WEBVTT 00:00:00.001 --> 00:00:04.740 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:04.740 --> 00:00:09.160 This is episode 270, recorded February 9th, 2022. 00:00:09.160 --> 00:00:10.580 I'm Michael Kennedy. 00:00:10.580 --> 00:00:11.680 And I'm Brian Okken. 00:00:11.680 --> 00:00:13.280 And I'm Dean Langsam. 00:00:13.280 --> 00:00:15.640 Dean, so great to have you on the show. 00:00:15.640 --> 00:00:16.300 Thank you. 00:00:16.300 --> 00:00:19.260 So often you help me with that start in the live chat. 00:00:19.260 --> 00:00:25.520 I know you're a big participant in the show, so we pulled you in and now here you are. 00:00:25.520 --> 00:00:25.940 Welcome. 00:00:25.940 --> 00:00:26.660 Thank you. 00:00:26.660 --> 00:00:27.520 Thank you. 00:00:27.660 --> 00:00:30.060 I've been a fan actually since episode one. 00:00:30.060 --> 00:00:31.180 I've been hearing this weekly. 00:00:31.180 --> 00:00:34.460 That goes back years, like five years or something. 00:00:34.460 --> 00:00:35.200 Yeah, it's about five years. 00:00:35.200 --> 00:00:39.860 I remember I moved apartments back then and I listened to Python. 00:00:39.860 --> 00:00:41.880 I didn't know Python as well back then. 00:00:41.880 --> 00:00:43.680 And I actually grow with the show. 00:00:43.680 --> 00:00:44.820 So that's very nice. 00:00:44.820 --> 00:00:45.480 That's fantastic. 00:00:45.480 --> 00:00:46.280 That's incredible. 00:00:46.280 --> 00:00:50.280 We've heard that from other people and that's just like mind-blowing to me. 00:00:50.280 --> 00:00:52.400 But yeah, it's cool. 00:00:52.400 --> 00:00:57.220 Yeah, I was taking like intro to data science classes in Coursera while listening to the show. 00:00:57.320 --> 00:01:00.660 And now other people call me a senior Python. 00:01:00.660 --> 00:01:02.520 So that was very nice. 00:01:02.520 --> 00:01:04.500 That's fantastic. 00:01:04.500 --> 00:01:05.760 And it does go fast. 00:01:05.760 --> 00:01:06.000 Yeah. 00:01:06.000 --> 00:01:07.120 So awesome. 00:01:07.120 --> 00:01:10.080 Thank you so much for joining us on the show. 00:01:10.080 --> 00:01:10.680 It's awesome. 00:01:10.680 --> 00:01:14.780 Before we get into it, I also want to say this episode is brought to you by Datadog. 00:01:14.780 --> 00:01:18.160 Check out their awesome stuff at pythonbytes.fm/datadog. 00:01:18.160 --> 00:01:20.320 I'll tell you more about that later. 00:01:20.880 --> 00:01:25.660 Right now, Python, I want to hear about a better Pygame loop. 00:01:25.660 --> 00:01:26.600 Brian, tell us about it. 00:01:26.600 --> 00:01:27.080 Yeah. 00:01:27.080 --> 00:01:29.940 So this is an article from Glyph. 00:01:29.940 --> 00:01:37.080 And Pygame is a package that's used for game programming a lot. 00:01:37.640 --> 00:01:39.480 And it's, I mean, a lot. 00:01:39.480 --> 00:01:45.780 And programming games is definitely, I think, it's one of the things I tried to do early on when I was a developer. 00:01:45.780 --> 00:01:55.340 And I think it's something that I think I encourage a lot of new developers to try out things like simple games because it's fun to learn coding that way. 00:01:56.040 --> 00:02:01.840 And it's, anyway, it's a big part of learning programming and the programming space. 00:02:01.840 --> 00:02:04.500 And with Python, it's pretty easy with Pygame. 00:02:04.500 --> 00:02:07.200 And there's a lot of tutorials out there. 00:02:07.200 --> 00:02:22.000 But one of the things that Glyph points out is a lot of the tutorials have this sort of simple while one loop where you, the main loop of a game where you just spin and wait for events and then handle the event or draw things or whatever. 00:02:22.000 --> 00:02:25.820 And then go back this and draw, you know, keep going. 00:02:26.020 --> 00:02:27.120 And this just happens forever. 00:02:27.120 --> 00:02:34.100 While one loop in programming is a busy loop and it's generally something that kind of has some issues. 00:02:34.100 --> 00:02:42.160 So Glyph is pointing out that some of the issues with this are that they, that it's, it's waste power for one. 00:02:42.160 --> 00:02:46.520 Your CPU is just spinning all the time when you're really not going to get events that fast. 00:02:46.520 --> 00:02:56.000 And then also there's a, there's a thing that I didn't know about called screen tearing, which is when you're drawing the screen at the same time you're trying. 00:02:56.000 --> 00:02:57.760 Writing to the screen buffer. 00:02:57.760 --> 00:02:58.780 Right. 00:02:58.780 --> 00:03:03.300 You're not waiting for the V sync 60, a hundred frames a second, whatever it is. 00:03:03.300 --> 00:03:03.480 Right. 00:03:03.480 --> 00:03:03.900 Yeah. 00:03:03.900 --> 00:03:08.960 So, and that can cause glitches in the game and it doesn't, it doesn't look as good. 00:03:09.460 --> 00:03:15.600 Py game does allow a V sync option, but apparently there's like some problem with that. 00:03:15.600 --> 00:03:25.740 So what really there's a, the article walks through both of these, both of these problems and the V sync fix, but, and the problems with that. 00:03:25.740 --> 00:03:34.920 But the end result really is he's got, it's actually interesting, interesting discussion about like really what's going on in py game. 00:03:34.920 --> 00:03:42.180 And he talks about like that, that there's really three jobs going on in the drawing and a game logic and the input handling all at once. 00:03:42.500 --> 00:03:44.440 And so this is a three thing. 00:03:44.440 --> 00:03:47.080 It's probably a good idea to do maybe async stuff. 00:03:47.080 --> 00:03:48.680 So things can work together. 00:03:49.520 --> 00:04:01.280 And, and the solution he came up with is still, I mean, it's definitely a larger loop, but it's not that big of a loop more complicated. 00:04:01.280 --> 00:04:10.260 And it's an async version to have some sleeps in there with some delays possibly, but a better loop for gaming. 00:04:10.260 --> 00:04:12.000 And it's, it's not that complicated. 00:04:12.000 --> 00:04:22.940 And actually if you're learning gaming while programming, hearing about this, these sorts of issues and, and trying to end learning how to solve it, it's probably just going to make you a better developer faster. 00:04:22.940 --> 00:04:25.800 So I think it's a good thing to, to look at this. 00:04:25.800 --> 00:04:26.640 Yeah. 00:04:26.640 --> 00:04:27.800 This looks really interesting. 00:04:27.800 --> 00:04:32.700 This game loop stuff, you know, it's, it's so often very much the same. 00:04:32.700 --> 00:04:41.640 And there's like these core elements like process input, you know, if the key's down or if there's a joystick attached, draw the scene, do the hit detection. 00:04:41.640 --> 00:04:43.600 And AI and game logic. 00:04:43.600 --> 00:04:45.580 And it's, it's almost always the same. 00:04:45.580 --> 00:04:49.740 Like this looks great as a way to tell me what I should be doing. 00:04:49.740 --> 00:04:58.420 And maybe the next step would be create a class that I just override, do the AI logic, draw the screen and just let that like not even be something I ever see. 00:04:58.420 --> 00:05:03.320 So this is ripe for a little bit of hiding away even this cool stuff. 00:05:03.320 --> 00:05:03.780 That's true. 00:05:03.780 --> 00:05:09.600 Maybe, maybe Pygame could extend a better built-in loop to hook into or something. 00:05:09.600 --> 00:05:10.520 Yeah. 00:05:11.140 --> 00:05:11.500 Yeah. 00:05:11.500 --> 00:05:23.540 I always think about, I always, I'm not actually used, do a lot of gaming on Python, but I always think about browsers, which are also kind of a loop that runs forever and renders stuff on your screen. 00:05:23.540 --> 00:05:26.920 And I think, well, the front end guys got it so easy, right? 00:05:26.920 --> 00:05:30.480 They don't, they just write the code and the browser does it for them. 00:05:30.480 --> 00:05:33.320 And I'm not sure if it works exactly the same. 00:05:34.100 --> 00:05:41.740 But maybe if someone manages to implement something that's like, just write your game and put it in this thing. 00:05:41.740 --> 00:05:47.820 Maybe this could attract more people into writing small games in Python. 00:05:48.320 --> 00:05:48.980 Yeah, absolutely. 00:05:48.980 --> 00:05:55.060 My thought is if you just sort of abstract that away, it's just 2D stuff, right? 00:05:55.060 --> 00:05:56.520 Which it's pretty easy to get into. 00:05:56.520 --> 00:06:08.060 I just listened to or watched a Netflix series called High Score, which is the history of video games going like way back to the Atari 2600 and Asteroid and whatnot. 00:06:08.320 --> 00:06:12.680 And there's this woman in here talks about how she got so inspired about just text-based games. 00:06:12.680 --> 00:06:16.940 So if you're learning to program, I definitely think games are a fun way. 00:06:16.940 --> 00:06:23.700 And often I think people might perceive that as like, well, I've got to write, you know, Angry Birds or something, which is fine. 00:06:23.700 --> 00:06:24.880 You can write that and that's super fun. 00:06:24.880 --> 00:06:32.060 But you can do a lot of stuff with just sort of text-based little fun story adventure type stuff as well. 00:06:32.060 --> 00:06:34.320 I got to check out that Netflix series. 00:06:34.320 --> 00:06:35.360 That sounds great. 00:06:35.360 --> 00:06:36.420 Yeah, yeah. 00:06:36.420 --> 00:06:44.520 I was just helping a friend writing like this small game and he's written this like with one thread and everything for this school project. 00:06:44.520 --> 00:06:50.160 And then he told me, well, but how do I show a score that like updates with the game? 00:06:50.160 --> 00:06:55.980 And then I thought like, no, for that you'll need multiple threads, a Pygame loop maybe and stuff like that. 00:06:55.980 --> 00:07:01.000 So if that could have been easier on him while learning Python, this could have been awesome. 00:07:01.000 --> 00:07:02.680 Yeah, absolutely. 00:07:03.080 --> 00:07:06.580 There's a lot of nice comments out in the live stream. 00:07:06.580 --> 00:07:11.340 Anthony says, I teach Pygame in my code club after school class. 00:07:11.340 --> 00:07:12.720 Smart kids, Pygame is great. 00:07:12.720 --> 00:07:17.020 So is Arcade, which is an alternative, an OpenGL-based alternative to Pygame. 00:07:17.020 --> 00:07:17.760 That's very cool. 00:07:18.340 --> 00:07:25.120 I do think having something visual for people when they're learning, it just, it reinforces things so much, right? 00:07:25.120 --> 00:07:32.120 Like writing that API back in that talks to database is great when you see the next three steps down the line, how it's going to enable something. 00:07:32.120 --> 00:07:35.640 But when you're getting started, you need quick feedback. 00:07:35.640 --> 00:07:36.260 Absolutely. 00:07:36.260 --> 00:07:37.300 All right. 00:07:37.880 --> 00:07:40.280 Well, let's talk about something else that's awesome here. 00:07:40.280 --> 00:07:42.200 I want to talk about SQLAlchemy. 00:07:42.200 --> 00:07:46.840 SQLAlchemy has been getting a lot of attention lately, and that's super cool. 00:07:46.840 --> 00:07:53.400 Mike Bayer released SQLAlchemy 2, which was the first async API version. 00:07:53.400 --> 00:07:57.120 So now you can use async and await with SQLAlchemy, which opens up lots of possibilities. 00:07:57.820 --> 00:08:05.300 Sebastian Ramirez released SQL Model, which is like a marriage of Pydantic and SQLAlchemy, which is also super neat. 00:08:05.300 --> 00:08:09.840 But there are many other things that you can do with SQLAlchemy that are really handy. 00:08:09.840 --> 00:08:16.460 So as all the awesome lists go, here's one for a curated list of SQLAlchemy. 00:08:16.460 --> 00:08:25.640 Now, first, just a word of warning from what I can tell, including the PR that I added yesterday, all the way back to the one in June 2020. 00:08:26.900 --> 00:08:30.400 It doesn't seem to be getting a whole lot of love, which is unfortunate. 00:08:30.400 --> 00:08:33.940 So it seems like it might be sort of stalled out. 00:08:33.940 --> 00:08:36.000 But that said, it's still a really good list of things. 00:08:36.000 --> 00:08:39.160 So I'll pull out a couple that I think are nice here. 00:08:39.160 --> 00:08:40.860 Which ones did I want to highlight? 00:08:40.860 --> 00:08:42.600 The first one is called Continuum. 00:08:42.600 --> 00:08:44.800 SQLAlchemy Continuum. 00:08:44.800 --> 00:08:46.220 And this is versioning. 00:08:46.220 --> 00:08:52.160 So imagine you would like to have a history or a record of changes to your database. 00:08:52.160 --> 00:08:54.320 Like maybe this is some sort of financial thing. 00:08:54.320 --> 00:09:02.300 And if you see changes, you want to be able to say, this person made this change on this date when they said, you know, update. 00:09:02.300 --> 00:09:04.260 Get the record. 00:09:04.260 --> 00:09:04.960 Make a change. 00:09:04.960 --> 00:09:07.360 And, you know, call commit on the SQLAlchemy session. 00:09:07.360 --> 00:09:12.160 So what this does is it will create versions of inserts, updates, and deletes. 00:09:12.160 --> 00:09:14.160 It won't store those. 00:09:14.300 --> 00:09:18.220 If there's not actually a change, it supports Olympic migrations. 00:09:18.220 --> 00:09:21.160 You can revert data objects and so on. 00:09:21.160 --> 00:09:23.760 So if you want that, SQLAlchemy Continuum. 00:09:23.760 --> 00:09:28.160 It's just like one of the many, many, many things in here, which is pretty awesome. 00:09:28.160 --> 00:09:30.320 Another one I wanted to highlight is UTC. 00:09:31.000 --> 00:09:38.880 So one of the challenges that people often run into is when you're storing stuff in the database, dates in particular, what time is that? 00:09:38.880 --> 00:09:45.320 Is that the time of the user who might be in a different time zone than the API endpoint that it was running at? 00:09:45.320 --> 00:09:45.860 Right. 00:09:45.900 --> 00:09:55.060 So it might be nice to be able to store time zone aware things and store them as UTC values so they're always the same. 00:09:55.060 --> 00:09:58.400 And then you can convert them back to the time zone, which is pretty cool. 00:09:58.400 --> 00:10:01.200 Another one is the SQLAlchemy Utils is pretty cool. 00:10:01.200 --> 00:10:06.080 So it's got things like choice type, which I'm guessing is basically enum. 00:10:06.080 --> 00:10:14.840 But country, JSON, URL, UUID, all of these different data types, data ranges, all kinds of stuff. 00:10:15.300 --> 00:10:18.960 ARM helpers, utility classes, and different things like that. 00:10:18.960 --> 00:10:21.580 So that's kind of a grab bag of them. 00:10:21.580 --> 00:10:22.320 Let's see. 00:10:22.320 --> 00:10:24.840 One also is called File Depot. 00:10:24.840 --> 00:10:27.180 There's cool stuff for processing images. 00:10:27.180 --> 00:10:33.740 You've got File Depot, which is a framework for easily storing and serving files out of your database on the web, 00:10:33.740 --> 00:10:38.600 as well as SQLAlchemy Image Attach, which is specifically about storing images in your database, 00:10:38.600 --> 00:10:41.460 which, by the way, we do, Brian, on Python Bytes. 00:10:41.460 --> 00:10:42.140 Cool. 00:10:42.660 --> 00:10:48.520 If you go to any page, any episode page, and you see that watch it on YouTube, that little thumbnail, 00:10:48.520 --> 00:10:52.640 we go get that dynamically from YouTube and then serve it up so we don't have to depend on YouTube. 00:10:52.640 --> 00:10:55.160 Anyway, that's pretty cool. 00:10:55.160 --> 00:10:56.120 Let's see. 00:10:56.120 --> 00:10:58.440 Maybe two more. 00:10:58.440 --> 00:11:00.400 There's Searchable. 00:11:01.040 --> 00:11:06.520 So if you want to add full-text search to your model, you can add, use this. 00:11:06.520 --> 00:11:11.120 And then it only supports Postgres because I'm sure it depends upon some core element there. 00:11:11.120 --> 00:11:15.220 But you can also do another one from MySQL as well, which is pretty cool. 00:11:15.220 --> 00:11:25.600 And then the last one is Schema Display, which generates basically graphs of your models and how they relate to each other, stuff like that, which is kind of neat. 00:11:25.600 --> 00:11:26.240 Nice. 00:11:26.240 --> 00:11:27.280 What do you all think? 00:11:27.280 --> 00:11:28.220 Cool stuff, right? 00:11:28.220 --> 00:11:28.600 Yeah. 00:11:28.600 --> 00:11:29.380 Very cool. 00:11:29.380 --> 00:11:30.720 Yeah. 00:11:30.720 --> 00:11:39.580 So if you're really bought into SQLAlchemy, you owe it to yourself to just flip through this list to just go like, wait, it can do that? 00:11:39.580 --> 00:11:41.520 I had no idea that it could do that, right? 00:11:41.520 --> 00:11:47.520 And just sort of see what are the other things that people built on top of here that I think would be super, super helpful. 00:11:47.520 --> 00:11:55.600 And by the way, my PR was really to say there's a layer called thin abstractions. 00:11:55.600 --> 00:12:04.440 And it says, you know, under the thin abstractions, we really should have us some SQL model because that thing is super popular straight out of the gate, right? 00:12:04.440 --> 00:12:05.980 So people should check this out. 00:12:05.980 --> 00:12:10.280 It's already got almost 7,000 stars and it's, what, a month old or something? 00:12:10.280 --> 00:12:10.960 That's crazy. 00:12:10.960 --> 00:12:11.600 Yeah. 00:12:11.600 --> 00:12:14.300 Maybe six weeks, but really, really new. 00:12:14.300 --> 00:12:15.980 Yeah. 00:12:15.980 --> 00:12:16.440 And. 00:12:16.440 --> 00:12:18.200 But the author, I mean. 00:12:18.200 --> 00:12:20.720 Yeah, exactly. 00:12:20.720 --> 00:12:21.620 I know. 00:12:21.620 --> 00:12:27.540 Brandon on the audience says, there should be a meta awesome list, like an awesome list of awesome lists. 00:12:27.540 --> 00:12:28.660 I'm sure there is. 00:12:28.660 --> 00:12:32.180 There is. 00:12:32.180 --> 00:12:32.800 I'm sure. 00:12:32.800 --> 00:12:35.320 And yeah, quite fun. 00:12:35.320 --> 00:12:37.260 I definitely recommend people check that out. 00:12:37.260 --> 00:12:38.180 All right. 00:12:38.760 --> 00:12:40.040 Dean, that brings us to your first item. 00:12:40.040 --> 00:12:40.680 Tell us about it. 00:12:40.680 --> 00:12:41.240 Yeah. 00:12:41.240 --> 00:12:45.360 So at work, I needed to write something that required threading. 00:12:45.360 --> 00:12:48.160 And I was very afraid of threading at the beginning. 00:12:48.160 --> 00:12:51.560 Basically, what we needed to do, we have some mechanism. 00:12:51.820 --> 00:13:02.240 I'm a data scientist and we need to take many queries at once and get them as Pandas data frames and save them to disk and later take all of them and work with them. 00:13:02.240 --> 00:13:07.740 And instead of writing, like sending them sequentially, I wanted to send a bunch of them together. 00:13:08.220 --> 00:13:19.840 And the bonus thing I found that is that when you release them to a threading, if you don't lock the threads or you don't wait for the threads, you can actually still work with the Jupyter notebook while waiting for the queries. 00:13:20.020 --> 00:13:21.860 So that was my main reasoning. 00:13:21.860 --> 00:13:30.500 And eventually, after I've written most of the code, I got this blog post called The Threadpool Executor in Python, The Complete Guide. 00:13:30.500 --> 00:13:32.740 So this is basically Jason Brownlee. 00:13:32.740 --> 00:13:38.060 He's a guy who's also the guy from Machine Learning Mastery, so I'm very familiar with him. 00:13:39.820 --> 00:13:49.680 It's a very long blog post, so you could kind of read it as an e-book or just access the stuff you need because it's like, I don't know, a two-hour read maybe. 00:13:49.680 --> 00:13:53.700 And he explains everything from the beginning. 00:13:53.700 --> 00:13:57.500 He explains what are Python threads, how to work with them. 00:13:57.500 --> 00:14:02.820 Then he introduces the Threadpool Executor, which is a more convenient way to use threads. 00:14:02.820 --> 00:14:10.980 He explains about the lifecycle of what does he do, how to do it then with a context manager and stuff like that. 00:14:10.980 --> 00:14:22.440 And eventually, what he talks about that other people do not when you search for a threading tutorial is actually about the complete lifecycle and then the usage patterns. 00:14:22.440 --> 00:14:26.640 And then he explains about IO bound versus CPU bound and everything. 00:14:26.640 --> 00:14:29.820 And he finishes off with the common questions. 00:14:29.820 --> 00:14:33.860 So this is like the link I've saved because I will forget it in a week. 00:14:33.860 --> 00:14:39.660 But the next time I need to, I just know I can come back to this and like read the common questions part. 00:14:39.660 --> 00:14:41.540 And yes, there are questions. 00:14:41.540 --> 00:14:44.060 The questions like, how do you stop running? 00:14:44.060 --> 00:14:44.720 There's a lot there, yeah. 00:14:44.720 --> 00:14:47.980 There is a lot there in this article, isn't there? 00:14:47.980 --> 00:14:48.580 Yeah, it's a lot. 00:14:48.580 --> 00:14:49.160 It's a lot. 00:14:49.160 --> 00:14:52.940 But the thing is, you can come back later and just take the stuff you need. 00:14:52.940 --> 00:14:57.840 Like I remember, I know I'm working, then I can ask myself, how do you set a chunk size in map? 00:14:57.840 --> 00:15:00.800 Well, it says there that you don't because that's for the process pool. 00:15:00.800 --> 00:15:02.300 But then I have another question. 00:15:02.300 --> 00:15:03.980 Maybe how do you cancel a running test? 00:15:03.980 --> 00:15:04.880 And the answer is that. 00:15:04.880 --> 00:15:11.720 So I think that's a good thing to have like to quickly access when you need to. 00:15:11.720 --> 00:15:19.880 And it finishes off with like, what's the difference from asyncIO, from threading.thread, from process pool executor. 00:15:19.880 --> 00:15:23.860 So that is a very helpful guide, very complete. 00:15:23.860 --> 00:15:33.660 And the entire blog actually explains, like it's an entire blog dedicated to the threading pool executor and the process pool executor. 00:15:33.660 --> 00:15:43.600 I love that it's covering the thread pool and process pools because it's easy for things to just completely get out of control. 00:15:43.600 --> 00:15:47.000 You know, as you throw more work at it, stuff can completely back up. 00:15:47.000 --> 00:15:49.680 So if you just say, create me a new thread and run that. 00:15:49.680 --> 00:15:51.220 And then another place, create me more threads. 00:15:51.220 --> 00:15:52.060 And I got a bunch more. 00:15:52.060 --> 00:15:54.200 Oh, look, now I have a thousand items to process. 00:15:54.200 --> 00:15:55.320 Create a thousand threads. 00:15:55.320 --> 00:15:55.580 Yeah. 00:15:55.580 --> 00:15:59.240 Each thread takes a lot of context switching to switch between. 00:15:59.240 --> 00:16:01.980 And they take a decent amount of memory and all sorts of stuff, right? 00:16:02.520 --> 00:16:05.700 Through the thread pool, you can say, queue up the work and run 10 at a time. 00:16:05.700 --> 00:16:12.580 Same for processes, which sort of sets an upper bound on how much concurrency you can deal with, right? 00:16:12.580 --> 00:16:12.960 Yeah. 00:16:12.960 --> 00:16:14.240 Yeah. 00:16:14.240 --> 00:16:15.200 This is cool. 00:16:15.200 --> 00:16:19.100 So you talked about solving some problems in Jupyter Notebook using this. 00:16:19.100 --> 00:16:20.900 What in particular were you trying to do? 00:16:20.900 --> 00:16:25.760 So basically, I can send, I know, a thousand queries. 00:16:25.760 --> 00:16:31.000 And once they get, like, we have big data and then they have a query that takes a part of it. 00:16:31.380 --> 00:16:35.720 Like, after maybe some group buys and limitations and stuff like that. 00:16:35.720 --> 00:16:38.340 And I want to take the data frame and save it. 00:16:38.340 --> 00:16:38.980 Right. 00:16:39.420 --> 00:16:48.260 And then once I have the entire data from all the queries, I want to join them or maybe do some, I don't know, some processing and then join everything. 00:16:48.260 --> 00:16:59.220 The thing is, after, like, 10 of those came back, I have a sample of my data that I can work with and try to manage and then have a code written. 00:16:59.220 --> 00:17:03.600 And while the other stuff is still written, I want to have that, like, I can play with it. 00:17:04.100 --> 00:17:13.520 So if I release the other things to the threads and they work in the background, the main thread of the Jupyter Notebook is open. 00:17:13.520 --> 00:17:16.360 And you can start working on the same notebook. 00:17:16.360 --> 00:17:27.620 Before then, I used to, like, open a notebook that's querying stuff, open a notebook that I'm playing with and, like, see that the file paths are the same. 00:17:27.620 --> 00:17:32.220 So I'm not confused with, like, some other directory of the other versioning of this data. 00:17:32.220 --> 00:17:34.280 And now it just works. 00:17:34.700 --> 00:17:35.300 Oh, that's really cool. 00:17:35.300 --> 00:17:43.460 And you can also, like, add a thread for, I know, with some visualizations of what's finished, what's eroded, what's, like, everything. 00:17:43.460 --> 00:17:44.240 Fantastic. 00:17:44.240 --> 00:17:45.860 Yeah, that sounds really good. 00:17:45.860 --> 00:17:49.000 I'm sure there's a lot of concurrency and parallelism in the data backend. 00:17:49.000 --> 00:17:52.340 It's just how do you sort of access that from Python, right? 00:17:52.340 --> 00:17:53.980 So how do you issue all those commands? 00:17:53.980 --> 00:17:54.720 Excellent. 00:17:54.720 --> 00:17:55.560 All right. 00:17:55.560 --> 00:17:56.960 Let's see. 00:17:56.960 --> 00:17:59.840 Brian, anything you want to add before I talk about Datadog? 00:17:59.840 --> 00:18:00.920 No. 00:18:01.920 --> 00:18:09.720 Some comments, like, Sam, Morley, concurrent futures is a much less painful way to work with them at a higher level. 00:18:09.720 --> 00:18:14.820 So maybe we could get an article on concurrent futures on the upside sometimes. 00:18:14.820 --> 00:18:15.820 Yeah, for sure. 00:18:15.820 --> 00:18:20.780 So the thread pool executor gets you back futures. 00:18:20.780 --> 00:18:30.720 And then part of what's explained in the blog post is how to work with futures, like, as completed or sequentially or, like, you decide your strategy, 00:18:30.720 --> 00:18:32.400 but you work with the futures. 00:18:32.400 --> 00:18:33.140 Nice. 00:18:33.140 --> 00:18:33.880 Okay. 00:18:33.880 --> 00:18:34.420 Cool. 00:18:34.420 --> 00:18:35.060 Yeah. 00:18:35.060 --> 00:18:35.420 Nice. 00:18:35.420 --> 00:18:41.780 And, of course, requisite shout out to Unsync, which is all sorts of awesome for this stuff. 00:18:41.780 --> 00:18:48.980 Unifies the API for direct threads for processes and AsyncIO. 00:18:49.520 --> 00:18:52.900 But what I want to tell you all about now is Datadog. 00:18:52.900 --> 00:18:54.020 Datadog is really awesome. 00:18:54.020 --> 00:18:56.320 You should really have insight into your applications. 00:18:56.320 --> 00:18:58.580 And that's what Datadog brings you. 00:18:58.580 --> 00:19:05.600 So Datadog is a real-time monitoring that unifies metrics, traces, logs into one tightly integrated platform. 00:19:06.360 --> 00:19:13.300 Their APM empowers developers to identify anomalies and resolve issues, especially around performance. 00:19:13.300 --> 00:19:22.700 You can begin collecting stack traces, visualize them as flame graphs, and organizing them into profile types, such as CPU bound, IO bound, and so on. 00:19:22.860 --> 00:19:35.080 And teams can even search specific profiles and correlate them to distributed traces to, you know, find things across different parts of your infrastructure and microservices and identify slow or underperforming code and then make it faster. 00:19:35.460 --> 00:19:37.860 Plus, you can use their APM live search. 00:19:37.860 --> 00:19:42.000 You can search across the full stream of all the traces over the last 15 minutes. 00:19:42.000 --> 00:19:46.440 So try Datadog APM for free with a 14-day trial. 00:19:46.440 --> 00:19:52.600 And then Datadog will send you one of these very cute doggy t-shirts, which who wouldn't want one of those, right? 00:19:52.600 --> 00:19:58.240 So visit high-dom-by-stud.fm slash Datadog or just click the link in your podcast player show notes to get started. 00:19:58.240 --> 00:19:59.140 Thanks, Datadog. 00:19:59.140 --> 00:20:01.580 And, Brian, back to you. 00:20:01.580 --> 00:20:02.380 Back to me. 00:20:02.380 --> 00:20:15.020 I was, I'm going to apologize whoever tweeted this, but somebody tweeted this out, a link to this article, and talking about chaining operators. 00:20:15.020 --> 00:20:19.300 So this is an article by Rodrigo Serrao. 00:20:19.300 --> 00:20:21.920 Py don'ts? 00:20:21.920 --> 00:20:23.220 Yeah. 00:20:23.220 --> 00:20:28.260 So I don't know what the py don'ts are about. 00:20:28.260 --> 00:20:29.360 Just, I don't know. 00:20:29.360 --> 00:20:31.860 Maybe he started blogging about things you shouldn't do in Python. 00:20:32.160 --> 00:20:38.260 Anyway, this article is called Chaining Comparison Operators, and I use chaining all the time. 00:20:38.260 --> 00:20:43.280 Mostly, I use it for simple things like, oh, let me find one. 00:20:43.280 --> 00:20:45.620 A is less than B, less than C. 00:20:45.620 --> 00:20:50.240 So ranges, like min, you know, my X value is between min and max. 00:20:50.240 --> 00:20:51.540 Yeah, that's really nice. 00:20:51.540 --> 00:20:51.760 Yeah. 00:20:51.760 --> 00:20:58.640 My hint on that, like, just tip for anybody doing that, always do them less than. 00:20:58.640 --> 00:21:01.840 Don't do greater than, because it's hard to do that. 00:21:01.840 --> 00:21:04.460 Anyway, so keep them like that. 00:21:04.460 --> 00:21:06.480 But this article is talking about other stuff. 00:21:06.480 --> 00:21:10.320 So this is pretty easy to think about, like the less than operators. 00:21:10.320 --> 00:21:12.460 So A is less than B, less than C. 00:21:12.460 --> 00:21:18.200 Is that really the same as A is less than B and B is less than C. 00:21:18.200 --> 00:21:19.420 It is that combination. 00:21:19.580 --> 00:21:21.380 That's what chained operators are. 00:21:21.380 --> 00:21:28.140 And the importance there is it doesn't really work for some operations. 00:21:28.140 --> 00:21:31.660 And it gets into, like, the equal operator. 00:21:31.660 --> 00:21:36.240 So you can do A equals B or equals C, which means they're all equal. 00:21:36.240 --> 00:21:36.820 Great. 00:21:36.820 --> 00:21:38.100 What about not equal? 00:21:38.100 --> 00:21:39.600 Does that work the same way? 00:21:40.560 --> 00:21:52.540 And it doesn't, because if you've got, like, A is not equal to B is not equal to C, it doesn't mean they're all different, because A and C still could be the same and have that pass. 00:21:53.080 --> 00:22:07.040 So this article, if you're working with chained expressions, which I think you should, if you're doing complicated things, it's way, I like it better than doing, having a bunch of ands in there, as long as that you can keep it readable. 00:22:07.040 --> 00:22:17.800 But this article talks through some of the, some of the gotchas inside of, and things to watch out for, like side effects and non-constants and things like that. 00:22:17.800 --> 00:22:20.720 So great discussion of chained operators. 00:22:21.340 --> 00:22:23.200 I hadn't even thought of doing this not equal to. 00:22:23.200 --> 00:22:23.960 This seems wrong. 00:22:23.960 --> 00:22:25.720 It just looks wrong. 00:22:25.720 --> 00:22:27.700 Yeah. 00:22:27.700 --> 00:22:31.440 But, yeah, don't do chained not equal. 00:22:31.440 --> 00:22:39.500 That's just, unless, and even if that's what you meant, that, like, A is not equal to B and B is not equal to C, but it's okay for A and C to be equal. 00:22:39.500 --> 00:22:41.980 That would be a terrible expression, because it's confusing. 00:22:41.980 --> 00:22:42.760 So don't do that. 00:22:42.760 --> 00:22:43.240 It is. 00:22:43.240 --> 00:22:43.640 Yeah. 00:22:43.640 --> 00:22:50.560 My favorite one of these chainings, like, X, you know, 7 less than X less than 10. 00:22:50.560 --> 00:22:50.960 Yeah. 00:22:51.040 --> 00:22:51.560 Something like that. 00:22:51.560 --> 00:22:51.980 That's nice. 00:22:51.980 --> 00:22:58.920 My favorite is converting X, if X is not none, else Y to just X or Y. 00:22:58.920 --> 00:22:59.620 Boom. 00:22:59.620 --> 00:23:01.880 That's so clean and so nice. 00:23:01.880 --> 00:23:07.180 And I never, coming from a C++ background and C#, I never thought that was possible. 00:23:07.180 --> 00:23:07.760 That's great. 00:23:07.760 --> 00:23:08.380 Yeah. 00:23:09.100 --> 00:23:10.180 Dean, what do you think about this? 00:23:10.180 --> 00:23:11.600 I love it. 00:23:11.600 --> 00:23:13.140 I use it a lot. 00:23:13.140 --> 00:23:15.260 It didn't always work. 00:23:15.260 --> 00:23:20.180 I think it's still not working with Pandas data frames or Pandas series and arrays. 00:23:20.720 --> 00:23:24.380 And I do wait for this to finally work. 00:23:24.380 --> 00:23:30.860 Arrays, when you do an array, like in NumPy or Pandas, when you do an array, it's less than some number. 00:23:31.240 --> 00:23:35.320 It returns a new array with true, like a Boolean array with true and false. 00:23:35.320 --> 00:23:40.640 And last time I checked was a few months ago, but the last time I checked, it didn't work. 00:23:40.640 --> 00:23:46.520 I couldn't do one is less than the series is less than two and get the Boolean array. 00:23:46.980 --> 00:23:50.800 So I'm waiting for this, but I love the concept a lot. 00:23:50.800 --> 00:23:51.640 Okay. 00:23:51.640 --> 00:23:52.120 That's good. 00:23:52.120 --> 00:23:52.600 Yeah. 00:23:52.600 --> 00:23:55.060 I had to consider the integration into Pandas. 00:23:55.060 --> 00:23:55.440 Yeah. 00:23:55.440 --> 00:23:56.100 But of course. 00:23:56.100 --> 00:24:03.860 I'm not sure how would you implement that with the regular data model of like Dunder, Dunder EQ or is this something else? 00:24:03.860 --> 00:24:04.340 I'm not sure. 00:24:04.340 --> 00:24:04.800 LTE. 00:24:04.800 --> 00:24:05.240 LTE. 00:24:05.240 --> 00:24:05.640 Yeah. 00:24:05.640 --> 00:24:06.120 Possibly. 00:24:06.120 --> 00:24:06.800 I'm not sure either. 00:24:06.800 --> 00:24:08.020 Yeah. 00:24:08.020 --> 00:24:15.120 There's probably some magic method and it might just expand out to less than and then and, you know, like the two tests basically. 00:24:15.120 --> 00:24:15.860 Probably does. 00:24:15.860 --> 00:24:16.840 Oh, cool. 00:24:16.900 --> 00:24:23.020 We should ask Brett Cannon to do a deep dive into what he changed off. 00:24:23.020 --> 00:24:26.740 He's pulling apart all the different parts of Python syntax, right? 00:24:26.740 --> 00:24:26.920 Yeah. 00:24:26.920 --> 00:24:27.600 All right. 00:24:27.600 --> 00:24:32.980 I want to give a quick shout out to Rich because it's one of our episodes. 00:24:32.980 --> 00:24:34.120 So we talk about Rich. 00:24:34.120 --> 00:24:37.320 I was going to talk about Anthony Shaw, but I didn't have enough information. 00:24:37.320 --> 00:24:41.240 So, I mean, he's the other person who needs a shout out in every show. 00:24:41.300 --> 00:24:48.060 So I want to talk about this article highlighting some tools by Martin Hines. 00:24:48.140 --> 00:24:48.540 Yeah. 00:24:48.540 --> 00:24:49.100 Martin Hines. 00:24:49.100 --> 00:24:49.700 Yeah. 00:24:49.700 --> 00:24:53.200 Well, creating beautiful tracebacks with Python, done exception hooks. 00:24:53.200 --> 00:24:55.660 So two things that I want to point out here. 00:24:55.660 --> 00:24:59.640 One, Python has an exception hook mechanism, which is pretty cool. 00:24:59.720 --> 00:25:08.440 So what you can do is you can create a function that has this signature of exception type, the actual exception and the traceback. 00:25:08.440 --> 00:25:09.700 So three arguments. 00:25:09.960 --> 00:25:19.380 And if you have a function like that, you can just go to the sys and say sys.accept hook equals that function, not calling it, of course, just passing the function as the value. 00:25:19.380 --> 00:25:22.460 And then whenever there's an exception, this will be called by Python. 00:25:22.460 --> 00:25:23.380 That's pretty cool, right? 00:25:23.380 --> 00:25:24.200 Yeah. 00:25:24.200 --> 00:25:28.580 So depending on what you want to do, like you could say, well, we're going to store all the errors. 00:25:28.580 --> 00:25:32.640 Like, let's imagine here's a scenario where you might make use of this. 00:25:33.020 --> 00:25:35.920 I'm going to create an app and I'm going to send it out. 00:25:35.920 --> 00:25:40.480 I'm going to use Py2App or Py2XE or just, you know, let people install it somehow. 00:25:40.480 --> 00:25:49.600 And then when it runs, I want to, it's going to run on their computers, but I want to gather up all the exceptions of all the users across the company or the research team or whatever. 00:25:49.600 --> 00:25:55.920 You could have this, submit this error along with other details right back to a database over an API, right? 00:25:55.920 --> 00:25:59.760 And then you could do like analytics, like, well, here's the most common error and so on. 00:25:59.820 --> 00:26:05.660 Of course, you could use Sentry or something like that, but maybe you're trying to gather some specific information that's different, right? 00:26:05.660 --> 00:26:07.960 So that's one of the types of things you could do with this. 00:26:07.960 --> 00:26:09.620 So I got a question before I go on. 00:26:09.620 --> 00:26:10.420 Yeah. 00:26:10.420 --> 00:26:13.600 So this doesn't catch the exception. 00:26:13.600 --> 00:26:16.400 It just, it doesn't interrupt the flow. 00:26:16.400 --> 00:26:18.860 It just, it just gets called when it happens. 00:26:18.860 --> 00:26:22.380 It doesn't, it doesn't catch the exception. 00:26:22.380 --> 00:26:27.560 It lets you basically change what kind of output comes from Python. 00:26:27.560 --> 00:26:32.600 So if you just wanted to print out like, here's a file where there was an error and here's the error message. 00:26:32.600 --> 00:26:33.080 Okay. 00:26:33.080 --> 00:26:34.480 Like you could do that, right? 00:26:34.480 --> 00:26:36.420 Or the type and then the message. 00:26:36.420 --> 00:26:39.360 I'm just noting the, noticing the example doesn't re-throw it. 00:26:39.360 --> 00:26:41.760 So you don't have to do that then. 00:26:41.760 --> 00:26:42.860 No, I don't believe so. 00:26:42.860 --> 00:26:44.080 And I'm not a hundred percent sure. 00:26:44.080 --> 00:26:47.180 I think the app, I think the process still ends. 00:26:47.400 --> 00:26:51.540 If it's just a regular running script rather than a web app. 00:26:51.540 --> 00:26:53.040 I think it still ends. 00:26:53.040 --> 00:26:54.940 But anyway, sorry. 00:26:54.940 --> 00:26:55.860 You get a different kind of output. 00:26:55.860 --> 00:26:56.240 Yeah, yeah. 00:26:56.240 --> 00:26:59.860 No, you just don't get the standard print output that Python gives you, right? 00:26:59.860 --> 00:27:02.680 So you could say, avoid printing the trace back if you wanted. 00:27:02.680 --> 00:27:04.720 You could just say this file on this line had this error. 00:27:04.720 --> 00:27:05.600 Oh, yeah. 00:27:05.600 --> 00:27:05.960 Okay. 00:27:05.960 --> 00:27:06.340 Nice. 00:27:06.340 --> 00:27:06.700 Okay. 00:27:06.700 --> 00:27:09.700 So it's easy enough to do. 00:27:09.700 --> 00:27:13.920 Like, for example, they have this function that they call that caused an error. 00:27:13.920 --> 00:27:17.940 And all you see when this crashes is there's a trace back. 00:27:17.940 --> 00:27:22.540 This file, this line in this module, here's the error message, right? 00:27:22.540 --> 00:27:25.220 Instead of the huge stack trace that might scare people. 00:27:25.220 --> 00:27:25.800 Okay. 00:27:25.800 --> 00:27:29.580 So, I mean, obviously you can use try and accept, but this is global, right? 00:27:29.580 --> 00:27:33.760 So even if some library is calling something and you're not catching it and like, right, 00:27:33.760 --> 00:27:35.180 it's catching everywhere. 00:27:35.180 --> 00:27:35.780 Okay. 00:27:35.780 --> 00:27:38.180 So then you could do more work about breaking that apart. 00:27:38.180 --> 00:27:39.760 And they talk about doing that. 00:27:39.760 --> 00:27:44.180 But the real interesting part is if you go and look at some options. 00:27:44.180 --> 00:27:48.160 So there are five, I believe there are five libraries mentioned in here that do really cool 00:27:48.160 --> 00:27:49.440 stuff for solving this. 00:27:49.440 --> 00:27:53.720 The first one is by Will McGugan's rich library. 00:27:53.720 --> 00:27:58.840 So you can just go from rich.traceback, import install, and then say install. 00:27:58.840 --> 00:28:00.100 Show locals is true. 00:28:00.100 --> 00:28:04.300 And then this also basically installs one of those global exception hooks. 00:28:05.220 --> 00:28:11.020 With the benefit being when you get the errors, what you get is a nice rich output. 00:28:11.020 --> 00:28:12.100 It's super pretty. 00:28:12.100 --> 00:28:14.260 It's pretty and it's useful. 00:28:14.260 --> 00:28:18.360 I mean, it's color highlighted so you can see where the error happened, but it also will 00:28:18.360 --> 00:28:23.200 print out in a really nice way with formatting and highlighting the locals, right? 00:28:23.580 --> 00:28:26.980 So what values were passed to that function when it's crashed? 00:28:26.980 --> 00:28:29.480 Well, here's a little table of those and so on. 00:28:29.480 --> 00:28:32.480 So this is really easy to identify. 00:28:32.480 --> 00:28:35.420 And at the very bottom, like a nice clear way to like, okay, what happened? 00:28:35.420 --> 00:28:38.320 So you can do this super simple version here. 00:28:38.320 --> 00:28:41.500 And there's also some manual ways to make rich print this type of stuff. 00:28:42.300 --> 00:28:46.960 Number two is better exceptions, which does similar stuff. 00:28:46.960 --> 00:28:51.540 You can see that it doesn't quite take over how the look and feel is so much, but it basically 00:28:51.540 --> 00:28:55.400 colorizes the standard look and feel of errors. 00:28:55.400 --> 00:28:59.000 So you can see, you know, which function, which error and so on. 00:28:59.000 --> 00:28:59.660 So that's pretty good. 00:28:59.660 --> 00:29:01.100 And there's pretty errors. 00:29:01.100 --> 00:29:02.460 Check out pretty errors. 00:29:02.460 --> 00:29:04.480 This looks pretty good, right? 00:29:05.100 --> 00:29:07.000 It's got a lot of like bold and highlights. 00:29:07.000 --> 00:29:11.740 You can really call out the error messages and the functions involved in the modules involved. 00:29:11.740 --> 00:29:13.060 Here's one for you, Dean. 00:29:13.060 --> 00:29:14.980 The built-in one to IPython. 00:29:14.980 --> 00:29:19.880 It has ultra TV for ultra traceback. 00:29:19.880 --> 00:29:21.420 And this is pretty nice, right? 00:29:21.420 --> 00:29:23.320 Actually, the IPython one's pretty good. 00:29:23.320 --> 00:29:26.080 Yeah, the Python one is really nice. 00:29:26.080 --> 00:29:31.040 And also I was planning to talk about it in the extras, but on IPython 8, which is pretty 00:29:31.040 --> 00:29:38.100 new, they even have this improved with some color coloring of exactly where the error happened. 00:29:38.100 --> 00:29:42.000 I think this uses the 310 part or something like that. 00:29:42.000 --> 00:29:42.960 Oh, awesome. 00:29:42.960 --> 00:29:43.620 Yeah, that's cool. 00:29:43.620 --> 00:29:47.020 We'll hear more about it when we talk about IPython 8 as well. 00:29:47.020 --> 00:29:47.540 Cool. 00:29:47.540 --> 00:29:51.500 Yeah, so that's built in, kind of if you're already on the data science stack. 00:29:51.500 --> 00:29:57.240 And then finally, stack printer, which you can give it a traceback and it will print that 00:29:57.240 --> 00:29:57.340 out. 00:29:57.340 --> 00:30:01.320 So you can sort of do like rich, you can say set exception hook and give it a theme 00:30:01.320 --> 00:30:02.760 like dark or whatever. 00:30:02.760 --> 00:30:05.880 And then it does this pretty nice printout as well. 00:30:05.880 --> 00:30:06.980 So these are all great. 00:30:06.980 --> 00:30:13.000 I'm personally liking the rich tracebacks version best, but this is really nice. 00:30:13.000 --> 00:30:13.260 Yeah. 00:30:13.260 --> 00:30:18.640 Connor out there in the audience says, wow, using show local SQL tree would have saved me hours 00:30:18.640 --> 00:30:19.540 and hours of time. 00:30:19.540 --> 00:30:20.500 And you and me both. 00:30:20.500 --> 00:30:20.800 Yeah. 00:30:20.800 --> 00:30:21.820 I feel the pain. 00:30:21.820 --> 00:30:23.480 I do too. 00:30:23.480 --> 00:30:27.880 So because a lot of times you're like, I know it crashed and it says none type does 00:30:27.880 --> 00:30:29.260 not have attribute, whatever. 00:30:29.260 --> 00:30:30.940 But like, why is it none? 00:30:30.940 --> 00:30:32.580 I need to go back three levels, right? 00:30:32.580 --> 00:30:34.480 Like, yes, it's so good. 00:30:34.480 --> 00:30:38.480 And then you find out you just forgot to return from the function. 00:30:38.480 --> 00:30:40.400 Yes, exactly. 00:30:41.340 --> 00:30:48.920 I was just debugging a test failure the other day and pytest has the option to throw a local. 00:30:48.920 --> 00:30:54.340 You can show locals with a crash or with every failure. 00:30:54.340 --> 00:31:03.560 And I forgot that the particular thing I was testing had like variables that were storing 00:31:03.560 --> 00:31:05.940 thousand element arrays. 00:31:06.900 --> 00:31:08.500 It just like went on for me. 00:31:08.500 --> 00:31:16.820 I believe the, I believe Rich's has a truncate variables where it'll do an ellipsis or something 00:31:16.820 --> 00:31:17.220 like that. 00:31:17.220 --> 00:31:18.000 I think. 00:31:18.000 --> 00:31:21.560 I mean, yeah, I'm not a hundred percent sure because I've been looking at all five of these 00:31:21.560 --> 00:31:22.320 Will's in the chat. 00:31:22.320 --> 00:31:22.960 We'll have to ask him. 00:31:22.960 --> 00:31:23.760 Will's in the chat. 00:31:23.760 --> 00:31:24.960 You'll have to give us a shout out. 00:31:24.960 --> 00:31:27.260 Will, I think, I think truncate is out there, right? 00:31:27.260 --> 00:31:28.400 I'm not a hundred percent sure. 00:31:28.400 --> 00:31:34.140 I think of how can I actually, so I talk with databases and sometimes the errors from the 00:31:34.140 --> 00:31:38.760 databases are like this big Java stack, Java trace. 00:31:38.760 --> 00:31:43.420 And then you need to like a lot of go a lot of apps. 00:31:43.420 --> 00:31:45.760 Sorry, something, some noise here. 00:31:45.760 --> 00:31:46.240 Sorry. 00:31:46.240 --> 00:31:51.340 You need to go a lot, a lot up in the browser to actually see the error. 00:31:51.580 --> 00:31:55.720 And if I could just shut it down and just give me the Python stuff. 00:31:55.720 --> 00:31:57.200 Cool. 00:31:57.200 --> 00:31:57.440 Yeah. 00:31:57.440 --> 00:32:02.440 I don't know what setting you set for that, but certainly with this mechanism, you could 00:32:02.440 --> 00:32:06.000 set it up so that like if the word Java appears, you just stop. 00:32:06.000 --> 00:32:06.680 Yeah. 00:32:06.680 --> 00:32:08.240 You just stop going back. 00:32:08.240 --> 00:32:08.820 Yeah. 00:32:08.820 --> 00:32:10.740 And Will says, yes, that's right. 00:32:10.740 --> 00:32:12.160 Thank you, Brian, for pulling that up. 00:32:12.160 --> 00:32:12.340 Yeah. 00:32:12.340 --> 00:32:12.640 Yeah. 00:32:12.640 --> 00:32:17.020 You can truncate it so that printing won't go completely insane because it could be gigabytes. 00:32:17.020 --> 00:32:18.880 I mean, it could be out of control, right? 00:32:18.880 --> 00:32:19.500 Yeah. 00:32:19.500 --> 00:32:19.540 Yeah. 00:32:19.540 --> 00:32:24.780 But I mean, even if they have a like reasonably large limit, sometimes it's just like, oh, 00:32:24.780 --> 00:32:28.160 I forgot that huge array was there and it's hard to see stuff. 00:32:28.160 --> 00:32:28.400 So. 00:32:28.400 --> 00:32:29.580 Yeah, absolutely. 00:32:29.580 --> 00:32:30.360 Yeah. 00:32:30.360 --> 00:32:31.540 All right. 00:32:31.540 --> 00:32:32.360 Over to you, Dean. 00:32:32.360 --> 00:32:37.120 Speaking of testing, Brian was talking about testing stuff and looking at the color and so 00:32:37.120 --> 00:32:37.240 on. 00:32:37.240 --> 00:32:37.420 Yeah. 00:32:37.420 --> 00:32:39.660 So I thought, Brian, this would be up your alley. 00:32:39.660 --> 00:32:44.400 So it's called Ways I Use Testing as a Data Scientist. 00:32:44.400 --> 00:32:46.160 It's by Peter Baumgartner. 00:32:46.800 --> 00:32:50.740 And I'm a data scientist, but I also love testing. 00:32:50.740 --> 00:32:56.780 The thing about testing with data science is sometimes it's not that clear what you should 00:32:56.780 --> 00:32:57.740 test for. 00:32:57.740 --> 00:32:58.320 Right. 00:32:58.380 --> 00:33:05.420 because sometimes some things we do are stochastic and then you could not actually test for stuff or stuff like that. 00:33:06.040 --> 00:33:14.820 So this blog talks about like the art of testing because sometimes like the it's not clear what you should test. 00:33:14.820 --> 00:33:18.760 And the more experience you get, you can actually see what's coming your way. 00:33:18.760 --> 00:33:27.420 And he talks about like data validation and he is throwing many packages that could help you. 00:33:27.420 --> 00:33:33.720 Packages like Pandera and Great Expectations that I think we've talked about before in the podcast. 00:33:34.200 --> 00:33:40.460 And also like the NumPy has some stuff like is closed. 00:33:40.460 --> 00:33:47.100 Checks for two numbers close to each other or array equal assert data frame equals in Pandas data frame. 00:33:47.100 --> 00:33:48.860 So he talks a lot about that. 00:33:48.860 --> 00:33:52.500 He also talks about using assert in your code. 00:33:52.500 --> 00:33:58.220 Like even if you had some ad hoc stuff of analysis, use assert within the code. 00:33:58.220 --> 00:33:59.840 Don't think about the tests later. 00:33:59.840 --> 00:34:03.460 Just think like where does this thing could hurt me? 00:34:03.460 --> 00:34:04.320 He gives an example. 00:34:05.200 --> 00:34:12.380 Maybe if I'm trying to join two data frames and I think they have the same shape, I want to check if they have the same IDs. 00:34:12.380 --> 00:34:16.040 So that way I know that the join works correctly. 00:34:16.040 --> 00:34:21.940 So he asserts that the length of the IDs is the same within the two data frames. 00:34:21.940 --> 00:34:25.300 And this is not even like real testing, we would say. 00:34:25.300 --> 00:34:27.160 It doesn't use some testing framework. 00:34:27.160 --> 00:34:30.080 It just says like write it within your code. 00:34:30.540 --> 00:34:40.800 It then continues to like hypothesis, which basically bombards the functions with a lot of ways to actually try to fail it. 00:34:40.800 --> 00:34:52.860 It continues with some other packages and eventually goes into a pytest and shows like how it would work with pytest and with like an approach that I haven't heard of. 00:34:52.860 --> 00:34:54.860 But it sounds good. 00:34:54.860 --> 00:34:57.980 Arrange, act, assert. 00:34:58.480 --> 00:35:08.920 Like arrange the data, then act on those things you want to check and then just assert if they are equal or almost equal and the thing you wanted to check for. 00:35:08.920 --> 00:35:13.360 Yeah, it's such a such a easy mistake to make. 00:35:13.360 --> 00:35:15.380 Like this number equal equal that number. 00:35:15.380 --> 00:35:15.980 Yep. 00:35:15.980 --> 00:35:18.800 And it's what when you do in science or data science. 00:35:19.540 --> 00:35:29.020 I'm glad he talks about structure because a lot of people that get into testing get get these giant tests that do do a little work, test something, do a little more work, test something. 00:35:29.020 --> 00:35:33.580 And then if it breaks, you're not sure what where the failure is. 00:35:33.580 --> 00:35:36.320 So this looks sounds fascinating. 00:35:36.320 --> 00:35:42.300 And actually, I'm not sure how I missed it, but I really want a way to compare an array for almost equal. 00:35:42.580 --> 00:35:45.060 So I'm going to have to go read that. 00:35:45.060 --> 00:35:49.780 Yeah, so NumPy and Pandas both have mechanisms for that. 00:35:49.780 --> 00:35:50.460 It's pretty great. 00:35:50.460 --> 00:35:50.980 Nice. 00:35:50.980 --> 00:35:51.500 Cool. 00:35:51.500 --> 00:35:52.580 Yeah. 00:35:52.580 --> 00:35:53.760 Very nice. 00:35:53.760 --> 00:35:55.520 I know this will be helpful to people. 00:35:55.520 --> 00:36:03.880 It's really I always wonder about testing data science stuff and machine learning things and so on where you get small perturbations, but they're fine. 00:36:04.020 --> 00:36:10.200 It's off by one millionth of some unit, but that's totally good. 00:36:10.200 --> 00:36:10.680 Those are equal. 00:36:10.680 --> 00:36:14.220 But it takes, I think, an extra level of thinking about it. 00:36:14.220 --> 00:36:20.440 So much people focus on, well, how do you get rid of your dependencies and how do you make sure that you don't talk to the real database when you do this? 00:36:20.440 --> 00:36:23.520 And that's one aspect that people focus on. 00:36:23.520 --> 00:36:27.780 But this working with science-y type stuff is its own specialty. 00:36:27.960 --> 00:36:34.240 Yeah, I think the entire community is a fairly new community, although it's not as new as it was. 00:36:34.240 --> 00:36:40.100 And I'm not sure we're on top of how to do tests in machine learning. 00:36:40.100 --> 00:36:43.560 We have many packages for that. 00:36:43.560 --> 00:36:45.820 We have many theories for that. 00:36:45.820 --> 00:36:51.760 But I'm not sure we have actually one solid good way. 00:36:51.760 --> 00:36:52.800 And maybe we shouldn't have. 00:36:52.800 --> 00:36:54.640 But it's a debate. 00:36:54.640 --> 00:36:56.440 Yeah, for sure. 00:36:56.440 --> 00:36:58.580 Same with the rest of the software world. 00:36:58.580 --> 00:36:59.380 So welcome. 00:36:59.380 --> 00:37:02.380 Thanks. 00:37:02.380 --> 00:37:02.680 Yeah. 00:37:02.680 --> 00:37:07.700 And Sam out in the live stream says, NumPy has an assert array almost equal in NumPy.testing. 00:37:07.700 --> 00:37:08.500 Nice. 00:37:08.500 --> 00:37:10.580 I just learned there's a NumPy.testing. 00:37:10.580 --> 00:37:11.020 That's cool. 00:37:11.020 --> 00:37:11.680 Yeah. 00:37:11.680 --> 00:37:13.820 Awesome. 00:37:13.820 --> 00:37:14.200 All right. 00:37:14.200 --> 00:37:17.900 Dean, while you have your screen up, do you have any extras you want to talk about? 00:37:17.900 --> 00:37:19.920 I know IPython 8 was a thing. 00:37:19.920 --> 00:37:20.220 Yeah. 00:37:20.220 --> 00:37:27.240 So IPython 8 was released last month after three years of waiting for a major version. 00:37:28.200 --> 00:37:31.500 It has a lot of new features, but this is the extra part. 00:37:31.500 --> 00:37:32.460 So I won't go over them. 00:37:32.460 --> 00:37:35.320 Just two and a half things I wanted to mention. 00:37:35.320 --> 00:37:37.340 It says that it's less code. 00:37:37.340 --> 00:37:38.960 And I love that. 00:37:38.960 --> 00:37:42.620 Like once you get better in a programming language, you understand it. 00:37:42.620 --> 00:37:43.820 You shouldn't write more code. 00:37:43.880 --> 00:37:45.260 You should delete code. 00:37:45.260 --> 00:37:47.820 And that's what those guys do. 00:37:47.820 --> 00:37:56.240 And the way they could have done that is by hiring a person through the NumFocus small development grants. 00:37:56.240 --> 00:37:57.720 And I think this is important. 00:37:57.720 --> 00:38:02.160 It's actually been talked a lot about after the Log4j stuff. 00:38:02.160 --> 00:38:06.100 It's been talked about like, well, those are three guys who worked tirelessly. 00:38:06.100 --> 00:38:13.080 They have their full-time jobs and they couldn't fix the Log4j stuff maybe as quickly as some other people wanted. 00:38:13.080 --> 00:38:18.620 But then you realize that they got donations of like a few hundred dollars within 10 years. 00:38:18.620 --> 00:38:22.020 And then after the Log4j, suddenly they got a thousand. 00:38:22.020 --> 00:38:28.140 So this, I think it shows you how the money could help open source stuff. 00:38:28.140 --> 00:38:36.520 And maybe if you use some package in a company, in some corporate, maybe try and think how you can give back money. 00:38:36.520 --> 00:38:43.700 Or even if you give back code, if you free up your developers to actually contribute, this is awesome. 00:38:43.700 --> 00:38:49.120 And the half thing just mentioned because it talks about the traceback. 00:38:49.120 --> 00:38:53.480 It shows that you can now see it's like colored. 00:38:53.480 --> 00:39:00.180 You can see on the screen, it's called the part where actually the arrow was, it's colored now. 00:39:00.180 --> 00:39:02.720 So it's very nice to see. 00:39:02.720 --> 00:39:10.620 Like the example shows you, you add the function three times, but only it fails on just one input of them. 00:39:10.620 --> 00:39:13.600 So it shows you which of the three times the function failed. 00:39:13.860 --> 00:39:14.260 Right. 00:39:14.260 --> 00:39:19.140 You call it the same thing like foo of zero plus foo of one plus foo of two. 00:39:19.140 --> 00:39:26.360 And it's the middle one that failed, not just line seven, but the second invocation with the value one where it failed, which that's awesome. 00:39:26.360 --> 00:39:27.440 Yeah, exactly. 00:39:27.440 --> 00:39:28.660 And the same. 00:39:28.660 --> 00:39:29.640 Well, that's right. 00:39:29.640 --> 00:39:34.900 I was going to say the same thing for indexing into, what is that, a data frame or something like that. 00:39:34.900 --> 00:39:38.580 Like it's, you're chaining together like bracket zero, bracket one, bracket zero. 00:39:38.580 --> 00:39:42.300 It's the second one trying to get to the one of zero. 00:39:42.300 --> 00:39:43.520 That was the one that failed there. 00:39:43.580 --> 00:39:48.340 That's really, those are hard to come back and find if you're not in a debugger. 00:39:48.340 --> 00:39:49.580 Like, well, which one of these failed? 00:39:49.580 --> 00:39:50.140 Like, great. 00:39:50.140 --> 00:39:53.360 Array index out of bounds on line three. 00:39:53.360 --> 00:39:54.860 Well, there's three of those happening. 00:39:54.860 --> 00:39:55.380 Which one? 00:39:55.380 --> 00:39:56.660 Yeah. 00:39:56.660 --> 00:39:57.340 Yeah, that's cool. 00:39:57.340 --> 00:40:01.980 And another thing is a tweet by Victor Steiner is a core dev. 00:40:02.720 --> 00:40:08.120 And he says, I mean, it's now time to deprecate the standard lib, URL lib module. 00:40:08.640 --> 00:40:12.960 And this is brought a lot of haters and fans. 00:40:13.600 --> 00:40:15.900 And I'm not sure what's my opinion yet. 00:40:15.900 --> 00:40:18.200 I'm not a heavy user of URL lib. 00:40:19.160 --> 00:40:20.960 But it opened up a debate. 00:40:20.960 --> 00:40:22.660 Like, we know how to do. 00:40:23.920 --> 00:40:24.200 Yeah. 00:40:24.200 --> 00:40:24.840 Yeah. 00:40:24.840 --> 00:40:25.840 That's really interesting. 00:40:25.840 --> 00:40:28.420 There are certain things in the standard library. 00:40:28.420 --> 00:40:29.200 You're like, yeah, yeah. 00:40:29.200 --> 00:40:31.420 I know that's there and you could use it, but you probably shouldn't use it. 00:40:31.420 --> 00:40:36.400 There's like so many better external choices that are so good that it would be kind of silly 00:40:36.400 --> 00:40:37.240 to bite them. 00:40:37.240 --> 00:40:37.420 Right. 00:40:37.420 --> 00:40:38.720 That's sort of the recommendation here. 00:40:39.160 --> 00:40:41.960 Yeah, but also some people don't like it. 00:40:41.960 --> 00:40:45.940 They have people there that say they hate dependencies. 00:40:45.940 --> 00:40:50.980 And sometimes you can do most of the work with the standard lib. 00:40:50.980 --> 00:40:57.580 And some of the tweets said, like, maybe deprecate the major parts that requests can do. 00:40:57.580 --> 00:41:01.020 But there are some other parts that are actually really needed. 00:41:01.020 --> 00:41:03.120 So maybe deprecate half of it. 00:41:03.120 --> 00:41:04.680 Yeah. 00:41:04.680 --> 00:41:07.440 I'm not sure if I'm about deprecating it. 00:41:08.120 --> 00:41:11.420 But, you know, it's one thing to say there are better choices. 00:41:11.420 --> 00:41:14.820 And we as a community recommend you probably just don't use this. 00:41:14.820 --> 00:41:20.500 But to deprecate it means to people who would rather go with a dependence, a lower level of 00:41:20.500 --> 00:41:24.800 dependencies, you're giving them warnings that they shouldn't be doing this when maybe, you 00:41:24.800 --> 00:41:26.480 know, it's unlikely it's going to actually vanish. 00:41:26.480 --> 00:41:26.780 Right. 00:41:26.780 --> 00:41:32.800 There's like a fallacy, though, that I think some people have that if it's in this, if they 00:41:32.800 --> 00:41:36.300 don't have dependency and it's in the standard, they're using something in the standard library, 00:41:36.300 --> 00:41:37.340 it's more solid. 00:41:38.080 --> 00:41:43.780 But I don't know if there's that many people working on URL lib right now. 00:41:43.780 --> 00:41:49.500 And some of the other parts that maybe people want to stop supporting. 00:41:49.500 --> 00:41:51.400 That's something very valid. 00:41:51.400 --> 00:41:53.180 Python still is an open source project. 00:41:53.180 --> 00:41:55.620 And we can make those decisions. 00:41:55.620 --> 00:41:56.240 Yeah. 00:41:56.240 --> 00:42:00.780 Victor actually says there are 40-year-old security issues in URL lib. 00:42:01.120 --> 00:42:04.520 So maybe it's better to use something outside of it. 00:42:04.520 --> 00:42:05.260 Yeah. 00:42:05.260 --> 00:42:06.180 Yeah. 00:42:06.180 --> 00:42:07.200 People want it to stay. 00:42:07.200 --> 00:42:09.500 But there's these issues. 00:42:09.500 --> 00:42:10.260 Yeah. 00:42:10.260 --> 00:42:11.440 I wonder if there's a way to go. 00:42:11.440 --> 00:42:13.400 Well, let's look at some of the libraries that are out there. 00:42:13.400 --> 00:42:19.140 Try to bring them in and just use their core to replicate that functionality. 00:42:19.340 --> 00:42:21.640 Not to say, let's just pick on requests. 00:42:21.640 --> 00:42:23.000 Bring requests in. 00:42:23.000 --> 00:42:26.440 Vendor a little bit of it in so it does what URL lib does. 00:42:26.440 --> 00:42:28.060 And just go look. 00:42:28.060 --> 00:42:30.000 Okay, this is the latest, greatest that we got. 00:42:30.000 --> 00:42:32.040 And everyone's been looking at requests already. 00:42:32.040 --> 00:42:32.840 I don't know. 00:42:32.840 --> 00:42:33.800 It could be interesting. 00:42:33.800 --> 00:42:34.640 Yeah. 00:42:35.040 --> 00:42:39.420 And then Brandon out in the audience points out there are also maybe environments where 00:42:39.420 --> 00:42:41.500 you can't install dependencies for security reasons. 00:42:41.500 --> 00:42:46.660 And so having things like URL lib allows you to do more with Python in those situations. 00:42:46.660 --> 00:42:50.000 But if there's security problems with URL lib, yeah, anyway. 00:42:50.000 --> 00:42:53.140 Yeah, just in some of the functions, you don't call those. 00:42:53.140 --> 00:42:53.680 No, I'm just kidding. 00:42:53.680 --> 00:42:55.920 All right, Brian, how about you? 00:42:55.920 --> 00:42:56.300 Extras? 00:42:56.300 --> 00:42:57.860 Just one extra. 00:42:57.860 --> 00:43:00.580 I brought this up last week. 00:43:00.580 --> 00:43:01.960 I'm currently not writing a book. 00:43:01.960 --> 00:43:03.440 Yay! 00:43:03.440 --> 00:43:06.760 So I want to write more blog posts. 00:43:06.760 --> 00:43:12.940 So one of the things I wanted to make sure that my blog migrated to pythontest.com. 00:43:12.940 --> 00:43:16.040 And now it has a blog setting. 00:43:16.040 --> 00:43:18.840 And I like it. 00:43:18.840 --> 00:43:19.400 It looks pretty, too. 00:43:19.400 --> 00:43:25.660 Instead of just pulling everything over from my old WordPress blog, I'm trying to edit it. 00:43:25.660 --> 00:43:28.380 So I'm up through 2012. 00:43:29.220 --> 00:43:34.960 I'm going to go oldest to newest and gradually do things, bring things in. 00:43:34.960 --> 00:43:38.500 So that's one of my side projects I'm working on. 00:43:38.500 --> 00:43:39.580 Yeah, that's a great side project. 00:43:39.580 --> 00:43:40.540 Nice. 00:43:40.540 --> 00:43:41.960 What's that running on? 00:43:41.960 --> 00:43:45.400 Is that like some static site generator or other hosted thing? 00:43:45.400 --> 00:43:48.540 It's Hugo hosted by a free Netlify account. 00:43:48.540 --> 00:43:49.620 Yeah. 00:43:49.620 --> 00:43:50.740 Netlify is pretty awesome. 00:43:50.740 --> 00:43:51.880 All right. 00:43:51.980 --> 00:43:54.560 I got a couple of things I want to give a quick shout out to. 00:43:54.560 --> 00:43:55.340 Yeah, Brandon. 00:43:55.340 --> 00:43:58.260 Brandon had the same question, but we got it. 00:43:58.260 --> 00:43:58.720 All right. 00:43:58.720 --> 00:44:03.340 First of all, I have two new my Python shorts, two new versions, two videos from there. 00:44:03.340 --> 00:44:04.580 I got beyond the list comprehension. 00:44:04.580 --> 00:44:06.800 So basically set and dictionary comprehensions. 00:44:06.800 --> 00:44:07.560 Fun stuff. 00:44:07.560 --> 00:44:08.380 Nice picture. 00:44:08.380 --> 00:44:09.580 Thank you. 00:44:09.760 --> 00:44:13.140 It's a little and it's like just a screenshot out of an animation. 00:44:13.140 --> 00:44:15.660 And then combining dictionaries. 00:44:15.660 --> 00:44:17.420 Python 310 way is the title of the article. 00:44:17.420 --> 00:44:21.480 It really should be 3.9, but I kind of want to communicate like if you're on the latest Python, 00:44:21.480 --> 00:44:22.680 how should you be doing it? 00:44:22.680 --> 00:44:25.560 It came out in 3.9, the features that are actually in there. 00:44:25.560 --> 00:44:27.700 Anyway, the pipe stuff. 00:44:27.700 --> 00:44:32.180 Dictionary 1, pipe, dictionary 2, pipe, dictionary 3, which is all fun. 00:44:32.180 --> 00:44:35.820 And then I wanted to talk about a feature over on pypi.org. 00:44:35.820 --> 00:44:36.980 I don't even know how I found this. 00:44:37.120 --> 00:44:39.620 Probably just like an accident, like bump the keyboard or something. 00:44:39.620 --> 00:44:43.460 But if I'm over here and you just want to search for something, forward slash. 00:44:43.460 --> 00:44:44.580 Now you can search. 00:44:44.580 --> 00:44:45.180 What? 00:44:45.180 --> 00:44:47.340 So they now have a beam in the browser. 00:44:47.340 --> 00:44:49.340 Exactly. 00:44:49.340 --> 00:44:53.160 So if you're on pypi.org and you want to search, forward slash. 00:44:53.160 --> 00:44:53.940 Yes. 00:44:53.940 --> 00:44:55.540 So that's pretty cool. 00:44:55.540 --> 00:44:56.260 Yep. 00:44:56.260 --> 00:44:56.720 All right. 00:44:56.720 --> 00:44:57.920 That's it for the extras. 00:44:57.920 --> 00:44:59.000 Nice. 00:44:59.000 --> 00:45:00.780 I don't even remember what my joke is. 00:45:00.780 --> 00:45:01.520 So that's good. 00:45:01.520 --> 00:45:02.540 That'd be fine. 00:45:02.540 --> 00:45:03.840 I mean, you're new to us all. 00:45:03.840 --> 00:45:05.860 You ready? 00:45:05.860 --> 00:45:06.520 Yeah. 00:45:06.940 --> 00:45:07.340 All right. 00:45:07.340 --> 00:45:08.060 Yeah. 00:45:08.060 --> 00:45:08.620 Here we go. 00:45:08.620 --> 00:45:09.320 Oh, yeah. 00:45:09.320 --> 00:45:13.160 This is another one of these sort of like frustration type of things. 00:45:13.160 --> 00:45:13.680 That's great. 00:45:13.680 --> 00:45:17.600 This comes from the Programming Humor Twitter account. 00:45:17.600 --> 00:45:21.680 You know, twitter.com slash Programming Humor, which is there's a lot of good stuff in there. 00:45:21.680 --> 00:45:22.920 Some that I really liked. 00:45:22.920 --> 00:45:24.620 I didn't want to necessarily put on the show. 00:45:24.620 --> 00:45:28.020 But this one is developers really frustrated. 00:45:28.020 --> 00:45:30.260 Like they're sucking in on their lips. 00:45:30.260 --> 00:45:31.160 They're pulling on their cheeks. 00:45:31.160 --> 00:45:32.500 They're going, oh, I hate this job. 00:45:32.500 --> 00:45:33.560 I hate my life. 00:45:33.560 --> 00:45:35.600 Why is this happening to me? 00:45:36.020 --> 00:45:36.620 Never mind. 00:45:36.620 --> 00:45:37.660 I misspelled a variable. 00:45:37.660 --> 00:45:40.620 Good to go. 00:45:40.620 --> 00:45:41.860 Yeah. 00:45:41.860 --> 00:45:42.540 Linting. 00:45:42.540 --> 00:45:42.840 Yeah. 00:45:42.840 --> 00:45:43.660 Linting is good. 00:45:43.660 --> 00:45:44.100 Linting. 00:45:44.100 --> 00:45:44.560 Indeed. 00:45:44.560 --> 00:45:45.240 Indeed. 00:45:45.240 --> 00:45:50.960 If you just flip through the Programming Humor one, it's pretty good. 00:45:50.960 --> 00:45:54.980 You know, this eight-year-old is learning Python after dealing with the syntax bug. 00:45:54.980 --> 00:45:59.920 She asked, if the computer knows it's missing a semicolon here, why won't it add it itself? 00:46:00.420 --> 00:46:01.120 I don't know. 00:46:01.120 --> 00:46:02.020 I really don't know. 00:46:02.020 --> 00:46:02.580 Yeah. 00:46:02.580 --> 00:46:04.300 Yeah. 00:46:04.300 --> 00:46:07.780 And like, so he follows up and says what he meant. 00:46:07.780 --> 00:46:10.000 He meant colon, not semicolon. 00:46:10.000 --> 00:46:12.040 But so many people are like, semicolon? 00:46:12.040 --> 00:46:13.780 We need to use a semicolon for Python. 00:46:13.780 --> 00:46:16.460 Exactly. 00:46:16.460 --> 00:46:17.780 There are uses. 00:46:17.780 --> 00:46:18.780 They're rare though. 00:46:18.780 --> 00:46:19.300 All right. 00:46:19.300 --> 00:46:21.720 Well, fantastic. 00:46:23.020 --> 00:46:24.600 That last one. 00:46:24.600 --> 00:46:26.520 See? 00:46:26.520 --> 00:46:27.920 Yeah. 00:46:27.920 --> 00:46:29.800 It shall not be spoken, but it's good, right? 00:46:29.800 --> 00:46:30.220 Okay. 00:46:30.220 --> 00:46:31.440 There's a lot of good stuff. 00:46:31.440 --> 00:46:33.560 I recommend people go flip through that Twitter account. 00:46:33.560 --> 00:46:34.440 Nice. 00:46:34.440 --> 00:46:36.060 Brian, thank you. 00:46:36.060 --> 00:46:37.420 As always, it's good to be back with you. 00:46:37.420 --> 00:46:38.600 It's good to be back. 00:46:39.200 --> 00:46:44.160 And Dean, thanks for coming on this side of the presentation and joining us for the show. 00:46:44.160 --> 00:46:45.240 Thanks for having me. 00:46:45.240 --> 00:46:47.860 Thanks for listening to Python Bytes. 00:46:47.860 --> 00:46:50.680 Follow the show on Twitter via at Python Bytes. 00:46:50.680 --> 00:46:53.880 That's Python Bytes as in B-Y-T-E-S. 00:46:53.880 --> 00:46:57.040 Get the full show notes over at pythonbytes.fm. 00:46:57.440 --> 00:47:02.280 If you have a news item we should cover, just visit Python Bytes.fm and click submit in the 00:47:02.280 --> 00:47:02.700 nav bar. 00:47:02.700 --> 00:47:04.820 We're always on the lookout for sharing something cool. 00:47:04.820 --> 00:47:09.700 If you want to join us for the live recording, just visit the website and click live stream 00:47:09.700 --> 00:47:12.600 to get notified of when our next episode goes live. 00:47:12.600 --> 00:47:17.180 That's usually happening at noon Pacific on Wednesdays over at YouTube. 00:47:17.180 --> 00:47:21.120 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:47:21.120 --> 00:47:24.840 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