WEBVTT 00:00:00.001 --> 00:00:05.240 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:05.240 --> 00:00:11.220 This is episode 92, recorded August 23rd, 2018. I'm Michael Kennedy. 00:00:11.220 --> 00:00:12.100 And I'm Brian Okken. 00:00:12.100 --> 00:00:13.000 Hey, Brian. How are you doing? 00:00:13.000 --> 00:00:13.840 I'm doing great. 00:00:13.840 --> 00:00:18.140 Yeah, same here. I'm excited to be back together. And I can't wait to talk about some of these things. 00:00:18.140 --> 00:00:23.900 We have some like amazing drop the mic sort of stuff coming up. So pretty excited to get into that. 00:00:23.900 --> 00:00:28.960 And also pretty excited that DigitalOcean is sponsoring this episode. So thank you, DigitalOcean. 00:00:28.960 --> 00:00:34.480 Check them out at pythonbytes.fm/digitalocean. Get $100 credit for new users. 00:00:34.480 --> 00:00:37.560 Right now, have we covered the fact that Python is popular? 00:00:37.560 --> 00:00:40.020 Yeah, but I wanted to cover it again. 00:00:40.020 --> 00:00:40.700 Why not? 00:00:40.700 --> 00:00:52.120 So Python is pretty popular. And for the second year in a row, it came out as the number one popular programming language in the IEEE survey. 00:00:52.520 --> 00:01:01.500 So there's a every year IEEE puts out a popular programming survey and that it's at the top isn't really news. 00:01:01.720 --> 00:01:09.380 What's really kind of cool about it, though, and we'll have a link to the results. It's an interactive results thing. I think we covered it last year. 00:01:09.540 --> 00:01:16.480 But this time, Python is number one, even in embedded. And that's the thing that stands out this year. 00:01:16.480 --> 00:01:25.480 Yeah. And it's worth pointing out that this survey is for electrical engineers. It's not like the same as, say, the Stack Overflow survey, which is maybe more developer focused. 00:01:25.480 --> 00:01:31.380 So it's like you saw another area where Python is sort of taking over, which is awesome. Embedded, like you say. 00:01:31.380 --> 00:01:37.180 The article that we're going to link to actually talks about whether or not this is real. 00:01:37.180 --> 00:01:46.300 And some people are doubting that Python really is that popular in embedded because, I mean, there's a lot of day-to-day job are embedded. 00:01:46.300 --> 00:01:48.360 The embedded work I do is in C++. 00:01:48.880 --> 00:01:54.940 There's a big range of what it means to be embedded. So they don't really define that. You have to kind of define that yourself. 00:01:54.940 --> 00:02:01.060 I define it mostly as you're developing software on a platform that's not the target platform. 00:02:01.060 --> 00:02:04.360 And the target platform doesn't really look like a computer. 00:02:04.360 --> 00:02:09.200 Like a Raspberry Pi or maybe like something embedded in your car or something like that? 00:02:09.200 --> 00:02:12.460 Yeah, definitely. But even it's a broad definition. 00:02:12.460 --> 00:02:18.540 So the embedded software I do actually runs on a Windows machine, but it doesn't look like a Windows machine. 00:02:18.620 --> 00:02:21.140 It looks like a, it's a testing equipment. 00:02:21.140 --> 00:02:23.860 It looks like a stereo component with dials and stuff. 00:02:23.860 --> 00:02:25.960 We don't run Python. We run C++. 00:02:25.960 --> 00:02:31.500 However, like you said, like Raspberry Pi, I think there's been a big push lately. 00:02:31.500 --> 00:02:33.560 I think that's where this is coming from. 00:02:33.560 --> 00:02:47.680 There's been a big push lately of trying to get people involved in more circuits and more real interactive programming with electronics through the use of MicroPython and CircuitPython on like Raspberry Pis and Arduinos. 00:02:48.360 --> 00:02:50.320 MicroBits and all that sort of stuff. 00:02:50.320 --> 00:02:55.580 And including the Mew editor that really tries to teach people right out of the get-go. 00:02:55.580 --> 00:03:00.640 So there is some doubt of whether or not really Python's the top for embedded, but I don't doubt it at all. 00:03:00.640 --> 00:03:02.200 I think it's going to stay. 00:03:02.200 --> 00:03:02.940 Yeah, I think it's cool. 00:03:02.940 --> 00:03:17.980 I mean, you could have the debate of, well, it's on professional shipping embedded devices that are for sale versus embedded in general, which like you say, could include $5 Adafruit type things that people play with or they're using in school, but it's not for sale per se. 00:03:18.120 --> 00:03:18.300 Right. 00:03:18.300 --> 00:03:25.900 There's a lot of people taking those Adafruit things and turning them into kits for people or products for people to like wear. 00:03:25.900 --> 00:03:33.460 And then the people that are learning on that are going to go into, it might go into other embedded jobs. 00:03:33.460 --> 00:03:43.560 And I think since Python is faster to develop in, there's a big incentive, I think, for people to use a higher level language and embedded if they can, even in a production level. 00:03:43.740 --> 00:03:45.500 Yeah, and if they are, it's got to be Python, right? 00:03:45.500 --> 00:03:46.380 Like what else would it be? 00:03:46.380 --> 00:03:46.740 Right. 00:03:46.740 --> 00:03:47.080 Yeah. 00:03:47.080 --> 00:03:47.340 Yeah. 00:03:47.340 --> 00:03:49.960 Maybe JavaScript, but that's a little shaky. 00:03:49.960 --> 00:03:50.940 Very interesting. 00:03:50.940 --> 00:03:51.320 All right. 00:03:51.320 --> 00:03:53.420 So are you ready for something incredible? 00:03:53.420 --> 00:03:54.060 Yeah. 00:03:54.060 --> 00:03:56.320 Is this drop the mic thing you're talking about? 00:03:56.320 --> 00:03:56.760 Yeah. 00:03:56.760 --> 00:03:58.980 Unfortunately, it's the second one, so I'll have to pick it back up. 00:03:58.980 --> 00:04:03.060 But let me work my way into this the way that it develops. 00:04:03.060 --> 00:04:06.840 So we have the mailing list, Python dev, okay? 00:04:06.840 --> 00:04:08.680 And that's where a lot of the core developers talk. 00:04:08.680 --> 00:04:09.300 Okay. 00:04:09.300 --> 00:04:19.080 On this list, someone was talking about finding a better way to test the C APIs instead of writing some C tests, some Python tests, and those kinds of things, right? 00:04:19.080 --> 00:04:21.080 You can be sympathetic with this, I'm sure. 00:04:21.080 --> 00:04:25.320 And they were saying, look, we need to test the C code inside CPython. 00:04:25.320 --> 00:04:29.560 And one way to do this would maybe we could use something like Cython. 00:04:29.560 --> 00:04:37.700 And we talked previously about how Cython lets you pretty easily integrate with C and Python at the same time. 00:04:38.100 --> 00:04:40.620 So there was some debate saying, you know, it might be a good idea. 00:04:40.620 --> 00:04:41.780 It might not be a good idea. 00:04:41.780 --> 00:04:44.700 Like on one hand, Ccython is changing a lot. 00:04:44.700 --> 00:04:51.360 It's a pretty big dependency to take as part of the core development component of Python itself. 00:04:51.360 --> 00:04:52.480 Things like that. 00:04:52.480 --> 00:04:57.000 But then something pretty incredible came along here. 00:04:57.000 --> 00:04:59.480 And I don't know how far along this is or whatever. 00:04:59.480 --> 00:05:03.140 But this guy named Yuri steps in and drops a bit of a bombshell. 00:05:03.140 --> 00:05:06.140 And he says, hey, you guys, this Cython thing is kind of cool. 00:05:06.140 --> 00:05:07.460 And I don't know if you want to do it. 00:05:07.460 --> 00:05:12.380 But over at Dropbox, we're working on a new compiler for Python called mypyC. 00:05:12.380 --> 00:05:23.300 And it just says, well, this mypyC will compile type annotated Python to an optimized C code, which ultimately compiles to machine instructions. 00:05:23.300 --> 00:05:24.320 That's pretty cool. 00:05:24.320 --> 00:05:25.040 How about that? 00:05:25.140 --> 00:05:27.660 So it's like Cython, right? 00:05:27.660 --> 00:05:33.620 And we've seen that where Cython can make things like 20 or 50 times faster in certain circumstances because it's compiled. 00:05:33.620 --> 00:05:35.340 Really interesting stuff there. 00:05:35.340 --> 00:05:39.320 But the problem with Cython is it's a superset of Python. 00:05:39.320 --> 00:05:40.640 You have like the C def. 00:05:40.640 --> 00:05:46.080 Like you basically write in a separate language that if you squint, you're like, wait a minute, is that Python? 00:05:46.080 --> 00:05:46.780 No, no, no. 00:05:46.820 --> 00:05:49.440 There's a lot of weird other stuff going on around here. 00:05:49.440 --> 00:05:51.100 This is not Python, but it's Python-like. 00:05:51.100 --> 00:05:55.220 And it doesn't even use the same type annotations because it sort of predated that. 00:05:55.220 --> 00:05:59.980 So Cython is good, but it has like this, you got to write in Cython, not Python. 00:05:59.980 --> 00:06:05.380 This mypyC thing they're talking about, this is straight Python 3.6 code. 00:06:05.380 --> 00:06:10.360 It runs on CPython, but you can compile it to machine instructions like Cython. 00:06:10.500 --> 00:06:11.980 It's not all of Python. 00:06:11.980 --> 00:06:13.620 It's a subset, right? 00:06:13.620 --> 00:06:15.080 It's a subset, exactly. 00:06:15.080 --> 00:06:21.240 And so I think, you know, the main thing is you have to type annotate everything so it can actually compile it. 00:06:21.240 --> 00:06:24.460 Like that's more or less the difference with Cython is you type annotate everything. 00:06:24.460 --> 00:06:26.800 But it's a different type. 00:06:26.800 --> 00:06:27.940 It's using Cython types. 00:06:27.940 --> 00:06:28.400 Right. 00:06:28.400 --> 00:06:31.300 But this is using just normal type annotations. 00:06:31.300 --> 00:06:33.640 It's pretty incredible. 00:06:33.640 --> 00:06:35.340 It says, look, this is standard Python. 00:06:35.340 --> 00:06:36.600 It's a subset of Python. 00:06:36.880 --> 00:06:40.180 And you can easily integrate with C libraries because of CFFI. 00:06:40.180 --> 00:06:44.720 You can even run it on PyPy because it's a subset of Python. 00:06:44.720 --> 00:06:47.220 So it's pretty cool. 00:06:47.220 --> 00:06:52.920 And if Dropbox releases a full-on compiler for Python, that would shock the world, I think. 00:06:52.920 --> 00:06:55.040 Yeah, definitely. 00:06:55.040 --> 00:07:06.400 Like even with this, with a subset, to be able to develop it in Python and then do some little extra work to go to the, to make sure you're able to compile it within this, this would be great. 00:07:06.400 --> 00:07:07.860 And the extra work is not much. 00:07:07.860 --> 00:07:09.880 It's like put type annotations on it. 00:07:09.880 --> 00:07:17.340 So maybe you have like three or four functions that you call that are like really called frequently or really a major portion of your time is spent there. 00:07:17.340 --> 00:07:19.240 And if you wrote it in like C, it would be way faster. 00:07:19.240 --> 00:07:23.540 Well, if you just put type annotations on it, you know, you could probably hit it with this thing. 00:07:23.540 --> 00:07:28.620 So pretty interesting little thread that got picked up there. 00:07:28.620 --> 00:07:33.460 We'll probably have to watch this and make, update people if we see any more progress. 00:07:33.460 --> 00:07:38.540 Yeah, what I did not see is here's a link to the Git repo or here's the announcement or anything like that. 00:07:38.740 --> 00:07:40.920 So we'll have to see if anything comes from it. 00:07:40.920 --> 00:07:46.240 But I think it's really interesting, you know, that, that we've sort of gotten to this place. 00:07:46.240 --> 00:07:52.100 Because from my understanding is Gito is not a huge fan of compiling Python code. 00:07:52.100 --> 00:07:57.420 It didn't see a need for this, but he must be involved in this because it's from Dropbox. 00:07:57.520 --> 00:08:00.120 And the main work he's doing at Dropbox is on my Pi. 00:08:00.120 --> 00:08:01.840 And it's all about this. 00:08:01.840 --> 00:08:05.080 So I suspect he's involved somehow, but I have no confirmation of that. 00:08:05.080 --> 00:08:05.340 Yeah. 00:08:05.340 --> 00:08:06.460 Okay, cool. 00:08:06.460 --> 00:08:06.780 Boom. 00:08:06.780 --> 00:08:07.340 Mic drop. 00:08:07.340 --> 00:08:08.720 Okay, I'll piggyback on that. 00:08:08.720 --> 00:08:11.340 Let's, let's talk about Netflix. 00:08:11.340 --> 00:08:13.040 They are doing such cool stuff with Python. 00:08:13.040 --> 00:08:13.600 Yeah. 00:08:13.600 --> 00:08:14.700 So it's Python and more. 00:08:14.880 --> 00:08:21.760 There's a kind of a really cool article called Beyond Interactive Notebook Innovation at Netflix. 00:08:21.760 --> 00:08:39.100 And as we've all known, Jupyter Notebooks have, and using the Jupyter system has kind of revolutionized a lot of data analysis and machine learning and, and quite a bit of that interactive, interacting with data environment. 00:08:39.300 --> 00:08:41.560 And that's true at Netflix as well. 00:08:41.560 --> 00:08:46.360 And that's what kind of this article is talking about is a lot of the cool things that they've done. 00:08:46.360 --> 00:08:51.120 And it's, it's over the last few years, they've seen a larger and larger growth. 00:08:51.120 --> 00:08:55.580 And they've also said, basically, this is our supplied data chain. 00:08:55.580 --> 00:09:02.800 So there's, I never really thought about all the different types of people that interact with data, but they, they listed, they have a little like diagram. 00:09:02.800 --> 00:09:12.140 They listed like eight, nine or 10 different job roles that interact with data a little bit differently, but they're all interacting with it. 00:09:12.140 --> 00:09:18.260 Like, you know, data experts or data analysts or algorithm engineers or data scientists. 00:09:18.260 --> 00:09:24.840 There's all these different things that I'm not quite sure what they all do different, but they're all interacting with these huge data sets. 00:09:24.840 --> 00:09:39.820 And in their effort to use notebooks and Jupyter to kind of streamline and make things easier for people in the process, they've came up with some really cool extra projects that they've open sourced or developing in the open. 00:09:39.820 --> 00:09:42.360 And one of them is, I'm going to run through a few of them. 00:09:42.360 --> 00:09:43.880 One of them is called Interact. 00:09:43.880 --> 00:09:51.660 And it's an alternative user interface that has some cool things like inline cell toolbars and drag and drop cells. 00:09:51.660 --> 00:09:55.580 I didn't know that you couldn't do that in Jupyter, but that's neat. 00:09:55.580 --> 00:09:58.460 And a built-in data explorer. 00:09:58.460 --> 00:10:11.260 And then if you've got a Jupyter notebook all set up with some data, well, it wouldn't be cool if you could have some of your states parameterized so you can run the whole notebook with different parameters. 00:10:11.260 --> 00:10:16.320 This is almost like making a notebook like a function you can call and pass it inputs and get outputs. 00:10:16.320 --> 00:10:17.040 Exactly. 00:10:17.040 --> 00:10:19.780 And that's what Papermill is partly. 00:10:19.780 --> 00:10:22.620 So there's a project called Papermill that parameterizes notebooks. 00:10:22.620 --> 00:10:32.120 And then it also does things like if you've parameterized a bunch, you can analyze the different set of parameterized runs and do some analysis on the set. 00:10:32.120 --> 00:10:37.300 And then there's, yeah, it's kind of making a notebook kind of, like you said, like a function that you can call. 00:10:37.520 --> 00:10:48.940 And then if you want to, if you're using that and you want to share it with other people with the data and with the results, they've put together a thing called commuter that lets people share them together, share them with each other. 00:10:48.940 --> 00:10:50.440 And then also scheduling. 00:10:50.440 --> 00:11:00.380 So there's a separate article that's attached at the end about how if you've got this function with different parameters and stuff, maybe you want to, it's, you want to test different live data. 00:11:00.380 --> 00:11:03.140 So you want to schedule it regularly or different times. 00:11:03.140 --> 00:11:07.380 They've got a notebook scheduling system in place. 00:11:07.380 --> 00:11:14.080 So all of this is, it's not like a super easy thing to jump into, but they're trying to make it easier for people. 00:11:14.080 --> 00:11:17.140 And this is actually some pretty neat things that they've shared with everybody. 00:11:17.140 --> 00:11:18.180 This is incredible. 00:11:18.180 --> 00:11:30.140 And if you look at this big infrastructure diagram they have, it's like, it's just this entire architecture and cloud platform for running their style of executable notebooks. 00:11:30.500 --> 00:11:35.540 Using this new UI on containers you can configure and schedule. 00:11:35.540 --> 00:11:37.140 It's really crazy. 00:11:37.140 --> 00:11:43.400 And you can even come down and say, I'd like to run on some container with four CPUs and 30 gigs of RAM. 00:11:43.400 --> 00:11:44.640 Go. 00:11:44.640 --> 00:11:45.640 Right? 00:11:45.640 --> 00:11:46.360 That's crazy. 00:11:46.360 --> 00:11:48.600 It's kind of amazing. 00:11:48.820 --> 00:12:01.320 And I'm trying to start exploring a little bit more of what people are doing with Jupyter notebooks and other parts of the Jupyter system because it's, it is sort of opening up a lot of different data analysis into the open, which is neat. 00:12:01.320 --> 00:12:01.720 Yeah. 00:12:01.720 --> 00:12:02.960 That is really cool. 00:12:02.960 --> 00:12:17.580 Something else I just see in here, and I don't know quite where this shows up, but in that same organization that released all of these projects on GitHub, you know, they're grouped into a bunch of projects under an org and the GitHub org style. 00:12:18.100 --> 00:12:21.400 There's a thing called VDOM for Python. 00:12:21.400 --> 00:12:22.040 What does it say? 00:12:22.040 --> 00:12:31.500 It says a virtual HTML DOM for Python, which is pretty interesting for creating basically HTML and whatnot, like divs and paragraphs and stuff. 00:12:31.500 --> 00:12:35.280 So that's also in here somewhere for whatever people want to do with that. 00:12:35.280 --> 00:12:35.760 Interesting. 00:12:35.760 --> 00:12:36.080 Yeah. 00:12:36.080 --> 00:12:38.780 So I feel like this one is massive too. 00:12:38.780 --> 00:12:40.440 Like this is a really major thing. 00:12:40.440 --> 00:12:45.820 And, you know, organizations that depend on this notebook style, like this could really be a big deal. 00:12:45.820 --> 00:12:48.400 And I like that they split it out into different projects. 00:12:48.400 --> 00:12:52.100 Like the parameterization idea is, is powerful. 00:12:52.100 --> 00:12:53.860 And maybe you can use that by itself. 00:12:54.340 --> 00:13:01.080 And if you don't quite like the user interface, and so they've got the interact there that you can use that instead. 00:13:01.080 --> 00:13:03.820 These are cool that they've split them out a bit. 00:13:03.820 --> 00:13:05.780 I don't know how much they're tied together. 00:13:05.780 --> 00:13:12.220 I expect things like Titus and commuter might depend on other pieces more than others. 00:13:12.220 --> 00:13:13.280 But it's neat. 00:13:13.280 --> 00:13:13.960 It's really neat. 00:13:14.080 --> 00:13:19.200 So another thing that is pretty cool that I want to talk about is DigitalOcean. 00:13:19.200 --> 00:13:22.120 And I've told you a bunch of things about it last time we talked about projects. 00:13:22.120 --> 00:13:22.760 And that's pretty awesome. 00:13:22.760 --> 00:13:25.100 Brian, you've heard of Heroku, right? 00:13:25.100 --> 00:13:25.580 Yeah. 00:13:25.580 --> 00:13:26.940 Do you know what Doku is? 00:13:26.940 --> 00:13:27.460 Doku? 00:13:27.460 --> 00:13:28.700 I think it's Doku, actually. 00:13:28.700 --> 00:13:29.380 No, I don't. 00:13:29.480 --> 00:13:34.420 So Doku is apparently a miniature Heroku powered by Docker. 00:13:34.420 --> 00:13:41.640 So if you want to have your own Heroku, you can spin up a Linux machine or machines and install this thing called Doku. 00:13:41.640 --> 00:13:45.020 And then treat it like Heroku for deploying and managing your code. 00:13:45.020 --> 00:13:45.660 Oh, that's neat. 00:13:45.660 --> 00:13:46.180 That's neat. 00:13:46.180 --> 00:13:51.780 So over at DigitalOcean, they have a one-click deploy for creating Doku servers. 00:13:51.780 --> 00:13:56.120 So you should go over there, create a new droplet, and you just choose, I want a Doku thing all set up. 00:13:56.120 --> 00:13:56.480 Click. 00:13:56.480 --> 00:13:57.420 Boom. 00:13:57.540 --> 00:13:59.760 You're ready to make your own little version of Heroku. 00:13:59.760 --> 00:14:00.260 Wow. 00:14:00.260 --> 00:14:01.220 That's really cool. 00:14:01.220 --> 00:14:01.960 Yeah, it's really cool. 00:14:01.960 --> 00:14:05.960 So they have all these cool one-click things to create different types of environments. 00:14:05.960 --> 00:14:09.280 And I just thought I'd highlight this Doku one because I know people use Heroku a lot. 00:14:09.280 --> 00:14:13.680 And if you want some flexibility, this is a really awesome way to do it. 00:14:13.680 --> 00:14:18.180 So anyway, check them out at pythonbytes.fm/DigitalOcean. 00:14:18.180 --> 00:14:22.700 Get $100 credit if you're a new user to play with Doku or whatever else you want to play with. 00:14:22.700 --> 00:14:23.740 All right. 00:14:23.840 --> 00:14:30.160 So remember a while ago, we talked about running a Python script as a systemd service. 00:14:30.160 --> 00:14:30.620 Yes. 00:14:30.620 --> 00:14:31.600 That was a while ago. 00:14:31.600 --> 00:14:32.080 It was. 00:14:32.080 --> 00:14:32.840 It was quite a while ago. 00:14:32.840 --> 00:14:35.600 I'll link to that when we talked about that as well, that episode. 00:14:35.600 --> 00:14:42.180 But the idea was I would like to take some Python code and make it, you know, turn on when I boot my Linux system. 00:14:42.480 --> 00:14:49.560 The idea was to take a Python script and make it just boot win Linux boots and run in the background. 00:14:49.560 --> 00:14:52.420 It's kind of like if you did a cron job, but you have more control, right? 00:14:52.420 --> 00:14:55.160 Like your app, your Python code is constantly running. 00:14:55.160 --> 00:14:58.020 If it crashes, it'll restart if you configure it that way and stuff. 00:14:58.020 --> 00:15:00.900 Well, that's great for Linux. 00:15:00.900 --> 00:15:02.720 But what about for Windows, right? 00:15:02.720 --> 00:15:06.020 So the equivalent infrastructure in Windows is called a Windows service. 00:15:06.020 --> 00:15:08.640 It starts when Windows starts. 00:15:08.640 --> 00:15:13.900 If you want it that way, it can run when you're logged out or it can run as a different user. 00:15:13.900 --> 00:15:16.380 It can run as a restricted account, all these things. 00:15:16.380 --> 00:15:23.920 So if you want your app to run basically as Windows runs, right, then you have to create this Windows service. 00:15:24.460 --> 00:15:29.380 Well, it turns out it's quite easy to do this in Python if you use the right libraries. 00:15:29.380 --> 00:15:32.960 So there's a cool article that just says how to create a Windows service in Python. 00:15:32.960 --> 00:15:38.660 And it's based on this project called PyWin32, which seems to have no documentation. 00:15:38.660 --> 00:15:41.960 Maybe I just missed it, but it doesn't seem at all obvious out of use. 00:15:41.960 --> 00:15:44.100 But it is on GitHub and pretty popular. 00:15:44.100 --> 00:15:47.100 But this article talks about how to create a Windows service. 00:15:47.100 --> 00:15:53.180 And the idea is you just derive from a certain class and you override a stop, a start, and a main method. 00:15:53.700 --> 00:15:57.220 And boom, you have Python code that runs as if it were part of Windows. 00:15:57.220 --> 00:15:58.720 Actually, this is awesome. 00:15:58.720 --> 00:15:59.760 I'll use this right away. 00:15:59.760 --> 00:16:01.180 Yeah, yeah, it's really cool. 00:16:01.180 --> 00:16:04.360 I don't have a good – I've written a couple of Windows services back in the day. 00:16:04.360 --> 00:16:07.560 I haven't had a chance or reason to do so recently. 00:16:07.560 --> 00:16:12.600 I did take that SystemD idea and do some really cool stuff around my courses for automation there. 00:16:12.600 --> 00:16:17.600 But if people are on Windows or they have to deploy to Windows, I think this is a great little example. 00:16:17.600 --> 00:16:23.260 So if that appeals to you, if that sounds like something you want to try to do, this is a really accessible way to do it. 00:16:23.260 --> 00:16:23.820 That's cool. 00:16:23.820 --> 00:16:24.440 Yeah, indeed. 00:16:24.440 --> 00:16:27.500 I'm glad that this came out because I thought about doing this before. 00:16:27.500 --> 00:16:28.760 I'm like, oh, man, I'm a smart guy. 00:16:28.760 --> 00:16:29.520 I can figure this out. 00:16:29.520 --> 00:16:31.520 I don't even know where to start looking. 00:16:31.520 --> 00:16:34.180 There's like weird registration stuff you have to do. 00:16:34.180 --> 00:16:34.820 But it's cool. 00:16:34.820 --> 00:16:41.440 You just say like the name of the service is this, the description of the service is this, the way it starts is like on system start or delayed or whatever. 00:16:41.440 --> 00:16:43.780 And then you just like run a command to install it. 00:16:43.780 --> 00:16:44.100 It's great. 00:16:44.100 --> 00:16:44.400 Nice. 00:16:44.400 --> 00:16:44.740 Yeah. 00:16:44.740 --> 00:16:47.560 You probably want to package it up though when you send it out. 00:16:47.940 --> 00:16:49.260 Yeah, you should package it. 00:16:49.260 --> 00:16:51.260 Have we talked about packaging before? 00:16:51.260 --> 00:16:52.020 I think so. 00:16:52.020 --> 00:16:52.840 Not enough. 00:16:52.840 --> 00:16:55.780 It's one of the things that should be more accessible. 00:16:55.780 --> 00:16:56.360 Okay. 00:16:56.360 --> 00:16:58.060 Well, let's talk about it again. 00:16:58.400 --> 00:17:03.200 So the Mamu Deshemi, I don't remember how long ago this was. 00:17:03.200 --> 00:17:04.520 I don't know if we covered it or not. 00:17:04.520 --> 00:17:11.060 But anyway, he wrote an article about different ways to package different levels of Python projects. 00:17:11.060 --> 00:17:13.160 And then he did a talk on it. 00:17:13.160 --> 00:17:17.720 And then now it's been all this information has been shared with others and edited. 00:17:17.720 --> 00:17:22.680 And it's part of the PyPA, the Python Package Authority documentation. 00:17:23.400 --> 00:17:26.300 And it's called an overview of packaging for Python. 00:17:26.300 --> 00:17:29.520 And it's kind of like how it's broken out. 00:17:29.520 --> 00:17:32.580 Because when you want to share Python code, there's different levels. 00:17:32.580 --> 00:17:35.620 So if you just want to share a module with somebody, there's that. 00:17:35.620 --> 00:17:41.660 And then so, I mean, it starts there and talks about how to just share a simple module with somebody else. 00:17:42.080 --> 00:17:50.360 But then you quickly get into packages with source distributions and wheels and possibly binary distributions and how to do that. 00:17:50.360 --> 00:17:55.500 And then, but that's complete packages are different than if you want to share an application. 00:17:55.500 --> 00:17:58.760 So I do, that's the part where I get fuzzy. 00:17:58.760 --> 00:18:02.820 But actually, this is a good starting point that hopefully they'll keep current. 00:18:02.980 --> 00:18:07.500 That talks about some of the different ways that you can share applications. 00:18:07.500 --> 00:18:10.260 Because, you know, sharing a web application is going to be different. 00:18:10.260 --> 00:18:17.340 And if you share it on Heroku, it's going to be different than sharing a desktop application or a command line application or something. 00:18:17.340 --> 00:18:24.280 And then also whether or not you want to assume people have Python or whether you want to package Python with it. 00:18:24.280 --> 00:18:27.660 And what do you do with the dependencies and all that. 00:18:27.860 --> 00:18:32.420 So we've talked about all this before and there's a lot of different solutions. 00:18:32.420 --> 00:18:38.320 And so this is a good starting point to take a look at if you want to share some code with somebody else. 00:18:38.320 --> 00:18:40.620 Start here and then get lost. 00:18:40.620 --> 00:18:41.500 There you go. 00:18:41.500 --> 00:18:42.000 I like it. 00:18:42.000 --> 00:18:43.880 It brings a lot of the stuff we talked about together. 00:18:43.880 --> 00:18:48.380 Like, for example, in the bring your own Python executable section. 00:18:48.380 --> 00:18:51.100 It's like, here's a whole bunch of ways to do this. 00:18:51.100 --> 00:18:55.280 You could use PyInstaller, CXFreeze, Constructor, which apparently I'd never heard of. 00:18:55.280 --> 00:18:56.600 OS Snap. 00:18:56.600 --> 00:18:57.220 OSnap. 00:18:57.580 --> 00:18:58.260 Another one. 00:18:58.260 --> 00:18:59.400 Like, there's a bunch of things in here. 00:18:59.400 --> 00:19:01.160 And I actually have only heard about half of them. 00:19:01.160 --> 00:19:02.200 So it's pretty cool. 00:19:02.200 --> 00:19:05.980 And since applications kind of range, everything else is fairly consistent. 00:19:05.980 --> 00:19:09.380 There's like one obvious right way to do it up through packages. 00:19:09.380 --> 00:19:15.800 And then it's with applications where it starts going off and feathering out into different solutions. 00:19:15.800 --> 00:19:19.300 I think it's because there's a lot of different requirements for applications. 00:19:19.300 --> 00:19:21.460 Different than just sharing source code. 00:19:21.460 --> 00:19:26.220 So I don't think it's a bad thing that we have a lot of different solutions right now. 00:19:26.300 --> 00:19:30.280 We've talked about how it'd be really nice if there was one obvious way. 00:19:30.280 --> 00:19:31.640 We're just not there yet. 00:19:31.640 --> 00:19:33.460 So here's a good list of some of them. 00:19:33.460 --> 00:19:33.720 Yep. 00:19:33.720 --> 00:19:34.560 That's really cool. 00:19:34.560 --> 00:19:35.520 Well done, Mahmood. 00:19:35.520 --> 00:19:44.640 So there was a little bit of drama a few weeks ago around PEP 572 about changing the Python language for an assignment in place assignment operator. 00:19:44.640 --> 00:19:45.020 Yeah. 00:19:45.020 --> 00:19:45.300 Remember that? 00:19:45.300 --> 00:19:52.240 And that was actually, that was part of the, one of the sort of straws that caused Guido to say, all right, I've had it with this. 00:19:52.240 --> 00:19:53.520 Tired of fighting over these things. 00:19:53.520 --> 00:19:53.860 I'm out. 00:19:53.860 --> 00:19:59.760 Well, here's a new PEP that I think proposes a bigger change to the Python language. 00:19:59.760 --> 00:20:00.980 This one is not accepted. 00:20:00.980 --> 00:20:01.980 It's in draft mode. 00:20:02.040 --> 00:20:03.220 So people can respond to it. 00:20:03.220 --> 00:20:08.140 However, I actually think the value this one brings is massive. 00:20:08.140 --> 00:20:09.320 It's a big deal though. 00:20:09.320 --> 00:20:10.200 It's a really big deal. 00:20:10.200 --> 00:20:15.760 So the idea is there's PEP 505 now, which is for none aware operators. 00:20:15.880 --> 00:20:18.560 And the idea is there are several languages that are proven. 00:20:18.560 --> 00:20:28.580 There's some nice design patterns or language patterns that short circuit working with none or null or whatever it is in the Swift. 00:20:28.580 --> 00:20:31.360 I forgot about what they call it there, but nothing, right? 00:20:31.360 --> 00:20:34.320 So there's basically two ways in which you work with this. 00:20:34.320 --> 00:20:40.920 One of these are called null coalescing operators, which lets you substitute a value if you have a none object. 00:20:40.920 --> 00:20:47.280 And null aware operators, which lets you chain operations regardless of whether with their null. 00:20:47.280 --> 00:20:52.080 So the two languages that are most popular that come to mind have to be C# and Swift. 00:20:52.080 --> 00:20:55.580 And both of these have deep support for this concept. 00:20:55.580 --> 00:21:06.500 Swift takes it to a whole other level to say you can't even assign the equivalent of none to a variable unless you mark it as nullable explicitly, which is way far out there. 00:21:06.500 --> 00:21:10.160 So the idea is like Python could benefit from this. 00:21:10.160 --> 00:21:13.440 So there's these two cases, the null coalescing and the null aware. 00:21:13.440 --> 00:21:15.280 So I'll give you the null coalescing, right? 00:21:15.280 --> 00:21:23.420 It's if you're going to do some kind of weird test, like you'd say something like value, if value is not none else missing, right? 00:21:23.420 --> 00:21:27.860 Like this big complicated thing could just be value, question mark, question mark, missing. 00:21:27.860 --> 00:21:31.700 It's either going to be the value, but if value is none, then it's the other value missing. 00:21:31.700 --> 00:21:32.920 What do you think of that, Brian? 00:21:32.920 --> 00:21:34.360 I'm still on the fence. 00:21:34.360 --> 00:21:34.720 Okay. 00:21:34.720 --> 00:21:38.160 This one to me actually doesn't offer a huge amount of value. 00:21:38.160 --> 00:21:39.000 It's okay. 00:21:39.400 --> 00:21:45.480 The next one to me is like pretty killer and more with the fluent style of calling functions chained together. 00:21:45.720 --> 00:21:53.220 So the null aware member access operator, it's the same basic PEP and the same basic structure, but it's used in a different case. 00:21:53.220 --> 00:21:56.740 It lets you chain these fluent interfaces together without testing for none. 00:21:56.740 --> 00:21:58.680 So suppose I have a user. 00:21:58.680 --> 00:21:59.920 The user has orders. 00:21:59.920 --> 00:22:04.360 The orders you can call a first operation, but if it's there's no orders, then it returns none. 00:22:04.360 --> 00:22:06.680 But if it does return one, the order has a name, right? 00:22:06.680 --> 00:22:09.300 So you would say like if user is none, return none. 00:22:09.300 --> 00:22:11.280 User.orders.first. 00:22:11.280 --> 00:22:14.160 If the first order is none, then we kind of bail out. 00:22:14.160 --> 00:22:18.700 But if you make it through all those tests, you can get first order.name. 00:22:18.940 --> 00:22:22.280 So that's a lot of code to be spoken over the air. 00:22:22.280 --> 00:22:28.820 But you would now with this new proposal say user?orders.first?name.name. 00:22:28.820 --> 00:22:30.320 And it would mean exactly the same thing. 00:22:30.320 --> 00:22:30.760 Okay. 00:22:30.840 --> 00:22:37.160 If you look at those two lines of code on digital paper, the amount of space on one and the other is ridiculous, right? 00:22:37.160 --> 00:22:37.500 Yeah. 00:22:37.500 --> 00:22:38.580 Sorry, I didn't mean to cut you off. 00:22:38.580 --> 00:22:48.520 First reading of the null aware operators, I was thinking it's just going to make the language more complicated to teach because when you're reading it, it isn't obvious what these things are doing. 00:22:48.520 --> 00:22:56.180 However, it does make the language smaller in that you can be more expressive in less code. 00:22:56.180 --> 00:22:57.880 And that's a good thing. 00:22:57.880 --> 00:23:05.880 I think it would make people more willing to effectively test for none when it could be none without cluttering the code, right? 00:23:05.880 --> 00:23:09.540 Like these two examples I gave you, it's like eight lines versus one line. 00:23:09.540 --> 00:23:11.620 You're willing to just go, oh, I just think it's there. 00:23:11.620 --> 00:23:12.360 I'll just go through it. 00:23:12.360 --> 00:23:16.320 And then you'll get none type does not have attributes such and such, right? 00:23:16.320 --> 00:23:17.560 Whatever the attribute is, right? 00:23:17.560 --> 00:23:18.640 You get that error all the time. 00:23:18.640 --> 00:23:20.060 It's one of the most common errors in Python. 00:23:20.060 --> 00:23:23.280 But if you just put the question marks in there, that totally goes away. 00:23:23.280 --> 00:23:28.460 That's the thing where if you don't think you need that problem, just think about how many times you see that. 00:23:28.460 --> 00:23:31.480 Like, you know, none does not have the index operator. 00:23:31.480 --> 00:23:32.100 Yes, exactly. 00:23:32.100 --> 00:23:32.700 Exactly. 00:23:32.700 --> 00:23:36.660 And this basically provides a much nicer way to deal with it. 00:23:37.100 --> 00:23:43.000 So my vote is for this feature, especially the null aware member access. 00:23:43.000 --> 00:23:43.380 Yeah. 00:23:43.380 --> 00:23:45.480 But I would be okay if Python never had it. 00:23:45.480 --> 00:23:49.380 But I do think the languages that do have it really make pretty good use of it. 00:23:49.380 --> 00:23:50.580 So it's in draft mode. 00:23:50.580 --> 00:23:53.740 People can give feedback before some sort of battle erupts over it. 00:23:53.740 --> 00:23:53.980 Yeah. 00:23:53.980 --> 00:23:54.920 I think it's good. 00:23:54.920 --> 00:23:55.160 Yeah. 00:23:55.160 --> 00:23:55.960 I do too. 00:23:55.960 --> 00:23:56.280 Cool. 00:23:56.280 --> 00:23:59.480 It'll let you have a second edition or third edition of your testing book, right? 00:23:59.480 --> 00:24:02.020 Because there's probably somewhere you got to put a question mark in there. 00:24:05.460 --> 00:24:06.860 You're just adding work for me. 00:24:06.860 --> 00:24:07.200 Thanks. 00:24:07.200 --> 00:24:08.800 Anyway, it's something for people to check out. 00:24:08.800 --> 00:24:09.580 Quite, quite good. 00:24:09.580 --> 00:24:11.800 I don't have anything extra to share with the folks. 00:24:11.800 --> 00:24:12.380 You, Brian? 00:24:12.380 --> 00:24:13.860 I've got a couple of things. 00:24:13.860 --> 00:24:22.420 I just noticed the other day that the PyCascades, it's going to be in February, February 23rd and 24th of 2019. 00:24:22.420 --> 00:24:24.560 And it's going to be in Seattle this time. 00:24:24.560 --> 00:24:27.440 It's the call for proposals is now open. 00:24:27.440 --> 00:24:28.300 Oh, that's sweet. 00:24:28.300 --> 00:24:29.260 You got a long time too. 00:24:29.260 --> 00:24:32.920 You got it now through October 21st to get in your proposal. 00:24:32.920 --> 00:24:34.460 Oh, I'm super excited about that. 00:24:34.460 --> 00:24:39.420 The opening, the inaugural PyCascades in Vancouver, BC last year was excellent. 00:24:39.420 --> 00:24:40.560 And I'm really glad I went. 00:24:40.560 --> 00:24:43.440 It was much smaller and it was a really intimate event. 00:24:43.440 --> 00:24:44.980 You got to meet a lot of the people who were there. 00:24:44.980 --> 00:24:47.320 And I'm looking forward to have it in Seattle. 00:24:47.320 --> 00:24:48.780 And it's coming to Portland next year. 00:24:48.780 --> 00:24:50.360 So that'll be even better. 00:24:50.360 --> 00:24:52.900 I'll try to go and I'll also try to submit something. 00:24:52.900 --> 00:24:53.640 That'll be good. 00:24:54.000 --> 00:25:05.040 The other thing I wanted to highlight is I just wanted to brag because I got a Test and Code episode out a week or so ago that was with David Heinemeyer Hansen. 00:25:05.040 --> 00:25:06.380 And it really went well. 00:25:06.380 --> 00:25:10.500 And he's not a Python person, but it's interesting information. 00:25:10.500 --> 00:25:12.040 So that was fun. 00:25:12.160 --> 00:25:12.300 All right. 00:25:12.300 --> 00:25:17.440 So for people who don't know who David Heinemeyer Hansen is, what's his claim to fame? 00:25:17.440 --> 00:25:18.540 Well, he's got lots. 00:25:18.540 --> 00:25:21.120 But he's the guy that made up Ruby on Rails. 00:25:21.120 --> 00:25:23.700 And he's one of the co-founders of Basecamp. 00:25:23.700 --> 00:25:24.120 Yeah. 00:25:24.120 --> 00:25:24.700 Just that. 00:25:24.700 --> 00:25:26.060 So that's pretty awesome. 00:25:26.060 --> 00:25:27.280 Yeah. 00:25:27.280 --> 00:25:28.620 It's really cool that he was on your show. 00:25:28.620 --> 00:25:31.300 And I'm looking forward to checking that out myself. 00:25:31.300 --> 00:25:39.900 It's definitely applicable to Python people as well because we talk about language agnostic things like just testing and stuff like that. 00:25:39.900 --> 00:25:40.340 Very cool. 00:25:40.340 --> 00:25:42.420 Well, thanks for the call out on PyCascades. 00:25:42.420 --> 00:25:42.960 Sure. 00:25:42.960 --> 00:25:44.660 PyCascades. 00:25:44.800 --> 00:25:49.580 But I also was catching up on your podcast, Talk Python. 00:25:49.580 --> 00:26:00.560 And they had a couple episodes that were people that learned Python after they didn't get CS degrees and learned Python later, programming later. 00:26:00.560 --> 00:26:05.360 And just the whole concept of that and listening to everybody's stories was very interesting. 00:26:05.360 --> 00:26:06.060 I like those. 00:26:06.060 --> 00:26:06.440 Yeah. 00:26:06.440 --> 00:26:06.820 Thank you. 00:26:06.820 --> 00:26:08.360 I did a two-part series on that. 00:26:08.480 --> 00:26:19.620 And I think it really connected to a lot of people because so many people find their way into our world of programming in Python without going through the traditional CS degree path. 00:26:19.620 --> 00:26:24.940 And I think it's just an example of a lot of people doing really great in alternate ways in. 00:26:24.940 --> 00:26:25.920 So, yeah, very cool. 00:26:25.920 --> 00:26:26.940 Well, good talking to you. 00:26:26.940 --> 00:26:27.980 And we'll talk next week. 00:26:27.980 --> 00:26:28.320 Yep. 00:26:28.320 --> 00:26:29.020 You as well. 00:26:29.020 --> 00:26:29.820 Catch you later. 00:26:29.820 --> 00:26:30.400 Bye, everyone. 00:26:32.880 --> 00:26:34.620 Thank you for listening to Python Bytes. 00:26:34.620 --> 00:26:37.180 Follow the show on Twitter via at Python Bytes. 00:26:37.180 --> 00:26:40.060 That's Python Bytes as in B-Y-T-E-S. 00:26:40.060 --> 00:26:43.480 And get the full show notes at pythonbytes.fm. 00:26:43.480 --> 00:26:47.840 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:26:47.840 --> 00:26:50.540 We're always on the lookout for sharing something cool. 00:26:50.540 --> 00:26:53.920 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:26:53.920 --> 00:26:57.560 Thank you for listening and sharing this podcast with your friends and colleagues.