Is Anaconda the best tool for data science? How Anaconda and the Anaconda Navigator can change the way you work.

Jacob Wert
5 min readFeb 9, 2021

Anaconda is “a distribution of the Python and R programming languages”, which is tailored specifically for scientific computing. Scientific computing includes general data science, machine learning, large-scale data processing, etc. How is Anaconda tailored specifically for scientific computing? By being an “easy-to-install package manager and environment manager.” Anaconda comes in several editions: individual, commercial, team and enterprise, but we’re going to focus on the individual edition as it is free. You can check out the documentation for the individual edition here. While you can operate Anaconda completely from the command line, they do offer Anaconda Navigator which has a GUI that some people might find more useful.

Why is Anaconda so useful?

Anaconda can help de-spaghettify your installed packages.

Anaconda allows you to create siloed environments where you can install the specific packages you need, without having to worry about packages negatively interacting with each other in other environments. Let’s try to break this down.

Anaconda is like a house, and each room is a different environment.

Each room can be unique with its own set of decorations, furniture and purpose. In a similar fashion, each environment in your installation of Anaconda can have their own packages installed. Besides the packages, you are even able to specify the specific version of Python that you have installed by environment. This is something that can be very helpful working in the world of science where you can run into programs that haven’t been updated in a long time. Let’s take a look at the home screen of the Anaconda Navigator.

Anaconda Navigator Home Screen

When starting the Anaconda Navigator, this is the first thing that you will see (as of the date of this publishing). On the left hand side, you have buttons to take you to the main four sections, “Home”, “Environments”, “Learning”, and “Community”. When you are at “Home”, you have the option to change the environment that you are running in, and can see any applications that you have installed specifically for that environment. Everyone starts off with an environment called “base (root)”. The specific applications installed can be selected when you download and install Anaconda, and you can always install something later if you wish.

Next to the drop-down menu, you will see a button called “Channels”. A channel is a location where Navigator and conda look for packages. We’ll come back to what “conda” is in a little bit. For most people, you won’t need to modify this, but if you are someone that has very specific packages, you might find this useful.

Each application is shown as a tile. The application icon is front and center, with the version number directly below, and a description above the big launch button. Clicking on the gear icon in the upper right hand corner gives you the options to install the application (if not installed already), update or remove it, or select a specific version. This goes back to the benefits of having environments. Environment A could have the latest version of Jupyter notebooks installed, while Environment B needs an older version for compatibility with another package.

Environment Manager

Moving down the menu bar on the left, we have the environment manager section. On the left (almost middle), you can see and search what environments you currently have created, create new environments, or delete unneeded ones. On the right hand side you can see a list of packages. You are able to filter them with the drop down menu to just the packages you have installed in that particular environment, which are not installed, which are updatable, which you have selected, or every package in the channels that you are searching on.

Creating a new environment is as straightforward as clicking on the “Create” button that has a plus sign. From there you can name your new environment, and select if you want Python and/or R installed, along with what version you would like. Once you click on “create”, your new environment will be created with a handful of default packages. However, if you want anything more than the bare minimum that you need for your environment to run, your first step should be to install any packages that you will need. To install a package via GUI, first set the drop down menu to be either “Not Installed” or “All” to see the available packages. You can either scroll through the entire list, or you can use the search bar in the upper right corner. Once you see the package that you want to install, click on the box to the left of the name, then click apply in the bottom right corner. Your desired package will now be installed!

Learning & Community

Learning and community are both pretty straightforward. Learning provides you will quick access links to documentation and some tutorials, and community contains links for events, forums, and social links. If you have any questions, be sure to check out these two sections!