WEBVTT 00:00:00.001 --> 00:00:04.640 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:04.640 --> 00:00:11.440 This is episode 134, recorded June 3rd, 2019. I'm Michael Kennedy. 00:00:11.440 --> 00:00:12.260 And I'm Brian Okken. 00:00:12.260 --> 00:00:13.980 And this episode is brought to you by DigitalOcean. 00:00:13.980 --> 00:00:18.640 I feel like I've been lied to. I feel like the world is not as I expected it to be. 00:00:18.640 --> 00:00:22.960 I was taught about planets first in school, and first I was told there were nine. 00:00:22.960 --> 00:00:28.320 And of course, I was told that Mercury was the closest to the sun, and then Venus, and then the Earth. 00:00:28.320 --> 00:00:32.900 And yet, you're telling me Python here may be disproving this? What's the story here? 00:00:32.900 --> 00:00:35.080 So which planet is closest to Earth? 00:00:35.080 --> 00:00:40.840 Mercury. Wait, no, Venus, because it goes Mercury, and then Venus, and then Earth, and then Mars, and so on. 00:00:40.840 --> 00:00:49.540 And the orbits are farther out, and if you look at it to scale, it looks like the orbit of Venus and the orbit of Earth are closer together. 00:00:49.540 --> 00:00:49.820 Yes. 00:00:49.820 --> 00:00:55.620 And that is true. However, they spend as much time the closest. 00:00:55.620 --> 00:01:10.520 On average, if you take the average, how of the exact locations and the exact distance or exact ish, actually all of the planets spend more time closer to the sun than they do to each other, because that's just how that works. 00:01:10.960 --> 00:01:18.840 And since Mercury is closest to the sun, it is on average closest to all the other planets. 00:01:18.840 --> 00:01:24.740 There's a nice video on this link, so I'm linking to an article called, I even did it wrong. 00:01:24.880 --> 00:01:31.560 So three scientists published a paper proving that Mercury, not Venus, is the closest planet to the Earth using Python. 00:01:31.560 --> 00:01:41.340 It's a nice article about using the graph, and they graph it all out, and it's kind of neat that you just watch it, and you're like, oh yeah, well, of course. 00:01:41.340 --> 00:01:47.820 As you watch these planets circle the sun, then you calculate the distance, and then you do an average. 00:01:48.260 --> 00:01:54.220 It's close, so the average, Mercury is closest to the Earth 43% of the time. 00:01:54.220 --> 00:01:58.280 Venus is closest to the Earth 37% of the time. 00:01:58.280 --> 00:02:00.460 So Mercury wins. 00:02:00.460 --> 00:02:00.840 Wow. 00:02:00.840 --> 00:02:01.120 Yeah. 00:02:01.120 --> 00:02:01.900 That's really wild. 00:02:01.900 --> 00:02:03.160 I guess that makes sense, right? 00:02:03.160 --> 00:02:11.020 Because all the planets are roughly, continuously the same distance from the sun, but they're often out of phase with each other, right? 00:02:11.020 --> 00:02:16.300 They might be on opposite sides of the sun, which puts them super far apart, even though their orbits could actually be close together. 00:02:16.300 --> 00:02:22.480 Yeah, and also the orbits that are far away from the sun, those take longer time, right? 00:02:22.480 --> 00:02:30.620 So they're spending more time really far from the sun, and if you're really far on the other side of the sun from another planet, you're, yeah, it's just far. 00:02:30.620 --> 00:02:31.380 Yeah, you're super far away. 00:02:31.380 --> 00:02:32.100 Yeah, absolutely. 00:02:32.100 --> 00:02:41.100 And they were linking the article and the video used a library called PyEphem, I think, P-E-Y-E-P-H-E-M. 00:02:41.580 --> 00:02:50.780 Apparently, according to Andrew Diedrich, that contributed this for this show, has been largely deprecated and replaced with another library called Skyfield. 00:02:50.780 --> 00:02:57.760 And I took a look at that, and it looks like a really fun library to easily get data about the solar system and stuff. 00:02:57.760 --> 00:02:58.700 Yeah, that looks super cool. 00:02:58.820 --> 00:03:06.280 I got to say, one of the areas that sparks the most wonder and amazement in the use of Python is astronomy, for me personally. 00:03:06.280 --> 00:03:16.340 Yeah, it's neat to even have some of these little things of, like, an average person can go, wait, I don't think that's right, and just map it out using some Python. 00:03:16.340 --> 00:03:16.760 Yeah. 00:03:16.860 --> 00:03:19.680 And then you can come up with these different conclusions. 00:03:19.680 --> 00:03:20.260 It's great. 00:03:20.260 --> 00:03:21.100 Yeah, super cool. 00:03:21.100 --> 00:03:22.920 Well, thanks for finding that one. 00:03:22.920 --> 00:03:23.300 That's great. 00:03:23.300 --> 00:03:30.020 Last week, I covered a couple of GitHub topics that I thought were really interesting, sponsors and Dependabot. 00:03:30.020 --> 00:03:35.620 And I'm back this time, this week, with a slightly different theme that also intersects GitHub. 00:03:35.860 --> 00:03:43.240 So I feel like I have a theme this week, and the theme is understanding languages, like the internals and the inner workings of languages better. 00:03:43.240 --> 00:03:49.500 So the first thing I want to talk about is this library that was released called GitHub Semantics. 00:03:49.500 --> 00:03:57.000 So GitHub, they have, as you know, many different programming languages and all sorts of cool analytics and reporting and whatnot. 00:03:57.000 --> 00:04:03.520 So they built this library that will help them make comparisons across languages. 00:04:03.520 --> 00:04:09.700 Like, if you wanted to ask the question of, what is the average psycholomatic complexity by language? 00:04:09.700 --> 00:04:13.500 Studying the top 100 repos or whatever, something like that. 00:04:13.500 --> 00:04:20.120 You could answer that across Python, TypeScript, JavaScript, Ruby, Go, and some other languages as well. 00:04:20.120 --> 00:04:20.620 Weird. 00:04:20.620 --> 00:04:21.540 That's cool. 00:04:21.540 --> 00:04:22.080 Isn't that cool? 00:04:22.080 --> 00:04:30.160 So it's not something in terms of, like, digging into it that a lot of Python developers are going to be super excited about because it's written in Haskell. 00:04:30.160 --> 00:04:36.420 But you can also just use it as a command line tool for parsing and analyzing and comparing source code across languages. 00:04:36.420 --> 00:04:36.980 Oh, neat. 00:04:36.980 --> 00:04:39.260 It does some interesting stuff. 00:04:39.260 --> 00:04:49.280 And, like, some of the things that you can do with it, or it has in it, I guess, is a flow-sensitive caching interpreter for imperative languages. 00:04:49.980 --> 00:04:55.080 It has an abstract interpreter for generating scope graphs within given program text. 00:04:55.080 --> 00:05:01.880 So if you want to compare scope across different languages, like, compare two different functions that are trying to do the same thing or something. 00:05:01.880 --> 00:05:06.940 And then a strategic rewriting system for open syntax terms. 00:05:06.940 --> 00:05:10.460 So not a whole lot of stuff that I feel like I'm going to be doing day to day. 00:05:10.540 --> 00:05:19.060 But if you're in a computer science program, if you're trying to study languages or make generalizations or maybe even tooling, I think this would be pretty interesting to check out. 00:05:19.060 --> 00:05:19.240 Yeah. 00:05:19.240 --> 00:05:20.460 Is it written in Haskell? 00:05:20.460 --> 00:05:20.840 It is. 00:05:20.840 --> 00:05:22.060 Yeah, it's written in Haskell. 00:05:22.060 --> 00:05:22.280 Yeah. 00:05:22.280 --> 00:05:22.840 Okay. 00:05:22.840 --> 00:05:23.240 Cool. 00:05:23.240 --> 00:05:23.440 Yep. 00:05:23.440 --> 00:05:25.520 And this one comes from Oren Carmi. 00:05:25.520 --> 00:05:27.320 So I just want to say thanks for sending that over. 00:05:27.320 --> 00:05:29.260 We always appreciate getting those ones passed along. 00:05:29.260 --> 00:05:30.040 What do you got next? 00:05:30.160 --> 00:05:32.700 Well, we've talked about black, I think, several times. 00:05:32.700 --> 00:05:34.240 And I like using black. 00:05:34.240 --> 00:05:39.820 One of the things that bothers me a little bit is having something change my code without me knowing about it. 00:05:39.820 --> 00:05:40.040 Yeah. 00:05:40.040 --> 00:05:47.820 There's a flag called black-check that will basically tell you if you ran it, this is what it would do. 00:05:47.820 --> 00:05:55.040 I also use flake8 to check stuff, like to sort of lint my code and help me understand what I'm... 00:05:55.040 --> 00:05:57.720 Actually, I like it that it doesn't... 00:05:57.720 --> 00:05:58.960 Flake8 doesn't change your stuff. 00:05:59.040 --> 00:06:01.020 It just tells you some information. 00:06:01.020 --> 00:06:04.620 And so you can train yourself to write in a more consistent way. 00:06:04.620 --> 00:06:07.720 So this is contributed by Nathan Clayton. 00:06:07.720 --> 00:06:20.360 There's a flake8 black plugin that can just run black check within a flake8 environment and tell you if you ran black, it would change this stuff along with all your other flake8 checks. 00:06:20.360 --> 00:06:24.460 I think this is pretty handy, and I'll try to include this into my workflow. 00:06:24.460 --> 00:06:28.240 Yeah, and that probably makes it pretty easy to plug in to continuous integration pipelines, 00:06:28.240 --> 00:06:32.020 because flake8 may already be part of some continuous integration check, right? 00:06:32.020 --> 00:06:36.140 A lot of people are hooking black into a pre-commit hook, which is great. 00:06:36.140 --> 00:06:37.180 You can also hook... 00:06:37.180 --> 00:06:40.460 If you're already hooking flake8 up in a pre-commit hook, you can do this as well. 00:06:40.460 --> 00:06:45.180 But having it, you can still slip stuff in and merges might muck things up. 00:06:45.180 --> 00:06:46.460 So yeah, this is cool. 00:06:46.460 --> 00:06:46.880 Yeah, yeah. 00:06:46.880 --> 00:06:47.280 Super cool. 00:06:47.360 --> 00:06:53.020 And you can't necessarily be sure everyone is running all the same tooling or whatever, right? 00:06:53.020 --> 00:06:54.780 So maybe they've changed it somehow. 00:06:54.780 --> 00:06:56.000 So this will check it. 00:06:56.000 --> 00:06:56.400 Very cool. 00:06:56.400 --> 00:06:57.060 That's a good one. 00:06:57.060 --> 00:06:57.480 All right. 00:06:57.480 --> 00:07:00.520 Now, before we get to the next one, I want to tell you all about DigitalOcean. 00:07:00.780 --> 00:07:06.360 They're sponsoring this episode, as they do many of them, and make this show certainly possible in some way. 00:07:06.360 --> 00:07:07.680 So thank you to them. 00:07:07.680 --> 00:07:10.300 And they are doing all sorts of cool stuff. 00:07:10.300 --> 00:07:11.940 You know, we use them for our infrastructure. 00:07:12.800 --> 00:07:15.460 They have great virtual machine support. 00:07:15.460 --> 00:07:21.480 But one of the things they recently rolled out was managed Postgres servers or as a service. 00:07:21.480 --> 00:07:27.160 So if you want to create something on Linux and get it up and running fast, affordable, simply, 00:07:27.160 --> 00:07:32.360 but you don't want to also become a DBA and manage your own database, your own backups and all that, 00:07:32.360 --> 00:07:36.680 you can just plug into their managed database service for Postgres over there. 00:07:36.680 --> 00:07:42.780 And that's one less thing to, you know, own as a puppy or to have to babysit all the time and patch and whatnot. 00:07:42.780 --> 00:07:45.280 So definitely recommend checking them out. 00:07:45.280 --> 00:07:46.220 They're doing cool stuff. 00:07:46.220 --> 00:07:51.680 So visit them over at pythonbystat.fm slash digitalocean, and you'll get a $50 credit for new users. 00:07:51.680 --> 00:07:55.740 Now, back onto my theme here for understanding languages. 00:07:55.740 --> 00:08:04.760 If you look at the popular editors these days, Brian, we have PyCharm and we have VS Code as the two, like, frontrunners, I would say, these days. 00:08:04.760 --> 00:08:08.220 It's kind of like if you want a big ID that's going to do everything for you, PyCharm. 00:08:08.220 --> 00:08:15.660 If you have a lighter weight preference, then maybe VS Code seems to be like what a lot of folks are trending towards. 00:08:15.660 --> 00:08:21.160 So there's this cool extension for VS Code called Python Preview. 00:08:21.160 --> 00:08:30.440 If you're a computer science student or if you're getting into Python and you want to have a good understanding of what's happening as you're writing code, 00:08:30.440 --> 00:08:32.520 I think using this extension is awesome. 00:08:32.720 --> 00:08:40.520 So click on the link there and just look at the little picture that gets drawn if you scroll down to the second picture or something like that there. 00:08:40.520 --> 00:08:40.960 Okay. 00:08:40.960 --> 00:08:41.860 This is neat. 00:08:41.860 --> 00:08:43.700 So what happens is you write some Python code. 00:08:43.700 --> 00:08:48.400 You maybe create a list, and then you're going to loop over it, and you're going to change the variable or something like that. 00:08:48.460 --> 00:08:57.000 And it will create a visualization of what the objects look like in memory, what the call stack looks like, the global frame is, and all that. 00:08:57.000 --> 00:09:03.040 And it will actually show all the pointer references between the variables and how they're changing and what type they are. 00:09:03.040 --> 00:09:10.260 All sorts of interesting stuff as it just sort of, like, explains what your code is doing visually if you were to run it. 00:09:10.260 --> 00:09:11.360 Oh, this is really cool. 00:09:11.360 --> 00:09:11.920 I like it. 00:09:11.920 --> 00:09:12.120 Yeah. 00:09:12.120 --> 00:09:12.900 It's really cool. 00:09:12.900 --> 00:09:16.220 It's free because it's just part of VS Code, so that's really nice. 00:09:16.220 --> 00:09:27.500 So if you're either a student or trying to learn the language or if you're a teacher, right, like, this is exactly the kind of stuff you're like, well, let's look at this code, and now I'm going to go over and draw it out on the whiteboard. 00:09:27.500 --> 00:09:30.580 Like, oh, you could just let this thing draw it perfectly, right? 00:09:30.580 --> 00:09:31.120 You know what I mean? 00:09:31.120 --> 00:09:31.380 Yeah. 00:09:31.380 --> 00:09:37.900 So I think this would actually be really good for an intro language course if you do any teaching or something like that. 00:09:37.900 --> 00:09:38.200 Yeah. 00:09:38.200 --> 00:09:39.100 I like it. 00:09:39.100 --> 00:09:39.280 Yeah. 00:09:39.280 --> 00:09:40.860 It reminds me of Python Tutor. 00:09:40.860 --> 00:09:53.440 It should remind you of Python Tutor because if you look at the GitHub repo for this extension, it actually says, I would like to thank a couple of projects for at least conceptually inspiring me. 00:09:53.440 --> 00:09:55.640 I don't know that there's any code sharing or anything like that. 00:09:55.640 --> 00:10:00.220 And the first one is Philip Guo's pythontutor.com site. 00:10:00.840 --> 00:10:01.280 Okay. 00:10:01.280 --> 00:10:02.320 Well, that makes sense. 00:10:02.320 --> 00:10:03.400 Yeah, it definitely makes sense. 00:10:03.400 --> 00:10:08.480 If you've played with Python Tutor before, that's a place to go explore Python, and you type it in there. 00:10:08.480 --> 00:10:13.220 And what's cool about this is, like, it just explores the code that you have open in your editor in a similar way. 00:10:13.220 --> 00:10:14.840 You can't step through it, I don't believe. 00:10:14.840 --> 00:10:16.320 But, yeah, it's pretty cool. 00:10:16.320 --> 00:10:16.560 Yeah. 00:10:16.560 --> 00:10:17.000 Neat. 00:10:17.000 --> 00:10:17.300 Awesome. 00:10:17.300 --> 00:10:18.000 All right. 00:10:18.000 --> 00:10:18.480 What's next? 00:10:18.480 --> 00:10:20.180 Something we haven't talked about before, huh? 00:10:20.180 --> 00:10:20.580 Packaging? 00:10:20.580 --> 00:10:22.440 Packaging and poetry. 00:10:22.440 --> 00:10:28.860 This is a nice article called Create and Publish a Python Package with Poetry by John Franey. 00:10:29.460 --> 00:10:34.260 There's been, occasionally people will say poetry is more for projects or something. 00:10:34.260 --> 00:10:36.340 But this is dealing with a Python package. 00:10:36.340 --> 00:10:43.980 And it walks through creating a new project and then customizing the pyproject.toml file. 00:10:44.240 --> 00:10:48.660 And then all the different settings within the toml and what do they mean and why you pick different ones. 00:10:48.660 --> 00:10:53.880 And then carries you through to, if it's something you were going to share with the rest of the world. 00:10:53.880 --> 00:10:57.960 I mean, one of the great things about all this is you don't have to share your code with the world. 00:10:57.960 --> 00:11:03.900 You might be just sharing it with your team members or just even making modularizing your own stuff. 00:11:04.080 --> 00:11:10.940 But if you do want to share it, it does show you how to use the test server at PyPI to try everything out and make sure it works. 00:11:10.940 --> 00:11:14.220 And then finally to publish directly to PyPI. 00:11:14.220 --> 00:11:14.540 Yeah. 00:11:14.540 --> 00:11:16.120 It looks like a really nice little write-up. 00:11:16.120 --> 00:11:20.220 And yeah, it's just typing poetry space publish. 00:11:20.220 --> 00:11:21.280 That's not terrible. 00:11:22.280 --> 00:11:22.640 Yeah. 00:11:22.640 --> 00:11:29.160 It doesn't talk about testing or integrating testing and talks into a project, which, and pytest, of course. 00:11:29.160 --> 00:11:31.260 But so that maybe that's an extension. 00:11:31.260 --> 00:11:33.220 But yeah, this is the packaging part. 00:11:33.220 --> 00:11:33.720 Yeah, cool. 00:11:33.720 --> 00:11:34.620 Maybe a follow-up article. 00:11:34.620 --> 00:11:35.360 That'd be great. 00:11:35.360 --> 00:11:36.420 Very nice one. 00:11:36.420 --> 00:11:41.560 So the last one that I want to cover is a realpython.com article by Logan Jones. 00:11:41.560 --> 00:11:43.840 We got to hang out with him a little bit at PyCon. 00:11:43.840 --> 00:11:44.660 That was fun. 00:11:44.660 --> 00:11:51.920 But this one is, I think, an interesting sort of thought-provoking one about Python and the language. 00:11:52.240 --> 00:11:54.840 So I think there's a lot to learn here for many folks. 00:11:54.840 --> 00:11:58.600 And it's called Python Pointers or Pointers in Python, What's the Point? 00:11:58.600 --> 00:12:06.440 So, I mean, first of all, like the question is like outside of C extensions and maybe like inside the CPython runtime. 00:12:06.440 --> 00:12:11.720 But, you know, in Python, the language itself, does Python even have pointers, right? 00:12:11.720 --> 00:12:12.620 Like, what do you think? 00:12:12.620 --> 00:12:14.480 How would you answer that question if somebody asked you that? 00:12:14.480 --> 00:12:16.860 It's mostly hidden from users. 00:12:16.860 --> 00:12:17.220 Exactly. 00:12:17.220 --> 00:12:17.480 Yeah. 00:12:17.480 --> 00:12:18.980 If someone asked me, does Python have pointers? 00:12:18.980 --> 00:12:20.640 I would say, yeah, of course it has pointers. 00:12:20.940 --> 00:12:31.360 But it really hinges on whether or not you define that as I can put an ampersand in front and then like plus plus it and move in the, you know, move along in like a string or move along in an array. 00:12:31.360 --> 00:12:35.360 Like, can I do pointer arithmetic and can I actually work at that level? 00:12:35.540 --> 00:12:41.200 Or is it I have variables that point out to things on the heap like pointers, right? 00:12:41.200 --> 00:12:44.520 Like, which of those has to pass the bar to be like it's a pointer, right? 00:12:44.520 --> 00:12:45.060 Yeah. 00:12:45.060 --> 00:12:50.920 And generally, a new person to point Python and programming altogether wouldn't even notice them and would say no. 00:12:50.920 --> 00:12:53.560 But, I mean, like names are all pointing to stuff. 00:12:53.560 --> 00:12:54.140 Exactly. 00:12:54.380 --> 00:12:58.040 So, what's really interesting is that could be a big misconception, right? 00:12:58.040 --> 00:13:06.620 Because if you think what you're passing around are values, but you actually have pointers, anytime you change one of those things, you're changing it everywhere, right? 00:13:06.620 --> 00:13:09.660 You have passed by reference, not passed by value semantics. 00:13:10.440 --> 00:13:16.580 And so, if you think I can pass this over, that thing can change it and it doesn't affect me like a normal value would, right? 00:13:16.580 --> 00:13:19.100 Like, whoops, that's a bit of a problem, right? 00:13:19.100 --> 00:13:26.080 We've seen like weird edge cases where like the default value of like an empty list in a function can be all sorts of badness for reasons like that. 00:13:26.080 --> 00:13:26.960 So, I don't know. 00:13:26.960 --> 00:13:27.640 It's pretty interesting. 00:13:27.640 --> 00:13:37.040 I think actually Python is more pointer heavy than most languages, more so than C#, more so than C++, right? 00:13:37.040 --> 00:13:45.080 Because in those languages, you at least have the option to define stuff on the stack and define value types like integers versus in pointers in C++, right? 00:13:45.080 --> 00:13:46.300 In Python, you really don't. 00:13:46.300 --> 00:13:49.780 Everything, even numbers and booleans and stuff are pointers, right? 00:13:49.780 --> 00:13:51.680 I didn't know that, but okay. 00:13:51.680 --> 00:13:52.540 Yeah. 00:13:52.540 --> 00:13:55.060 So, like true is a pointer out on the heap. 00:13:55.060 --> 00:13:57.420 The number seven is a pointer on the heap. 00:13:57.420 --> 00:14:00.620 So, what it means is like all sorts of, you know, it really helps you. 00:14:00.620 --> 00:14:03.200 If you look, and the article goes through a lot of this. 00:14:03.200 --> 00:14:12.380 If you look deeply at like the memory structure of what you get, you know, each reference type, which I'll call them, you know, thing in Python. 00:14:12.380 --> 00:14:17.400 Each object, so everything, has a reference count it has to keep track of. 00:14:17.880 --> 00:14:32.020 It has a type, like so, in the pi object that actually gets allocated when you say the number one or whatever, that has actually a field up there that says this is an integer right now. 00:14:32.020 --> 00:14:35.900 This pi object that it's pointing at is supposed to be an integer, and it has a value. 00:14:36.560 --> 00:14:46.700 So, if you think of, I've got this simple little piece of data, like the number one or the character C, it's not just one byte or four bytes or whatever. 00:14:46.700 --> 00:14:48.560 It's many of those. 00:14:48.560 --> 00:14:50.800 I don't know, 2025, 28. 00:14:50.800 --> 00:14:57.600 I'm not sure exactly what the number is, but there's a lot of stuff coming along with that, what would otherwise look like a really simple little value. 00:14:57.740 --> 00:14:58.600 Yeah, okay. 00:14:58.600 --> 00:14:58.980 Okay. 00:14:58.980 --> 00:15:06.360 There's some nice drawings in there about like that names are pointing to objects, and objects all have types and values and reference counts. 00:15:06.360 --> 00:15:08.600 And yeah, this is good. 00:15:08.600 --> 00:15:09.620 I probably need to. 00:15:09.620 --> 00:15:16.880 Yeah, I think people should read this who haven't deeply thought about like this whole reference type scenario and what that means. 00:15:16.880 --> 00:15:17.540 It's pretty cool. 00:15:17.900 --> 00:15:20.300 My takeaway is like Python has pointers. 00:15:20.300 --> 00:15:29.560 All the variables in Pythons are pointers, but they're like these safe reference type, wrapper type things that you don't directly have to worry about, allocate, whatever, right? 00:15:29.560 --> 00:15:29.900 Yeah. 00:15:29.900 --> 00:15:30.640 So, pretty cool. 00:15:30.640 --> 00:15:38.080 There's one interesting little thing that I saw in the article that Logan talked about, and that was about interning objects. 00:15:38.740 --> 00:15:45.020 So, when I said earlier before, when I say the number one, it actually allocates a Py object on the heap, sort of. 00:15:45.020 --> 00:15:55.620 There was a Py object allocated on the heap that corresponds to one, but the first 256, something like that, numbers are what are called interned, and so are certain strings. 00:15:55.620 --> 00:16:04.500 And that means like if you allocate a one over here and you allocate a one over there, those are actually reusing the same object, right? 00:16:04.500 --> 00:16:11.120 So, they pre-allocate like the basic numbers and strings, and then you just change where your pointer points. 00:16:11.120 --> 00:16:13.400 Like that's just part of like Python startup. 00:16:13.400 --> 00:16:13.780 Okay. 00:16:13.780 --> 00:16:14.080 Right? 00:16:14.080 --> 00:16:14.980 So, that's called interning. 00:16:14.980 --> 00:16:19.580 And the interesting bit is that you can manually intern things. 00:16:19.580 --> 00:16:22.720 Like I can go to the system and say, this thing, I want to intern it. 00:16:22.720 --> 00:16:24.220 So, if someone else tries to create one of them. 00:16:24.220 --> 00:16:24.640 Oh, really? 00:16:24.640 --> 00:16:26.800 Just, you know, don't reallocate it. 00:16:26.800 --> 00:16:29.980 Just share this sort of global one-off thing I've created. 00:16:30.380 --> 00:16:36.480 And so, it says you can intern strings, and this can be useful for the performance on dictionary lookedups. 00:16:36.480 --> 00:16:47.120 Because if the strings that are the keys of the dictionary are interned, then a dictionary lookup is a pointer comparison versus a string comparison. 00:16:47.120 --> 00:16:49.040 Okay. 00:16:49.040 --> 00:16:49.980 Okay. 00:16:49.980 --> 00:16:50.780 Isn't that cool? 00:16:50.780 --> 00:16:51.040 Yeah. 00:16:51.040 --> 00:16:54.320 So, basically, it's an is versus a double equal, right? 00:16:54.320 --> 00:16:54.820 Okay. 00:16:54.900 --> 00:17:01.820 And if you've got a big dictionary and you're like going crazy on it and the keys are kind of complicated, that could potentially make it quite a bit faster. 00:17:01.820 --> 00:17:03.420 Like much, much faster. 00:17:03.420 --> 00:17:09.920 It's like, are these two integers or longs the same versus I got to go through like 20 characters and compare them? 00:17:09.920 --> 00:17:12.700 Well, so, there must be some reason why we don't just intern everything. 00:17:12.700 --> 00:17:14.380 If you intern it, it won't expire. 00:17:14.380 --> 00:17:15.400 It won't be cleaned up. 00:17:15.400 --> 00:17:16.300 Oh, okay. 00:17:16.300 --> 00:17:16.800 Yeah, yeah. 00:17:16.800 --> 00:17:22.440 So, I mean, like if you're not going to keep it around and share it, you would basically be doing manually memory management stuff. 00:17:22.440 --> 00:17:23.300 All right. 00:17:23.300 --> 00:17:26.020 So, all of that is, I think, is super interesting and worth people checking out. 00:17:26.020 --> 00:17:30.500 But now the question comes down like, can I have ampersand X? 00:17:30.500 --> 00:17:33.580 Can I get my actual pointer or star X, right? 00:17:33.580 --> 00:17:37.680 Can I do real pointer stuff in Python? 00:17:37.680 --> 00:17:42.700 And, of course, Python has to interact with C, and C functions often take this type of pointer, that type of pointer. 00:17:42.700 --> 00:17:44.460 So, how does that work, right? 00:17:44.820 --> 00:17:49.020 So, in C++, we have like inline assembly. 00:17:49.020 --> 00:17:50.620 You can kind of drop down to a lower bit. 00:17:50.620 --> 00:17:55.100 In C#, you have unsafe mode because C# has reference types just like Python. 00:17:55.100 --> 00:17:59.460 But there's a way to say, turn that off and let me actually do pointer arithmetic on it. 00:17:59.460 --> 00:18:02.640 So, the question is, can we have that in Python? 00:18:02.640 --> 00:18:05.700 And actually, if you use Cython, you can. 00:18:05.700 --> 00:18:11.560 Like in Python code, you can say, this is a pointer, and you can dereference its address by saying ampersand and stuff like that. 00:18:11.560 --> 00:18:13.160 That's one way. 00:18:13.160 --> 00:18:15.600 And you can also work with the C types library. 00:18:15.600 --> 00:18:22.320 So, I can create like an integer pointer by allocating a C type and then pass it around and share it and stuff. 00:18:22.320 --> 00:18:23.420 Okay. 00:18:24.160 --> 00:18:32.400 So, yes, if you need to dig down and you need to like truly simulate what pointers really, really are, you can actually do that in Python as well. 00:18:32.400 --> 00:18:33.840 And there's two ways to do it there. 00:18:33.840 --> 00:18:34.520 Okay. 00:18:34.520 --> 00:18:37.140 One requires Cython and one is just like straight CPython. 00:18:37.140 --> 00:18:38.220 All right. 00:18:38.220 --> 00:18:40.840 Have you had enough language lessons for today, Ryan? 00:18:41.080 --> 00:18:43.660 Or you could just not worry about it so much. 00:18:43.660 --> 00:18:44.840 But, okay. 00:18:45.440 --> 00:18:45.760 Exactly. 00:18:45.760 --> 00:18:52.120 I do think it's worth thinking about this question and like what it looks like in Python if you haven't thought about it before. 00:18:52.120 --> 00:18:58.280 And I think the thing before, the preview Python plugin or extension for VS Code, I think would also help with this. 00:18:58.280 --> 00:18:58.500 Yeah. 00:18:58.580 --> 00:19:01.560 Yeah, because it has the little lines that are really the references and so on. 00:19:01.560 --> 00:19:01.760 Yep. 00:19:01.760 --> 00:19:03.560 So, that's it for all of our topics today. 00:19:03.560 --> 00:19:04.360 Anything else? 00:19:04.360 --> 00:19:06.580 Any extra stuff you want to just throw out there real quick? 00:19:06.580 --> 00:19:11.420 Well, I was going to talk about the PSF fundraiser, but you've got it on your list too. 00:19:11.420 --> 00:19:12.040 So. 00:19:12.720 --> 00:19:15.920 Yeah, it looks like I, in terms of writing it down, I beat you to it. 00:19:15.920 --> 00:19:17.240 So, I guess I'll say something about it. 00:19:17.240 --> 00:19:19.680 So, the PSF is doing a fundraiser. 00:19:19.680 --> 00:19:20.960 I'm linked over to it. 00:19:20.960 --> 00:19:21.940 You should definitely check it out. 00:19:21.940 --> 00:19:29.760 On the previous episode, we also sort of talked about that you can buy a PyCharm license and some of that money, like 30% or something, goes towards the PSF. 00:19:29.760 --> 00:19:32.480 Or you can just directly contribute to it. 00:19:32.480 --> 00:19:34.780 So, I've linked to the blog post that they just posted. 00:19:34.780 --> 00:19:43.640 Now, the deadline was supposed to be June 1st, I believe, but they didn't raise enough money, which is mind-blowing to me. 00:19:43.640 --> 00:19:48.300 But they didn't raise enough money, so they've extended it until June 30th. 00:19:48.300 --> 00:19:55.420 So, please click on the link, donate something, and more importantly, maybe try to get your company to donate. 00:19:55.420 --> 00:19:55.940 Right? 00:19:55.940 --> 00:20:03.500 If your company deeply depends on Python having a vibrant ecosystem, these are tax-deductible if you pay taxes in the U.S., so definitely check it out. 00:20:03.500 --> 00:20:13.660 People don't know the PSF money goes to help fund workshops and conferences, and they help pay meetup fees around so that a lot of the local Python meetups don't have to pay their own fees. 00:20:13.660 --> 00:20:19.660 They do a lot of sponsorship and financial aid of different projects that should be funded but aren't otherwise. 00:20:19.660 --> 00:20:21.460 So, it's a good thing. 00:20:21.460 --> 00:20:22.280 Donate to it. 00:20:22.280 --> 00:20:22.640 Absolutely. 00:20:22.640 --> 00:20:23.260 All right. 00:20:23.260 --> 00:20:24.880 I think it's time for a joke. 00:20:24.880 --> 00:20:25.200 Yeah. 00:20:25.200 --> 00:20:26.560 Quick little one here at the end. 00:20:26.560 --> 00:20:28.100 This one comes from Jay Miller. 00:20:28.300 --> 00:20:34.560 And the question is, what did the developer name his newborn son? 00:20:34.560 --> 00:20:35.840 Of course, Jason. 00:20:35.840 --> 00:20:36.920 Jason. 00:20:36.920 --> 00:20:37.660 That's right. 00:20:37.660 --> 00:20:39.200 Oh, dear. 00:20:39.200 --> 00:20:39.860 Oh, wow. 00:20:39.860 --> 00:20:40.200 Yeah. 00:20:40.200 --> 00:20:40.980 Yeah, that's a good one. 00:20:40.980 --> 00:20:41.760 Thank you, Jay. 00:20:41.760 --> 00:20:43.640 And Brian, thanks for being here. 00:20:43.640 --> 00:20:44.000 All right. 00:20:44.000 --> 00:20:44.420 Thank you. 00:20:44.420 --> 00:20:44.760 Bye. 00:20:44.760 --> 00:20:45.060 Yep. 00:20:45.060 --> 00:20:45.300 See ya. 00:20:45.440 --> 00:20:47.220 Thank you for listening to Python Bytes. 00:20:47.220 --> 00:20:49.740 Follow the show on Twitter via at Python Bytes. 00:20:49.740 --> 00:20:52.600 That's Python Bytes as in B-Y-T-E-S. 00:20:52.600 --> 00:20:55.840 And get the full show notes at pythonbytes.fm. 00:20:55.840 --> 00:21:00.040 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:21:00.040 --> 00:21:02.740 We're always on the lookout for sharing something cool. 00:21:02.740 --> 00:21:05.860 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:21:05.860 --> 00:21:09.280 Thank you for listening and sharing this podcast with your friends and colleagues.