WEBVTT 00:00:00.001 --> 00:00:04.720 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:04.720 --> 00:00:09.600 This is episode 118, recorded February 19th, 2019. 00:00:09.600 --> 00:00:10.760 I'm Michael Kennedy. 00:00:10.760 --> 00:00:11.580 And I'm Brian Okken. 00:00:11.580 --> 00:00:14.140 And this episode is brought to you by DigitalOcean. 00:00:14.140 --> 00:00:16.780 Check them out at pythonbytes.fm/DigitalOcean. 00:00:16.780 --> 00:00:17.760 More on that later. 00:00:17.760 --> 00:00:20.040 Right now, Brian, it's... 00:00:20.040 --> 00:00:21.200 Man, it is cold outside. 00:00:21.200 --> 00:00:22.720 It's not bad. 00:00:22.720 --> 00:00:25.300 I would say it's frozen. 00:00:25.300 --> 00:00:26.520 Okay. 00:00:26.520 --> 00:00:28.400 Oh, yeah. 00:00:28.660 --> 00:00:30.340 The whole transition thing. 00:00:30.340 --> 00:00:31.180 You're so good at that. 00:00:31.180 --> 00:00:33.960 The first topic is frozen Flask. 00:00:33.960 --> 00:00:37.460 This was sent in by somebody who I probably should have written their name down. 00:00:37.460 --> 00:00:39.660 But it was recommended by a listener. 00:00:39.660 --> 00:00:42.320 And it's actually pretty darn cool. 00:00:42.320 --> 00:00:45.660 So I've been playing with static site generators a little bit. 00:00:45.660 --> 00:00:54.380 And here's the idea is if you already have Flask application up, and it's mostly static-y content anyway, 00:00:54.380 --> 00:00:55.380 or it's got some... 00:00:55.980 --> 00:00:58.620 Yeah, activity is more JavaScript-y or something. 00:00:58.620 --> 00:01:05.540 You can just freeze that into a statically servable web thing with frozen Flask. 00:01:05.540 --> 00:01:06.600 And it's pretty painless. 00:01:06.600 --> 00:01:08.060 Frozen Flask looks so cool. 00:01:08.060 --> 00:01:09.180 Yeah, it really does. 00:01:09.620 --> 00:01:18.480 And then I also stumbled across a tutorial that's called Dead Easy Yet Powerful Static Website Generator with Flask. 00:01:18.480 --> 00:01:20.360 And it talks about frozen Flask. 00:01:20.360 --> 00:01:31.420 And it also talks about an extension to Flask called Flask Flat Pages, which allows you to have a bunch of pages generated from Markdown. 00:01:31.420 --> 00:01:32.260 Oh, that's pretty cool. 00:01:32.260 --> 00:01:33.240 Yeah, I haven't heard of that. 00:01:33.240 --> 00:01:33.720 That's nice. 00:01:33.880 --> 00:01:35.200 And it's pretty fun. 00:01:35.200 --> 00:01:37.180 I'm going to warn you, I tried to go through this tutorial. 00:01:37.180 --> 00:01:39.300 It's still got a lot of great information. 00:01:39.300 --> 00:01:40.140 It's well written. 00:01:40.140 --> 00:01:41.200 It's just a little... 00:01:41.200 --> 00:01:44.080 Since it was from 2012, it's a little out of date. 00:01:44.080 --> 00:01:45.960 Some of the imports don't quite work. 00:01:45.960 --> 00:01:47.360 So you'll have to do some Googling. 00:01:47.360 --> 00:01:51.020 The frozen Flask that we also link to has some really cool... 00:01:51.020 --> 00:01:54.300 It's some pretty easy tutorials on there too. 00:01:54.440 --> 00:01:55.540 So it looks fun. 00:01:55.540 --> 00:01:56.680 Yeah, that looks really cool. 00:01:56.680 --> 00:02:04.960 There's always this tension between having a data-driven website where maybe I've got a bunch of podcast episodes or something. 00:02:04.960 --> 00:02:05.780 And they all have notes. 00:02:05.780 --> 00:02:09.300 But I want to have a whole bunch of stuff around them and have that generated. 00:02:09.300 --> 00:02:13.280 And of course, that would be like a regular Flask data-driven app, right? 00:02:13.280 --> 00:02:17.100 But if I'm going to do that with a static thing, how do I do that? 00:02:17.100 --> 00:02:19.060 Do I create a bunch of static Markdown? 00:02:19.060 --> 00:02:20.060 How do I mix it all together? 00:02:20.060 --> 00:02:20.960 It can be painful, right? 00:02:21.300 --> 00:02:24.820 So Frozen Flask is like, well, you create the dynamic thing, but then you host it. 00:02:24.820 --> 00:02:26.800 You don't have to deal with the infrastructure hosting. 00:02:26.800 --> 00:02:30.980 You just put HTML and CSS somewhere and it runs great, right? 00:02:30.980 --> 00:02:31.300 Yeah. 00:02:31.300 --> 00:02:34.280 Like, for instance, I was thinking it would be a neat way to do this. 00:02:34.280 --> 00:02:38.160 I was always potentially looking for ways I can use something also. 00:02:38.160 --> 00:02:45.920 Like if I've got something internal for our test team, for instance, and we want to look at all of our test data and analytics and all that stuff, 00:02:45.920 --> 00:02:49.000 having a more dynamic site would be really neat. 00:02:49.180 --> 00:02:57.680 But then also a periodic generation of like a version report or something as a static site. 00:02:57.680 --> 00:02:57.860 Yeah. 00:02:57.860 --> 00:02:59.540 This would be useful for something like that. 00:02:59.540 --> 00:02:59.760 Yeah. 00:02:59.760 --> 00:03:05.120 And so place that I just want to throw out is like an extra for this part that I have no experience with, 00:03:05.120 --> 00:03:06.720 but seems super cool. 00:03:06.720 --> 00:03:09.500 And I think you also are familiar with this is Netlify. 00:03:09.500 --> 00:03:10.040 Yeah. 00:03:10.040 --> 00:03:10.340 Yeah. 00:03:10.400 --> 00:03:14.940 So Netlify is a place specifically for hosting static sites. 00:03:14.940 --> 00:03:18.600 So it says build, deploy, and manage modern web projects. 00:03:18.600 --> 00:03:24.820 And it gives you a global CDN, continuous integration, and automatic free HTTPS, 00:03:24.820 --> 00:03:27.920 I think even your own domain name, 100% for free. 00:03:27.920 --> 00:03:32.600 There are ways to pay for it, but so much of it is for free that this seems like a really good option. 00:03:32.820 --> 00:03:32.960 Yeah. 00:03:32.960 --> 00:03:34.840 And they're not sponsoring this episode. 00:03:34.840 --> 00:03:35.600 It's just cool. 00:03:35.600 --> 00:03:37.180 That's not, that's, that's just something that's cool. 00:03:37.180 --> 00:03:37.600 That's right. 00:03:37.600 --> 00:03:38.600 Digital Ocean. 00:03:38.600 --> 00:03:40.020 They are sponsoring this episode. 00:03:40.800 --> 00:03:47.760 So last week, I think it was, we spoke about the new PEP that is for Dunder PyPackages. 00:03:47.760 --> 00:03:48.060 Yeah. 00:03:48.060 --> 00:03:49.680 And you said that's awesome, except. 00:03:49.680 --> 00:03:50.060 Yeah. 00:03:50.060 --> 00:03:52.680 It's awesome, but. 00:03:52.680 --> 00:03:58.020 And one of the challenges you said was, well, if you just want to have packages and put them in there 00:03:58.020 --> 00:04:00.140 and run your app, that's totally good. 00:04:00.140 --> 00:04:01.300 It'll pick it up and run. 00:04:01.300 --> 00:04:12.820 But the big, the thing that was missing was to run the command line binary apps like pytest or Tox or P-Serve or, you know, like MicroWSGI, 00:04:12.820 --> 00:04:18.580 all those things that would then get stuffed into that place that is really nearly impossible to put into your path, right? 00:04:18.580 --> 00:04:18.860 Yeah. 00:04:18.860 --> 00:04:19.400 Yeah. 00:04:19.400 --> 00:04:20.600 Well, not impossible, but. 00:04:20.600 --> 00:04:22.780 Not impossible, but it's a, it's a super pain. 00:04:22.780 --> 00:04:24.920 Like every time you create a virtual environment to add it. 00:04:24.920 --> 00:04:25.340 Yeah. 00:04:25.340 --> 00:04:26.560 To the path, right? 00:04:26.640 --> 00:04:38.100 So, so Chad Smith, who had worked on that before, well, we mentioned who was part of the, this Python loc for like local Python project to let you do like a preview interaction with that. 00:04:38.100 --> 00:04:46.580 Sent us a message and said, Hey, actually you should know about my other project called PIPX, I guess for a pip executable or something like that. 00:04:46.580 --> 00:04:51.720 And the idea is it will execute binary Python packages in isolated environments. 00:04:51.720 --> 00:04:56.360 And the closest analogy analogy to this is probably PIPC, which we have spoken about before. 00:04:56.560 --> 00:04:56.800 Oh yeah. 00:04:56.800 --> 00:04:57.260 Okay. 00:04:57.260 --> 00:05:07.780 So, so the idea is, yeah, you can create these, you can basically, instead of pip install a thing, you can PIPX install a thing and it'll automatically create a virtual environment for you. 00:05:07.780 --> 00:05:09.700 Install it and its dependencies. 00:05:09.700 --> 00:05:11.480 And even it, it's even nice. 00:05:11.480 --> 00:05:17.620 It makes sure your pip is up to date, which is almost never the case when you create a new virtual environment and it just installs it there. 00:05:17.620 --> 00:05:19.280 And then it puts it in the path. 00:05:19.800 --> 00:05:29.800 It puts it in a location on your system that when you install PIPX is in the path, but is not in the restricted part of your operating system. 00:05:29.800 --> 00:05:32.260 So you can pip install anything you need to run. 00:05:32.260 --> 00:05:36.180 So you can run it from the command line in your path without doing sudo. 00:05:36.180 --> 00:05:36.520 Okay. 00:05:36.520 --> 00:05:42.260 So the entry points are going to all go from a bunch of different virtual environments are all going to go to the same place, I guess. 00:05:42.260 --> 00:05:42.520 Yes. 00:05:42.520 --> 00:05:44.580 It does a bunch of like linking back. 00:05:44.580 --> 00:05:45.020 Okay. 00:05:45.020 --> 00:05:49.000 Some place that's in the path back to the various virtual environments it manages. 00:05:49.000 --> 00:05:49.360 Okay. 00:05:49.420 --> 00:05:50.260 This is a very cool. 00:05:50.260 --> 00:05:51.040 Now I'm excited. 00:05:51.040 --> 00:05:51.320 Yeah. 00:05:51.320 --> 00:05:52.540 Now I'm even more excited. 00:05:52.540 --> 00:05:52.760 Yeah. 00:05:52.760 --> 00:05:53.960 This is, this is super cool. 00:05:53.960 --> 00:06:04.320 So you can safely install these packages into virtual environments, which is where they belong, but globally, but without messing up other things or, or, you know, put them all into one virtual environment. 00:06:04.320 --> 00:06:05.040 That's in your path. 00:06:05.040 --> 00:06:05.280 Yeah. 00:06:05.280 --> 00:06:07.440 You can do things like list the stuff that's installed. 00:06:07.440 --> 00:06:10.080 So you can see which of the, which it's kind of like brew. 00:06:10.080 --> 00:06:13.540 You can say, what have I installed here through PIPX, which is pretty cool. 00:06:13.540 --> 00:06:18.180 I'd also run binaries out of the pie package directory as well. 00:06:18.680 --> 00:06:21.260 So it's kind of a companion tool to Python Locke as well. 00:06:21.260 --> 00:06:21.580 Okay. 00:06:21.580 --> 00:06:22.180 Nice. 00:06:22.180 --> 00:06:25.760 Being a Gen Xer, I'm, I'm a sucker for anything with X at the end. 00:06:25.760 --> 00:06:26.160 I know. 00:06:26.160 --> 00:06:26.680 It's amazing. 00:06:26.680 --> 00:06:27.680 It's, it's the best. 00:06:27.680 --> 00:06:31.340 So basically you just pip install PIPX. 00:06:31.340 --> 00:06:36.440 And then from then on, you can say pip install like PT Python, which is an awesome thing. 00:06:36.440 --> 00:06:38.800 You could pip install pytest. 00:06:38.800 --> 00:06:41.440 You could pip install all sorts of things, glances. 00:06:41.440 --> 00:06:46.720 And there's a couple of ways in which it's sort of like Pipsy, but also better. 00:06:46.720 --> 00:06:50.420 So one of the things that I really like, you know, I made the analogy to homebrew before 00:06:50.420 --> 00:06:53.800 is you can go and say PIPX upgrade all. 00:06:54.200 --> 00:06:58.920 And so everything that you've installed throughout the last six months or whatever with PIPX, 00:06:58.920 --> 00:07:03.280 it'll go and find those and see if there's any updates for any of your little Python utilities 00:07:03.280 --> 00:07:03.820 you've installed. 00:07:03.820 --> 00:07:04.320 Oh, cool. 00:07:04.320 --> 00:07:05.380 That's cool, right? 00:07:05.380 --> 00:07:05.600 Yeah. 00:07:05.600 --> 00:07:05.860 Yeah. 00:07:05.860 --> 00:07:09.200 And then there's also a comparison, little matrix thing by matrix. 00:07:09.200 --> 00:07:11.900 I mean, one dimensional single line. 00:07:11.900 --> 00:07:16.300 Anyway, there's a comparison to PIPC and it talks about how PIPX is under active development. 00:07:16.300 --> 00:07:19.700 PIPC isn't in a bunch of other stuff. 00:07:19.700 --> 00:07:22.140 You know, it's based on Python three, six and so on. 00:07:22.140 --> 00:07:24.720 It uses V and V not virtual environment. 00:07:24.780 --> 00:07:27.260 And there's just a bunch of little niceties. 00:07:27.260 --> 00:07:31.640 And, you know, one that made me smile is it prints out emojis when it's doing stuff. 00:07:31.640 --> 00:07:35.980 It prints out little happy face, like little indicator emojis on the terminal, which, you 00:07:35.980 --> 00:07:36.900 know, makes me happy. 00:07:36.900 --> 00:07:37.260 Yeah. 00:07:37.260 --> 00:07:38.660 What's the other thing that does that? 00:07:38.660 --> 00:07:40.000 PIPenv does that, right? 00:07:40.000 --> 00:07:40.300 Yeah. 00:07:40.300 --> 00:07:40.780 Yeah. 00:07:41.020 --> 00:07:43.860 And yeah, Kubernetes, some of the Kubernetes tools do as well. 00:07:43.860 --> 00:07:48.640 It's a trend I'm seeing coming along here, but there's a lot of smiley, happy little things 00:07:48.640 --> 00:07:50.700 on the terminal, which is, you know, it doesn't hurt. 00:07:50.700 --> 00:07:50.920 Yeah. 00:07:50.920 --> 00:07:51.200 Yeah. 00:07:51.200 --> 00:07:52.040 What's the next thing you got for us? 00:07:52.040 --> 00:07:58.500 This is an article from Vicky Boykus that's called Data Science is Different Now. 00:07:58.500 --> 00:08:02.880 It's a fairly lengthy article, but it's some pretty good information. 00:08:02.880 --> 00:08:04.640 So here's the situation we've got. 00:08:04.640 --> 00:08:06.760 We've been talking about data science. 00:08:06.760 --> 00:08:08.980 Everybody's been talking about, say, data science. 00:08:09.600 --> 00:08:11.500 There's just a lot of buzz around it. 00:08:11.500 --> 00:08:16.920 So there's a lot of people with just a little bit of training in it going through book camps 00:08:16.920 --> 00:08:20.020 or MOOCs or, is that how you pronounce MOOCs? 00:08:20.020 --> 00:08:20.480 MOOCs? 00:08:20.480 --> 00:08:21.540 I think so, yeah. 00:08:21.540 --> 00:08:21.840 Okay. 00:08:21.840 --> 00:08:25.480 M-O-O-C, the Massive Online Courses, yeah. 00:08:25.480 --> 00:08:25.840 Yeah. 00:08:25.840 --> 00:08:26.280 Okay. 00:08:26.280 --> 00:08:30.400 Or they're self-taught or they get a remote degree or some other training and they want to 00:08:30.400 --> 00:08:31.540 get a data science job. 00:08:31.540 --> 00:08:38.100 So there's a whole bunch of people trying to get into data science and this partly can become 00:08:38.100 --> 00:08:38.740 a problem. 00:08:39.120 --> 00:08:44.940 So the market, she notes that the market can be very, very discouraging for this flood 00:08:44.940 --> 00:08:50.040 of beginners because since there's a lot of beginners, there's a lot of people. 00:08:50.040 --> 00:08:52.560 The jobs are also becoming more advanced. 00:08:52.560 --> 00:08:54.880 They have more specific requirements. 00:08:54.880 --> 00:09:00.660 And also just the job description is sort of vague. 00:09:00.760 --> 00:09:02.840 So what is it you're actually going to be doing? 00:09:02.840 --> 00:09:07.800 And when people are actually doing it in the workplace, it seems a lot more like engineering 00:09:07.800 --> 00:09:10.720 than this shiny new thing that we've been talking about. 00:09:11.960 --> 00:09:12.260 That's right. 00:09:12.260 --> 00:09:18.180 It's just a kind of a good view of reality check for data science, people interested in getting 00:09:18.180 --> 00:09:19.180 into it. 00:09:19.180 --> 00:09:23.640 But there's a, she also ends with some advice, not just a complaining about it. 00:09:23.720 --> 00:09:27.500 But her advice is for people that wanted to get into it. 00:09:27.500 --> 00:09:32.640 Her first bullet point is don't get into data science, which I just laughed. 00:09:32.640 --> 00:09:33.720 I actually laughed out loud. 00:09:33.720 --> 00:09:35.220 I'm like, that's awesome advice. 00:09:35.360 --> 00:09:40.440 But no, basically, she said, just don't, don't follow the crowd because following what everybody 00:09:40.440 --> 00:09:42.840 else is doing is not going to differentiate you. 00:09:42.840 --> 00:09:48.960 And she recommends a backdoor approach is of like starting as a junior developer or in DevOps 00:09:48.960 --> 00:09:54.520 or project management or something like that and learn a lot of data science stuff on the 00:09:54.520 --> 00:10:00.280 job and then build that up into a position that you can work at the company as a data scientist. 00:10:00.800 --> 00:10:05.660 You know, that sounds like a pretty good bit of advice because I feel like data scientists 00:10:05.660 --> 00:10:09.280 are a little more on their own than software developers. 00:10:09.280 --> 00:10:09.680 Yeah. 00:10:09.680 --> 00:10:16.160 Like at a company, there'll be five or 10 software developers working on one web app or one other 00:10:16.160 --> 00:10:17.400 automation project. 00:10:17.400 --> 00:10:21.440 But how often do you have five to 10 data scientists all working on the same problem? 00:10:21.440 --> 00:10:21.880 Right. 00:10:21.880 --> 00:10:26.420 I feel like they're all kind of doing their own thing a lot of times, which as a beginner 00:10:26.420 --> 00:10:27.780 can make it more difficult. 00:10:27.780 --> 00:10:28.080 Right. 00:10:28.080 --> 00:10:32.140 So maybe get some of these engineering skills through the approach that you're just talking 00:10:32.140 --> 00:10:35.660 about and then branch out into that more solo side of things. 00:10:35.660 --> 00:10:35.980 Yeah. 00:10:35.980 --> 00:10:41.320 And then she also has like some skill, a whole list of specific skills for like, let's say 00:10:41.320 --> 00:10:45.540 you did get a junior developer job or you got some other job, which skills should you beef 00:10:45.540 --> 00:10:45.960 up on? 00:10:45.960 --> 00:10:49.680 So she's got a bunch of listed, a bunch of skills to try to beef up on that. 00:10:49.680 --> 00:10:50.320 So yeah. 00:10:50.320 --> 00:10:50.740 What are they? 00:10:50.740 --> 00:10:54.820 Creating Python packages, putting R into production, which I don't like that one. 00:10:54.820 --> 00:10:55.780 You should use Python. 00:10:55.780 --> 00:10:57.800 But anyway, what do I know? 00:10:57.800 --> 00:10:58.880 We'll talk more about that later. 00:10:58.880 --> 00:11:01.680 Optimizing Spark jobs so they run more efficiently. 00:11:01.680 --> 00:11:03.060 I don't even know what that means. 00:11:03.060 --> 00:11:04.420 Version controlling data. 00:11:04.420 --> 00:11:05.800 Ooh, that sounds neat. 00:11:05.800 --> 00:11:08.200 Making models and data reproducible. 00:11:08.200 --> 00:11:10.160 Version controlling SQL. 00:11:10.160 --> 00:11:11.560 Building and maintaining. 00:11:11.560 --> 00:11:12.080 That sounds hard. 00:11:12.080 --> 00:11:14.280 Clean data in data lakes. 00:11:14.280 --> 00:11:16.260 I love data lakes and data swamps. 00:11:16.260 --> 00:11:17.540 It's just funny. 00:11:17.540 --> 00:11:20.260 Tooling for time series. 00:11:20.260 --> 00:11:21.680 Forecasting at scale. 00:11:21.680 --> 00:11:22.920 Scaling, sharing. 00:11:22.920 --> 00:11:24.560 Sharing of Jupyter Notebooks. 00:11:24.780 --> 00:11:26.580 Thinking about systems for clean data. 00:11:26.580 --> 00:11:27.780 And lots of JSON. 00:11:27.780 --> 00:11:28.600 Yeah, that's cool. 00:11:28.600 --> 00:11:29.500 All right. 00:11:29.500 --> 00:11:30.260 That sounds good. 00:11:30.260 --> 00:11:31.200 Like a good advice. 00:11:31.200 --> 00:11:31.580 Yeah. 00:11:31.580 --> 00:11:32.380 That's a good pick. 00:11:32.380 --> 00:11:38.940 Speaking of good stuff and sponsored episodes, this one is brought to you by DigitalOcean. 00:11:38.940 --> 00:11:42.800 And they, you know, it's February 19th as we're recording this, Brian. 00:11:42.960 --> 00:11:45.960 And not long ago, five days ago, was Valentine's Day, right? 00:11:45.960 --> 00:11:46.280 Yeah. 00:11:46.280 --> 00:11:46.520 Yeah. 00:11:46.520 --> 00:11:50.320 So DigitalOcean, they actually had a Valentine's Day present for the world. 00:11:50.320 --> 00:11:52.880 And their present was database as a service. 00:11:52.880 --> 00:11:59.460 So until now, you would go over to DigitalOcean, you create a server, and then you would have to put your own database on there. 00:11:59.460 --> 00:12:03.260 Or use some other external service, like Object Rocket or something like that. 00:12:03.800 --> 00:12:10.260 So now you can get managed Postgres databases on DigitalOcean, which is pretty sweet if you're going to work with Postgres. 00:12:10.260 --> 00:12:15.340 So you just check the box, and they handle all the operational bits for you. 00:12:15.340 --> 00:12:15.940 It's pretty cool, right? 00:12:15.940 --> 00:12:16.840 Yeah, that is cool. 00:12:16.840 --> 00:12:17.760 Yeah. 00:12:17.760 --> 00:12:18.280 Yeah, definitely. 00:12:18.280 --> 00:12:22.740 So check them out at pythonbytes.fm/DigitalOcean. 00:12:22.740 --> 00:12:26.580 New users get $100 credit to play around with Postgres or whatever else you want to play with. 00:12:26.580 --> 00:12:29.060 And, yeah, they're a very good service. 00:12:29.060 --> 00:12:30.900 Definitely can recommend them. 00:12:30.900 --> 00:12:33.280 So this next one is pretty interesting. 00:12:33.580 --> 00:12:39.320 It's not super actionable yet, Brian, but it starts to approach something that I'm really passionate about. 00:12:39.320 --> 00:12:42.060 So the thing I want to tell you about is Rust Python. 00:12:42.060 --> 00:12:47.700 So we have the language Rust, we have the language Python, and somehow they come together. 00:12:47.700 --> 00:12:51.640 So the Rust doesn't replace the Python, it replaces the C in CPython. 00:12:51.640 --> 00:12:57.860 The idea is, like, here's a new interpreter, a new runtime built on Rust instead of on C. 00:12:57.860 --> 00:12:58.200 Wow. 00:12:58.200 --> 00:12:58.440 Yeah. 00:12:58.440 --> 00:13:03.160 So that feels like a more modern type of language to use than just C, right? 00:13:03.240 --> 00:13:04.320 To build a foundation. 00:13:04.320 --> 00:13:07.280 And that might be a little bit nicer if they could get that working really well. 00:13:07.280 --> 00:13:07.540 Yeah. 00:13:07.540 --> 00:13:07.800 Yeah. 00:13:07.800 --> 00:13:08.980 And it's Python 3 only. 00:13:08.980 --> 00:13:10.480 So 3, 5, and above. 00:13:10.480 --> 00:13:13.420 And basically, the interpreter is written in Rust. 00:13:13.420 --> 00:13:16.200 And this comes to us from Frederick Averpil. 00:13:16.200 --> 00:13:17.880 So thanks, Frederick, for sending that over. 00:13:17.880 --> 00:13:19.560 But, yeah, this looks pretty interesting. 00:13:19.560 --> 00:13:21.580 And I would say it's definitely under active development. 00:13:21.700 --> 00:13:23.900 When I was getting ready for this recording, I looked at the site. 00:13:23.900 --> 00:13:27.340 And the GitHub said, last commit an hour ago. 00:13:27.340 --> 00:13:29.360 So it feels like people are working on it. 00:13:29.360 --> 00:13:31.160 You always, you know, wonder, is this thing real? 00:13:31.160 --> 00:13:32.180 Are people doing anything? 00:13:32.180 --> 00:13:36.900 Now, I say that because they're not really ready to, like, do much. 00:13:36.940 --> 00:13:39.700 Like, you can create a REPL and you can run some basic Python and stuff. 00:13:39.700 --> 00:13:45.040 But, like, there's not a great standard library written with Rust foundations and things like that. 00:13:45.040 --> 00:13:46.460 So there's a lot of work to be done. 00:13:46.460 --> 00:13:53.680 But the goals are to have a full Python 3 environment entirely in Rust with no CPython bindings or no C bindings, really. 00:13:53.680 --> 00:13:57.080 And a clean implementation without compatibility hacks. 00:13:57.080 --> 00:13:57.460 Okay. 00:13:57.460 --> 00:14:03.720 Yeah, so if you want to practice some Rust and play around, there's a lot of things that you could do here that might be fun. 00:14:03.720 --> 00:14:08.640 I think people who played with this would probably understand regular CPython better as well, right? 00:14:08.640 --> 00:14:12.060 Obviously, if you reimplement it in another language, that would be good. 00:14:12.060 --> 00:14:17.360 But they have a bunch of tasks on the issue trackers and they're labeled very nicely, like, good first issue and stuff. 00:14:17.360 --> 00:14:18.720 So if people want to get started, they can do that. 00:14:18.720 --> 00:14:25.020 Yeah, also kind of be a fun getting in in the early stages sort of a thing to jump in and help out. 00:14:25.020 --> 00:14:25.440 Exactly. 00:14:25.620 --> 00:14:28.880 Like in five years, if people keep working on this, this will be really hard to contribute to. 00:14:28.880 --> 00:14:30.040 But right now, it'd be really easy. 00:14:30.040 --> 00:14:30.440 Yeah. 00:14:30.440 --> 00:14:31.740 Relatively speaking. 00:14:31.740 --> 00:14:34.480 So why do I think this is super cool? 00:14:34.480 --> 00:14:41.100 Well, Rust comes from Mozilla and Rust has good support for compiling to WebAssembly, right? 00:14:41.100 --> 00:14:41.680 Oh, okay. 00:14:41.680 --> 00:14:48.260 And WebAssembly is the much better, faster, stronger way of running something in the browser. 00:14:48.920 --> 00:15:03.320 So if this thing got off the ground and it actually worked in a meaningful way and it could be compiled to WebAssembly, well, then, all of a sudden, we have a super awesome Python implementation in the browser on the client side. 00:15:03.320 --> 00:15:03.700 Yeah. 00:15:03.700 --> 00:15:08.320 So we could have Python really full stack on both ends, the server and the client. 00:15:08.320 --> 00:15:08.540 Yeah. 00:15:08.540 --> 00:15:09.460 Yeah. 00:15:09.460 --> 00:15:10.580 So it would be super cool. 00:15:10.580 --> 00:15:13.480 And this is one possible path down that. 00:15:13.480 --> 00:15:13.920 I don't know. 00:15:13.920 --> 00:15:15.100 I don't know that that's the goal. 00:15:15.100 --> 00:15:20.820 But they do talk about WebAssembly being an option in sort of a toy sense because it's not really quite ready yet. 00:15:20.820 --> 00:15:21.880 But there's some stuff there. 00:15:21.880 --> 00:15:24.480 So anyway, that was what really got my interest. 00:15:24.480 --> 00:15:26.580 I'm like, okay, another implementation address. 00:15:26.580 --> 00:15:27.300 It's kind of interesting. 00:15:27.300 --> 00:15:28.940 Like, oh, wait, WebAssembly support? 00:15:28.940 --> 00:15:31.340 That would be a big deal if that came on. 00:15:31.700 --> 00:15:36.840 Maybe in a few years we'll see something like a book called Python Script, just the good parts. 00:15:36.840 --> 00:15:37.960 Exactly. 00:15:37.960 --> 00:15:39.880 Very cool. 00:15:39.880 --> 00:15:40.160 All right. 00:15:40.160 --> 00:15:40.820 Well, what's the next one? 00:15:40.820 --> 00:15:43.480 The next one is this is an article. 00:15:43.480 --> 00:15:48.440 Actually, it's kind of a team up between Mike Driscoll and RealPython. 00:15:48.440 --> 00:15:54.240 So Mike Driscoll wrote an article on RealPython called Jupyter Notebook, an Introduction. 00:15:54.240 --> 00:16:01.040 And, of course, we've talked about Jupyter Notebooks before and a lot of stuff around data and around that. 00:16:01.520 --> 00:16:05.320 However, this is actually a tutorial that I think was kind of missing. 00:16:05.320 --> 00:16:11.080 And it's not all the cool things you can do with it, but it's, I know I want to use it. 00:16:11.080 --> 00:16:12.700 No, really, how do I start? 00:16:12.700 --> 00:16:20.720 And it really starts from installing it, of course, up to and then just kind of walks through some of the real basics, 00:16:20.720 --> 00:16:26.860 like creating a new, once you get it up and loaded, you've got a blank notebook or a blank interface, 00:16:26.860 --> 00:16:28.820 and you can create a new notebook in there. 00:16:29.280 --> 00:16:38.820 And then even something simple, like I didn't know that it was so easy to change the name just by clicking on the name of the untitled notebook that you get right away. 00:16:38.820 --> 00:16:41.740 And you can click and rename it something else. 00:16:41.740 --> 00:16:43.500 It talks about how to run cells. 00:16:43.500 --> 00:16:48.500 I think I played with Notebooks for quite a while before I learned the shortcut of Shift-Enter. 00:16:48.500 --> 00:16:48.760 Yep. 00:16:48.760 --> 00:16:55.820 And then it goes through just some of the menu options and some of the neat features that you might not be obvious if you didn't think of exploring them before. 00:16:55.820 --> 00:16:59.300 Like File has a save and checkpoint. 00:16:59.300 --> 00:17:04.660 As you're working on a notebook, you can, it's kind of like a version control thing built into it. 00:17:04.700 --> 00:17:07.640 You can just save it, save at different checkpoints and move on. 00:17:07.640 --> 00:17:08.080 That's cool. 00:17:08.080 --> 00:17:17.480 The edit menu has, I wouldn't have thought to look there for things like splitting and merging cells or moving cells around, but it's in there. 00:17:17.480 --> 00:17:20.980 And then the cell menu has a whole bunch of cool ways to run. 00:17:21.220 --> 00:17:29.320 I knew you could run a cell or run everything, but there's also things like run a subset or run everything above this cell or below this cell or something. 00:17:29.320 --> 00:17:29.880 Yeah. 00:17:29.880 --> 00:17:30.560 Excellent. 00:17:30.560 --> 00:17:38.220 At first, when I was reading this, there's a couple of questions that I have right away when I start with the notebooks that I, that wasn't covered. 00:17:38.220 --> 00:17:48.500 So I'm going to go ahead and push promote these practices right away is when you're installing Jupyter, if you're installing it with pip, do it in a virtual environment. 00:17:48.500 --> 00:17:55.840 And then all the installs within your notebooks will go in that environment and you can keep those separate from the rest of your work of everything else. 00:17:55.840 --> 00:18:03.440 Once you have a notebook up, you can pip install within there now with the percent pip install that works now. 00:18:03.440 --> 00:18:07.220 Thanks to, I think at least Jake VanderPlas publicized it. 00:18:07.220 --> 00:18:11.240 I'm not sure if he did the work or not, but so that's kind of really what I wanted to talk to about. 00:18:11.240 --> 00:18:14.320 This is a really good, I think a good article to send to people. 00:18:14.320 --> 00:18:21.280 If you're working with notebooks and you want to share something with somebody else and they've never experienced them before, just send them to this also. 00:18:21.280 --> 00:18:21.500 Yeah. 00:18:21.500 --> 00:18:23.820 It's a good resource and, you know, very comprehensive. 00:18:23.820 --> 00:18:26.980 So definitely a good one that Mike Driscoll put up there. 00:18:26.980 --> 00:18:34.140 Now I want to round out the show talking about the Python developer survey results from 2018. 00:18:34.140 --> 00:18:35.420 This is the end of 2018. 00:18:35.560 --> 00:18:38.800 So this is pretty fresh from the PSF and JetBrains. 00:18:38.800 --> 00:18:43.840 We talked about their stuff a year ago when they did it for 2017 and in 2017. 00:18:43.840 --> 00:18:46.480 So this is the next year. 00:18:46.480 --> 00:18:47.000 All right. 00:18:47.000 --> 00:18:47.760 Have you seen this yet, Brian? 00:18:47.760 --> 00:18:49.640 I've only briefly looked through it. 00:18:49.640 --> 00:18:49.880 Yeah. 00:18:49.940 --> 00:18:52.400 So there's a couple of interesting things that I pulled out. 00:18:52.400 --> 00:18:58.740 And one of them is that Python usage as a main language. 00:18:58.740 --> 00:19:02.580 So one of the questions I say is, so you're here because you use Python. 00:19:02.580 --> 00:19:04.780 Is that because you use it as a secondary thing? 00:19:04.780 --> 00:19:08.980 Like maybe you're a C++ developer, but you use it for DevOps or something like that. 00:19:08.980 --> 00:19:10.940 Or do you use it as your main language? 00:19:10.940 --> 00:19:15.520 The answer of Python is my main language is up 5% from last year. 00:19:15.520 --> 00:19:16.100 That's awesome. 00:19:16.100 --> 00:19:16.360 Yeah. 00:19:16.360 --> 00:19:21.380 So now it's 84% of the people and it was 79 last year. 00:19:21.380 --> 00:19:27.640 So when the PSF conducted the survey, the other big thing to talk about maybe is what do you use Python for? 00:19:27.640 --> 00:19:34.240 And the number one thing is data analysis and then web development and then machine learning. 00:19:34.240 --> 00:19:34.640 Okay. 00:19:34.640 --> 00:19:37.660 That sounds like a lot of data science and probably a lot of Jupyter stuff happening. 00:19:37.660 --> 00:19:38.260 Yeah. 00:19:38.260 --> 00:19:39.440 Between those top three, right? 00:19:39.440 --> 00:19:44.680 And also, so one of the things that's cool about this survey is now they've been doing it a few years is they have a comparison, 00:19:44.680 --> 00:19:46.880 like a sort of a trend predictor. 00:19:46.880 --> 00:19:49.640 Is the derivative positive or negative on this trend here? 00:19:49.640 --> 00:19:53.760 And so I put those numbers down for 2018 and 2017. 00:19:53.760 --> 00:19:59.400 So for data analysis, it's 59% of the people now use that for data analysis, use Python for data analysis. 00:19:59.400 --> 00:20:01.240 That's up 8%. 00:20:01.240 --> 00:20:01.660 Oh, wow. 00:20:01.660 --> 00:20:03.360 And, you know, it's above web development. 00:20:03.360 --> 00:20:07.680 So you might think it's a zero-sum game, like more people are not doing web and now they're doing data science. 00:20:07.960 --> 00:20:10.480 But no, like web development's up 2%. 00:20:10.480 --> 00:20:12.300 Machine learning is up 7%. 00:20:12.300 --> 00:20:13.860 Your R comment and all that, right? 00:20:13.860 --> 00:20:15.520 So pretty interesting. 00:20:15.520 --> 00:20:20.340 And the other thing they let you do is they let you ask for people who answer this question, 00:20:20.340 --> 00:20:25.920 let's partition their answers by whether Python is their main language or it's just kind of a secondary thing they do. 00:20:26.380 --> 00:20:36.680 So the main takeaway of this section is that web development is the only category with a large gap separating main users of Python versus secondary ones, 00:20:36.680 --> 00:20:42.080 like 56% versus 36%, 20% difference depending on how you use Python. 00:20:42.080 --> 00:20:43.840 All the other ones are pretty similar. 00:20:43.840 --> 00:20:44.160 Okay. 00:20:44.160 --> 00:20:44.920 Cross this too. 00:20:45.040 --> 00:20:46.920 Yeah, I want to point out a couple other things. 00:20:46.920 --> 00:20:47.220 Okay. 00:20:47.220 --> 00:20:54.400 From the survey, 93% say that they're involved with software testing or writing automated tests. 00:20:54.400 --> 00:20:56.400 That's pretty cool, right? 00:20:56.400 --> 00:20:56.960 That's awesome. 00:20:56.960 --> 00:20:59.640 Or at least maybe that's professional versus hobby. 00:20:59.640 --> 00:21:00.780 I don't know. 00:21:00.780 --> 00:21:01.940 Yeah, anyway. 00:21:01.940 --> 00:21:02.860 Yeah, but it's quite high. 00:21:02.860 --> 00:21:05.760 And they said they ask you which test frameworks you're using. 00:21:05.760 --> 00:21:08.340 For test framework, pytest is number one. 00:21:08.340 --> 00:21:09.300 Of course. 00:21:09.300 --> 00:21:10.520 Of course. 00:21:10.520 --> 00:21:11.160 That's pretty cool. 00:21:11.520 --> 00:21:21.300 Another one that's interesting is kind of like last year they said sort of scientific computing, data-oriented, like data analysis, machine learning, whatnot. 00:21:21.300 --> 00:21:23.800 How does that compare to web development? 00:21:23.800 --> 00:21:29.560 And it's now 27% web development, 28% scientific development, they call it. 00:21:29.560 --> 00:21:31.120 It's like data science plus machine learning. 00:21:31.120 --> 00:21:34.940 Also, Python versus legacy Python, super good news here. 00:21:34.940 --> 00:21:41.240 Of the surveyed folks, 84% are using Python 3 versus 16% for legacy Python. 00:21:41.440 --> 00:21:42.600 Yeah, that's awesome. 00:21:42.600 --> 00:21:43.660 So that's really good. 00:21:43.660 --> 00:21:45.260 And it also is growing very rapidly. 00:21:45.260 --> 00:21:48.580 So like 9% more people are using modern Python, which is great. 00:21:48.580 --> 00:21:52.980 Web frameworks, Flask is now the most popular web framework on the survey. 00:21:52.980 --> 00:21:54.200 Django was last year. 00:21:54.200 --> 00:21:54.920 Yeah, that's interesting. 00:21:54.920 --> 00:21:56.880 Yeah, but they're still really close. 00:21:56.880 --> 00:21:58.280 They're neck and neck for sure. 00:21:58.280 --> 00:22:02.780 Then it goes Tornado and then Pyramid and then Databases, Postgres, MySQL, SQLite. 00:22:02.780 --> 00:22:04.260 Those are the top three relational. 00:22:04.480 --> 00:22:08.180 And then MongoDB is tailing along in the NoSQL bit. 00:22:08.180 --> 00:22:12.000 And then ORMs, SQLAlchemy, and Django ORM are tied for that. 00:22:12.000 --> 00:22:14.700 Anyway, a bunch of like, that's just skimming the service. 00:22:14.700 --> 00:22:17.940 There's a ton of cool little interactive bits you can go explore the survey with. 00:22:17.940 --> 00:22:18.200 Yeah. 00:22:18.200 --> 00:22:18.800 Wow. 00:22:18.800 --> 00:22:19.420 Neat. 00:22:19.420 --> 00:22:19.640 Yeah. 00:22:19.640 --> 00:22:20.360 So good stuff. 00:22:20.360 --> 00:22:22.780 And yeah, people should go check it out. 00:22:22.780 --> 00:22:24.040 Of course, the links in the show notes. 00:22:24.040 --> 00:22:26.260 All right, Brian, you got anything else you want to share? 00:22:26.260 --> 00:22:28.020 That's not one of our main topics. 00:22:28.020 --> 00:22:33.140 Yeah, Nina mentioned to us, you can pronounce her name, but I have trouble with this. 00:22:33.140 --> 00:22:33.700 Zacharena? 00:22:33.700 --> 00:22:34.100 Yeah. 00:22:34.100 --> 00:22:34.880 Zacharenko? 00:22:34.880 --> 00:22:38.820 She hit us up and said, if we could plug this, it'd be great. 00:22:38.820 --> 00:22:42.880 Mentored sprints for diverse beginners are something that's at PyCon. 00:22:42.880 --> 00:22:47.360 I don't know if they've always been a thing, but that's something that's available this year. 00:22:47.360 --> 00:22:51.220 It's a newcomer's introduction to contributing to an open source project. 00:22:51.220 --> 00:22:52.940 And we have a link in the show notes. 00:22:52.940 --> 00:22:53.600 Yeah, cool. 00:22:53.600 --> 00:22:58.420 You do have to both, there's a call for applications, both for projects and for participants. 00:22:58.420 --> 00:23:00.600 That's open until March 14th. 00:23:00.600 --> 00:23:05.380 And a highlight, if you're wondering if this event is for you, it definitely is. 00:23:05.380 --> 00:23:07.520 And we'd love to have you take part of it. 00:23:07.520 --> 00:23:08.520 So that's a neat thing. 00:23:08.520 --> 00:23:14.640 Yeah, and they'll take place on Saturday, May 4th from 2.30 to 6.30 at the main PyCon. 00:23:14.640 --> 00:23:15.480 So that's cool. 00:23:15.480 --> 00:23:15.940 Yeah, neat. 00:23:15.940 --> 00:23:16.700 Yeah, that's a great thing. 00:23:16.700 --> 00:23:18.140 I'm glad we get a chance to highlight it. 00:23:18.140 --> 00:23:18.520 All right. 00:23:18.520 --> 00:23:18.800 All right. 00:23:18.800 --> 00:23:20.180 Do you got a joke for us? 00:23:20.180 --> 00:23:21.060 I don't have a joke. 00:23:21.060 --> 00:23:21.840 All right, I grabbed one. 00:23:22.180 --> 00:23:26.020 This one plays a little better in cartoon form, but I'm going to try it in audio. 00:23:26.020 --> 00:23:26.500 Okay. 00:23:26.500 --> 00:23:26.760 Okay. 00:23:26.760 --> 00:23:33.600 So if you have some pseudocode in like a sample.txt file or something like that, what's the easiest 00:23:33.600 --> 00:23:35.040 way to convert that to Python? 00:23:35.040 --> 00:23:35.640 I don't know. 00:23:35.640 --> 00:23:35.940 How? 00:23:35.940 --> 00:23:37.960 You change the extension to .py. 00:23:37.960 --> 00:23:43.920 For some reason, the pseudocode is very similarly written to Python. 00:23:43.920 --> 00:23:46.220 And of course, it speaks to Python's readability, right? 00:23:46.220 --> 00:23:49.640 So maybe you got to throw a colon in there somewhere, but yeah, pretty much. 00:23:50.140 --> 00:23:51.100 And I have an extra. 00:23:51.100 --> 00:23:52.620 I actually have two extras for you. 00:23:52.620 --> 00:23:52.900 Okay. 00:23:52.900 --> 00:23:54.860 The first one is this Python song. 00:23:54.860 --> 00:24:01.500 And it takes a bunch of like modern songs and it converts them into like learning chapters. 00:24:01.500 --> 00:24:05.040 So you learn things like you learn about Python basics, like looping and variables. 00:24:05.040 --> 00:24:06.540 You learn about classes. 00:24:06.540 --> 00:24:07.860 You learn about web design. 00:24:08.080 --> 00:24:10.520 So there's like a little different song for each one. 00:24:10.520 --> 00:24:15.000 It's these folks somewhere like Sweden or Norway or somewhere like that. 00:24:15.000 --> 00:24:15.500 I can't remember. 00:24:15.500 --> 00:24:16.080 Maybe Denmark. 00:24:16.080 --> 00:24:23.700 And they put together this really funny like six and a half minute set of little songs all 00:24:23.700 --> 00:24:24.740 put together into chapters. 00:24:24.740 --> 00:24:25.940 And it's all about Python. 00:24:25.940 --> 00:24:28.220 I'm totally going to subject my daughter to this. 00:24:28.220 --> 00:24:28.520 Yeah. 00:24:28.520 --> 00:24:28.840 Yeah. 00:24:28.840 --> 00:24:29.700 It's pretty funny. 00:24:29.700 --> 00:24:31.640 And then I'll throw one more in there really quick for you. 00:24:31.640 --> 00:24:33.080 I did my speaking of daughters. 00:24:33.160 --> 00:24:35.840 My daughter literally sent this in like about an hour ago. 00:24:35.840 --> 00:24:39.940 And speaking of all the data analysis and data science folks, I think this one might 00:24:39.940 --> 00:24:40.300 resonate. 00:24:40.300 --> 00:24:41.260 I'll let you. 00:24:41.260 --> 00:24:42.400 I'll let you be the judge. 00:24:42.400 --> 00:24:45.860 So an infinite number of mathematicians walk into a bar. 00:24:45.860 --> 00:24:48.460 The first one tells the bartender he wants a beer. 00:24:48.460 --> 00:24:50.680 The second one tells the bartender he wants half a beer. 00:24:50.680 --> 00:24:53.240 The third one says she wants a fourth of a beer. 00:24:53.240 --> 00:24:57.480 The bartender interrupts and puts two beers on the table and says, people, you really need 00:24:57.480 --> 00:24:58.140 to learn your limits. 00:24:58.140 --> 00:25:01.940 Okay. 00:25:01.940 --> 00:25:02.620 That's funny. 00:25:02.620 --> 00:25:04.060 It's pretty good, right? 00:25:04.060 --> 00:25:04.940 Yeah, definitely. 00:25:04.940 --> 00:25:05.500 Pretty good. 00:25:05.500 --> 00:25:06.660 All right. 00:25:06.660 --> 00:25:07.200 Super. 00:25:07.200 --> 00:25:07.720 Okay. 00:25:07.720 --> 00:25:10.580 Well, Brian, these joke segments always make me smile. 00:25:10.580 --> 00:25:11.640 Yeah, definitely. 00:25:11.640 --> 00:25:12.720 Yeah. 00:25:12.720 --> 00:25:14.680 Thanks for doing the episode as always. 00:25:14.680 --> 00:25:15.120 All right. 00:25:15.120 --> 00:25:15.620 Thank you. 00:25:15.620 --> 00:25:15.860 Yep. 00:25:15.860 --> 00:25:18.240 And I guess we will see everyone at Pi Cascades, right? 00:25:18.240 --> 00:25:18.620 Oh, yeah. 00:25:18.620 --> 00:25:19.940 That will be the next thing. 00:25:19.940 --> 00:25:20.500 That's this week. 00:25:20.500 --> 00:25:20.860 Yeah. 00:25:20.860 --> 00:25:23.140 We head out in like three days or something like that for the conference. 00:25:23.140 --> 00:25:25.920 So if you're going to be there, hopefully we'll get a chance to meet you. 00:25:25.980 --> 00:25:27.900 Otherwise, see you some other place. 00:25:27.900 --> 00:25:28.320 All right. 00:25:28.320 --> 00:25:28.920 Bye. 00:25:28.920 --> 00:25:29.140 Bye. 00:25:29.140 --> 00:25:30.960 Thank you for listening to Python Bytes. 00:25:30.960 --> 00:25:33.480 Follow the show on Twitter via at Python Bytes. 00:25:33.480 --> 00:25:36.380 That's Python Bytes as in B-Y-T-E-S. 00:25:36.380 --> 00:25:39.780 And get the full show notes at pythonbytes.fm. 00:25:39.780 --> 00:25:44.120 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:25:44.120 --> 00:25:46.840 We're always on the lookout for sharing something cool. 00:25:46.840 --> 00:25:50.220 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:25:50.220 --> 00:25:53.740 Thank you for listening and sharing this podcast with your friends and colleagues.