Going on a journey

Posted on 2019-11-10 14:32 +0000 in Python • Tagged with Python • 3 min read

It's hardly a revelation to say that learning a new programming language, or even learning software development at all, is even more difficult if you don't have an actual problem to solve. I know I'm not alone in having pet projects that, when faced with a new environment, I'll code up a version of that project as a way to get familiar with and understand a language's idioms while implementing something I know well.

Personally, my two favourites are a puzzle called 5x5 (here, here, here, here, here, here and here), and writing a library or even a full application to read Norton Guide database files (here, here, here, here, here, here, here and here). Both are fun to work on, have practical uses, and both have the benefit of being solved problems (for me) that let me concentrate on the "how do I do X in this language/toolkit/environment/framework/etc?".

Even with those two as my goto projects, I'm always open to new small problems that might be fun to apply to languages I do know, or languages I want to get to know (internally at work we have a fun "league" of sorts, writing a particular hamming distance calculation tool in different languages, for example).

A few days ago, via this repo on GitHub, I discovered this fun little problem. Right away I could see the benefit in it. As a "go away and code up a solution" interview question it strikes me as near-perfect. It's obviously not hard to solve, but it touches on some basic but important aspects of software development and so will allow the developer to show off how they approach things.

There's so many different approaches to it too. Even in a single language, I could imagine having some fun writing the smallest code to solve the problem, the most idiomatic code to solve the problem, the most supportable and well-documented code to solve the problem, etc. And then there's the thing I talk about above: knowing the solution and knowing it's easy, you can then use it to learn the idiomatic way of solving the problem in new languages.

Even better, the README of the original repo links to solutions others have written. Knowing the problem, and knowing the solution, you can then go and read other people's code and learn something about different styles and different languages.

Over the next few weeks, as I get free time, I think I might just do this. Take the "Journeys" problem and write versions in different languages I work with, or know, and also use it to get to know languages I've yet to know or use heavily (I'm especially keen to try a version in Julia -- a language I really like the look of and want to find a reason to use).

Meanwhile, yesterday, I had a quick go at a first version in Python (aimed at Python 3.8 or higher): https://github.com/davep/journeys.py

I set out to try and write something that was fairly idiomatic Python, which uses tools that I tend to employ when working on Python projects (pipenv, make, etc), and which also used something I've never quite found a need for so far in my usual coding, but which I can see being useful and helpful.

I even threw in a couple of uses of PEP 572!

I can see me tinkering with this some more over the next few days. I can even see me writing a very different implementation in Python, just for the fun of it.

I think that's what I like about this little problem. It's a good way to do a bit of programming exercise; it's like the perfect way to do the programming equivalent of going for a short run.

My Pylint shame

Posted on 2019-11-04 20:39 +0000 in Python • Tagged with Python, fish • 3 min read

I first got into Python in the mid-to-late 1990s. It's so far back that I think the copy of Programming Python that I have (sadly in storage at the moment) might be a first edition. I probably fell out of the habit of using Python some time in the early 2000s (that was when I met Ruby). It was only 22 months ago that I started using Python a lot thanks to a change of employer.

As you might imagine, much had changed in the 15+ years since I'd last written a line of Python in anger. So, early on, I made a point of making Pylint part of my development process. All my projects have a make lint make target. All of my projects lint the code when I push to master in the company GitLab instance. These days I even use flycheck to keep me honest as I write my code; mostly gone are the days where I don't know of problems until I do a make lint.

Leaning on Pylint in the early days of my new position made for a great Python refresher for me. Now, I still lean on it to make sure I don't make daft mistakes.


Pylint and I don't always agree. And that's fine. For example, I really can't stand Pylint's approach to whitespace, and that is a hill I'll happily die on. Ditto the obsession with lines being no more than 80 characters wide (120 should be fine thanks). As such any project's .pylintrc has, as a bare minimum, this:



Beyond that though, aside from one or two extras that pertain to particular projects, I'm happy with what Pylint complains about.

There are exceptions though. There are times, simply due to the nature of the code involved, that Pylint's insistence on code purity isn't going to work. That's where I use its inline block disabling feature. It's handy and helps keep things clean (I won't deploy code that doesn't pass 10/10), but there is always this nagging doubt: if I've disabled a warning in the code, am I ever going to come back and revisit it?

To help me think about coming back to such disables now and again, I thought it might be interesting to write a tool that'll show which warnings I disable most. It resulted in this fish abbr:

abbr -g pylintshame "rg --no-messages \"pylint:disable=\" | awk 'BEGIN{FS=\"disable=\";}{print \$2}' | tr \",\" \"\n\" | sort | uniq -c | sort -hr"

The idea here being that it produces a "Pylint hall of shame", something like this:

  12 wildcard-import
  12 unused-wildcard-import
   8 no-member
   6 invalid-name
   5 no-self-use
   4 import-outside-toplevel
   4 bare-except
   2 unused-argument
   2 too-many-public-methods
   2 too-many-instance-attributes
   2 not-callable
   2 broad-except
   1 wrong-import-position
   1 wrong-import-order
   1 unused-variable
   1 unexpected-keyword-arg
   1 too-many-locals
   1 arguments-differ

To break the pipeline down:

rg --no-messages "pylint:disable="

First off, I use ripgrep (if you don't, you might want to have a good look at it -- I find it amazingly handy) to find everywhere in the code in and below the current directory (the --no-messages switch just stops any file I/O errors that might result from permission issues -- they're not interesting here) that contains a line that has a Pylint block disable (if you tend to format yours differently, you'll need to tweak the regular expression, of course).

I then pipe it through awk:

awk 'BEGIN{FS="disable=";}{print $2}'

so I can lazily extract everything after the disable=.

Next up, because it's a possible list of things that can be disabled, I use tr:

tr "," "\n"

to turn any comma-separated list into multiple lines.

Having got to this point, I sort the list, uniq the result, while prepending a count (-c), and then sort the result again, in reverse and sorting the numbers based on how a human would read the result (-hr).

sort | uniq -c | sort -hr

It's short, sweet and hacky, but does the job quite nicely. From now on, any time I get curious about which disables I'm leaning on too much, I can use this to take stock.

pydscheck -- A quick hack that keeps slowly growing

Posted on 2019-10-26 13:19 +0100 in Python • Tagged with Python, documentation • 3 min read

Something I always try to do when I'm coding is be consistent. I feel this is important. While people's coding standards may differ, I think different approaches are easier to handle if someone has been consistent with their style across all of their code.

This also stands for documentation too.

In my current position, I do a lot of Python coding, and one of the things I like about Python (there are things I don't like too, but that's not for now) is that it has doc-strings (just like my favourite language). I use them extensively, ensuring every function and method has some form of documentation, and generally I use Sphinx to generate documentation from those doc-strings.

Early on I was bothered by the fact that, just by the simple act of making typos, I wasn't keeping the form of the doc-strings consistent. And in this case it was a really simple thing that was bugging me. Normally, if I'm writing a single-line doc-string, I'll write like this:

def one_liner():
    """Here is a one-line doc-string."""

So far, so good. But, if the doc-string is a multi-liner, I prefer the ending quotes to be on a line of their own, like this:

def multi_liner():
    """Here is the first line.
    Here is another line.
    Here is the final line.

But, sometimes, by accident, I'd leave a doc-string like this:

def multi_liner():
    """Here is the first line.
    Here is another line.
    Here is the final line.""""

While it's really not a big deal, it would bug me and every time I found one like this I'd "fix" it.

Eventually, it bugged me enough that I decided I was going to write a little tool to find all such instances in my code and report them. My first approach was to think "I could just do this with some regexp magic", which was really a bad idea. Then I though, I know, I should use this as an excuse to to play with Python's ast library.

That worked really well! I had the first version of the code up and running in no time. It was simple but did the job. It ran through Python code I threw at it and alerted me to both missing doc-strings, and doc-strings with the ending I didn't like.

That served me for a while, until one day I realised that it wasn't quite doing the job correctly; it was only really looking at top-level functions and top-level methods in classes. Sometimes, not often, but sometimes, I'll define functions within functions, and I feel they deserve documentation too. So then I modified the code to ensure it walked every part of the AST.

Since then, when I've run into new things and had new ideas, pydscheck has grown and grown. I've added checks that all mentioned parameters have a type; I've added checks that any function/method that returns something actually documents the return value; I've added checks that any documentation of a returned value includes its type; I've added checks that any function or method that yields a value documents that fact; I've added checks that ensure that every parameter is documented in some way.

Each time I've done this it's helped uncover issues in my code's documentation that could be cleaner, and it's also given me a pet project to slowly better understand Python's AST.

It could be that there are better tools out there, I'd have thought that a good doc-string linting tool would be something someone had already written. But this time around I was happy to NIH it because I needed a fun learning exercise that would also have some benefits for my day-to-day work.

I'll caveat this with the fact that it's very particular to how I work and how I like my documentation to look, but if it sounds useful, here it is: https://github.com/davep/pydscheck.

There's still lots I could do with it. First off I should really properly package it up so it can be installed as a command line tool via pip. Other things that would be handy would be to allow some form of customisation of how it works. I'm sure there's other fun things I can do with it too.

That's part of the fun of having a pet project: you can tinker when you like and also get benefits from it as you use it.

A little speed issue with openpyxl

Posted on 2018-06-02 13:16 +0100 in Python • Tagged with Python, openpyxl • 4 min read

It's been very quiet on the blogging front, I'm afraid, mostly for the reasons I wrote about back in December last year. In that time I've been really very busy with work (in a good way, in a very good way) and there's not a whole lot of time to be toying with pet projects at home.

However, finding myself with a spare hour or so, I wanted to write about something I did run into as part of some development at work, and which I thought might be worth writing about in case it helps someone else.

Recently I've needed to write a library of code for loading data from Excel Workbooks. Given that the vast majority of coding I do at the moment is in Python, it made sense to make use of openpyxl. The initial prototype code I wrote worked well and it soon grew into a full-blown library that'll be used in a couple of work-related projects.

But one thing kept niggling me... It just wasn't as fast as I'd expected. The workbooks I'm pulling data from aren't that large, and yet it was taking a noticeable number of seconds to read in the data, and when I let the code have a go at a directory full of such workbooks... even the fan on the machine would ramp up.

It didn't seem right.

I did a little bit of profiling and could see that the code was spending most of its time deep in the guts of some XML-parsing functions. While I know that an xlsx file is pretty much an XML document, it seemed odd to me that it would take so much time and effort to pull the data out from it.

Given that I had other code to be writing, and given that the workbook-parsing code was "good enough" for the moment, I moved on for a short while.

But, a couple of weeks back, I had a bit of spare time and decided to revisit it. I did some more searching on openpyxl and speed issues and almost everything I found said that the common problem was failing to open the workbook in read_only mode. That can't have been my problem because I'd being doing that from the very start.

Eventually I came across a post somewhere (sorry, I've lost it for now -- I'll try and track it down again) that suggested that openpyxl was very slow to read from a workbook if you were reading one cell at a time, rather than using generators. The suggestion being that every time you pull a value form a cell, it has to parse the whole sheet up to that cell. Generators, on the other hand, would allow access to all the cells during one parse.

This seemed a little unlikely to me -- I'd have expected the code to cache the parsing results or something like that -- but it also would explain what I was seeing. So I decided to give it a test.

openpyxl-speed-issue is a version of the tests I wrote and ran and they absolutely show that there's a huge difference between cell-by-cell access vs generator access.

Code like this:

for row in range( 1, sheet.max_row + 1 ):
    for col in range( 0, sheet.max_column ):
        value = sheet[ row ][ col ].value

is far slower than something like this:

for row in wb[ "Test Sheet" ].rows:
    for cell in row:
        value = cell.value

Here's an example of the difference in time, as seen on my iMac:

$ make test
pipenv run time ./read-using-generators
        1.59 real         0.44 user         0.04 sys
pipenv run time ./read-using-peeking
       25.02 real        24.88 user         0.10 sys

As you can see, the cell-by-cell approach is about 16 times slower than the generator approach.

In most circumstances the generator approach would make most sense anyway, and in any other situation I probably would have used it and never have noticed this. However, the nature of the workbooks I need to pull data from means I need to "peek ahead" to make decisions about what I'm doing, so a more traditional loop over, with an index, made more sense.

I can easily "fix" this by using the generator approach to build up a two-dimensional array of cells, acquired via the generator; so I can still do what I want and benefit from using generators.

In conclusion: given that I found it difficult to find information about my speed issue, and given that the one off-hand comment I saw that suggested it was this wasn't exactly easy to find, I thought I'd write it all down too and create a repository of some test code to illustrate the issue. Hopefully someone else will benefit from this in the future.