Maintaining Py’n’APL Part 1: The Beginning

Py’n’APL is an interface between APL and Python that allows you to run Python code from within APL and APL code from within Python. This interface was originally developed by Dyalog Ltd intern Marinus Oosters, who presented it in a webinar and at Dyalog ’17. I subsequently talked about Py’n’APL at Dyalog ’21, where I promised to update it and make it into an awesome and robust tool.

I’ve now stared at Py’n’APL’s code base for longer than I’m proud to admit, but without any proper goals and some basic project management this has been as effective in cleaning it up as a Magikarp’s Splash – in other words, it has had no effect.

For that reason, and in another attempt to take up the maintenance of Py’n’APL, I have decided to start blogging about my progress. This will be a way for me to share with the world what it feels like to take up the maintenance of a project that you aren’t necessarily very familiar with.

(By the way, Py’n’APL is open source and has a very permissive licence. This means that, like me, you can also stare at the source code; it also means that you can go to GitHub, star the project, fork it, and play around with it!)


There are some obvious tasks that I need to do, like testing Py’n’APL thoroughly. This will help make Py’n’APL more robust, it will certainly uncover bugs, and it will help me to document Py’n’APL capabilities. The Python side will be tested with pytest and the APL side will be tested with CITA, which is a Continuous Integration Tool for APL.

The code base also needs to be updated. Py’n’APL currently supports Python 2 up to Python 3.5. At the time of writing this blog post, Python 2 has been in end-of-life for more than 2 years and Python 3.7 is reaching end of life in a couple of months. In other words, there is no overlap between the original Python versions supported and the Python versions that an application should currently support. In addition, Dyalog has progressed from v16.0 to v18.2, and the new tools available with the later versions are also likely to be useful.

Another big thing that should be done (and that would pay high dividends) is to update the project management of the Python part of Py’n’APL. By using the appropriate tooling, we make it easier to clone the (open source) repository so that others can poke around, play with it, modify it, and/or contribute.

The First Commits

Let GitHub commit 4283176f4ffd7f1067f216c1459306cdbc49189a be the starting point of my documented journey. At this point in time, I have two handfuls of commits on the branch master that fixed a (simple) issue with a Python import and added the usage examples I showed at Dyalog ’21. So, what will my first commits look like?

Setting up Poetry

The first thing I decided to do was to set up Poetry to manage the packaging and dependencies of the Python-side of code. By using Poetry, isolating whatever I do to/with the Python code from all the other (Python) things I have on my computer becomes trivial and it makes it very easy to install the package pynapl on my machine.

Auto-Formatting the Source Code

Another thing that I did was to use black (which I added as a development dependency to Poetry) to auto-format all the Python code in the repository. I imagine that this might sound surprising if you come from a different world! But if you look at the commit in question, you will see that although that commit was a big one, the changes were only at the level of the structure of the source code; by using a tool like black, I can play with a code base that is consistently formatted and – most importantly – that is formatted like every other Python project I have taken a look at. This consistency in the Python world makes it easier to read code, because the structure of the code on the page is always the same. This means that there is one less thing for my brain to worry about, which my brain appreciates!

In a typical Python project using black, or any other formatter, the idea is that the formatter is used frequently so that the code always has that consistent formatting style; the idea is not to occasionally insert an artificial commit that is just auto-formatting.

Fixing (Star) Imports

The other major minor change that I made was fixing (star) imports across the Python source code. Star imports look like from module_name import * and are like )LOADing a whole workspace in APL – you will gain access to whatever is inside the workspace you loaded. In Python, star imports are typically discouraged because after a star import you have no idea what names you have available, nor do you know what comes from where, which can be confusing if you star imported multiple modules. Instead, if you need the tools foo and bar from the module module_name, you should import the module and use the tools as and, or import the specific names that you need: from module_name import foo, bar.

I therefore went through the Py’n’APL Python source code and eliminated all the star imports, replacing them by the specific imports that were needed. (OK, not quite all star imports; the tests still need to be reworked.) As well as fixing star imports, I also reordered the imports for consistency and removed imports that were no longer needed.

Python 2-Related Low-Hanging Fruit

To get started with my task of removing old Python 2 code, I decided to start with some basic trimming. For example, there were plenty of instances where the code included conditional assignments that depended on the major version of Python (2 or 3) that were supposed to homogenise the code, making it look as much as possible like Python 3. I could remove those because I know we will be running Python 3. Another fairly basic and inconsequential change I could make was removing the explicit inheriting from object when creating classes (this was needed in Python 2, but not in Python 3).

Explicit Type Checking and Duck Typing

Python is a dynamically-typed language, and sometimes you might need to make use of duck typing to ensure that you are working with the right kind of objects. At Dyalog Ltd we are very fond of ducks, but duck typing is something else entirely:

If it walks like a duck and if it quacks like a duck then it must be a duck.

In other words, in Python we tend to care more about what an object can do (its methods) than what the object is (its type). The Py’n’APL source code included many occurrences of the built-in type and I went through them, replacing them with isinstance to implement better duck typing.

What Happens Next?

These are some of the main changes that I have made so far; they happen to be mostly inconsequential and all on the Python side of the code. Of course, I won’t be able to maintain Py’n’APL by only making inconsequential changes, so more substantial changes will come next. I also need to take a look at the APL code and see what can and what needs to be done there. Although I haven’t looked at the APL code as much as at the Python code, I have a feeling that I will not need to make as many changes there. Fingers crossed!

This blog post covers (approximately) the changes included in this GitHub diff.

4 thoughts on “Maintaining Py’n’APL Part 1: The Beginning

  1. Good stuff! I wonder how you have resisted naming it APL’Py, pronounced apple pie?

    Apart from that, have you ever tried to map APL arrays to Python’s NumPy arrays? As NumPy (or duck-typing-compatible arrays) are the gateway to many AI workhorses, this might be an effective way to bridge the two worlds.

    Kind regards, Martin

    • Hey Martin,

      I wasn’t around when Py’n’APL was created/named, but I think Py’n’APL (pronounced “pineapple”) is a great pun as well!
      As for NumPy, Py’n’APL already uses it to represent arrays when possible, although maybe that’s not very clear yet.

      Best regards, Rodrigo.

  2. This is great news, and you’ve given a very clear account of your work so far. Thank you!

    I’m looking forward to reading about future progress.