#170 Visualize this: Visualizing Python's visualization ecosystem

WEBVTT
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Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds.
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This is episode 170, recorded February 19th, 2020. I am Brian Okken.
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And I'm Michael Kennedy.
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And this episode is brought to you by DigitalOcean. They've got something cool we're going to share
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with you a little later. But first, tell me about whatever you're going to tell me about first.
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I want to kick things off by building a picture for you. A picture of something awesome and yet
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something fairly complex. The Python visualization space. I don't know how you feel about it,
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but whenever I think, oh my gosh, I gotta go make some cool pictures and do neat things with Python,
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I'm like, matplotlib, bokeh, py, py plot, what else should I learn? All these other things.
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And then if I want to do this graph, there's a lot of stuff out there. So Price and Daniel sent across
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a website, a resource for anyone who feels like me and says, ah, there's like kind of an
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overwhelming sense of all the things happening. And this is called PyViz at pyviz.org.
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And basically, the idea with PyViz is they've categorized and mind mapped all the things around
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Python data visualization and they've grouped it into different stuff. So the way they describe it
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is they say it's an open platform for helping users decide on the best open source Python data
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visualization tools for their purpose with links, overviews, comparisons, examples, and tutorials.
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That's cool.
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Yeah, sounds good, right? So they have different sections that you can go check out. Overview of all
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the packages, which is like a nice visual graph relationship thing. High level overview of the
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tools, a live table comparing maturity, popularity, and support. Also a special focus on building
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dashboards that are like interactive web things that people can interact with for scientific
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visualization for rendering embedded three-dimensional objects. And like I said, some tutorials and
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examples. Also some of the examples talking about like, if you have this kind of data, here's how
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you visualize it. What do you think?
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Yeah, I like it that the subset is still a lot of work if you know what you want to do and you still
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have to try to figure out what you want to do with it. But at least it's broken it down into
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just a handful of things that you have to figure out.
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Yeah, absolutely. Well, it doesn't entirely alleviate the challenge of this. But like,
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for example, if you go to the overview section, you can see there's three main sections, JavaScript,
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matplotlib, and OpenGL. And that might sound like, I thought this was Python. What's up? Like these are,
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I'm going to work with Python and get the outcome as a JavaScript thing on a web page or something I can
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get into matplotlib or something I can get into like an OpenGL window for interaction and whatnot.
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So yeah, there's a bunch of stuff here that you can work with. But I think it's interesting to see the
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relationships as well.
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Well, we had a visualization thing that we used at work that we had originally done a JavaScript side one.
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And I think that we're going to switch and go back to a matplotlib side, mostly because it's whether or not it's
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happening on the front end or the back end. That's a division that we have to make, made that decision.
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But you know, it's only software, you can change it.
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That's right. It's not concrete.
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Yeah, just get another intern.
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Indeed, indeed. All right. Well, I definitely am going to make use of this site if I've got some
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visualization to do. And yeah, there's a couple of things I'm really would like to up the sort of
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reporting exploration game for both the course side of things, understanding what users are up to,
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but also for the podcasts, right? So we can understand how our listeners are listening a
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little bit better. So I'll see what I can do around that for us. It might involve one of these.
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Yeah, that'd be cool. I'm intrigued by the breakdown of some of the dashboard ones.
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Yeah. Would you say that that project's awesome that I was thinking about?
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I think so. Yeah. It's probably going to be an awesome project.
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What else is awesome?
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Oh, well, Zen is awesome. I actually like a lot of these. I have to admit it,
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I like the awesome sites, the collections, awesome collections of different things,
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usually thrown up on GitHub. And this one's also on GitHub.
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They're usually more concrete, though. They're more like awesome async servers in Python or awesome
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web frameworks.
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Right. But we want to be a little reflective here. We've got a rabbit hole of Zen with
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the awesome Zen of Python. Yeah, I was trying to make a bad pun of a rabbit hole lot of Zen.
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So anyway, so the Zen of Python is a thing. So you import this and you see it. And we've covered,
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I think, at least one article talking about that. But somebody put together a collection of articles
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and video and talks and some other things about the Zen of Python, because people have thought about
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it and written about it. And I like it. A couple of things I wanted to point out. Al Swigert has a
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nice one that's a Zen of Python explained. It's a really quick reference. If you're new and are
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curious about the Zen of Python and you read it and you don't quite get what the heck that is,
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I think that he did a good start. I'll probably get his name wrong. But Moshe Zetka has a meditations on
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the Zen of Python, which is probably a longer couple paragraphs per item, a little bit longer,
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but I like his take on it as well. And one of the things I just learned recently is that there's a
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reference to the increment and decrement operator within the Zen of Python, or not the increment and
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decrement, but post decrement and pre decrement from C is like something that trips up C people.
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There's actually a joke embedded in the Zen of Python about that.
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How nice. Yeah, if it has to be an interview question about what incrementing a variable does,
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it might be too complicated.
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Yeah.
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What happens if you test if, you know, plus plus X of this thing or whatever, right? Like that's,
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yeah, not good. So although I do wish Python had a X plus plus, you know, sometimes I could go.
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Yeah. Just an easy increment or decrement operator would be good, but we don't need two.
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No, that's right.
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I got to admit, I read in the, now we're getting down the C world in a for loop. It's recommended
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that you do a pre-increment because there's a little bit of savings on complex objects to do
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pre-increment, but give me a break. It's I plus plus at the end of a for loop. I always do that.
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Yeah, it is. Definitely is. So I'm glad Python doesn't even have a for loop.
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We have for loops, just not the same.
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Yeah.
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For I equals zero.
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Yeah. Pretty sweet. Yeah. No, this is a cool art, the cool find here. I like this.
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The Xenopython is something that's frequently spoken about, but it's very terse and there's
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not a lot of explanation about it. So, you know, it's like one of those things I remember being
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new going, Oh, the Xenopython. How cool. What does it mean? You know what I mean? Like,
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could I get an example for each line, please? It would be nice. You know?
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Yeah. I'm also including a link. I think it's important to read the Xenopython as a joke
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and here's why. And it talks about a few aspects of it as to basically don't take it too seriously. And
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Python is a language that you're really not supposed to take too seriously. Yes, it's serious. We can get
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a lot of work done, but don't get freaked out on stuff.
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It's named after Monty Python after all. Come on. Yeah, exactly. Lighten up people.
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You know what else is awesome? Kubernetes. Kubernetes and DigitalOcean.
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And DigitalOcean, yes. Yep. So there is an enormous amount of material on the web about
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Kubernetes available both online and in print. So the DigitalOcean community realized that it's a
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little, you know, you might have trouble figuring it all out, even though there's a lot of information.
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So they developed a Kubernetes for full stack developers course to provide a thorough,
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well-maintained and approachable reference set of tutorials for anyone who would like to start
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learning about Kubernetes. And if that wasn't cool enough, they also realized that some people don't
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want to take courses. They want to read about it. So they compiled the course curriculum into an ebook
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to make it available for more people. So you can see that if you go to pythonbytes.fm
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slash do dash kube and you can download the book and visit the course. And as always, you can visit
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pythonbytes.fm/digital ocean to get a hundred dollar credit for new users to build something awesome.
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I think we should just keep rolling things that are awesome. Yeah. Yeah.
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So Matt Harrison inadvertently recommended this next item to us.
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Really?
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I mean, like very indirectly, but he did. So he posted on Twitter how he is absolutely loving
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jupitext. J-U-P-Y-T-E-X, jupitext. And I didn't even see it on Twitter. No, Twitter emailed
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me a summary of popular tweets, which said Matt Harrison liked this. So thank you, Matt.
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Interesting. Okay.
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Okay. So Jupyter Notebooks are awesome. They're super cool for exploring data and they're really
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powerful for people who are doing like more exploration rather than writing software, right?
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You can run the first bits. They might take a while, but then you can slice and dice stuff
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to visualize it and explore it as much as you want. However, they're not all the way awesome
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because if I put one of those things into say GitHub and you were to check it out and I were
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to be messing with running it and that was getting like live data. So non-deterministic results and
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you did the same and then we both put it back. It keeps like those results and stuff. And it's not
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very friendly in Git, right? Another thing that would be nice would be sometimes you want to
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explore stuff in Jupyter, but then productize it, turn it into like a script of its natural language,
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right? It supports a bunch of languages now. It would be nice to be able to go. And here's the one we run
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where the comment, the text becomes comments or whatever. So basically that's what JupyterText
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does. It will take Jupyter notebooks and turn them into markdown documents or Python scripts.
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Wow.
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And then you put those in source control or whatever, right? You can work with those.
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So you could then export it out and work with it in like PyCharm or something like that. You also get
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meaningful diffs of what changed when you look at the differences of those. So this thing's really cool.
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You can export the Jupyter notebooks in all these different script languages. Currently it supports,
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ready for this. Julia, Python, R, Bash, Scheme, Clojure, MATLAB, Octave, which is the open source
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MATLAB, C++, UKDB+, IDL, TypeScript, JavaScript, Scala, Rust, PowerShell, C#, F Sharp, and my favorite
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behind Python, robot framework.
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Nice.
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I just want to work in the robot framework. What language did you know? I know, robot. Oh, really?
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Cool.
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Yeah. Anyway, people who are working in Jupyter notebooks, this looks super helpful. And
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Matt, thanks for having a popular tweet that got emailed to me.
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Yeah. So can you do Scheme in Jupyter?
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You must be able to.
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You must be able to. I did Scheme as my CS101 course.
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Yeah, me too.
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And I did a couple of CS courses.
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And I'm not a fan of all the close parentheses, to be honest. I'm like, what a crazy language
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this is. But yeah, apparently you can.
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Nice.
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I guess I would rather do that over like, rather than through like an X term or whatever.
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But yeah, apparently you can.
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Nice.
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Pretty cool. Anyway, so if you work on the Jupyter notebooks and you want to be able to export
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them in these cool ways and do other stuff as well, check that out.
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Well, one of the things I want to talk about is stuff that we iterate over.
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Okay.
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This is a failed attempt as a transition.
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So a great article called a tour of Python iter tools and iter tools is one of those things
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that you kind of, it's so cool, but you kind of blast past it when you learn it. And you,
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when you're learning Python, it's a little bit, a little bit more advanced than you want,
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but this is cool stuff. This article is by Martin Heinz and he's just kind of briefly
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describes what handful of the different tools within iter tools and a library called more iter
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iter tools does. And all of these are around iterables and iterating over things.
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And you wouldn't think right off the bat that you could do so much, but I'm going to highlight
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a few of these, not all of them for iter tools. He talks about compress, accumulate cycle and T.
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And I had not been familiar with cycle and T, but they're pretty cool. So iterables,
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some of them that they like they're empty once you go over it once and T allows you to make
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multiple references to one interval. That's pretty neat. And cycle is so cool. It allows you to like,
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so you, if you iterate over like say a set or a list of items, you can just iterate through it once,
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but cycle allows you to just keep going over and over and over again. It's like a never ending
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repeating iterable. And, the little example in the document or in this article that shows you how to
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easily iterate through characters to make a never ending spinner on a command line. So it's kind of
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fun.
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Oh yeah. That's cool. Yeah. You just put the different ones and a list and then you just cycle
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it.
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Yeah. The note is that like for things like games and stuff like that, if you iterate through,
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through different players, you might have to do that. But there's a lot of cases where I want to,
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like maybe zip through a couple of lists, but I, it's really just a site. One of the lists is a
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cycle. You can kind of do that. It's cool. More iter tools has a whole bunch of things like he's
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highlighting different things that are different ways to chop up your iterations. Like there's a
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divide that just divides it into different sub iterables.
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Like paging almost, right? Like process them 25 at a time or something.
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Yeah. So there's a whole bunch of different ones. Like in partition is like, if you have a,
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an expression to that can be mapped to true or false on any of your elements, it'll split the,
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the interval into two intervals based on that predicate, which is pretty cool.
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A side effect that I never knew about. So if you want to add another, another function
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that gets called with each element during iteration, wow, this is cool. I think that's neat.
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I don't know what to use it for, but I'm going to come up with a use. There's a bucketizing to be
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able to do a bunch of buckets, map reduce, just a whole bunch of neat things. And I think this is a
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good, if you think you, you've like sunk your teeth into Python and you want to like take it to the next
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level, this is a good article to, to learn iter tools.
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This is a super interesting and iter tools is definitely one of those things where you feel like
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I'm always surprised that there's extra stuff. Collections as well. You know,
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it's just like, wow, there's also that thing. Why did I not ever use that thing? You know,
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there's some things about programming that I know some people say they'll, when they come to a problem
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and they need a solution for it, then they'll look up. That's how they learn new things. But you're
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right with collections and iter tools, you don't know to look it up because you don't know there's
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a solution there. So these are good things to like skim through so that you just have seen them once.
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So that later when you run into a problem, you're like, oh, there's a thing in iter tools that I can
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use for this. Yeah. I know that that's true even before I look, but I don't know what it is.
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I just know if I go dive around in there, it's probably something I can use at some point.
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Yeah, that's a cool one. And it's nice to see it put together and highlighted like that.
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I just really want to talk about this next one. Okay. Okay. All right. So this next one is called
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JustPy. And the idea is I want to create a web application that allows me to do single page
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app, cool, interactive, front end framework type stuff that I would do with something like say Vue.js,
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but I don't want to write JavaScript. So it's pretty interesting. It's a web framework that lives on a
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server. So you put it on, you know, your standard, it actually supports async stuff. So you'd put it on
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like a async server, like Starlette or something like that, or uvicorn. And then you express how the pages
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are supposed to look in Python. And it creates the JavaScript that's going to make that happen. And then
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you're going to create, I guess, web sockets or some form of back and forth back to the server to run your
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Python code as people interact with it. Okay. Using effectively like Ajax type of interaction. So yeah,
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it lets you create these front end and, you know, sort of full stack web apps with no front end programming.
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And the way it works is like I said, it has no front end back end distinction. All the programming is
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done on the back end, allowing a simpler, more productive way and more Pythonic way to
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create a web app, but it intercepts the relevant events on the front end and sends them back to
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the back end to be processed, either talking to a database or just like having the Python logic there.
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Neat. Yeah. It's a really, really different take. And it's been getting a lot of traction.
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My personal thought on it, the verdict's a little bit out. I'm not sure how I feel about it,
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but I definitely think it's interesting and people should check it out to see what they think about it.
00:17:10.020 --> 00:17:15.340
It's really based on like classes and components. So you can create these components,
00:17:15.340 --> 00:17:19.740
which then can be put onto the page. So like here's a login component or like a
00:17:19.740 --> 00:17:25.180
component that displays a list of podcast episodes or whatever. You can put those in different places
00:17:25.180 --> 00:17:31.760
and you can create these custom components and they can either be just straight Python where you
00:17:31.760 --> 00:17:36.840
create kind of the DOM in Python. That's part of the, if it was more HTML based, I'd be happier,
00:17:36.980 --> 00:17:43.380
but it does also support HTML components that then work with it and even has like interactive
00:17:43.380 --> 00:17:45.260
charts and grids. So that's pretty cool.
00:17:45.260 --> 00:17:51.200
Yeah. I see what you're saying. It's kind of neat, but also if you don't want to learn HTML and
00:17:51.200 --> 00:17:53.060
JavaScript, learn this instead.
00:17:53.380 --> 00:17:58.580
Yes, exactly. It's kind of like that. It's like too much avoiding HTML. I think it's awesome that the
00:17:58.580 --> 00:18:04.220
logic of it is in Python and this, this no distinction stuff is really cool about the front and back end.
00:18:04.360 --> 00:18:14.200
I just wish I would design the site in HTML and CSS and then plug in the liveness through this, not as much of the content definition.
00:18:14.520 --> 00:18:26.700
That said, Quasar, I suspect many listeners are not familiar with the Quasar library, but the Quasar library is a cool library that's got all sorts of stuff built on top of Vue.js.
00:18:26.700 --> 00:18:33.040
So their little slogan is build high performance Vue.js user interfaces in record time.
00:18:33.040 --> 00:18:40.520
So you can see they've got like, think of kind of like what Bootstrap did for web design is kind of doing that for Vue.js, right?
00:18:40.520 --> 00:18:53.240
You get some like cool stuff going on there. And the reason I randomly diverted sideways and started talking about Vue.js and Quasar is because JustPy is built on libraries that you would know.
00:18:53.240 --> 00:19:03.820
So Starlette and uvicorn for the server side, but Vue.js for the front end, that means the Quasar library automatically plugs in its components and functionalities into JustPy.
00:19:03.820 --> 00:19:08.680
So that gives it like actually a huge boost. Like, oh, there's already this really cool library that just plugs into it.
00:19:08.680 --> 00:19:09.740
Oh, nice. Okay.
00:19:10.020 --> 00:19:25.200
That gives it a boost. But yeah, I wish you could define more stuff in HTML and then bring it. Now you can say like, here's an HTML page. I'm going to import it and like work with it. But it's not, it doesn't seem to be my first reading that that's like the natural way to go.
00:19:25.200 --> 00:19:26.740
Yeah. Okay.
00:19:26.740 --> 00:19:31.100
But nonetheless, I love that this exists. It looks really interesting and people should check it out.
00:19:31.100 --> 00:19:31.420
Nice.
00:19:31.420 --> 00:19:34.000
Nice. I wonder what the maintenance story like is around it though.
00:19:34.000 --> 00:19:42.340
Yeah. I'm curious. So we want to talk about maintenance and I'm not sure why this article is titled modularity for maintenance.
00:19:42.560 --> 00:19:49.860
So modularity for maintenance, it's a bunch of automation tools that you can use to help maintain your open source projects.
00:19:49.860 --> 00:20:11.400
And it's really terse with links, but it's got continuous integration and talks and linting tools and type checking and dependencies and security scanning and using code coverage and formatting help and releasing and all these automation tools that help you, which is actually, it's really cool to have all these listed down in one place.
00:20:11.480 --> 00:20:27.660
And I like that. If you've done all this for a bunch of projects and then you start a new project or maybe you split one project into two projects, just the overhead of making, setting all that stuff up again for another project is in itself a lot of work.
00:20:27.660 --> 00:20:43.340
So he's hoping for somebody to make so that you can save some time. But all of us also, I never really thought about all of these time-saving things you actually end up having to set up again for each project.
00:20:43.340 --> 00:20:45.280
Are they actually saving you time?
00:20:45.280 --> 00:20:46.080
Yeah, right.
00:20:46.140 --> 00:20:49.180
It depends. It depends how long you use the project. Probably they are, but yeah.
00:20:49.180 --> 00:21:00.740
Probably. But I guess it is interesting to think about the extra overhead and you have to have some of these require login credentials and stuff that you have to set up.
00:21:00.740 --> 00:21:08.660
But actually, I really like all this list and I kind of want to go take one of my projects and see if I can make sure I'm hitting all of these things.
00:21:08.820 --> 00:21:21.740
Yeah, it's super cool. And I just last night used Cookie Cutter, the new Python package template from Cookie Cutter to create a Python package that I'm working on.
00:21:21.740 --> 00:21:30.540
So yeah, this is super real. I'm like, oh, I bet there's a Cookie Cutter thing to set up like the setup file just right and integrate talks and whatnot. And yeah, let's hit it with that.
00:21:30.800 --> 00:21:32.640
Where was this new Cookie Cutter thing?
00:21:32.640 --> 00:21:42.940
It's not super new. If you go to Cookie Cutter, there's like a place called, a park called Pantry Full of Cookies or something like that. Let me go. I'll pull up it really quick.
00:21:42.940 --> 00:21:53.140
Under the documentation on cookiecutter.readthedocs, there's a pantry full of cookie cutters. And then the first four are for making packages.
00:21:53.360 --> 00:22:06.260
So there's Audrey's ultimate Python package project template creator thing. There's a minimal package for pip installable projects and a couple of others. The one I used was the second one, I believe.
00:22:06.260 --> 00:22:13.260
No, third one. I don't know. I can't remember anymore. And it was on a different computer, so I can't get to the history of it.
00:22:13.260 --> 00:22:13.500
Okay.
00:22:13.500 --> 00:22:22.740
But yeah, anyway, these are really cool because basically it sets it up. I used the one that was the minimal, PyPackage minimal. It automatically sets it up to work with pytest.
00:22:22.740 --> 00:22:28.300
And it has the setup Py all generated based on what you tell it and stuff like that. It's pretty cool.
00:22:28.300 --> 00:22:32.800
Okay. Well, okay. If it's not using Flit, I don't know if it's really minimal.
00:22:32.800 --> 00:22:42.920
But the thing is, it doesn't have all these things that Glyph's talking about, right? It does have Travis CI integration in there.
00:22:42.920 --> 00:22:48.020
But there's a lot of things that it could be added, and it's pretty neat. Somebody could fork that and then make it.
00:22:48.020 --> 00:22:51.120
Yeah, I definitely think so. So, yeah, hopefully.
00:22:51.340 --> 00:22:55.440
Yeah. Nice. All right. Well, I guess that's it, huh? What else we got? What do you want to let people know about?
00:22:55.440 --> 00:23:04.560
Here's a quick one. Not really big enough to warrant a whole topic, but I noticed Sentry is helping to fund some open source projects.
00:23:04.560 --> 00:23:10.160
They got some money from somebody else. They decided to match it and pass it along.
00:23:10.760 --> 00:23:17.700
And they're going to help fund Black, PyPI, pytest, StructLog, and a Rust tool called Gimli.
00:23:17.700 --> 00:23:18.340
Cool.
00:23:18.340 --> 00:23:26.040
And that's pretty neat. I like the... Actually, I haven't played with StructLog too much. I've played with it a little bit, but all the others I use all the time.
00:23:26.040 --> 00:23:32.140
Oh, yeah. Very nice. Yeah. Sentry, a nice open source-based company. Funding open source. Pretty nice.
00:23:32.820 --> 00:23:37.360
The circle of life. Except for Sentry's not eating any of these, but other than that.
00:23:37.360 --> 00:23:41.600
Nice. Okay.
00:23:41.700 --> 00:23:48.480
Thanks. All right. A couple of quick announcements on my end. Just launched a new course, Python for Absolute Beginners.
00:23:48.480 --> 00:23:59.420
So this is like, hey, I didn't take CS at college. What the heck is all this stuff about when people talk about memory allocation and variables and data structures, but actually really getting started with Python.
00:23:59.420 --> 00:24:03.520
So that's a super fun one. People can check that out. Build a lot of games, but learn a lot of Python.
00:24:04.000 --> 00:24:14.020
And then also related on Talk Python training, started streaming a couple of our latest courses where possible, including this one I just announced, is high DPI.
00:24:14.020 --> 00:24:25.060
So almost 4K streams, and it's like night and day. It's so awesome to sit there and look at the screen and go, that is like as if I'm looking at a real monitor.
00:24:25.060 --> 00:24:27.000
Just back in time.
00:24:27.000 --> 00:24:33.240
So anyway, I'm trying that out. Hopefully, so far it's been working well for people, but if for some reason it doesn't, people can send me a note.
00:24:33.940 --> 00:24:36.600
Here's an interesting one that comes from Anthony Sotili.
00:24:36.600 --> 00:24:49.620
Amazon Web Services has decided to no longer publish AWS CLI, their package, AWS CLI, to PyPI, pulling a 700 million download package off of PyPI.
00:24:49.620 --> 00:24:51.360
Think of all the bandwidth they're saving.
00:24:51.360 --> 00:24:53.080
That's weird.
00:24:53.080 --> 00:24:58.000
So this is not their Python library, potentially.
00:24:58.000 --> 00:25:03.620
This is the CLI tool that people who don't even do Python would use.
00:25:03.880 --> 00:25:04.140
Right.
00:25:04.140 --> 00:25:05.420
It's just implemented in there.
00:25:05.420 --> 00:25:07.520
So it's not a huge deal.
00:25:07.520 --> 00:25:10.920
It's not like a giant, oh my gosh, what's happening to the world sort of thing.
00:25:10.920 --> 00:25:12.000
But it's also strange.
00:25:12.000 --> 00:25:21.200
So what's more strange is I'm linking to Anthony Sotili's Twitter thread that talks about this, but then that links over to GitHub where there's more conversation.
00:25:21.540 --> 00:25:23.000
On one hand, it makes sense, right?
00:25:23.000 --> 00:25:31.140
Like, why use pip to distribute a library that is not specifically for Python developers?
00:25:31.140 --> 00:25:35.580
But the other bit is, well, the replacement's an unversioned zip file of script.
00:25:35.580 --> 00:25:39.860
So I don't know which is better.
00:25:39.860 --> 00:25:40.680
It's a little bit funky.
00:25:41.260 --> 00:25:45.340
And then finally, people might have noticed some weirdness with the podcast.
00:25:45.340 --> 00:25:48.340
I don't know what has gone on, but something happened.
00:25:48.340 --> 00:25:49.800
I think it was at Apple.
00:25:49.800 --> 00:25:58.120
On Talk Python, it all of a sudden started showing four episodes, some from three or four years ago, as the latest.
00:25:58.640 --> 00:26:02.840
And you might say, well, maybe something changed in the database and the dates got wrong or the number.
00:26:02.840 --> 00:26:04.520
Like, we changed nothing.
00:26:04.520 --> 00:26:07.780
Didn't change the RSS feed generator.
00:26:07.780 --> 00:26:09.080
We didn't change the data.
00:26:09.080 --> 00:26:10.980
It just all of a sudden started going crazy.
00:26:10.980 --> 00:26:23.880
And my theory is they've put some kind of limit on the size because we were shipping like 2.5 megs of XML for each request, especially for Python bytes because the show notes are large.
00:26:24.800 --> 00:26:30.840
And I think it just went like, no, but its response to no was just to do something really weird.
00:26:30.840 --> 00:26:42.180
So on Python bytes, we now are showing just the last half year because the alternative was to have things massively broken in the RSS and the podcast players.
00:26:42.180 --> 00:26:45.680
So if you want the older ones, they're still available on the website.
00:26:45.680 --> 00:26:47.100
We're not doing anything like that.
00:26:47.100 --> 00:26:52.240
But this was the fix that I had to put in to get things to work again on the teams.
00:26:52.240 --> 00:26:53.020
I don't know what happened.
00:26:53.020 --> 00:26:54.500
I think it's a fair tradeoff.
00:26:54.640 --> 00:26:55.160
Yeah, I do too.
00:26:55.160 --> 00:26:58.840
And also, like, it means stuff refreshes faster for people.
00:26:58.840 --> 00:27:02.360
They're not pulling 2.5 megs, like, all day long with their podcast players.
00:27:02.360 --> 00:27:05.420
I don't mind on the web server, but for them, they might mind.
00:27:05.420 --> 00:27:05.700
Yeah.
00:27:05.700 --> 00:27:09.340
They don't pull it all unless they download every episode or something, right?
00:27:09.340 --> 00:27:14.460
Well, it's constantly pulling that 2.5 megs over and over and over, right?
00:27:14.460 --> 00:27:18.980
Like, if you pull up the tail of the web server, it's just going toom, toom, toom against that over and over and over.
00:27:19.480 --> 00:27:23.780
Then it pulls the actual episodes it finds that are different.
00:27:23.780 --> 00:27:24.760
Oh, right.
00:27:24.760 --> 00:27:25.060
Okay.
00:27:25.060 --> 00:27:25.640
Got it.
00:27:25.640 --> 00:27:25.700
Yeah.
00:27:25.700 --> 00:27:27.280
But it's hitting the full-on RSS.
00:27:27.280 --> 00:27:32.300
So now the RSS speed is like 250K, which is still pretty big, but it's not 2.5 megs.
00:27:32.300 --> 00:27:32.740
Oh, yeah.
00:27:32.740 --> 00:27:33.720
Sounds smaller.
00:27:33.800 --> 00:27:37.320
Are you a fan of physics, Einstein, relatively?
00:27:37.320 --> 00:27:38.820
I like gravity.
00:27:38.820 --> 00:27:39.240
It's good.
00:27:39.240 --> 00:27:39.520
Yeah.
00:27:39.520 --> 00:27:39.860
Yeah.
00:27:39.860 --> 00:27:42.680
And you're also a fan of software quality.
00:27:42.680 --> 00:27:43.080
Yep.
00:27:43.080 --> 00:27:45.560
As most software people should be, I suspect.
00:27:45.560 --> 00:27:49.400
But did you know that E equals MC squared is-
00:27:49.400 --> 00:27:51.260
I have heard that on t-shirts before.
00:27:51.260 --> 00:27:55.320
It's a statement about programming, really.
00:27:55.320 --> 00:28:00.920
I didn't realize Einstein was into this, but it's about, like, testing and code quality and bug rates and whatnot.
00:28:00.920 --> 00:28:01.340
How?
00:28:01.340 --> 00:28:06.380
Because E equals MC squared is errors equals more code squared.
00:28:06.380 --> 00:28:13.000
So as you add code, the errors increase at the square of the amount of code, basically.
00:28:13.000 --> 00:28:14.840
And that seems not totally untrue.
00:28:14.840 --> 00:28:15.820
Yeah, that's funny.
00:28:15.820 --> 00:28:16.680
But yeah, you're right.
00:28:16.680 --> 00:28:18.180
It's sort of kind of true.
00:28:18.180 --> 00:28:18.500
Yeah.
00:28:18.500 --> 00:28:23.820
I'm not sure if it's the square or it's, like, 1.5 or maybe it's, like-
00:28:23.820 --> 00:28:25.460
It's definitely nonlinear, though.
00:28:25.460 --> 00:28:29.760
You know, some polynomial, but it's nonlinear and it grows.
00:28:29.760 --> 00:28:30.520
Yeah, for sure.
00:28:30.520 --> 00:28:30.720
Yeah.
00:28:30.720 --> 00:28:31.100
For sure.
00:28:31.160 --> 00:28:32.800
Anyway, E equals MC squared.
00:28:32.800 --> 00:28:33.840
That's the joke for the week.
00:28:33.840 --> 00:28:36.260
First law of software quality, E equals MC squared.
00:28:36.260 --> 00:28:36.760
Nice.
00:28:36.760 --> 00:28:37.580
I like it.
00:28:37.580 --> 00:28:38.240
Awesome.
00:28:38.240 --> 00:28:38.980
All right.
00:28:38.980 --> 00:28:39.160
Yeah.
00:28:39.160 --> 00:28:39.540
Thanks.
00:28:39.540 --> 00:28:39.900
Bye.
00:28:39.900 --> 00:28:40.260
Bye.
00:28:40.260 --> 00:28:42.500
Thank you for listening to Python Bytes.
00:28:42.500 --> 00:28:44.960
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00:28:47.980 --> 00:28:50.880
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00:28:50.880 --> 00:28:55.940
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00:28:58.240 --> 00:29:02.200
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