Shareable Haskell with Jupyter!

In the last couple weeks, we've discussed a couple options for Haskell IDEs, like Atom and IntelliJ. But there's another option I'll talk about this week. Both our IDE setups are still most useful for fully-fledged projects. But if you're writing some quick and dirty one-off code, they can be a little cumbersome to work with.

This other option is Jupyter with IHaskell. It's like IPython, for those who have used that. I got the idea when the good folks at Tweag made a blog post with it. Jupyter was originally intended for making quick Python data science scripts. It allows a nice UI for making data visualizations. Thanks to the hard work of Andrew Gibiansky, there is a Haskell kernel for Jupyter! In this article, we'll discuss some quick approaches to using it.

IHaskell is actually a great tool when you're first learning Haskell! If you've never programmed in Haskell before, you can read our Liftoff Series and follow along with the code examples! You can write them using IHaskell instead of making a Stack project!


If you want to make a full-fledged Jupyter notebook, you'll need to install Jupyter first. The most heavy-duty but easiest way would be to use the Anaconda distribution. But there are also other options like pip.

After that, you'll need to install the Haskell kernel for it. Unfortunately, you can't do this on a Windows machine. You either need a Mac, Linux or a virtual box. The instructions for these systems are well documented in the README. In short, you need to sort out your Python dependencies, grab the Github repo, and build the project.

Now if you're on Windows, or you don't want to install the full Jupyter system, you can try out IHaskell online. Head to this Binder page, make a new notebook, and get cracking!

Making a Basic Example

In our notebook, we can write Haskell code as if we're in a file, but evaluate it as if we're in GHCI. A quick look at the .cabal file will reveal the libraries we have easy access to in this notebook setup. We can see for instance that we have stalwarts such as mtl, aeson, and split. Using this last library, we can write the following snippet:


Then we press shift+enter to finish the cell and it gets evaluated. Evaluations work as in GHCI. Anything you assign to a variable name will be usable later on in your notebook. Then the final expression you put will get printed. So we'll see output like so:


Then we can use the items we named in another snippet like this:


Since we also have access to the Aeson library, we can serialize our list like so:


As a final note it is easy to create multiline definitions and use those! This is a big improvement over GHCI. It would be very annoying to define a new data type, for instance:


Exporting the Notebook

Now one of the awesome things about Jupyter notebooks is that it's easy to share your work! There's an option off the file menu for downloading your notebooks. There are a great many options, including Haskell source files, pdfs, and HTML documents. These last two can be extremely useful if you want to make a presentation!



As I move forward with MMH, I'm definitely going to explore using notebooks like this more. It should provide a better reader experience than what I have now. I'll also be looking at migrating some of our existing permanent content to Jupyter. The lack of Windows functionality for Haskell is unfortunate, but I'll find a way around it.

Jupyter IHaskell is a great way to get familiar with the basics of Haskell without downloading any of the tools. But at some point, you'll need these! Read our Liftoff Series and download our Beginners Checklist to find out more!