WEBVTT 00:00:00.001 --> 00:00:04.940 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:04.940 --> 00:00:10.360 This is episode 288, recorded June 14th, 2022. 00:00:10.360 --> 00:00:11.460 I'm Michael Kennedy. 00:00:11.460 --> 00:00:12.680 And I am Brian Okken. 00:00:12.680 --> 00:00:13.780 Brian, how are you doing? 00:00:13.780 --> 00:00:15.180 I'm excellent today. 00:00:15.180 --> 00:00:16.440 I hear you're a little busy. 00:00:16.440 --> 00:00:21.680 But it's just, you know, being a parent and having side jobs and stuff like that. 00:00:21.680 --> 00:00:24.080 Of course. Well, it's better than the alternative. 00:00:24.080 --> 00:00:28.280 Definitely. I was talking to somebody this weekend about like their one job 00:00:28.280 --> 00:00:30.160 and trying to balance job and life. 00:00:30.160 --> 00:00:33.060 And I'm like, I don't even remember what that's like with just one job. 00:00:33.060 --> 00:00:36.580 I know. Or you have a job where you go to work and you do the work. 00:00:36.580 --> 00:00:39.820 And then when you go home, there's no real reason to do the job anymore. 00:00:39.820 --> 00:00:41.080 So you can just step away from it. 00:00:41.080 --> 00:00:41.980 It sounds glorious. 00:00:41.980 --> 00:00:45.780 And yet I continue to choose the opposite, which I also love. 00:00:45.780 --> 00:00:50.520 All right. Well, speaking of stuff people might love, you want to kick us off with your first item? 00:00:50.520 --> 00:00:52.200 Yeah, we're going to talk about polar bears. 00:00:52.200 --> 00:00:53.960 No, not polar bears. 00:00:53.960 --> 00:00:55.900 A project called Polars. 00:00:56.280 --> 00:00:59.160 And actually, it's like super fun and cool. 00:00:59.160 --> 00:01:02.840 So Polars was suggested to us by actually several listeners. 00:01:02.840 --> 00:01:05.180 We got several people sent in. 00:01:05.180 --> 00:01:07.440 And I'm sorry, I don't have their names, but thank you. 00:01:07.440 --> 00:01:09.640 Always send great stuff our way. 00:01:09.640 --> 00:01:10.480 We love it. 00:01:10.480 --> 00:01:17.620 But Polars is billed as a lightning fast data frame library for Rust and Python. 00:01:17.620 --> 00:01:21.060 And it is written in Python. 00:01:21.060 --> 00:01:23.140 No, it's written in Rust. 00:01:23.140 --> 00:01:31.600 But they have a full API is present in Python. 00:01:31.600 --> 00:01:34.820 And it's kind of neat, actually, how they've done it. 00:01:34.820 --> 00:01:39.720 So we've got up on the screen the splash screen for the Polars project. 00:01:40.540 --> 00:01:43.080 There's a user guide and API reference, of course. 00:01:43.080 --> 00:01:48.840 But one of the things I wanted to talk about is some of their why you would consider it. 00:01:48.840 --> 00:01:52.020 So Polars is lightning fast data frame library. 00:01:52.020 --> 00:01:54.360 It uses an in-memory query engine. 00:01:54.360 --> 00:01:59.340 And it says it's embarrassingly parallel in execution. 00:01:59.660 --> 00:02:04.580 And it has cache-efficient algorithms and expressive API. 00:02:04.580 --> 00:02:10.460 And they say it makes it perfect for efficient data wrangling, data pipelines, snappy APIs, and so much more. 00:02:10.460 --> 00:02:13.620 But I just also is fun. 00:02:13.620 --> 00:02:16.200 I played with it a little bit. 00:02:16.200 --> 00:02:17.400 It's zippy and fun. 00:02:17.400 --> 00:02:26.780 They have both the ability to do lazy execution and eager execution, whichever you prefer for your use. 00:02:26.780 --> 00:02:28.400 It's multi-threaded. 00:02:28.780 --> 00:02:33.800 It has a notion of single instruction, multiple data. 00:02:33.800 --> 00:02:37.260 I'm not exactly sure what that means, but makes it faster, apparently. 00:02:37.260 --> 00:02:48.040 And I was looking through the whole, the introductory user's guide is actually written like a very well-written book. 00:02:48.040 --> 00:02:53.820 And it looks like the whole guide, as far as I can tell, is written for the Python API. 00:02:53.820 --> 00:02:58.400 So I think that was part of the intent all along, is to write it quickly in Rust. 00:02:58.400 --> 00:03:03.280 expose it to Rust users also, but also run it with Python. 00:03:03.280 --> 00:03:07.980 And it's just really pretty clean and super fast. 00:03:08.120 --> 00:03:16.660 One of these benchmark results performance things was, it's like Spark was taking 332 seconds and they took 43 seconds. 00:03:18.660 --> 00:03:27.080 It's 100 million rows and it's just like, let's load up a couple of pieces of data or something. 00:03:27.080 --> 00:03:27.780 Right. 00:03:27.980 --> 00:03:41.480 So there's a lot of focus on this, making sure that it's fast, especially when you don't need everything, like doing lazy evaluation or making sure you do multiprocessing. 00:03:41.480 --> 00:03:53.920 One of the things I thought was really kind of cool about it, I was looking through the documentation, is there's a section on, that says, it was a section that was talking about parallelization. 00:03:54.060 --> 00:03:56.500 It says, do not kill the parallelization. 00:03:56.500 --> 00:04:07.460 Because with Python, we know we, there is, basically there's ways to use polars that can kill parallel processing because of the GIL. 00:04:07.460 --> 00:04:16.280 If you're using, if you don't do it the way they've set it up, you can use it in a way that makes it a little slower, I guess, is what I'm saying. 00:04:16.280 --> 00:04:22.360 But, so there's a section on this talking about the polar expressions, polars expressions. 00:04:22.740 --> 00:04:33.980 And these are all set up so that you can pass these expressive queries into polars and have it run in the background and just make things really fast. 00:04:33.980 --> 00:04:41.400 And sort of skirt around the GIL because you're doing all the work in the Rust part of the world. 00:04:41.400 --> 00:04:44.620 And then collecting the data later. 00:04:44.620 --> 00:04:47.540 So there's like a set up the query and then collect the query. 00:04:47.540 --> 00:04:49.140 That's kind of cool. 00:04:49.140 --> 00:04:52.540 So, anyway, I just thought this is a really, looks fun. 00:04:52.540 --> 00:04:57.460 And it's, it's just a, there's nothing to, you don't have to do, know that it's in Rust. 00:04:57.460 --> 00:05:00.560 You just say pip install polars and it works. 00:05:00.560 --> 00:05:00.980 So. 00:05:00.980 --> 00:05:01.940 Yeah, that's great. 00:05:01.940 --> 00:05:06.080 Out in the audience, Tharab asks, why Rust and not C? 00:05:06.080 --> 00:05:10.300 Maybe an example there is Pandas versus this. 00:05:10.300 --> 00:05:12.860 Also, probably the person who wrote it just really likes Rust. 00:05:12.860 --> 00:05:17.340 And I think Rust has a little bit more thread safety than straight C does. 00:05:17.340 --> 00:05:18.460 I'm not 100% sure. 00:05:18.460 --> 00:05:22.520 But this uses threads, as you point out, whereas the other one, Pandas. 00:05:22.520 --> 00:05:24.000 And others in C don't. 00:05:24.000 --> 00:05:27.560 I also think that we're going to see a lot more of things like this. 00:05:27.560 --> 00:05:37.420 Like, because I think some of the early faster packages for Python were written in C because Rust wasn't around or it wasn't mature enough. 00:05:37.420 --> 00:05:44.940 But I think we're going to see more people saying, well, I want it to be closer to the processor for some of this stuff. 00:05:45.460 --> 00:05:46.560 Why not Rust? 00:05:46.560 --> 00:05:51.100 Because I think Rust is a cleaner development environment than C right now. 00:05:51.100 --> 00:05:52.600 Yeah, I agree. 00:05:52.600 --> 00:05:53.400 Absolutely. 00:05:53.400 --> 00:05:55.280 It's just a more modern language, right? 00:05:55.280 --> 00:05:57.740 You know, C is keeping up. 00:05:57.740 --> 00:06:00.960 C is never going to be old, I don't think. 00:06:00.960 --> 00:06:01.640 But yeah. 00:06:01.640 --> 00:06:02.160 Yeah. 00:06:02.480 --> 00:06:02.840 Yeah, yeah. 00:06:02.840 --> 00:06:06.920 I don't mean to say that C is out, not modern in the sense that people are not using it. 00:06:06.920 --> 00:06:12.600 But it doesn't embrace in its sort of natural form the most, you know, smart pointers and things like that. 00:06:12.600 --> 00:06:12.880 Yeah. 00:06:12.880 --> 00:06:14.940 And there's C++ maybe, but not C. 00:06:14.940 --> 00:06:21.520 There's safety features built into Rust to make sure you don't, just make it easier to not do dumb things. 00:06:21.820 --> 00:06:22.640 I guess. 00:06:22.640 --> 00:06:23.440 Let's put it that way. 00:06:23.440 --> 00:06:25.080 Indeed. 00:06:25.080 --> 00:06:25.740 All right. 00:06:25.740 --> 00:06:29.460 Well, let's jump on to my first item, which is a follow up from last week. 00:06:29.460 --> 00:06:31.620 Python developer survey 2021. 00:06:31.620 --> 00:06:33.100 Yes, you heard that right. 00:06:33.100 --> 00:06:33.880 I know it's 2022. 00:06:33.880 --> 00:06:36.440 These are the results from the survey that was at the end of last year. 00:06:36.440 --> 00:06:42.700 So let's, I'm going to kind of skim through this and just hit on some of the main ideas here. 00:06:42.700 --> 00:06:46.460 There's a ton of information and I encourage people to go over and scroll through it. 00:06:46.460 --> 00:06:51.620 This is done in conjunction with the folks over at JetBrains, the PyCharm team and all that. 00:06:51.620 --> 00:06:58.320 So it was collected and analyzed by the JetBrains folks, but put together independently by the PSF, right? 00:06:58.320 --> 00:07:01.540 So it's intended to not be skewed in any way towards them. 00:07:01.540 --> 00:07:02.080 All right. 00:07:02.080 --> 00:07:06.700 So first thing is if you're using Python, is it your main language or your secondary language? 00:07:06.700 --> 00:07:12.260 84% of the people say it's their main language with 16% picking up the balance of not so much. 00:07:12.260 --> 00:07:14.300 It's been pretty stable over the last four years. 00:07:14.300 --> 00:07:15.080 What do you think of this, Brian? 00:07:15.080 --> 00:07:20.380 I think that there's a lot of people like me. 00:07:20.540 --> 00:07:24.780 I think that it started out as my secondary language and now it's my main language. 00:07:24.780 --> 00:07:25.940 Yeah. 00:07:25.940 --> 00:07:26.460 Interesting. 00:07:26.460 --> 00:07:27.000 Yeah. 00:07:27.000 --> 00:07:27.960 And it got sucked in. 00:07:27.960 --> 00:07:29.760 Like, ah, maybe I'll use it to test my C stuff. 00:07:29.760 --> 00:07:30.980 Wait, actually, this is kind of nice. 00:07:30.980 --> 00:07:31.800 Maybe I'll do more of this. 00:07:31.800 --> 00:07:32.020 Yeah. 00:07:32.020 --> 00:07:39.160 There's always the next question or analysis is always fraught with weird overlaps. 00:07:39.380 --> 00:07:42.880 But I like the way they ask this a little bit better than a lot of times. 00:07:42.880 --> 00:07:45.080 It says Python usage with other languages. 00:07:45.080 --> 00:07:47.120 What other languages do you use Python with? 00:07:47.120 --> 00:07:51.820 Rather than maybe a more general one where they ask, well, what is the most popular language? 00:07:51.820 --> 00:07:54.640 And you'll see weird stuff like, well, most people code in CSS. 00:07:54.640 --> 00:07:57.360 Like, I'm a full stack CSS developer. 00:07:57.360 --> 00:07:58.080 Like, no, you're not. 00:07:58.120 --> 00:07:59.180 Just everyone has to use it. 00:07:59.180 --> 00:07:59.840 Like, what is this? 00:07:59.840 --> 00:08:00.840 It's a horrible question. 00:08:00.840 --> 00:08:01.800 Right. 00:08:01.800 --> 00:08:05.180 So this is like, if you're doing Python, what other languages do you bring into the mix? 00:08:05.180 --> 00:08:07.240 And I guess maybe just hit the top five. 00:08:07.240 --> 00:08:09.840 JavaScript, because you might be doing front and back end. 00:08:09.840 --> 00:08:11.660 HTML, CSS, same reason. 00:08:11.660 --> 00:08:14.560 Bash shell, because you're doing automation build, so on. 00:08:14.560 --> 00:08:15.020 SQL. 00:08:15.020 --> 00:08:15.720 SQL. 00:08:15.720 --> 00:08:19.260 I'm surprised there's that much direct SQL, but there it is. 00:08:19.260 --> 00:08:21.640 And then C and C++, speaking of that language. 00:08:21.640 --> 00:08:27.480 Also, to sort of address the thing that I brought up before, Rust is at 6%. 00:08:27.480 --> 00:08:28.960 Last year, it was at 5%. 00:08:28.960 --> 00:08:30.860 So it's compared to C at 30. 00:08:30.860 --> 00:08:33.240 And 29, so they both grew by 1% this year. 00:08:33.240 --> 00:08:33.800 Okay. 00:08:33.800 --> 00:08:34.340 Yeah. 00:08:34.340 --> 00:08:36.260 I think they both grew. 00:08:36.260 --> 00:08:36.900 That's interesting. 00:08:36.900 --> 00:08:37.920 Yeah, exactly. 00:08:37.920 --> 00:08:43.640 Another thing that people might want to pay attention to is you'll see year over year stuff 00:08:43.640 --> 00:08:46.240 all over the place in these reports, because they've been doing this for a while. 00:08:46.240 --> 00:08:53.160 So like the top bar that's darker or sorry, brighter is this year, but they always also 00:08:53.160 --> 00:08:54.100 put last year. 00:08:54.100 --> 00:08:56.580 So for example, people are doing less bash. 00:08:56.580 --> 00:09:00.300 You can see like it's lower bar is higher and they're doing less PHP. 00:09:00.300 --> 00:09:03.140 Probably means they love themselves a little bit more. 00:09:03.140 --> 00:09:05.860 Don't go home crying. 00:09:05.860 --> 00:09:06.120 Okay. 00:09:06.120 --> 00:09:07.780 Let's see. 00:09:07.780 --> 00:09:09.920 Languages for web and data science. 00:09:09.920 --> 00:09:13.240 This is kind of like if you're doing these things, what to use more. 00:09:13.240 --> 00:09:16.840 So if you're doing data science, you do more SQL is your most common thing. 00:09:16.840 --> 00:09:21.940 If you're doing web surprise, JavaScript and HTML is the most common other thing. 00:09:21.940 --> 00:09:22.400 Yeah. 00:09:22.400 --> 00:09:23.460 Let's see. 00:09:23.460 --> 00:09:25.380 What do you use Python for? 00:09:25.380 --> 00:09:27.200 Work and personal is 50%. 00:09:27.200 --> 00:09:30.160 Personal is 29 and work 20%. 00:09:30.160 --> 00:09:34.120 Kind of interesting that more people use it for side projects. 00:09:34.120 --> 00:09:37.500 If they use it for just one or the other of work or personal. 00:09:37.500 --> 00:09:40.200 I guess people who know Python at work, they want to go home. 00:09:40.200 --> 00:09:40.660 They're like, you know what? 00:09:40.660 --> 00:09:41.940 I could automate my house with this too. 00:09:42.020 --> 00:09:42.400 Let's do that. 00:09:42.400 --> 00:09:45.420 I think that, yeah, I would take it like that. 00:09:45.420 --> 00:09:48.020 I think more people, it isn't just even automated your house. 00:09:48.020 --> 00:09:49.560 It's just playing around with it at home. 00:09:49.560 --> 00:09:53.180 Like, yeah, I heard about this, this new web framework, FastAPI. 00:09:53.180 --> 00:09:54.180 I want to try it out. 00:09:54.180 --> 00:09:55.220 Things like that. 00:09:55.220 --> 00:09:55.480 So. 00:09:55.480 --> 00:09:56.440 Yeah, absolutely. 00:09:56.440 --> 00:09:58.980 I'm going to skip down here through a bunch of stuff. 00:09:58.980 --> 00:10:01.580 What do you use Python for the most? 00:10:01.580 --> 00:10:04.200 Web development, but that fell year over year. 00:10:04.200 --> 00:10:06.840 Data analysis stayed the same year over year. 00:10:06.840 --> 00:10:08.520 Machine learning fell year over year. 00:10:08.880 --> 00:10:09.920 And a bunch of stuff. 00:10:09.920 --> 00:10:14.860 But so sort of the growth areas year over year are education and desktop development. 00:10:14.860 --> 00:10:17.860 And then other, I think it's pretty. 00:10:17.860 --> 00:10:19.500 Also game development doubled. 00:10:19.500 --> 00:10:21.260 Doubled from one to two percent. 00:10:21.260 --> 00:10:25.260 I mean, from one to two is probably like there was, you know, that might be within the margin 00:10:25.260 --> 00:10:26.180 of error type of thing. 00:10:26.180 --> 00:10:27.180 But still, it doubled. 00:10:27.180 --> 00:10:28.780 But I think just the other. 00:10:28.780 --> 00:10:30.420 No, other didn't grow. 00:10:30.420 --> 00:10:31.840 There's just, I think it's just more spread out. 00:10:31.840 --> 00:10:32.340 I don't know. 00:10:32.380 --> 00:10:34.480 Because there's still, I think, same number of people using Python. 00:10:34.480 --> 00:10:34.980 All right. 00:10:34.980 --> 00:10:36.220 Are you a data scientist? 00:10:36.220 --> 00:10:37.660 One third, yes. 00:10:37.660 --> 00:10:38.480 Two thirds, no. 00:10:38.480 --> 00:10:42.180 That's that fits with my mental model of the Python space. 00:10:42.180 --> 00:10:47.320 One third data science, one third web and API, and one third massively diverse other. 00:10:47.320 --> 00:10:48.920 The way I see the ecosystem. 00:10:48.920 --> 00:10:50.920 Python three versus two. 00:10:51.040 --> 00:10:55.920 I think we're asymptotically as a limit approaching Python three only. 00:10:55.920 --> 00:11:03.180 But year over year, it goes 25% from 2017, then 16% Python two, then 10%, then six, then 00:11:03.180 --> 00:11:03.560 five. 00:11:03.560 --> 00:11:06.820 And then there's just huge code bases that are stuck on Python two. 00:11:06.820 --> 00:11:12.420 Like some of the big banks have like 5,000 Python developers working on Python two code bases 00:11:12.420 --> 00:11:16.380 that are so specialized and tweaked that they can't just swap out stuff. 00:11:16.380 --> 00:11:19.180 So, you know, that might represent 5% bank usage. 00:11:19.180 --> 00:11:19.660 I don't know. 00:11:19.660 --> 00:11:23.860 I just, I feel bad for you. 00:11:23.860 --> 00:11:25.320 We're rooting for you. 00:11:25.320 --> 00:11:27.320 Everybody out there using Python two. 00:11:27.320 --> 00:11:28.480 Stick in there. 00:11:28.480 --> 00:11:29.480 Let's approach that limit. 00:11:29.480 --> 00:11:30.080 Yeah, yeah. 00:11:30.080 --> 00:11:32.920 Let's divide by n factorial, not n for your limit there. 00:11:32.920 --> 00:11:33.280 Let's go. 00:11:33.280 --> 00:11:33.860 Get in there. 00:11:33.860 --> 00:11:34.440 All right. 00:11:34.440 --> 00:11:37.660 Python three nine is the most common version. 00:11:37.660 --> 00:11:42.600 Three 10 being 16% and three eight being 27% versus 35. 00:11:42.600 --> 00:11:44.360 So that's, that's pretty interesting. 00:11:44.360 --> 00:11:44.940 Yeah. 00:11:44.940 --> 00:11:47.780 I feel like this is, hey, this is what comes with my Linux. 00:11:47.780 --> 00:11:49.320 This is what comes with my Docker. 00:11:49.440 --> 00:11:51.500 So I'm using that, but maybe it's more. 00:11:51.500 --> 00:11:52.260 Yeah. 00:11:52.260 --> 00:11:56.360 It's interesting because you and I like our interesting space because we're always looking 00:11:56.360 --> 00:11:57.220 at the new stuff. 00:11:57.220 --> 00:12:00.760 So I, I'm at, I'm at three 10 and I can't wait to jump to three 11. 00:12:00.760 --> 00:12:01.940 Yeah. 00:12:01.940 --> 00:12:05.040 And actually I've switched to three 11 for some projects. 00:12:05.040 --> 00:12:09.580 So, but there's a lot of people that was like, man, Python's pretty good. 00:12:09.580 --> 00:12:11.060 And then it's been good for a while. 00:12:11.060 --> 00:12:13.380 So I don't need a lot of the new features. 00:12:13.380 --> 00:12:14.100 So. 00:12:14.100 --> 00:12:15.460 Yeah, for sure. 00:12:15.460 --> 00:12:19.980 I'm going to later talk about something that might shift that. 00:12:19.980 --> 00:12:20.460 Yeah. 00:12:20.460 --> 00:12:21.280 To the right. 00:12:22.100 --> 00:12:25.020 I've actually been thinking like, should I maybe install three 11 beta? 00:12:25.020 --> 00:12:27.200 See how stable that is on the servers. 00:12:27.200 --> 00:12:28.780 We'll see. 00:12:28.780 --> 00:12:29.660 That might be a bad choice. 00:12:29.660 --> 00:12:31.080 Might be a good choice. 00:12:31.080 --> 00:12:32.160 That's what's okay. 00:12:32.160 --> 00:12:34.120 where do you install Python from? 00:12:34.120 --> 00:12:35.080 38%? 00:12:35.080 --> 00:12:37.960 Just download the thing from Python.org and run with that. 00:12:37.960 --> 00:12:38.340 Yeah. 00:12:38.620 --> 00:12:46.240 The next most common option is, to install it via your OS package manager, apt, homebrew, 00:12:46.240 --> 00:12:46.620 whatever. 00:12:46.620 --> 00:12:47.320 Yeah. 00:12:47.320 --> 00:12:51.300 And Alvaro has a great little recommendation out there for people who are stuck on Python 00:12:51.300 --> 00:12:51.640 too. 00:12:51.640 --> 00:12:54.600 There probably is a support group for Python two users. 00:12:54.600 --> 00:12:57.140 Hi, my name is Brian and I use Python too. 00:12:57.140 --> 00:12:59.880 Hi, Brian. 00:12:59.880 --> 00:13:02.340 All right. 00:13:02.340 --> 00:13:08.520 Another one I thought was pretty interesting is, the packaging stuff, the isolation stuff, 00:13:08.600 --> 00:13:13.100 um, before we get there really quick, web frameworks, FastAPI continues to grow. 00:13:13.100 --> 00:13:13.760 Yeah. 00:13:13.760 --> 00:13:15.140 Pretty strong here. 00:13:15.140 --> 00:13:22.280 We've got Flask is now maybe within the margin error, but just edged ahead of Django, but fast 00:13:22.280 --> 00:13:25.180 API almost doubled in usage over the last year. 00:13:25.180 --> 00:13:28.060 It grew nine percentage points, but it was at 12% last year. 00:13:28.060 --> 00:13:32.360 And so now it's at 21%, which is, that's a pretty big chunk to take out of established 00:13:32.360 --> 00:13:32.740 frameworks. 00:13:32.740 --> 00:13:33.260 Yeah. 00:13:33.260 --> 00:13:36.060 Well, and it looks like the third is none. 00:13:36.060 --> 00:13:37.440 I haven't tried that yet. 00:13:38.300 --> 00:13:38.560 Yeah. 00:13:38.560 --> 00:13:42.360 It gets a lot of attribute errors, but it's, it's really efficient because it doesn't do 00:13:42.360 --> 00:13:42.760 much work. 00:13:42.760 --> 00:13:43.040 Yeah. 00:13:44.040 --> 00:13:44.400 Yeah. 00:13:44.400 --> 00:13:48.640 People, who maybe don't know FastAPI, the name would indicate it's only for building 00:13:48.640 --> 00:13:51.360 APIs, but you can build web apps with it as well. 00:13:51.360 --> 00:13:52.380 And it's pretty good at that. 00:13:52.380 --> 00:13:59.040 Especially if you check out Michael's courses, he's got like two courses on building web apps 00:13:59.040 --> 00:14:00.320 with the fast eggs. 00:14:00.500 --> 00:14:01.500 I do. 00:14:01.500 --> 00:14:01.500 I do. 00:14:01.500 --> 00:14:04.680 And I also have a, some, some sort of template extensions for it that make it easier. 00:14:04.680 --> 00:14:05.220 All right. 00:14:05.220 --> 00:14:06.360 Data science libraries. 00:14:06.360 --> 00:14:08.360 I don't know how I feel about this one. 00:14:08.360 --> 00:14:09.040 Do you use NumPy? 00:14:09.040 --> 00:14:12.160 Well, yes, but if you use other libraries, then you also use NumPy. 00:14:12.160 --> 00:14:14.980 So yeah, it's like all of these are using NumPy. 00:14:14.980 --> 00:14:16.460 So exactly. 00:14:16.460 --> 00:14:17.600 Exactly. 00:14:17.600 --> 00:14:18.100 Yeah. 00:14:18.480 --> 00:14:19.840 a bunch of other stuff. 00:14:19.840 --> 00:14:21.080 Look at that for unit testing. 00:14:21.080 --> 00:14:22.700 Would it surprise you that pytest is winning? 00:14:22.700 --> 00:14:23.580 No. 00:14:23.580 --> 00:14:26.140 They just overtook Num this year, didn't it? 00:14:26.140 --> 00:14:30.940 yeah. 00:14:30.940 --> 00:14:31.880 So. 00:14:31.880 --> 00:14:32.620 All right. 00:14:32.620 --> 00:14:37.800 ORMs, SQLAlchemy is ahead and then there's, Django ORM. 00:14:37.800 --> 00:14:39.280 Django is tied to Django. 00:14:39.280 --> 00:14:40.380 SQLAlchemy is broad. 00:14:40.380 --> 00:14:41.500 So there's, there's that. 00:14:41.500 --> 00:14:45.800 And then kind of the none of the ORM world is raw SQL at 16%. 00:14:45.800 --> 00:14:46.620 That's pretty interesting. 00:14:47.060 --> 00:14:51.400 Postgres is the most common database by far at 43%. 00:14:51.400 --> 00:14:54.060 Then you have SQLite, which is a little bit of a side case. 00:14:54.060 --> 00:14:56.160 You can use it directly, but it's also used for development. 00:14:56.160 --> 00:14:59.920 And then MySQL, the MongoDB, and then Redis and Microsoft SQL Server. 00:14:59.920 --> 00:15:00.260 So. 00:15:00.260 --> 00:15:00.800 Yeah. 00:15:00.800 --> 00:15:01.320 Huh. 00:15:01.320 --> 00:15:05.260 Actually SQL Server and Oracle are higher than I would have expected, even though, you know, 00:15:05.260 --> 00:15:06.180 but it's okay. 00:15:06.180 --> 00:15:10.480 Well, I think what you're going to find is that there's like certain places, especially 00:15:10.480 --> 00:15:15.420 in the enterprise where it's like, we're a SQL Server shop or we're an Oracle shop and 00:15:15.420 --> 00:15:16.860 our DBAs manage our databases. 00:15:16.860 --> 00:15:21.220 So here you, you put in a, you file a ticket and they'll create a database for you. 00:15:21.220 --> 00:15:21.480 Yeah. 00:15:21.480 --> 00:15:25.460 Or there's a, there's already an existing database and you're connecting to it or something. 00:15:25.460 --> 00:15:25.900 Yep. 00:15:25.900 --> 00:15:26.260 Yep. 00:15:26.260 --> 00:15:26.780 Exactly. 00:15:26.780 --> 00:15:27.420 Exactly. 00:15:27.420 --> 00:15:28.020 All right. 00:15:28.020 --> 00:15:28.780 Let's keep going. 00:15:28.780 --> 00:15:29.920 Cloud platforms. 00:15:29.920 --> 00:15:31.880 AWS is at the top. 00:15:31.880 --> 00:15:36.860 Then you've got Google cloud at 50% and then GCP, Google cloud platform, then Azure, then 00:15:36.860 --> 00:15:38.100 Heroku, DigitalOcean. 00:15:38.100 --> 00:15:39.780 Linode has made it on the list here. 00:15:39.960 --> 00:15:45.280 So, you know, a former or sometimes sponsor of the show, it's good for them. 00:15:45.280 --> 00:15:48.420 And let's see, do you run, how do you run stuff in the cloud? 00:15:48.420 --> 00:15:49.200 Let's skip over this. 00:15:49.200 --> 00:15:53.160 I think a bunch of interesting, a few more interesting things and we'll, we'll call it compared to 00:15:53.160 --> 00:16:00.400 2020 Linux and macOS popularity decreased by 5% while windows usage has risen by 10%. 00:16:00.400 --> 00:16:00.920 Wow. 00:16:00.920 --> 00:16:01.500 Yeah. 00:16:01.500 --> 00:16:06.160 Where the windows people now double more than double the macOS people and are almost rivaling 00:16:06.160 --> 00:16:06.780 the Linux people. 00:16:06.780 --> 00:16:10.600 That's, I think that's just, towards the growth of Python. 00:16:10.600 --> 00:16:16.040 I think, Python's just making it more into everybody's using it sort of thing. 00:16:16.040 --> 00:16:16.440 Yeah. 00:16:16.440 --> 00:16:18.960 And there's also a windows subsystem for Linux. 00:16:18.960 --> 00:16:24.200 It's been coming along pretty strong, which makes windows a more viable, more on have, have 00:16:24.200 --> 00:16:26.300 more parity with your cloud targets. 00:16:26.300 --> 00:16:26.520 Right. 00:16:26.520 --> 00:16:27.120 Yeah. 00:16:27.120 --> 00:16:29.720 And it feels like out in the audience is because of WSL. 00:16:29.720 --> 00:16:29.940 Yeah. 00:16:29.940 --> 00:16:30.240 Maybe. 00:16:30.240 --> 00:16:30.680 Yeah. 00:16:30.680 --> 00:16:31.300 Okay. 00:16:31.300 --> 00:16:33.280 let's see a few more things. 00:16:33.280 --> 00:16:34.080 Documentation. 00:16:34.080 --> 00:16:34.440 It's cool. 00:16:34.440 --> 00:16:36.400 They're asking about like what documentation frameworks you use. 00:16:36.400 --> 00:16:38.000 This one's interesting to me. 00:16:38.000 --> 00:16:40.920 What's your main editor, VS Code or PyCharm? 00:16:40.920 --> 00:16:44.460 I asked this question a lot at the end of talk Python and it feels like VS Code, VS Code, 00:16:44.460 --> 00:16:49.140 VS Code, VS Code is what people are saying all the time, but it's 35% VS Code, 31% PyCharm 00:16:49.140 --> 00:16:50.540 and Brian right there for you. 00:16:50.540 --> 00:16:52.040 7% Vim, but. 00:16:52.040 --> 00:16:53.420 Okay. 00:16:53.420 --> 00:16:54.420 Yeah. 00:16:54.420 --> 00:16:55.860 I just teased you. 00:16:55.860 --> 00:16:56.180 Yeah. 00:16:56.180 --> 00:16:59.280 To be fair, it's both VS Code. 00:16:59.280 --> 00:17:00.220 It's, it's, it's all three. 00:17:00.220 --> 00:17:00.660 Yeah. 00:17:00.660 --> 00:17:01.340 Or top. 00:17:01.340 --> 00:17:01.700 Yeah. 00:17:01.700 --> 00:17:03.560 Top four for me, but yeah. 00:17:03.560 --> 00:17:04.420 Yeah, exactly. 00:17:04.420 --> 00:17:07.780 Well, often you probably just use Vim bindings within the other two, right? 00:17:07.780 --> 00:17:08.020 Yep. 00:17:08.020 --> 00:17:08.440 Yeah. 00:17:08.740 --> 00:17:09.640 Let's see. 00:17:09.640 --> 00:17:17.120 I think also maybe another interesting breakdown is that if you look at the usage scenarios 00:17:17.120 --> 00:17:21.620 or the type of development done with the editors, you get different answers. 00:17:21.620 --> 00:17:28.960 So like for data science, you've got more PyCharm and for web development, I think, hold on, 00:17:28.960 --> 00:17:29.420 I have that right. 00:17:29.420 --> 00:17:30.400 Oh, interesting. 00:17:30.400 --> 00:17:32.440 For data science, you have a lot more VS Code. 00:17:32.440 --> 00:17:36.860 For web development, you have more PyCharm and you have a lot less other in data science, 00:17:36.860 --> 00:17:37.920 AKA Jupyter. 00:17:38.180 --> 00:17:39.140 I suspect it. 00:17:39.140 --> 00:17:39.820 Yeah. 00:17:39.820 --> 00:17:39.900 Yeah. 00:17:39.900 --> 00:17:40.520 Okay. 00:17:40.520 --> 00:17:42.220 How did you learn about your editor? 00:17:42.220 --> 00:17:43.040 By far? 00:17:43.040 --> 00:17:45.360 Or first one here is from a friend. 00:17:45.360 --> 00:17:49.360 So basically friends like push editors, like drug dealers, like gotta get out. 00:17:49.360 --> 00:17:50.120 What are you doing on that thing? 00:17:50.120 --> 00:17:50.520 Get in here. 00:17:50.620 --> 00:17:55.880 No, I think it's like, if I'm, if I'm watching somebody do something cool, I want to do it 00:17:55.880 --> 00:17:57.220 also because it looks helpful. 00:17:57.220 --> 00:17:57.680 Yeah, exactly. 00:17:57.680 --> 00:17:59.780 You sit down next to your friend and you're like, how did you do that? 00:17:59.780 --> 00:18:00.400 That's awesome. 00:18:00.400 --> 00:18:01.440 I want that feature, right? 00:18:01.440 --> 00:18:02.460 I think you're probably right. 00:18:02.460 --> 00:18:03.080 Okay. 00:18:03.080 --> 00:18:05.080 Let's just bust down a few things better. 00:18:05.080 --> 00:18:09.700 One, do you know, or what do you think about the new developer in residence role? 00:18:09.700 --> 00:18:12.420 This is Lucas Schillinga that's going on right now. 00:18:12.420 --> 00:18:14.560 77% are like, the what? 00:18:14.560 --> 00:18:16.440 Never heard of it. 00:18:18.060 --> 00:18:23.100 Maybe like we got, we got a little more advocacy job to do here, but he's been doing a great 00:18:23.100 --> 00:18:28.680 job really speeding things up and sort of greasing the wheels of open source contributions 00:18:28.680 --> 00:18:29.280 and whatnot. 00:18:29.280 --> 00:18:35.460 I, yeah, I'm going to take it like design because if design's done well, nobody knows it's there. 00:18:35.460 --> 00:18:37.440 And yeah, I think the same thing. 00:18:37.440 --> 00:18:41.160 I think if he's doing his job really, really well, most people won't notice things will 00:18:41.160 --> 00:18:41.620 just work. 00:18:41.620 --> 00:18:42.060 Yep. 00:18:42.060 --> 00:18:43.180 Yeah. 00:18:43.180 --> 00:18:44.860 Quick real time follow up. 00:18:44.860 --> 00:18:46.940 Felix out in the audience says, I use PyCharm because of Michael. 00:18:47.660 --> 00:18:53.180 It should have been one of the options in the survey because of Michael. 00:18:53.180 --> 00:18:54.760 Oh, come on. 00:18:54.760 --> 00:18:55.220 That's awesome. 00:18:55.220 --> 00:18:57.360 But no, let's see. 00:18:57.360 --> 00:18:58.600 There's a bunch of questions about that. 00:18:58.600 --> 00:19:00.620 And the final thing I want to touch on is Python packaging. 00:19:00.620 --> 00:19:03.240 Let's see here. 00:19:03.240 --> 00:19:06.500 Which tools related to Python packaging do you use directly? 00:19:06.500 --> 00:19:08.400 And we've talked about poetry. 00:19:08.740 --> 00:19:13.560 We've talked about Flit, pipenv and so on. 00:19:13.560 --> 00:19:18.480 And 81% of the people are like, I use pip for packaging. 00:19:20.480 --> 00:19:22.200 As opposed to Flit or something. 00:19:22.200 --> 00:19:25.960 And then sort of parallel to that is for virtual environment. 00:19:25.960 --> 00:19:28.620 Do you use the, you know, what do you use for virtual environments basically? 00:19:28.620 --> 00:19:29.100 Yeah. 00:19:29.100 --> 00:19:34.580 Like 42% is like, I just use the built-in one or I use the virtual ENV wrapper. 00:19:34.580 --> 00:19:39.400 And then it's like poetry, pipenv talks and so on. 00:19:39.400 --> 00:19:39.980 There's a few. 00:19:40.080 --> 00:19:40.900 I don't know what this is. 00:19:40.900 --> 00:19:42.780 Yeah. 00:19:42.780 --> 00:19:47.760 Well, I'm glad they included that because one of the original questions didn't include 00:19:47.760 --> 00:19:49.540 like the built-in VENV. 00:19:49.540 --> 00:19:52.680 And that's, I think that's what most people use. 00:19:52.680 --> 00:19:53.820 It is. 00:19:53.820 --> 00:19:54.140 Yeah. 00:19:54.140 --> 00:19:54.680 Yeah. 00:19:54.680 --> 00:19:55.220 Absolutely. 00:19:55.220 --> 00:19:55.880 All right. 00:19:55.880 --> 00:19:58.540 Well, I think there's more in my progress bar here. 00:19:58.540 --> 00:20:00.060 This is a super detailed report. 00:20:00.060 --> 00:20:02.000 I'm linking to it in the show notes. 00:20:02.000 --> 00:20:05.180 So just go over there and check it out if you want to see all the cool graphs and play 00:20:05.180 --> 00:20:06.440 with the interactive aspects. 00:20:06.440 --> 00:20:09.300 But thanks again to the PSF and JetBrains for putting this together. 00:20:09.420 --> 00:20:13.880 It's really good to have this insight and these projections of where things are going. 00:20:13.880 --> 00:20:14.340 Yeah. 00:20:14.340 --> 00:20:15.260 Hey. 00:20:15.260 --> 00:20:15.580 All right. 00:20:15.580 --> 00:20:17.220 I'm going to grab the next one. 00:20:17.220 --> 00:20:19.200 Ooh, we did this smoothly this time. 00:20:19.200 --> 00:20:19.880 Nice. 00:20:19.880 --> 00:20:28.600 So JIN config is just JIN actually, but the project's called JIN config. 00:20:28.600 --> 00:20:32.520 And it's kind of a neat little thing. 00:20:32.520 --> 00:20:35.340 It's a different way to think about configuration files. 00:20:35.340 --> 00:20:41.180 So like you have, you have your PyProject or you have .toml files. 00:20:41.180 --> 00:20:42.780 You could have .any files. 00:20:42.780 --> 00:20:45.660 There's a lot of ways to have configuration files. 00:20:45.660 --> 00:20:53.980 But JIN takes the perspective of, oh, well, what if you just, what if you're not really 00:20:53.980 --> 00:20:57.600 into all of that stuff and you're a machine learning person and you just have a whole bunch 00:20:57.600 --> 00:21:00.080 of stuff to configure and you're changing stuff a lot? 00:21:00.400 --> 00:21:01.800 Maybe let's make it easier. 00:21:01.800 --> 00:21:10.280 So I actually came across this because of Vincent Warmerdam. 00:21:10.280 --> 00:21:15.220 He's got an excellent intro to JIN on his comcode site. 00:21:15.600 --> 00:21:22.620 And the idea is you've got this, you just have for a function that you want to, in your 00:21:22.620 --> 00:21:26.160 code, you got some code and you have like part of it that you want configurable. 00:21:26.160 --> 00:21:30.900 You just slap a JIN configurable decorator onto it. 00:21:30.900 --> 00:21:36.240 And then all of the parameters to that function are now something that can show up in a config 00:21:36.240 --> 00:21:36.540 file. 00:21:36.540 --> 00:21:38.160 And it's not in any file. 00:21:38.160 --> 00:21:41.340 I actually don't know the exact syntax, but it just kind of looks like Python. 00:21:41.340 --> 00:21:45.680 It's a, you just have names. 00:21:45.680 --> 00:21:51.260 Like in the, in this example that I'm showing, there's a, there's a file called simulate and 00:21:51.260 --> 00:21:55.540 there's actually a function called simulate and a parameter called in samples. 00:21:55.540 --> 00:22:02.300 And in your config file, you can just say simulate.insamples equals a hundred or something like that. 00:22:02.980 --> 00:22:03.400 Oh, wow. 00:22:03.400 --> 00:22:08.920 This is like, it basically sets the default parameters for all your functions you're calling. 00:22:08.920 --> 00:22:09.580 Yeah. 00:22:09.580 --> 00:22:13.060 The ones that you want to be configurable and you just do that. 00:22:13.060 --> 00:22:20.240 Now it's still where you can still set defaults within your code and, and just like you normally 00:22:20.240 --> 00:22:20.620 would. 00:22:20.620 --> 00:22:24.800 And then, and then you can configure the ones that you want to be different than the defaults. 00:22:24.800 --> 00:22:26.560 So that's a, that's a possibility. 00:22:26.560 --> 00:22:30.180 And there's a whole bunch of, I'm going to expand this a little bit. 00:22:30.520 --> 00:22:36.080 There's a whole bunch of different things that Vincent goes through like required settings. 00:22:36.080 --> 00:22:39.480 You can have a, you can specify like a dot. 00:22:39.480 --> 00:22:40.580 What is it? 00:22:40.580 --> 00:22:43.400 Gin dot required as a function. 00:22:43.400 --> 00:22:49.360 And it makes it so that, or as your parameter, and then it makes it so that your user has to 00:22:49.360 --> 00:22:50.520 put it in their config file. 00:22:50.520 --> 00:22:52.060 That's kind of cool. 00:22:52.380 --> 00:22:58.500 And then you can also, if you don't want somebody to configure something, you can, you can mark 00:22:58.500 --> 00:23:03.240 it as, oh, he's got blacklist the end samples. 00:23:03.240 --> 00:23:07.440 So if you want, like in this example, he's got a simulate function with two parameters, random 00:23:07.440 --> 00:23:08.540 funk and end samples. 00:23:08.540 --> 00:23:12.920 You want people to configure the random funk, but you don't want them to touch the end samples. 00:23:12.920 --> 00:23:15.480 You can, you can say, don't do that. 00:23:15.660 --> 00:23:17.460 So, it's kind of neat. 00:23:17.460 --> 00:23:19.440 There's a whole bunch of cool features around it. 00:23:19.440 --> 00:23:25.160 Like, like being able to specify different functions so you can name things and, do 00:23:25.160 --> 00:23:29.480 it around like, like to say like in his example, he's got random functions. 00:23:29.660 --> 00:23:35.600 And if you, you can specify, you know, one of the other, one of the other, like a random 00:23:35.600 --> 00:23:39.400 triangle function, you can specify a function and assign it to that. 00:23:39.400 --> 00:23:41.000 He's got, named things. 00:23:41.000 --> 00:23:48.420 it's a really, it, it's a interesting way to think about configuration and the, the, 00:23:48.420 --> 00:23:55.340 the motivation section of the documentation for gin says, that often modern machine learning 00:23:55.340 --> 00:24:01.740 experiments require just configuring a whole bunch of parameters and, and then you're 00:24:01.740 --> 00:24:02.700 tweaking them and stuff. 00:24:02.700 --> 00:24:08.280 And, and, and to have that be as easy as possible and as simple as possible, because 00:24:08.280 --> 00:24:12.520 it is, and you're going to add some and take some away and things like that because some 00:24:12.520 --> 00:24:18.000 things you want configured and then you decide not to not having to go through a config parser 00:24:18.000 --> 00:24:22.180 system, and just making it as trivial as possible to add parameters. 00:24:22.180 --> 00:24:23.600 I think it's a really cool idea. 00:24:23.600 --> 00:24:24.080 So. 00:24:24.520 --> 00:24:25.160 It is a cool idea. 00:24:25.160 --> 00:24:28.440 It reminds me of like dependency injection a little bit. 00:24:28.440 --> 00:24:28.680 Yeah. 00:24:28.680 --> 00:24:33.640 you know, where you would like configure, say like if somebody asks for a function 00:24:33.640 --> 00:24:38.640 that implements this or that, that goes here, like this is the data access layer to use, or 00:24:38.640 --> 00:24:40.960 here's the ORM I want you to pick this time. 00:24:40.960 --> 00:24:45.140 It's not super common in Python, but it's pretty common in a lot of languages. 00:24:45.140 --> 00:24:46.800 And it feels a little bit like that. 00:24:46.800 --> 00:24:48.220 Can we configure stuff? 00:24:48.220 --> 00:24:53.700 So we have these parameters that we might use for testing or something, but it just, they get 00:24:53.700 --> 00:24:54.540 filled in automatically. 00:24:54.540 --> 00:24:55.040 Yeah. 00:24:55.040 --> 00:24:56.820 Even FastAPI has that for example. 00:24:56.820 --> 00:24:57.320 Yeah. 00:24:57.320 --> 00:24:57.380 Yeah. 00:24:57.380 --> 00:24:58.760 so cool. 00:24:58.760 --> 00:25:03.260 Somebody in the audience says it isn't, isn't Jen used with go. 00:25:03.260 --> 00:25:09.440 and I'm not sure about that, but it, Jen is, is not an officially supported Google 00:25:09.440 --> 00:25:14.400 product, but it's under the Google, like a GitHub repo group. 00:25:14.400 --> 00:25:19.560 So maybe, yeah, maybe it does look very Python like though for the config files. 00:25:19.560 --> 00:25:20.020 And that's cool. 00:25:20.020 --> 00:25:20.380 Yeah. 00:25:20.380 --> 00:25:21.040 Good one. 00:25:21.040 --> 00:25:21.400 All right. 00:25:21.400 --> 00:25:23.920 let me switch back before I swap over. 00:25:23.920 --> 00:25:24.900 Okay, here we go. 00:25:24.900 --> 00:25:30.160 Now this next one, I think universally will be well accepted. 00:25:30.160 --> 00:25:33.520 Although the comment section about it was a little bit rough and tumble. 00:25:33.520 --> 00:25:37.100 Nonetheless, I think it should be universally exciting to everyone. 00:25:37.240 --> 00:25:44.200 And this comes to us from Eduardo Orochena who sent over this article that said the, what's 00:25:44.200 --> 00:25:44.480 it called? 00:25:44.480 --> 00:25:48.500 The Python 311 performance benchmarks are looking fantastic. 00:25:48.500 --> 00:25:50.040 And oh boy, are they? 00:25:50.400 --> 00:25:54.160 So we're talking beta code six months out, right? 00:25:54.160 --> 00:25:56.680 And still, still we've got some pretty neat stuff. 00:25:56.680 --> 00:26:02.740 So this, this links over to an article with that same title by Michael Larabel basically 00:26:02.740 --> 00:26:07.900 says, look, we took a whole bunch of different performance benchmarks for Python and ran them 00:26:07.900 --> 00:26:12.360 on Python 311 beta, which this is the thing I was hinting at. 00:26:12.360 --> 00:26:17.160 Like you might really want to consider this for if you're thinking, should we upgrade from 00:26:17.160 --> 00:26:18.120 nine to 10? 00:26:18.120 --> 00:26:20.120 Maybe you want to just go straight to 11. 00:26:20.480 --> 00:26:20.660 Right. 00:26:20.660 --> 00:26:25.060 I mean, you know, sort of a side thought, Brian, isn't it awesome that the one that 00:26:25.060 --> 00:26:28.380 goes like crazy performance, this one goes to 11. 00:26:28.380 --> 00:26:33.200 All right. 00:26:33.200 --> 00:26:39.920 So they show all the stuff that they're testing on, like AMD Ryzen 16 core 32 with hyper threading 00:26:39.920 --> 00:26:41.220 the motherboard. 00:26:41.220 --> 00:26:44.800 I mean, like down to the motherboard and the chipset and the memory and all that. 00:26:44.800 --> 00:26:46.880 So a pretty decent stuff. 00:26:47.000 --> 00:26:50.480 And then also the build commands and all sorts of things here. 00:26:50.480 --> 00:26:52.620 So pretty repeatable, I think. 00:26:52.620 --> 00:26:53.100 Yeah. 00:26:53.100 --> 00:27:00.200 Rather than just like, hey, I ran it and here's a graph without, without axes or something like 00:27:00.200 --> 00:27:00.440 that. 00:27:00.640 --> 00:27:03.300 So you can kind of click through here and you see some pictures and it says, all right, 00:27:03.300 --> 00:27:07.400 well, there's the Pybench, which I think is like the standard simple one. 00:27:07.400 --> 00:27:08.520 It says, look at this. 00:27:08.520 --> 00:27:14.260 The Python 311 beta is faster than 310, which by the way, was slightly slower than the previous 00:27:14.260 --> 00:27:14.660 ones. 00:27:14.660 --> 00:27:16.480 But you know, what is that? 00:27:16.480 --> 00:27:17.740 10% or something. 00:27:17.740 --> 00:27:20.780 So already actually 16% better. 00:27:20.780 --> 00:27:22.320 So that's already pretty awesome. 00:27:22.320 --> 00:27:24.040 But there's a whole bunch of other ones. 00:27:24.040 --> 00:27:25.520 They did one called Go. 00:27:25.880 --> 00:27:27.580 I don't know what these benchmarks are. 00:27:27.580 --> 00:27:31.200 This does, I don't think this has anything to do with the language Go, just the name of 00:27:31.200 --> 00:27:31.580 the benchmark. 00:27:31.580 --> 00:27:33.780 And then there's two to three and chaos. 00:27:33.780 --> 00:27:34.860 That one sounds like the funnest. 00:27:34.860 --> 00:27:39.280 But if you look at this Go one, this one is like almost 50% faster. 00:27:39.280 --> 00:27:41.260 50% faster. 00:27:41.260 --> 00:27:42.480 That's insane, right? 00:27:42.480 --> 00:27:42.960 Yeah. 00:27:42.960 --> 00:27:43.500 Wow. 00:27:43.500 --> 00:27:49.740 And you come down to the two to three is, these are all estimates, 25, 20% faster, 00:27:49.740 --> 00:27:53.120 say 40% faster with the chaos one. 00:27:53.120 --> 00:27:57.980 Come down to the float operations and Python 310 was already better than the others. 00:27:57.980 --> 00:28:00.400 But this is again, maybe 30% faster. 00:28:00.400 --> 00:28:03.700 And let's roll into the next page. 00:28:03.700 --> 00:28:05.620 You just kind of see this across the board. 00:28:05.620 --> 00:28:06.360 Better, better. 00:28:06.360 --> 00:28:07.840 Some of them are super better. 00:28:07.840 --> 00:28:10.940 Some are like a little bit better, like Pathlib's better, but not crazy. 00:28:11.340 --> 00:28:14.720 Ray tracing is like, again, 40% better here. 00:28:14.720 --> 00:28:16.160 And you keep going. 00:28:16.160 --> 00:28:21.180 There's another one with this huge crypto IAES, some sort of encryption thing. 00:28:21.180 --> 00:28:25.260 So there's just a bunch of, a couple of these are, there's like this one at the end, you're 00:28:25.260 --> 00:28:26.620 like, oh wait, this one got way worse. 00:28:26.620 --> 00:28:30.460 Be careful because it says more is better on this composition. 00:28:30.460 --> 00:28:31.820 I guess is the results here. 00:28:31.820 --> 00:28:36.300 Like how much more computing power do you get per CPU cycle or whatever? 00:28:36.300 --> 00:28:37.440 What is that? 00:28:37.440 --> 00:28:38.800 That's a massive jump. 00:28:38.880 --> 00:28:43.480 You saw a little bit better improvements from 3.8 to 3.9, 3.9 to 3.10. 00:28:43.480 --> 00:28:50.120 But 3.10 to 11 is like a 40%, yeah, 41% better on the beta before it's even final. 00:28:50.120 --> 00:28:50.680 Wow. 00:28:50.680 --> 00:28:51.820 That's pretty exciting, right? 00:28:51.820 --> 00:28:52.920 That's very exciting. 00:28:52.920 --> 00:28:59.780 And actually, I think, I'm curious what some of these negative comments are, but the interesting 00:28:59.780 --> 00:29:06.160 thing is to run lots of different metrics and lots of different benchmarks and having them 00:29:06.160 --> 00:29:12.800 all be, it's faster kind of means that, I mean, I take it as, you know, your mileage may vary, 00:29:12.800 --> 00:29:16.560 but it's going to be better for whatever you're doing, probably. 00:29:16.560 --> 00:29:17.220 Yeah. 00:29:17.220 --> 00:29:17.660 Yeah. 00:29:17.660 --> 00:29:17.900 Yeah. 00:29:17.900 --> 00:29:20.560 It feels like this is a thing you could just install and things get better. 00:29:20.560 --> 00:29:25.160 The negative comments are mostly like, well, if Python was so slow, it could be made this 00:29:25.160 --> 00:29:26.760 faster than Python is a crappy language. 00:29:26.760 --> 00:29:29.640 It's pretty much, I've summed up like 65 comments right there. 00:29:31.640 --> 00:29:39.400 By the way, so I interviewed Guido Van Rassam and Mark Shannon a little while ago about 00:29:39.400 --> 00:29:43.780 this whole project about making Python five times, not 40%, but five times faster. 00:29:43.780 --> 00:29:47.960 And the goal is to make it a little bit faster like this, each release for five releases in 00:29:47.960 --> 00:29:48.240 a row. 00:29:48.240 --> 00:29:51.180 And because of compounding, that'll get you to like 5%. 00:29:51.180 --> 00:29:53.360 So it looks like they're delivering, which is awesome. 00:29:53.360 --> 00:29:53.920 Yeah. 00:29:53.920 --> 00:29:54.680 This is good. 00:29:54.680 --> 00:29:55.180 Yeah. 00:29:55.180 --> 00:29:55.780 Well, cool. 00:29:55.780 --> 00:29:56.400 All right. 00:29:56.400 --> 00:29:57.980 Yeah. 00:29:57.980 --> 00:29:59.760 I think that's it for all of our items. 00:29:59.760 --> 00:30:00.440 Yeah. 00:30:00.440 --> 00:30:01.100 Got any extras? 00:30:01.580 --> 00:30:06.700 no, I was going to pull up the, so yeah, the, the, this one goes to 11. 00:30:06.700 --> 00:30:08.980 If people don't know that that's a spinal tap reference. 00:30:08.980 --> 00:30:12.740 Yeah, exactly. 00:30:12.740 --> 00:30:13.600 All right. 00:30:13.600 --> 00:30:15.000 I got a few extras to throw out real quick. 00:30:15.000 --> 00:30:19.120 Python three, 10, five is out with a bunch of bug fixes. 00:30:19.120 --> 00:30:23.840 Like what happens if you create an F string that doesn't have a closing curly and just a 00:30:23.840 --> 00:30:25.380 bunch of crashes and bug fixes. 00:30:25.380 --> 00:30:29.160 So if you've been running into issues, you know, maybe there's a decent amount of stuff in 00:30:29.160 --> 00:30:29.880 the changelog here. 00:30:29.880 --> 00:30:30.520 Nice. 00:30:30.840 --> 00:30:31.640 People can check that out. 00:30:31.640 --> 00:30:36.320 Also real quick, people might, if they're on a Mac, they might check out Raycast, which 00:30:36.320 --> 00:30:41.100 is a replacement for the command space spotlight thing that has like all these developer plugins. 00:30:41.100 --> 00:30:46.160 So you can do like interact with your GitHub repo through command space and stuff. 00:30:46.160 --> 00:30:48.420 You can create a lot of things. 00:30:48.420 --> 00:30:49.420 And there's a bunch of extensions. 00:30:49.420 --> 00:30:58.660 like, this thing's free, at least for not for team, if you're not on a team, but there's a bunch of different, things you can get that are full, like managing 00:30:58.660 --> 00:31:04.760 processes, doing searches, VS Code project management from command space and whatnot. 00:31:04.760 --> 00:31:05.760 So I'm going to do a lot of things. 00:31:05.760 --> 00:31:05.760 I'm going to do a lot of things. 00:31:05.760 --> 00:31:06.760 I'm going to do a lot of things. 00:31:06.760 --> 00:31:07.760 I'm going to do a lot of things. 00:31:07.760 --> 00:31:07.760 I'm going to do a lot of things. 00:31:07.760 --> 00:31:08.760 I'm going to do a lot of things. 00:31:08.760 --> 00:31:09.760 I'm going to do a lot of things. 00:31:09.760 --> 00:31:09.760 I'm going to do a lot of things. 00:31:09.760 --> 00:31:10.760 I'm going to do a lot of things. 00:31:10.760 --> 00:31:11.760 I'm going to do a lot of things. 00:31:11.760 --> 00:31:12.760 I'm going to do a lot of things. 00:31:12.760 --> 00:31:13.760 I'm going to do a lot of things. 00:31:13.760 --> 00:31:14.760 I'm going to do a lot of things. 00:31:14.760 --> 00:31:15.760 I'm going to do a lot of things. 00:31:15.760 --> 00:31:16.760 I'm going to do a lot of things. 00:31:16.760 --> 00:31:17.760 I'm going to do a lot of things. 00:31:17.760 --> 00:31:18.760 I'm going to do a lot of things. 00:31:18.760 --> 00:31:19.760 I'm going to do a lot of things. 00:31:19.760 --> 00:31:20.760 I'm going to do a lot of things. 00:31:20.760 --> 00:31:21.760 I'm going to do a lot of things. 00:31:21.760 --> 00:31:22.760 I'm going to do a lot of things. 00:31:22.760 --> 00:31:23.760 I'm going to do a lot of things. 00:31:23.760 --> 00:31:24.760 I'm going to do a lot of things. 00:31:24.760 --> 00:31:25.760 I'm going to do a lot of things. 00:31:25.760 --> 00:31:26.760 I'm going to do a lot of things. 00:31:26.760 --> 00:31:27.760 I'm going to do a lot of things. 00:31:27.760 --> 00:31:28.760 I'm going to do a lot of things. 00:31:28.760 --> 00:31:30.760 which ties really well back to the PSF survey. 00:31:30.760 --> 00:31:32.760 We talked about, well, what framework do you use? 00:31:32.760 --> 00:31:34.760 What data science framework do you use? 00:31:34.760 --> 00:31:36.760 Or what web framework do you want to use? 00:31:36.760 --> 00:31:39.760 Django or Flask or FastAPI or what? 00:31:39.760 --> 00:31:42.760 So here's one that is a pretty interesting analysis. 00:31:42.760 --> 00:31:46.760 And the title is, why wouldn't you choose Parrot for your next application? 00:31:46.760 --> 00:31:48.760 Not a framework, but literally a Parrot. 00:31:48.760 --> 00:31:51.760 And this is compared to machine learning. 00:31:51.760 --> 00:31:56.760 So it has like this breakdown of features, like a featured table. 00:31:56.760 --> 00:31:58.760 And it has a parrot, which literally just has a picture of a parrot. 00:31:58.760 --> 00:32:00.760 And this is machine learning algorithms with a neural network. 00:32:00.760 --> 00:32:02.760 And then it lists off the features. 00:32:02.760 --> 00:32:03.760 Learns random phrases. 00:32:03.760 --> 00:32:04.760 Check, check. 00:32:04.760 --> 00:32:08.760 Doesn't understand anything about what it learns. 00:32:08.760 --> 00:32:09.760 Check, check. 00:32:09.760 --> 00:32:11.760 Occasionally speaks nonsense. 00:32:11.760 --> 00:32:12.760 Check, check. 00:32:12.760 --> 00:32:13.760 It's a cute birdie parrot. 00:32:13.760 --> 00:32:15.760 Check, fail. 00:32:15.760 --> 00:32:19.760 Why wouldn't you choose this, Brian? 00:32:19.760 --> 00:32:21.760 This is funny. 00:32:21.760 --> 00:32:22.760 I love it. 00:32:22.760 --> 00:32:24.760 Yeah, it's pretty good. 00:32:24.760 --> 00:32:25.760 Pretty good stuff. 00:32:25.760 --> 00:32:28.760 I actually reminds me of like, I have to pull up this article. 00:32:28.760 --> 00:32:33.760 So I was reading about some machine learning stuff to try to get models like even closer 00:32:33.760 --> 00:32:34.760 and closer to reality. 00:32:34.760 --> 00:32:36.760 There's a whole bunch of tricks people do. 00:32:36.760 --> 00:32:42.760 And then, and then there's some analysis of like, sometimes it's actually not doing anything 00:32:42.760 --> 00:32:44.760 more than just a linear regression. 00:32:44.760 --> 00:32:45.760 So, yeah. 00:32:45.760 --> 00:32:47.760 Try simple for an if statement. 00:32:47.760 --> 00:32:48.760 Yeah. 00:32:48.760 --> 00:32:49.760 Yeah, yeah, yeah, yeah. 00:32:49.760 --> 00:32:50.760 For sure. 00:32:50.760 --> 00:32:53.760 They're using artificial intelligence to make the computer decide. 00:32:53.760 --> 00:32:54.760 No, it's an if statement. 00:32:54.760 --> 00:32:58.760 Like, it's just computers deciding things the old fashioned way. 00:32:58.760 --> 00:32:59.760 Yeah, yeah. 00:32:59.760 --> 00:32:59.760 So. 00:32:59.760 --> 00:33:00.760 Awesome. 00:33:00.760 --> 00:33:00.760 All right. 00:33:00.760 --> 00:33:01.760 Well, thanks for being here. 00:33:01.760 --> 00:33:02.760 Thank you. 00:33:02.760 --> 00:33:03.760 Thanks everyone for listening.