Show HN: MPL Plotter – Python library to make technical plots more efficiently

I get your point, but disagree. I agree mpl is very powerful and you can do almost anything with it, but the biggest problem with mpl is the default behaviour for many things is just plain horrible. Take subplots, axis labels and ticks. By default pretty much any attempt will yield overlapping/obscured labels or way too much whitespace. Tight_layout helps a bit but in my experience you essentially have to manually tweak spacing and margins every time.

Similarly things like traditional x/y plots with a centre axis with arrows are much harder than they should be (it can be done but is really quite a bit of hassle).

And there are lots of other, similar issues. I suspect the OP is correct and it will be very difficult to replace mpl for producing publish quality plots (I think for interactive plots it's much easier and already done), despite it's shortcomings, because it is so powerful.

I agree with your assessment. An important thing that you point out though, is that it takes regular use to get a good functional and flexible wrapper.

I occasionally (once every 4 months or so) have to create some plots for reports or analysis, and they are almost always very weird, dissimilar to previous plots. It always ends up being a huge pain, even if sometime in the past I've done something kind of like them (this is especially true for any kind of dynamic plots).

I've ended up building a pretty easy to modify plotting suite out of DearPyGui. I cannot recommend DPG enough, especially for high throughput or large scale plots. Immediate mode programming is a joy to work with, and it's pretty awesome being able to quickly toggle on and off any dataset in realtime.

Try seaborn. It's a decent wrapper.

It seems like seaborn would be a step backwards if you're looking for fast plotting. Perhaps I got the wrong impression, but this month I spent some time helping a coworker migrate to plain matplotlib as the seaborn plots were taking minutes to process while my matplotlib based code took half a second.

How large are you talking about? Most of the time, your best option is to plot a representative sample.

Datashader can handle some pretty big sets. https://datashader.org/

Hey! Thanks a lot for checking it out! TL;DR: 1. Between Seaborn and MPL Plotter, I believe it comes to taste more than anything, and (somewhat arguably), I think MPL Plotter is a bit more concise. I really like how you can expand its functionality with Matplotlib, but that applies to both! 2. I recommend Datashader (https://datashader.org/) (HoloViz is super cool) and Vispy (https://vispy.org/). I found Vispy's documentation a bit lacking some time ago, but they probably have improved it since then, and it's very capable. Lastly, check Taichi (https://taichi.graphics/), might not be a conventional data representation library (or rather, not only), but it's amazing and worth a look.

To add some more depth to the Seaborn comparison, and not being an expert Seaborn user, I'd say:

1. MPL Plotter is lighter (but also with less wide-ranging plot options) 2. In my experience, MPL Plotter's presets (most importantly, the defaults from which you build your plots up) are more suitable for technical papers than Seaborn's.

And perhaps a bit more arguably (again, I'm not a Seaborn expert, please do correct me if you think otherwise):

3. I believe MPL Plotter gives you more fine-grained control over your plot. That's is for you to plot and customize as far as Matplotlib will take you in one line, while most Seaborn examples I've seen use pyplot snippets. 4. And following with the above, I believe the syntax is a little more concise.

Personally I like that MPL Plotter is fundamentally Matplotlib, so I can use any Matplotlib customization I might need seamlessly, and, if useful enough, add it later on as a method in MPL Plotter itself, which would be a bit harder on such an established project as Seaborn. It's just tastes at that point, and the flexibility of being a small project.

Cheers!