WEBVTT 00:00:00.001 --> 00:00:05.800 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:05.800 --> 00:00:11.340 This is episode 35, recorded July 18th, 2017. I'm Michael Kennedy. 00:00:11.340 --> 00:00:12.540 And I'm Brian Okken. 00:00:12.540 --> 00:00:17.320 And we've got some really cool stuff to start out with. Do you have any comments maybe to kick us off, Brian? 00:00:17.320 --> 00:00:20.100 Well, I like to comment about just about everything. 00:00:20.100 --> 00:00:28.100 But to kick off, we've got an article from, I think it's Philip Troner, and it's called Python Quirks Comments. 00:00:28.100 --> 00:00:37.680 I kind of like this article. When you're looking at source code, there's definitely comments that start with pound that are obviously just comments to other coders. 00:00:37.680 --> 00:00:46.640 But then also sometimes, since we have doc strings in there, some people have learned that you can just put strings or other objects in your code. 00:00:46.640 --> 00:00:50.820 And as long as they're not referenced by anything else, it just acts like a comment. 00:00:51.040 --> 00:00:57.420 But this is an article taking a look at the abstract syntax tree and also taking a look at timing them. 00:00:57.420 --> 00:00:59.000 And obviously... 00:00:59.000 --> 00:01:03.580 Yeah. The fundamental question was, is there a reason to prefer one over the other? 00:01:03.580 --> 00:01:06.600 Is there a performance difference between them? Things like that, right? 00:01:06.600 --> 00:01:07.580 Yeah, definitely. 00:01:07.840 --> 00:01:17.040 And I have seen it even... I haven't seen it in a lot of big open source projects, but I've seen it in just random, like, Python code that I look at from coworkers or whatever. 00:01:17.040 --> 00:01:18.740 People will comment out... 00:01:18.740 --> 00:01:23.260 Even commenting out a chunk of code with the three quotes just to block it out. 00:01:23.260 --> 00:01:24.420 It's not good. 00:01:24.680 --> 00:01:29.540 It actually leaves that object in your code and can slow things down a bit. 00:01:29.540 --> 00:01:38.080 Yeah. So he did a bunch of testing and the hash or pound comments that actually get grayed out, like, those literally do not appear in the abstract syntax tree. 00:01:38.080 --> 00:01:41.260 So once the PYC file is generated, they're gone, right? 00:01:41.260 --> 00:01:44.620 Like, they literally don't appear in the resulted executed code. 00:01:44.620 --> 00:01:51.580 However, if you have, like, triple quote for doc strings, that actually gets set to the dunder doc property, I think. 00:01:52.100 --> 00:01:59.060 But those appear, if you have other ones, those just execute, they get allocated, and then they immediately get dereferenced and garbage collected. 00:01:59.060 --> 00:02:00.460 But those steps happen, right? 00:02:00.460 --> 00:02:00.820 Yeah. 00:02:00.820 --> 00:02:08.720 Yeah, one of the things that made me think about, because I've been adding, for the code examples for the book, I've been adding doc strings. 00:02:08.720 --> 00:02:11.300 And I'm curious. 00:02:11.300 --> 00:02:12.900 I'd like to do a similar test. 00:02:12.900 --> 00:02:17.300 I think I might take his code example and do a similar test on, like, size of doc strings. 00:02:17.520 --> 00:02:28.500 If you do, like, a, I don't know, a 10-character one-liner versus 100, 200 characters of a huge doc string, does it make a difference for performance at all? 00:02:28.500 --> 00:02:28.960 Yeah, yeah. 00:02:28.960 --> 00:02:29.460 Pretty interesting. 00:02:29.460 --> 00:02:34.780 I'm definitely a fan of the hash comment unless, you know, it's literally for doc strings. 00:02:34.920 --> 00:02:50.040 Yeah, and one of the things that, this is outside of the article, but I, in a comment recently, I can't remember where, a discussion about this was that doc strings are information for your user or for the user of a function or something. 00:02:50.040 --> 00:02:57.420 But the compound or hash comments are information for a future developer, and I like that. 00:02:57.420 --> 00:03:06.420 Yeah, that's the, imagine a psychopath who's having a bad day 10 years from now is inheriting your code, and they know where you live. 00:03:06.420 --> 00:03:08.640 You want to leave them some comments to help them feel happy. 00:03:08.640 --> 00:03:09.220 Yeah. 00:03:11.620 --> 00:03:17.020 All right, so, you know what I get if I open up my terminal, I type python3-V. 00:03:17.020 --> 00:03:18.060 I don't know, what do you get? 00:03:18.060 --> 00:03:22.520 I get 362, because python362 is out, I think today even. 00:03:22.520 --> 00:03:22.960 Awesome. 00:03:22.960 --> 00:03:24.300 Yeah, very, very cool. 00:03:24.300 --> 00:03:28.380 And I was blown away at how much stuff is in here. 00:03:28.380 --> 00:03:30.480 And I think these are mostly fixes. 00:03:30.480 --> 00:03:33.460 I didn't see, I don't think there were any new features. 00:03:33.460 --> 00:03:34.820 I think that's coming in 37. 00:03:35.620 --> 00:03:39.500 But holy moly, there are a lot of changes, and it's pretty interesting. 00:03:39.500 --> 00:03:45.380 So I pulled out a few just to highlight, and I'm highlighting for a variety of reasons. 00:03:45.380 --> 00:03:50.940 So I broke them in, well, they broke them in, too, and I grabbed them from four categories here. 00:03:50.940 --> 00:03:54.820 There's a few others that I decided not to touch on, like changes to idle, don't care. 00:03:54.820 --> 00:03:59.260 But security, that I very much care about. 00:03:59.260 --> 00:04:03.380 So the security ones, we have these changes, and they've got a bunch of numbers. 00:04:03.380 --> 00:04:09.460 You guys can look them up, but prevent environment variable injection into subprocesses on Windows. 00:04:09.460 --> 00:04:16.720 So prevent other things from freaking out or taking over what the system looks like for Python. 00:04:16.720 --> 00:04:19.200 Or this one is kind of scary. 00:04:19.200 --> 00:04:24.820 Upgrade expat copy from 220 to 221 gets fixed. 00:04:24.820 --> 00:04:29.980 You have multiple security vulnerabilities, and all these loops, integer overflow, 00:04:30.420 --> 00:04:35.660 regressions of other bugs, counter hash flooding, all these things that are like, you know, 00:04:35.660 --> 00:04:39.600 probably someone where in there, there's a really bad vulnerability. 00:04:39.600 --> 00:04:44.620 Also parsing the host, URL lib, and things like that. 00:04:44.620 --> 00:04:49.660 So there's a bunch of security fixes, not just, you know, features or whatever. 00:04:49.660 --> 00:04:52.220 So if you can, you should probably install this. 00:04:52.320 --> 00:05:02.800 Yeah, just definitely from my first glance at it this morning, it just seemed like it's just better for security and other changes to definitely install it. 00:05:02.800 --> 00:05:06.580 But I didn't see anything that jumped out of like, oh, I was waiting for this. 00:05:06.580 --> 00:05:08.320 Now I can use it or anything like that. 00:05:08.320 --> 00:05:09.060 Yeah, yeah, yeah. 00:05:09.060 --> 00:05:12.800 I don't think that I think this is only bug fixes and security fixes. 00:05:13.140 --> 00:05:15.320 No features until 3.7. 00:05:15.320 --> 00:05:15.700 Okay. 00:05:15.700 --> 00:05:17.420 And maybe you run a Mac, right? 00:05:17.420 --> 00:05:18.860 So maybe I'm just dense. 00:05:18.860 --> 00:05:22.400 But to upgrade, you just download the new one and install it. 00:05:22.400 --> 00:05:24.220 There isn't a way to just upgrade, is there? 00:05:24.220 --> 00:05:25.320 Well, you can use brew. 00:05:25.320 --> 00:05:28.220 I actually installed it off of python.org. 00:05:28.220 --> 00:05:31.600 So to upgrade that version, I think you got to keep rolling. 00:05:31.600 --> 00:05:33.040 You just get the next one and run it. 00:05:33.040 --> 00:05:33.520 That's fine. 00:05:34.020 --> 00:05:40.120 But if you brew install Python 3, then you can brew upgrade Python 3, which is kind of maybe what I'll do in my next Mac. 00:05:40.120 --> 00:05:40.820 Or I don't know. 00:05:40.820 --> 00:05:41.920 I'm happy with what I got now. 00:05:41.920 --> 00:05:43.860 But I'm going to try to use a homebrew more. 00:05:43.860 --> 00:05:44.580 I'm starting to love it. 00:05:44.580 --> 00:05:50.400 I actually, so if you do, if you have 361 and you install 362, you just have both of them there, right? 00:05:50.400 --> 00:05:52.400 Yes, I think so. 00:05:52.400 --> 00:05:55.040 But certainly if I type Python 3, now that's 362. 00:05:55.040 --> 00:05:55.520 Okay. 00:05:55.520 --> 00:05:59.220 Well, I like having both around anyway for testing multiple versions, but. 00:05:59.220 --> 00:05:59.940 Yeah, sure. 00:06:00.040 --> 00:06:04.380 And then there's some things, some tools like virtual environment wrapper, but slightly different. 00:06:04.380 --> 00:06:05.760 I'm forgetting the name right now. 00:06:05.760 --> 00:06:08.740 That'll let you like get all the versions and flip between them and whatnot. 00:06:08.740 --> 00:06:09.220 It's pretty cool. 00:06:09.220 --> 00:06:09.620 All right. 00:06:09.620 --> 00:06:09.900 All right. 00:06:09.900 --> 00:06:16.220 So some other ones, core and built-in stuff like the parsing of f-strings with backslashes apparently is broken. 00:06:16.220 --> 00:06:19.540 Segmentation faults when you are working with dictionaries. 00:06:19.540 --> 00:06:21.340 Those are never used in Python anywhere. 00:06:21.340 --> 00:06:28.480 When you're like changing them while searching, you know, if your Python just goes away, your web app keeps crashing, like all sorts of bad stuff. 00:06:28.480 --> 00:06:29.580 Control C. 00:06:29.580 --> 00:06:35.320 When you're inside of a yield from or a wait call gets fixed and all these different things. 00:06:35.320 --> 00:06:36.940 So tons of fixes there. 00:06:36.940 --> 00:06:44.120 The library gets race condition fixes for some signal delivery and wake up for file stuff. 00:06:44.120 --> 00:06:49.400 The lib2to3 now understands f-strings, race conditions, windows. 00:06:49.400 --> 00:06:50.280 Oh my God. 00:06:50.280 --> 00:06:51.040 This one is awesome. 00:06:51.040 --> 00:06:55.800 If you work on windows or you teach people Python who work on windows, you can cheer for this. 00:06:55.800 --> 00:06:56.960 This is amazing. 00:06:57.660 --> 00:07:03.400 Windows now will locate msbuild.exe instead of vcvarsallbat. 00:07:03.400 --> 00:07:14.040 It is so much more reliable to find msbuild on windows than it is that stupid old vcvarsallbat thing for like all the C compilations. 00:07:14.040 --> 00:07:17.860 So that means pip install a thing on windows should get more reliable. 00:07:17.860 --> 00:07:21.560 So there's about 40 more of these types of fixes. 00:07:22.140 --> 00:07:24.780 So one, I wanted to share the news. 00:07:24.780 --> 00:07:25.640 How awesome is this? 00:07:25.640 --> 00:07:34.100 I also wanted to hit on some of those things, especially the security stuff, because we're coming up quickly on the end of legacy Python, right? 00:07:34.100 --> 00:07:34.600 Yes. 00:07:34.600 --> 00:07:34.900 Yeah. 00:07:34.900 --> 00:07:37.760 Legacy Python has to have some of these in there. 00:07:37.760 --> 00:07:40.620 Like people discovered these and now here are these problems that are uncovered. 00:07:41.380 --> 00:07:44.680 In 2020, these problems are going to stay in Python 2. 00:07:44.680 --> 00:07:50.540 So the sooner you can get to Python 3 so these changes keep coming to you rather than become just, oh, that's a security vulnerability. 00:07:50.540 --> 00:07:51.940 Sorry, you have to live with that. 00:07:51.940 --> 00:07:56.260 Just one more reason to upgrade to Python 3 for those holdouts out there. 00:07:56.260 --> 00:07:57.120 Yeah, definitely. 00:07:57.120 --> 00:08:05.580 And one of the things looking at this list, I just have to say, give a big thank you to everybody that worked on all this so that I don't have to work on things like this. 00:08:05.900 --> 00:08:06.600 Yes, thank you. 00:08:06.600 --> 00:08:07.000 That's awesome. 00:08:07.000 --> 00:08:07.820 It's all getting better. 00:08:07.820 --> 00:08:08.420 Yeah. 00:08:08.420 --> 00:08:08.880 Cool. 00:08:08.880 --> 00:08:09.300 All right. 00:08:09.300 --> 00:08:19.200 Speaking of contributing to open source projects, a lot of us feel like we're not good enough or maybe we don't know enough or our experience isn't rich enough or whatever, right? 00:08:19.200 --> 00:08:20.240 That's a huge problem. 00:08:20.240 --> 00:08:20.520 Yeah. 00:08:20.520 --> 00:08:23.260 I think everybody has gone through that. 00:08:23.260 --> 00:08:31.360 I mean, definitely everybody that's now contributing to open source has had an initial time where they felt like whether they knew enough about something. 00:08:31.360 --> 00:08:34.600 And so make sure I get her name right. 00:08:34.780 --> 00:08:41.560 Adrienne Lowe, who does coding with knives and has spoken at a couple of PyCons and other places. 00:08:41.560 --> 00:08:42.840 Yeah, she's great. 00:08:42.840 --> 00:08:46.000 She wrote a contributing to open source projects. 00:08:46.000 --> 00:08:50.320 Well, she wrote a thing on GitHub called Imposter Syndrome Disclaimer. 00:08:50.320 --> 00:08:55.340 Essentially, it's in places where you have how to contribute to your project. 00:08:55.560 --> 00:09:03.860 She'd like you to add this or think about adding this little disclaimer to people that maybe don't think that they're ready to do it. 00:09:04.300 --> 00:09:06.220 And it's kind of great wording. 00:09:06.220 --> 00:09:09.780 It has things including saying, I want your help. 00:09:09.780 --> 00:09:10.780 No, really, I do. 00:09:10.780 --> 00:09:12.700 There might be a little voice. 00:09:12.700 --> 00:09:14.160 I'm just quoting right out of this. 00:09:14.160 --> 00:09:24.200 There might be a little voice inside you that tells you that you're not ready, that you need to do one more tutorial or learn another framework or read a few more blog posts before you're ready. 00:09:24.580 --> 00:09:34.420 But I assure you that's not the case and goes on to, like, tell you to point to your contributing guidelines and then also to comment about other stuff. 00:09:34.420 --> 00:09:35.940 And here's another quote. 00:09:35.940 --> 00:09:37.380 And you don't just have to write code. 00:09:37.380 --> 00:09:42.360 You can help out by writing documentation tests or even giving feedback about this. 00:09:42.620 --> 00:09:48.260 And we talked about this in one of our previous episodes of many ways you can contribute to open source projects. 00:09:48.260 --> 00:09:54.140 But I think that this is a great idea to put it right in your contributing guidelines for your project. 00:09:54.140 --> 00:09:55.420 Yeah, really nice work, Adrian. 00:09:55.420 --> 00:09:59.540 If you guys were at PyCon, she was the host of the art museum dinner. 00:09:59.540 --> 00:10:01.040 And this is really great. 00:10:01.040 --> 00:10:04.020 She does a bunch for the community and contributes to many projects. 00:10:04.020 --> 00:10:07.020 So I know she's been on both sides of this. 00:10:07.020 --> 00:10:09.700 And I do think having this on your projects will help. 00:10:09.700 --> 00:10:11.100 She'd like to collect examples. 00:10:11.100 --> 00:10:12.000 So I've got a link. 00:10:12.160 --> 00:10:14.200 We've got a link in the show notes for where she's. 00:10:14.200 --> 00:10:19.180 And just or just get a hold of her and say you're taking this and contributing. 00:10:19.180 --> 00:10:20.920 So, yeah. 00:10:20.920 --> 00:10:21.780 Yeah, sounds good. 00:10:21.780 --> 00:10:23.760 Yeah, nicely done. 00:10:23.760 --> 00:10:26.240 And you just grab that and you can drop it into your project. 00:10:26.240 --> 00:10:28.280 It's just like a markdown file or something like that. 00:10:28.280 --> 00:10:28.560 Yeah. 00:10:28.560 --> 00:10:31.260 So, Michael, do you have any dark secrets that you want to share? 00:10:31.260 --> 00:10:34.740 I think we all have dark secrets and I don't really want to talk about it. 00:10:34.740 --> 00:10:36.700 But it's time to get it out in the open. 00:10:36.700 --> 00:10:41.700 And so the next thing I want to talk about is an article, a pretty deep article from MIT Technology 00:10:41.700 --> 00:10:45.080 Review called The Dark Secret at the Heart of AI. 00:10:45.080 --> 00:10:47.700 So we've touched on this a few times. 00:10:47.700 --> 00:10:49.620 It's kind of a nice follow-up from last week. 00:10:50.100 --> 00:10:51.660 There's a huge problem with AI. 00:10:51.660 --> 00:10:55.360 And we've had statistical models and we can look at the model. 00:10:55.360 --> 00:10:57.160 We can see things that it's predicting. 00:10:57.160 --> 00:11:02.520 But as we move farther and farther into things like deep learning, the machine doesn't know 00:11:02.520 --> 00:11:03.320 why it knows a thing. 00:11:03.320 --> 00:11:05.040 We don't know why it knows a thing. 00:11:05.040 --> 00:11:07.320 But we can teach it a thing and then it does that thing. 00:11:07.320 --> 00:11:07.500 Right? 00:11:08.080 --> 00:11:13.000 Even the creators of these deep learning models can't explain why it makes a decision. 00:11:13.000 --> 00:11:16.280 You can't like set a break point and step through and go, oh, this is the if case. 00:11:16.280 --> 00:11:17.220 Yes, of course, here. 00:11:17.220 --> 00:11:18.680 There's none of that. 00:11:18.680 --> 00:11:23.400 It's like I've taught it a bunch of stuff and now it somehow knows and then I ask it a question. 00:11:23.400 --> 00:11:26.620 So they gave a really interesting example to kick off this article. 00:11:26.620 --> 00:11:31.500 They said, last year, an experimental vehicle made by NVIDIA was just like any other automated 00:11:31.500 --> 00:11:34.380 car was released somewhere in New Jersey, I think it was. 00:11:34.380 --> 00:11:39.860 And they said, but it was unlike anything demonstrated by Google, Tesla, or GM. 00:11:39.860 --> 00:11:42.600 And it shows the rising power of AI. 00:11:42.600 --> 00:11:46.400 The car didn't follow instructions by being programmed or engineered. 00:11:46.400 --> 00:11:52.600 They basically taught this car how to drive by having it watch humans drive. 00:11:52.600 --> 00:11:54.740 and then they put it out on the road. 00:11:54.740 --> 00:11:55.440 Oh, wow. 00:11:55.440 --> 00:11:55.760 Yeah. 00:11:55.760 --> 00:11:59.360 And so it was really weird. 00:11:59.360 --> 00:12:02.260 Like the results seemed to do what human drivers did. 00:12:02.260 --> 00:12:07.980 But if it did something different, how do you understand or debug it or even change it to make 00:12:07.980 --> 00:12:09.140 those decisions differently? 00:12:09.140 --> 00:12:12.300 Like if it crashes into a tree, it sits at a light. 00:12:12.300 --> 00:12:17.380 Or there's always the hilarious joke that people seem to play on these cars is like draw like 00:12:17.380 --> 00:12:20.640 what looks like painted white lines in a circle around it. 00:12:20.640 --> 00:12:22.240 It can't get out. 00:12:22.540 --> 00:12:27.120 You know, like, but if it does an unexpected thing, how do you debug it or change it? 00:12:27.120 --> 00:12:32.780 That's really the secret is we, even the developers of AI and AI itself, they don't know how they 00:12:32.780 --> 00:12:33.000 work. 00:12:33.000 --> 00:12:33.360 Yeah. 00:12:33.360 --> 00:12:38.200 And there's also things that when I think about this stuff, I don't, I'm fairly optimistic 00:12:38.200 --> 00:12:42.720 about the self-driving cars and I'll be one of the first to grab one if I can afford one. 00:12:42.980 --> 00:12:58.360 But the, there's always the question of like, okay, so if, if a car comes up to say, decide whether or not to crash you and your family into a tree or take out a whole glob of school children, what does it do? 00:12:58.360 --> 00:12:58.620 Yeah. 00:12:59.060 --> 00:13:03.140 And yeah, I don't, that's the sort of moral questions. 00:13:03.140 --> 00:13:05.580 I don't know how to deal with how people are going to debug that. 00:13:05.580 --> 00:13:06.340 So for sure. 00:13:06.340 --> 00:13:10.520 And if you get the AI to do that, maybe like, how do you know it's always going to make the right choice? 00:13:10.520 --> 00:13:11.240 You don't. 00:13:11.300 --> 00:13:15.000 It's probably statistically better than humans, but still it's, it's an interesting question. 00:13:15.000 --> 00:13:19.740 So they basically say like, you know, how do you understand what the system does? 00:13:19.740 --> 00:13:21.700 Like, why does it make the decision that does it? 00:13:21.700 --> 00:13:22.880 You can't really ask it right now. 00:13:22.880 --> 00:13:26.560 It's difficult to design a system so that it could explain what it does. 00:13:26.560 --> 00:13:30.220 Like people don't, can't explain always why they do what they do precisely. 00:13:30.220 --> 00:13:32.940 And so it's interesting. 00:13:32.940 --> 00:13:39.820 One of the consequences that might be coming really soon, this is in the EU is there's an argument being made 00:13:39.820 --> 00:13:47.940 that you have to be able to get machines and AIs to tell you why it reached a conclusion as a fundamental legal right. 00:13:47.940 --> 00:13:48.720 Oh, wow. 00:13:48.720 --> 00:13:49.440 Okay. 00:13:49.440 --> 00:13:55.920 So if I'm told I have cancer and I go crazy and I burn all my life savings, oh, sorry, glitch in the whopper core. 00:13:55.920 --> 00:13:56.840 You're fine. 00:13:56.840 --> 00:13:59.980 You know, you want to, you know, want to know why. 00:13:59.980 --> 00:14:05.920 If I'm denied a loan, if I'm denied the ability to buy a house, if I'm denied a job, right? 00:14:05.920 --> 00:14:08.200 These are like serious, serious questions. 00:14:08.200 --> 00:14:12.120 So basically they kind of round out that it didn't go all the way. 00:14:12.120 --> 00:14:20.540 There's a lot to cover in this article, but last thing for us is he said, we've never before built machines that operate in ways their creators don't understand. 00:14:20.540 --> 00:14:27.260 How well can we expect to communicate and get along with intelligent machines that could be unpredictable and inscrutable? 00:14:27.260 --> 00:14:28.340 Crazy, huh? 00:14:28.340 --> 00:14:28.840 Yeah. 00:14:29.660 --> 00:14:30.700 Yeah, definitely. 00:14:30.700 --> 00:14:36.980 I'm optimistic with you as well, but it's just a very, it's interesting that philosophy and morality is starting to become part of programming. 00:14:36.980 --> 00:14:37.280 Yep. 00:14:37.280 --> 00:14:42.380 We definitely have machines now that I think of more that one person doesn't understand. 00:14:42.380 --> 00:14:46.140 But yeah, I think the biggest consequence for us is that we're going to have programs. 00:14:46.140 --> 00:14:48.200 We can't debug or understand why they do things. 00:14:48.200 --> 00:14:50.100 That's going to be a bizarre program in the future. 00:14:50.220 --> 00:14:53.380 Before we move on, did you say the Whopper Corps? 00:14:53.380 --> 00:14:55.020 I did say the Whopper Corps. 00:14:55.020 --> 00:14:57.140 Is there a computer based on a hamburger? 00:14:57.140 --> 00:14:58.180 No, no. 00:14:58.180 --> 00:15:00.740 That's from the war games. 00:15:00.740 --> 00:15:01.580 Oh, okay. 00:15:01.580 --> 00:15:08.320 Remember they had to hack into the Whopper Corps because that machine, they had to teach it to play tic-tac-toe against itself or something. 00:15:08.320 --> 00:15:09.240 Nice reference. 00:15:09.240 --> 00:15:10.160 Thank you. 00:15:10.160 --> 00:15:19.000 Yeah, I always think it's great that in that movie you can get from Colorado Springs to Bainbridge Island in a helicopter. 00:15:19.000 --> 00:15:22.000 Like on one take of gas. 00:15:22.000 --> 00:15:23.360 Not possible. 00:15:23.360 --> 00:15:24.420 Anyway. 00:15:24.420 --> 00:15:25.040 Awesome. 00:15:25.040 --> 00:15:26.080 All right. 00:15:26.080 --> 00:15:30.520 So let's proceed safely back to the three A's of testing patterns and away from this philosophy stuff. 00:15:30.520 --> 00:15:30.820 Yeah. 00:15:30.820 --> 00:15:33.300 So actually, I loved seeing this. 00:15:33.300 --> 00:15:34.840 So this is an article. 00:15:34.840 --> 00:15:36.040 I didn't write his name down. 00:15:36.040 --> 00:15:36.480 Sorry. 00:15:36.820 --> 00:15:40.400 Called Arrange Act Assert Patterns for Python Developers. 00:15:40.400 --> 00:15:49.580 And I am a, you know, I'm a big, and this is a, the Arrange Act Assert Pattern is a structure for how to set up test cases. 00:15:49.580 --> 00:15:58.920 And this is, you know, a fairly gentle, easy introduction, basically just telling people to not have big, long, spaghetti test code. 00:15:58.920 --> 00:16:01.680 Your test code should be something structured. 00:16:01.680 --> 00:16:03.080 And this is a decent structure. 00:16:03.260 --> 00:16:08.220 And the Arrange Part is get yourself ready to do whatever you're going to do is the setup part. 00:16:08.220 --> 00:16:11.120 Act is whatever thing you're testing. 00:16:11.120 --> 00:16:13.700 And the Asserts Part is where you check. 00:16:13.700 --> 00:16:21.880 So the important thing is don't go back and do a whole bunch of, try to do as many test cases as you can that all of the Asserts are at the end. 00:16:21.940 --> 00:16:24.900 And you don't do more actions and do more Asserts. 00:16:24.900 --> 00:16:26.320 I wrote a list. 00:16:26.320 --> 00:16:29.900 There's other names that people might know it by, like given, when, then. 00:16:29.900 --> 00:16:34.400 That's often attributed to behavior-driven development, but it's essentially the same pattern. 00:16:34.400 --> 00:16:40.060 And I did cover it in a couple places on pythontesting.net and also in testing code. 00:16:40.440 --> 00:16:44.480 Yeah, the links are both in the show notes there, yeah, for the episode and the article. 00:16:44.480 --> 00:16:57.080 But more, I'm pleased with more people, more people being like one so far, more other Python developers writing for targeting developers and teaching people how to, how they should set up their tests. 00:16:57.080 --> 00:16:58.080 So that's... 00:16:58.080 --> 00:16:59.620 Yeah, and it's such a simple pattern. 00:16:59.900 --> 00:17:06.060 But I find when I follow up my code, my tests are more focused and they're not less rambly. 00:17:06.060 --> 00:17:06.860 So I think it's good. 00:17:06.860 --> 00:17:15.540 Yeah, and also you have less chance of something going, like a test failing, you pretty much know what's wrong instead of a test failing. 00:17:15.540 --> 00:17:17.680 And it might be one of 15 different things. 00:17:17.680 --> 00:17:18.460 Yep, for sure. 00:17:18.460 --> 00:17:25.280 All right, so last thing I want to cover is to shine a bit of a bright light on the future of Python. 00:17:25.280 --> 00:17:29.780 So everyone out there listening, you are in a good place, let me tell you. 00:17:29.780 --> 00:17:33.780 In terms of being interested and working in Python right now. 00:17:33.780 --> 00:17:40.960 So there's another really deep article by this company called SourceD, Sourced, not entirely sure. 00:17:40.960 --> 00:17:44.200 But their mission, they're not super Python focused. 00:17:44.200 --> 00:17:45.820 I think they're doing mostly Go stuff. 00:17:45.820 --> 00:17:49.800 But their mission is to build the first AI that understands code. 00:17:49.800 --> 00:17:52.180 Speaking of AI, pretty interesting. 00:17:52.180 --> 00:17:55.100 So they wrote this really long blog post. 00:17:55.100 --> 00:17:57.600 There's a decent amount of data science and math in there. 00:17:57.600 --> 00:18:02.340 And it's called Analyzing GitHub, How Developers Change Programming Languages Over Time. 00:18:02.340 --> 00:18:11.120 So we've talked before, Brian, how Python is the number one most active language on GitHub, right? 00:18:11.120 --> 00:18:14.140 For active, non-trivial projects, things like that. 00:18:14.140 --> 00:18:18.520 And I think JavaScript was number one because everybody has JavaScript in their web apps, right? 00:18:18.520 --> 00:18:18.720 Yeah. 00:18:18.800 --> 00:18:22.160 So this is a different question, but kind of similar. 00:18:22.160 --> 00:18:26.040 Not what is just most popular, but how is it changing over time? 00:18:26.040 --> 00:18:27.840 Where are those trends going to? 00:18:27.840 --> 00:18:30.420 If people are changing languages, where do they change from? 00:18:30.420 --> 00:18:36.100 And so they have these cool Gantt charts that they've studied 4.5 million GitHub users, 00:18:36.100 --> 00:18:39.520 over 393 different languages, and 10 terabytes of code. 00:18:39.620 --> 00:18:43.760 And they said, given one of those 4.5 million users, how do we visualize them? 00:18:43.760 --> 00:18:44.640 How do we think about them? 00:18:44.640 --> 00:18:49.620 And they've got a Gantt chart of like, as they transition from one language to another over time. 00:18:49.620 --> 00:18:55.440 And this is based on an original article by Eric Bernhardsen. 00:18:55.440 --> 00:18:56.800 He's at Google. 00:18:56.800 --> 00:18:59.220 And the name of his article is pretty interesting as well. 00:18:59.220 --> 00:19:03.040 The Eigen Vector of Why We Move from Language X to Language Y. 00:19:03.860 --> 00:19:04.460 All right. 00:19:04.460 --> 00:19:06.500 So this takes us- 00:19:06.500 --> 00:19:07.600 I love me a good Eigen Vector. 00:19:07.600 --> 00:19:09.680 I do love me a good Eigen Vector as well. 00:19:09.680 --> 00:19:10.500 It tells you where you're going. 00:19:10.500 --> 00:19:16.600 So this is a slightly different approach, more of a data science, less of a statistical approach, 00:19:16.600 --> 00:19:16.980 I believe. 00:19:16.980 --> 00:19:20.900 And they said, look, first of all, we're going to not include JavaScript because JavaScript 00:19:20.900 --> 00:19:24.220 is like spread amongst all these projects, right? 00:19:24.220 --> 00:19:26.560 Hey, my Pyramid project has JavaScript. 00:19:26.560 --> 00:19:28.620 That Ruby on Rails project, it's got JavaScript. 00:19:28.620 --> 00:19:29.640 Everything is JavaScript. 00:19:29.640 --> 00:19:34.740 So it's super hard to make reasonable claims about JavaScript because it's such a complementary 00:19:34.740 --> 00:19:35.220 language. 00:19:35.220 --> 00:19:38.000 So they said, like, we can't reason about this, so put it to the side. 00:19:38.000 --> 00:19:39.460 Take that for what it's worth. 00:19:39.460 --> 00:19:44.960 And they said, we're going to look at the most popular languages on GitHub. 00:19:44.960 --> 00:19:46.340 And they do a whole bunch of work. 00:19:46.340 --> 00:19:50.540 And they come up with this stationary distribution of a Markov chain. 00:19:50.540 --> 00:19:51.180 How about that? 00:19:51.720 --> 00:19:59.020 And what they find out is the number one most stable language at GitHub is Python. 00:19:59.020 --> 00:20:06.540 And interestingly, its stability level is higher by almost 50% than the amount of code as a 00:20:06.540 --> 00:20:07.880 percentage of it on GitHub. 00:20:07.880 --> 00:20:09.620 So it's really, really stable. 00:20:09.620 --> 00:20:11.320 And then behind that... 00:20:11.320 --> 00:20:12.340 So what do you mean by stable? 00:20:12.340 --> 00:20:16.040 People, once they get to Python, are least likely to move away from Python. 00:20:16.040 --> 00:20:16.480 Okay. 00:20:16.480 --> 00:20:16.780 Yeah. 00:20:17.680 --> 00:20:18.660 I believe that's right. 00:20:18.660 --> 00:20:19.240 Interpretation. 00:20:19.240 --> 00:20:21.500 Then there's Java, which is also very stable. 00:20:21.500 --> 00:20:25.300 There's C, then C++, then PHP, then Ruby, then C#. 00:20:25.300 --> 00:20:26.400 And then it goes on and on and on. 00:20:26.400 --> 00:20:29.020 So they make some claims based on this. 00:20:29.020 --> 00:20:34.420 They say, Python, at 16.1%, appears to be the most attractive language followed closely 00:20:34.420 --> 00:20:35.040 by Java. 00:20:35.040 --> 00:20:40.440 It's especially interesting since 11.3% of all code on GitHub is written in Python. 00:20:40.440 --> 00:20:43.940 So it's more attractive than its level of code would imply. 00:20:43.940 --> 00:20:47.060 They said, there are some languages that are repulsive. 00:20:47.060 --> 00:20:49.180 That's my wording, not theirs. 00:20:49.180 --> 00:20:54.680 But they said, although there are 10 times more lines of code on GitHub and PHP rather than Ruby, 00:20:54.680 --> 00:20:57.940 they have the same level of stationary attractiveness. 00:20:57.940 --> 00:21:01.220 So much less reason to be attracted to Ruby. 00:21:01.220 --> 00:21:03.060 But if you're there, you're more likely to stick. 00:21:03.060 --> 00:21:05.320 And so they said, what about sticking to a language? 00:21:05.580 --> 00:21:10.820 Developers coding in one of the five most popular languages, Java, C, C++, PHP, and Ruby, 00:21:10.820 --> 00:21:15.420 are most likely to switch to Python with a 22% chance on average. 00:21:15.420 --> 00:21:16.100 How about that? 00:21:16.100 --> 00:21:16.380 Yeah. 00:21:16.380 --> 00:21:19.300 So people who like Python are most likely to stay there. 00:21:19.300 --> 00:21:22.740 And people who are one of the five most popular languages are most likely to move there as well. 00:21:22.740 --> 00:21:27.380 I think it also didn't, I don't know, I haven't read this article, but I think it also goes to 00:21:27.380 --> 00:21:32.620 the fact how easy it is to think of something that you could solve that you could share with 00:21:32.620 --> 00:21:35.060 somebody else on a project with Python. 00:21:35.060 --> 00:21:41.680 For instance, I've programmed C++ all my career, but I've never contributed any C++ code to GitHub. 00:21:41.680 --> 00:21:42.820 Yeah, that's for sure. 00:21:42.820 --> 00:21:45.000 It's definitely a more open source friendly language as well. 00:21:45.000 --> 00:21:46.740 A few more random stats. 00:21:46.740 --> 00:21:53.300 They say visual basic developers are very likely to move to C# with a 92% chance of that. 00:21:53.300 --> 00:21:58.960 And people using numerical and statistical environments such as Fortran, MATLAB, or R are most likely 00:21:58.960 --> 00:22:06.500 to switch to Python using this measure of analysis, whereas Eric, the base blog, the original blog, 00:22:06.500 --> 00:22:08.320 was suggesting they might move to C. 00:22:08.320 --> 00:22:14.360 So pretty interesting little article there about stability and attractiveness of projects. 00:22:14.360 --> 00:22:20.960 That correlates with other, I guess, anecdotal things that we've heard of more people migrating, 00:22:20.960 --> 00:22:23.020 especially in data science, to Python. 00:22:23.020 --> 00:22:27.280 Yeah, it seems totally believable to me, given all the other pieces of information and studies 00:22:27.280 --> 00:22:28.820 we've seen and talked about. 00:22:28.820 --> 00:22:29.120 Yeah. 00:22:29.120 --> 00:22:29.720 Nice. 00:22:29.720 --> 00:22:30.080 Nice. 00:22:30.080 --> 00:22:34.260 So I think I would say, you know, if you're thinking about where do I bet my career, that's 00:22:34.260 --> 00:22:37.760 another positive sign that Python's probably a good spot to hang on to for a while. 00:22:37.760 --> 00:22:38.660 Long while. 00:22:38.660 --> 00:22:39.640 All right. 00:22:39.700 --> 00:22:40.740 Well, that's the news. 00:22:40.740 --> 00:22:42.260 Anything else going on, Brian? 00:22:42.260 --> 00:22:46.140 I wasn't there, but EuroPython got wrapped up last week. 00:22:46.140 --> 00:22:50.940 But I did have some stickers, some rocket stickers, Hits a Ride. 00:22:50.940 --> 00:22:51.560 Nice. 00:22:51.560 --> 00:22:54.520 They blasted all the way over to, what was that, Spain? 00:22:54.520 --> 00:22:56.400 Yeah, Italy, I think. 00:22:56.400 --> 00:22:56.840 Oh, Italy. 00:22:56.840 --> 00:22:57.540 Yeah, yeah, that's right. 00:22:57.540 --> 00:23:00.840 And had a bunch of stickers handed out to promote the book. 00:23:00.840 --> 00:23:02.300 So that's fun. 00:23:02.300 --> 00:23:06.620 So I got one more week to finish it, and then it'll be done. 00:23:06.620 --> 00:23:08.460 You must be looking forward to that. 00:23:08.460 --> 00:23:08.900 Yeah. 00:23:09.460 --> 00:23:10.380 So how about you? 00:23:10.380 --> 00:23:10.980 What's going on? 00:23:10.980 --> 00:23:11.240 Awesome. 00:23:11.240 --> 00:23:12.360 Not too much. 00:23:12.360 --> 00:23:13.360 I'm really enjoying summer. 00:23:13.360 --> 00:23:18.520 I'm actually working on some apps, some very interesting apps from my training courses. 00:23:18.520 --> 00:23:21.180 Not that I'm going to teach, but to like deliver stuff. 00:23:21.180 --> 00:23:23.460 So more to come on that right now. 00:23:23.460 --> 00:23:23.960 I'm just writing. 00:23:23.960 --> 00:23:24.480 Okay. 00:23:24.480 --> 00:23:25.320 See how it comes out in the end. 00:23:25.320 --> 00:23:25.520 Yeah. 00:23:25.520 --> 00:23:26.200 Very fun. 00:23:26.200 --> 00:23:31.980 Hey, one of the things that you brushed by fairly, I know you talked about it a lot somewhere 00:23:31.980 --> 00:23:35.620 else, maybe, but you were Python for entrepreneurs course. 00:23:35.620 --> 00:23:37.200 It's freaking awesome. 00:23:37.200 --> 00:23:38.220 Thank you, man. 00:23:38.220 --> 00:23:38.940 Thank you very much. 00:23:39.220 --> 00:23:40.640 I think more people should check it out. 00:23:40.640 --> 00:23:42.440 And I don't think it's just for entrepreneurs. 00:23:42.440 --> 00:23:48.620 I think it's a good top to bottom Python for web plus front end and back end. 00:23:48.620 --> 00:23:51.100 It's a nice thing for people to look at. 00:23:51.100 --> 00:23:52.000 Thank you so much. 00:23:52.000 --> 00:23:52.180 Yeah. 00:23:52.180 --> 00:23:54.600 And it's officially, officially done as of last week. 00:23:54.600 --> 00:23:56.600 So it's finally ready for the world. 00:23:56.600 --> 00:23:58.280 Thanks a bunch for the shout out. 00:23:58.280 --> 00:23:58.560 Yeah. 00:23:58.560 --> 00:24:00.980 And Matt Makai helped you with that, I believe. 00:24:00.980 --> 00:24:01.420 Yep. 00:24:01.420 --> 00:24:02.800 Matt Makai from Full Stack Python. 00:24:03.000 --> 00:24:04.120 We are happy to be done. 00:24:04.120 --> 00:24:06.660 And we're planning our next thing that we're going to do. 00:24:06.660 --> 00:24:07.100 Yeah. 00:24:07.100 --> 00:24:08.680 You guys did a good job on that. 00:24:08.680 --> 00:24:09.440 So cool. 00:24:09.440 --> 00:24:10.100 Thanks a bunch. 00:24:10.100 --> 00:24:12.420 And yeah, it's super good to see your book coming out as well. 00:24:12.420 --> 00:24:13.580 It's very fun, isn't it? 00:24:13.580 --> 00:24:13.980 Yeah. 00:24:13.980 --> 00:24:14.320 Awesome. 00:24:14.320 --> 00:24:14.660 All right. 00:24:14.660 --> 00:24:15.500 Catch up next week. 00:24:15.500 --> 00:24:15.880 You bet. 00:24:15.880 --> 00:24:16.580 Catch you next week. 00:24:16.680 --> 00:24:17.500 Thanks for being here, Brian. 00:24:17.500 --> 00:24:18.520 Thanks everyone for listening. 00:24:18.520 --> 00:24:21.540 Thank you for listening to Python Bytes. 00:24:21.540 --> 00:24:24.100 Follow the show on Twitter via at Python Bytes. 00:24:24.100 --> 00:24:27.000 That's Python Bytes as in B-Y-T-E-S. 00:24:27.000 --> 00:24:30.400 And get the full show notes at pythonbytes.fm. 00:24:30.400 --> 00:24:34.760 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:24:34.760 --> 00:24:37.460 We're always on the lookout for sharing something cool. 00:24:37.460 --> 00:24:40.840 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:24:40.840 --> 00:24:44.480 Thank you for listening and sharing this podcast with your friends and colleagues.