WEBVTT 00:00:00.001 --> 00:00:05.120 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:05.120 --> 00:00:11.500 This is episode 105, recorded November 21st on Turkey Eve. 00:00:11.500 --> 00:00:11.860 Yeah. 00:00:11.860 --> 00:00:13.140 And I'm Michael Kennedy. 00:00:13.140 --> 00:00:14.040 And I'm Brian Okken. 00:00:14.040 --> 00:00:15.440 Hey, Brian, are you ready for Turkey Eve? 00:00:15.440 --> 00:00:16.580 No, not at all. 00:00:16.580 --> 00:00:17.200 Yeah. 00:00:17.200 --> 00:00:18.020 Are you? 00:00:18.020 --> 00:00:20.580 I'm getting there, I'm getting there. I just picked up a whole bunch of stuff. 00:00:20.580 --> 00:00:22.620 We're going to cook a turkey and it's going to be crazy. 00:00:22.620 --> 00:00:24.000 Nice. 00:00:24.000 --> 00:00:24.800 So, yeah, yeah. 00:00:24.800 --> 00:00:27.960 We bought a smoked turkey, so it's already, all the work's done. 00:00:27.960 --> 00:00:29.060 Yeah, that's the way to do it. 00:00:29.060 --> 00:00:30.160 Just save yourself the trouble. 00:00:30.160 --> 00:00:37.280 I don't know how many people out there listening who don't go through this Thanksgiving thing actually cook turkeys, but it's kind of stressful and it takes forever. 00:00:37.280 --> 00:00:38.840 It's like many hours. 00:00:38.840 --> 00:00:45.780 Yeah, and then for some reason everybody wants to eat like dinner at 2 o'clock in the afternoon or something weird like that. 00:00:45.780 --> 00:00:47.880 Yeah, well, they've probably been drinking since 10, so, you know, that's why. 00:00:47.880 --> 00:00:52.620 All right, so for those of you who celebrate Thanksgiving, happy Thanksgiving. 00:00:52.620 --> 00:00:57.680 For those of you who don't, happy email Black Friday spam week. 00:00:58.120 --> 00:00:59.980 And hopefully you got some deals. 00:00:59.980 --> 00:01:01.420 Yes. 00:01:01.420 --> 00:01:12.120 So, thinking of crazy things, it's pretty awesome that DigitalOcean is giving people $100 credit for creating an account over at pythonbytes.fm/DigitalOcean. 00:01:12.120 --> 00:01:13.560 Tell you more about that later. 00:01:13.560 --> 00:01:21.840 You know, one thing I like about Thanksgiving is it lets you sort of look back on history and like think about your family and like kind of just older stuff, right? 00:01:21.980 --> 00:01:23.120 Yeah, it does. 00:01:23.120 --> 00:01:23.560 It's nice. 00:01:23.560 --> 00:01:24.880 And maybe look at some old pictures. 00:01:24.880 --> 00:01:31.780 Yeah, like, but sometimes it would be great if they weren't like, you know, old and black and white and boring and crummy. 00:01:31.780 --> 00:01:32.280 Definitely. 00:01:32.280 --> 00:01:37.380 There's a project and it's Python based and machine learning called Deoldify. 00:01:37.380 --> 00:01:38.360 Deoldify. 00:01:38.560 --> 00:01:39.620 The name says it all. 00:01:39.620 --> 00:01:43.960 Yeah, it would be actually a great name for a health club too, I think. 00:01:44.360 --> 00:01:47.400 What it is, is I can only sort of describe it. 00:01:47.400 --> 00:01:50.360 What it does is it takes a black and white photo and makes it look awesome. 00:01:50.360 --> 00:01:55.980 And like does a whole bunch of cleanup of sharpening and then colorizing it. 00:01:56.140 --> 00:01:57.800 You kind of have to look at these pictures. 00:01:57.800 --> 00:01:58.780 They're kind of amazing. 00:01:58.780 --> 00:02:07.620 One of the things I like about actual old pictures when they started to put take color photos and do colorizing, there's a lot of color. 00:02:07.620 --> 00:02:21.220 It wasn't muted, whereas a lot of the, some of the colorizing algorithms that are out are kind of just come default to obvious things like grass is green and make everything kind of brownish and stuff. 00:02:21.400 --> 00:02:32.380 And it, it just makes it a little bit better than black and white, but not really, but these really pop like adding purple and greens and reds and, and all sorts of awesome things. 00:02:32.380 --> 00:02:33.320 These are glorious. 00:02:33.320 --> 00:02:34.960 Like the old soda shop one. 00:02:34.960 --> 00:02:36.580 It's just so amazing. 00:02:36.580 --> 00:02:38.500 Do you know how it gets the color right? 00:02:38.500 --> 00:02:41.160 Like why does it not make people, you know, purple or something? 00:02:41.160 --> 00:02:46.880 Well, I mean, things like people are sort of an easy thing because they're going to be kind of people colored. 00:02:46.880 --> 00:02:51.380 You know, if we get too far into this, 00:02:51.380 --> 00:02:53.920 the how it works, I really can't tell you. 00:02:53.920 --> 00:02:59.740 Well, it does use deep learning, which means like if you get too deep down, nobody knows how it works in some sense, right? 00:02:59.740 --> 00:03:02.080 Like you've taught it sort of by example. 00:03:02.080 --> 00:03:02.380 Yeah. 00:03:02.380 --> 00:03:03.160 Yeah. 00:03:03.160 --> 00:03:15.580 It's, it's definitely based on self attenuation, generative adversarial networks and progressively growing the GANs with a two timescale update rule and generator loss in two parts. 00:03:16.060 --> 00:03:19.700 I have no idea what those words mean, but that's part of it. 00:03:19.700 --> 00:03:21.420 I think it's a really cool project. 00:03:21.420 --> 00:03:34.040 I mean, you know, what would be like a great Christmas gift or something would be to like get ahold of some old photos, do this against them, and then take them and like turn that into a photo book for like your grandparents or something. 00:03:34.040 --> 00:03:34.580 Wouldn't that be awesome? 00:03:34.720 --> 00:03:35.660 Yeah, it would be. 00:03:35.660 --> 00:03:38.860 And I'm actually, I can't wait to try playing with this. 00:03:38.860 --> 00:03:51.120 What I'm going to, with our main link in this, in the show notes of this is an interview with, it's a text-based interview with Jason Antic, the developer of Dealdify, talking about why he developed it and how he developed it. 00:03:51.320 --> 00:03:53.520 And it's a, it's kind of a really fun article. 00:03:53.520 --> 00:03:58.720 So if you're curious about this, either just play with it, try it, or, you know, read the article. 00:03:58.720 --> 00:03:59.900 I really like these visual things. 00:03:59.900 --> 00:04:05.700 I think it very clearly highlights like how amazing just this whole deep learning and machine learning thing is, you know? 00:04:05.700 --> 00:04:06.020 Yeah. 00:04:06.020 --> 00:04:14.340 And this is a sort of a fun application of it that I would have never thought we could make our lives better by making these photos better. 00:04:14.540 --> 00:04:17.640 I mean, I wouldn't have thought machine learning would be for that, but it's cool. 00:04:17.640 --> 00:04:18.400 Yeah, it's very cool. 00:04:18.400 --> 00:04:23.520 So remember last time how you said Brett Cannon was going to be mad at me? 00:04:23.520 --> 00:04:24.740 Yeah. 00:04:24.740 --> 00:04:30.500 Well, Brett Cannon will probably be happy because I got a lot of items on VS Code this week and they're pretty awesome. 00:04:30.500 --> 00:04:34.960 So I didn't do it to make up for it, but I'm happy to feature them. 00:04:34.960 --> 00:04:38.400 So this first one has to do with IoT. 00:04:38.880 --> 00:04:45.360 And I think IoT is one of these amazing things that often is like below the surface, right? 00:04:45.360 --> 00:04:47.900 Like when smartphones came out, it was so amazing. 00:04:47.900 --> 00:04:49.600 Everybody saw it and you're like, show me that thing. 00:04:49.600 --> 00:04:50.180 That's incredible. 00:04:50.180 --> 00:04:59.020 But, you know, the fact that, I don't know, your refrigerator is slightly smarter or your microwave or your doorbell, I think it's less obvious. 00:04:59.020 --> 00:05:04.680 But there's tons of IoT stuff out there and the ability to do it in Python these days is great, right? 00:05:04.680 --> 00:05:10.720 Yeah, and it's amazing how the IoT everywhere is sort of just make, it's no big deal anymore. 00:05:10.720 --> 00:05:11.640 It's so fast. 00:05:11.640 --> 00:05:14.860 Yeah, and the price is coming down, the energy consumption is coming down. 00:05:14.860 --> 00:05:15.260 It's great. 00:05:15.260 --> 00:05:24.440 So the thing that I want to tell folks about is something that Jason Percor told us about called PlatformIO IDE for VS Code. 00:05:24.440 --> 00:05:33.920 So PlatformIO is this platform for interacting with all sorts of IoT and small device type of programming examples. 00:05:33.920 --> 00:05:35.580 And it has tons of languages. 00:05:35.580 --> 00:05:42.460 And what they've done is they've built in a debugging, their framework, all that stuff into Visual Studio Code. 00:05:42.460 --> 00:05:43.180 Oh, that's cool. 00:05:43.180 --> 00:05:46.960 And what's cool is because it's Visual Studio Code, it has like all sorts of mixed mode understanding. 00:05:46.960 --> 00:05:49.480 So if you're doing C++, embedded C++, great. 00:05:49.480 --> 00:05:51.480 If you're doing Python, great. 00:05:51.480 --> 00:05:52.980 It knows about all these things. 00:05:53.420 --> 00:05:54.440 So super cool. 00:05:54.440 --> 00:05:58.140 It has a cross-platform, of course, because it's just VS Code basically. 00:05:58.140 --> 00:05:59.780 Built-in debugger. 00:05:59.780 --> 00:06:00.720 Check this out. 00:06:00.720 --> 00:06:03.160 Remote unit testing on your device. 00:06:03.160 --> 00:06:03.800 That's pretty sweet. 00:06:03.800 --> 00:06:06.200 Firmware updates, all that sort of stuff. 00:06:06.200 --> 00:06:07.340 Yeah, neat. 00:06:07.340 --> 00:06:15.740 Yeah, so if you're working on like Arduinos or something like that, it's sort of comparable to the Arduino IDE, which I haven't actually used very much. 00:06:16.220 --> 00:06:18.840 But it's sort of that kind of thing. 00:06:18.840 --> 00:06:20.120 Now, there's a few features. 00:06:20.120 --> 00:06:25.140 I think like firmware updates and a few other things are paid, but it's like $9 a month. 00:06:25.140 --> 00:06:26.180 But there's also a free version. 00:06:26.180 --> 00:06:28.000 So it's a little bit like PyCharm in that sense. 00:06:28.000 --> 00:06:30.240 Like there's a paid edition and a free community edition. 00:06:30.500 --> 00:06:31.460 So it's pretty cool. 00:06:31.460 --> 00:06:35.440 The PlatformIO core itself is written in Python, which is awesome. 00:06:35.440 --> 00:06:37.180 But it's written in legacy Python. 00:06:37.180 --> 00:06:37.920 Boo! 00:06:37.920 --> 00:06:38.660 Hiss, hiss. 00:06:38.660 --> 00:06:41.300 But that's okay. 00:06:41.900 --> 00:06:48.380 And then finally, Jason put together a video of when he, sort of an unboxing of the IDE, but it's digital. 00:06:48.380 --> 00:06:49.820 So you don't really take it out of anything. 00:06:49.820 --> 00:06:51.560 But when he first sort of test drove it. 00:06:51.560 --> 00:06:52.080 Oh, that's neat. 00:06:52.080 --> 00:06:52.980 Going through it. 00:06:52.980 --> 00:06:54.160 So it's like a 15-minute video. 00:06:54.160 --> 00:06:59.100 So if you really are interested in doing this and seeing how it works, you can check out Jason's video, which I also link there. 00:06:59.100 --> 00:06:59.500 Nice. 00:06:59.500 --> 00:06:59.920 Cool. 00:06:59.920 --> 00:07:00.180 Yeah. 00:07:00.180 --> 00:07:03.300 So IoT, definitely a cool option here. 00:07:03.300 --> 00:07:06.220 You might be collecting a lot of data on those IoT devices, right? 00:07:06.220 --> 00:07:12.000 You could have them all grabbing data from all over the world every five seconds, streaming it back, drop it into somewhere. 00:07:12.000 --> 00:07:13.100 You want to look at it, right? 00:07:13.100 --> 00:07:13.460 Yeah. 00:07:13.460 --> 00:07:16.580 You might want to make that into some visualization. 00:07:16.580 --> 00:07:17.520 Yes. 00:07:17.520 --> 00:07:19.480 And there's a lot of options. 00:07:19.480 --> 00:07:30.000 So I actually, I'm in the middle of this problem right now with a work project of trying to decide which visualization libraries to use on a particular problem. 00:07:30.000 --> 00:07:30.840 Oh, let's draw a graph. 00:07:30.840 --> 00:07:31.300 Oh, wait. 00:07:31.300 --> 00:07:33.020 There's 300 ways to do that. 00:07:33.020 --> 00:07:33.480 Why is this? 00:07:33.480 --> 00:07:33.840 Yeah. 00:07:35.320 --> 00:07:41.300 There's an article that's on the Anaconda blog called Python Data Visualization 2018. 00:07:41.300 --> 00:07:43.380 Why are there, why so many libraries? 00:07:43.380 --> 00:07:46.960 It talks about, you know, how like there's some of them are map. 00:07:46.960 --> 00:07:48.580 There's matplotlib, of course. 00:07:48.580 --> 00:07:52.820 And there's a whole bunch of others that are based on matplotlib. 00:07:52.820 --> 00:07:55.260 And then whether or not you use JavaScript. 00:07:55.260 --> 00:07:58.780 And then, you know, if you use JSON or something else. 00:07:58.780 --> 00:08:00.180 And then there's WebGL. 00:08:00.180 --> 00:08:08.740 And anyway, it's a nice breakdown of the different tools and kind of the data behind it. 00:08:08.740 --> 00:08:11.900 Like what data and stuff are they're using. 00:08:12.420 --> 00:08:15.780 And then also what you can do with them, what plot types you can do. 00:08:15.780 --> 00:08:27.020 And generally, if you're like, if you're using, for instance, geographical data, then you probably maybe look should look at matplotlib with cartoppy or a few others. 00:08:27.020 --> 00:08:30.340 They've got got some examples and they bucket them together. 00:08:30.340 --> 00:08:32.160 So it's less of a search. 00:08:32.160 --> 00:08:35.820 You don't have to look at everything to see if you should look at it. 00:08:35.820 --> 00:08:37.860 And also kind of what your data size is. 00:08:37.860 --> 00:08:41.980 And I very much appreciated this to kind of narrow things down. 00:08:41.980 --> 00:08:42.700 Yeah, it's really cool. 00:08:42.700 --> 00:08:54.220 And there's a great visualization as well from Jake Vanderplass, which it's kind of like a mind map of related, linked together, different visualization libraries. 00:08:54.420 --> 00:08:57.600 So it has like, well, here's D3 as one of the major hubs. 00:08:57.600 --> 00:08:58.800 And then you've got matplotlib. 00:08:58.800 --> 00:09:04.100 And maybe for matplotlib, you might want to use scikit plot, which is derived based on that. 00:09:04.100 --> 00:09:09.220 Or if you're using D3, you might use, I don't know, D3 poe or whatever, like one of these other things. 00:09:09.220 --> 00:09:19.080 But it also says, well, you can combine matplotlib and D3.js and get MPLD map plot something three, like a combination. 00:09:19.080 --> 00:09:21.200 MPL D3, I guess. 00:09:21.200 --> 00:09:21.680 I don't know. 00:09:22.040 --> 00:09:28.120 But the relationships, so maybe, oh, if I'm using this, there's this other library that also has some other features I could combine. 00:09:28.120 --> 00:09:28.640 I think it's cool. 00:09:28.640 --> 00:09:30.260 Yeah, they all kind of interrelate. 00:09:30.260 --> 00:09:35.560 And then I wanted to point out that there's some, sometimes you're just like, I just want something simple. 00:09:35.560 --> 00:09:39.580 I really was looking for this example about a year ago, and I wish I would have found it. 00:09:39.640 --> 00:09:56.080 But there's an example I'm going to link to that's just a really short article and then a link to a gist, which is just a single file containing Flask application that both generates and displays a couple matplotlib charts. 00:09:57.080 --> 00:10:02.160 So, Matt Laplod lib might be the simplest thing, but it isn't obvious right off the bat. 00:10:02.160 --> 00:10:03.440 How do you encode? 00:10:03.440 --> 00:10:08.860 How do you generate one of these things and then link to it in a web page and all that sort of stuff? 00:10:08.860 --> 00:10:10.400 So, this is nice. 00:10:10.400 --> 00:10:10.900 Nice, nice. 00:10:10.900 --> 00:10:18.320 I don't have a reason to draw graphs too much, but I'm starting to think maybe some BI type stuff might be in my future. 00:10:18.320 --> 00:10:19.640 That would be interesting to learn. 00:10:19.640 --> 00:10:22.380 And I'll definitely have to come back to this to figure out how to look at it. 00:10:22.380 --> 00:10:22.660 Yeah. 00:10:22.660 --> 00:10:22.900 Cool. 00:10:22.900 --> 00:10:30.400 So, before we get on to the next one, which will also make Brett Cannon happy, I think, is I want to tell you quickly about DigitalOcean. 00:10:30.400 --> 00:10:35.900 So, DigitalOcean, great hosting provider, super affordable, really rock solid. 00:10:35.900 --> 00:10:40.620 And one of the things that's cool that they do is they've created this idea of projects. 00:10:40.620 --> 00:10:46.240 Because often you go into your cloud provider and you've got these testing servers and all this stuff. 00:10:46.240 --> 00:10:47.540 You're like, well, what goes with what? 00:10:47.800 --> 00:10:50.120 Is this thing, can I turn this server off? 00:10:50.120 --> 00:10:51.520 Is it being used, right? 00:10:51.520 --> 00:10:53.580 What group is it related with? 00:10:53.580 --> 00:10:54.980 So, they have this idea of projects. 00:10:54.980 --> 00:11:02.020 And you can group your droplets, which are VMs and load balancers and domains and IPs and all that, into these different projects. 00:11:02.020 --> 00:11:07.580 So, you can treat them individually as like all of this infrastructure goes with this site. 00:11:07.580 --> 00:11:09.720 And that infrastructure goes with that site and stuff like that. 00:11:09.720 --> 00:11:10.820 So, very cool. 00:11:10.820 --> 00:11:15.400 You should check out what they've got going on over there at pythonbytes.fm/digitalocean. 00:11:15.820 --> 00:11:19.500 And like I said, you'll get $100 free credit if you create a new account there. 00:11:19.500 --> 00:11:21.260 So, very nice stuff. 00:11:21.260 --> 00:11:27.040 Now, speaking of the cloud and speaking of Visual Studio Code, I want to tell you all about Coder.com. 00:11:27.040 --> 00:11:30.240 So, Coder.com is a thing I recently learned about. 00:11:30.240 --> 00:11:33.040 They reached out to me for some stuff on Talk Python. 00:11:34.040 --> 00:11:36.800 And after playing with it, it is pretty cool. 00:11:36.800 --> 00:11:41.560 So, you know how VS Code is an Electron.js app, right? 00:11:41.560 --> 00:11:41.880 Okay. 00:11:41.880 --> 00:11:42.200 Okay. 00:11:42.200 --> 00:11:44.840 So, in Electron.js, there's kind of a hidden Chrome. 00:11:44.840 --> 00:11:46.100 The thing that you see is a window. 00:11:46.100 --> 00:11:47.300 That's really Chrome. 00:11:47.300 --> 00:11:51.200 Just like a headless Chrome type of thing running some kind of HTML there. 00:11:51.200 --> 00:11:55.400 And then it talks to Node.js over like a local transport of some sort. 00:11:55.400 --> 00:11:55.720 Okay. 00:11:55.800 --> 00:11:56.980 What if you could separate those? 00:11:56.980 --> 00:12:01.240 What if the Node.js backend lived in like a server that was really awesome? 00:12:01.240 --> 00:12:03.560 And then you could just put the front end anywhere like a website. 00:12:03.560 --> 00:12:05.380 That's kind of how it's designed to work. 00:12:05.380 --> 00:12:05.900 Yeah. 00:12:05.900 --> 00:12:06.480 Yeah. 00:12:06.720 --> 00:12:07.820 It seems possible, right? 00:12:07.820 --> 00:12:09.840 So, basically, that's like Coder.com. 00:12:09.840 --> 00:12:13.660 So, it's full Visual Studio code but in your browser on awesome hardware. 00:12:13.660 --> 00:12:15.980 So, you can write code in your browser. 00:12:15.980 --> 00:12:21.800 And because it's full Visual Studio code, you get like all the 4,000 extensions, all the different languages. 00:12:21.800 --> 00:12:29.340 And it's not some like cheapo imitation sort of sometimes the autocomplete works IDE in the cloud. 00:12:29.340 --> 00:12:32.760 It's like literally just Visual Studio but working in your browser. 00:12:33.100 --> 00:12:41.040 So, it's really, you know, once you fire it up and you get going, it's like within a few minutes, you're like, oh, wait, that's not the regular top bar for my app. 00:12:41.040 --> 00:12:41.800 That's a browser window. 00:12:41.800 --> 00:12:43.100 But you would just kind of forget. 00:12:43.100 --> 00:12:45.040 And it has some other features. 00:12:45.040 --> 00:12:50.100 So, like it runs, every time you create a project, your project lives in a Docker container. 00:12:50.100 --> 00:12:56.160 And you get root access to install and configure that Linux Docker machine however you want. 00:12:56.160 --> 00:12:59.560 So, you can do basically whatever you want in the cloud on this thing. 00:12:59.660 --> 00:13:06.560 And there's a like a paid feature, you can push a button and it will give you up to 96 cores of computational power. 00:13:06.560 --> 00:13:08.600 You get like a certain amount of free, like five hours. 00:13:08.600 --> 00:13:10.400 But after that, it's a paid feature. 00:13:10.400 --> 00:13:11.460 But still, it's pretty awesome. 00:13:11.460 --> 00:13:21.060 So, if you want to work on Linux and you're on, say, Windows and you need like hardcore Linux, not just a VM but something that's really running solid, you can use this. 00:13:21.060 --> 00:13:24.400 If you're on a Chromebook or you're on a tablet, right, this would be awesome. 00:13:24.400 --> 00:13:25.620 All sorts of stuff. 00:13:25.720 --> 00:13:29.220 Another thing that's really cool is it has Google Docs-like collaboration. 00:13:29.220 --> 00:13:31.960 So, multiple people can be working on the same file at the same time. 00:13:31.960 --> 00:13:32.580 Oh, that's neat. 00:13:32.580 --> 00:13:32.800 Yeah. 00:13:32.800 --> 00:13:33.500 It's pretty awesome. 00:13:33.500 --> 00:13:35.920 So, anyway, I wanted to tell people about it. 00:13:35.920 --> 00:13:37.900 They just launched a few weeks ago. 00:13:37.900 --> 00:13:39.680 And I think it's pretty awesome. 00:13:39.680 --> 00:13:41.020 They have really good Python support. 00:13:41.020 --> 00:13:46.120 You can obviously pip install all the stuff you need because you just open the terminal and you have root access. 00:13:46.120 --> 00:13:47.520 So, do what you like to it. 00:13:47.520 --> 00:13:50.640 It looks like it has a whole bunch of plugins available, too. 00:13:50.640 --> 00:13:52.900 It isn't just bare VS Code. 00:13:52.900 --> 00:13:53.320 Yeah, no. 00:13:53.320 --> 00:13:54.540 It's super cool, that thing. 00:13:54.540 --> 00:13:56.460 So, quite the neat project. 00:13:56.460 --> 00:14:01.020 Now, just for transparency, they did sponsor Talk Python To Me, but this is not an ad. 00:14:01.020 --> 00:14:03.000 This is just like I learned about it because of that. 00:14:03.000 --> 00:14:04.160 And I'm like, oh, this is pretty cool. 00:14:04.160 --> 00:14:05.260 I'll tell people about it here. 00:14:05.260 --> 00:14:07.260 So, anyway, something worth checking out. 00:14:07.260 --> 00:14:09.980 And the free option is pretty cool. 00:14:09.980 --> 00:14:15.940 You get like three gigs of disk space, one gig of RAM for free forever, pretty much. 00:14:15.940 --> 00:14:16.760 So, it's pretty cool. 00:14:16.760 --> 00:14:17.880 That's generous. 00:14:17.880 --> 00:14:18.660 Yeah. 00:14:18.660 --> 00:14:19.400 Yeah, not bad. 00:14:19.400 --> 00:14:19.820 All right. 00:14:19.820 --> 00:14:20.420 What's your next one? 00:14:20.420 --> 00:14:23.140 This one, it just popped up on my news feed. 00:14:23.140 --> 00:14:24.360 I think it was this morning. 00:14:24.360 --> 00:14:33.420 This is a Forbes article about Guido and trying to bring more women into the Python community. 00:14:33.420 --> 00:14:37.560 Some of the stuff I didn't, I knew things, some of these things were happening, 00:14:37.840 --> 00:14:42.820 but I didn't know some of the impetus and the, I mean, I was glad, but I didn't really 00:14:42.820 --> 00:14:43.620 understand why. 00:14:43.620 --> 00:14:50.680 But apparently, in 2015, somebody just mentioned to him, like, weird, there's no women in the 00:14:50.680 --> 00:14:52.000 core, in Python core. 00:14:52.000 --> 00:14:52.360 Why? 00:14:52.360 --> 00:14:59.520 And, you know, he talks about a lot of projects having that problem, or at least a situation 00:14:59.520 --> 00:15:05.500 where there's not a lot of women contributing, and you kind of have to examine why that's going 00:15:05.500 --> 00:15:05.860 on. 00:15:05.860 --> 00:15:13.640 And so, he took on putting out an offer saying that he would mentor women that wanted to become 00:15:13.640 --> 00:15:14.340 core developers. 00:15:14.340 --> 00:15:20.400 And, of course, we know Marietta was the first, and that's awesome. 00:15:20.400 --> 00:15:26.260 And now there's four altogether, and he just wants that to keep growing. 00:15:27.060 --> 00:15:30.240 And it's also kind of a nice talk. 00:15:30.240 --> 00:15:34.780 It's an interview that happened after he stepped down, saying that he's definitely going to be, 00:15:34.780 --> 00:15:36.700 stay involved with Python. 00:15:36.700 --> 00:15:42.080 It's just, he doesn't want to be part of all the everyday fighting and stuff. 00:15:42.080 --> 00:15:42.600 Right. 00:15:42.860 --> 00:15:48.200 And just, I kind of read this also, and I was thinking about, there was a comment that Python 00:15:48.200 --> 00:15:52.660 was worse than the average, just commercial engineering team. 00:15:52.660 --> 00:15:54.020 That is troublesome. 00:15:54.240 --> 00:15:59.060 But I think a lot of, even depending on what kind of field you're in, this might still be 00:15:59.060 --> 00:15:59.480 a problem. 00:15:59.480 --> 00:16:06.480 And I think people need to examine their own hiring processes and how you get people involved 00:16:06.480 --> 00:16:07.220 in your project. 00:16:07.220 --> 00:16:09.320 And whether it's open source or whether it's commercial. 00:16:09.320 --> 00:16:17.460 And take a look about really, one of the comments was, some teams will say there's no women in the 00:16:17.460 --> 00:16:19.300 core because nobody's been interested. 00:16:19.460 --> 00:16:25.260 Nobody's asked, but that's probably a little insincere if you take a look about, about how, 00:16:25.260 --> 00:16:27.100 how the commit bit happens. 00:16:27.100 --> 00:16:32.540 It's, it's often a kind of a nebulous, fuzzy, we trust this person sort of a thing. 00:16:32.540 --> 00:16:34.620 And a lot of hiring is that way too. 00:16:34.620 --> 00:16:40.840 And I, the argument of that a lot of hiring people say is, well, I would hire a woman or 00:16:40.840 --> 00:16:45.480 somebody, other underrepresented group, but nobody applied. 00:16:45.480 --> 00:16:47.680 And I think that's just kind of BS. 00:16:47.680 --> 00:16:50.840 You have to go out of your way to try to find a larger pool. 00:16:50.840 --> 00:16:52.700 And so this is just a good reminder. 00:16:52.700 --> 00:16:53.740 Yeah, it's a great reminder. 00:16:53.740 --> 00:16:56.600 And Geter did super important work there. 00:16:56.600 --> 00:17:03.400 I said it before, but I think I've been to different types of programming conferences and 00:17:03.400 --> 00:17:10.840 coming to a Python conference is a much better experience in feeling like this group of people 00:17:10.840 --> 00:17:17.920 represents the general population of the world compared to other ones where, you know, not 00:17:17.920 --> 00:17:20.060 so much more, not at all, basically. 00:17:20.060 --> 00:17:24.740 So I think that's a really positive aspect of what's happening for the community. 00:17:24.740 --> 00:17:29.060 And it's great that Forbes, I mean, Forbes, that's a huge place to cover this. 00:17:29.120 --> 00:17:30.140 So that's pretty awesome. 00:17:30.140 --> 00:17:30.420 Yeah. 00:17:30.420 --> 00:17:35.880 Now on your hiring thought, a lot of jobs, I think, get filled by first asking all the team 00:17:35.880 --> 00:17:38.220 members who work there, who do you know and who's awesome? 00:17:38.220 --> 00:17:39.120 Who should we hire? 00:17:39.120 --> 00:17:44.120 And when that process fails and it's like, fine, we'll put like a general, put a general 00:17:44.120 --> 00:17:46.240 job offer out there that everybody might get. 00:17:46.460 --> 00:17:51.220 But I can certainly see how that would create reinforcing groups because the team that you 00:17:51.220 --> 00:17:55.860 have would know people most likely like them, right? 00:17:55.860 --> 00:17:56.180 Yeah. 00:17:56.180 --> 00:18:02.220 And also the, some of it's the crucible process of the hiring process of, as well, 00:18:02.220 --> 00:18:08.020 even if you have people that are decent candidates, I don't remember where I have this reference 00:18:08.020 --> 00:18:15.760 from, but I've heard a comment that more frequently a male will apply for a job where he meets at least 00:18:15.760 --> 00:18:22.880 two of the qualifications, but more likely a woman will only apply for jobs where she meets 00:18:22.880 --> 00:18:26.380 all of the qualifications and there's a difference right there. 00:18:26.380 --> 00:18:31.120 So you have to, it has to start from the very beginning of be very careful what you put in 00:18:31.120 --> 00:18:35.180 your required qualifications because it will eliminate a bunch of people. 00:18:35.180 --> 00:18:35.600 Yeah. 00:18:35.600 --> 00:18:36.680 I think that's really interesting. 00:18:36.680 --> 00:18:40.680 I speak to my wife about this sometimes and she's like, well, guys will just say, they'll 00:18:40.680 --> 00:18:42.680 just, they can do this thing or, or whatever. 00:18:42.680 --> 00:18:46.120 And they can't, they don't have all this, the experience or everything that they need 00:18:46.120 --> 00:18:48.620 and they'll just do it and then they'll get the job and women don't. 00:18:48.620 --> 00:18:50.800 And I don't know, she wasn't really saying that was unfair. 00:18:50.800 --> 00:18:51.500 She's just saying. 00:18:51.500 --> 00:18:52.460 It is the way it is. 00:18:52.460 --> 00:18:53.300 So you have to be aware of it. 00:18:53.300 --> 00:18:54.120 It is the way it is. 00:18:54.180 --> 00:18:58.920 But I mean, on one hand, let's put this out here as an encouragement to women to say, 00:18:58.920 --> 00:19:03.540 look, if you have most of the, it's a wishlist. 00:19:03.540 --> 00:19:08.400 And if you have most of the things or the important ones, even just apply, right? 00:19:08.400 --> 00:19:12.520 I mean, there's so many opportunities for JIT learning, just in time learning. 00:19:12.520 --> 00:19:17.080 Like if you know three out of the six things, but you could take some online classes or something 00:19:17.080 --> 00:19:20.320 or read a book on the others, like just apply. 00:19:20.320 --> 00:19:20.760 Yeah. 00:19:20.760 --> 00:19:26.100 The other end of it is often part of the hiring process isn't really on qualifications. 00:19:26.100 --> 00:19:28.000 It's on fit. 00:19:28.000 --> 00:19:30.360 Do they seem like they already fit into the team? 00:19:30.360 --> 00:19:30.760 Yeah. 00:19:30.760 --> 00:19:34.880 And you have to be really careful with that because you, you might end up with a whole 00:19:34.880 --> 00:19:36.480 bunch of people that are kind of the same. 00:19:36.480 --> 00:19:36.800 Yeah. 00:19:36.800 --> 00:19:38.360 I think that one's, that one's trickier. 00:19:38.360 --> 00:19:39.380 These needs attention. 00:19:39.380 --> 00:19:39.780 Okay. 00:19:39.900 --> 00:19:45.800 So the last one that I want to cover is from a very inspirational woman named Annalena Popkus. 00:19:45.800 --> 00:19:50.020 And I had her back on Talk Python just last week, actually. 00:19:50.020 --> 00:19:51.600 That was an incredible interview too. 00:19:51.600 --> 00:19:52.360 Thank you. 00:19:52.360 --> 00:19:53.160 Yeah. 00:19:53.160 --> 00:19:53.540 Thank you. 00:19:53.540 --> 00:19:54.860 She was such a great guest. 00:19:54.860 --> 00:19:58.420 And so many people wrote to me and said, wow, she was really inspirational. 00:19:58.420 --> 00:19:59.880 And I think she was. 00:19:59.880 --> 00:20:05.000 And one of the things that was inspirational was not long ago, she had no programming skills. 00:20:05.380 --> 00:20:11.120 Then she decided to go to a computer science master's program in machine learning. 00:20:11.120 --> 00:20:16.440 And now she's working at Microsoft's AI residency program in just a few short years, which is 00:20:16.440 --> 00:20:16.800 incredible. 00:20:16.800 --> 00:20:20.120 And the thing that I want to tell people about is directly from that whole path. 00:20:20.120 --> 00:20:27.260 So she put together a GitHub repository of Python notebooks, Jupyter notebooks that are basically 00:20:27.260 --> 00:20:31.460 explorations to understand the core algorithms of machine learning. 00:20:31.460 --> 00:20:31.920 Nice. 00:20:31.920 --> 00:20:32.400 Yeah. 00:20:32.400 --> 00:20:37.860 Not, I want to create, you know, import scikit learning and feed this function a few things, 00:20:37.860 --> 00:20:42.360 but with no dependencies, how do we build up these algorithms and what does that look like? 00:20:42.360 --> 00:20:42.820 Cool. 00:20:42.820 --> 00:20:48.440 So if you're out there and you are doing data science or want to get into data science, she 00:20:48.440 --> 00:20:51.240 has a bunch of really nice notebooks that you can check out. 00:20:51.240 --> 00:20:52.760 I'll link to most of them here. 00:20:52.760 --> 00:20:55.020 And this is built in modern Python. 00:20:55.020 --> 00:20:56.280 So hurry for that. 00:20:56.280 --> 00:21:02.340 So anyway, she built this for herself to learn these algorithms as she did this sort of career 00:21:02.340 --> 00:21:02.820 progression. 00:21:02.820 --> 00:21:05.820 And maybe, you know, throwing it out there is going to help a lot of people. 00:21:05.820 --> 00:21:07.840 It already has like 2,500 GitHub stars. 00:21:07.840 --> 00:21:09.100 So I guess it will be. 00:21:09.100 --> 00:21:10.500 Plus it has a perceptron. 00:21:10.500 --> 00:21:13.440 I don't even know what a perceptron is, but that's a really cool word. 00:21:13.440 --> 00:21:13.840 I know. 00:21:13.840 --> 00:21:17.620 It's like a psychic power used by the machine learning algorithm. 00:21:17.620 --> 00:21:21.160 No, I have no idea what it is either, but it sounds awesome. 00:21:21.160 --> 00:21:25.680 I'm sure there's data science people rolling their eyes at us for not knowing this stuff. 00:21:25.680 --> 00:21:26.100 I'm sure. 00:21:26.100 --> 00:21:29.980 Yeah, I was going to make a good joke about another algorithm to follow up on that, but 00:21:29.980 --> 00:21:30.560 I just failed. 00:21:30.560 --> 00:21:32.020 So we're just going to leave it there. 00:21:32.020 --> 00:21:35.460 But yeah, so check out Annalena's work if you're getting into data science. 00:21:35.460 --> 00:21:36.060 That's pretty cool. 00:21:36.060 --> 00:21:37.880 Well, that's it for our main items, Brian. 00:21:37.880 --> 00:21:38.780 What else you got for us? 00:21:38.780 --> 00:21:40.260 Any little extra things you want to throw up? 00:21:40.260 --> 00:21:47.040 Just somebody mentioned to me the other day that AWS Lambda now supports Python 3.7, which 00:21:47.040 --> 00:21:48.020 is awesome. 00:21:48.020 --> 00:21:49.280 Yeah, you can use data classes. 00:21:49.280 --> 00:21:50.860 Yeah. 00:21:51.340 --> 00:21:51.760 It's cool. 00:21:51.760 --> 00:21:54.020 And it wasn't like it upgraded from 3.6, I think. 00:21:54.020 --> 00:21:55.320 I think it was something before that. 00:21:55.320 --> 00:21:56.820 They were like, well, when is 3.6 coming? 00:21:56.820 --> 00:21:58.160 And they just said, you know, skip that. 00:21:58.160 --> 00:21:58.660 3.7. 00:21:58.660 --> 00:21:59.720 Oh, they don't have 3.6? 00:21:59.720 --> 00:22:00.400 Well, they might. 00:22:00.400 --> 00:22:04.080 Now, I think that they were saying they were waiting for 3.6, and then actually 3.7 came 00:22:04.080 --> 00:22:04.220 out. 00:22:04.220 --> 00:22:07.860 I could be wrong about that, but I feel like this was a big jump in the 3 world. 00:22:07.860 --> 00:22:11.520 Yeah, I was so happy with 3.6, but now that 3.7's here, I'm like, yeah, I'll just use 00:22:11.520 --> 00:22:11.900 3.7. 00:22:11.900 --> 00:22:12.780 Yeah, that's awesome. 00:22:12.780 --> 00:22:13.200 How about you? 00:22:13.200 --> 00:22:14.200 Got anything else? 00:22:14.200 --> 00:22:17.660 Remember you asked me what Shiboken meant from last time? 00:22:17.660 --> 00:22:18.040 Yeah. 00:22:18.040 --> 00:22:19.420 It has a super deep meaning. 00:22:19.420 --> 00:22:21.500 Actually, no, it doesn't mean anything. 00:22:21.500 --> 00:22:22.900 It's just a random name. 00:22:22.900 --> 00:22:25.320 I wonder if it was, what was that, Hack Your Name? 00:22:25.320 --> 00:22:28.320 They could have probably just named it for Hack Your Name, right? 00:22:28.320 --> 00:22:28.560 Yeah. 00:22:28.680 --> 00:22:31.060 Yeah, so right now I get Stilus for Hack Your Name. 00:22:31.060 --> 00:22:35.160 That's pretty much as good as Shiboken, but Shiboken, I actually linked to the blog post 00:22:35.160 --> 00:22:39.540 that says there's no name, but it's still a pretty cool C++ interrupt thing. 00:22:39.540 --> 00:22:45.880 Also, people out there listening, if you want to nominate somebody to become a PSF fellow, 00:22:45.880 --> 00:22:52.260 Python Software Foundation fellow, this is a group of folks who every quarter, five people 00:22:52.260 --> 00:22:56.380 are nominated and put on the roster as having done something awesome for the Python community 00:22:56.380 --> 00:22:57.640 in general. 00:22:57.640 --> 00:23:04.200 So I'm linking to the place where you can read what this is about and then nominate somebody. 00:23:04.200 --> 00:23:07.560 So if someone out there is doing something cool, nominate them. 00:23:07.560 --> 00:23:09.580 That way you have a chance of being in this group. 00:23:09.580 --> 00:23:10.260 Are you a fellow? 00:23:10.260 --> 00:23:11.240 I am actually. 00:23:11.240 --> 00:23:12.140 Very surprising. 00:23:12.140 --> 00:23:16.920 I didn't even know about Python Software Foundation fellows and somebody nominated me and they 00:23:16.920 --> 00:23:17.820 said, hey, you got the award. 00:23:17.820 --> 00:23:18.560 I said, wait, what? 00:23:18.560 --> 00:23:21.040 So yeah, whoever nominated me, thank you so much. 00:23:21.040 --> 00:23:21.540 That was awesome. 00:23:21.540 --> 00:23:22.160 I am. 00:23:22.160 --> 00:23:22.620 Yes. 00:23:22.760 --> 00:23:23.460 Yeah, that's cool. 00:23:23.460 --> 00:23:26.140 So it would be waste if somebody were to nominate you again. 00:23:26.140 --> 00:23:26.840 Yeah. 00:23:26.840 --> 00:23:29.580 The link I actually sent is a Twitter thing that says, please don't nominate me. 00:23:29.580 --> 00:23:30.760 Here's how to nominate somebody. 00:23:30.760 --> 00:23:31.860 Oh, okay. 00:23:31.860 --> 00:23:32.760 Thanks. 00:23:32.760 --> 00:23:34.560 Yeah, yeah. 00:23:34.560 --> 00:23:34.660 Cool. 00:23:34.660 --> 00:23:35.580 But that's pretty awesome. 00:23:35.580 --> 00:23:35.900 All right. 00:23:35.900 --> 00:23:40.940 So anyway, I'm sure that would really make somebody's week if they got that and they probably 00:23:40.940 --> 00:23:42.120 don't even expect it. 00:23:42.120 --> 00:23:42.360 Yeah. 00:23:42.360 --> 00:23:42.720 Nice. 00:23:42.720 --> 00:23:43.020 Cool. 00:23:43.020 --> 00:23:43.480 All right. 00:23:43.480 --> 00:23:45.180 Well, that's it for this week, Brian. 00:23:45.180 --> 00:23:45.760 All right. 00:23:45.760 --> 00:23:46.900 Well, we'll talk to you next week. 00:23:46.900 --> 00:23:47.520 Yeah, you bet. 00:23:47.520 --> 00:23:48.280 Go forth in Turkey. 00:23:48.600 --> 00:23:50.120 Thank you for listening to Python Bytes. 00:23:50.120 --> 00:23:52.660 Follow the show on Twitter via at Python Bytes. 00:23:52.660 --> 00:23:55.540 That's Python Bytes as in B-Y-T-E-S. 00:23:55.540 --> 00:23:58.980 And get the full show notes at pythonbytes.fm. 00:23:58.980 --> 00:24:03.300 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:24:03.300 --> 00:24:06.000 We're always on the lookout for sharing something cool. 00:24:06.000 --> 00:24:09.400 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:24:09.400 --> 00:24:12.920 Thank you for listening and sharing this podcast with your friends and colleagues. 00:24:12.920 --> 00:24:13.920 Thanks. 00:24:13.920 --> 00:24:43.900 Thank you.