On this page, I’ll outline some of the key concepts and common uses to get you up and running with conda. A conda cheatsheet is also available under the “Cheatsheets” section in the main navigation bar.

To fully familiarize yourself with conda, I strongly recommend going through the official 20-minute Conda User Guide.

Key Concepts

Conda Commands

We’ll be using the conda tool through its command-line interface rather than a typical graphical user interface (GUI) application. If you are unfamiliar with the command line, this will take some getting used to but once you get the hang of it, it will make working with conda and Python much easier.

The conda command is the main interface for using the conda tool for managing your Python packages. From the command line, you simply run:

conda command [optional arguments will go here]

where “command” is the name of the command you want to run. Commands exist to install new packages, create new environments, and much more.

Starting and running conda

We will run conda from the command line but the specifics of this will depend on your operating system.

Windows

Open the Start menu, search for and open the “Anaconda Prompt”. This application provides a command line interface where the conda tool is properly load, initialized, and ready to be used. Note that you cannot use the default “Command Prompt” application to use conda because it doesn’t know how to load conda properly.

MacOS

The Terminal app should be used on MacOS to use conda. You can also use any Terminal emulator (such as iTerm2). Simply open the Terminal application and the conda command should be ready to use.

Conda Channels

Conda “channels” are the locations where packages are located. Channels are typically remote, hosted in the cloud, and when you specify a channel, conda will search the remote database for the right package and download it to your local computer.

By default, packages will be downloaded from the defaults channel, which hosts thousands of packages and is managed by the makers of the Anaconda distribution. A full list of packages is available here.

There is also a community-managed channel known as conda-forge that hosts thousands of packages. It includes many of the packages on the “defaults” channel but also popular packages that are widely-used but not quite essential enough for the “defaults” channel. A list of maintained packages is available here.

For less well known packages, there is a higher likelihood the package will be hosted on conda forge. For that reason, we will prefer downloading and installing packages from conda forge in this course.

Conda Environments

The conda tool not only lets you download and install packages, but you can group those packages together into environments. By default, the Miniconda Python distribution creates an environment named base. We will create a new environment specifically for this course that will hold all of the packages needed for the entire semester.

Environments become particularly useful when working with lots of packages, packages that have a lot of dependencies, or packages that are difficult to install. When environments become too large, it can be difficult to install a new package that satisfies all of the existing package dependencies. For that reason, we will create a fresh, new environment to install the packages we need to use during this course.

Conda vs pip

The other widely used method for installing packages is via the pip command. The commands are similar in a lot of ways but with some key differences. The pip command installs packages from the Python Package Index and is designed to install Python-only packages.

The main advantage of conda is that it is cross-platform and can handle dependencies that are written in C (or other languages) and will automatically handle the compiling process during installation. Many of the packages we use in this course have complex dependencies written in C, and conda will make installation of these packages much easier.

In this course, we’ll be using conda to install packages. Generally speaking, if you already are using conda to manage environments, it’s best to try to install packages with conda and if the package is not available, then try using pip.

See this article for more information about conda and pip.

Common Uses

Managing environments and installing packages will be done by executing the conda command in the command line. Below are some of the most common commands that we will use in this class.

Important: All of the examples below should be run in the Terminal app (MacOS) or Anaconda Prompt (Windows). See the Starting and running conda section above for more detail.

Getting help with the conda command

The conda command has a built-in help function. From the command line (Anaconda Prompt on Windows or Terminal on MacOS) run,

conda --help

which will print out info about individual commands:

conda is a tool for managing and deploying applications, environments and packages.

Options:

positional arguments:
  command
    clean        Remove unused packages and caches.
    compare      Compare packages between conda environments.
    config       Modify configuration values in .condarc. This is modeled
                 after the git config command. Writes to the user .condarc
                 file (/Users/nhand/.condarc) by default.
    create       Create a new conda environment from a list of specified
                 packages.
    help         Displays a list of available conda commands and their help
                 strings.
    info         Display information about current conda install.
    init         Initialize conda for shell interaction. [Experimental]
    install      Installs a list of packages into a specified conda
                 environment.
    list         List linked packages in a conda environment.
    package      Low-level conda package utility. (EXPERIMENTAL)
    remove       Remove a list of packages from a specified conda environment.
    uninstall    Alias for conda remove.
    run          Run an executable in a conda environment. [Experimental]
    search       Search for packages and display associated information. The
                 input is a MatchSpec, a query language for conda packages.
                 See examples below.
    update       Updates conda packages to the latest compatible version.
    upgrade      Alias for conda update.

To find out more info about a specific sub-command, you can run:

conda command --help

For example, for more info about the arguments (both required and optional) needed to install packages, use: conda install --help.

Making sure you are running the latest conda version

The first thing you should do is make sure you are running the latest conda version.

From the command line (Anaconda Prompt on Windows or Terminal on MacOS), run

conda update -n base -c defaults conda

Listing the available environments

The default environment when first installing Anaconda is called 'base'. You can list the currently installed Python environments by running the following command from the command line (Anaconda Prompt on Windows or Terminal on MacOS):

conda env list

The currently active environment will have a '*' next to it. You should see the 'base' environment as well as any other environments you have created.

Creating your initial environment

Throughout this course, we will maintain an environment called 'musa-550-fall-2022' to install and manage all of the packages needed throughout the semester.

The packages in an environment are specified in a file typically called environment.yml. The environment file for this course is stored in the course-materials repository on Github and a copy is also stored in the cloud on anaconda.org.

It is recommended to create the 'musa-550-fall-2022' environment on your local computer using the environment file stored in the cloud on anaconda.org.

First, we need to make sure the anaconda-client package is installed locally. This will ensure that conda can interface with anaconda.org. From the command line (Anaconda Prompt on Windows or Terminal on MacOS), run:

conda install anaconda-client -n base

Then, create your environment with conda env create:

conda env create pennmusa/musa-550-fall-2022

If you list your local environments, you should now see the 'musa-550-fall-2022' environment listed.

Activating your environment

Environments must first be “activated” before the packages are available to use. To activate the environment for this course, you can run the following from the command line (Anaconda Prompt on Windows or Terminal on MacOS):

conda activate musa-550-fall-2022

Now, all of the packages in this environment will be available when we run Python.

Finding the active environment

To see the active environment, list the available environments. The active environment will be listed with a ‘*’ next to its name.

From the command line (Anaconda Prompt on Windows or Terminal on MacOS),

conda env list

Listing the installed packages

If you have already activated the musa-550-fall-2022 environment, you can list all of the installed packages.

From the command line (Anaconda Prompt on Windows or Terminal on MacOS),

conda list

Activating the base environment

To activate the 'base' default environment, run from the command line (Anaconda Prompt on Windows or Terminal on MacOS):

conda deactivate

Note that you should always use the 'musa-550-fall-2022' environment to do the analysis in this course, making sure it is the activated environment when using Python.

Deleting an environment

Note that you cannot create a new environment with the same name as an existing environment. If your environment becomes corrupted or you run into issues, it is often easiest to delete the environment and start over. To do, you can run the following commands from the command line (Anaconda Prompt on Windows or Terminal on MacOS):

conda deactivate

conda env remove --name musa-550-fall-2022

Updating an existing environment

The environment we are using throughout the course might be need to be updated during the course. For example, we might want to update to include a newly released version of a package.

You can update your local environment via the following command. From the command line (Anaconda Prompt on Windows or Terminal on MacOS):

conda env update pennmusa/musa-550-fall-2022

This command will ensure that the 'musa-550-fall-2022' environment on your local computer matches the environment specified by the environment.yml file stored in the cloud for the course.

Installing specific packages

You shouldn’t need to install any individual packages into the 'musa-550-fall-2022' environment. But for reference, you could install specific packages into the active environment using from the command line (Anaconda Prompt on Windows or Terminal on MacOS):

conda install package_name