WEBVTT 00:00:00.001 --> 00:00:04.880 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:04.880 --> 00:00:09.340 This is episode 94, recorded September 5th, 2018. I'm Michael Kennedy. 00:00:09.340 --> 00:00:10.320 And I'm Brian Okken. 00:00:10.320 --> 00:00:11.180 Hey, Brian. How are you doing? 00:00:11.180 --> 00:00:13.000 I'm doing really good. Yeah. 00:00:13.000 --> 00:00:17.300 Excellent. Doing very well. The sun is shining. Summer has not left us yet. 00:00:17.300 --> 00:00:21.640 It's not that great for productivity, but it's definitely good for keeping the spirits up. 00:00:21.640 --> 00:00:27.900 You know what else is keeping my spirits up is my DigitalOcean servers, the ones running this site and many others. 00:00:28.340 --> 00:00:31.720 They've been working perfectly. So they've been going really, really strong. 00:00:31.720 --> 00:00:33.240 And we'll tell you more about them later. 00:00:33.240 --> 00:00:38.980 But in the meantime, if you want to check them out, pythonbytes.fm/DigitalOcean, get $100 credit for new users. 00:00:38.980 --> 00:00:47.820 Brian, when I was in the C++ world, the C# world, design patterns were like this massive thing. 00:00:47.820 --> 00:00:49.940 And you had to know all the design patterns. 00:00:49.940 --> 00:00:53.940 And there was like dependency injection and IOC containers and all this stuff. 00:00:53.960 --> 00:01:02.240 And I feel like Python doesn't have as much rigor around it because you don't have to jump through so many hoops to make certain things happen, I guess. 00:01:02.240 --> 00:01:03.080 What do you think? 00:01:03.080 --> 00:01:04.460 Yeah, I think so. 00:01:04.460 --> 00:01:08.140 And it's actually something that's interesting because I came from the C++ world. 00:01:08.140 --> 00:01:10.560 So patterns were a thing in C# also? 00:01:10.560 --> 00:01:10.920 Oh, yeah. 00:01:11.020 --> 00:01:11.140 Okay. 00:01:11.140 --> 00:01:15.760 Well, I don't even know who the gang of four are. 00:01:15.760 --> 00:01:21.080 But there were four authors that wrote the design patterns book. 00:01:21.080 --> 00:01:21.840 Let's see. 00:01:21.840 --> 00:01:27.420 Eric Gamma, Richard Helm, Ralph Johnson, and I'm not going to try to pronounce that last one. 00:01:27.420 --> 00:01:28.020 John something. 00:01:28.740 --> 00:01:37.960 Anyway, gosh, in the 90s, if you were in C++ or C#, apparently you read this book or others around design patterns. 00:01:37.960 --> 00:01:43.180 And then when I got into Python, I was a little curious whether that was a thing in Python or not. 00:01:43.180 --> 00:01:48.280 But I haven't really heard much other than I haven't really needed it. 00:01:48.280 --> 00:01:49.760 A lot of this stuff isn't really needed. 00:01:49.760 --> 00:01:58.000 What I think is interesting is there's those patterns that you see from the gang of four and the sort of derivative ones, derivative books and thinking. 00:01:58.000 --> 00:02:00.340 And a lot of it, like you say, is not needed. 00:02:00.340 --> 00:02:03.240 But there are other patterns that are really useful and come in. 00:02:03.240 --> 00:02:06.860 Like, for example, meta classes, for example, or decorators. 00:02:06.860 --> 00:02:08.820 Or there's other stuff, right? 00:02:08.820 --> 00:02:10.100 Generator methods. 00:02:10.100 --> 00:02:17.540 All sorts of stuff that is in here that don't appear in the gang of four because C++ or Smalltalk just didn't do that. 00:02:17.540 --> 00:02:19.580 They were highly based on Smalltalk, actually. 00:02:19.580 --> 00:02:20.380 Their patterns. 00:02:20.380 --> 00:02:20.720 Right. 00:02:20.720 --> 00:02:27.820 Well, one of the things that caught my attention today was a tweet by, gosh, who's this guy? 00:02:27.820 --> 00:02:28.040 Brandon Rhodes. 00:02:28.040 --> 00:02:29.260 Brandon Rhodes, yep. 00:02:29.260 --> 00:02:34.060 And he's doing, he's got a site called pythonpatterns.guide. 00:02:34.060 --> 00:02:42.940 And it has, he's sort of going through a lot of different, I think he's going through the gang of four book. 00:02:42.940 --> 00:02:50.060 But he might be also doing other, pulling together other design pattern things that he's talked. 00:02:50.060 --> 00:02:53.100 Yeah, he's pulling together information from talks and writing. 00:02:53.100 --> 00:02:55.140 And I think he's creating more information, too. 00:02:55.140 --> 00:03:00.280 But there are a whole bunch of these, trying to apply some of these patterns to Python. 00:03:00.660 --> 00:03:03.860 And kind of sometimes different ways to do it. 00:03:03.860 --> 00:03:05.760 So you can do things in different ways. 00:03:05.760 --> 00:03:13.560 And so far, he's got abstract factory pattern, the builder pattern, factory method, composite, decorator. 00:03:13.560 --> 00:03:15.120 Yeah, we definitely have decorators. 00:03:15.460 --> 00:03:20.580 And then things like monkey patches and iterators, things like that. 00:03:20.580 --> 00:03:28.060 And how that applies, I'm glad that somebody that knows what they're talking about is trying to figure out, how does this all apply in Python? 00:03:28.060 --> 00:03:31.040 And I haven't really dug too much into this. 00:03:31.040 --> 00:03:34.900 I just think it's a neat resource to try to read about some of these. 00:03:34.900 --> 00:03:36.960 Yeah, I definitely think it's a really neat resource. 00:03:36.960 --> 00:03:41.380 And Brandon has some interesting thinking on design patterns and architectures. 00:03:41.680 --> 00:03:47.680 He gave a super counterintuitive talk called Clean Architecture. 00:03:47.680 --> 00:03:49.660 I think it was at Pi Ohio a couple years ago. 00:03:49.660 --> 00:03:53.380 And when I first started watching it, I was like, I just disagree with everything you're saying. 00:03:53.380 --> 00:03:55.140 This just seems so wrong. 00:03:55.140 --> 00:03:58.440 And then after 10 minutes, I'm like, but wait a minute. 00:03:58.440 --> 00:03:59.620 I think it's right. 00:03:59.620 --> 00:04:02.000 I think I've been thinking about this all wrong. 00:04:02.000 --> 00:04:07.840 And it really caught my attention because I didn't agree with it so much. 00:04:07.840 --> 00:04:11.000 But then I'm like, wow, this is really compelling what you're telling me. 00:04:11.080 --> 00:04:12.680 So maybe I need to rethink what I'm thinking. 00:04:12.680 --> 00:04:18.520 And whenever I have that feeling, I'm like, whoa, I need to pay attention because I might learn something really good here. 00:04:18.520 --> 00:04:20.700 Yeah, so that's a good point. 00:04:20.700 --> 00:04:25.400 I'm not necessarily saying, since I haven't really dug through this too much, I'm not sure. 00:04:25.400 --> 00:04:28.860 I mean, I respect Brandon as a smart guy. 00:04:28.860 --> 00:04:34.440 I expect that there's some really great stuff in here, but you may not agree with all of it. 00:04:34.440 --> 00:04:38.260 So we'll try to dig up a link to that Clean Architecture too because that sounds interesting. 00:04:38.260 --> 00:04:39.120 It's super interesting. 00:04:39.120 --> 00:04:39.540 Yeah, yeah. 00:04:39.540 --> 00:04:40.580 It's definitely a good one. 00:04:40.980 --> 00:04:41.820 Cool. 00:04:41.820 --> 00:04:42.680 Well, thanks for bringing this up. 00:04:42.680 --> 00:04:44.300 I love these Python patterns. 00:04:44.300 --> 00:04:51.660 And I love sort of the how would these traditional, more formalized patterns actually look in our language. 00:04:51.660 --> 00:04:53.420 And there's a lot of interesting examples there. 00:04:53.420 --> 00:04:53.700 Yeah. 00:04:53.700 --> 00:04:54.860 What do we got next? 00:04:54.860 --> 00:04:56.760 Well, we got this thing called Arctic. 00:04:56.760 --> 00:04:57.760 Arctic. 00:04:57.760 --> 00:05:04.900 Arctic is an API framework over top of MongoDB and Pandas. 00:05:04.900 --> 00:05:10.220 And the idea is this is a thing that's been around since around 2012. 00:05:10.220 --> 00:05:16.440 And its sole purpose is analyzing time series data super fast. 00:05:16.440 --> 00:05:25.060 So one of their headline is basically Arctic millions of rows per second of time data in Python. 00:05:25.060 --> 00:05:27.600 So that is really quite impressive. 00:05:27.600 --> 00:05:29.960 I can tell you a lot of the ODMs and ORMs and stuff. 00:05:29.960 --> 00:05:31.740 They don't do millions of records per second. 00:05:32.100 --> 00:05:38.280 So the idea is that it basically bakes in pandas and numpies and all those kinds of things. 00:05:38.620 --> 00:05:41.520 And it has an underlying data store that's backed by MongoDB. 00:05:41.520 --> 00:05:46.060 And it actually uses the binary low-level communication. 00:05:46.060 --> 00:05:52.380 So instead of trying to store all the data and then bringing it back and deserializing each row, 00:05:52.380 --> 00:05:57.340 I think what it does is it actually just stores the binary data of pandas. 00:05:57.340 --> 00:06:00.500 And it'll pickle numpy arrays and stuff like that. 00:06:00.500 --> 00:06:04.620 And just exchanges the memory structure and just pulls it straight back and go, 00:06:04.620 --> 00:06:05.440 yep, here it is. 00:06:05.440 --> 00:06:06.200 Let's look at it. 00:06:06.200 --> 00:06:07.120 And it's pretty cool. 00:06:07.120 --> 00:06:07.520 Yeah. 00:06:08.120 --> 00:06:09.160 Yeah, definitely. 00:06:09.160 --> 00:06:13.820 I mean, there's a lot of applications that use just huge amounts of time series data. 00:06:13.820 --> 00:06:14.420 Yeah. 00:06:14.420 --> 00:06:22.840 So they say the two big areas they think it's useful is IoT, little tiny IoT devices that maybe Python is running on, 00:06:22.840 --> 00:06:24.900 and financial analysis. 00:06:24.900 --> 00:06:32.400 So it's sort of been extracted out of the work that this financial company called MAN, AHL, 00:06:32.400 --> 00:06:36.560 I've never heard of them, but I think they're mostly an Asian company, but also in the U.S., 00:06:36.560 --> 00:06:38.320 around investment and so on. 00:06:38.320 --> 00:06:40.120 So they've been working on this. 00:06:40.120 --> 00:06:49.560 And they actually have some numbers on how this thing performs relative to other types of projects that they pursued or other things that were available. 00:06:50.220 --> 00:06:54.840 So they talk about the different kinds of data that they store and analyze for stock trading and analysis. 00:06:54.840 --> 00:07:01.040 And they say, look, we have this sort of data that's for one day, a whole bunch of it, maybe 10,000 rows. 00:07:01.040 --> 00:07:04.360 And they can work with those 10,000 rows in four milliseconds. 00:07:05.140 --> 00:07:11.180 And they say, compare that to what we were getting out of SQL Server, which was 2.2 seconds. 00:07:11.180 --> 00:07:14.920 So, you know, 500 times slower, which is pretty incredible. 00:07:14.920 --> 00:07:18.840 And they have this other tick data, like, you know, the stock ticker type of data. 00:07:19.580 --> 00:07:26.380 They can say in one second they can process 3.5 megs worth of that data in Python or 15 megs in Java. 00:07:26.380 --> 00:07:33.260 And there was some other project that they were trying to improve over called OtherTick, which took like 40 seconds versus one. 00:07:33.260 --> 00:07:40.240 So really, really interesting, high-performance, database-backed time series. 00:07:40.240 --> 00:07:41.540 Neat. 00:07:41.660 --> 00:07:41.780 Yeah. 00:07:41.780 --> 00:07:49.000 So if you're into Pandas, NumPy, and you've got to store and query a bunch of time series, whatever the reason, this is probably worth checking out. 00:07:49.000 --> 00:07:52.020 And it's also tested with pytest, which is pretty cool, right? 00:07:52.020 --> 00:07:52.840 Well, of course. 00:07:52.840 --> 00:07:55.980 Any real project is tested with pytest. 00:07:55.980 --> 00:07:56.840 That's right. 00:07:56.840 --> 00:07:57.700 Of course. 00:07:57.700 --> 00:08:08.720 So one of the things I really like about the Python community is the fact that there's so much sharing of information out of conferences and meetups and things like that. 00:08:08.860 --> 00:08:13.060 So we have another thing you found here for PyCon, right? 00:08:13.060 --> 00:08:13.600 Yeah. 00:08:13.600 --> 00:08:18.740 So the PyCon – I don't remember when it was, but PyCon Australia wasn't too long ago. 00:08:18.740 --> 00:08:22.160 And they've already got all the videos up. 00:08:22.160 --> 00:08:25.220 And we have a link to the PyCon Australia videos. 00:08:25.220 --> 00:08:31.260 And I've got quite a few of them queued up that I'd like to listen to and watch. 00:08:31.260 --> 00:08:33.400 I'm kind of bad about videos, actually. 00:08:33.400 --> 00:08:40.900 I often just listen to them and then go back and look at the slide parts of information that I wanted to capture. 00:08:40.900 --> 00:08:43.200 But I like listening to talks as well. 00:08:43.200 --> 00:08:48.920 But there's one from Mark Smith, which he always amuses me because his Twitter handle is Judy2K. 00:08:48.920 --> 00:08:50.640 And he won't tell me why. 00:08:50.640 --> 00:08:55.040 But his talk is how to publish a package on PyPI. 00:08:55.040 --> 00:08:56.700 And that's the one I've watched so far. 00:08:57.140 --> 00:08:58.500 There's a lot of great talks there, though. 00:08:58.500 --> 00:09:04.400 But I think this one's a great one that it – the end punchline is use cookie cutter. 00:09:04.400 --> 00:09:09.540 But he blasts through not using cookie cutter, all this sort of stuff you have to do to get up. 00:09:09.540 --> 00:09:12.840 And, you know, it's – every little piece makes sense and it's not difficult. 00:09:12.840 --> 00:09:14.340 But there are a lot of different little pieces. 00:09:14.340 --> 00:09:17.520 But he goes through this entire thing in like less than half an hour. 00:09:17.900 --> 00:09:24.600 And so that's pretty impressive to watch him talk about all the different pieces and why they're there and what they're used for. 00:09:24.600 --> 00:09:31.420 So that's a good one to sort of understand what's going on in the packaging world in a very short amount of time. 00:09:31.420 --> 00:09:32.260 Oh, that's really cool. 00:09:32.260 --> 00:09:33.980 Yeah, there's a bunch of cool ones here. 00:09:33.980 --> 00:09:36.340 A couple in MicroPython, actually. 00:09:36.340 --> 00:09:39.780 So one writing fast and efficient MicroPython code. 00:09:39.780 --> 00:09:42.260 And the other is async.io in MicroPython. 00:09:42.260 --> 00:09:43.700 Both of those are pretty cool. 00:09:43.700 --> 00:09:46.300 Kind of tie into what we were just talking about previously. 00:09:46.300 --> 00:09:53.680 Yeah, and then there's like – gosh, there's solid APIs and there's – it looks like a lot of good stuff. 00:09:53.680 --> 00:10:03.420 And I know that Australia is – since it's a big travel burden to other places, other PyCons, you'll see some speakers there that you're not going to see other places. 00:10:03.420 --> 00:10:04.360 So that's cool. 00:10:04.360 --> 00:10:04.780 Yeah, absolutely. 00:10:04.780 --> 00:10:06.040 And they have 88 videos. 00:10:06.040 --> 00:10:07.240 So that's pretty solid. 00:10:07.240 --> 00:10:07.660 Yeah. 00:10:07.660 --> 00:10:08.180 Quite cool. 00:10:08.180 --> 00:10:08.720 That's a good one. 00:10:08.720 --> 00:10:12.960 So before we move on, I'll tell you about another cool thing, DigitalOcean. 00:10:12.960 --> 00:10:14.580 All right, so I'm a big fan. 00:10:14.920 --> 00:10:21.760 And so one of the things that they've released – we talked about this just a couple of times, not very much – is this idea of projects. 00:10:22.380 --> 00:10:35.800 So when you go into your name, your cloud provider, you might have a bunch of servers, a bunch of ESP storage type things, virtual storage blocks, load balancers, all sorts of stuff. 00:10:35.800 --> 00:10:38.340 And it's really hard to know what goes with what. 00:10:38.340 --> 00:10:41.520 Do you have a staging environment, a production environment, all that kind of stuff, right? 00:10:41.520 --> 00:10:42.960 So how do you organize that? 00:10:43.040 --> 00:10:55.240 So DigitalOcean has come up with this feature called projects that lets you group things like your droplets, that's virtual machines, and floating IPs, and back storage-like spaces into these different use cases. 00:10:55.240 --> 00:10:59.200 So you know, yeah, actually, we're done with this project. 00:10:59.200 --> 00:11:06.240 So we can turn that server off and destroy it and not like the fear of, I don't think we're using this one, but I'm not going to destroy it. 00:11:06.240 --> 00:11:08.840 I'm not going to delete it because what if I'm wrong, right? 00:11:08.840 --> 00:11:12.360 So a very cool feature you can take advantage of for all of their stuff. 00:11:12.360 --> 00:11:18.280 Check them out at pythonbytes.fm/DigitalOcean, and they'll give you $100 credit for new users. 00:11:18.280 --> 00:11:18.920 That's awesome. 00:11:19.240 --> 00:11:20.600 Hey, let's talk about another cloud provider. 00:11:20.600 --> 00:11:23.160 I'll write it on the back of that. 00:11:23.160 --> 00:11:37.180 So one of the ways that you can run your code on the internet is like I just described with DigitalOcean, like I do for our stuff, is to create some virtual machines and various other pieces and sort of use it as so-called infrastructure as a service, right? 00:11:37.180 --> 00:11:38.080 IaaS. 00:11:38.080 --> 00:11:41.940 But you might also use platform as a service, like here's my code, run it. 00:11:41.940 --> 00:11:45.300 So Google App Engine, Heroku, those types of things. 00:11:45.300 --> 00:11:45.780 Okay. 00:11:45.780 --> 00:11:54.880 So Google App Engine has a pretty interesting announcement, and it's interesting for both it's good now and like, oh my gosh, I can't believe it was like that. 00:11:54.880 --> 00:12:03.280 So the announcement is that Google App Engine has released their second generation runtimes, which the Python one is now based on Python 3.7. 00:12:03.280 --> 00:12:04.500 That's pretty awesome, right? 00:12:04.500 --> 00:12:05.220 It is. 00:12:05.220 --> 00:12:06.260 You want to run some code. 00:12:06.260 --> 00:12:06.860 Boom. 00:12:06.860 --> 00:12:08.220 Here's my Python 3.7. 00:12:08.220 --> 00:12:09.240 So that's really good. 00:12:09.240 --> 00:12:11.420 You might think, oh, Michael, what was the previous one? 00:12:11.420 --> 00:12:12.040 3.6? 00:12:12.040 --> 00:12:13.300 3.5? 00:12:13.540 --> 00:12:15.780 No, I believe the previous one was 2.7. 00:12:15.780 --> 00:12:16.760 Oh, no. 00:12:16.760 --> 00:12:17.460 Until now. 00:12:17.460 --> 00:12:22.000 Like if you were using Google App Engine, I believe you had to use a legacy Python, period. 00:12:22.000 --> 00:12:26.320 Until that was like mid-2018 that I just said that. 00:12:26.320 --> 00:12:28.940 That wasn't like a statement around 2012 or something. 00:12:28.940 --> 00:12:30.140 That was just now. 00:12:30.140 --> 00:12:33.580 But let bygones be bygones. 00:12:33.580 --> 00:12:35.940 And now it's Python 3.7, which is pretty awesome. 00:12:36.400 --> 00:12:38.880 So apparently it's a pretty big upgrade. 00:12:38.880 --> 00:12:39.880 You get a bunch of new things. 00:12:39.880 --> 00:12:46.800 Like, for example, it's based on their new sandbox container, sort of Docker-like things. 00:12:46.800 --> 00:12:48.960 It removes a bunch of restrictions. 00:12:48.960 --> 00:12:56.380 Like, in addition to only running on the old Python, legacy Python, you could only use a white-labeled set of packages. 00:12:56.380 --> 00:13:00.140 And now in the new Google App Engine, you can use arbitrary packages. 00:13:00.140 --> 00:13:03.320 Just put them in a requirements file, which is pretty sweet. 00:13:03.320 --> 00:13:04.220 That's a big change. 00:13:04.580 --> 00:13:05.920 It is a pretty big change. 00:13:05.920 --> 00:13:06.260 Yeah. 00:13:06.260 --> 00:13:09.500 So a lot of cool things like autoscaling and things that are a little bit easier as well. 00:13:09.500 --> 00:13:16.460 So anyway, if you're interested in Google App Engine's platform as a service for Python, it just got many, many times better. 00:13:16.460 --> 00:13:16.900 Yeah. 00:13:16.900 --> 00:13:17.600 Neat. 00:13:17.600 --> 00:13:17.840 Yeah. 00:13:17.840 --> 00:13:18.220 Yeah. 00:13:18.220 --> 00:13:24.440 So, Brian, I typically write my code in Python files, not really in notebooks per se. 00:13:24.440 --> 00:13:25.460 How about you? 00:13:25.460 --> 00:13:26.980 Yeah, mostly in files. 00:13:26.980 --> 00:13:31.380 But I'm trying to learn Jupyter Notebooks some and utilize them. 00:13:31.540 --> 00:13:37.940 They're kind of fun, especially in data science-y realms or looking at plotting data and stuff. 00:13:37.940 --> 00:13:38.800 Notebooks are fun. 00:13:38.800 --> 00:13:39.480 Yeah. 00:13:39.480 --> 00:13:46.720 But there was a person named Joel Gruse that says he does not like notebooks. 00:13:46.720 --> 00:13:49.540 And Joel is notable because he's not like a random dude on the internet. 00:13:49.880 --> 00:13:54.160 But Joel Gruse has written a book called Data Science from Scratch. 00:13:54.160 --> 00:13:57.120 He's done a lot of work in data science, things like that. 00:13:57.120 --> 00:13:59.580 I've even had him on Talk Python many moons ago. 00:13:59.980 --> 00:14:00.380 Yeah. 00:14:00.380 --> 00:14:03.940 And this wasn't just like a one-off comment. 00:14:03.940 --> 00:14:07.000 He gave this talk at JupyterCon. 00:14:07.000 --> 00:14:09.480 And that's kind of hilarious. 00:14:09.480 --> 00:14:14.400 But the video for that is not available yet as far as I couldn't find it. 00:14:14.400 --> 00:14:17.500 So because that was just recently or still going on, I'm not sure. 00:14:17.500 --> 00:14:19.620 But the slides are up. 00:14:19.620 --> 00:14:20.600 He put the slides up. 00:14:20.960 --> 00:14:23.200 And for one, it puts me to shame. 00:14:23.200 --> 00:14:28.220 You know, this presentation has got so many animations and pictures and stuff. 00:14:28.220 --> 00:14:30.180 Plus, it's like I haven't even got through it yet. 00:14:30.180 --> 00:14:32.020 It's like 100 pages long or more. 00:14:32.020 --> 00:14:33.860 But it's really good. 00:14:33.860 --> 00:14:44.360 But it's a serious discussion about some of the issues with the problems with notebooks that people new to notebooks don't quite get. 00:14:44.360 --> 00:14:48.660 And people old to notebooks just sort of know it and don't really think about it anymore. 00:14:48.660 --> 00:14:52.120 And one of the big ones is that there's hidden state. 00:14:52.120 --> 00:14:59.480 And so, essentially, we think of files as, like you said, we normally work in files. 00:14:59.480 --> 00:15:04.000 So they get run from top to bottom, except for, you know, functions don't get run. 00:15:04.000 --> 00:15:05.880 They get interpreted as functions. 00:15:05.880 --> 00:15:08.860 And then when they are run, they run top to bottom, essentially. 00:15:08.860 --> 00:15:10.880 And notebooks are not like that. 00:15:10.880 --> 00:15:15.960 You can jump around and execute different bits of code in different orders if you feel like it. 00:15:15.960 --> 00:15:21.180 And that stateness can lead to weird, confusing things. 00:15:21.180 --> 00:15:23.960 So it's just like a gotcha to know about. 00:15:24.080 --> 00:15:36.600 And then he goes on to talk about some of the issues where if you start learning how to code with notebooks, you may end up, you know, developing some bad habits like importing notebooks instead of just trying to. 00:15:36.600 --> 00:15:43.700 I mean, like, that's a thing apparently you can do is you can define some functions in a notebook and then import them into another notebook. 00:15:43.700 --> 00:15:49.200 Well, I mean, wouldn't it be better to just put them in a different library, in a package or a library? 00:15:49.200 --> 00:15:50.640 Use the package, use the library. 00:15:50.640 --> 00:15:51.000 Exactly. 00:15:51.320 --> 00:15:51.620 Yeah. 00:15:51.620 --> 00:16:05.520 So some of those, and, you know, I'm highlighting this not because I think notebooks are evil, but because I think it's important to start to listen to people saying, you know, listen to a voice that says they aren't a silver bullet. 00:16:05.520 --> 00:16:07.860 They have their issues also. 00:16:07.860 --> 00:16:11.960 And we just need to be careful and talk and make sure you don't fall into those traps. 00:16:11.960 --> 00:16:13.180 Yeah, these are really interesting. 00:16:13.180 --> 00:16:16.000 And these are certainly issues to look out for. 00:16:16.000 --> 00:16:19.480 And while this is a funny presentation, I cannot wait to watch this video. 00:16:19.480 --> 00:16:26.440 Joel, if you're listening, please let us know when it's out or if someone else sees it come out, shoot us a note, either email or Twitter, because this is fantastic. 00:16:26.440 --> 00:16:26.900 Yeah. 00:16:26.900 --> 00:16:36.440 Plus, also, like, I can't even imagine how long it took to put together this presentation because it's, yeah, there's a lot of animations in there and it's quite a riot. 00:16:36.440 --> 00:16:37.520 It is quite a riot. 00:16:37.520 --> 00:16:37.760 Yeah. 00:16:37.760 --> 00:16:38.780 Anyway, there's that. 00:16:38.920 --> 00:16:42.100 Just the other side of maybe notebooks aren't awesome. 00:16:42.100 --> 00:16:42.480 Yeah. 00:16:42.480 --> 00:16:44.680 It's pretty, pretty, pretty interesting. 00:16:44.680 --> 00:16:56.180 So we've had a couple of conversations around the various peps and stuff that have been maybe causing some kerfuffle in the community. 00:16:56.180 --> 00:17:02.500 Obviously, the biggest one was a PEP 572 about the in-place assignments. 00:17:03.040 --> 00:17:08.260 And that was the thing with all the stress around it that Guido said, hey, after this, I'm, this is my last one. 00:17:08.260 --> 00:17:08.960 I've given my all. 00:17:08.960 --> 00:17:09.640 I'm out of here. 00:17:09.640 --> 00:17:11.340 You guys, it's up to you. 00:17:11.340 --> 00:17:16.160 We actually had Brett Cannon and Carol Willing on episode 87 to talk all about that. 00:17:16.160 --> 00:17:16.340 Right. 00:17:16.340 --> 00:17:21.420 And one of the things that we talked about was what comes next. 00:17:21.420 --> 00:17:22.480 Right. 00:17:22.480 --> 00:17:31.300 If, if it's not down to Guido to make the final decisions, which is how it has worked, how will the Python community decide, you know, what it's up to? 00:17:31.300 --> 00:17:33.360 So, yeah. 00:17:33.360 --> 00:17:38.780 So Barry Warsaw has published five peps at least around this. 00:17:38.780 --> 00:17:40.900 And I don't think this is a decision. 00:17:40.900 --> 00:17:45.960 It's sort of a structure to further the conversation and make a decision. 00:17:46.260 --> 00:17:53.320 So he just published not too long ago, PEP 8000, which is Python language government proposal overview. 00:17:53.320 --> 00:17:55.820 And I don't know if this is common in peps. 00:17:55.820 --> 00:18:01.080 I haven't seen it that much, but it's like a gathering of other peps that are specific details. 00:18:01.080 --> 00:18:05.320 So there's PEP 8,001, 8,002, 8,10, and 8,11. 00:18:05.800 --> 00:18:10.440 The first two are about voting and ways in which this government might work. 00:18:10.440 --> 00:18:16.780 And then the higher ones, the 8,10s are actual proposed models. 00:18:16.780 --> 00:18:19.520 And there's a third one in 8,12 that I forgot to put in the notes. 00:18:19.520 --> 00:18:28.900 And so there's for the government styles, government styles, we have the BDFL governance model is one of the proposed options, 00:18:28.900 --> 00:18:33.420 which is to elect a new person who is the final decider. 00:18:33.420 --> 00:18:34.040 Right. 00:18:34.680 --> 00:18:40.820 Basically, GEDO step down, who is going to take that place to now participate in that way? 00:18:40.820 --> 00:18:46.980 We also have the council governance model, which we talked about interesting things like should there be an even or odd number of people in the council? 00:18:46.980 --> 00:18:50.400 And then the last one, I think, let me pull that up. 00:18:50.400 --> 00:18:52.200 I think it is community. 00:18:52.200 --> 00:18:54.840 Yeah, the community governance model. 00:18:54.840 --> 00:18:57.580 And that one's a little more free form. 00:18:57.580 --> 00:19:02.540 So these are all different ways of possibly arranging and solving that problem. 00:19:02.660 --> 00:19:05.420 And there's a lot of examples like, let's see how Rust did it. 00:19:05.420 --> 00:19:09.660 Let's see how OpenStack manages their organization and so on. 00:19:09.660 --> 00:19:11.860 So there's a lot of concrete stuff there. 00:19:12.280 --> 00:19:13.580 Anyway, that's pretty cool. 00:19:13.580 --> 00:19:13.860 Yeah, that's interesting. 00:19:13.860 --> 00:19:21.320 If you have a strong thought on this and you want to participate, get in there, make comments, let people know what you're thinking. 00:19:21.320 --> 00:19:23.180 Because it's still open. 00:19:23.180 --> 00:19:24.500 It's not anything decided. 00:19:24.500 --> 00:19:25.200 Right? 00:19:25.200 --> 00:19:26.560 It's still up in the air. 00:19:26.560 --> 00:19:29.720 So if you want to have a say, now is the time to make statements. 00:19:29.720 --> 00:19:30.260 Wow. 00:19:30.360 --> 00:19:34.020 It's like government working in our own community. 00:19:34.020 --> 00:19:34.860 What? 00:19:34.860 --> 00:19:36.120 Incredible. 00:19:36.120 --> 00:19:37.220 Incredible. 00:19:37.220 --> 00:19:38.340 Yeah. 00:19:38.340 --> 00:19:38.940 Yeah. 00:19:38.940 --> 00:19:39.640 So this is pretty cool. 00:19:39.640 --> 00:19:43.580 I don't know where it's going to go, but I like that it's all laid out like this. 00:19:43.580 --> 00:19:49.460 My guess is it's going to go down the council model, maybe with, I don't know. 00:19:49.520 --> 00:19:51.100 I think it's going to go down the council model, but we'll see. 00:19:51.100 --> 00:19:51.700 Yeah. 00:19:51.700 --> 00:20:04.280 I think that whatever they do, they need, they should, if there's a council, they should have to meet together to make decisions and pass around like a talking stick or something. 00:20:04.280 --> 00:20:05.080 Yes. 00:20:05.080 --> 00:20:08.040 I love it. 00:20:08.040 --> 00:20:11.620 Oh, we could come up with something weird that they have to follow. 00:20:11.620 --> 00:20:14.860 How about the Python staff of power that you were carrying around? 00:20:14.860 --> 00:20:15.540 Yeah. 00:20:15.920 --> 00:20:21.600 But then, you know, should it be the blue and yellow one or should it be the green and yellow one? 00:20:21.600 --> 00:20:22.120 I don't know. 00:20:22.120 --> 00:20:23.280 That is a big question. 00:20:23.280 --> 00:20:23.620 Yeah. 00:20:23.620 --> 00:20:24.240 Yeah. 00:20:24.240 --> 00:20:24.500 Yeah. 00:20:24.500 --> 00:20:24.860 I don't know. 00:20:24.860 --> 00:20:28.160 So, sorry, green and gold. 00:20:28.160 --> 00:20:32.080 So people in Australia say it's gold, not yellow, but it looks yellow to me. 00:20:32.080 --> 00:20:32.380 Yeah. 00:20:32.380 --> 00:20:34.100 I definitely, I thought that stick was a big hit. 00:20:34.100 --> 00:20:35.880 I don't know if people don't know what you're talking about. 00:20:35.880 --> 00:20:37.380 What should they Google to find the stick? 00:20:37.380 --> 00:20:40.160 I think it's Pythonic staff of enlightenment. 00:20:40.160 --> 00:20:40.980 I don't know. 00:20:40.980 --> 00:20:41.340 Is that right? 00:20:41.340 --> 00:20:42.560 That's got to do it. 00:20:42.560 --> 00:20:45.140 How many hits on Google can there be for that? 00:20:45.440 --> 00:20:46.140 I don't know. 00:20:46.140 --> 00:20:46.500 Awesome. 00:20:46.500 --> 00:20:48.240 So, yeah, they should have to pass that thing around. 00:20:48.240 --> 00:20:49.300 All right. 00:20:49.300 --> 00:20:51.540 Well, that's it for our items this week. 00:20:51.540 --> 00:20:53.040 You got anything extra you want to share with folks? 00:20:53.040 --> 00:20:54.620 I don't, actually. 00:20:54.620 --> 00:20:56.540 Just trudging along. 00:20:56.540 --> 00:20:58.760 We got a couple more testing codes out. 00:20:58.760 --> 00:20:59.320 Yeah. 00:20:59.320 --> 00:20:59.800 Very nice. 00:20:59.800 --> 00:21:00.220 How about you? 00:21:00.220 --> 00:21:03.680 I've got, of course, some TalkPilot stuff queued up to be released shortly. 00:21:03.680 --> 00:21:07.600 I have been recording some courses, which are going to be awesome. 00:21:07.600 --> 00:21:10.420 And I'm very excited about them, doing a bunch of stuff in parallel. 00:21:10.420 --> 00:21:12.860 So I'll let you know when that's sort of further along. 00:21:13.240 --> 00:21:16.560 But I do have two things I want to talk about this week really quickly. 00:21:17.160 --> 00:21:19.900 One is we got a message on Twitter. 00:21:19.900 --> 00:21:22.360 And I don't have the name of who sent us. 00:21:22.360 --> 00:21:23.640 This was John, actually. 00:21:23.640 --> 00:21:24.920 Thanks, John. 00:21:24.920 --> 00:21:31.480 Who sent us this heads up that Brian Granger, one of the guys behind iPython and Jupyter and 00:21:31.480 --> 00:21:35.440 all that stuff from the very early days, is giving a free webcast. 00:21:35.800 --> 00:21:38.300 And it's an ACM-sponsored thing. 00:21:38.300 --> 00:21:43.460 So it's Project Jupyter from computational notebooks to large-scale data science with sensitive data. 00:21:43.460 --> 00:21:46.000 So if that sounds interesting to you, I put the link in there. 00:21:46.000 --> 00:21:47.620 It's this Friday. 00:21:47.620 --> 00:21:50.180 This episode probably will come out on Thursday. 00:21:50.180 --> 00:21:52.900 So you've got to take action right away if you're listening. 00:21:52.900 --> 00:21:54.780 There's probably a recording or something afterwards. 00:21:54.780 --> 00:21:55.600 You can check that out. 00:21:55.600 --> 00:21:59.180 The other thing is, you know, we talk sometimes about the popularity of Python. 00:21:59.180 --> 00:21:59.500 Yeah. 00:21:59.600 --> 00:22:02.160 So I don't want to beat this one to death too much. 00:22:02.160 --> 00:22:03.540 It's not really worth its own item. 00:22:03.540 --> 00:22:07.640 But Python continues to climb yet another ranking. 00:22:07.640 --> 00:22:13.960 So the TOB index is one of the more well-respected, more long-running ways of ranking programming languages. 00:22:13.960 --> 00:22:18.740 And I think when we started this podcast, Python was either fifth or sixth. 00:22:18.740 --> 00:22:20.440 I think it was sixth on this list. 00:22:20.440 --> 00:22:22.100 It is now third. 00:22:22.100 --> 00:22:24.080 Probably because of the podcast. 00:22:24.080 --> 00:22:25.760 Certainly partly because of it. 00:22:25.760 --> 00:22:26.460 Yeah. 00:22:27.200 --> 00:22:30.820 But that may be a very small part or more maybe it's meaningful. 00:22:30.820 --> 00:22:36.400 But what's really interesting is it's now above C++, C#, JavaScript. 00:22:36.400 --> 00:22:37.740 It's way above JavaScript. 00:22:37.740 --> 00:22:39.200 And JavaScript's going down. 00:22:39.200 --> 00:22:41.220 It's above Ruby. 00:22:41.220 --> 00:22:42.940 It's above many, many things. 00:22:42.940 --> 00:22:47.380 What it's not above is it is not above Java or C. 00:22:47.380 --> 00:22:50.960 And not only is it not above them, but it's like half. 00:22:50.960 --> 00:22:54.800 So it's like 7.6% to C is 15.4%. 00:22:55.320 --> 00:22:59.640 It's going to be a long time, if ever, until it gets to a 2 or a 1. 00:22:59.640 --> 00:23:02.700 But it's definitely doing quite well. 00:23:02.700 --> 00:23:03.280 Yeah. 00:23:03.280 --> 00:23:04.360 So, yeah. 00:23:04.360 --> 00:23:06.380 What is the TOB index? 00:23:06.380 --> 00:23:06.820 Anyway. 00:23:06.820 --> 00:23:07.780 I'll have to look into that. 00:23:07.780 --> 00:23:08.100 Yeah. 00:23:08.100 --> 00:23:12.520 If you look into it, they talk about their philosophy and where they measure stuff from and so on. 00:23:12.520 --> 00:23:15.120 It's been a long time since I read it, so I don't remember the details. 00:23:15.120 --> 00:23:17.660 But they do lay out where the ranking comes from. 00:23:17.660 --> 00:23:18.080 Okay. 00:23:18.080 --> 00:23:18.320 Cool. 00:23:18.320 --> 00:23:18.580 Yeah. 00:23:18.580 --> 00:23:18.920 All right. 00:23:18.920 --> 00:23:19.840 Well, that's it for this week. 00:23:20.040 --> 00:23:21.160 Thanks for chatting with me, Brian. 00:23:21.160 --> 00:23:21.620 Thank you. 00:23:21.620 --> 00:23:21.880 Bye. 00:23:21.880 --> 00:23:22.140 You bet. 00:23:22.140 --> 00:23:22.400 Bye. 00:23:22.400 --> 00:23:25.160 Thank you for listening to Python Bytes. 00:23:25.160 --> 00:23:27.720 Follow the show on Twitter via at Python Bytes. 00:23:27.720 --> 00:23:30.600 That's Python Bytes as in B-Y-T-E-S. 00:23:30.600 --> 00:23:34.020 And get the full show notes at pythonbytes.fm. 00:23:34.020 --> 00:23:38.360 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:23:38.360 --> 00:23:41.060 We're always on the lookout for sharing something cool. 00:23:41.060 --> 00:23:44.460 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:23:44.460 --> 00:23:48.080 Thank you for listening and sharing this podcast with your friends and colleagues.