WEBVTT 00:00:00.001 --> 00:00:05.920 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:05.920 --> 00:00:11.400 This is episode 34, recorded July 12, 2017. 00:00:11.400 --> 00:00:12.740 I'm Michael Kennedy. 00:00:12.740 --> 00:00:13.600 And I'm Brian Okken. 00:00:13.600 --> 00:00:15.400 And we've got some great stuff to share with you. 00:00:15.400 --> 00:00:19.640 But Brian, just let everyone know, this episode is brought to you by Rollbar. 00:00:19.640 --> 00:00:23.120 So check them out at pythonbytes.fm/rollbar for some good stuff. 00:00:23.120 --> 00:00:24.080 We'll tell you about that later. 00:00:24.080 --> 00:00:25.640 Right now, I want to hear about logging. 00:00:25.640 --> 00:00:31.520 Yeah, I'm one of those people that know that I should use logging more instead of just print statements. 00:00:31.520 --> 00:00:38.020 But to say it like mildly, the standard library logging package is non-intuitive. 00:00:38.020 --> 00:00:39.140 It's definitely not obvious. 00:00:39.140 --> 00:00:40.380 Yeah, you're like, I got a logger. 00:00:40.380 --> 00:00:40.980 It doesn't work. 00:00:40.980 --> 00:00:41.340 Why? 00:00:41.340 --> 00:00:42.100 What do I do now? 00:00:42.100 --> 00:00:43.920 Yeah, the setting up the config and stuff. 00:00:43.920 --> 00:00:46.740 And I get, anyway, I still get confused about it. 00:00:46.740 --> 00:00:53.100 So I am welcome to look at, or I welcome new logging packages to try to clean that up. 00:00:53.100 --> 00:00:56.700 And there's an article that came out, easy Python logging with Daiquiri. 00:00:56.700 --> 00:01:02.680 And Daiquiri is a logger that works a little bit. 00:01:02.680 --> 00:01:04.840 It's a little bit cleaner and works pretty good. 00:01:04.840 --> 00:01:06.480 I played around with it a little bit. 00:01:06.480 --> 00:01:10.880 But it uses colors in the terminal, of course, which is nice. 00:01:10.880 --> 00:01:12.540 It supports file logging. 00:01:12.540 --> 00:01:16.400 And it's supposedly journal D, which I have no idea what that is. 00:01:16.400 --> 00:01:18.480 I like some of the features of it. 00:01:18.480 --> 00:01:21.840 And what I really like about it is that it just sort of works right away. 00:01:22.020 --> 00:01:23.440 You don't have to do much setup. 00:01:23.440 --> 00:01:27.840 Although you do have to call, like, a get logger and call a setup function. 00:01:27.840 --> 00:01:28.740 But it's not too bad. 00:01:28.740 --> 00:01:33.220 Yeah, it's really similar to the built-in logging, actually, which is super nice. 00:01:33.220 --> 00:01:36.300 And the two things that I like, one is the, well, three things. 00:01:36.300 --> 00:01:38.460 It works just like you create it, and it works. 00:01:38.460 --> 00:01:40.180 You don't have to go and configure it a bunch. 00:01:40.180 --> 00:01:47.160 The other thing is that it prints color messages to standard out or standard error. 00:01:47.600 --> 00:01:50.100 So if a thing is an error, it comes out red. 00:01:50.100 --> 00:01:54.660 If it's good, it comes out, like, bluish or, you know, warnings or yellow, things like that. 00:01:54.660 --> 00:01:54.900 Yeah. 00:01:54.900 --> 00:02:00.040 It does have some, I can't remember what the dependencies are, but it does have some dependencies 00:02:00.040 --> 00:02:02.620 that tripped me up at first when I was installing it. 00:02:02.620 --> 00:02:03.280 Don't know why. 00:02:03.280 --> 00:02:04.980 But then it was easy to install. 00:02:04.980 --> 00:02:05.600 Not too bad. 00:02:05.600 --> 00:02:05.880 Yeah. 00:02:06.260 --> 00:02:09.660 The other thing that's sweet is it does native logging of exceptions. 00:02:09.660 --> 00:02:14.260 So even if you don't catch the exception, it'll, like, print out the exception details, right? 00:02:14.260 --> 00:02:14.760 Oh, yeah. 00:02:14.760 --> 00:02:15.200 That's cool. 00:02:15.200 --> 00:02:15.720 I missed that. 00:02:15.720 --> 00:02:16.180 Yeah, yeah. 00:02:16.180 --> 00:02:16.640 That's slick. 00:02:16.640 --> 00:02:22.140 And then as I was thinking about this today, I just saw somebody mentioning log zero. 00:02:22.140 --> 00:02:23.980 So I went and checked that out this morning. 00:02:23.980 --> 00:02:26.660 And there's a link to it in our show notes. 00:02:26.900 --> 00:02:34.040 But it's also pretty clean, and it has no external dependencies and also does color on the output. 00:02:34.040 --> 00:02:39.180 And a little bit, I mean, if you're doing a quick and dirty thing, it's a little bit, you 00:02:39.180 --> 00:02:40.780 don't, there's no setup required at all. 00:02:40.780 --> 00:02:42.400 Yeah, that's pretty interesting. 00:02:42.400 --> 00:02:42.820 Anyway. 00:02:42.820 --> 00:02:48.000 And the logo of that one is like a beaver with a hard hat and, like, some plans, which is pretty 00:02:48.000 --> 00:02:48.340 interesting. 00:02:48.340 --> 00:02:48.660 Yeah. 00:02:48.660 --> 00:02:54.300 One of the things I'd like to say about Daiquiri is I really had to check my spelling on it. 00:02:54.300 --> 00:02:57.200 The first time I downloaded it, pip installed it, no problem. 00:02:57.200 --> 00:03:00.800 And then I went to import it, and I spelled Daiquiri wrong. 00:03:00.800 --> 00:03:04.380 So it's a D-A-I-Q-U-I-R-I. 00:03:04.380 --> 00:03:09.820 Yeah, I'm not sure I would spell that correctly off the, you know, just off the top of my head 00:03:09.820 --> 00:03:10.060 either. 00:03:10.060 --> 00:03:12.720 But both are fun projects, so check them out. 00:03:12.720 --> 00:03:13.120 Yeah, yeah. 00:03:13.120 --> 00:03:15.580 If you're throwing logging in there, also check out Logbook, another good one. 00:03:15.580 --> 00:03:16.320 Logbook, okay. 00:03:16.320 --> 00:03:19.300 It's by Armin Roedeker from Flask. 00:03:19.300 --> 00:03:19.980 I'll check that out. 00:03:19.980 --> 00:03:22.840 All right, so I have kind of a deep article, a deep thought for us on the next one. 00:03:23.100 --> 00:03:27.760 Python is probably the biggest space where artificial intelligence is done, right? 00:03:27.760 --> 00:03:33.720 Like a lot of the AI machine learning algorithms come out for Python first, or at least come 00:03:33.720 --> 00:03:35.960 with Python right out of the box, right? 00:03:35.960 --> 00:03:43.440 So TensorFlow, Keras, and a bunch of the other things that you might do would probably be done 00:03:43.440 --> 00:03:44.180 in Python. 00:03:44.940 --> 00:03:49.380 So there's this interesting article that was in the New York Times of all places that I'm picking 00:03:49.380 --> 00:03:52.420 it from called The Real Threat of Artificial Intelligence. 00:03:52.420 --> 00:03:59.360 And, you know, we so often hear people talk about AI and the singularity and super scary stuff. 00:03:59.360 --> 00:04:04.640 Like Elon Musk last year said that he's pretty sure that we live in a simulation, which I'm 00:04:04.640 --> 00:04:05.740 pretty sure is silly. 00:04:05.740 --> 00:04:07.080 What do you think about that? 00:04:07.080 --> 00:04:08.020 That's pretty silly. 00:04:08.160 --> 00:04:08.280 Yeah. 00:04:08.280 --> 00:04:10.840 I mean, it's a fun mind game to think. 00:04:10.840 --> 00:04:13.720 It's like a philosophy, you know, prove you exist sort of thing. 00:04:13.720 --> 00:04:15.460 But how real is it? 00:04:15.460 --> 00:04:17.180 And even if it is real, what can you do about it? 00:04:17.180 --> 00:04:17.440 Whatever. 00:04:17.440 --> 00:04:18.060 It doesn't matter. 00:04:18.820 --> 00:04:23.260 But this article kind of says, all right, let's look at really what the implications 00:04:23.260 --> 00:04:27.100 of AI for society are, right? 00:04:27.100 --> 00:04:33.040 So AI is going to be reshaping work and what jobs mean. 00:04:33.040 --> 00:04:39.280 And I'm not sure if it'll affect programming or not, whether AI will actually replace programmers 00:04:39.280 --> 00:04:41.000 or we'll just program AI. 00:04:41.000 --> 00:04:41.820 You know what I mean? 00:04:41.820 --> 00:04:43.980 Like we just might shift around a little bit. 00:04:43.980 --> 00:04:47.660 But if you do anything with AI, I recommend you check this out. 00:04:47.660 --> 00:04:51.040 It's really interesting on the effect that it's going to have on the industry, basically 00:04:51.040 --> 00:04:52.640 on society in general. 00:04:52.640 --> 00:04:58.720 And some of the things they said, well, like, you know, there's so many jobs that are going 00:04:58.720 --> 00:05:00.540 to be dramatically affected by this. 00:05:00.540 --> 00:05:04.360 Like I was just telling you before we started, like the number one job for men in the United 00:05:04.360 --> 00:05:05.180 States is driving. 00:05:05.180 --> 00:05:05.640 Yeah. 00:05:05.640 --> 00:05:11.600 And the combination between self-driving cars and self-driving trucks and the various other 00:05:11.600 --> 00:05:17.640 ways that automation is going to happen could easily take that down to very small 00:05:17.640 --> 00:05:19.060 percentage of what it is today. 00:05:19.060 --> 00:05:21.180 What's that going to mean for society? 00:05:21.180 --> 00:05:23.420 So these kinds of things are interesting to think about. 00:05:23.420 --> 00:05:29.400 The other part was sort of geopolitical, like how do countries sort of evolve with AI? 00:05:29.400 --> 00:05:35.260 So once there's an AI that can solve a problem, there's probably one that is way better than 00:05:35.260 --> 00:05:37.100 all the other attempts because they have more data. 00:05:37.100 --> 00:05:38.320 Think of Google, right? 00:05:38.320 --> 00:05:41.160 There are other search engines, but they don't come close to Google, right? 00:05:41.160 --> 00:05:41.700 For example. 00:05:42.380 --> 00:05:44.040 And it would probably be like that for AI. 00:05:44.040 --> 00:05:50.800 And so the countries that own the powerful AIs will be even more sort of unequal with the 00:05:50.800 --> 00:05:51.340 rest of the world. 00:05:51.340 --> 00:05:54.340 So if you're thinking about AI, you're doing AI and machine learning stuff. 00:05:54.340 --> 00:05:56.680 This is a really interesting philosophical read. 00:05:56.680 --> 00:05:57.140 Yeah. 00:05:57.140 --> 00:06:02.860 It's also not just the people with the AI, but also the people with the data sets that control 00:06:02.860 --> 00:06:03.620 the data sets. 00:06:03.620 --> 00:06:05.060 That's a huge part of it, right? 00:06:05.060 --> 00:06:05.400 Absolutely. 00:06:05.400 --> 00:06:09.360 Who's collecting the data now that can be fed to these AIs that make them stand out? 00:06:09.560 --> 00:06:09.800 Yeah. 00:06:09.800 --> 00:06:15.960 One of the things that I liked about this article was this kind of AI sometimes can seem still 00:06:15.960 --> 00:06:20.280 far out, even when we're trying to think about like driving cars and stuff. 00:06:20.280 --> 00:06:27.420 But the model that right now a use for AI is just any time where you take a whole bunch of 00:06:27.420 --> 00:06:33.320 data to use algorithms to decide on what decision for a particular circumstance. 00:06:34.160 --> 00:06:39.180 And like, for instance, all of the information about somebody that they turn in with their 00:06:39.180 --> 00:06:41.740 loan application, whether or not you should give them a loan. 00:06:41.740 --> 00:06:47.180 Well, I think it's going to be pretty fast if like a lot of loan representatives are replaced 00:06:47.180 --> 00:06:49.000 by AI machines. 00:06:49.000 --> 00:06:50.820 I can see that basically be instantaneous. 00:06:50.820 --> 00:06:51.360 Yeah. 00:06:51.360 --> 00:06:52.160 Here's my information. 00:06:52.160 --> 00:06:52.680 I want a loan. 00:06:52.680 --> 00:06:53.420 Okay, you can have it. 00:06:53.420 --> 00:06:54.140 No, you can't have it. 00:06:54.140 --> 00:06:54.660 Here's why. 00:06:54.660 --> 00:06:55.100 Yeah. 00:06:55.100 --> 00:06:55.460 You know what I mean? 00:06:55.740 --> 00:06:59.840 And also like first level, I mean, they even talked about customer service. 00:06:59.840 --> 00:07:05.480 And when we've got like voice recognition things going on that are pretty darn good. 00:07:05.620 --> 00:07:11.240 I mean, they're not great, but they're well enough to where your first level of contact 00:07:11.240 --> 00:07:15.800 through a phone service is probably going to be a computer not too long from now. 00:07:15.800 --> 00:07:16.120 Yeah. 00:07:16.120 --> 00:07:19.720 Certainly with all these like smart speaker type things, it's going to push that technology 00:07:19.720 --> 00:07:20.200 way out. 00:07:20.200 --> 00:07:26.760 And this article does have a bit of a dark view on the world, but just think of the stuff 00:07:26.760 --> 00:07:27.460 that AI can do. 00:07:27.460 --> 00:07:30.500 There's going to be a bunch of really amazing and positive stuff. 00:07:30.500 --> 00:07:35.140 Like one practical example, then we'll move on is shout out to the partially derivative 00:07:35.140 --> 00:07:37.860 guys, the other podcasts that I heard this on. 00:07:37.860 --> 00:07:44.720 They interviewed some people who are using data science and AI type stuff to make basically 00:07:44.720 --> 00:07:47.320 to solve police violence problems. 00:07:47.320 --> 00:07:55.580 And they said, if they use machine learning to figure out that if a policeman goes and handles 00:07:55.580 --> 00:08:00.920 like something terrible, like a suicide case, and then is immediately sent back out and 00:08:00.920 --> 00:08:05.340 pulls over somebody who talks back to them, there's a much higher chance of police violence 00:08:05.340 --> 00:08:09.200 against that driver because they're still upset from that previous thing. 00:08:09.200 --> 00:08:12.180 And so, you know, that was discovered through like data science and things like this. 00:08:12.180 --> 00:08:16.580 So there'll be a bunch of good stuff coming out of it, but some interesting effects on society 00:08:16.580 --> 00:08:17.320 and the jobs here. 00:08:17.320 --> 00:08:18.960 Anyway, it's something to keep track of. 00:08:18.960 --> 00:08:19.800 Yeah, it's nice. 00:08:19.800 --> 00:08:20.020 Yep. 00:08:20.020 --> 00:08:20.980 All right. 00:08:20.980 --> 00:08:26.440 So let's move from sort of society philosophy to the philosophy of physics and science applied 00:08:26.440 --> 00:08:27.040 to programming. 00:08:27.040 --> 00:08:31.240 There was an article called the three laws of config dynamics. 00:08:31.240 --> 00:08:34.980 And yes, that's config dynamics for like configuration files. 00:08:34.980 --> 00:08:38.360 And I ran into this recently myself. 00:08:38.360 --> 00:08:44.440 I've got, and they talk, the article starts out talking about how a project can like the inception 00:08:44.440 --> 00:08:47.020 of the birth of a configuration file for a project. 00:08:47.020 --> 00:08:53.240 And it often centers around something like what you're connected to, what database or 00:08:53.240 --> 00:08:56.800 what service you're connected to, and being able to control that. 00:08:56.800 --> 00:08:57.080 Right. 00:08:57.080 --> 00:09:00.760 Soon as you have like a dev database and a production database. 00:09:00.760 --> 00:09:01.160 Yeah. 00:09:01.160 --> 00:09:01.640 Then. 00:09:01.640 --> 00:09:02.020 Yeah. 00:09:02.020 --> 00:09:05.380 Like in the book I'm working on, I had a config file to control. 00:09:05.380 --> 00:09:08.180 It's a little task list application. 00:09:08.560 --> 00:09:11.920 And I support both Mongo and tiny DB. 00:09:11.920 --> 00:09:16.140 And so I have to tell the application which one to use and where it is. 00:09:16.140 --> 00:09:18.080 And those are things that just happen. 00:09:18.080 --> 00:09:22.900 But the article after come up with like how they're created in the first place talks 00:09:22.900 --> 00:09:24.000 about these three laws. 00:09:24.000 --> 00:09:30.500 And one of them was a config values can be transformed from one form to another, but can not be created 00:09:30.500 --> 00:09:31.420 or destroyed. 00:09:31.780 --> 00:09:37.200 And one of the examples of that is some people recommend putting the config values in environmental 00:09:37.200 --> 00:09:38.120 variables instead. 00:09:38.120 --> 00:09:43.000 But it just doesn't really solve the problem because people just create little scripts to, 00:09:43.000 --> 00:09:45.080 to write their environmental variables. 00:09:45.080 --> 00:09:47.340 And then now you've got a config file again. 00:09:47.580 --> 00:09:49.960 I love the parody to the three laws of thermodynamics. 00:09:49.960 --> 00:09:55.300 So the total length of config file can only increase over time. 00:09:55.300 --> 00:10:01.980 And I think that's just, if left unchecked, one of the recommendations there is to regularly 00:10:01.980 --> 00:10:08.160 evaluate your values in your config file and see if you can condense them or combine them 00:10:08.160 --> 00:10:09.280 or somehow limit them. 00:10:09.280 --> 00:10:09.520 Yeah. 00:10:09.520 --> 00:10:11.240 That's a really, that was a really interesting one. 00:10:11.240 --> 00:10:16.500 And I thought actually, you know, it was a good reminder that these things need maintenance 00:10:16.500 --> 00:10:21.180 and trimming and, and clean up just like the rest of your code or it can get nasty. 00:10:21.180 --> 00:10:26.400 And this one surprised me when I read it at first is the last one is a number three, 00:10:26.400 --> 00:10:32.200 the length of a perfect config file in a development environment is exactly equal to zero. 00:10:32.200 --> 00:10:37.600 And that's saying you should be able to run an application with no configuration file. 00:10:37.600 --> 00:10:43.880 And it should be like fairly valid because otherwise, if it isn't, then you've got to have some 00:10:43.880 --> 00:10:48.720 way to have each developer come up with what their default config file should be. 00:10:48.720 --> 00:10:49.080 Right. 00:10:49.080 --> 00:10:54.600 And so, you know, for example, you could have the dev database hard coded in, but if it's 00:10:54.600 --> 00:10:59.420 passed some other value, like a production or staging database, it'll just use that instead. 00:10:59.420 --> 00:11:01.800 So you don't need to specify the dev database. 00:11:01.800 --> 00:11:06.360 Similarly for like app on email, just go like, if there's nothing here, we just don't send email. 00:11:06.780 --> 00:11:10.540 But if there's something, we'll send it to this, you know, SMTP server and so on. 00:11:10.540 --> 00:11:10.940 Yeah. 00:11:10.940 --> 00:11:11.220 Yeah. 00:11:11.220 --> 00:11:14.360 And then it said, it also talked about Docker helping it. 00:11:14.360 --> 00:11:18.820 And since I'm not a Docker user, I just kind of got lost on that section. 00:11:18.820 --> 00:11:26.520 But yeah, I feel like, like we said before, Docker shifts programming experience to ops experience in a lot of ways. 00:11:26.620 --> 00:11:37.380 The way they said that Docker can help is with a Docker, you can consistently name the machines, the same thing in production and in development. 00:11:37.380 --> 00:11:38.220 And it's the same. 00:11:38.220 --> 00:11:40.260 So like the database server could just be called DB. 00:11:40.260 --> 00:11:44.240 The API address could just be API or whatever. 00:11:44.240 --> 00:11:48.420 And like you have different containers running in production versus development or whatever. 00:11:48.420 --> 00:11:48.980 Oh, okay. 00:11:48.980 --> 00:11:50.140 That makes sense. 00:11:50.140 --> 00:11:54.200 So you can hard code the name, but change the infrastructure to just match it. 00:11:54.200 --> 00:11:57.560 So I want to talk about Jupyter notebooks next. 00:11:57.560 --> 00:11:59.600 But before we do, let's talk about Rollbar. 00:11:59.600 --> 00:12:02.220 So Rollbar has been a longtime sponsor of the show. 00:12:02.220 --> 00:12:07.140 And I'm sure a lot of you know, right, the job of Rollbar is you integrate it into your app, especially web apps. 00:12:07.140 --> 00:12:09.700 If there's any kind of error, it gives you tons of information. 00:12:09.700 --> 00:12:12.500 So I'll touch on a few things that I haven't last time. 00:12:12.500 --> 00:12:16.560 So they support Python, of course, which is great. 00:12:16.560 --> 00:12:19.140 And, you know, that's why they're a big part of this community. 00:12:19.140 --> 00:12:22.900 But they actually support 26 languages and frameworks. 00:12:22.900 --> 00:12:26.740 So Python, but also Node, .NET, and they even support Flash. 00:12:26.740 --> 00:12:32.340 So if you want to get error handling your Flash apps that you somehow got stuck with, plug it in. 00:12:32.340 --> 00:12:36.480 And another thing that they have that's pretty cool is they have what's called people tracking. 00:12:36.480 --> 00:12:39.500 So if you have users, like anybody logged into your app, 00:12:39.500 --> 00:12:43.620 one of the problems is if somebody reports an error to you, they say, 00:12:43.620 --> 00:12:45.040 I did this thing and it didn't work. 00:12:45.040 --> 00:12:48.820 You're like, oh, gosh, I've got to go troll through the log files and find it, speaking of logging. 00:12:49.100 --> 00:12:53.700 But here with Rollbar, you can actually associate that error with a logged in user. 00:12:53.700 --> 00:13:02.000 And then you can go to your dashboard and say, find that user and see all the errors and workflow or flow through your app they ran into to find this error. 00:13:02.000 --> 00:13:03.100 So pretty cool. 00:13:03.100 --> 00:13:06.200 Check them out at pythonbytes.fm/Rollbar. 00:13:06.200 --> 00:13:08.040 It helps support the show and it's a cool product. 00:13:08.240 --> 00:13:22.720 Before we get on to Jupyter Notebooks, I have to say I just recently cleaned out my office and found my goodie bag from PyCon and found a Rollbar sticker and slapped it on my laptop because I'm really proud that Rollbar sponsors the show. 00:13:22.720 --> 00:13:23.600 That's awesome. 00:13:23.600 --> 00:13:24.880 Very, very cool. 00:13:24.880 --> 00:13:25.140 Yeah. 00:13:25.140 --> 00:13:28.000 My Rollbar t-shirt is quite threadbare these days. 00:13:28.360 --> 00:13:28.800 Okay. 00:13:28.800 --> 00:13:32.880 So let's talk about five tips for getting started with Jupyter Notebooks. 00:13:32.880 --> 00:13:34.960 How are you with Jupyter Notebooks? 00:13:34.960 --> 00:13:35.700 Do you use them often? 00:13:35.700 --> 00:13:36.260 No. 00:13:36.260 --> 00:13:37.060 Yeah, I don't either. 00:13:37.060 --> 00:13:41.160 I mean, mostly I do web stuff with tons of files and like they all got to fit together. 00:13:41.160 --> 00:13:44.080 So the workflow is just not that way for me. 00:13:44.080 --> 00:13:50.120 I also often feel like, you know, I should really just get in the habit of firing up a Jupyter Notebook for certain types of work. 00:13:50.120 --> 00:13:59.880 And so this is a nice little article, I think is by the ActiveState folks, about a couple of things you can do to just like start really quickly and easily with Jupyter Notebooks. 00:13:59.880 --> 00:14:04.620 So the five tips are don't put your entire code into a single cell, right? 00:14:04.620 --> 00:14:08.180 You have these little cells, you put little fragments of code and the cells like work together. 00:14:08.180 --> 00:14:10.420 So you get the output of each step. 00:14:10.420 --> 00:14:15.220 So if you break it apart, right, you get kind of, you can execute the steps independently. 00:14:15.220 --> 00:14:17.240 You can have the data flow from one to the next. 00:14:17.240 --> 00:14:17.700 That's cool. 00:14:17.700 --> 00:14:19.320 There's different types of cells. 00:14:19.320 --> 00:14:24.100 Like you can put markdown or you can put Python code or you can put a picture, all kinds of stuff. 00:14:24.100 --> 00:14:26.420 So you can kind of build up a story with a code. 00:14:26.420 --> 00:14:29.620 You can execute shells with shift enter. 00:14:29.620 --> 00:14:31.700 This is not the ActiveState guys. 00:14:31.700 --> 00:14:33.820 This is Esri, ESRI. 00:14:33.820 --> 00:14:39.020 Anyway, you can explore like maps because you can integrate their ArcGIS stuff into it. 00:14:39.020 --> 00:14:43.360 And they have a really cool way to get information about modules you might not know about. 00:14:43.360 --> 00:14:49.200 So you could type, say, like import Daiquiri and then Daiquiri question mark and hit tab. 00:14:49.200 --> 00:14:51.680 And it'll like give you a big summary of what you can do with Daiquiri. 00:14:51.680 --> 00:14:52.140 Oh, wow. 00:14:52.140 --> 00:14:52.480 Cool. 00:14:52.560 --> 00:14:55.280 Anyway, there's a few simple things you do with Jupyter. 00:14:55.280 --> 00:14:59.320 Hopefully this inspires you to check it out and try it for when it makes sense. 00:14:59.320 --> 00:14:59.980 All right. 00:14:59.980 --> 00:15:01.240 What's next? 00:15:01.240 --> 00:15:02.320 Oh, coupling. 00:15:02.320 --> 00:15:03.460 Coupling versus decoupling. 00:15:03.460 --> 00:15:04.280 Tell me about coupling. 00:15:04.280 --> 00:15:05.500 Coupling versus decoupling. 00:15:05.500 --> 00:15:08.380 So this is an article by Kent Beck. 00:15:08.380 --> 00:15:14.500 And I've followed Kent Beck since I was reading about extreme programming and test-driven development. 00:15:14.500 --> 00:15:14.760 Yeah. 00:15:14.820 --> 00:15:21.700 He's one of the original agile manifesto guys, the extreme programming, pair programming, all that stuff from back in the day, right? 00:15:21.700 --> 00:15:21.900 Yeah. 00:15:21.900 --> 00:15:25.100 And I ran across this article and I was confused. 00:15:25.100 --> 00:15:26.420 At first I was confused. 00:15:26.420 --> 00:15:29.500 It's called cost of coupling versus cost of decoupling. 00:15:29.960 --> 00:15:34.640 And I'm like, why is he writing on like posting this on Facebook? 00:15:34.640 --> 00:15:36.380 But he works at Facebook. 00:15:36.380 --> 00:15:37.580 So that makes sense. 00:15:37.580 --> 00:15:42.060 So anyway, two elements are coupled with respect to a given change. 00:15:42.060 --> 00:15:45.720 If the change, changing one element implies changing the other. 00:15:45.720 --> 00:15:47.180 So that makes sense for coupling. 00:15:47.180 --> 00:15:55.180 So when you're writing tightly coupled code, it's hard to change things because things, every time you change one thing, you got to change a bunch. 00:15:55.340 --> 00:16:00.500 Like if you've got, if you do copy and paste coding all over the place, then if you fix the algorithm, you got to fix it everywhere. 00:16:00.500 --> 00:16:08.300 The other thing, other places where coupling often creeps up is very tightly coupled test and implementation. 00:16:08.300 --> 00:16:15.260 So if you, with mocks and whatever, and if you, if you decide to change, change part of the design, you got to change all the tests. 00:16:15.260 --> 00:16:18.420 So some people are okay with that, whatever. 00:16:18.420 --> 00:16:21.920 But, and decoupled code is the opposite of that. 00:16:21.920 --> 00:16:25.000 It follows the dry principle and uses smaller components or whatever. 00:16:25.000 --> 00:16:32.420 But the nice decoupled code also takes more time to write and design initially. 00:16:32.420 --> 00:16:42.140 So the article is just talking about thinking about the costs of both and making sure and understanding that there are places for both of them in your development. 00:16:42.140 --> 00:16:45.760 And Kent splits it into explore and extract. 00:16:45.760 --> 00:16:51.620 And that's, maybe those words are more meaningful to him, but that isn't very obvious to me. 00:16:51.940 --> 00:16:59.180 So the explore phase being like a spike projects or your first draft of your implementation or happy path. 00:16:59.360 --> 00:17:06.520 The point of that, your first writing through a project is just learning about it, answering questions quickly and, 00:17:06.520 --> 00:17:13.460 and learning enough about the, whatever you're working in to, to ask better questions as you go along. 00:17:13.460 --> 00:17:18.440 And then the extract phase is more like a final draft or architected stuff. 00:17:18.440 --> 00:17:24.400 Economies of scale take over and you need to minimize the cost of changes so that you can, 00:17:24.460 --> 00:17:26.800 you know, you know, the opposite of that. 00:17:26.800 --> 00:17:28.500 Yeah. 00:17:28.500 --> 00:17:32.200 Well, I think one of the things that's interesting about this is, you know, it's, 00:17:32.200 --> 00:17:37.040 it's easy to like study design patterns or these types of things and say, 00:17:37.040 --> 00:17:39.560 Oh, I'm going to apply this to like everything I do from now. 00:17:39.560 --> 00:17:40.360 Cause this is amazing. 00:17:40.820 --> 00:17:47.500 But if you're building something that's 5,000 lines and is like, just, you are going to work on it. 00:17:47.500 --> 00:17:54.220 You're just going to be spending extra effort to make these, to apply these patterns and this decoupling and whatnot. 00:17:54.220 --> 00:17:57.560 When you could just write it and be done. 00:17:57.560 --> 00:18:04.140 Whereas if a team of 10 people is working on it and it's a hundred thousand lines and it's got to live a long time and be maintainable and evolve. 00:18:04.340 --> 00:18:06.920 Well then, you know, these things make a lot more sense, right? 00:18:06.920 --> 00:18:08.660 So definitely think about the trade-offs. 00:18:08.660 --> 00:18:08.960 Yeah. 00:18:08.960 --> 00:18:17.780 And also as you're like a more, as your project goes into maturity, the code base is going to get larger and it's going to cover a little corner cases. 00:18:17.780 --> 00:18:27.360 So it is necessarily going to be your tests are going to cover those little corner cases and they're going to be more tightly coupled as well. 00:18:27.360 --> 00:18:34.540 So it was a good to think about and to make sure that you okay with hacking things together at first, if, if you're learning stuff. 00:18:34.540 --> 00:18:34.760 Yeah. 00:18:34.760 --> 00:18:36.740 Sometimes it's worth it just to hack it together. 00:18:36.740 --> 00:18:37.500 All right. 00:18:37.500 --> 00:18:41.000 Last one for me is just an inspirational little project. 00:18:41.000 --> 00:18:46.020 Something a lot of people out there ask me like, Hey Michael, I want to get started in programming in Python. 00:18:46.360 --> 00:18:55.680 I want to build up some kind of portfolio that I could show people to get a job or to get a promotion or whatever. 00:18:55.680 --> 00:18:56.020 Right. 00:18:56.020 --> 00:19:03.080 So the guy who was over at PyBytes, not Python bytes, PyBytes, P-Y-B-I-T-E-S. 00:19:03.080 --> 00:19:04.940 That's Bob and Julian. 00:19:04.940 --> 00:19:08.940 You can see the link in the site, the show notes. 00:19:08.940 --> 00:19:11.920 They did a hundred days of code challenge. 00:19:11.920 --> 00:19:12.620 Have you heard of this, Brian? 00:19:12.620 --> 00:19:15.260 Well, I've been sort of following them along with this. 00:19:15.260 --> 00:19:26.140 They're pretty good at giving information about what they're up to because they had part of their challenge was they wrote a bot that would automatically tweet the process, their progress each day. 00:19:26.140 --> 00:19:27.060 And there's a hundred days. 00:19:27.060 --> 00:19:29.300 So they were just going after it. 00:19:29.300 --> 00:19:33.140 One of the things I wasn't clear on, did they start the hundred days of code or did they? 00:19:33.140 --> 00:19:35.360 I don't think they started it. 00:19:35.360 --> 00:19:37.100 I think they said, we're going to do it for Python. 00:19:37.220 --> 00:19:37.340 Okay. 00:19:37.340 --> 00:19:37.840 Yeah. 00:19:37.840 --> 00:19:41.960 And so they wrote, they found out they have all sorts of stats and pictures and stuff. 00:19:41.960 --> 00:19:44.440 So the article, the writeup that we're referencing is really cool. 00:19:44.440 --> 00:19:49.880 They wrote about 5,000 lines of code split across obviously 100 scripts per day. 00:19:50.760 --> 00:19:55.860 And the number of lines was like between 50 and 200 per day, which is kind of an interesting thing. 00:19:55.860 --> 00:19:57.080 They've got a whole bunch of projects. 00:19:57.080 --> 00:20:00.460 They've got 10 projects that they built that they showcased. 00:20:00.460 --> 00:20:02.540 And they're really, some of them are really neat. 00:20:02.540 --> 00:20:05.900 Like I said, they auto tweeted their progress. 00:20:06.840 --> 00:20:12.540 They ran some stuff, some analysis across their scripts, their 100 scripts and figured out they 00:20:12.540 --> 00:20:14.600 used exactly 100 modules. 00:20:14.600 --> 00:20:15.440 Wow. 00:20:15.440 --> 00:20:16.880 So weird coincidence, right? 00:20:16.880 --> 00:20:19.220 So a hundred different modules as part of it. 00:20:19.220 --> 00:20:22.960 And their next a hundred day project is going to be around Django. 00:20:22.960 --> 00:20:27.980 So if you're interested in Django, you know, follow them on Twitter or however they send out 00:20:27.980 --> 00:20:29.460 their messages and about this stuff. 00:20:29.460 --> 00:20:33.880 But anyway, if you're interested in like getting started, check out their article. 00:20:34.160 --> 00:20:39.680 It may help you have some concrete things, small concrete daily things you can do. 00:20:39.680 --> 00:20:42.440 And a hundred days later, you'll have a lot more experience. 00:20:42.440 --> 00:20:42.780 Yeah. 00:20:42.780 --> 00:20:44.620 I think it was Bob that was at PyCon. 00:20:44.620 --> 00:20:45.220 Yes. 00:20:45.220 --> 00:20:45.500 Yeah. 00:20:45.500 --> 00:20:49.220 I ran into Bob and talked with him a little bit and great guy. 00:20:49.220 --> 00:20:50.940 And I just, I like what they're doing. 00:20:50.940 --> 00:20:55.140 They have like one person, I can't remember who, which is which, but one of them already 00:20:55.140 --> 00:20:56.380 knew Python to begin with. 00:20:56.380 --> 00:21:01.140 And the other one was like brand new, had never used Python before as they, as they started 00:21:01.140 --> 00:21:02.340 the PyBytes thing. 00:21:02.340 --> 00:21:03.380 So yeah. 00:21:03.380 --> 00:21:04.020 Very, very cool. 00:21:04.400 --> 00:21:04.660 All right. 00:21:04.660 --> 00:21:05.480 What else you got for us? 00:21:05.480 --> 00:21:06.580 That's, that's our news for the week. 00:21:06.580 --> 00:21:07.600 What's up with you? 00:21:07.600 --> 00:21:08.840 Good review of your book, right? 00:21:08.840 --> 00:21:09.420 I just read it. 00:21:09.420 --> 00:21:09.820 Yeah. 00:21:09.820 --> 00:21:16.240 I ran across the first review I've seen so far for the book by Chris Shaver and well, the 00:21:16.240 --> 00:21:20.660 link in the show notes, but it was nice to see a book review of it. 00:21:20.660 --> 00:21:23.700 And it's fun to have more and more people reading it. 00:21:23.700 --> 00:21:24.600 So it's good. 00:21:24.600 --> 00:21:25.000 Yeah. 00:21:25.100 --> 00:21:29.860 It was really a good write-up and summarized it well and gave it a good vote of confidence. 00:21:29.860 --> 00:21:31.200 So I think it's well-deserved. 00:21:31.200 --> 00:21:31.540 Nice. 00:21:31.540 --> 00:21:32.080 How about you? 00:21:32.080 --> 00:21:41.560 Finally, after a very long time, long journey, kind of book-like duration, the Python for Entrepreneurs 00:21:41.560 --> 00:21:46.340 course has finally officially been finished and is ready to go. 00:21:46.340 --> 00:21:52.380 So it's came in around 19 hours of content and you get it at talkpython.fm/launch. 00:21:52.380 --> 00:21:55.440 So I'm super excited to be done with that course. 00:21:55.440 --> 00:21:58.060 It came out great and move on to writing new courses. 00:21:58.060 --> 00:21:58.600 Awesome. 00:21:58.820 --> 00:21:58.940 Yeah. 00:21:58.940 --> 00:22:03.680 It took so long, I actually wrote two other courses and finished them while that was getting 00:22:03.680 --> 00:22:04.080 finished. 00:22:04.080 --> 00:22:07.240 Well, would that have been faster? 00:22:07.240 --> 00:22:08.640 Had you focused on one? 00:22:08.640 --> 00:22:10.600 No, because I was waiting on other people. 00:22:10.600 --> 00:22:11.480 I was waiting on editors. 00:22:11.480 --> 00:22:13.420 I was waiting on Matt to do his sections. 00:22:13.420 --> 00:22:17.500 I was waiting on PyCharm to fix a bug so I could finish another second. 00:22:17.500 --> 00:22:18.780 There's just like lots of waiting. 00:22:18.780 --> 00:22:20.080 So many things. 00:22:20.080 --> 00:22:20.880 Okay. 00:22:20.880 --> 00:22:21.660 But it's all good. 00:22:21.660 --> 00:22:22.180 It's all done. 00:22:22.180 --> 00:22:22.640 Wonderful. 00:22:22.640 --> 00:22:23.020 All right. 00:22:23.020 --> 00:22:23.580 Yeah. 00:22:23.580 --> 00:22:26.300 So good to see our projects getting out to the world. 00:22:26.300 --> 00:22:28.140 Well, thanks again for talking with me this week. 00:22:28.140 --> 00:22:28.560 You bet. 00:22:28.560 --> 00:22:29.680 And thank you everyone for listening. 00:22:29.680 --> 00:22:30.720 See you next time. 00:22:30.720 --> 00:22:31.160 Bye, Brian. 00:22:31.160 --> 00:22:31.400 Bye. 00:22:31.400 --> 00:22:34.060 Thank you for listening to Python Bytes. 00:22:34.060 --> 00:22:36.620 Follow the show on Twitter via at Python Bytes. 00:22:36.620 --> 00:22:39.520 That's Python Bytes as in B-Y-T-E-S. 00:22:39.520 --> 00:22:42.920 And get the full show notes at pythonbytes.fm. 00:22:42.920 --> 00:22:47.280 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:22:47.280 --> 00:22:49.980 We're always on the lookout for sharing something cool. 00:22:49.980 --> 00:22:53.360 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:22:53.360 --> 00:22:56.980 Thank you for listening and sharing this podcast with your friends and colleagues.