WEBVTT 00:00:00.001 --> 00:00:04.720 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:04.720 --> 00:00:10.580 This is episode 196, recorded August 19th, 2020. 00:00:10.580 --> 00:00:12.100 And I am Brian Okken. 00:00:12.100 --> 00:00:12.980 And I'm Michael Kennedy. 00:00:12.980 --> 00:00:15.880 And actually, we have a sponsor this week, Datadog. 00:00:15.880 --> 00:00:16.680 Thank you, Datadog. 00:00:16.680 --> 00:00:16.960 Yeah. 00:00:16.960 --> 00:00:18.340 Thanks, Datadog. 00:00:18.340 --> 00:00:20.680 First off, I want to talk about Django a little bit. 00:00:20.680 --> 00:00:23.980 I've always heard Django is super easy, and that's why people choose it, 00:00:23.980 --> 00:00:28.900 because it's really easy to get started, and it has all these things that make working with Django easy, and so on, right? 00:00:28.900 --> 00:00:31.040 Yeah, I think there's a lot going for it. 00:00:31.040 --> 00:00:33.200 The community seems pretty awesome. 00:00:33.200 --> 00:00:34.680 There's a lot of tutorials. 00:00:34.680 --> 00:00:37.020 There's a lot of expertise that they can help you out. 00:00:37.020 --> 00:00:44.040 So there's an interesting article by Dan Verrazzo called Surviving Django, If You Care About Databases. 00:00:44.040 --> 00:00:51.340 So, I mean, Surviving Django, right off the start, it's an odd title for an article about Django. 00:00:51.340 --> 00:00:57.800 It's going to be kind of hard to summarize, but basically, the take on it is a little bit of a... 00:00:57.800 --> 00:01:02.000 There's a lot of people that are going to be able to do that. 00:01:02.000 --> 00:01:02.940 It's a lot of people that are going to be able to do that. 00:01:02.940 --> 00:01:14.440 And it's an interesting perspective, but the gist of it really centers around that there's a lot of parts of Django that seem to be database agnostic. 00:01:14.440 --> 00:01:17.880 So you could use MySQL or Postgres or something else. 00:01:18.080 --> 00:01:22.280 But he says, kind of in reality, people don't do that. 00:01:22.280 --> 00:01:24.460 People don't really switch databases that much. 00:01:24.460 --> 00:01:33.500 So if you really want to utilize the database and some of the great things about whatever database you pick, maybe not being database agnostic is good. 00:01:33.840 --> 00:01:44.800 Also, he talks about how to set up schemas and database migrations using the database, not using the built-in Django stuff. 00:01:44.800 --> 00:01:48.100 It seems a little bit more like, why would I do that? 00:01:48.100 --> 00:01:51.100 It seems more technical than I want to do with Django. 00:01:51.480 --> 00:01:53.180 But there is some reasoning around it. 00:01:53.180 --> 00:01:58.260 And then he also shows exactly how to do this, how to do migrations, how to do schemas. 00:01:58.260 --> 00:02:00.040 And it really doesn't look that bad. 00:02:00.040 --> 00:02:06.020 The interesting take, I was curious about what the rest of the Django community would feel about this. 00:02:06.140 --> 00:02:10.140 But then after the article, there's comments on the article. 00:02:10.140 --> 00:02:19.040 There's a really nice civilized discussion between the author and someone named Paolo Melchior, I think, and Andrew Godwin. 00:02:19.040 --> 00:02:20.840 Definitely have heard of Andrew before. 00:02:20.840 --> 00:02:25.060 And some others talking about basically that take. 00:02:25.060 --> 00:02:33.940 And one interesting comment was articles like this that point out some of the pitfalls of there possibly are pitfalls with Django. 00:02:33.940 --> 00:02:38.760 And some well-written articles are a good way to point those out. 00:02:38.760 --> 00:02:44.400 And because there's a lot of fans of Django that really aren't going to talk about the bad parts. 00:02:44.400 --> 00:02:46.720 And this isn't necessarily the bad part. 00:02:46.720 --> 00:02:47.900 It's just something to be aware of. 00:02:48.460 --> 00:02:56.500 Another really interesting comment by Andrew was, I agree that at some point in a project or company's life, when it's big enough, 00:02:56.500 --> 00:03:01.260 SQL migrations are the way to go instead of the Django migrations. 00:03:01.260 --> 00:03:06.700 Migrations in the out-of-box state are mostly there to supplement rapid prototyping. 00:03:06.700 --> 00:03:16.760 Like a lot of Django, it can be removed or ignored progressively if and when you outgrow the single set of design constraints when you chose them. 00:03:16.960 --> 00:03:24.200 So that kind of take of using Django's migrations and all the agnostic stuff might be good early on. 00:03:24.200 --> 00:03:30.100 And then maybe slowly going towards using your database more later. 00:03:30.100 --> 00:03:30.680 Yeah. 00:03:30.680 --> 00:03:31.640 That's an interesting take. 00:03:31.640 --> 00:03:32.220 Yeah, that's cool. 00:03:32.220 --> 00:03:35.360 A bit of a practicality beats purity on both ends there. 00:03:35.360 --> 00:03:41.360 This article also made me really appreciate the Django community because this was not a flame war. 00:03:41.360 --> 00:03:44.840 This was a civilized discussion about a technical topic. 00:03:44.840 --> 00:03:46.000 What, on the internet? 00:03:46.000 --> 00:03:46.640 For real? 00:03:46.640 --> 00:03:46.980 Yeah. 00:03:46.980 --> 00:03:49.320 It was great. 00:03:49.320 --> 00:03:50.220 Yeah, that's really cool. 00:03:50.220 --> 00:03:53.440 However, you know, a few comments. 00:03:53.440 --> 00:04:00.940 One, I've switched from one database back in to another three or four times on major projects as you're like, you know what? 00:04:00.940 --> 00:04:04.340 This is just not doing it or it's outgrown this or whatever. 00:04:04.340 --> 00:04:05.460 So it happens. 00:04:05.460 --> 00:04:09.340 But at the same time, like that's usually not my SQL to Postgres. 00:04:09.440 --> 00:04:14.520 It's usually like relational to non-relational or something massive where it's going to require rewrite anyway. 00:04:14.520 --> 00:04:23.520 So I do like the idea of saying you have this capability to be completely agnostic, but you're working with the lowest common denominator there. 00:04:23.720 --> 00:04:27.300 And that's usually not the best choice if you're writing an application. 00:04:27.300 --> 00:04:31.500 Maybe if you're working with a library, tons of people are going to use it in ways you don't anticipate. 00:04:31.740 --> 00:04:35.840 But if it's an application, you know how it's going to be used most often. 00:04:36.020 --> 00:04:36.120 Yeah. 00:04:36.120 --> 00:04:45.440 Also, some of those speed and speed improvements you can get out of a database, you really can't do too much of with the agnostic front end. 00:04:45.440 --> 00:04:48.880 You kind of need to know the specifics of that database. 00:04:48.880 --> 00:04:49.780 Yeah, pretty cool. 00:04:49.780 --> 00:04:55.860 For this next one, I want to talk about an interesting pattern that Python uses, I guess. 00:04:55.860 --> 00:04:57.040 Interesting technique. 00:04:57.040 --> 00:04:59.240 So you know the ID function, right? 00:04:59.240 --> 00:05:05.660 You can say ID of a thing and it'll give you a number back and it basically tells you what it is, like where it is in memory. 00:05:05.660 --> 00:05:06.660 Are you familiar with this? 00:05:06.660 --> 00:05:07.840 I guess I don't use this. 00:05:07.840 --> 00:05:08.080 Yeah. 00:05:08.080 --> 00:05:16.560 So if you want to know, like if I'm giving two variables, are they actually referring to the same object or do they just have the same value? 00:05:16.560 --> 00:05:17.460 Right? 00:05:17.460 --> 00:05:25.060 Like if I had a dictionary and I want to know, is it the same dictionary or does it just have the same keys and the same values for those keys? 00:05:25.500 --> 00:05:30.100 You can say ID of one thing and ID the other. 00:05:30.100 --> 00:05:32.380 And in CPython, that'll actually give you the memory address. 00:05:32.380 --> 00:05:40.340 But in all Python, that gives you a unique identifier that is guaranteed to be different if they're different objects, the same if it's the same object, right? 00:05:40.340 --> 00:05:40.620 Okay. 00:05:40.620 --> 00:05:40.980 Okay. 00:05:40.980 --> 00:05:53.540 So one of the things that Python does that's really interesting, and this is all research I've pulled up from working on my Python for memory management course that is probably out by the time that this comes out. 00:05:53.540 --> 00:05:56.240 But you don't have to take that to care about this. 00:05:56.240 --> 00:06:02.040 So one of the things that's really interesting in Python is everything is a pointer, right? 00:06:02.040 --> 00:06:12.040 Allocated on the heap, including numbers and strings and other small stuff that might be allocated on the stack in languages like C# or C++ or whatever, right? 00:06:12.780 --> 00:06:21.660 So numbers in Python are way more expensive than they are in languages that treat them as value types rather than reference types. 00:06:21.660 --> 00:06:33.440 So for example, the number four uses 28 bytes of memory in Python, whereas the number four could use one, two, four, or eight in the languages that treat them as value types, depending if they're like shorts or longs or whatever. 00:06:33.580 --> 00:06:33.740 Okay. 00:06:33.740 --> 00:06:33.940 Right? 00:06:33.940 --> 00:06:39.040 So there's this cool design pattern called the flyweight pattern. 00:06:39.040 --> 00:06:41.420 And I'll just give you the quick rundown on that. 00:06:41.420 --> 00:06:43.780 So flyweight is a software design pattern. 00:06:43.780 --> 00:06:49.740 A flyweight is an object that minimizes memory usage by sharing as much data as possible with similar objects. 00:06:49.740 --> 00:06:50.220 Right? 00:06:50.220 --> 00:06:51.040 So that's from Wikipedia. 00:06:51.040 --> 00:06:51.780 I'll link over to that. 00:06:52.280 --> 00:06:54.540 In Python, Python does that for numbers. 00:06:54.540 --> 00:07:10.400 So if you compute, like through some mathematical function, if you compute the number 16 and then some other way you compute the number 16 and then somewhere else you parse a string, the number 16, those are all literally the same 16 in memory. 00:07:10.400 --> 00:07:10.780 Okay. 00:07:10.780 --> 00:07:11.140 Okay? 00:07:11.140 --> 00:07:13.480 Because 16 is pretty common. 00:07:13.480 --> 00:07:20.100 But if you computed 423, the three different ways, that would be three copies of 423. 00:07:20.800 --> 00:07:25.420 So Python uses this flyweight pattern for the numbers from negative 5 to 256. 00:07:25.420 --> 00:07:30.340 And you'll only ever have one of those in the language, in the runtime. 00:07:30.340 --> 00:07:36.960 But beyond 256 or below negative 5, those are always recreated. 00:07:36.960 --> 00:07:37.820 Isn't that interesting? 00:07:37.820 --> 00:07:38.880 It is very interesting. 00:07:38.880 --> 00:07:39.140 Yeah. 00:07:39.140 --> 00:07:41.220 So it doesn't matter how they come out. 00:07:41.220 --> 00:07:49.000 Basically, if the runtime is going to generate the number, say, 7 as an integer, it's going to use the same 7, which is pretty cool. 00:07:49.420 --> 00:07:51.660 I actually have some example code that people can play with. 00:07:51.660 --> 00:07:59.500 It creates like two lists of a whole bunch of numbers, separate ways, and then says, you know, are these the same number or not, which is pretty cool. 00:07:59.500 --> 00:08:00.820 I was just playing with it right now. 00:08:00.820 --> 00:08:07.640 So you can, if you assign x to 1, you can do an ID of both x and 1, and it'll show up as the same number. 00:08:07.760 --> 00:08:13.220 But if you assign x to minus 10, x and minus 10 are different IDs. 00:08:13.220 --> 00:08:13.860 Isn't that funky? 00:08:13.860 --> 00:08:14.580 Yeah. 00:08:14.580 --> 00:08:17.860 It's because the numbers in Python are extra expensive. 00:08:17.860 --> 00:08:23.400 So Python takes special care to not recreate these very common numbers. 00:08:23.400 --> 00:08:26.120 And apparently, very common means negative 5 to 256. 00:08:26.380 --> 00:08:34.820 Anyway, I thought that might be interesting to people, this flyweight design pattern concept and then applied to the numbers might be interesting. 00:08:34.820 --> 00:08:36.940 And there's a little example code that I included it there. 00:08:36.940 --> 00:08:40.680 So it's not quite an article, but it's like an idea with some code. 00:08:40.880 --> 00:08:40.960 Yeah. 00:08:40.960 --> 00:08:45.200 So can you, I mean, as a user, can I use the flyweight pattern in Python for other stuff? 00:08:45.200 --> 00:08:46.040 You totally should. 00:08:46.040 --> 00:08:46.520 Yeah. 00:08:46.520 --> 00:08:54.120 Like imagine you've got some objects you're creating and instead of recreating them over and over, they're being used in a lot of places. 00:08:54.120 --> 00:08:59.240 You could totally create some kind of like shared lookup for certain common ones. 00:08:59.240 --> 00:09:03.920 Like maybe you create, you're creating states and the state has a bunch of information about it. 00:09:03.920 --> 00:09:06.260 Like US states or countries or something. 00:09:06.260 --> 00:09:08.860 But then you often have to go like, all right, what state is this? 00:09:08.860 --> 00:09:09.720 Give me that information. 00:09:09.720 --> 00:09:10.160 Right. 00:09:10.160 --> 00:09:11.520 You don't need to necessarily recreate that. 00:09:11.520 --> 00:09:14.920 You could just create 50 states, keep them in memory and never allocate them again. 00:09:14.920 --> 00:09:15.380 Okay. 00:09:15.380 --> 00:09:22.660 I guess I'm like caching and memoization are ways to do something similar, but with only one thing at a time. 00:09:22.660 --> 00:09:23.100 Exactly. 00:09:23.100 --> 00:09:27.860 The big important thing here to make this work correctly is they have to be immutable. 00:09:27.860 --> 00:09:28.620 Right. 00:09:28.620 --> 00:09:35.620 Because if one person gets the state Georgia and it has certain values and another person gets it, oh, it has a new county. 00:09:35.620 --> 00:09:36.240 Let's add that. 00:09:36.240 --> 00:09:36.880 And like, wait a minute. 00:09:36.880 --> 00:09:37.340 That's not. 00:09:38.000 --> 00:09:44.060 I've now not recreated a different thing or like, you know, so it's got to be immutable, which is why it works for numbers. 00:09:44.060 --> 00:09:46.180 And you could do it for strings and things like that. 00:09:46.180 --> 00:09:46.460 Okay. 00:09:46.460 --> 00:09:46.840 Cool. 00:09:46.840 --> 00:09:47.560 Yeah. 00:09:47.560 --> 00:09:47.980 Pretty cool. 00:09:47.980 --> 00:09:50.260 Something else that's really cool is Datadog. 00:09:50.380 --> 00:09:52.940 So thank you, Datadog for sponsoring this episode. 00:09:52.940 --> 00:09:54.300 Let me ask you a question. 00:09:54.300 --> 00:09:57.540 Do you have an app in production that's slower than you like? 00:09:57.540 --> 00:10:00.980 It's performance all over the place, sometimes fast, sometimes slow. 00:10:00.980 --> 00:10:02.820 Now here's an important question. 00:10:02.820 --> 00:10:04.080 Do you know why? 00:10:04.080 --> 00:10:05.980 With Datadog, you will. 00:10:05.980 --> 00:10:10.260 You can troubleshoot your app's performance with Datadog's end-to-end tracing. 00:10:10.820 --> 00:10:16.440 Use the detailed flame graphs to identify bottlenecks and latency in that finicky app of yours. 00:10:16.440 --> 00:10:20.940 Be the hero that got the app back on track with your company. 00:10:20.940 --> 00:10:26.120 Get started today with a free trial at pythonbytes.fm/Datadog. 00:10:26.120 --> 00:10:26.420 Awesome. 00:10:26.420 --> 00:10:27.120 Thanks, Datadog. 00:10:27.120 --> 00:10:28.020 You know what else is awesome? 00:10:28.020 --> 00:10:28.560 What is awesome? 00:10:28.560 --> 00:10:34.020 Pip installing a thing that when I pip install something and it happens right away and it's 00:10:34.020 --> 00:10:39.900 not like 30 seconds of compile time, like say MicroWhisgy is, to get the thing installed 00:10:39.900 --> 00:10:44.900 and I don't have to have like MSBuild or VCVarsBat set up right or whatever. 00:10:44.900 --> 00:10:47.020 Yeah. 00:10:47.020 --> 00:10:49.240 So definitely I'm grateful for wheels. 00:10:49.240 --> 00:10:54.620 It was still a world that we didn't, there was less wheels in it when we started this podcast. 00:10:54.620 --> 00:10:55.560 I'm pretty sure. 00:10:55.560 --> 00:10:55.940 Yep. 00:10:56.260 --> 00:11:02.400 Most of the common packages, a lot of them have migrated to distributing wheels and package 00:11:02.400 --> 00:11:04.560 authors have had to care about this a lot. 00:11:04.560 --> 00:11:10.680 And so I want to talk about this article that's on the RealPython blog from Brad Solomon called 00:11:10.680 --> 00:11:13.020 What are Python Wheels and Why Should We Care? 00:11:13.020 --> 00:11:18.240 One of the things I really love about this is like I said, a lot of package authors have already 00:11:18.240 --> 00:11:21.220 gone through this and understand some of the ramifications. 00:11:21.540 --> 00:11:27.440 But as a normal casual user of pip install, we don't really think about it. 00:11:27.440 --> 00:11:33.060 But this is the first half of this article talks about kind of what the user's perspective is. 00:11:33.060 --> 00:11:34.820 And it's kind of a nice look. 00:11:34.820 --> 00:11:41.080 When you say pip install something and it's a cool because it as an example, I'm glad they list an example. 00:11:41.080 --> 00:11:46.780 And it's a particular version of MicroWhisky because most packages are wheels now. 00:11:46.880 --> 00:11:50.840 But if you install something that is not a wheel, it's probably a tarball. 00:11:50.840 --> 00:11:53.940 And I don't know if there's other options other than tarballs. 00:11:53.940 --> 00:11:57.660 But anyway, a tarball is something that ends in tar.gz. 00:11:57.660 --> 00:12:00.460 So it's a tarred and zipped. 00:12:00.460 --> 00:12:03.300 And that's a whole bunch of Unix speak that you don't really have to care about. 00:12:03.300 --> 00:12:07.020 But it downloads this blob of stuff and then unpacks it. 00:12:07.220 --> 00:12:12.660 And then pip calls setup and some other stuff to build the wheel after you download it. 00:12:12.660 --> 00:12:16.340 And then it labels it and then it installs it. 00:12:16.340 --> 00:12:18.020 There's a whole bunch of steps in there. 00:12:18.020 --> 00:12:19.880 Plus, it's calling setup.py. 00:12:19.880 --> 00:12:21.760 So there could be really any code in there. 00:12:21.760 --> 00:12:23.440 And so that's kind of creepy. 00:12:23.440 --> 00:12:31.640 The difference is often with if you actually have a wheel instead of a tarball, pip install will just pull this down and install it. 00:12:31.640 --> 00:12:33.260 And it doesn't call setup.py. 00:12:33.260 --> 00:12:40.860 That's really nice, actually, because one of the things I think a lot of people don't realize until they're like, oh, wait, what just happened? 00:12:40.860 --> 00:12:46.360 When you pip install something, you're running semi-arbitrary code off of the internet. 00:12:46.360 --> 00:12:48.080 That's not ideal. 00:12:48.080 --> 00:12:48.440 Right. 00:12:48.440 --> 00:12:50.020 With the wheels, you don't have to run. 00:12:50.020 --> 00:12:54.400 Because basically that runs the setup.py in the sdisk version, I believe. 00:12:54.400 --> 00:12:59.380 So this is really nice that wheels can cut out that Python execution bit. 00:12:59.380 --> 00:13:00.260 It cuts that out. 00:13:00.380 --> 00:13:03.000 Plus, also, I'm not sure what the technology is here. 00:13:03.000 --> 00:13:08.160 Well, I think it's probably just it's already pre-compiled and there's operating system specifics. 00:13:08.160 --> 00:13:12.940 But wheels tend to be smaller than the tarballs, so they download a lot faster. 00:13:12.940 --> 00:13:15.580 Wheels have a bunch of stuff in the name. 00:13:15.580 --> 00:13:17.440 And it's not just random stuff. 00:13:17.440 --> 00:13:18.880 It's specific stuff. 00:13:18.880 --> 00:13:21.600 But it talks about what distribution it is. 00:13:21.600 --> 00:13:22.640 It's got the version number. 00:13:22.640 --> 00:13:27.780 It's got maybe build identifiers and which Python it's for. 00:13:27.780 --> 00:13:31.400 If it's Python 2 versus Python 3 or a specific version. 00:13:31.400 --> 00:13:35.140 And then the platform is one of the important bits. 00:13:35.140 --> 00:13:43.320 So if you have compiled code, then there's kind of a different CI pipeline to try to build all those wheels. 00:13:43.580 --> 00:13:46.400 But on the user end, we don't have to care about it. 00:13:46.400 --> 00:13:56.880 So one of the different things is one of the interesting bits about going and moving towards wheels is there's a whole bunch, often a whole bunch of different, whole bunch of packages up there. 00:13:56.880 --> 00:14:02.360 And that's something that users will see if they look at what downloads are available. 00:14:02.360 --> 00:14:03.860 There'll be this whole slew of stuff. 00:14:04.560 --> 00:14:06.400 And for the most part, you don't have to care about that. 00:14:06.400 --> 00:14:09.860 If you do pip install, it'll just pick the right one for your operating system. 00:14:09.860 --> 00:14:24.300 However, it's good to be aware of those because if you are creating like a cache of stuff at your, if you have your office or something, you may want to cache more of those depending on what operating systems are being used around. 00:14:24.300 --> 00:14:26.920 So that little discussion, I think, is pretty cool. 00:14:26.920 --> 00:14:27.360 Absolutely. 00:14:27.360 --> 00:14:29.260 Anyway, I'm not going to get too much into it. 00:14:29.260 --> 00:14:33.400 This is a good article for, yeah, I use wheels, but what are they? 00:14:33.580 --> 00:14:36.500 And this is, this doesn't get too deep into it, but it's nice. 00:14:36.500 --> 00:14:36.800 Yeah. 00:14:36.800 --> 00:14:38.300 Well, wheels are definitely nice. 00:14:38.300 --> 00:14:41.160 And another solid article from RealPython. 00:14:41.160 --> 00:14:41.960 So very nice. 00:14:41.960 --> 00:14:43.320 You know what else is good? 00:14:43.320 --> 00:14:44.400 Pandas. 00:14:44.400 --> 00:14:46.580 I've heard that Pandas does a lot of cool stuff. 00:14:46.580 --> 00:14:48.280 Now, actually, Pandas is really, really cool. 00:14:48.280 --> 00:14:50.460 You could do a whole bunch of interesting things with it. 00:14:50.460 --> 00:14:55.060 And Jack McHugh, he's been on fire lately. 00:14:55.060 --> 00:14:58.800 He's created all these different projects that he keeps sending them over and like, oh, this is another one I found. 00:14:58.800 --> 00:15:00.140 He's like, no, this is another one I created. 00:15:00.140 --> 00:15:01.940 And a lot of them are cool. 00:15:02.120 --> 00:15:04.320 One of the things he created was awesome Python bytes. 00:15:04.320 --> 00:15:05.860 So hat tip to Jake on that. 00:15:05.860 --> 00:15:06.220 That's cool. 00:15:06.220 --> 00:15:08.780 Like all the awesome stuff that we happen to have covered periodically. 00:15:08.780 --> 00:15:11.740 But this one is called Pandas Alive. 00:15:11.740 --> 00:15:19.980 And so, Brian, to get the experience of this one, you need to open it up and just scroll through the readme on the GitHub page and just look at the animations. 00:15:20.720 --> 00:15:27.320 So you probably have seen these racing histograms or racing bar charts that show stuff happening over time. 00:15:27.320 --> 00:15:30.980 Like here's the popularity of web browsers all the way back from 1993. 00:15:31.420 --> 00:15:35.480 Where it was Mozilla and then Netscape and then IE and then, you know, whatever. 00:15:35.480 --> 00:15:37.780 And you see them like growing and moving over time. 00:15:37.780 --> 00:15:38.060 Yeah. 00:15:38.060 --> 00:15:48.600 So this is a package that if you have a Pandas data frame in a really simple format where the columns are basically the different things you want to graph. 00:15:49.160 --> 00:15:51.980 And they're all arranged by a common date. 00:15:51.980 --> 00:15:53.000 And they just have numbers. 00:15:53.000 --> 00:16:02.640 You can turn that into a really cool like bar chart race type of thing or line graph race where it's just this animation of those over time of the dates that you have in there. 00:16:02.640 --> 00:16:03.740 Oh, I really like this. 00:16:03.740 --> 00:16:04.420 Isn't this cool? 00:16:04.620 --> 00:16:04.780 Yeah. 00:16:04.780 --> 00:16:07.760 And the, I mean, like the race charts and stuff, those are cool. 00:16:07.760 --> 00:16:13.660 But then you can also do the, like the line, the line graphs, the growing, zooming. 00:16:13.660 --> 00:16:14.120 Yeah. 00:16:14.120 --> 00:16:14.280 Yeah. 00:16:14.280 --> 00:16:19.800 You can do like line graphs and you can do other types of things, little plot, scatter plot type things. 00:16:19.800 --> 00:16:23.580 You can also do pie charts, but you can even have them together. 00:16:23.580 --> 00:16:25.560 So you can have maps. 00:16:25.560 --> 00:16:31.340 So if you want to have a map evolving over time with like different countries or counties fading in and out, 00:16:32.060 --> 00:16:36.180 you could have like those two graphs animated side by side at the same time. 00:16:36.180 --> 00:16:42.180 So you could have like the chart of the bars as well as the map all animated together in like one graph. 00:16:42.180 --> 00:16:42.440 Cool. 00:16:42.440 --> 00:16:43.300 Seems pretty awesome. 00:16:43.300 --> 00:16:44.380 Well done, Jack. 00:16:44.380 --> 00:16:47.720 It's based on, I believe, matplotlib. 00:16:47.720 --> 00:16:53.640 And basically it'll render a bunch of different matplotlib renderings into an animated GIF. 00:16:53.640 --> 00:16:59.480 So all you have to do is just go like dataframe.plotanimated, give it a file name, and then this happens. 00:16:59.480 --> 00:17:00.140 Oh, that's cool. 00:17:00.260 --> 00:17:03.220 So then you can just generate this GIF and then put it wherever. 00:17:03.220 --> 00:17:03.780 Exactly. 00:17:03.780 --> 00:17:04.620 You can put it on your website. 00:17:04.620 --> 00:17:05.680 You can put it wherever you want. 00:17:05.680 --> 00:17:07.360 You can share it on Twitter, I guess, even. 00:17:07.360 --> 00:17:07.960 Right? 00:17:07.960 --> 00:17:15.140 But it doesn't require like a JavaScript backend running something and your Jupyter notebook and then all that kind of stuff to wire up. 00:17:15.140 --> 00:17:17.200 Like, no, it's just an animated GIF that comes out. 00:17:17.200 --> 00:17:17.480 Neat. 00:17:17.480 --> 00:17:18.520 This is mesmerizing. 00:17:18.520 --> 00:17:19.740 I could just watch these all day. 00:17:19.740 --> 00:17:21.400 You could watch it for quite a while. 00:17:21.400 --> 00:17:22.560 So, yeah. 00:17:22.560 --> 00:17:29.700 Anyway, really think that's a cool project if you want to visualize data over time, which, you know, there's a lot of good reasons to do that. 00:17:29.700 --> 00:17:32.700 One of the things that it has there is animated maps. 00:17:32.700 --> 00:17:35.120 But maps are something else also. 00:17:35.120 --> 00:17:39.240 There's also a map function, which has nothing to do with geographic maps. 00:17:39.440 --> 00:17:44.460 You probably learned a Python a long time ago, but do you remember being surprised by map and all? 00:17:44.460 --> 00:17:48.600 Yeah, map and all those things, they always confuse me and I've always tried to basically avoid them. 00:17:48.600 --> 00:17:51.820 And I've successfully mostly done that. 00:17:51.820 --> 00:17:53.340 But I know also. 00:17:53.340 --> 00:17:53.900 Yeah, yeah. 00:17:53.900 --> 00:17:55.240 I also know how useful they can be. 00:17:55.240 --> 00:17:56.340 So tell us about it. 00:17:56.340 --> 00:18:01.220 This is an article from Catherine Hancock's How to Use the Python Map Function. 00:18:01.220 --> 00:18:06.200 And I know we're sure people have heard of maps and map, the map function. 00:18:06.620 --> 00:18:09.580 It's an extremely useful function, a useful thing. 00:18:09.580 --> 00:18:11.580 So it's a built-in. 00:18:11.580 --> 00:18:16.200 And what it does, if you're not familiar with it, it takes two or more parameters. 00:18:16.200 --> 00:18:19.860 The first parameter to map is the function that you want to apply. 00:18:19.860 --> 00:18:25.820 And then, like, let's say if you give it as the second argument an iterable, like a list or something, 00:18:25.820 --> 00:18:32.020 it takes that function that you passed in and applies it to absolutely every element of the iterable, the other one. 00:18:32.720 --> 00:18:42.460 So, like, if I have quick, like, the normal often use is using a lambda function or something to apply some, like, quick thing. 00:18:42.460 --> 00:18:48.000 Like, if I want to do x times squared, x times squared, x times two or x squared or something like that. 00:18:48.000 --> 00:18:50.040 And apply that to every element. 00:18:50.040 --> 00:18:51.060 You can do that. 00:18:51.060 --> 00:18:52.680 And you can make one list into another. 00:18:53.440 --> 00:19:03.940 I think it's good for people to, like, read about them every once in a while if they're not using them often because they do come in handy in places that you, all the time, for me at least. 00:19:04.100 --> 00:19:09.020 So, it's not an obvious thing if you're not used to this sort of a function from other languages. 00:19:09.020 --> 00:19:11.040 I wasn't coming from C. 00:19:11.040 --> 00:19:11.380 Yeah. 00:19:11.380 --> 00:19:14.100 And maybe Perl has something like this, but I never used it. 00:19:14.100 --> 00:19:16.900 So, that's the normal use of applying it. 00:19:16.900 --> 00:19:20.860 One of the things I like about this tutorial is it goes through a few different things. 00:19:20.860 --> 00:19:27.380 So, applying lambdas to a list or an iterable, and then the function that you apply doesn't have to be a lambda. 00:19:27.380 --> 00:19:32.280 It could be your own user-defined function, or it could be a built-in function that you map to it. 00:19:32.280 --> 00:19:40.260 I want to, like, warn people, the part where she's talking about the user-defined function, it's oddly complex for some reason. 00:19:40.260 --> 00:19:48.140 I'm not sure why this was made so complex, because a user-defined function just works like anything, any other function that's using for map. 00:19:48.300 --> 00:20:00.160 But one of the things that I even got out of it is I had forgotten that map applies the function to the iterable one element at a time, and it doesn't do it ahead of time. 00:20:00.160 --> 00:20:03.180 So, like, for instance, and I'm like, really? 00:20:03.180 --> 00:20:08.040 And I had to, like, prove it to myself by putting a print statement or something in a function to do it. 00:20:08.040 --> 00:20:15.820 But what happens is, like, let's say I've got iterable hooked up to grab, like, a huge data chunk out of a stream or something. 00:20:15.980 --> 00:20:23.760 I can apply some function to each element as I'm pulling it out and using map to do that, so I can iterate over map. 00:20:23.760 --> 00:20:28.820 So map returns a map object, which, whatever, it doesn't matter. 00:20:28.820 --> 00:20:35.500 It's just every element that you use, if you use it as an iteration, is the answer after you apply the function. 00:20:35.500 --> 00:20:37.800 It's like a custom generator type thing. 00:20:37.980 --> 00:20:38.260 Yeah. 00:20:38.260 --> 00:20:46.360 And then if you want it as something solid, you can convert it to a list or a tuple or something like that if you want to do everything. 00:20:46.360 --> 00:20:47.480 I'm done with generators. 00:20:47.480 --> 00:20:48.100 Throw it in a list. 00:20:49.440 --> 00:20:50.820 There's some honesty here, too. 00:20:50.820 --> 00:20:55.460 The other thing I often forget about map is that you can map it across. 00:20:55.460 --> 00:21:01.060 If you have a function that takes multiple arguments, you can pass it multiple iterables. 00:21:01.060 --> 00:21:04.160 And it'll take, you know, element-wise each one. 00:21:04.160 --> 00:21:12.640 So, like, the nth element out of each list and apply, pass it to the function and then return the answer to that, which is cool. 00:21:12.700 --> 00:21:19.780 The other thing, a good comment in this, because it's a similar problem area, is comprehensions kind of do the same thing. 00:21:19.780 --> 00:21:22.940 So when would you use map versus comprehension? 00:21:22.940 --> 00:21:32.040 And the advice in this article is comprehensions are very useful for smaller data sets, but often for large data sets, map can be more powerful. 00:21:32.040 --> 00:21:33.300 So that's reasonable. 00:21:33.300 --> 00:21:40.840 And sometimes you want to do operations that if you had to go over different collections of data would make a really nasty-looking comprehension and stuff. 00:21:40.840 --> 00:21:41.820 So, yeah, cool. 00:21:41.820 --> 00:21:42.120 Yep. 00:21:42.240 --> 00:21:46.560 You also can do, like, pandas type of things a little bit, like multiplying vectors, right? 00:21:46.560 --> 00:21:52.200 Like, if I've got two lists and I want to have pieces put together, like that power example that's in there, right? 00:21:52.200 --> 00:22:04.260 It'll take the first element of the first one, the second element of the second one, and then apply the function and generate a new list, effectively, that has, like, as if you had sort of done vector multiplication, which is cool. 00:22:04.260 --> 00:22:07.140 Or, like, cross, I don't know, cross multiplication. 00:22:07.140 --> 00:22:07.700 Yeah. 00:22:07.900 --> 00:22:14.520 I often use map also when I want to muck with something and it seems a little cleaner to me to iterate through something. 00:22:14.520 --> 00:22:21.000 If I know I'm looking for something and I'm not going to get the end of the data or I'm using endless data. 00:22:21.100 --> 00:22:21.260 Nice. 00:22:21.260 --> 00:22:25.940 So, we spoke earlier about databases and I've got another one for us. 00:22:25.940 --> 00:22:28.680 This cool thing called AutoMigrate. 00:22:28.680 --> 00:22:29.880 It's a project called AutoMigrate. 00:22:29.880 --> 00:22:30.240 Okay. 00:22:30.420 --> 00:22:38.560 So, what it does is it's kind of like you talked about Django migrations and we also have SQLAlchemy migrations with Olympic. 00:22:38.560 --> 00:22:52.240 But some people, either they're not using an ORM at all, in which case those tools are useless, or they want to very carefully write the SQL scripts that control their databases. 00:22:52.240 --> 00:22:58.340 Like, some people, there's, like, a group of DBAs that manage the database and that's that, right? 00:22:58.340 --> 00:23:01.380 We're not going to run just random tooling against the database. 00:23:01.380 --> 00:23:03.860 We're going to run scripts that are very carefully considered. 00:23:04.660 --> 00:23:16.880 So, this AutoMigrate thing, what it will do is if you have a, those DDL, data definition language scripts that say create table, add column, and so on. 00:23:16.880 --> 00:23:21.620 All it has to do is have the script that will say, here's how we create something from scratch. 00:23:21.620 --> 00:23:25.460 You put that into GitHub and then you make changes to it. 00:23:25.460 --> 00:23:30.380 Like, to add a column, I go and edit the create table thing and I just type in the new column in there. 00:23:30.580 --> 00:23:39.440 And what this will do is it'll look at your git history and it'll do diffs on the create table statements and it will generate the migration scripts from that. 00:23:39.440 --> 00:23:40.520 Oh, that's really cool. 00:23:40.520 --> 00:23:41.140 That's neat, right? 00:23:41.140 --> 00:23:48.640 So, all you got to do is, like, maintain the, here's how I create the database and it'll actually go, well, to go from this version to that version, here's the script that would actually do it. 00:23:48.640 --> 00:23:49.760 It'll do all that stuff for you. 00:23:49.760 --> 00:23:50.460 Oh, nice. 00:23:50.460 --> 00:23:50.780 Yeah. 00:23:50.780 --> 00:23:58.360 So, if that's your flow, if your flow is to work with these DDL files, these SQL files, this seems like a great tool. 00:23:58.480 --> 00:24:08.820 Now, they do say, oh, this is way better than, like, an ORM or something because in those, like Alembic, what you have to do is you have to go and write the migration scripts. 00:24:08.820 --> 00:24:10.160 Here's how you migrate up. 00:24:10.160 --> 00:24:11.320 Here's how you migrate down. 00:24:11.320 --> 00:24:19.080 But they left out a little important thing, --auto generate, which looks at all of your classes in your database and go, here's the difference. 00:24:19.080 --> 00:24:23.440 We automatically wrote that for you, which I think is way nicer even than this project. 00:24:23.440 --> 00:24:28.560 So, I think Alembic is better, but the big requirement there is you are using SQLAlchemy. 00:24:28.560 --> 00:24:43.960 If you're not using SQLAlchemy to do these migrations, then this tool, but you're using these scripts instead to define your database, like, I'm sure a lot of, like, especially the larger companies where there's, like, a database team or, like, DBAs and so on are doing. 00:24:44.100 --> 00:24:46.060 And this seems like a really cool project for it. 00:24:46.060 --> 00:24:46.320 Yeah. 00:24:46.320 --> 00:24:49.580 That said, the converse is actually pretty cool. 00:24:49.580 --> 00:24:54.980 So, what it can do is it can look at a database and it will generate your SQLAlchemy files for you. 00:24:54.980 --> 00:24:56.140 That's pretty cool. 00:24:56.140 --> 00:24:56.820 That's nice. 00:24:56.980 --> 00:25:04.040 Yeah, it'll generate ORM definitions from SQL, right, using the SQLAlchemy generator, which is pretty awesome. 00:25:04.040 --> 00:25:12.080 So, you can say, here is my create table scripts, generate me the corresponding SQLAlchemy thing to match that. 00:25:12.080 --> 00:25:14.720 So, in that direction, it's pretty awesome also. 00:25:14.720 --> 00:25:15.760 So, which does that? 00:25:15.760 --> 00:25:16.960 This one, this auto migrate. 00:25:16.960 --> 00:25:22.800 It'll look at your DDL, like, create these table scripts, and it'll turn it into Python SQLAlchemy classes. 00:25:23.360 --> 00:25:27.880 But the reverse, it was saying, like, oh, it's painful to use Olympic in the other direction. 00:25:27.880 --> 00:25:32.260 But if you use the auto generate feature of Olympic, then it's also not painful. 00:25:32.260 --> 00:25:36.380 But there's certainly a couple of use cases that are pretty awesome here. 00:25:36.380 --> 00:25:46.460 One, like, starting from all the create stuff, like, given a database, just ramp me up to getting a SQLAlchemy set of classes that'll talk to it as quick as possible. 00:25:46.460 --> 00:25:47.220 That's really cool. 00:25:47.220 --> 00:25:55.820 If I've got a schema change, is there a version number that's stored in the database somewhere to say which version of the schema is being used? 00:25:55.820 --> 00:25:58.580 Yeah, I have no idea about this thing. 00:25:58.580 --> 00:26:01.780 With SQLAlchemy and Olympic, there is a version number. 00:26:01.780 --> 00:26:04.180 It says, I'm version some hash. 00:26:04.180 --> 00:26:07.420 And then all the migrations, one of those is the hash. 00:26:07.420 --> 00:26:11.460 And each migration says, the one that came before me is this, and the one that comes after me is that. 00:26:11.540 --> 00:26:15.140 They can look at an existing database and say, your version X. 00:26:15.140 --> 00:26:16.200 Yes, exactly. 00:26:16.200 --> 00:26:18.160 For Olympic, I have no idea about this thing. 00:26:18.160 --> 00:26:27.740 This thing could potentially look at the table, basically run it, like, script this create table stuff for me, and then look at that compared to what it has. 00:26:27.740 --> 00:26:29.700 Potentially, I have no idea if it's that smart, though. 00:26:29.700 --> 00:26:29.980 Okay. 00:26:29.980 --> 00:26:31.900 Yeah, but it looks like it could be handy for a lot of folks. 00:26:31.900 --> 00:26:36.140 Well, I've had a rough week, so I've got no extra stuff. 00:26:36.140 --> 00:26:37.280 No extra stuff? 00:26:37.280 --> 00:26:38.380 No extra stuff. 00:26:38.420 --> 00:26:39.460 I don't have too much either. 00:26:39.460 --> 00:26:40.740 I have a little bit. 00:26:40.740 --> 00:26:45.240 I just want to give a shout out that we have a ton of new courses coming. 00:26:45.240 --> 00:26:55.720 And I want to just encourage people, if they're interested in these, to go to training. talkpython.fm/getnotified and put the email there if they haven't created an account or signed up there before. 00:26:55.720 --> 00:27:00.360 Because we have Excel, moving from Excel to Python with pandas coming out. 00:27:00.360 --> 00:27:03.080 We have getting started with data science coming out. 00:27:03.080 --> 00:27:05.340 We have Python memory management tips coming out. 00:27:05.960 --> 00:27:08.860 Those all three will probably be within, like, a couple of weeks. 00:27:08.860 --> 00:27:12.560 And then getting started with Git and Python design patterns as well. 00:27:12.560 --> 00:27:13.640 So there's a bunch of cool stuff. 00:27:13.640 --> 00:27:16.900 If you want to hear about any of those, just be sure to get on the mail list. 00:27:16.900 --> 00:27:17.440 Oh, wow. 00:27:17.440 --> 00:27:18.000 That's cool. 00:27:18.000 --> 00:27:21.460 If I didn't talk to you every week, I would totally get on this mailing list. 00:27:21.460 --> 00:27:22.580 Awesome. 00:27:22.580 --> 00:27:25.040 Actually, I think I'm already on it. 00:27:25.040 --> 00:27:26.000 I'm sure you are. 00:27:26.000 --> 00:27:28.320 Because you do talk to me, though, you get jokes. 00:27:28.320 --> 00:27:28.900 Definitely. 00:27:29.080 --> 00:27:30.800 But everybody who listens gets them also. 00:27:30.800 --> 00:27:31.280 That's right. 00:27:31.280 --> 00:27:33.880 This is a fun game to play. 00:27:33.880 --> 00:27:40.240 The idea is you take some actual, legitimate, classical painting. 00:27:40.240 --> 00:27:53.520 And if you go to an art gallery, it'll say, like, flowers in bloom, oil canvas, Monet 1722, or something like that, 00:27:53.580 --> 00:27:54.860 like, in the little placard underneath. 00:27:54.860 --> 00:28:00.740 So the game is to reinterpret these paintings in modern tech speak. 00:28:00.740 --> 00:28:01.880 Okay? 00:28:01.880 --> 00:28:02.240 Yeah. 00:28:02.340 --> 00:28:03.720 So here, I'll do the first one. 00:28:03.720 --> 00:28:07.220 I put three in the show notes that people can check out. 00:28:07.220 --> 00:28:09.700 I'll describe this to you, and then I'll read the little thing. 00:28:09.700 --> 00:28:18.340 So there's, like, a ship that seems to be on fire with some extremely strong guys trying to drag the ship out of the water. 00:28:18.340 --> 00:28:20.800 Maybe, no, they're pushing it into the water. 00:28:20.800 --> 00:28:24.100 And a bunch of folks on the edge sitting off. 00:28:24.100 --> 00:28:25.360 It's like a Viking ship. 00:28:25.360 --> 00:28:27.160 I think they're actually cremating. 00:28:27.160 --> 00:28:27.860 Somebody's sitting out. 00:28:27.920 --> 00:28:33.360 Anyway, it's this historical picture, and it says, the placard says, 00:28:33.360 --> 00:28:36.920 Engineers remove dead code after dropping a feature flag. 00:28:36.920 --> 00:28:42.620 Sir Frank Bernard Dixie, 1893, oil on canvas. 00:28:42.620 --> 00:28:45.400 You want to do the next one? 00:28:45.400 --> 00:28:45.980 Oh, sure. 00:28:45.980 --> 00:28:47.240 Pull it up. 00:28:47.240 --> 00:28:48.360 Oh, okay. 00:28:48.360 --> 00:28:50.340 How do I describe this? 00:28:50.340 --> 00:28:53.180 This is like a picture. 00:28:53.180 --> 00:28:54.540 It's a Picasso picture. 00:28:54.540 --> 00:28:57.560 It's like an abstract violin that explodes or something. 00:28:58.400 --> 00:28:58.560 Yeah. 00:28:58.560 --> 00:29:00.880 It's hard to tell, really, what's going on. 00:29:00.880 --> 00:29:02.280 It kind of looks like a violin. 00:29:02.280 --> 00:29:05.720 And the title is CSS Without Comments. 00:29:05.720 --> 00:29:06.440 That's good. 00:29:06.440 --> 00:29:10.180 Pablo Picasso, 1912. 00:29:10.180 --> 00:29:10.800 All right. 00:29:10.800 --> 00:29:11.620 The last one. 00:29:11.620 --> 00:29:12.860 The last one we'll do. 00:29:12.860 --> 00:29:14.840 By the way, there's hundreds of these. 00:29:14.840 --> 00:29:15.660 They're all really good. 00:29:15.660 --> 00:29:18.560 So this one is a little disturbing. 00:29:18.560 --> 00:29:23.400 There's a person who looks deathly ill with a bunch of, like, gargoyles over them. 00:29:23.400 --> 00:29:30.200 A priest with a crucifix, kind of glowing, apparently trying to ward off the gargoyles. 00:29:30.200 --> 00:29:35.220 And the placard says, experienced developer deploys hotfix on production. 00:29:35.220 --> 00:29:38.900 Francisco Goya, oil on canvas, circa 1788. 00:29:38.900 --> 00:29:40.040 That's good. 00:29:40.040 --> 00:29:40.300 Yeah. 00:29:40.300 --> 00:29:41.920 So there's just so many of these. 00:29:41.920 --> 00:29:43.040 You can go through them all day. 00:29:43.040 --> 00:29:43.900 It's really fun. 00:29:43.900 --> 00:29:46.720 Didn't PyCon do that once at, like, one of the PyCons? 00:29:46.860 --> 00:29:47.880 I think you might have been with us. 00:29:47.880 --> 00:29:53.740 I know Chris Medina, Kelsey Hightower, and I were walking around the Portland Art Museum, 00:29:53.740 --> 00:29:56.000 like, basically playing this game. 00:29:56.000 --> 00:29:57.340 We were, like, coming up with a placard. 00:29:57.340 --> 00:29:57.880 It was fun. 00:29:57.880 --> 00:29:58.680 And were you there for that? 00:29:58.680 --> 00:29:59.180 You might have been. 00:29:59.180 --> 00:30:00.000 No, I wasn't. 00:30:00.000 --> 00:30:01.160 I missed that one, but. 00:30:01.160 --> 00:30:02.320 That was good. 00:30:02.320 --> 00:30:04.620 Remember that one when you could go to conferences? 00:30:04.620 --> 00:30:06.420 If there were people around you? 00:30:06.420 --> 00:30:07.020 Other people? 00:30:07.020 --> 00:30:08.020 Close? 00:30:08.020 --> 00:30:08.760 It was weird. 00:30:08.760 --> 00:30:13.400 Actually, we don't need anybody to contact us and tell us that we have no idea when different 00:30:13.400 --> 00:30:14.300 painters were alive. 00:30:15.040 --> 00:30:15.820 But thanks. 00:30:15.820 --> 00:30:16.640 And cool. 00:30:16.640 --> 00:30:17.760 Good for you if you know it. 00:30:17.760 --> 00:30:18.860 Awesome. 00:30:18.860 --> 00:30:19.920 Yeah, these are really good. 00:30:19.920 --> 00:30:23.720 If you enjoy this kind of stuff, there's hundreds of fun pictures to go through. 00:30:23.720 --> 00:30:28.600 And I think it's also amusing that we often pick visual jokes for an audio format. 00:30:28.600 --> 00:30:29.660 So, sure. 00:30:29.660 --> 00:30:30.260 Why not? 00:30:30.260 --> 00:30:31.280 Do it hard. 00:30:31.280 --> 00:30:31.840 That's what burgers do. 00:30:31.840 --> 00:30:32.880 Let's do it with, that's right. 00:30:32.880 --> 00:30:34.020 Let's do it with abstract art. 00:30:34.020 --> 00:30:34.300 Yeah. 00:30:34.300 --> 00:30:37.320 Yeah, it's funny. 00:30:37.320 --> 00:30:38.240 Anyway. 00:30:38.240 --> 00:30:38.540 Awesome. 00:30:38.540 --> 00:30:39.220 All right. 00:30:39.220 --> 00:30:39.720 Well, thanks, Brian. 00:30:39.720 --> 00:30:40.180 Thank you. 00:30:40.180 --> 00:30:40.420 Yep. 00:30:40.420 --> 00:30:40.600 Bye. 00:30:40.600 --> 00:30:40.860 Bye. 00:30:40.860 --> 00:30:42.560 Thank you for listening to Python Bytes. 00:30:42.560 --> 00:30:45.020 Follow the show on Twitter at Python Bytes. 00:30:45.160 --> 00:30:48.060 That's Python Bytes, as in B-Y-T-E-S. 00:30:48.060 --> 00:30:50.940 And get the full show notes at Python Bytes.fm. 00:30:50.940 --> 00:30:56.000 If you have a news item you want featured, just visit Python Bytes.fm and send it our way, 00:30:56.000 --> 00:30:58.060 where I was on the lookout for sharing something cool. 00:30:58.060 --> 00:31:02.240 This is Brian Okken, and on behalf of myself and Michael Kennedy, thank you for listening 00:31:02.240 --> 00:31:04.540 and sharing this podcast with your friends and colleagues.