed)
- Install all of their dependencies in myenv
- Choose the latest version of every one of those packages (as long as it works with every other package being installed)
That last point is a mouthful, but it basically means that conda will try to avoid any conflicts between package dependencies.
Note: conda stores all of your environments in one location on your computer, so it doesn’t matter what directory you are in when you create an environment.
2️⃣ conda activate myenv
This activates the myenv environment, such that you are now working in the myenv “workspace”.
In other words:
- If you now type python or jupyter lab (for example), you’ll be running the Python or JupyterLab that is installed in myenv.
- If you then type import pandas, you’ll be importing the pandas that’s installed in myenv.
Note: Activating an environment does not change your working directory.
3️⃣ conda list
This lists all of the packages that are installed in the active environment (along with their version numbers). If you followed my commands above, you’ll see python, jupyter, pandas, matplotlib, scikit-learn, and all of their dependencies.
4️⃣ conda env list
This lists all of the conda environments on your system, with an asterisk (*) next to the active environment.
5️⃣ conda deactivate
This exits the active environment, which will usually take you back to the “base” environment (which was created by Anaconda or Miniconda during installation).
6️⃣ conda env remove -n myenv
This permanently deletes the myenv environment. You can’t delete the active environment, so you have to deactivate myenv (or activate a different environment) first.
Going further
If you want to learn more about conda environments, check out this section of conda’s user guide:
If you want a broader view of conda and its capabilities, check out this section:
Or, share your question in the comments below and I’m happy to help! 👇
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This article is originally published at https://www.dataschool.io/
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