WEBVTT 00:00:00.220 --> 00:00:04.640 This is Python Bytes. Python headlines and news delivered directly to your earbud. 00:00:04.640 --> 00:00:11.040 It's episode 13 on Monday the 13th. Oh my god. Recorded February 13th, 2017. 00:00:11.040 --> 00:00:17.140 Hey Brian, I want to start by saying thanks and hello. I want to say thanks to a new sponsor of 00:00:17.140 --> 00:00:21.440 the show. We have a new sponsor. That's wonderful. We do. And I want to say thank you to Metis. 00:00:21.440 --> 00:00:25.960 They're a data science training company. We have some boot camp type stuff that's really excellent 00:00:25.960 --> 00:00:31.580 and I'll tell everyone about that later. But I kind of like to learn about alternate Python 00:00:31.580 --> 00:00:38.860 implementations and where they are these days. We had heard about Piston, which is spelled P-Y-S-T-O-N, 00:00:38.860 --> 00:00:45.660 developed by Dropbox and an announcement came out at the end of last month saying that Piston is no 00:00:45.660 --> 00:00:52.740 longer to be supported by Dropbox. Yeah, so this is a really interesting development. It's a little 00:00:52.740 --> 00:00:59.380 unfortunate. I don't really know how to interpret as a positive or negative. I mean, it could be just 00:00:59.380 --> 00:01:03.540 there was an experiment. We're moving on to the next experiment. But what it was is an alternate 00:01:03.540 --> 00:01:09.480 version implementation. I have a hard time calling an interpreter when it's actually JIT compiled, but 00:01:09.480 --> 00:01:17.720 alternate interpreter for Python. And the idea was it supported Python 2.7 and it did JIT compilation 00:01:17.720 --> 00:01:26.300 instead of interpreting. So its main goal was to be a very fast but highly compatible alternate 00:01:26.300 --> 00:01:33.540 implementation of Python 2.7. Yeah. And so there's a couple of blog posts, one on the Piston blog and 00:01:33.540 --> 00:01:43.820 one on by Kevin Modzilewski and one on his own site, kevmod.com. And it looks like there's lots of 00:01:43.820 --> 00:01:50.620 reasons around it. But the general flow of it is that the cost and benefit, it cost more than they were hoping 00:01:50.620 --> 00:01:56.920 and it benefited them not as much as they were hoping. Yeah, so we look at the different implementations of 00:01:56.920 --> 00:02:04.200 Python. We've got obviously CPython and its versions, but we've got Piston, we've got Unladen Swallow, 00:02:04.680 --> 00:02:13.380 we've got Cython, we've got PyPy, P-Y-P-Y, IronPython, Jython. There's a bunch of different ones and they all have these 00:02:13.380 --> 00:02:20.760 trade-offs. And so they were actually able to make Piston pretty quick in the benchmark story. So they got it up to 95% 00:02:20.760 --> 00:02:28.820 faster than CPython 2.7. But they also ran into other drawbacks and they found like actually when it ran the Dropbox 00:02:29.260 --> 00:02:37.760 code itself, it was only 10% faster, but it put a lot more memory pressure on it because it does GC instead of reference counting. 00:02:37.760 --> 00:02:46.540 I've got mixed feelings about it. I like that the project was out in the open, open source, and people could watch this experiment. 00:02:46.540 --> 00:02:54.040 I wasn't really thrilled that they were targeting 2.7 to start with. The outcome of them just, I guess, not working on it anymore. 00:02:54.040 --> 00:02:58.900 It totally makes sense, but it is, I guess, it's just a thing. It's interesting. 00:02:58.900 --> 00:03:04.640 Yeah, I think it's a case of a couple of things. I mean, nobody said it, but I feel like, you know, 00:03:04.640 --> 00:03:10.700 moving to Python 3 is also causing like a sort of a rethinking of this as well. Like if they're going to be moving to Python 3, 00:03:10.700 --> 00:03:16.220 then they'd have to redo this work and so on. One of the problems or criticisms, mild criticisms, let's say, 00:03:16.220 --> 00:03:22.380 was that this was like a from scratch thing. It didn't build on existing stuff. So it didn't build on PyPy 00:03:22.380 --> 00:03:30.780 because PyPy wants to be fast at the cost of compatibility and to run a multi-million line Python application that is Dropbox. 00:03:30.780 --> 00:03:36.500 Compatibility is like super, super critical. So they're like, well, we can't have any incompatibility, 00:03:36.500 --> 00:03:42.200 but it wasn't directly based on CPython, even though it took a bunch of pieces in. And so I don't know, 00:03:42.200 --> 00:03:47.880 it was like this sort of other experiment where the thing that gives me the most hope right now is what 00:03:47.880 --> 00:03:53.780 Brett Cannon and Dino Veland are working on with Pigeon, P-Y-J-I-O-N at Microsoft, where they're trying 00:03:53.780 --> 00:03:57.940 to add like JIT plugin capabilities to the existing CPython. 00:03:57.940 --> 00:04:00.780 Yeah. There is some more CPython news. 00:04:00.780 --> 00:04:03.960 There's big CPython news. CPython 00:04:03.960 --> 00:04:08.340 has just as of three days ago 00:04:08.340 --> 00:04:10.920 moved. It used to be 00:04:10.920 --> 00:04:19.180 long, long ago in 2006, it moved to Subversion, SVN. And from 2006 to 2011, it was on Subversion. 00:04:19.180 --> 00:04:21.060 Then it moved to Mercurial. 00:04:21.060 --> 00:04:25.700 What's kind of interesting because that's when Git came out and Mercurial, they both, all this whole 00:04:25.700 --> 00:04:31.880 distributed version control system story kind of happened around then. And Mercurial is interesting 00:04:31.880 --> 00:04:36.040 because it's written in Python. Like for example, Facebook does some really interesting things 00:04:36.040 --> 00:04:41.380 with Mercurial. And so it seemed like a natural home for Python to live on Mercurial. 00:04:41.380 --> 00:04:44.520 But, you know, GitHub has really been 00:04:44.520 --> 00:04:47.980 the place where people go to do open source. 00:04:47.980 --> 00:04:52.240 Not just a container for open source stuff like SourceForge used to be or something, 00:04:52.240 --> 00:04:55.560 but there's like a whole social ecosystem where everybody 00:04:55.560 --> 00:04:58.440 comes and collaborates in GitHub. 00:04:58.620 --> 00:05:04.400 And so the big news is as of Friday, Python, CPython and a few of its pieces have moved 00:05:04.400 --> 00:05:05.800 to GitHub. 00:05:05.800 --> 00:05:10.880 I think that's great. And I think that one of the great things about it is the culture around 00:05:10.880 --> 00:05:15.200 GitHub. Whether you're a pro GitHub person or not, that's where a lot of people look for 00:05:15.200 --> 00:05:19.920 opportunities to help out in open source projects. It's a higher incentive for people to get involved 00:05:19.920 --> 00:05:21.780 with helping to maintain CPython. 00:05:21.780 --> 00:05:25.620 Yeah, absolutely. Yeah, I think that's actually the main motivation, which is really interesting. 00:05:25.620 --> 00:05:30.040 So there's a link to the mailing list where they talk about it. We'll put that in the show notes. 00:05:30.040 --> 00:05:32.340 There's obviously the link to the repository in GitHub. 00:05:32.340 --> 00:05:35.640 There's a Reddit thread where people are talking about this. 00:05:35.640 --> 00:05:38.000 And a lot of people are bemoaning the fact 00:05:38.000 --> 00:05:41.620 that it's not on something totally open source. 00:05:41.620 --> 00:05:44.700 It's not on something that is 00:05:44.700 --> 00:05:48.440 based on Python. Like it used to be on a source control system 00:05:48.440 --> 00:05:49.940 built on top of Python. 00:05:49.940 --> 00:05:52.400 And while that's cool, 00:05:52.400 --> 00:05:57.580 like you shouldn't, my opinion is you shouldn't do that at the cost of people loving to work on the 00:05:57.580 --> 00:06:04.120 project. So the real thing that you want to read to get the deep insight on this is at snarky.ca. 00:06:04.120 --> 00:06:08.600 So this is Brett Cannon's blog, and it's the history behind the decision to move Python to GitHub. 00:06:08.600 --> 00:06:13.520 And he talks about how he worked with a bunch of people to come to this decision. 00:06:13.740 --> 00:06:23.540 And he says, you know, back in 2007, it became really obvious that working on the source code and doing code reviews and collaboration was actually a burden. 00:06:23.540 --> 00:06:28.240 There were people submitting, you know, patches and stuff. 00:06:28.380 --> 00:06:33.020 And they would sit there for maybe years before somebody actually reviewed it. 00:06:33.020 --> 00:06:39.560 Like how excited would you be to work on a project to contribute to open source if you submit a patch and it doesn't get looked at for a year, right? 00:06:39.560 --> 00:06:47.280 And so the workflow on GitHub is just so much easier for pull requests and reviewing it. 00:06:47.280 --> 00:06:53.980 And so basically, Brett said, like, I want it to be so easy that I could take my tablet and sit on the beach where I have some kind of internet connectivity 00:06:53.980 --> 00:06:56.600 and review pull requests and chill out. 00:06:56.600 --> 00:06:58.300 Like, that's just not how it works now. 00:06:58.300 --> 00:07:00.820 Like, you've got to have a machine with SSH keys and all sorts of stuff. 00:07:00.820 --> 00:07:08.560 So the main driver behind this is trying to make it easier for core developers to be more responsive to people who contribute to the project, 00:07:08.560 --> 00:07:09.920 which I think is super positive. 00:07:09.920 --> 00:07:12.260 Yeah, I think I'm really excited about it. 00:07:12.260 --> 00:07:17.960 And actually, I had never really considered contributing myself, but I definitely consider it more now. 00:07:17.960 --> 00:07:21.480 Yeah, I definitely consider it more accessible now. 00:07:21.600 --> 00:07:25.640 And I think this is absolutely the right move, even if it's breaking some conventions. 00:07:25.640 --> 00:07:28.780 Like, it's moving off of Python as the thing that supports it. 00:07:28.780 --> 00:07:35.080 It's moving off a fully, fully open source thing to, like, this somewhat closed thing, which is GitHub. 00:07:35.080 --> 00:07:36.440 But still, I think it's great. 00:07:36.440 --> 00:07:44.340 So remember last time when we talked about the top 10 Python articles of 2016? 00:07:44.340 --> 00:07:44.980 Yes. 00:07:45.160 --> 00:07:49.460 Yeah, so I said, well, let's just take a survey of the kind of category of these things. 00:07:49.460 --> 00:07:54.440 And I would say, like, a full two-thirds or more of those were data science. 00:07:54.440 --> 00:07:56.880 Data science is definitely important in Python. 00:07:56.880 --> 00:07:59.100 So I want to tell you about our sponsor, Metis. 00:07:59.100 --> 00:08:03.800 So Metis is a data science training company based in New York, San Francisco, Chicago, and Seattle. 00:08:04.400 --> 00:08:10.040 And they provide full-time immersive boot camps and evening courses that you can come. 00:08:10.040 --> 00:08:17.000 So if you have a full-time job, you can go, like, two nights a week for six weeks and take classes on all sorts of data science things. 00:08:17.000 --> 00:08:19.520 Machine learning and things like that. 00:08:19.700 --> 00:08:32.280 If you are wanting to learn more about data science, especially in a hands-on way, you live in New York or San Francisco, or you're willing to spend some time there, check out thisismetis.com slash talkpython. 00:08:32.280 --> 00:08:34.940 And maybe we'll feature your article next year. 00:08:34.940 --> 00:08:37.280 Yeah, that actually sounds pretty interesting. 00:08:37.280 --> 00:08:40.180 Yeah, I would love to go do more data science. 00:08:40.180 --> 00:08:43.360 So TensorFlow, they've got classes on that, all sorts of cool stuff. 00:08:43.360 --> 00:08:45.720 So check it out, thisismetis.com slash talkpython. 00:08:45.720 --> 00:08:47.020 All right. 00:08:47.160 --> 00:08:53.220 You know what they do a lot of in data science, a lot of functional programming, a lot of expressions, things like that, right? 00:08:53.220 --> 00:08:53.920 Yeah. 00:08:53.920 --> 00:08:55.580 Actually, I don't know. 00:08:55.580 --> 00:08:57.120 I'm not a data science person. 00:08:57.120 --> 00:08:59.180 The ones I've hung around seem to. 00:08:59.180 --> 00:09:05.280 There's an article called Using Functional Programming in Python Like a Boss, Generators, Iterators, and Decorators. 00:09:05.280 --> 00:09:07.260 And these are generators and iterators. 00:09:07.260 --> 00:09:12.900 Okay, all three of those, generators, iterators, and decorators are things that trip up intermediate Python people. 00:09:12.900 --> 00:09:15.580 Beginner people just ignore them for now. 00:09:16.120 --> 00:09:25.360 But this article is a Jupyter Notebook, which I like this trend of people just publishing Jupyter Notebooks as articles. 00:09:25.360 --> 00:09:27.580 It actually is good. 00:09:27.580 --> 00:09:29.880 And there's some good diagrams in here. 00:09:29.880 --> 00:09:37.060 Although the use of really large images, it's kind of annoying to me, but it's okay. 00:09:37.460 --> 00:09:47.160 Anyway, this article talks about, it also talks about just basically what a function is and what things can act like functions. 00:09:47.160 --> 00:09:53.620 And then talks about generators, iterators, and containers and how they all work together to make your code more expressive. 00:09:53.920 --> 00:09:59.040 And it seems like kind of an esoteric sort of thing to think about, but I think it's good. 00:09:59.040 --> 00:10:02.700 And this is a good, I like this article about thinking about this stuff. 00:10:02.700 --> 00:10:10.460 Yeah, I really think this is one of those things that when you come to Python from other languages, it takes you a while to get into these, right? 00:10:10.540 --> 00:10:20.060 Like the concept of a generator is quite different to somebody coming from C or I think Java, I don't think Java has this, things like that. 00:10:20.060 --> 00:10:23.720 But once you get the Zen of them, they're amazing. 00:10:23.720 --> 00:10:25.320 And they make such a big difference. 00:10:25.580 --> 00:10:29.160 But it's really a nice mental shift. 00:10:29.160 --> 00:10:31.920 Yeah, so I really think it's great that he's covering it. 00:10:31.920 --> 00:10:43.180 Also one of those topics that I don't think I remember when I finally grokked what people were talking about, but it took like three or four different articles talking about it for it to sink in for me. 00:10:43.180 --> 00:10:44.200 So here's another one. 00:10:44.200 --> 00:10:44.840 Yeah, excellent. 00:10:44.840 --> 00:10:45.720 Yeah, so we'll link to that one. 00:10:45.720 --> 00:10:54.560 And by some strange coincidence, just so everybody knows, the way that we pick things for this show is I go find a couple of interesting things. 00:10:54.560 --> 00:10:56.900 Brian goes and finds a couple of interesting things. 00:10:56.900 --> 00:10:59.260 We throw them together and then we just talk about them. 00:10:59.260 --> 00:11:06.800 And it happens to be the very next one is the same person in a Jupyter Notebook, but it's actually a totally different topic. 00:11:06.800 --> 00:11:08.640 But it's presented in a similar way. 00:11:08.640 --> 00:11:12.340 So this one is called It's Metaclasses All the Way Down. 00:11:12.760 --> 00:11:18.200 This is a quick but fairly deep dive into what metaprogramming is. 00:11:18.200 --> 00:11:24.020 And it starts out by saying, look, metaprogramming is a technique for writing computer programs that can treat themselves as data. 00:11:24.020 --> 00:11:28.160 So you can introspect, generate, and or modify them while they're running. 00:11:28.160 --> 00:11:29.580 And that's definitely Python. 00:11:29.580 --> 00:11:32.820 You can, at runtime, create types. 00:11:32.820 --> 00:11:39.140 You can add functions or even remove features from classes and objects, right? 00:11:39.300 --> 00:11:48.420 But mostly metaprogramming comes down to metaclasses where you can actually control what the class does. 00:11:48.420 --> 00:11:51.780 So there's all sorts of interesting examples of how you might use them. 00:11:51.780 --> 00:11:59.280 It starts out with an interesting quote saying, look, metaclasses are deeper magic than 99% of the users should ever worry about. 00:12:00.120 --> 00:12:02.660 If you wonder whether you need them, you don't. 00:12:02.660 --> 00:12:06.200 So if you don't, like, that was a quote by Tim Peters. 00:12:06.200 --> 00:12:11.240 If you don't deeply understand, like, yeah, this is totally, I'm going to solve this with a metaclass, like, you probably shouldn't mess with it. 00:12:11.240 --> 00:12:16.580 But they are the power that makes a lot of the magic inside Python happen. 00:12:16.980 --> 00:12:18.600 So a couple of examples. 00:12:18.600 --> 00:12:20.740 One is Django RRM. 00:12:20.740 --> 00:12:24.820 We could substitute SQLAlchemy ORM or PeeWee ORM models, right? 00:12:24.820 --> 00:12:32.040 So when you define, like, a model in Django or SQLAlchemy, you put what looks like column types with database constraints as the fields of the class. 00:12:32.040 --> 00:12:36.560 But then when you interact with them at runtime, they look like strings and integers and dates, right? 00:12:36.560 --> 00:12:45.700 And that transition, that sort of two-faced behavior of these things that they can be used in a database query in one way but then act as normal objects as others, that's metaclasses. 00:12:45.700 --> 00:12:55.240 Another example, there's a cool example about using them to make abstract classes and abstract functions that have to be implemented by things that inherit it and so on. 00:12:55.240 --> 00:12:58.800 Again, this is a nice Jupyter notebook. 00:12:58.800 --> 00:13:03.120 It has some cool pictures, but it really does help you understand this stuff pretty well with nice examples. 00:13:03.120 --> 00:13:03.780 Yeah. 00:13:03.780 --> 00:13:04.180 Yeah. 00:13:04.180 --> 00:13:05.020 I like this, too. 00:13:05.020 --> 00:13:07.560 It's funny that we both picked the same person's article. 00:13:07.560 --> 00:13:08.300 It's a small world. 00:13:08.300 --> 00:13:09.260 Yeah. 00:13:09.260 --> 00:13:16.160 I think we're kind of going on the stuff that most people don't need train right now. 00:13:16.160 --> 00:13:18.840 But next up is lambdas. 00:13:18.840 --> 00:13:27.460 And speaking of another thing that I think newbies get a little confused by, but there are lambda functions. 00:13:27.460 --> 00:13:36.000 Lambda functions are just unnamed functions that are, you can put the entire expression on one line. 00:13:36.000 --> 00:13:39.000 And so they're convenient in a lot of places. 00:13:39.000 --> 00:13:44.560 So here's an article from Dan Bader titled, Lambda functions in Python, what are they good for? 00:13:45.200 --> 00:13:50.580 And what I like about this article is it's just a fairly quick jump into it. 00:13:50.580 --> 00:13:57.580 I think everybody that thinks of themselves as a little bit more than just a beginner in Python should at least understand lambda functions. 00:13:58.140 --> 00:14:03.760 And the thing I really like about this article is it talks about places to not use them. 00:14:03.760 --> 00:14:10.620 Because when you finally get your head around lambdas, all of a sudden it's your hammer and everything looks like a nail. 00:14:10.620 --> 00:14:19.420 He talks about a couple of places like creating class methods and other places where maybe you shouldn't use a lambda for that. 00:14:19.420 --> 00:14:20.640 You can, but don't. 00:14:20.640 --> 00:14:27.380 And then also there's some places where list comprehensions and dictionary comprehensions are wonderful. 00:14:27.760 --> 00:14:32.920 And they're way more expressive, but you could use lambdas in those two cases. 00:14:32.920 --> 00:14:33.720 Yeah, absolutely. 00:14:33.720 --> 00:14:38.680 I think the trick with lambdas that make them so wonderful is like there's little places in your code. 00:14:38.680 --> 00:14:46.020 You're like, if I could just pass a behavior here, I don't want to go out of where I am and write a function and then like give it a name and then say the name here. 00:14:46.020 --> 00:14:53.820 I just want to go, yeah, sort by this reversed or, you know, grab this value out and transform it or something. 00:14:53.820 --> 00:14:56.960 And lambda functions are beautiful for that. 00:14:57.180 --> 00:14:58.980 They're not so beautiful for writing your whole program. 00:14:58.980 --> 00:15:07.760 Yeah, actually sort is one of those, one of those places where one of the parameters is you can pass in a key or how to find the key. 00:15:07.760 --> 00:15:13.340 And it's, I've used lambdas a lot there because I don't really need a named function. 00:15:13.340 --> 00:15:15.440 I just need a function to pass in and do the work. 00:15:15.440 --> 00:15:16.740 Yeah, I've got a list of customers. 00:15:16.740 --> 00:15:17.900 I want to sort by last name. 00:15:17.900 --> 00:15:20.720 So lambda, C goes to C dot, last name, done. 00:15:20.720 --> 00:15:21.320 Beautiful. 00:15:21.320 --> 00:15:22.520 Yeah, it's great. 00:15:22.520 --> 00:15:25.260 So you wanted to do a quick follow-up on Jopronto. 00:15:25.920 --> 00:15:28.280 That's our super, super fast web framework, right? 00:15:28.280 --> 00:15:28.880 Theoretically. 00:15:28.880 --> 00:15:29.060 Yep. 00:15:29.060 --> 00:15:31.820 Yeah, that's what we were talking about that last week. 00:15:31.820 --> 00:15:37.940 But I ran across a Reddit thread that we'll post in the show notes. 00:15:38.500 --> 00:15:47.400 And it is titled, sticking with Flask versus switching to one of the new faster frameworks. 00:15:47.400 --> 00:16:03.020 And the Jopronto author actually posted on this and said, yeah, if you're looking at Jopronto, please read the read me there because I've noted that it's not ready for production, basically. 00:16:03.680 --> 00:16:08.640 So there's, he's not even, he's something about, I'll just read it. 00:16:08.640 --> 00:16:14.960 He's, as stated in the read me, you should not build anything serious with Jopronto now because it's going to probably eat your laundry. 00:16:14.960 --> 00:16:18.620 Remember, it's hand coded in C and this needs a lot of testing. 00:16:18.620 --> 00:16:23.740 On top of that, I plan to do several iterations of API changes in a largely incompatible way. 00:16:24.300 --> 00:16:28.440 I hope, though, it's going to make a serious player in the field one day. 00:16:28.440 --> 00:16:30.880 I applaud the author for putting that out there. 00:16:30.880 --> 00:16:37.740 He's basically developing in the open, but he's not promising to stick with the same model or the same API. 00:16:37.740 --> 00:16:39.260 And I think that's completely fair. 00:16:39.260 --> 00:16:40.560 And he's pretty open about it. 00:16:40.560 --> 00:16:42.040 Yeah, that's really great of him. 00:16:42.040 --> 00:16:50.640 And it's, you know, he seriously is optimizing this stuff in some really interesting but out there ways. 00:16:50.840 --> 00:16:53.320 And so, and all that part is done in C, right? 00:16:53.320 --> 00:17:02.160 And so, it probably needs a little bit of careful validation before it becomes a thing that you really, you know, depend on for your e-commerce site or whatever. 00:17:02.160 --> 00:17:04.940 But it's definitely, it's got so much promise, that project. 00:17:04.940 --> 00:17:06.000 So, I hope he gets there. 00:17:06.000 --> 00:17:07.700 Oh, we're already done. 00:17:07.700 --> 00:17:08.740 So, there we go. 00:17:08.740 --> 00:17:09.380 That's it. 00:17:09.380 --> 00:17:11.460 That's the news we got for this week. 00:17:11.460 --> 00:17:14.120 Anything you want to share with everyone while we're wrapping things up, Brian? 00:17:14.120 --> 00:17:16.620 No, nothing, nothing right now. 00:17:16.620 --> 00:17:17.260 All right. 00:17:17.260 --> 00:17:17.600 Awesome. 00:17:17.600 --> 00:17:19.140 Well, how about you? 00:17:19.280 --> 00:17:20.540 I'm cranking out some classes. 00:17:20.540 --> 00:17:25.340 I just am pretty much finished up my Consuming Restful Services course. 00:17:25.340 --> 00:17:26.820 So, stay tight. 00:17:26.820 --> 00:17:27.680 You know, hang tight for that. 00:17:27.680 --> 00:17:30.100 It's going to be out in maybe a week or so. 00:17:30.100 --> 00:17:30.800 All right. 00:17:30.800 --> 00:17:31.280 Cool. 00:17:31.280 --> 00:17:31.880 Yeah. 00:17:31.880 --> 00:17:34.300 Fun to be building new things for everyone. 00:17:34.300 --> 00:17:34.780 All right. 00:17:34.780 --> 00:17:36.440 Well, thank you everyone for listening. 00:17:36.440 --> 00:17:38.440 Brian, thanks for chatting with me today. 00:17:38.440 --> 00:17:39.520 Thank you. 00:17:39.520 --> 00:17:39.860 Yep. 00:17:39.860 --> 00:17:40.540 See you all later. 00:17:41.840 --> 00:17:43.600 Thank you for listening to Python Bytes. 00:17:43.600 --> 00:17:46.160 Follow the show on Twitter via at Python Bytes. 00:17:46.160 --> 00:17:49.060 That's Python Bytes as in B-Y-T-E-S. 00:17:49.060 --> 00:17:52.500 And get the full show notes at pythonbytes.fm. 00:17:52.500 --> 00:17:56.820 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:17:56.820 --> 00:17:59.520 We're always on the lookout for sharing something cool. 00:17:59.520 --> 00:18:02.920 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:18:02.920 --> 00:18:06.540 Thank you for listening and sharing this podcast with your friends and colleagues.