WEBVTT 00:00:00.001 --> 00:00:04.800 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:04.800 --> 00:00:09.360 This is episode 164, recorded January 9, 2020. 00:00:09.360 --> 00:00:10.920 And I'm Brian Okken. 00:00:10.920 --> 00:00:11.820 And I'm Michael Kennedy. 00:00:11.820 --> 00:00:14.400 And this episode is brought to you by Datadog. 00:00:14.400 --> 00:00:16.100 We'll talk more about them later. 00:00:16.100 --> 00:00:19.120 But speaking of data, data-driven journalism. 00:00:19.120 --> 00:00:20.300 Yeah, let's start there. 00:00:20.300 --> 00:00:25.580 So there's this project called CJ Workbench, or just Workbench, something like this. 00:00:25.580 --> 00:00:29.600 And this recommendation comes to us from Michael Palowski. 00:00:29.600 --> 00:00:32.780 Sorry if I kind of mispronounced your name there, but thank you, Michael. 00:00:32.780 --> 00:00:42.420 And it's data-driven journalism as a service, as a platform, along with training on how to use it. 00:00:42.420 --> 00:00:46.860 Not training on journalism, but training on basically how to do data journalism. 00:00:46.860 --> 00:00:48.360 So it's a pretty cool idea. 00:00:48.360 --> 00:00:49.060 That is neat. 00:00:49.060 --> 00:00:49.340 Yeah. 00:00:49.500 --> 00:00:54.720 And so what you do is you can go and say, point it at, I don't know, some website. 00:00:54.720 --> 00:00:56.980 And say, I want you to scrape this from the website. 00:00:56.980 --> 00:00:59.240 And then you go and you fill in, like, this UI. 00:00:59.240 --> 00:01:03.340 You fill in and say, okay, now I want to filter out the empty columns. 00:01:03.340 --> 00:01:07.060 And now I want to do this and that transform and other transforms and so on. 00:01:07.060 --> 00:01:12.660 And so it basically lets you do kind of what you might do with a Jupyter notebook. 00:01:13.300 --> 00:01:16.740 But there's no code for the journalist, which is pretty cool. 00:01:16.740 --> 00:01:21.740 And so you might wonder, well, if there's no code, why are we talking about it? 00:01:21.740 --> 00:01:25.140 There's all sorts of software that does interesting stuff that we don't talk about. 00:01:25.140 --> 00:01:30.400 Well, one of the things you can do is if you find that the built-in features are not enough, 00:01:30.400 --> 00:01:35.640 then you write a few lines of Python and you start extending it for the journalist using Python, 00:01:35.640 --> 00:01:37.080 which is a pretty cool way. 00:01:37.240 --> 00:01:41.140 So instead of you worrying about the UI and all the basic stuff like loading CSVs 00:01:41.140 --> 00:01:44.080 and working with data frames and all that, you just say, use this. 00:01:44.080 --> 00:01:45.880 And if we got to write a little Python code, we will. 00:01:45.880 --> 00:01:50.580 You can use their system and just add your own little code snippets to it? 00:01:50.580 --> 00:01:51.240 Yeah, exactly. 00:01:51.240 --> 00:01:55.640 So if you look at the features, it has modules to scrape, clean, analyze, and visualize data. 00:01:55.640 --> 00:02:01.080 It's got built-in training, like it'll teach you how to use the feature that you've got built in. 00:02:01.080 --> 00:02:06.880 It connects to things like social and Google Drive and APIs, like JSON endpoints, I think. 00:02:07.420 --> 00:02:10.400 It has recorded workflows, so it's really repeatable. 00:02:10.400 --> 00:02:11.540 Versioning. 00:02:11.540 --> 00:02:18.140 But finally, you can write custom modules in Python and then add them to the little drop-down module library that you can use. 00:02:18.140 --> 00:02:18.920 Oh, that's neat. 00:02:18.920 --> 00:02:19.580 Yeah. 00:02:19.580 --> 00:02:19.880 Yeah. 00:02:19.880 --> 00:02:25.000 Plus, this is all in GitHub, so that if you really want to hack the back end, you can also. 00:02:25.000 --> 00:02:25.700 Yeah, exactly. 00:02:25.700 --> 00:02:26.740 It's open source. 00:02:26.740 --> 00:02:27.300 It's on GitHub. 00:02:27.300 --> 00:02:29.340 You can go grab it, download it, and run with it. 00:02:29.340 --> 00:02:31.240 But they also have it online as a service. 00:02:31.240 --> 00:02:33.500 So you can decide, do you want to run your own server? 00:02:33.500 --> 00:02:36.020 Do you want to use their public server that's in beta? 00:02:36.620 --> 00:02:37.120 Or whatnot. 00:02:37.120 --> 00:02:44.280 So I covered this partly because I think it's really cool, and I also covered it because, boy, I think the world needs journalism right now. 00:02:44.280 --> 00:02:44.800 Yes. 00:02:44.800 --> 00:02:48.740 And, yeah, more people saying, I don't know if I believe your analysis. 00:02:48.740 --> 00:02:50.580 I want to do my own analysis on it. 00:02:50.580 --> 00:02:52.900 Yeah, and here you don't have to commit to a huge amount of work. 00:02:52.900 --> 00:02:54.760 It's cool you pointed all these live sources. 00:02:54.760 --> 00:03:04.000 Like, you can point it at some website, and it can, like, real-time refresh the live data that it's scraping for your article so it can evolve over time and whatnot. 00:03:04.000 --> 00:03:04.580 It's pretty cool. 00:03:04.580 --> 00:03:05.580 I might have to try this. 00:03:05.580 --> 00:03:06.160 Yeah. 00:03:06.160 --> 00:03:06.680 Yeah, nice. 00:03:07.280 --> 00:03:09.360 Have we covered GUIs yet on this podcast? 00:03:09.360 --> 00:03:12.840 I think I do remember way back when at some point we talked about GUIs, right? 00:03:12.840 --> 00:03:14.660 Yeah, at least once. 00:03:14.660 --> 00:03:15.040 Yeah. 00:03:15.040 --> 00:03:15.740 So... 00:03:15.740 --> 00:03:16.600 What do you got for us this time? 00:03:16.600 --> 00:03:18.780 Well, so here's another one, another GUI. 00:03:18.780 --> 00:03:20.200 This one's called Remy. 00:03:20.200 --> 00:03:23.160 The Remy comes from Remote Interface Library. 00:03:23.160 --> 00:03:25.060 I'm not sure why it's called that. 00:03:25.060 --> 00:03:25.940 All GUIs are essentially... 00:03:25.940 --> 00:03:27.140 Why is it not Remy? 00:03:27.140 --> 00:03:28.480 Remy, I don't know. 00:03:28.480 --> 00:03:29.660 No, no, it's cool. 00:03:29.660 --> 00:03:34.320 But Remy, a platform-independent Python GUI library for your applications. 00:03:34.320 --> 00:03:43.680 And yes, we've covered things like this a lot of, like, okay, I want to just scrap the idea of a native user interface, and we'll just use web user interfaces. 00:03:43.680 --> 00:03:47.220 The thing that I liked about this, it's so small. 00:03:47.220 --> 00:03:48.440 There's no dependencies. 00:03:48.440 --> 00:03:55.920 I'm not sure why they're pointing you to do a pip install from their GitHub repo, but I'm guessing they're not really that great about... 00:03:55.920 --> 00:03:58.920 updating the PyPI version, because it's there also. 00:03:58.920 --> 00:04:01.760 But anyway, I did this morning. 00:04:01.760 --> 00:04:03.320 Grabbed a... 00:04:03.320 --> 00:04:06.880 Just did a pip install of Remy, but from their Git repo. 00:04:06.880 --> 00:04:11.740 And then I tried their little sample basic app, and I'm like, is it really this easy? 00:04:11.740 --> 00:04:16.720 And yeah, this is like 30 lines of code or less to just write. 00:04:16.720 --> 00:04:19.120 And if you run it, just Python bloop. 00:04:19.120 --> 00:04:23.280 Oh, first of all, when you pip install it, it installs the package, and then that's it. 00:04:23.280 --> 00:04:24.700 It doesn't install anything else. 00:04:25.080 --> 00:04:28.040 Because it's only dependent on the standard library, which is cool. 00:04:28.040 --> 00:04:36.860 And then you start it up, and it runs a little server on your thing, and it's really easy to throw buttons together and stuff. 00:04:36.860 --> 00:04:46.480 It looks pretty good enough, and the code is simple enough that I thought I'd include it, because there's a lot of cases where, like in an internal team, where you have... 00:04:47.020 --> 00:04:57.100 If you want to throw something together where people have access to some Python data or some Python analysis with a user interface, and something like this would be cool. 00:04:57.100 --> 00:04:58.200 And it's neat. 00:04:58.200 --> 00:04:59.100 Yeah, it's pretty neat. 00:04:59.200 --> 00:05:07.680 It's like a Python-only electron.js-ish type of thing without the HTML part. 00:05:07.680 --> 00:05:14.300 So you define your app, and you programmatically define, like, I want a button here, and I want a label there. 00:05:14.300 --> 00:05:17.480 And when I click the button, I want it to call this other Python function. 00:05:17.660 --> 00:05:28.140 So you just write pure Python, and then it turns the GUI definition into HTML, shows that in a server, and then calls back to your code, something like that, which is pretty cool, right? 00:05:28.140 --> 00:05:33.360 Yeah, and it feels similar to working with TK back in the day. 00:05:33.360 --> 00:05:40.100 Within the Python code, you create a label object and a button object, and then you put them in a container. 00:05:40.100 --> 00:05:49.420 There's no packing so far that I can see, but it has different types of containers that you append them, and then just throw those in a box. 00:05:49.420 --> 00:05:52.640 So, yeah, like you said, you don't have to write any of the HTML. 00:05:52.640 --> 00:05:54.340 It's just all Python code. 00:05:54.620 --> 00:05:58.360 So, yeah, for what it's worth, it's kind of simple. 00:05:58.360 --> 00:06:02.860 Yeah, it's a nice, simple little thing, and it looks pretty cool, and yeah, I kind of like it. 00:06:02.860 --> 00:06:10.060 One of the things I was thinking about doing was, sorry, was we have a switch box that's controlled through. 00:06:10.060 --> 00:06:19.260 We've got a Python thing that controls the switches, and sometimes it'd be nice to be able to just see what the current setup is and just have a web page that just shows that. 00:06:19.260 --> 00:06:22.700 It'd be really easy to write in Python, kind of hard to do. 00:06:22.960 --> 00:06:31.060 I don't want to deal with a whole user interface sort of thing, but having that, I think I could probably throw that together with this tool in like half an hour. 00:06:31.060 --> 00:06:38.380 Yeah, and it's a proper local application, so it can look around on the system and do what it needs to do. 00:06:38.380 --> 00:06:40.260 It needs to be really a web server. 00:06:40.260 --> 00:06:40.580 Yeah. 00:06:40.580 --> 00:06:46.960 Although you can expose, I saw there's a way to tell it to listen on the network, broader interface, not just local host. 00:06:47.040 --> 00:06:51.160 So you can't expose this thing to sort of the world, for what that's worth. 00:06:51.160 --> 00:06:52.740 I don't know if it's a good idea or not, but you could. 00:06:52.740 --> 00:06:58.720 Like I said, we would do it within our internal network, so there's no malicious code flying around internally. 00:06:58.720 --> 00:06:59.800 Yeah, yeah. 00:06:59.800 --> 00:07:00.900 Yeah, cool. 00:07:00.900 --> 00:07:01.180 Anyway. 00:07:01.180 --> 00:07:09.920 You know, I'm starting to get excited again about Qt after Ogie Moore's presentation at the last PDX Python West meetup. 00:07:09.920 --> 00:07:11.680 Like that looked pretty compelling, actually. 00:07:11.680 --> 00:07:12.100 Yeah. 00:07:12.100 --> 00:07:14.000 What a slick program that was. 00:07:14.000 --> 00:07:14.700 And zippy. 00:07:14.700 --> 00:07:16.080 Very zippy. 00:07:16.080 --> 00:07:16.980 Nice. 00:07:16.980 --> 00:07:18.480 Speaking of slick, Datadog. 00:07:18.740 --> 00:07:19.060 Yeah. 00:07:19.060 --> 00:07:31.320 Python Bytes today is sponsored by Datadog, a cloud-scale monitoring analytics platform that unifies metrics, logs, and distributed traces from your Python application. 00:07:31.320 --> 00:07:43.920 The Datadog tracing client auto instruments popular frameworks and libraries such as Django, Flask, Postgres, and AsyncIO even, so you can quickly get deep visibility into your application. 00:07:44.620 --> 00:07:48.080 Trace requests across services boundaries with flame graphs. 00:07:48.080 --> 00:07:49.260 I like flame graphs. 00:07:49.260 --> 00:07:55.200 Correlate traces with logs and metrics and plot your application architecture with the service map. 00:07:55.200 --> 00:08:02.420 Sign up for free for a free trial at pythonbytes.fm/Datadog and you'll receive a free t-shirt. 00:08:02.420 --> 00:08:02.720 Nice. 00:08:02.720 --> 00:08:04.060 Yeah, they're doing good stuff. 00:08:04.060 --> 00:08:04.760 Thanks, Datadog. 00:08:04.760 --> 00:08:08.280 This next one, Brian, you wanted it, but I got it. 00:08:08.280 --> 00:08:09.400 Yeah, you got it first. 00:08:09.400 --> 00:08:10.080 It's cool. 00:08:10.080 --> 00:08:11.040 I'm excited about it. 00:08:11.160 --> 00:08:16.560 Yeah, so there's a couple of cool libraries for building CLIs. 00:08:16.560 --> 00:08:19.680 Click is probably the most prevalent one. 00:08:19.680 --> 00:08:21.480 We've got argpars. 00:08:21.480 --> 00:08:32.540 But people who do FastAPI, the cool new API framework, these folks have now gone, let me just double check that it's actually the same organization. 00:08:32.540 --> 00:08:32.920 Yep. 00:08:33.020 --> 00:08:33.520 Yes, good. 00:08:33.520 --> 00:08:34.480 Yeah, same work. 00:08:34.480 --> 00:08:34.820 Perfect. 00:08:34.820 --> 00:08:34.860 Perfect. 00:08:34.860 --> 00:08:40.220 So they've gone and created a CLI definition library like Click. 00:08:40.920 --> 00:08:43.140 And I believe it's actually based on Click. 00:08:43.140 --> 00:08:44.280 Yeah, it's based on Click. 00:08:44.280 --> 00:08:47.200 So you might wonder, well, what does it do? 00:08:47.200 --> 00:08:49.320 The name gives you a little bit of a hint. 00:08:49.320 --> 00:08:51.000 It's based on Python type hints. 00:08:51.000 --> 00:08:53.660 So Typer looks at the various hints. 00:08:53.660 --> 00:08:56.140 It can, you can say like, I have a function. 00:08:56.140 --> 00:08:58.820 It takes a name, which is, you know, name colon stir. 00:08:58.820 --> 00:08:59.680 It takes a string. 00:08:59.880 --> 00:09:01.060 And then you just use it. 00:09:01.060 --> 00:09:09.200 And when you go run it, it'll say, hey, this thing that we ran, in order to call it, you have to specify a name and the name has to be a string and so on. 00:09:09.200 --> 00:09:15.700 So it just looks at the arguments and the types of the methods you're trying to run, in the simple case at least, and it uses that. 00:09:15.700 --> 00:09:16.540 That's pretty cool, huh? 00:09:16.540 --> 00:09:17.340 It's super cool. 00:09:17.340 --> 00:09:17.700 Yeah. 00:09:17.700 --> 00:09:20.040 And then you can grow in more complexity. 00:09:20.040 --> 00:09:21.640 Like you can have sub commands. 00:09:21.640 --> 00:09:24.480 You can have help on all the commands. 00:09:24.480 --> 00:09:26.040 You can actually get autocomplete. 00:09:26.380 --> 00:09:30.340 You can install autocomplete in your shell, all kinds of cool stuff. 00:09:30.340 --> 00:09:31.580 So you can create a little app. 00:09:31.580 --> 00:09:33.220 You say, here's a command and here's a command. 00:09:33.220 --> 00:09:35.980 And then, of course, the commands are functions. 00:09:35.980 --> 00:09:37.360 Those functions take arguments. 00:09:37.360 --> 00:09:38.400 The arguments have types. 00:09:38.400 --> 00:09:40.740 They can have default values, all that kind of stuff. 00:09:40.740 --> 00:09:42.240 So you can try to run it. 00:09:42.240 --> 00:09:47.620 It'll give you help pretty much automatically, which, yeah, this really speaks to me the way this works. 00:09:47.620 --> 00:09:49.080 I could definitely see myself using it. 00:09:49.080 --> 00:09:49.260 Yeah. 00:09:49.260 --> 00:09:55.820 So a really good, clean command line interface that has, if you're writing a tool with a command line interface, 00:09:56.000 --> 00:09:58.320 you're going to want help associated with it. 00:09:58.320 --> 00:10:02.940 You're going to want description of the different methods and what they do. 00:10:02.940 --> 00:10:07.880 But also, with as little boilerplate as possible, you don't want to clutter up your code. 00:10:07.880 --> 00:10:11.560 And as far as I'm concerned, right now, typer wins. 00:10:11.560 --> 00:10:17.280 There's hardly any extra overhead that you have to add to make this work other than type hits. 00:10:17.280 --> 00:10:23.460 And if you're not using a main or something, you have to put a little decorator on the different entry points in your program. 00:10:23.460 --> 00:10:25.200 But really clean. 00:10:25.200 --> 00:10:26.880 I'm totally going to start using this. 00:10:26.880 --> 00:10:27.680 It's really nice. 00:10:27.680 --> 00:10:32.880 And because it can leverage so much of the natural program itself, right? 00:10:32.940 --> 00:10:36.340 The argument names, the fact that it has arguments, the argument types. 00:10:36.340 --> 00:10:38.060 That's really helpful for it, yeah. 00:10:38.060 --> 00:10:47.680 So you can also integrate things like Colorama and click, which is based on, will automatically start using color for its output. 00:10:47.680 --> 00:10:50.700 If you just have Colorama around, you can also use click completion. 00:10:50.700 --> 00:10:56.400 And typer, this is the part I was talking about, will automatically configure it to provide completion for all the shells. 00:10:56.540 --> 00:10:57.920 That's really cool, too. 00:10:57.920 --> 00:11:02.980 So the one thing I was concerned about is it's based on click. 00:11:02.980 --> 00:11:06.800 And one of the things I love about click is that it has a good testing interface. 00:11:06.800 --> 00:11:11.200 You can write tests against your command line app as if you're writing from the command line, 00:11:11.200 --> 00:11:15.280 but you don't actually have to spawn a new subpros to test things. 00:11:15.280 --> 00:11:23.420 And yes, typer has the same sort of model where you can write your tests against the interface and not have to do a subprocess. 00:11:23.420 --> 00:11:24.460 Oh, that's really cool. 00:11:24.460 --> 00:11:25.300 Yeah, nice. 00:11:25.460 --> 00:11:29.760 Because, yeah, you don't have to start it basically shell out and see what happens, right? 00:11:29.760 --> 00:11:33.460 Or like issue the command line directly to the shell to see what happens. 00:11:33.460 --> 00:11:34.380 Yeah, really good job. 00:11:34.380 --> 00:11:39.700 Yeah, the other thing I like about this is you could say, well, we don't want the API of click. 00:11:39.700 --> 00:11:44.700 So we're going to start from zero and rebuild our version of what this whole world looks like. 00:11:44.700 --> 00:11:50.060 But what they did is said that we're going to build a new interface for writing against this, but on top of click. 00:11:50.060 --> 00:11:54.360 So things like Colorama and the completion just plug in seamlessly. 00:11:54.560 --> 00:11:55.520 I think that's a cool lesson. 00:11:55.520 --> 00:11:56.120 Yeah, definitely. 00:11:56.120 --> 00:11:59.080 Also, it's a smaller project for them to have to maintain, too. 00:11:59.080 --> 00:11:59.740 Yeah, exactly. 00:11:59.740 --> 00:11:59.980 Yeah. 00:11:59.980 --> 00:12:00.380 Exactly. 00:12:00.380 --> 00:12:01.180 Cool. 00:12:01.180 --> 00:12:01.460 All right. 00:12:01.460 --> 00:12:04.460 This next one that you got here is from Chris Moffitt, right? 00:12:04.460 --> 00:12:04.920 Yes. 00:12:04.920 --> 00:12:05.560 And this is actually... 00:12:06.120 --> 00:12:08.820 So I'm hoping this is completely still accurate. 00:12:08.820 --> 00:12:10.380 I imagine it is. 00:12:10.380 --> 00:12:14.620 The interface for Matplotlib hasn't changed too awfully much. 00:12:14.620 --> 00:12:20.260 I know they've had some cool releases, but I want to talk about an article called Effectively Using Matplotlib. 00:12:20.780 --> 00:12:22.740 And this is a 2017 article. 00:12:22.740 --> 00:12:28.960 But in the middle of the introduction, he says, at first, I think I was a little premature in dismissing Matplotlib. 00:12:28.960 --> 00:12:33.640 To be honest, I did not quite understand it and how to use it effectively in my workflow. 00:12:33.860 --> 00:12:37.640 And I think that matches my understanding of Matplotlib as well. 00:12:37.640 --> 00:12:43.080 I mean, when I first needed to put charts or graphs or something, of course, you try Matplotlib. 00:12:43.080 --> 00:12:43.820 And then you're... 00:12:43.820 --> 00:12:47.180 You kind of hit in the face with the complexity of the interface. 00:12:47.180 --> 00:12:56.620 And you know you can do the simple thing simple, but how to do something a little bit more less than simple or more than, you know, more complex is a little daunting. 00:12:56.620 --> 00:12:58.960 And then I went out and looked for other alternatives. 00:12:58.960 --> 00:13:02.260 But now I think the alternatives have their issues too. 00:13:02.260 --> 00:13:05.900 So I'm ready to go back and take a serious look at Matplotlib. 00:13:05.900 --> 00:13:13.380 This article is a really good look at kind of understanding it and at least Chris's workflow, how to use it. 00:13:13.560 --> 00:13:22.500 And one of the things I like is the description that one of the reasons why Matplotlib's interface might be a little confusing is because it's coming from... 00:13:22.500 --> 00:13:23.880 It's really got two interfaces. 00:13:23.880 --> 00:13:27.080 It does a Matlab-like state-based interface. 00:13:27.080 --> 00:13:33.580 And I'm not a Matlab user, but I know that a lot of people have come from Matlab to Matplotlib. 00:13:33.580 --> 00:13:39.100 And so that sort of interface is important to have that transition be easy for a lot of people. 00:13:39.100 --> 00:13:43.300 But then there's also an object-based interface that makes a lot more sense. 00:13:43.300 --> 00:13:49.060 And that's Chris's recommendation is to use the object-based interface instead. 00:13:49.060 --> 00:13:55.740 And so now after he's gone through it a few times and he has some recommendations. 00:13:55.740 --> 00:14:06.700 And his recommendations are to first learn the basic Matplotlib terminology, specifically what is figure and axes and around those. 00:14:06.700 --> 00:14:12.440 And what's difficult for me is that those are just normal words that I think I already understand them. 00:14:12.440 --> 00:14:16.720 But I need to make sure I understand what Matplotlib thinks those things are. 00:14:16.860 --> 00:14:21.480 Yeah, sometimes it's just hard to work with Matplotlib unless you find an example, right? 00:14:21.480 --> 00:14:22.620 A lot of times what I... 00:14:23.620 --> 00:14:26.700 My goal is to hunt the internet for something that looks like I want. 00:14:26.700 --> 00:14:29.180 Figure out if I can find the Python code that created it. 00:14:29.180 --> 00:14:30.340 And then I'm good. 00:14:30.340 --> 00:14:30.660 Yeah. 00:14:30.660 --> 00:14:35.000 He does have an example in this, which is kind of walks through at least one example. 00:14:35.000 --> 00:14:40.380 But if you really want to kind of get your head around it to start with using pandas with Matplotlib. 00:14:41.380 --> 00:14:49.040 So yeah, you start your visualization with basic pandas plotting and then add, if you want it more complex, try Seaborn. 00:14:49.040 --> 00:14:57.140 And then if you want to customize it a little bit more, he said use Matplotlib native stuff to customize the visualization. 00:14:57.140 --> 00:14:59.960 And I wouldn't have thought to use that workflow. 00:14:59.960 --> 00:15:01.420 So that's kind of nice. 00:15:01.420 --> 00:15:09.060 It also includes this really kind of cool handy reference that there's a little graphic that he created that has like all the different... 00:15:09.060 --> 00:15:16.200 What all the axes are and if you want to customize a certain part of a graph, what that name is within Matplotlib so you know how to look it up. 00:15:16.200 --> 00:15:17.740 And that's often... 00:15:17.740 --> 00:15:21.560 The secret of programming anymore is to know what specific word to Google. 00:15:21.560 --> 00:15:23.480 So I think that's a good thing. 00:15:23.480 --> 00:15:23.900 Nice. 00:15:23.900 --> 00:15:29.780 And also I want to remind folks, long-time listeners of PyLustrator. 00:15:29.780 --> 00:15:35.280 I'm not really sure about the pronunciation like Illustrator, but Py for the first two words. 00:15:35.280 --> 00:15:36.780 I'll put a link in the show notes. 00:15:36.780 --> 00:15:40.380 And what it is, is it lets you take a basic Matplotlib plot. 00:15:40.380 --> 00:15:45.980 It launches a GUI where you customize the look and feel and then it'll output the code that would have done that for you. 00:15:45.980 --> 00:15:46.420 Oh, cool. 00:15:46.420 --> 00:15:46.680 Yeah. 00:15:46.680 --> 00:15:50.240 We talked about that in episode 137, which is like a year ago or something. 00:15:50.240 --> 00:15:51.820 But anyway, still good stuff. 00:15:51.820 --> 00:15:52.900 So I'll put a link in there for that. 00:15:52.900 --> 00:15:56.680 I'm also including a link in a almost related section. 00:15:57.120 --> 00:15:59.640 Maybe there's an article about this, but I haven't found it. 00:15:59.640 --> 00:16:03.000 There's a Stack Overflow answer that has a little... 00:16:03.000 --> 00:16:06.020 The code you need to stick a map... 00:16:06.020 --> 00:16:14.000 Generate in a Matplotlib graph and put it in a Flask file on the fly without having to save it to a file. 00:16:14.000 --> 00:16:14.400 Oh, yeah. 00:16:14.400 --> 00:16:15.880 Like a live dashboard type thing. 00:16:15.880 --> 00:16:16.260 That's cool. 00:16:16.260 --> 00:16:16.520 Yeah. 00:16:16.520 --> 00:16:16.840 Nice. 00:16:16.940 --> 00:16:23.700 Speaking about doing stuff on the web, if you're using Django, there's a cool little library that'll let you do stuff in the background. 00:16:23.700 --> 00:16:36.460 Maybe you want to do some query and update some kind of report or you want to send out a bunch of emails or recompute some indexes or recompute some search results or something like that. 00:16:36.460 --> 00:16:42.040 And it's going to take a little while and you don't want to just have it block on your main request. 00:16:42.160 --> 00:16:43.960 There's a bunch of different things you could do. 00:16:43.960 --> 00:16:49.060 You could set up a separate server with like Celery or RabbitMQ or something like that. 00:16:49.060 --> 00:16:52.180 But that's a whole other set of servers and it just gets way more complicated. 00:16:52.180 --> 00:16:56.660 Sometimes you want to just run it in the background just in the same process, right? 00:16:56.660 --> 00:16:59.120 It's easy to share data because you just pass the pointers. 00:16:59.120 --> 00:17:02.060 So there's this thing called Django Simple Task. 00:17:02.060 --> 00:17:09.080 And it allows you to run background tasks in Django 3 without requiring any services or workers. 00:17:09.080 --> 00:17:09.540 Oh, cool. 00:17:09.540 --> 00:17:14.080 Yeah, basically the reason it has to be Django 3 is it's based on AsyncIO. 00:17:14.080 --> 00:17:18.600 And Django 3 is the first version that uses ASGI. 00:17:18.600 --> 00:17:24.280 And so you actually need to run it in ASGI server like uvicorn or something like that. 00:17:24.280 --> 00:17:28.440 So be really aware that you have to run it in its async way for this to work. 00:17:28.440 --> 00:17:34.680 But basically when it starts, when the web app starts, there's a queue created and some workers to listen to the queue. 00:17:35.060 --> 00:17:41.540 And then you just go to any function you want to run and you just call defer on it and that'll kick it off in the background. 00:17:41.540 --> 00:17:43.120 And you could do that now. 00:17:43.120 --> 00:17:46.200 Like you could create a thread and just launch it as a background thread. 00:17:46.620 --> 00:17:49.280 But one, you don't get like a thread pooling. 00:17:49.280 --> 00:18:01.500 But the other is when it's time for the worker process to shut down, like you're doing a new deploy or something, it'll actually wait until its background work gets done before it allows you to restart it, at least in a friendly way. 00:18:01.500 --> 00:18:03.060 So that's cool. 00:18:03.160 --> 00:18:12.600 And you can either give it a asyncio coroutine or just a function and it delegates it, either runs it or delegates it to a thread pool if it's not async, things like that. 00:18:12.600 --> 00:18:15.740 So it's pretty cool and it's really easy to use, like ridiculously easy. 00:18:15.740 --> 00:18:16.580 Oh, that's neat. 00:18:16.580 --> 00:18:16.820 Yeah. 00:18:17.060 --> 00:18:23.460 So there's a simple little example I put in the show notes where it just imports the deferred operation method. 00:18:23.460 --> 00:18:28.240 It has two tasks, one of them which uses time.sleep that requires threading. 00:18:28.400 --> 00:18:31.800 And one uses asyncio because it's an async method. 00:18:31.800 --> 00:18:33.440 So it would run on the async event loop. 00:18:33.440 --> 00:18:39.020 And in the view, you can just say defer one, defer method one, defer method two, and then do your regular work. 00:18:39.020 --> 00:18:41.480 And that kicks it off but doesn't wait for the response. 00:18:41.480 --> 00:18:50.520 But does it in a slightly more durable, better way than just like launching threads or kicking it off on its own little background thing to go run unmonitored. 00:18:50.520 --> 00:19:01.860 So in this example that nobody can see, but they will if they look at the show notes, will the HTTP response happen and the view return before the defer tasks get run? 00:19:01.860 --> 00:19:08.220 They might get started, but because in both of them they sleep for a second to kind of, that's their work, right? 00:19:08.220 --> 00:19:09.200 They're sleeping for a second. 00:19:09.200 --> 00:19:14.880 So certainly the response would be sent back to the user before those are done. 00:19:14.880 --> 00:19:19.180 And this is a cool interface because the two tasks, one of them is async and one is not. 00:19:19.180 --> 00:19:22.180 And the defer call doesn't, you can't tell. 00:19:22.180 --> 00:19:22.440 Right. 00:19:22.440 --> 00:19:23.560 You don't have to treat it differently. 00:19:23.560 --> 00:19:25.100 But if you can do async, great. 00:19:25.100 --> 00:19:27.700 It'll do that on the async event loop, I think. 00:19:27.700 --> 00:19:32.440 Otherwise, it just puts it into a thread pool, which is pretty cool that you don't have to worry. 00:19:32.440 --> 00:19:34.420 You can just kind of go, here's some background stuff. 00:19:34.420 --> 00:19:37.060 If it's IO driven, we'll do it this way. 00:19:37.060 --> 00:19:38.960 Otherwise, just do it that way, which is, I like it. 00:19:38.960 --> 00:19:39.260 It's nice. 00:19:39.260 --> 00:19:40.340 Yeah, really nice. 00:19:40.340 --> 00:19:40.920 Cool. 00:19:40.920 --> 00:19:41.160 Cool. 00:19:41.160 --> 00:19:41.540 All right. 00:19:41.540 --> 00:19:42.960 What's this last one you got for us? 00:19:43.820 --> 00:19:45.800 Well, so something happened recently. 00:19:45.800 --> 00:19:52.120 I've got a handful of, I don't open source, work on open source projects really as much 00:19:52.120 --> 00:19:52.760 as I'd like to. 00:19:52.760 --> 00:19:56.080 I'd like to work, do some more work on open source projects. 00:19:56.080 --> 00:19:59.620 But I have a few that I maintain myself. 00:19:59.620 --> 00:20:04.860 But project is in air quotes I'm doing because they're really pretty small projects. 00:20:04.860 --> 00:20:11.980 And a couple of them, cards and submark are things that I just created to help with teaching 00:20:11.980 --> 00:20:13.360 people how testing works. 00:20:13.760 --> 00:20:16.180 They're not really intended to be useful projects. 00:20:16.180 --> 00:20:19.980 And then another one was a plugin for pytest. 00:20:19.980 --> 00:20:23.700 Actually, I do use it and it's useful for me. 00:20:23.700 --> 00:20:29.360 But I started getting, like in the last handful of months, I started getting pull requests and 00:20:29.360 --> 00:20:30.460 issues and stuff. 00:20:30.460 --> 00:20:33.180 And I didn't know anybody was using it at all. 00:20:33.180 --> 00:20:34.440 So this was surprising to me. 00:20:34.440 --> 00:20:38.300 But I wanted to highlight, I don't know if, I can't believe we haven't covered this or maybe 00:20:38.300 --> 00:20:38.920 we already have. 00:20:39.360 --> 00:20:42.900 There's a website called PyPI stats.org. 00:20:42.900 --> 00:20:43.820 Yeah, that's news to me. 00:20:43.820 --> 00:20:44.400 I hadn't heard of it. 00:20:44.400 --> 00:20:46.220 It's just a simple interface. 00:20:46.220 --> 00:20:48.380 Now, the backend might be complicated. 00:20:48.820 --> 00:20:55.000 But in the interface, it just has like a little box and you plug in a Python package and it 00:20:55.000 --> 00:21:00.000 tells you what the download stats, actually quite a bit of history too, of different packages. 00:21:00.000 --> 00:21:04.120 And so I use that for all of my different projects. 00:21:04.380 --> 00:21:09.420 And sure enough, cards was downloaded like, I don't know, less than 400 times in the last 00:21:09.420 --> 00:21:13.160 month, submark less than 70 times in the last month. 00:21:13.160 --> 00:21:20.280 And then pytest-check, which is a little app or a plugin that lets you have multiple failures 00:21:20.280 --> 00:21:22.540 per test, but keep going during the test. 00:21:22.740 --> 00:21:28.140 That was downloaded just a little less than 20,000 times in the last month. 00:21:28.140 --> 00:21:29.960 So clearly that one's more. 00:21:29.960 --> 00:21:34.700 And that's the reason why I'm having more people try and tell me what's wrong with it. 00:21:34.700 --> 00:21:36.780 So I guess I have to start maintaining. 00:21:36.780 --> 00:21:39.020 Yeah, this is a super cool interface. 00:21:39.020 --> 00:21:43.640 You get all kinds of graphs and ability to slice it by dates and whatnot. 00:21:43.640 --> 00:21:45.240 And yeah, I like it. 00:21:45.240 --> 00:21:46.300 And there's also a top 20. 00:21:46.300 --> 00:21:50.420 So you can look at like really what are the top 20 packages on PyPI. 00:21:50.780 --> 00:21:57.380 And that's why I say that mine are small peanuts because at the most, it's like 19,000 a month. 00:21:57.380 --> 00:22:02.340 But the top 20 all get over 1.3 million downloads per day. 00:22:02.340 --> 00:22:02.940 Wow. 00:22:02.940 --> 00:22:03.480 Yeah. 00:22:03.480 --> 00:22:05.160 And you can look at them, look at the trends. 00:22:05.160 --> 00:22:05.900 That's pretty interesting. 00:22:05.900 --> 00:22:08.560 There's probably some cool stuff to go through here. 00:22:08.560 --> 00:22:14.220 And I guess if you were trying to do predictive stuff or analytics, there's probably a lot of 00:22:14.220 --> 00:22:14.840 things to be done here. 00:22:14.840 --> 00:22:15.620 Yeah, definitely. 00:22:15.620 --> 00:22:18.980 Also, just occasionally, I'm just the top 20. 00:22:18.980 --> 00:22:24.060 I like because I was occasionally I'm curious about that of like, what are the top packages 00:22:24.060 --> 00:22:24.700 on PyPI? 00:22:24.700 --> 00:22:25.940 What do people use it? 00:22:25.940 --> 00:22:26.080 Yeah. 00:22:26.080 --> 00:22:28.160 They weren't what I expected. 00:22:28.160 --> 00:22:33.280 I mean, some of the ones I obviously expected were, but I think right now URLlib was the top 00:22:33.280 --> 00:22:33.520 top. 00:22:33.520 --> 00:22:35.080 And I didn't expect that. 00:22:35.080 --> 00:22:36.120 So yeah. 00:22:36.120 --> 00:22:36.280 Yeah. 00:22:36.280 --> 00:22:37.780 URLlib 3 specifically. 00:22:37.780 --> 00:22:38.740 But yeah. 00:22:38.840 --> 00:22:40.680 It must be used by a whole bunch of other stuff. 00:22:40.680 --> 00:22:41.640 Like 6. 00:22:41.640 --> 00:22:44.160 I don't think people are specifically downloading 6. 00:22:44.160 --> 00:22:45.980 It's the 2 or 3. 00:22:46.060 --> 00:22:51.000 Like I think Request uses both of those and Certify and that like hits the top 4. 00:22:51.000 --> 00:22:53.240 So Python. 00:22:53.240 --> 00:22:54.700 DateUtil is a good one up there. 00:22:54.700 --> 00:22:55.520 Boto Core. 00:22:55.520 --> 00:22:56.360 Pretty interesting. 00:22:56.360 --> 00:23:03.540 I use Boto 3, which depends on Boto 4, but it kind of makes me frustrated because Boto 3 won't 00:23:03.540 --> 00:23:07.340 use the latest version of Boto Core, but it's all made by the same people. 00:23:07.340 --> 00:23:10.120 Anyway, there's a bunch of interesting stuff up here. 00:23:10.120 --> 00:23:10.300 Yeah. 00:23:10.300 --> 00:23:12.160 I think I'm going to have to look at this some more. 00:23:12.160 --> 00:23:13.440 Pip is in the top 20. 00:23:13.440 --> 00:23:15.020 Nice. 00:23:15.020 --> 00:23:16.360 Got to get started somewhere. 00:23:16.360 --> 00:23:17.480 Yeah. 00:23:17.480 --> 00:23:18.080 All right. 00:23:18.080 --> 00:23:19.600 Well, that's it for all of our main items. 00:23:19.600 --> 00:23:20.620 What you got for the extras? 00:23:20.620 --> 00:23:22.980 Well, I wanted to actually just ask for some help. 00:23:22.980 --> 00:23:30.000 The last episode we put out was a live recording of the January Python PDX West meetup. 00:23:30.000 --> 00:23:32.660 Actually, not the entire meetup, but just a- 00:23:32.660 --> 00:23:34.840 Flash in case people haven't been paying attention. 00:23:34.840 --> 00:23:36.080 They're near Portland. 00:23:36.080 --> 00:23:39.620 There's a second Python meetup, which runs out in Hillsborough. 00:23:39.620 --> 00:23:41.780 Much better for Westside folks. 00:23:41.780 --> 00:23:43.120 So yeah, you put that together. 00:23:43.120 --> 00:23:43.480 Good job. 00:23:43.480 --> 00:23:43.800 Yeah. 00:23:43.800 --> 00:23:47.720 And one of the things that we wanted to do with this one was to, I got this from a lot 00:23:47.720 --> 00:23:48.100 of people. 00:23:48.100 --> 00:23:53.640 And in the tech community, well, the Westside has a lot of high-tech stuff. 00:23:53.640 --> 00:23:56.400 There's a lot of people coming from other languages to Python. 00:23:56.400 --> 00:24:00.880 And so they might not be beginner programmers, but they're beginners at Python. 00:24:01.380 --> 00:24:08.060 We've had this before, people saying some beginner-friendly content per at least one topic per meetup so 00:24:08.060 --> 00:24:09.600 that people aren't lost with everything. 00:24:09.600 --> 00:24:14.000 And yeah, we kind of forgot this last time, but I enjoyed the topics. 00:24:14.000 --> 00:24:19.080 I'd like to hear from other people as to what sort of topics would be good at a meetup for 00:24:19.080 --> 00:24:20.360 Python beginner-friendly. 00:24:20.360 --> 00:24:21.840 And just let us know. 00:24:21.840 --> 00:24:22.620 I think that'd be cool. 00:24:22.720 --> 00:24:22.840 Yeah. 00:24:22.840 --> 00:24:28.280 Well, it's easy to get super into focusing on what's the most latest, coolest, hottest 00:24:28.280 --> 00:24:29.020 thing. 00:24:29.020 --> 00:24:35.100 But there's a lot of folks who really just appreciate setting the ground, a good foundation. 00:24:35.100 --> 00:24:36.940 And so, yeah, I think that's a good topic. 00:24:36.940 --> 00:24:37.180 Yeah. 00:24:37.180 --> 00:24:41.420 And one of the things I'd like to do is possibly get a list of those sorts of things so that 00:24:41.420 --> 00:24:44.540 we can try to ask people to put together something. 00:24:45.000 --> 00:24:52.340 Like one of the meetups I did, like just describing how pip and virtual environment work and how 00:24:52.340 --> 00:24:52.900 to use those. 00:24:52.900 --> 00:24:57.700 And there's a lot of basic stuff that people don't even think about maybe that they should 00:24:57.700 --> 00:24:58.180 talk about. 00:24:58.180 --> 00:25:00.100 And it'd be good to hear more. 00:25:00.260 --> 00:25:03.320 I also want to try to get other people to do it because you and I could probably do 00:25:03.320 --> 00:25:05.100 those sorts of beginner-friendly ones. 00:25:05.100 --> 00:25:05.380 Sure. 00:25:05.380 --> 00:25:07.240 It'd be fun to have other speakers too. 00:25:07.240 --> 00:25:07.860 Yeah, absolutely. 00:25:07.860 --> 00:25:09.260 It would be very fun. 00:25:09.260 --> 00:25:14.620 If people are willing to get to Portland on a Tuesday evening, then they can come give a 00:25:14.620 --> 00:25:14.980 presentation. 00:25:14.980 --> 00:25:15.500 Yeah. 00:25:15.500 --> 00:25:16.540 One more thing. 00:25:16.540 --> 00:25:19.120 I want to do a shout out to PyCascades 2020. 00:25:19.120 --> 00:25:20.400 I can't remember when. 00:25:20.400 --> 00:25:21.700 It's in February coming up. 00:25:21.700 --> 00:25:22.920 February, I believe. 00:25:22.920 --> 00:25:25.080 I believe it's like 8th and 9th. 00:25:25.080 --> 00:25:25.800 Let me double check. 00:25:25.800 --> 00:25:29.120 It is February 8th and 9th in Portland, Eastside. 00:25:29.120 --> 00:25:29.740 Wonderful. 00:25:29.980 --> 00:25:31.740 So people should get tickets so they could come. 00:25:31.740 --> 00:25:36.460 And if you haven't bought your ticket yet, check the show notes because we've got a discount 00:25:36.460 --> 00:25:37.760 code for 10% off. 00:25:37.760 --> 00:25:38.420 Yeah, absolutely. 00:25:38.420 --> 00:25:39.460 Yeah, it's right there at the bottom. 00:25:39.460 --> 00:25:43.380 I don't really have anything this week for extras to share. 00:25:43.380 --> 00:25:47.980 But one thing that did come out and caught my attention yesterday that I think I might as 00:25:47.980 --> 00:25:54.600 well just throw out that's kind of cool is Firefox 72 is out and it has come with anti-fingerprinting, 00:25:54.600 --> 00:25:56.560 which is pretty exciting. 00:25:56.560 --> 00:25:58.320 So I don't know how many people are aware. 00:25:58.320 --> 00:25:59.260 I don't know if you're aware, Brian. 00:25:59.700 --> 00:26:04.300 That even with ad blockers and blocking like tracking cookies and stuff, there's all these 00:26:04.300 --> 00:26:07.360 weird things that you can do to figure out. 00:26:07.360 --> 00:26:09.760 Actually, it's that same person, right? 00:26:09.760 --> 00:26:15.740 Like you can ask, what are all the installed fonts and what version of OS do you have? 00:26:15.740 --> 00:26:17.380 And here's a canvas thing. 00:26:17.380 --> 00:26:22.280 Let's run some stuff on there and like try to detect weird CPU cycles and other kinds of 00:26:22.280 --> 00:26:26.680 there's all these like weird things you can do in browsers and pretty much uniquely identify 00:26:26.680 --> 00:26:28.740 everyone running a web browser. 00:26:28.920 --> 00:26:29.580 Oh, that's lame. 00:26:29.580 --> 00:26:30.780 Even without cookies. 00:26:30.780 --> 00:26:31.200 Yeah. 00:26:31.200 --> 00:26:33.680 So Firefox is trying to fight back against that. 00:26:33.680 --> 00:26:36.140 So I think that's pretty awesome and worth checking out. 00:26:36.140 --> 00:26:38.880 I'll actually link to ours Technica article about it. 00:26:38.880 --> 00:26:39.520 Okay, cool. 00:26:39.520 --> 00:26:39.800 Yep. 00:26:40.160 --> 00:26:42.600 Well, I guess that leaves us with a joke, doesn't it? 00:26:42.600 --> 00:26:43.000 Yeah. 00:26:43.140 --> 00:26:47.280 Last time we did a joke about describing different software programming jobs. 00:26:47.280 --> 00:26:52.700 This one, you're going to have to imagine that people who are fans of various programming 00:26:52.700 --> 00:26:57.680 languages are in like a literature course and they have to write an essay. 00:26:57.680 --> 00:26:57.980 Okay? 00:26:57.980 --> 00:26:58.400 Okay. 00:26:58.500 --> 00:26:58.740 Okay. 00:26:58.740 --> 00:27:00.200 Have you pulled up the picture? 00:27:00.200 --> 00:27:00.680 Yes. 00:27:00.680 --> 00:27:01.580 I'm already laughing. 00:27:01.580 --> 00:27:01.940 All right. 00:27:01.940 --> 00:27:02.140 Cool. 00:27:02.140 --> 00:27:07.700 So basically there's professor looking person with glasses trying to read like a paper, a 00:27:07.700 --> 00:27:12.580 physical paper and a student looking at it, getting the feedback from the professor. 00:27:12.580 --> 00:27:15.760 This is important because at the end it gets interesting with the pictures. 00:27:15.760 --> 00:27:19.000 So for Python, it says, this is plagiarism. 00:27:19.000 --> 00:27:20.920 You can't just import essay. 00:27:20.920 --> 00:27:28.240 For Java, it says, I'm two pages in and I have no idea what you're saying. 00:27:28.240 --> 00:27:29.240 Assembling. 00:27:29.240 --> 00:27:30.120 Assembling. 00:27:30.120 --> 00:27:30.620 Pretty appropriate. 00:27:30.620 --> 00:27:30.980 Yeah. 00:27:30.980 --> 00:27:31.560 Assembling. 00:27:31.560 --> 00:27:34.600 Do you really have to redefine every word in the English language? 00:27:34.600 --> 00:27:39.520 With C, it says, this is great, but you forgot to add a null terminator. 00:27:39.520 --> 00:27:40.680 Now I'm just reading garbage. 00:27:40.680 --> 00:27:47.160 I don't really know if I understand the C++ one, but it says, I asked for one copy, not 00:27:47.160 --> 00:27:49.300 400 and the professor's buried in books. 00:27:49.300 --> 00:27:51.340 Oh, I don't get that either, but. 00:27:51.340 --> 00:27:51.560 Yeah. 00:27:51.560 --> 00:27:52.020 Okay. 00:27:52.020 --> 00:27:52.660 Unix Shell. 00:27:52.660 --> 00:27:53.440 This one's pretty good. 00:27:53.440 --> 00:27:55.240 I don't have permission to read this. 00:27:55.920 --> 00:28:02.020 LaTeX, which you often use to describe scientific papers and super elaborate mathematical formulas, 00:28:02.020 --> 00:28:07.160 says, your paper makes no sense to me, but it's the most beautiful thing I've ever laid 00:28:07.160 --> 00:28:08.020 eyes on. 00:28:08.020 --> 00:28:11.100 And the final one is the HTML programmer. 00:28:11.100 --> 00:28:18.760 And instead of holding a paper, the professor's holding a pot and they just say, this is a flower 00:28:18.760 --> 00:28:19.080 pot. 00:28:19.080 --> 00:28:21.180 What is it? 00:28:21.380 --> 00:28:23.080 I don't get it, but it's funny. 00:28:23.080 --> 00:28:23.440 Yeah. 00:28:23.440 --> 00:28:24.540 I don't either. 00:28:24.540 --> 00:28:25.280 All right. 00:28:25.280 --> 00:28:26.820 So anyway, I thought that was pretty fun. 00:28:26.820 --> 00:28:27.960 You all can check it out. 00:28:27.960 --> 00:28:30.560 The comic is linked in there, but it's pretty fun. 00:28:30.560 --> 00:28:31.820 You can't just import essay. 00:28:31.820 --> 00:28:32.720 This is plagiarism. 00:28:32.720 --> 00:28:34.520 All right. 00:28:34.520 --> 00:28:35.780 Well, thanks a lot, Michael. 00:28:35.780 --> 00:28:36.400 Yeah, you bet. 00:28:36.400 --> 00:28:37.080 Thanks, Brian. 00:28:37.080 --> 00:28:37.720 Good to be here. 00:28:37.720 --> 00:28:39.720 Thank you for listening to Python Bytes. 00:28:39.720 --> 00:28:42.180 Follow the show on Twitter at Python Bytes. 00:28:42.180 --> 00:28:45.200 That's Python Bytes as in B-Y-T-E-S. 00:28:45.200 --> 00:28:48.100 And get the full show notes at Pythonbytes.fm. 00:28:48.100 --> 00:28:53.160 If you have a news item you want featured, just visit Pythonbytes.fm and send it our way, 00:28:53.160 --> 00:28:55.220 where I was on the lookout for sharing something cool. 00:28:55.220 --> 00:28:59.400 This is Brian Okken, and on behalf of myself and Michael Kennedy, thank you for listening 00:28:59.400 --> 00:29:01.640 and sharing this podcast with your friends and colleagues.