WEBVTT 00:00:00.001 --> 00:00:05.640 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:05.640 --> 00:00:09.820 This is episode 42, recorded September 6, 2017. 00:00:09.820 --> 00:00:11.240 I'm Michael Kennedy. 00:00:11.240 --> 00:00:12.160 And I'm Brian Okken. 00:00:12.160 --> 00:00:14.400 And we have the band back together again. 00:00:14.400 --> 00:00:15.100 How about that, Brian? 00:00:15.100 --> 00:00:20.020 Thank you so much for carrying the Python Bytes news banner while I was gone. 00:00:20.020 --> 00:00:22.200 And then I get to pay you back straight away a little bit. 00:00:22.200 --> 00:00:28.420 Yeah, it was fun listening to one with you and I'm blanking right now. 00:00:28.420 --> 00:00:28.940 Yeah, Miguel. 00:00:29.140 --> 00:00:29.640 Yeah, Miguel. 00:00:29.640 --> 00:00:30.620 It was great. 00:00:30.620 --> 00:00:33.240 And then it was kind of fun to talk with some new people. 00:00:33.240 --> 00:00:35.220 But I'm not ready to replace you yet. 00:00:35.220 --> 00:00:35.920 Yeah, no, no. 00:00:35.920 --> 00:00:41.500 I think this idea of maybe every now and then having someone else drop in and give it a fresh set of topics might be cool. 00:00:41.500 --> 00:00:43.000 But yeah, no, it's great to be back together. 00:00:43.000 --> 00:00:47.380 So before we get started, I just want to say thanks to Datadog for sponsoring this show. 00:00:47.380 --> 00:00:52.580 You can check out their special offer at pythonbytes.fm/Datadog, which is actually really cool. 00:00:52.580 --> 00:00:53.820 You get something out of it. 00:00:53.820 --> 00:00:54.880 So that's sweet. 00:00:55.400 --> 00:01:00.760 But let's talk about what a couple of the one of the prolific Python developers out there. 00:01:00.760 --> 00:01:03.720 Just in one week, what he's been up to. 00:01:03.720 --> 00:01:05.680 Yeah, I think it was the last week. 00:01:05.820 --> 00:01:08.700 Kenneth Reitz has been very prolific. 00:01:08.700 --> 00:01:14.220 And I don't know if he's got insomnia and he's not sleeping lately. 00:01:14.220 --> 00:01:17.100 But he's put out a bunch of stuff recently. 00:01:17.100 --> 00:01:23.180 And instead of spreading it out entirely for our entire episode, we're just going to lump it all together. 00:01:23.380 --> 00:01:25.200 This basically could be an entire episode. 00:01:25.200 --> 00:01:26.000 Yeah, it could. 00:01:26.000 --> 00:01:26.780 But, you know. 00:01:26.780 --> 00:01:27.980 That would be wrong. 00:01:27.980 --> 00:01:28.680 That would be wrong. 00:01:28.680 --> 00:01:30.800 So first off, I think this is cool. 00:01:30.800 --> 00:01:36.260 So if you're a Mac user, maybe you use Homebrew to install stuff. 00:01:36.260 --> 00:01:41.440 And one of the things he's put together is Homebrew Pythons, which is a tap. 00:01:41.440 --> 00:01:44.000 I didn't know there was a thing called Homebrew Taps. 00:01:44.620 --> 00:01:52.440 But it's a way you can get all Python 2.5 through 3.6 installed easily with just a couple of command lines. 00:01:52.440 --> 00:01:53.440 Yeah, it's beautiful. 00:01:53.440 --> 00:01:56.880 You just basically register Kenneth's tap. 00:01:56.880 --> 00:02:02.780 And then you just say, brew install Python dash 3.6 or 2.7 or whatever. 00:02:02.780 --> 00:02:05.520 I really like brew. 00:02:05.520 --> 00:02:07.820 And I've been starting to use it more and more for my Mac. 00:02:07.820 --> 00:02:13.340 The next time I set up a fresh operating system, which seems to be about like yearly, I get frustrated and format the thing. 00:02:14.040 --> 00:02:21.940 So next time I'm going to just brew install as much as I can, like MongoDB, Python, Node.js, all the things that you need. 00:02:21.940 --> 00:02:22.520 It's beautiful. 00:02:22.520 --> 00:02:22.980 Yeah. 00:02:22.980 --> 00:02:23.800 Okay. 00:02:23.800 --> 00:02:24.960 Well, moving on. 00:02:24.960 --> 00:02:39.780 Next up, we've got a request threads, which is a, I think it's something built on top of requests that uses Twisted's deferred objects as a return object for requests. 00:02:40.260 --> 00:02:48.340 And so you can use it with either async and await or with Twisted to have asynchronous reading through requests easily. 00:02:48.520 --> 00:02:49.900 Yeah, it's really, really cool. 00:02:49.900 --> 00:02:55.120 So basically you create this async session and you just say await session dot get. 00:02:55.120 --> 00:02:56.580 So you have actually two ways. 00:02:56.580 --> 00:03:00.280 You can do async await Python 3.6 style, which is really cool. 00:03:00.280 --> 00:03:06.500 Or you can use Twisted's deferreds, which work in basically every version of Python. 00:03:06.500 --> 00:03:09.340 So you actually can pick and it's really, really cool. 00:03:10.000 --> 00:03:12.120 So I'm looking forward to seeing that rock. 00:03:12.120 --> 00:03:16.180 And it basically looks like the last activity on everything was a couple days ago. 00:03:16.180 --> 00:03:17.460 So yeah, this is kind of new. 00:03:17.460 --> 00:03:18.200 It's kind of cool. 00:03:18.200 --> 00:03:23.640 And also, before we move on, I really enjoyed your, you interviewed somebody about Twisted recently. 00:03:23.640 --> 00:03:24.340 Oh, yeah, yeah. 00:03:24.340 --> 00:03:24.800 That was Glyph. 00:03:24.800 --> 00:03:28.020 That was the most recent episode that's out, but probably not by the time. 00:03:28.020 --> 00:03:29.440 That was 127 on Talk Python. 00:03:29.440 --> 00:03:29.740 Thanks. 00:03:29.740 --> 00:03:31.200 Yeah, he's doing amazing stuff. 00:03:31.280 --> 00:03:33.340 So that kind of riffs on this as well, for sure. 00:03:33.340 --> 00:03:33.760 Okay. 00:03:33.760 --> 00:03:38.500 Now thinking about instead of threads, you might want to just have a background task. 00:03:38.500 --> 00:03:40.360 And Kenneth has that too. 00:03:40.360 --> 00:03:45.180 He's got a repo called background, which just runs stuff in the background. 00:03:45.180 --> 00:03:45.920 Yeah, it's beautiful. 00:03:45.920 --> 00:03:50.820 You basically put like a decorator on a function and then it becomes this async background thing 00:03:50.820 --> 00:03:52.960 that you just kick off like fire and forget style. 00:03:52.960 --> 00:03:54.320 It's really, really cool. 00:03:54.320 --> 00:03:56.080 Like you can do it for CLIs. 00:03:56.080 --> 00:03:57.140 You can do it for web apps. 00:03:57.140 --> 00:04:00.780 I use this in the Talk Python training site, this type of stuff. 00:04:00.880 --> 00:04:02.300 I don't use this because it's new. 00:04:02.300 --> 00:04:08.380 But like when I first built it and, you know, gave it the capability to like email thousands 00:04:08.380 --> 00:04:09.540 of people and update. 00:04:09.540 --> 00:04:13.960 It turns out that that's really bad to do in a serial way on the main thread. 00:04:13.960 --> 00:04:17.920 So you got to kick that kind of stuff off the background threads or things like Celery. 00:04:17.920 --> 00:04:19.560 And this is a really cool way to do it. 00:04:19.560 --> 00:04:24.080 I know that there's Celery and other things, but having some simple way to just ease into 00:04:24.080 --> 00:04:28.040 multi-process programming and it's really hardly any code at all. 00:04:28.040 --> 00:04:28.280 Right. 00:04:28.280 --> 00:04:29.580 There's no extra service. 00:04:29.840 --> 00:04:31.120 It just runs in process. 00:04:31.120 --> 00:04:32.080 It's simple and easy. 00:04:32.080 --> 00:04:32.440 Yeah. 00:04:32.440 --> 00:04:36.600 Last up from Kenneth, although I'm sure I'm missing something that he's been doing recently, 00:04:36.600 --> 00:04:39.940 is setup.py for humans. 00:04:39.940 --> 00:04:42.280 And this isn't something you run. 00:04:42.280 --> 00:04:49.620 It's just he's got a repo that's an example setup file, which he directly off of it, this 00:04:49.620 --> 00:04:55.440 repo exists to provide an example setup.py file that can be used to bootstrap your next Python 00:04:55.440 --> 00:04:55.940 project. 00:04:55.940 --> 00:05:00.900 It includes some advanced patterns and best practices, as well as some commented out nice 00:05:00.900 --> 00:05:01.440 to haves. 00:05:01.440 --> 00:05:05.780 And it had a quick look through it and it looks actually, I like it. 00:05:05.780 --> 00:05:06.000 Yeah. 00:05:06.220 --> 00:05:08.500 So get out there and make that module package, right? 00:05:08.500 --> 00:05:09.160 Very, very cool. 00:05:09.160 --> 00:05:09.400 Okay. 00:05:09.400 --> 00:05:13.680 And so Kenneth, either slow down or we're going to have to have another podcast just to talk 00:05:13.680 --> 00:05:14.520 about your stuff. 00:05:14.520 --> 00:05:18.660 And he has his own podcast, which is pretty long form. 00:05:18.780 --> 00:05:20.400 So maybe he covers this stuff there as well. 00:05:20.400 --> 00:05:20.920 Yeah, maybe. 00:05:20.920 --> 00:05:22.400 I'm not sure, but yeah, definitely. 00:05:22.400 --> 00:05:23.660 All right. 00:05:23.660 --> 00:05:28.200 So up next is a really positive thing, but it sounds kind of negative. 00:05:28.200 --> 00:05:30.200 I want to talk about the Python death clock. 00:05:30.200 --> 00:05:30.980 Dun, dun, dun. 00:05:30.980 --> 00:05:32.140 Dun, dun, dun. 00:05:32.140 --> 00:05:37.160 So I'm over here on pythonclock.org. 00:05:37.160 --> 00:05:42.200 And it says, Python 2.7 will retire in two years, seven months, five days, seven hours, 00:05:42.200 --> 00:05:44.720 33 minutes, and 35 seconds. 00:05:44.720 --> 00:05:47.580 So that's pretty neat, right? 00:05:47.580 --> 00:05:48.540 So here's this clock. 00:05:48.540 --> 00:05:56.620 There's just a running animated countdown till the 2020 timeframe when Python 2.7 is going 00:05:56.620 --> 00:06:00.240 to be no longer maintained when it goes out of support and things like that. 00:06:00.240 --> 00:06:00.480 Yeah. 00:06:00.480 --> 00:06:06.240 And so this is the get, it's counting to what a best guess for when PyCon in 2020 is. 00:06:06.240 --> 00:06:06.460 Yeah. 00:06:06.460 --> 00:06:07.180 Isn't that interesting? 00:06:07.180 --> 00:06:12.000 There's no, when they said they're going to retire Python 2.7, they didn't say we're going 00:06:12.000 --> 00:06:17.320 to retire it on June 1st, 2020, or we're going to retire it on December 31st, 2020. 00:06:17.320 --> 00:06:20.580 It's just, you know, within that year, kind of. 00:06:20.580 --> 00:06:22.440 So there's a couple of interesting things here. 00:06:22.440 --> 00:06:26.160 This guy's like, all right, so when would be the ideal date to retire this? 00:06:26.160 --> 00:06:28.420 It would be PyCon 2020, of course. 00:06:28.420 --> 00:06:34.080 So he says, I hereby suggest we make PyCon 2020 the official end of life date. 00:06:34.080 --> 00:06:37.480 And we throw a massive party to celebrate all that Python 2 has done for us. 00:06:37.480 --> 00:06:40.760 Python 2, thank you for your years of faithful service. 00:06:40.760 --> 00:06:42.700 Python 3, your time is now. 00:06:42.700 --> 00:06:43.120 Sorry. 00:06:43.120 --> 00:06:53.060 So yeah, I think this is a really cool idea to sort of encourage people to move from legacy 00:06:53.060 --> 00:06:55.040 Python over to modern Python. 00:06:55.040 --> 00:07:01.400 And it's fun to just, you know, have up if you're, when you're leaving your desktop open, 00:07:01.400 --> 00:07:06.020 going to have some coffee, you can just remind everybody around you that Python 2 is dying. 00:07:06.140 --> 00:07:06.460 That's right. 00:07:06.460 --> 00:07:12.440 Or if your non-technical manager says, we can't really afford to upgrade that old Python 2 code, 00:07:12.440 --> 00:07:15.160 you can say, well, here's the Python 2 death clock. 00:07:15.160 --> 00:07:16.460 So this is our timeframe. 00:07:16.460 --> 00:07:19.820 This is an upper bound for when we should get to this. 00:07:19.820 --> 00:07:20.100 Yeah. 00:07:20.200 --> 00:07:22.720 So this other one that you want to talk about is pretty interesting. 00:07:22.720 --> 00:07:28.420 It's kind of a design pattern, almost a pushback on some of the more intense design patterns, 00:07:28.420 --> 00:07:28.880 I would say. 00:07:28.880 --> 00:07:29.240 Yeah. 00:07:29.240 --> 00:07:35.380 So one of the things that I always push, I don't always, but I, my red flag goes up when 00:07:35.380 --> 00:07:41.860 everybody, whenever anybody follows something dogmatically, like just this is a best practice, 00:07:41.860 --> 00:07:42.900 follow it or else. 00:07:42.900 --> 00:07:44.000 I don't, I don't like that. 00:07:44.000 --> 00:07:48.640 This is a article called small functions considered harmful. 00:07:48.640 --> 00:07:54.560 And it just takes a look, it takes a look at, there's a quote from here, general programming 00:07:54.560 --> 00:08:00.360 advice doled out invariably seems to extol the elegance and efficacy of small functions. 00:08:00.360 --> 00:08:06.520 And I have seen, I've seen that like this, the author has to an extreme where sometimes people 00:08:06.520 --> 00:08:12.020 will go to the point where they're writing very small functions, like one line functions. 00:08:12.440 --> 00:08:18.280 And that, and I think that's okay for some cases, like complicated math, maybe it makes 00:08:18.280 --> 00:08:23.140 sense, but small functions that are only used in one place, it actually doesn't help. 00:08:23.140 --> 00:08:28.280 And so there's, she goes through and talks about quite a few problems with small functions, 00:08:28.280 --> 00:08:32.960 including like just making your, there's a lot of fun things that she talks about, but 00:08:32.960 --> 00:08:37.500 I think that just makes it code harder to read to me and especially to newcomers. 00:08:37.700 --> 00:08:43.340 One thing I didn't think about was if you're using classes a lot, adding more class functions, 00:08:43.340 --> 00:08:47.820 just a kind of a litters up your class namespace, your interface. 00:08:47.820 --> 00:08:51.400 So you, it's harder to tell what the class is supposed to be doing. 00:08:51.400 --> 00:08:51.660 Yeah. 00:08:51.660 --> 00:08:55.400 I mean, you almost, you almost got to go to the level of like using double underscores to 00:08:55.400 --> 00:08:57.000 make that stuff not show up. 00:08:57.540 --> 00:08:57.900 Right. 00:08:57.900 --> 00:08:58.260 Yeah. 00:08:58.260 --> 00:08:58.480 Right. 00:08:58.480 --> 00:08:59.300 Sort of hide it. 00:08:59.300 --> 00:09:00.660 So, okay, these are the internal things. 00:09:00.660 --> 00:09:01.920 These are little stuff I broke it into. 00:09:01.920 --> 00:09:04.420 I have mixed feelings about this. 00:09:04.420 --> 00:09:07.060 I am sympathetic to what Cindy says. 00:09:07.060 --> 00:09:09.700 I feel like she has a really good point. 00:09:09.700 --> 00:09:16.580 And certainly some of these like dry principles, some of the design patterns taken too much to 00:09:16.580 --> 00:09:19.000 an extreme are really painful. 00:09:19.000 --> 00:09:26.860 Like I would say one of the hardest applications I've ever worked on is this like 50 to a hundred 00:09:26.860 --> 00:09:32.460 thousand lines of code that was just fully abstract everything. 00:09:32.660 --> 00:09:37.840 design pattern, this dependency injection that, and just every time I wanted to do something 00:09:37.840 --> 00:09:39.060 like, where does this live? 00:09:39.060 --> 00:09:40.900 How can I tell what it does? 00:09:40.900 --> 00:09:43.400 I know like somehow it's assembled at runtime. 00:09:43.400 --> 00:09:47.700 So this thing happens, but like there's so many pieces and patterns contributing to it. 00:09:47.700 --> 00:09:49.220 It was really, really not good. 00:09:49.220 --> 00:09:51.300 And I think that's what she's kind of riffing on. 00:09:51.300 --> 00:09:57.900 But I also think there's, it doesn't hurt to necessarily encourage people to write small 00:09:57.900 --> 00:10:00.520 functions, but if they get too small, it is certainly a problem. 00:10:00.640 --> 00:10:06.940 My, my rule of thumb that I use is does the function need documentation to tell what it 00:10:06.940 --> 00:10:07.240 does? 00:10:07.240 --> 00:10:09.200 If it does, it's probably wrong. 00:10:09.200 --> 00:10:14.480 Now that, that doesn't necessarily apply to like, it's an API in a public package and you've 00:10:14.480 --> 00:10:15.220 never seen it. 00:10:15.220 --> 00:10:15.740 Right. 00:10:15.740 --> 00:10:19.180 But in your own code, like do you need comments to tell you what that function does? 00:10:19.180 --> 00:10:21.640 Or does a short, simple name tell you what it does? 00:10:21.640 --> 00:10:27.080 Like that, I feel like anytime I'm about to write a comment and Martin Fowler, I think it's 00:10:27.080 --> 00:10:29.180 Martin Fowler has a really interesting way of talking about comments. 00:10:29.180 --> 00:10:32.280 He says comments are deodorant for bad code. 00:10:32.280 --> 00:10:37.100 So I feel like if I'm about to write a comment about a function, I'm like, oh, this probably 00:10:37.100 --> 00:10:38.060 just should be rewritten. 00:10:38.060 --> 00:10:40.260 So it's really clear what it does. 00:10:40.260 --> 00:10:44.720 It's certainly a tension, but I guess maybe the warning is don't take it too much to the 00:10:44.720 --> 00:10:45.060 extreme. 00:10:45.060 --> 00:10:47.840 It makes it at least as bad or as bad. 00:10:48.040 --> 00:10:52.800 And I want to touch on one more thing because I've been thinking about agile and agility 00:10:52.800 --> 00:10:56.060 a lot lately, especially with refactoring code. 00:10:56.220 --> 00:11:03.900 And one of the comments she brings up is that breaking up your code into small pieces can create premature 00:11:03.900 --> 00:11:04.900 abstractions. 00:11:04.900 --> 00:11:11.240 So you're breaking it up into multiple pieces and that makes it harder to, it might make it 00:11:11.240 --> 00:11:14.840 harder to refactor once you completely understand the problem space. 00:11:15.020 --> 00:11:22.100 So I think the time to break up things is after you've completely mostly solved the problem 00:11:22.100 --> 00:11:25.400 and you're just making it maintainable for the future. 00:11:25.400 --> 00:11:25.700 Yeah. 00:11:25.700 --> 00:11:26.520 That's an interesting point. 00:11:26.520 --> 00:11:30.440 Like once you fully understand and have solved the problem, like apply some refactoring tooling 00:11:30.440 --> 00:11:32.160 or concepts against it. 00:11:32.160 --> 00:11:36.180 And then it's much more likely to be stable and you've got the big picture and then you 00:11:36.180 --> 00:11:36.660 break it up. 00:11:36.660 --> 00:11:36.940 Sure. 00:11:36.940 --> 00:11:37.440 I agree. 00:11:37.440 --> 00:11:38.800 Nice. 00:11:39.220 --> 00:11:40.360 You know what I've been thinking about lately? 00:11:40.360 --> 00:11:40.720 What? 00:11:40.720 --> 00:11:41.380 Datadog. 00:11:41.380 --> 00:11:41.980 Oh yeah. 00:11:41.980 --> 00:11:42.860 Yeah. 00:11:42.860 --> 00:11:44.640 So Datadog, they're sponsoring this episode. 00:11:44.640 --> 00:11:45.220 Thank you guys. 00:11:45.220 --> 00:11:52.340 What they do is basically they let you have an entire view of your entire system, not just 00:11:52.340 --> 00:11:52.840 your application. 00:11:52.840 --> 00:11:57.260 So you've got your Python code, but maybe your Python code is running on a web server. 00:11:57.260 --> 00:11:58.480 It's built on Flask. 00:11:58.480 --> 00:11:59.880 It's talking to MongoDB. 00:11:59.880 --> 00:12:04.280 It's hosted on a set of scaled out Ubuntu servers or Nginx and MicroWSGI. 00:12:04.280 --> 00:12:08.880 You can actually integrate Datadog into all those things that I've mentioned and get an 00:12:08.880 --> 00:12:15.460 entire view of how your overall system, those things all taken as a whole work, not just 00:12:15.460 --> 00:12:20.160 logs or performance monitoring for your Python app, which is pretty awesome if you have a 00:12:20.160 --> 00:12:21.100 big distributed system. 00:12:21.100 --> 00:12:21.460 Yeah. 00:12:21.460 --> 00:12:21.840 Yeah. 00:12:21.840 --> 00:12:27.620 So they actually have a cool little tutorial you can take at pythonbytes.fm/Datadog 00:12:27.620 --> 00:12:31.900 and it takes just a few minutes and they'll even send you a cool Datadog t-shirt if you 00:12:31.900 --> 00:12:32.600 do the tutorial. 00:12:32.600 --> 00:12:35.580 So check it out at pythonbytes.fm/Datadog. 00:12:35.580 --> 00:12:36.700 It helps support the show. 00:12:36.700 --> 00:12:37.020 Yeah. 00:12:37.020 --> 00:12:37.500 Yeah. 00:12:37.600 --> 00:12:40.040 If I do the tutorial, can I get a t-shirt even? 00:12:40.040 --> 00:12:40.780 You can get a t-shirt. 00:12:40.780 --> 00:12:41.980 I don't think you've been excluded. 00:12:41.980 --> 00:12:42.420 Okay. 00:12:42.420 --> 00:12:42.800 Great. 00:12:42.800 --> 00:12:43.180 Awesome. 00:12:43.180 --> 00:12:43.300 Yeah. 00:12:43.300 --> 00:12:44.340 We could both go get a t-shirt. 00:12:44.340 --> 00:12:44.760 Awesome. 00:12:44.760 --> 00:12:48.740 So we talked about the death clock a little bit. 00:12:48.740 --> 00:12:54.260 Let's talk about more sugar, less of a stick type of a thing to encourage people to use 00:12:54.260 --> 00:12:54.800 modern Python. 00:12:54.800 --> 00:12:56.340 I ran across this site. 00:12:56.340 --> 00:12:58.740 I can't remember where I found it, but it's really cool. 00:12:58.840 --> 00:13:00.620 It's called Y-Python 3. 00:13:00.620 --> 00:13:06.380 And what it is, is if you pull it up, there's a simple little code sample of some Python 3 00:13:06.380 --> 00:13:08.300 feature that's awesome that you can't use. 00:13:08.300 --> 00:13:10.660 And there's a button like, show me another cool feature. 00:13:10.660 --> 00:13:14.800 And it'll randomly generate another code sample of something that you could do. 00:13:15.500 --> 00:13:17.040 And so I hit it a few, isn't that cool? 00:13:17.040 --> 00:13:18.460 So I hit it, it's really simple and quick. 00:13:18.460 --> 00:13:19.480 So I hit it a few times. 00:13:19.480 --> 00:13:20.520 I got annotations. 00:13:20.520 --> 00:13:25.560 So type annotations like functions, say it takes two integers and returns a string or something 00:13:25.560 --> 00:13:25.980 like that. 00:13:25.980 --> 00:13:30.200 Keyword only arguments, which is pretty cool. 00:13:30.320 --> 00:13:35.620 So a way to say like, you're only allowed, you can basically say like, you must pass these 00:13:35.620 --> 00:13:38.380 certain arguments as keywords, which was not a thing in Python 2. 00:13:38.380 --> 00:13:44.700 Yield from for basically consuming generators and turning them into generators, enums, and 00:13:44.700 --> 00:13:45.220 a bunch more. 00:13:45.220 --> 00:13:48.040 You can sit there and keep clicking, give me another random sample. 00:13:48.040 --> 00:13:48.920 It's fun, right? 00:13:48.920 --> 00:13:49.640 That is nice. 00:13:49.640 --> 00:13:50.240 Yeah, cool. 00:13:50.240 --> 00:13:51.880 So just go play around with that. 00:13:51.880 --> 00:13:55.640 Actually, there was a few things that I learned about that I didn't know, like the secrets. 00:13:55.640 --> 00:13:58.880 I just have no reason to really play with the cryptographic secret stuff, but that's 00:13:58.880 --> 00:14:00.520 apparently new in Python 3 and pretty nice. 00:14:00.520 --> 00:14:00.860 Yeah. 00:14:00.860 --> 00:14:01.180 Nice. 00:14:01.180 --> 00:14:06.480 So let's talk about something a little above the code, but something happens a lot in technical 00:14:06.480 --> 00:14:06.940 spaces. 00:14:06.940 --> 00:14:07.400 Yeah. 00:14:07.400 --> 00:14:07.620 Drinking. 00:14:07.620 --> 00:14:09.060 Drinking, definitely. 00:14:09.060 --> 00:14:14.400 And speaking of secrets as well, there's a secret that is not so much as a secret that 00:14:14.400 --> 00:14:16.100 not everybody drinks alcohol. 00:14:16.100 --> 00:14:22.060 So I actually got, I thought about this a lot because Trey Hunter brought it up recently on 00:14:22.060 --> 00:14:28.500 Twitter and he said that a lot of the conferences and tech events that he goes to, he, and I'll 00:14:28.500 --> 00:14:28.880 quote him. 00:14:28.880 --> 00:14:34.100 I feel, I sometimes feel excluded when events include nice alcohol, but cheap soda. 00:14:34.100 --> 00:14:36.580 And I've never really thought about that before. 00:14:36.580 --> 00:14:38.260 And I want everybody else to think about it. 00:14:38.260 --> 00:14:45.900 And so there's this acronym called ENAB, E-A-N-A-B, which is equally attractive, non-alcoholic 00:14:45.900 --> 00:14:46.340 beverage. 00:14:46.340 --> 00:14:48.740 And I think it's a cool idea. 00:14:48.740 --> 00:14:49.140 Yeah. 00:14:49.140 --> 00:14:56.620 So there's a, Trey also linked to us to, got us onto a Stanford site because there is like 00:14:56.620 --> 00:14:59.000 an alcohol.stanford site, which is cool. 00:14:59.000 --> 00:15:03.900 ENABs are required for all Stanford parties and it's a, it's a cool idea. 00:15:04.000 --> 00:15:08.340 So if you're going to have like, like, let's say we have a get together and you got like 00:15:08.340 --> 00:15:13.860 some specialty beers, we'll go out and try to find some specialty local sodas also, or 00:15:13.860 --> 00:15:18.020 make sure that you have a nice, if you've got a spiked punch, make a non-alcoholic version. 00:15:18.380 --> 00:15:24.280 And then the Stanford site also has quite a few mocktail recipes that look easy and delicious. 00:15:24.280 --> 00:15:25.340 Yeah, that's cool. 00:15:25.340 --> 00:15:27.020 And those are pretty easy to do, right? 00:15:27.020 --> 00:15:31.540 Like here in Portland, I know we have a bunch of like locally brewed root beers, for example, 00:15:31.540 --> 00:15:32.700 and things like that. 00:15:32.700 --> 00:15:32.920 Yeah. 00:15:32.920 --> 00:15:33.600 So yeah. 00:15:33.600 --> 00:15:34.500 Hot lip sodas. 00:15:34.500 --> 00:15:34.760 Yum. 00:15:34.760 --> 00:15:35.180 Yeah. 00:15:35.180 --> 00:15:35.980 Hot lip sodas. 00:15:35.980 --> 00:15:39.340 Those are really nice and not alcoholic unless you spike them. 00:15:39.340 --> 00:15:44.800 And I think most events now think about having non-alcoholic stuff too, but it's the equally 00:15:44.800 --> 00:15:49.920 attractive part of make sure that you're not getting cheap stuff if you're going all out 00:15:49.920 --> 00:15:50.560 on the alcohol. 00:15:50.560 --> 00:15:55.700 You could have either that really cool minty cocktail or RC Cola. 00:15:55.700 --> 00:15:56.280 Yeah. 00:15:56.280 --> 00:16:00.900 And if you're in the Portland area and you are serving cocktails, let me know and I'll show 00:16:00.900 --> 00:16:01.120 up. 00:16:01.120 --> 00:16:02.240 Yeah, yeah, exactly. 00:16:02.240 --> 00:16:03.540 I'll be there with you guys. 00:16:03.540 --> 00:16:03.840 Yeah. 00:16:03.840 --> 00:16:04.200 Awesome. 00:16:04.200 --> 00:16:06.340 So there's probably a theme. 00:16:06.340 --> 00:16:09.520 I don't know, maybe I was just in a particular mood today or something because I've kind of 00:16:09.520 --> 00:16:14.780 chosen a related type of topics, but we've touched a few times on the podcast. 00:16:14.780 --> 00:16:20.820 And I keep coming back to this, not just to like be a cheerleader and rah, rah, rah, 00:16:20.820 --> 00:16:25.420 things are great, but you know, people bet their careers at least short term on studying 00:16:25.420 --> 00:16:30.800 certain technologies and pursuing one thing over the other, show Node.js over, you know, 00:16:30.800 --> 00:16:33.040 Python and web apps or something like this, right? 00:16:33.040 --> 00:16:38.000 So there's a really interesting article from Stack Overflow and Stack Overflow, I think is 00:16:38.000 --> 00:16:42.020 killing it in terms of deep insight into the developer community. 00:16:42.140 --> 00:16:44.300 And the title is not so bad here. 00:16:44.300 --> 00:16:46.420 It's The Incredible Growth of Python. 00:16:46.420 --> 00:16:47.260 How about that? 00:16:47.260 --> 00:16:49.280 Yeah, this is actually a fascinating article. 00:16:49.280 --> 00:16:50.560 So I'm glad that you brought it up. 00:16:50.560 --> 00:16:50.780 Yeah. 00:16:50.780 --> 00:16:53.860 So I just, it came out today, like just a few hours ago. 00:16:53.860 --> 00:16:55.080 I'm like, we have to cover this today. 00:16:55.080 --> 00:16:58.180 So the idea is they recently wrote it. 00:16:58.180 --> 00:16:59.020 This is not this article. 00:16:59.020 --> 00:17:03.720 Previously, they wrote an article exploring how wealthy countries, those that are defined 00:17:03.720 --> 00:17:08.920 by high, as high income countries by the World Bank tend to visit a slightly different set 00:17:08.920 --> 00:17:13.640 of technologies than the counterpart of developers in the world. 00:17:13.640 --> 00:17:19.740 And they said, well, if we actually look at that set, the largest difference we saw was among 00:17:19.740 --> 00:17:22.580 a Python programmers and the Python programming language. 00:17:23.120 --> 00:17:30.060 And in high countries, Python is even more popular, more extreme in its popularity than things 00:17:30.060 --> 00:17:35.440 like Stack Overflow trends, Google trends, and other Python, other language rankings would 00:17:35.440 --> 00:17:36.140 make it out to be. 00:17:36.140 --> 00:17:41.520 So they basically said, in this article, we're going to make a case that Python has a solid 00:17:41.520 --> 00:17:45.180 claim to being the fastest growing major programming language, period. 00:17:45.180 --> 00:17:46.320 Yeah. 00:17:46.400 --> 00:17:48.120 So did you look through some of the graphs in there? 00:17:48.120 --> 00:17:48.460 Yeah. 00:17:48.460 --> 00:17:53.480 Well, the one that I didn't understand, hoping you can explain to me, is the predicting future 00:17:53.480 --> 00:17:57.420 growth looks like an even larger gap between Python and everything else. 00:17:57.420 --> 00:17:57.680 Yeah. 00:17:57.680 --> 00:17:57.900 Yeah. 00:17:57.900 --> 00:18:05.020 So they started out by saying in June 2017 was the first month that Python was the most visited 00:18:05.020 --> 00:18:07.580 tag on Stack Overflow, period. 00:18:07.580 --> 00:18:12.880 That's, of course, all those conversations restricted to these high income nations, right? 00:18:12.980 --> 00:18:16.820 US, Europe, Canada, Australia, those types of places. 00:18:16.820 --> 00:18:23.140 But if you restrict your attention to those areas, Python is by far the most visited. 00:18:23.140 --> 00:18:27.120 It's actually grown two and a half times in popularity since 2012. 00:18:27.120 --> 00:18:34.200 And there's one that compares it to, there's a graph called Python compared to smaller growing 00:18:34.200 --> 00:18:35.440 technologies. 00:18:35.440 --> 00:18:37.000 But what was the first one? 00:18:37.000 --> 00:18:38.220 You asked me about which one? 00:18:38.220 --> 00:18:39.600 Looking at future growth. 00:18:39.600 --> 00:18:40.180 Oh, right, right. 00:18:40.180 --> 00:18:41.360 The prediction of future growth. 00:18:41.420 --> 00:18:47.520 So they said, look, if we're going to look at these graphs over from 2012 until now. 00:18:47.520 --> 00:18:50.760 And then the guy who wrote this article is actually a data scientist. 00:18:50.760 --> 00:18:54.020 And he said he used to do a little Python, but he only does R. So he doesn't really have 00:18:54.020 --> 00:18:56.400 much of a horse in this race. 00:18:56.400 --> 00:19:02.500 But he said, let's apply statistical modeling to the growth curve out to 2020. 00:19:02.500 --> 00:19:05.060 And if you look at that, it's just like crazy. 00:19:05.180 --> 00:19:09.280 So they say, well, maybe there's going to be a back and forth with Java as students go 00:19:09.280 --> 00:19:10.720 back to school this month. 00:19:10.720 --> 00:19:15.320 And they're going to look up issues for their homework because Java's taught a lot of the 00:19:15.320 --> 00:19:16.380 first year computer science. 00:19:16.380 --> 00:19:19.520 But if you follow this trend, it's like, it's domination. 00:19:19.520 --> 00:19:21.000 It's basically what he said. 00:19:21.000 --> 00:19:25.200 There's another one that's really interesting because I feel like people compare this a lot. 00:19:25.200 --> 00:19:27.620 It says Python compared to smaller growing technologies. 00:19:27.620 --> 00:19:32.820 So Python often gets compared to things like Go and Rust in the data science world, it gets 00:19:32.820 --> 00:19:34.580 compared to R and so on. 00:19:34.580 --> 00:19:42.400 And there's a graph showing it just like 10, 20 times more popular and steeper growth curves 00:19:42.400 --> 00:19:46.420 than all of those languages, which I think is really interesting because people kind of see 00:19:46.420 --> 00:19:51.160 these shiny new languages and go, oh my gosh, this is, I should just abandon Python and go 00:19:51.160 --> 00:19:52.360 write Go or whatever. 00:19:52.360 --> 00:19:56.320 And that may be true for some particular case, but not in general. 00:19:56.320 --> 00:19:56.680 Yeah. 00:19:56.680 --> 00:20:04.480 And some of the strong languages that we have been around for a while, like PHP, C++, Java, 00:20:04.480 --> 00:20:10.780 it's interesting to see that those charts are, they've been relatively, either relatively 00:20:10.780 --> 00:20:13.960 flat or slightly declining for the last few years. 00:20:13.960 --> 00:20:14.200 Yeah. 00:20:14.200 --> 00:20:17.600 A lot of the standard well-known ones, the ones you named, right? 00:20:17.600 --> 00:20:22.080 They are definitely slowly, slowly trending down, which is not amazing for them. 00:20:22.080 --> 00:20:22.440 Yeah. 00:20:22.440 --> 00:20:22.760 Yeah. 00:20:22.760 --> 00:20:24.560 So also related, there's another article. 00:20:24.780 --> 00:20:30.120 I almost covered this one, but in fact, this one was just so, so neat and had so much data 00:20:30.120 --> 00:20:32.240 behind it that I thought I'd rather cover this. 00:20:32.240 --> 00:20:36.640 But there's another one that says Python overtakes R to become the leader in data science and machine 00:20:36.640 --> 00:20:37.340 learning platforms. 00:20:37.340 --> 00:20:38.940 So these are all of course related. 00:20:38.940 --> 00:20:41.000 So I linked to that one in there as well. 00:20:41.000 --> 00:20:41.500 Yeah. 00:20:41.500 --> 00:20:44.740 And finally, one, one wrap up is this was applied to high income countries. 00:20:44.740 --> 00:20:49.660 If you apply to countries that are not in that group, they said Python has the exact same 00:20:49.660 --> 00:20:51.080 growth curve. 00:20:51.080 --> 00:20:53.400 It's just, it's starting at a lower point. 00:20:53.400 --> 00:20:57.140 So they expect in the future in a few years that to also be able to make these kinds of 00:20:57.140 --> 00:21:00.080 statements there, but there's like a lag and they talk a little bit about that. 00:21:00.080 --> 00:21:00.720 It's kind of interesting. 00:21:00.720 --> 00:21:00.920 Yeah. 00:21:00.920 --> 00:21:05.360 I'll have to go back and read those other article because I don't understand why the difference 00:21:05.360 --> 00:21:06.520 between different countries. 00:21:06.520 --> 00:21:09.600 I didn't get a good solid answer there either. 00:21:09.600 --> 00:21:09.900 Okay. 00:21:09.900 --> 00:21:10.460 Cool. 00:21:10.460 --> 00:21:11.920 But worth definitely worth checking out. 00:21:11.920 --> 00:21:13.480 Hey, that's our six, man. 00:21:13.480 --> 00:21:14.960 So that is our six. 00:21:15.080 --> 00:21:15.640 That was fun. 00:21:15.640 --> 00:21:16.860 What's up with you? 00:21:16.860 --> 00:21:17.980 What's any news? 00:21:17.980 --> 00:21:20.160 Well, I've been doing a couple of things. 00:21:20.160 --> 00:21:27.320 I've been learning the joyful bit of the decay of online courses and been re-recording some 00:21:27.320 --> 00:21:32.580 of my online courses, sections of them as the web properties they depended upon, like web 00:21:32.580 --> 00:21:35.400 services or something, change their model or went away. 00:21:35.400 --> 00:21:36.380 So that was fun. 00:21:36.380 --> 00:21:41.360 But it's all good to be able to keep that stuff fresh. 00:21:41.360 --> 00:21:44.080 Also, I'm going to pie cascades. 00:21:44.080 --> 00:21:49.640 So this is a new pie con in Vancouver, British Columbia, beautiful place of the world in 00:21:49.640 --> 00:21:50.680 January 2018. 00:21:50.680 --> 00:21:54.900 So I already booked my flight and I'm trying to get you to go and you're on the fence, man. 00:21:54.900 --> 00:21:56.780 Yeah, I really want to go. 00:21:56.780 --> 00:22:01.340 I don't know what my January is going to look like yet, but I should decide soon so I can 00:22:01.340 --> 00:22:01.780 get a flight. 00:22:01.780 --> 00:22:02.640 Yeah, you definitely should. 00:22:02.640 --> 00:22:03.520 Awesome. 00:22:03.520 --> 00:22:03.940 How about you? 00:22:03.940 --> 00:22:04.860 What else is new? 00:22:04.860 --> 00:22:07.800 Well, I just got back from Germany. 00:22:08.300 --> 00:22:11.860 And one of the things I did there was I met up with the... 00:22:11.860 --> 00:22:18.420 I want to shout out to everybody at the Munich Python user group that like less than a week's 00:22:18.420 --> 00:22:22.620 notice decided to come together and hang out with me for an evening, which is like totally 00:22:22.620 --> 00:22:23.000 awesome. 00:22:23.000 --> 00:22:23.940 And it was a lot of fun. 00:22:23.940 --> 00:22:27.900 We talked about pytest, of course, but we talked about quite a few other things as well. 00:22:27.900 --> 00:22:28.180 Yeah. 00:22:28.180 --> 00:22:28.880 That's awesome. 00:22:28.880 --> 00:22:30.960 They were able to put that together on a short notice for you. 00:22:31.080 --> 00:22:31.320 Yeah. 00:22:31.320 --> 00:22:34.580 And then something happened just today and I didn't even know it was going to happen. 00:22:34.580 --> 00:22:37.500 So the pytest book I'm now no longer working on. 00:22:37.500 --> 00:22:43.620 The last beta was beta 5, but beta 6 just came out today, which is the... 00:22:43.620 --> 00:22:46.920 I wasn't involved at all, but it's all the copy editing changes. 00:22:46.920 --> 00:22:48.380 They fixed all my typos. 00:22:48.380 --> 00:22:48.920 Oh, nice. 00:22:48.920 --> 00:22:53.320 So this stuff has been pushed off to the editors and they're rolling it back out to the readers, 00:22:53.320 --> 00:22:53.520 huh? 00:22:53.520 --> 00:22:53.880 Yeah. 00:22:53.880 --> 00:22:56.140 And so it's just a... 00:22:56.140 --> 00:23:00.160 There's some magic machine now that turns it into a book, a physical book. 00:23:00.500 --> 00:23:05.460 Does your book have a version number, like 1.3, 0.7, 0.6? 00:23:05.460 --> 00:23:06.700 No. 00:23:06.700 --> 00:23:07.520 It should. 00:23:07.520 --> 00:23:12.980 Well, I mean, all of it's stored in revision control, so it has versions that way. 00:23:12.980 --> 00:23:13.320 Yeah. 00:23:13.320 --> 00:23:16.040 It just has a SHA, really long, hard to make any sense of. 00:23:16.040 --> 00:23:17.620 Awesome. 00:23:17.620 --> 00:23:17.860 All right. 00:23:17.860 --> 00:23:19.480 Well, it's good to be back together with you, Brian. 00:23:19.480 --> 00:23:24.080 I think we're both going to be relatively stable for next few episodes probably, so that'll 00:23:24.080 --> 00:23:24.340 be good. 00:23:24.340 --> 00:23:25.240 Yeah, definitely. 00:23:25.240 --> 00:23:26.100 So thanks a lot. 00:23:26.100 --> 00:23:28.900 Thank you for listening to Python Bytes. 00:23:28.900 --> 00:23:31.460 Follow the show on Twitter via at Python Bytes. 00:23:31.460 --> 00:23:34.360 That's Python Bytes as in B-Y-T-E-S. 00:23:34.360 --> 00:23:37.800 And get the full show notes at pythonbytes.fm. 00:23:37.800 --> 00:23:42.120 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:23:42.120 --> 00:23:44.820 We're always on the lookout for sharing something cool. 00:23:44.820 --> 00:23:48.220 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:23:48.700 --> 00:23:51.840 Thank you for listening and sharing this podcast with your friends and colleagues.