Installing and Managing Python with Conda
Conda is an open-source, cross-platform, language-agnostic package manager and environment management system that allows you to quickly install, run, and update packages within your work environment(s).
Here we will cover:
What are packages?
Creating a Conda environment
Useful Conda commands
Time to learn: 20 minutes
What are Packages?
A Python package is a collection of modules, which in turn, are essentially Python scripts that contain published functionality. There are Python packages for data input, data analysis, data visualization, etc. Each package offers a unique toolset and may have its own unique syntax rules.
Package management is useful because you may want to update a package for one of your projects, but keep it at the same version in other projects to ensure that they continue to run as expected.
We recommend you install Miniconda. You can do that by following the instructions for you machine.
Miniconda only comes with the
conda package management system; it is a pared-down version of the full Anaconda Python distribution.
Installing Anaconda takes longer and takes up more disk space, but provides you with more functionality: Jupyter, Spyder (a Python-specific integrated development platform or IDE), as well as other immediately installed packages. The interface of Anaconda is great if you are uncomfortable with the terminal.
We recommend Miniconda for two reasons:
It’s quicker and takes up less disk space.
It encourages you to install only the packages you need in reproducible isolated environments for specific projects. This is generally a more robust way to work with open source tools.
Once you have
conda via the Miniconda installer, the next step is to create an environment and install packages.
Creating a Conda Environment
A conda environment is an interoperable collection of specific versions of packages or libraries that you install and use for a specific workflow. The conda package manager takes care of dependencies so everything works together in a predictable way. One huge advantage of using environments is that any changes you make to one environment will not affect your other environments at all, so you are much less likely to “break” something!
To create a new Conda environment, type
conda create --name and the name of your environment in your terminal, and then specify any packages that you would like to have installed. For example, to install a Jupyter-ready environment called
conda create --name sample_environment python jupyterlab
Once the environment is created, you need to activate it in the current terminal session (see below)
It is a good idea to create new environments for different projects because since Python is open source, new versions of the tools are released very frequently. Isolated environments help guarantee that your script will use the same versions of packages and libraries and should run the same as you expect it to. Similarly, it is best practice to NOT modify your
Useful Conda commands
Some other Conda commands that you will find useful include:
Activating a specific environment
conda activate sample_environment
Deactivating the current environment
Checking what packages/versions are installed in the current environment
Installing a new package into current environment
conda install somepackage
Installing a specific version of a package into current environment
conda install somepackage=0.17
Updating all packages in the current environment to the latest versions
conda update --all
Checking what conda environments you have
conda env list
Deleting an environment
conda env remove --name sample_environment
Lots more information is in the conda documentation or this handy conda cheat sheet
If you’re not a command line user, the Anaconda navigator offers GUI functionality for selecting environments and installing packages.
Conda is a package and environment management system that allows you to quickly install, run, and update packages within your work environment(s). This is important for gathering all of the tools necessary for your workflow. Conda can be managed in the command line or in the Anacond GUI.