WEBVTT 00:00:00.001 --> 00:00:05.500 This is Python Bytes, episode number one, recorded Monday, November 7th, 2016. 00:00:05.500 --> 00:00:13.560 Hello, everybody, and welcome to Python Bytes, where we deliver Python headlines directly to your earbuds. 00:00:13.560 --> 00:00:17.980 This is one of your hosts, Michael Kennedy, along here with Brian Okken. 00:00:17.980 --> 00:00:18.960 Hey, Brian, how's it going? 00:00:18.960 --> 00:00:20.860 It's going great. 00:00:20.860 --> 00:00:23.600 I'm super excited to be doing this podcast with you. 00:00:23.600 --> 00:00:29.240 I'm really thrilled to be bringing these little tidbits to everyone each week or biweekly, 00:00:29.240 --> 00:00:30.440 whatever we would decide to go with. 00:00:30.440 --> 00:00:32.480 Yeah, I'm really excited, too. 00:00:32.480 --> 00:00:36.500 I think this is definitely something that's missing out in the community right now. 00:00:36.500 --> 00:00:39.480 Yeah, the goal is you subscribe to Python Bytes. 00:00:39.480 --> 00:00:46.120 And if you listen every week, you'll basically get a lot of the top headlines that you need to know about. 00:00:46.120 --> 00:00:47.700 So let's jump right into the headlines. 00:00:47.700 --> 00:00:53.740 The first one that I want to talk about is PyData, specifically PyData from Washington, D.C. 00:00:53.740 --> 00:00:56.700 The videos for this conference are out. 00:00:56.700 --> 00:00:59.300 So if you didn't make it to PyData, D.C. 00:00:59.300 --> 00:01:07.060 And you want to check them out, there are 64 videos, which is about 30, 35 hours worth of content on YouTube right now. 00:01:07.220 --> 00:01:16.020 And so a couple of the videos that I thought were really great, you know, looking through them, where one of them was Elasticsearch and Redis, how and when to use them. 00:01:16.020 --> 00:01:18.980 And I don't really know too much about Elasticsearch. 00:01:18.980 --> 00:01:21.420 So I'm really excited to watch this video and learn more about it. 00:01:21.420 --> 00:01:23.580 Another one is the one by Renee Tate. 00:01:23.820 --> 00:01:27.540 She hosts a podcast and a website called Becoming a Data Scientist. 00:01:27.540 --> 00:01:33.160 And she did a talk on advice from her podcast guests about becoming a data scientist. 00:01:33.160 --> 00:01:34.220 So I think that's really cool. 00:01:34.220 --> 00:01:41.860 One that was also cool was visual diagnostics for more information about machine learning and basically tuning your machine learning. 00:01:42.300 --> 00:01:50.500 And then one about data reproducibility and scientific computing called You Got Your Engineering and My Data Science Addressing the Reproducibility Crisis. 00:01:51.040 --> 00:01:53.080 Yeah, I was really impressed with these videos. 00:01:53.080 --> 00:01:56.700 And there's a couple of things that I really liked about them. 00:01:56.700 --> 00:02:05.680 I like the format that shows you can see both the slides and the presenter on a little kind of a picture in picture sort of thing of the presenter talking as well. 00:02:05.680 --> 00:02:06.600 I think that was done well. 00:02:06.600 --> 00:02:14.900 I also really like the, man, these are like 35 minute or half an hour presentations and they're just packed with data. 00:02:14.900 --> 00:02:18.220 Yeah, it's not like people are trying to fill an hour, hour and a half. 00:02:18.220 --> 00:02:19.980 They're like, here's the essence, let's go. 00:02:19.980 --> 00:02:21.040 And it's really great. 00:02:21.040 --> 00:02:23.880 And, you know, I definitely agree with you on the quality. 00:02:23.880 --> 00:02:39.000 I'm really, really impressed with all the Python conferences about how high quality their AV work is and how good of an effort they make to basically record and share, not just with the attendees, but with everybody, whatever went on there. 00:02:39.000 --> 00:02:40.220 Yeah, it's pretty impressive. 00:02:40.220 --> 00:02:41.300 All right, what do you got for us, Brian? 00:02:41.300 --> 00:02:42.740 Oh, all right. 00:02:42.740 --> 00:02:48.460 One of the things that came up is another tutorial about EasyArgs. 00:02:48.920 --> 00:02:54.160 It's a command line, command line to library for making arguments better. 00:02:54.160 --> 00:02:56.840 I actually didn't take too much of a look at it. 00:02:56.840 --> 00:03:06.940 I just think it's one of the things I wanted to point out was it's kind of interesting that we have yet another way to make command line arguments easier to parse. 00:03:07.700 --> 00:03:11.180 I've lost track now of how many extra libraries there are. 00:03:11.540 --> 00:03:12.460 Yeah, let's see. 00:03:12.460 --> 00:03:12.560 Yeah. 00:03:12.560 --> 00:03:12.560 Let's see. 00:03:12.560 --> 00:03:16.720 Arg pars, click, doc opt, and a bunch more, right? 00:03:16.720 --> 00:03:18.380 Yeah. 00:03:18.500 --> 00:03:28.880 It seemed, I was just thinking that it seems like a rite of passage now that, so if you're serious about being a Python developer and open source and blogging about it, you have to do two things. 00:03:28.880 --> 00:03:33.780 You have to write a tutorial about how generators work, and you have to make your own command line library. 00:03:33.780 --> 00:03:34.800 That's awesome. 00:03:34.800 --> 00:03:36.020 I think you might be right there. 00:03:36.500 --> 00:03:44.920 I haven't done either of those, but I actually do like, I'm making fun of it a little bit, but I'm glad that people are doing it. 00:03:45.140 --> 00:03:58.120 If everybody's trying to solve the problem, it means there's a lot of people not happy with the current solutions, and I'm one of those people that likes to write command line tools, so I say more power to them to write another. 00:03:58.120 --> 00:03:59.280 Yeah, absolutely. 00:03:59.280 --> 00:04:00.040 More power to them. 00:04:00.040 --> 00:04:00.500 That's awesome. 00:04:00.500 --> 00:04:09.660 So the next one that I wanted to talk about is something called safety-db, which is on GitHub, and of course, we'll put all the links to everything we're talking about in the show notes. 00:04:09.660 --> 00:04:11.040 You can check them out in your podcast player. 00:04:11.340 --> 00:04:21.500 But safety-db is interesting because when we write and deploy our applications, especially web applications because they have the biggest surface area and they touch the most people, right? 00:04:21.500 --> 00:04:22.920 They're publicly out on a server. 00:04:22.920 --> 00:04:24.980 We don't just deploy our code. 00:04:24.980 --> 00:04:31.960 We deploy all the packages that we're using in our code and then the packages that those packages depend upon and so on. 00:04:31.960 --> 00:04:38.900 So, for example, on my training website, I've got a mailing list, and I use MailChimp for that. 00:04:39.280 --> 00:04:43.900 MailChimp itself depends upon DocOpt, speaking of command line parsers and so on. 00:04:43.900 --> 00:04:49.160 How do I keep track of all these things and know if there's a security vulnerability? 00:04:49.160 --> 00:04:53.020 Like, what if there's a problem in DocOpt that would let people hack into my server? 00:04:53.020 --> 00:04:54.500 I didn't install DocOpt. 00:04:54.500 --> 00:04:55.900 Maybe it's not even on my mind. 00:04:55.900 --> 00:04:57.600 I don't even realize that it got installed. 00:04:58.420 --> 00:05:11.540 This GitHub set of data keeps that information current so I can check, hey, does my project have any vulnerabilities that I may or may not even be aware of through this sort of hierarchy of dependencies I'm using? 00:05:11.540 --> 00:05:12.300 Wow. 00:05:12.300 --> 00:05:16.060 I didn't realize it was that in full. 00:05:16.620 --> 00:05:20.740 Yeah, so I'm just starting to get into more of the web development aspect of it. 00:05:20.740 --> 00:05:27.080 But I do, I mean, just even with what I do now, I depend on MailChimp and other services. 00:05:27.080 --> 00:05:30.880 I didn't even think about that, of looking at their vulnerabilities. 00:05:30.880 --> 00:05:32.200 Yeah, isn't that crazy? 00:05:32.200 --> 00:05:40.380 There was something with one of the libraries that Flask is built upon a while ago that caused some big hoopla. 00:05:40.380 --> 00:05:43.360 And I'm sure it got patched right away and it was no big deal. 00:05:43.360 --> 00:05:48.380 But, you know, if you don't know that you need to go patch your system, that's a problem. 00:05:48.380 --> 00:05:50.020 So, SafetyDB. 00:05:50.020 --> 00:05:51.360 Yeah, great. 00:05:51.360 --> 00:05:51.740 Cool. 00:05:51.740 --> 00:05:52.400 I'll check it out. 00:05:52.400 --> 00:05:52.940 Check it out. 00:05:52.940 --> 00:05:53.620 All right. 00:05:53.620 --> 00:06:00.780 So, next up, we've got a GitHub project that came to my attention this week called Fast Style Transfer. 00:06:00.780 --> 00:06:07.780 And it's an interesting library that uses TensorFlow, which I've never used TensorFlow. 00:06:07.780 --> 00:06:08.960 Have you used TensorFlow, Mike? 00:06:08.960 --> 00:06:17.000 No, I'm just starting to work on a project, a data science project that is probably using TensorFlow. 00:06:17.000 --> 00:06:19.240 But I've only heard good things. 00:06:19.240 --> 00:06:20.260 I have not yet used it. 00:06:20.260 --> 00:06:20.860 Okay. 00:06:20.860 --> 00:06:27.760 Well, this one is something that makes me want to give it a try because the demo on it looks really cool. 00:06:27.760 --> 00:06:36.420 You take a painting or a picture of some artist's style and you can apply that style to another picture, like your own picture. 00:06:36.420 --> 00:06:38.320 And I've seen something like that before. 00:06:38.320 --> 00:06:42.260 But what I'm really excited about is this will apply it to videos as well. 00:06:42.260 --> 00:06:53.160 And I've always, I guess, ever since the AHA's Take On Me video, I've liked that notion of like an artistic style over a short video. 00:06:53.500 --> 00:06:54.580 It just seems like fun. 00:06:54.580 --> 00:06:55.580 That sounds really fun. 00:06:55.580 --> 00:07:01.600 So, I could like take my training videos and turn them into like a Van Gogh type of thing? 00:07:01.600 --> 00:07:02.100 Yeah. 00:07:02.100 --> 00:07:03.540 Yeah, that'd be great. 00:07:03.540 --> 00:07:04.200 That'd be awesome. 00:07:04.200 --> 00:07:05.640 I don't know if people could read your slides. 00:07:05.640 --> 00:07:09.200 But they would feel very creative while they're watching it. 00:07:09.200 --> 00:07:15.000 No, I can't think of actually any commercial reason why I would try this. 00:07:15.180 --> 00:07:17.260 But the artist in me wants to give it a shot. 00:07:17.260 --> 00:07:18.320 No, it sounds really cool. 00:07:18.320 --> 00:07:19.400 And a chance to play a TensorFlow. 00:07:19.400 --> 00:07:20.400 Also cool. 00:07:20.400 --> 00:07:20.780 Yeah. 00:07:20.780 --> 00:07:25.260 So, another big piece of news that came out this week is pip. 00:07:25.260 --> 00:07:28.080 So, pip has a full new major version. 00:07:28.080 --> 00:07:29.540 PIP 9 is out. 00:07:29.540 --> 00:07:31.920 And it comes with a couple of new features. 00:07:32.180 --> 00:07:37.840 One is the ability to check the installed package dependencies to see if everything is set up correctly there. 00:07:37.840 --> 00:07:41.340 You can use pip show in a less verbose way. 00:07:41.340 --> 00:07:51.220 You can also say pip list and give it a not required option, which will show you all the packages that aren't there because they're a dependency on a thing. 00:07:51.220 --> 00:07:53.140 They're sort of top level, which is really cool. 00:07:53.140 --> 00:07:56.320 There's a ton of fixes that came as part of this release. 00:07:56.320 --> 00:08:06.700 And as you might expect with any major release of some new thing, four days later, pip 901 was released with five fixes for bugs that were introduced in pip 9. 00:08:06.860 --> 00:08:07.340 Yeah. 00:08:07.340 --> 00:08:12.200 And actually, the list of what's in pip 9 is big. 00:08:12.200 --> 00:08:12.960 It is big. 00:08:12.960 --> 00:08:18.860 And one of the things that is cool is there's features in there that I didn't even know existed. 00:08:18.860 --> 00:08:22.820 So, like, the pip check is, like, that's really cool. 00:08:22.820 --> 00:08:23.800 I'm excited about that. 00:08:23.800 --> 00:08:28.540 But also, they're changing the list format, or they're going to. 00:08:28.540 --> 00:08:35.460 So, if you do pip list now on 9, it'll give you a warning that in the future, the default format will be a column format. 00:08:35.540 --> 00:08:40.020 And the column format, you can add now with a --format equals column. 00:08:40.020 --> 00:08:41.940 And it's really easy to read. 00:08:41.940 --> 00:08:43.580 So, I'm excited about that. 00:08:43.580 --> 00:08:43.920 Yeah. 00:08:43.920 --> 00:08:45.720 I'm really excited that this is here as well. 00:08:45.720 --> 00:08:49.500 Packaging is such a foundational, important thing in the whole ecosystem. 00:08:49.500 --> 00:08:53.260 And so, anything that makes that better, I'm for it. 00:08:53.260 --> 00:08:53.980 Yeah. 00:08:53.980 --> 00:08:59.520 And I use, like most people, I'm using virtual environments all over the place. 00:08:59.520 --> 00:09:03.860 And keeping track of what's in every one of them is pretty cool. 00:09:03.860 --> 00:09:04.580 Yeah, it definitely is. 00:09:05.000 --> 00:09:05.480 Okay. 00:09:05.480 --> 00:09:12.920 So, I'm going to jump to one, the next topic that you brought up, which is a Reddit. 00:09:12.920 --> 00:09:23.760 Actually, it's a sort of a blog post and a Jupyter project about the new MacBook Pro and what Reddit users think of the MacBook Pro. 00:09:24.580 --> 00:09:28.520 And I, at first, you brought this up and I'm like, this is ridiculous. 00:09:28.520 --> 00:09:33.240 I know that everybody's upset, like half the people are upset and half the people are excited. 00:09:33.300 --> 00:09:38.440 But most of the people I talk to are developers and they're kind of ticked about all the changes of the MacBook Pro. 00:09:38.440 --> 00:09:48.100 But this Reddit article or this article about incorporating Reddit, it's not about the specifics of that. 00:09:48.100 --> 00:09:49.520 It's using sentiment. 00:09:49.760 --> 00:10:03.600 It's talking about sentiment analysis to analyze all the words used in these Reddit threads and try to determine from the words if people are excited or upset about it. 00:10:03.920 --> 00:10:06.420 And I had never even heard of sentiment analysis. 00:10:06.700 --> 00:10:14.040 So, I think it's neat to show a data analysis project that is topical on something that people are talking about right now. 00:10:14.040 --> 00:10:17.000 Yeah, because we all have a thing that we care about. 00:10:17.000 --> 00:10:23.500 Either we work for a company that makes a product or has some sort of public persona or we have an open source project. 00:10:23.500 --> 00:10:26.060 And it would be nice to know, are people liking my work? 00:10:26.180 --> 00:10:30.340 And this ability to apply sentiment analysis to it, it sounds really cool. 00:10:30.340 --> 00:10:33.500 And I think the MacBook Pro is a great thing to just grab because it's timely. 00:10:33.500 --> 00:10:47.360 Yeah, and I think it'd be kind of neat to – it was interesting to apply it to that and probably safer than applying it to something like current politics because, you know, you don't want to get in the middle of that right now. 00:10:47.360 --> 00:10:48.320 No, no, no, no. 00:10:48.320 --> 00:10:52.120 It's T minus 28 hours or something. 00:10:52.120 --> 00:10:53.400 Nobody wants to get in the middle of that. 00:10:53.400 --> 00:10:54.080 Yeah, cool. 00:10:54.080 --> 00:10:55.860 So, I really thought that was neat as well. 00:10:56.600 --> 00:10:58.680 Personally, I have a MacBook Pro I'm holding out. 00:10:58.680 --> 00:11:00.400 I'm not buying this new one for a while. 00:11:00.400 --> 00:11:02.160 I'm going to see where this whole thing shakes out. 00:11:02.160 --> 00:11:07.700 Yeah, I guess I didn't want to get too much into it, but I was curious about that, if you were going to get a new one or not. 00:11:07.700 --> 00:11:14.140 No, I was ready to, you know, go customize it, turn all the knobs to 11 and say, ship it now. 00:11:14.140 --> 00:11:21.140 But it's just, you know, I think there's too many things that are kind of weird and there's not enough of a benefit, so I'm just going to stick with what I got. 00:11:21.400 --> 00:11:22.300 Yeah, I guess. 00:11:22.300 --> 00:11:28.620 And maybe I'm just not doing anything complicated because I'm on a couple-year-old MacBook Air. 00:11:28.620 --> 00:11:31.440 I just don't need very much power, I guess. 00:11:31.440 --> 00:11:33.300 Yeah, that's good news. 00:11:33.300 --> 00:11:34.400 Okay. 00:11:34.500 --> 00:11:36.620 All right, well, that rounds out our news items for the week. 00:11:36.620 --> 00:11:37.940 But, you know, what else? 00:11:37.940 --> 00:11:38.760 What do you got going on? 00:11:38.760 --> 00:11:41.980 I know you've got the Test of Code podcast going. 00:11:41.980 --> 00:11:43.100 You're working on a book. 00:11:43.100 --> 00:11:46.120 You just did some presentations at a conference. 00:11:46.120 --> 00:11:48.800 Why don't you tell people, like, what's the news around your life? 00:11:49.180 --> 00:12:02.040 Yeah, well, I'm very grateful for November to roll around because there's – I know that – I know a lot of people in startups and I guess I've heard that people are working in San Francisco and stuff, work like crazy hours. 00:12:02.040 --> 00:12:03.460 But I'm not used to that. 00:12:03.460 --> 00:12:08.520 I'm an older engineer and I like my 40-hour work weeks. 00:12:08.520 --> 00:12:11.280 But October has been insane. 00:12:11.280 --> 00:12:16.760 I've been doing our projects at work, which I can't talk about and most of the people listening wouldn't care about. 00:12:16.760 --> 00:12:19.940 But we've been doing, like, 60-hour weeks lately. 00:12:19.940 --> 00:12:25.400 And on top of that, I was doing the Pacific Northwest Software Quality Conference. 00:12:25.720 --> 00:12:28.200 And I got to talk about – it was a couple weeks ago. 00:12:28.200 --> 00:12:30.520 I got to talk at that conference. 00:12:30.520 --> 00:12:32.520 And that was a 90-minute talk. 00:12:32.520 --> 00:12:35.480 And that's actually when I was looking at the PyData videos. 00:12:35.480 --> 00:12:44.760 I was impressed with all the information people got out in 30 minutes because it's, like, three times the amount of content that I got out in, like, 90 minutes. 00:12:44.760 --> 00:12:48.600 So – but it was a good experience to talk. 00:12:49.080 --> 00:12:57.600 I'm actually kind of glad that my first conference experience was a long one because I sure got my jitters out of the way. 00:12:57.600 --> 00:13:00.340 And I'm excited to do a future one. 00:13:00.340 --> 00:13:03.320 And the book's coming along good. 00:13:03.320 --> 00:13:05.980 I'm working with an editor. 00:13:05.980 --> 00:13:11.620 And this upcoming book is focused on pytest alone. 00:13:11.620 --> 00:13:12.980 Yeah, that's excellent. 00:13:12.980 --> 00:13:14.820 What's the title so people know what to look for? 00:13:14.820 --> 00:13:16.720 We don't have a title yet. 00:13:16.720 --> 00:13:19.020 The book about pytest. 00:13:19.020 --> 00:13:20.880 Yeah, the book about pytest. 00:13:20.880 --> 00:13:27.280 But I will definitely share more information with this podcast as it becomes available. 00:13:27.280 --> 00:13:28.440 Yeah, yeah, beautiful. 00:13:28.440 --> 00:13:29.620 How about you? 00:13:29.620 --> 00:13:30.340 What's going on with you? 00:13:30.340 --> 00:13:35.260 Well, I've been on an absolute terror of recording Talk Python in me episodes. 00:13:35.260 --> 00:13:38.780 I recorded six weeks' worth last week. 00:13:38.780 --> 00:13:44.880 So that's – given that I spend about 10 hours of research and prep time on each, that meant a very long week. 00:13:44.880 --> 00:13:47.560 But I'm really, really happy with what I got out. 00:13:47.560 --> 00:13:50.320 The next three that I have coming, you know, we talked about pip. 00:13:50.320 --> 00:13:56.980 And there's actually a bit of a crisis in the Python ecosystem's core open source infrastructure. 00:13:56.980 --> 00:14:02.260 And so I'm doing a panel session, including the guy who works on PyPI and pip, Donald Suft. 00:14:02.260 --> 00:14:06.680 And that's called Are We Failing to Fund Python's Core Infrastructure? 00:14:06.760 --> 00:14:10.480 So that comes out this week and also did something which I love the title, 00:14:10.480 --> 00:14:12.960 parsing horrible things with Python with Eric Rose. 00:14:12.960 --> 00:14:20.320 And then Martin Peters, who is either the top or one of the top guys at Stack Overflow as a user, 00:14:20.320 --> 00:14:25.860 a contributor, an answerer, has over half a million reputation in Python. 00:14:25.860 --> 00:14:30.160 And I talked to him about a bunch of the things going on with questions and answers 00:14:30.160 --> 00:14:33.380 and hard problems that they found on Stack Overflow. 00:14:33.380 --> 00:14:35.180 And that's just super revealing. 00:14:35.180 --> 00:14:36.680 I've learned a ton by doing that. 00:14:36.680 --> 00:14:37.200 Wow. 00:14:37.200 --> 00:14:38.680 I can't wait to talk about these. 00:14:38.680 --> 00:14:40.740 Now, is Donald still on PIP? 00:14:40.740 --> 00:14:43.520 Donald is still on pip. 00:14:43.520 --> 00:14:47.680 But the short, short, short version is he was working at HP. 00:14:47.680 --> 00:14:51.240 HP went through a huge bunch of layoffs, cut his whole division. 00:14:51.240 --> 00:14:55.460 And by way of doing that, basically defunded PyPI. 00:14:55.460 --> 00:14:56.160 Wow. 00:14:56.420 --> 00:15:01.240 So there's other companies like Rackspace and a couple of others. 00:15:01.240 --> 00:15:08.420 I'm sorry, I'm forgetting the names that contribute tons of resources in terms of computing and server and bandwidth. 00:15:08.420 --> 00:15:13.080 Like the bandwidth and infrastructure charges for PyPI are $40,000 a month. 00:15:13.080 --> 00:15:13.880 Wow. 00:15:13.940 --> 00:15:17.380 But there's no funding for people to keep that infrastructure running. 00:15:17.380 --> 00:15:19.280 So anyway, that's what that show is about. 00:15:19.280 --> 00:15:20.600 I can't wait for that. 00:15:20.600 --> 00:15:21.220 Yeah, yeah. 00:15:21.220 --> 00:15:22.160 Check it out if you guys are interested. 00:15:22.160 --> 00:15:22.800 All right. 00:15:22.800 --> 00:15:23.280 Oh, yeah. 00:15:23.280 --> 00:15:27.980 I forgot to mention, I do have, I did talk with Rafael Pierzina. 00:15:27.980 --> 00:15:29.180 I'm probably getting that wrong. 00:15:29.180 --> 00:15:34.940 But that's a podcast coming up for testing code focused on pytest and Cookie Cutter. 00:15:34.940 --> 00:15:36.820 So that's coming up soon. 00:15:36.820 --> 00:15:37.740 Absolutely. 00:15:37.740 --> 00:15:38.300 All right. 00:15:38.300 --> 00:15:43.580 Well, thank you, everybody, for listening to this very first episode of Python Bytes. 00:15:43.580 --> 00:15:46.720 Brian, thank you for launching a new podcast with me. 00:15:46.720 --> 00:15:47.860 I think it's going to be a lot of fun. 00:15:47.860 --> 00:15:48.760 I hope people enjoy it. 00:15:48.760 --> 00:15:49.980 I think it'll be fun, too. 00:15:49.980 --> 00:15:50.560 Thank you, Michael. 00:15:50.560 --> 00:15:51.620 Yeah, absolutely. 00:15:51.620 --> 00:15:56.060 So if you are out there listening and you're like, oh, I have a great news item I want to send you guys, 00:15:56.060 --> 00:15:58.520 just visit pythonbytes.fm. 00:15:58.520 --> 00:16:02.880 There's a way right in the menu bar to click and actually send us your news. 00:16:02.880 --> 00:16:06.240 So if you find something that's cool and you want us to cover it in the next episode, 00:16:06.240 --> 00:16:08.580 be sure to send it our way so that it's on our radar. 00:16:08.580 --> 00:16:09.600 All right. 00:16:09.600 --> 00:16:10.380 Thank you, everyone. 00:16:10.380 --> 00:16:11.040 Talk to you later. 00:16:11.040 --> 00:16:11.640 Bye, Brian. 00:16:11.640 --> 00:16:12.240 Bye. 00:16:12.240 --> 00:16:14.900 Thank you for listening to Python Bytes. 00:16:14.900 --> 00:16:17.460 Follow the show on Twitter via at Python Bytes. 00:16:17.460 --> 00:16:20.360 That's Python Bytes as in B-Y-T-E-S. 00:16:20.360 --> 00:16:23.760 And get the full show notes at pythonbytes.fm. 00:16:23.760 --> 00:16:28.120 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:16:28.120 --> 00:16:30.860 We're always on the lookout for sharing something cool. 00:16:30.860 --> 00:16:34.220 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:16:34.220 --> 00:16:37.840 Thank you for listening and sharing this podcast with your friends and colleagues.