WEBVTT 00:00:00.001 --> 00:00:05.780 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:05.780 --> 00:00:10.680 This is episode 74, recorded on April 17, 2018. 00:00:10.680 --> 00:00:11.780 I'm Michael Kennedy. 00:00:11.780 --> 00:00:12.580 And I'm Brian Okken. 00:00:12.580 --> 00:00:13.600 And I'm Matt Harrison. 00:00:13.600 --> 00:00:14.540 Yeah, that's right. 00:00:14.540 --> 00:00:17.180 We've got a special guest here on the show. 00:00:17.180 --> 00:00:22.760 So Brian and I decided to invite Matt Harrison to mix things up a little bit and bring a slightly different perspective. 00:00:22.760 --> 00:00:28.740 And we decided we're going to try this little experiment from time to time, you know, once a month, once every six weeks, something like that. 00:00:28.980 --> 00:00:30.660 So, Matt, welcome to the show. 00:00:30.660 --> 00:00:31.200 Thanks. 00:00:31.200 --> 00:00:31.980 Thanks for having me. 00:00:31.980 --> 00:00:32.580 Good to be here. 00:00:32.580 --> 00:00:33.280 Good to have you here. 00:00:33.280 --> 00:00:36.220 So before we get into the topics, I just want to say thank you to Datadog. 00:00:36.220 --> 00:00:37.860 They're sponsoring this episode. 00:00:37.860 --> 00:00:41.080 Check them out at pythonbytes.fm/Datadog. 00:00:41.080 --> 00:00:42.320 I'll tell you more about them later. 00:00:42.320 --> 00:00:47.180 Brian, you've been running your own open source project lately, and it's been fun, right? 00:00:47.180 --> 00:00:48.780 Yeah, I started a little project. 00:00:48.780 --> 00:00:49.620 I called it Cards. 00:00:49.620 --> 00:00:52.300 But the intent was to – it's on GitHub. 00:00:52.300 --> 00:01:01.040 And the intent is to talk about, like, how I'm going to go about testing it using all the – what I think of as good test methodologies. 00:01:01.420 --> 00:01:06.880 But doing it in the open on an open source project, I'm getting contributors already, which is really cool. 00:01:06.880 --> 00:01:07.360 That's awesome. 00:01:07.360 --> 00:01:08.700 I don't really know the conventions. 00:01:08.700 --> 00:01:14.520 And even though I've been programming for decades, I'm kind of new to the – actually contributing to open source. 00:01:14.720 --> 00:01:19.100 Well, and it's different on GitHub than it was, say, in Subversion or something like that, right? 00:01:19.100 --> 00:01:22.120 There's the whole Git flow and PRs and that whole thing, right? 00:01:22.120 --> 00:01:32.740 Yeah, and how to deal with, like – how to deal with branching with – branching and forking with GitHub versus doing something out in the open on GitHub is one thing. 00:01:32.740 --> 00:01:37.080 But actually running it like a normal open source project is completely different. 00:01:37.780 --> 00:01:45.000 So I had a – actually, Anthony Shaw did a pull request on Cards, and he did a really good job on it. 00:01:45.000 --> 00:01:49.360 And I was – one of the things is he started it with a WIP for a work in progress. 00:01:49.360 --> 00:01:51.940 And I didn't know that was a convention already. 00:01:51.940 --> 00:02:00.880 So I looked around, and there's some – if people are new to this and want to learn more, I'm sure everybody out there already knows about open source stuff. 00:02:00.880 --> 00:02:01.380 But anyway. 00:02:01.380 --> 00:02:02.960 I would not make that assumption. 00:02:02.960 --> 00:02:04.040 I definitely would. 00:02:04.160 --> 00:02:13.100 I mean, it feels that way, but I think it's more of a dark matter sort of experience where, like, you see the stuff that people are doing. 00:02:13.100 --> 00:02:15.560 But that doesn't mean everyone is participating. 00:02:15.560 --> 00:02:18.920 And many people, I think, are just wanting to get into it. 00:02:18.920 --> 00:02:19.700 What do you think, Matt? 00:02:19.700 --> 00:02:24.560 I've been using open source since the mailing list days, and you had to talk over the mailing list. 00:02:24.560 --> 00:02:28.000 And so GitHub, when it first came out, was sort of a big change for me. 00:02:28.220 --> 00:02:39.900 And I think a lot of people use GitHub as just, I'm going to throw my source over the fence and see what happens to it, rather than maintaining it as a real open source project and trying to include community and get contributions. 00:02:39.900 --> 00:02:42.640 So I'm curious to see how this works. 00:02:42.640 --> 00:02:49.520 And I think, like Michael said, don't make the assumption that everyone knows how to do these things, even though some people are doing it. 00:02:49.520 --> 00:02:51.800 There's certainly a lot of people who can learn from this. 00:02:51.800 --> 00:02:53.560 There's a couple of things I wanted to throw out. 00:02:53.560 --> 00:02:58.600 This is from 2015 already, but it's an article called How to Write the Perfect Pull Request. 00:02:58.600 --> 00:03:05.540 And it kind of talks about the philosophy sort of both with approaching the pull request when you're first getting started. 00:03:05.540 --> 00:03:11.180 And it even talks about the WIP trick to tell the person that owns the repo, you're not done with it. 00:03:11.180 --> 00:03:12.960 You just kind of like early feedback on it. 00:03:12.960 --> 00:03:23.300 And then there's some advice on offering feedback to pull request submitters and also responding to that feedback as the submitter as well. 00:03:23.300 --> 00:03:26.760 It's a short read, but there's some good tips there. 00:03:26.760 --> 00:03:34.120 And then I'm also really excited about VM's new book coming out from Pragmatic called Forge Your Future with Open Source. 00:03:34.120 --> 00:03:37.200 And it includes things like how to deal with pull requests and everything. 00:03:37.200 --> 00:03:39.060 That sounds like such an interesting book. 00:03:39.060 --> 00:03:39.720 I love it. 00:03:39.800 --> 00:03:51.800 And I just like to say, like, I think getting using open source and getting involved for someone who wants to get a break into learning Python or learning a library or even getting a job. 00:03:51.800 --> 00:03:53.560 That's an awesome way to do it. 00:03:53.560 --> 00:03:54.200 I agree. 00:03:54.200 --> 00:03:59.440 And one of the problems people run into is they work in a place that doesn't use Python. 00:03:59.440 --> 00:04:04.580 And so they don't have a place to actually practice Python outside of just like toy things. 00:04:04.580 --> 00:04:11.700 So contributing to open source lets you make a meaningful contribution, even though maybe you're a Java or .NET shop and they're like Python? 00:04:11.700 --> 00:04:12.000 No. 00:04:12.000 --> 00:04:16.960 Or yeah, they might even be using Subversion or some other source control. 00:04:16.960 --> 00:04:17.700 Absolutely. 00:04:17.700 --> 00:04:18.380 Cool. 00:04:18.380 --> 00:04:18.860 This is a good one. 00:04:18.860 --> 00:04:19.460 This is a good one, Brian. 00:04:19.460 --> 00:04:23.000 So, Matt, you and I both do in-person training periodically. 00:04:23.580 --> 00:04:27.300 And I just did a class last week, I guess, a short class. 00:04:27.300 --> 00:04:34.880 And it was a lot of folks coming from a traditional sort of MATLAB, Mathematica background and moving into things like Jupyter. 00:04:34.880 --> 00:04:36.700 And I think that this might be a trend. 00:04:36.700 --> 00:04:37.100 What do you think? 00:04:37.340 --> 00:04:43.640 Yeah, I think we're seeing Jupyter changing from early adopter to sort of normalcy. 00:04:43.640 --> 00:04:45.680 I found a thing going around Twitter. 00:04:45.680 --> 00:04:53.720 Paul Romer, who's an economy professor at NYU, tweeted about his experience with Mathematica and Jupyter. 00:04:53.720 --> 00:04:59.900 And he referenced an article in The Atlantic about both of those products, Mathematica and Jupyter. 00:05:00.620 --> 00:05:04.280 And for me, like I said, I've been involved with open source for a long time. 00:05:04.280 --> 00:05:08.800 You don't often see stuff in The Atlantic or professors, economy professors. 00:05:08.800 --> 00:05:10.520 This isn't a computer science professor. 00:05:10.520 --> 00:05:14.660 This is an economy professor posting about Python and Jupyter. 00:05:14.660 --> 00:05:16.720 So, really cool stuff there. 00:05:16.720 --> 00:05:18.820 This is such an interesting find. 00:05:18.820 --> 00:05:24.140 And if you open, people should open up the Atlantic link that you're linking to here. 00:05:24.140 --> 00:05:26.740 Because, wow, that's a pretty provocative picture. 00:05:26.740 --> 00:05:28.780 There's like a formal paper. 00:05:29.320 --> 00:05:31.540 It says the scientific paper is obsolete. 00:05:31.540 --> 00:05:36.060 And there's like a paper, like an academic paper, that's literally on fire. 00:05:36.060 --> 00:05:38.300 Like in an animated way, like full screen. 00:05:38.300 --> 00:05:38.840 It's crazy. 00:05:38.840 --> 00:05:45.640 Yeah, and it makes reference to the discovery of gravitational waves and how there was a paper on that. 00:05:45.640 --> 00:05:50.580 But they also, along with that, published a Jupyter notebook where you could go out on your own. 00:05:50.580 --> 00:05:53.540 And you could look at their code, look at their data. 00:05:53.940 --> 00:06:02.000 And it had embedded text in it as well and basically discover gravitational waves or go through the same sequence and reproduce their science. 00:06:02.000 --> 00:06:03.660 So, I thought that was pretty cool. 00:06:03.660 --> 00:06:06.360 A quote from Mr. Romer. 00:06:06.360 --> 00:06:13.680 He says that Jupyter is the new open source alternative to Mathematica that is well on its way to becoming the standard for exchanging research results. 00:06:13.880 --> 00:06:14.420 I agree. 00:06:14.420 --> 00:06:23.180 I think academics has been too dependent on these couple of big, really expensive lock-in type of things like Mathematica and MATLAB. 00:06:23.180 --> 00:06:25.520 I'm also thinking of journals and stuff like that. 00:06:25.520 --> 00:06:29.120 This sort of open source paper in the form of Jupyter kind of touches on both of those. 00:06:29.120 --> 00:06:29.880 Brian, what do you think? 00:06:29.960 --> 00:06:31.640 I'm actually really excited about all that. 00:06:31.640 --> 00:06:42.560 I was just listening to a topic not too long ago about how the – actually, it was one of your podcasts about the academic journals that are – a lot of times nobody actually follows the steps along. 00:06:42.560 --> 00:06:48.140 But having the code out on Jupyter notebooks just allows everybody to go and follow along right there. 00:06:48.480 --> 00:07:02.100 One of the main points of these articles isn't that there's a notebook per se, but the compelling reason for using Python and Jupyter is not necessarily that the technology is better, but that there's a huge community around it. 00:07:02.100 --> 00:07:12.740 So, you know, they make the argument that the Wolfram notebook might be prettier or whatnot, but you have so many people who are contributing to these open source projects. 00:07:12.880 --> 00:07:19.500 You've got Matplotlib for graphics, SymPy for symbolic math, NumPy, SciPy, Pandas, NLTK. 00:07:19.500 --> 00:07:28.120 And, you know, if you look at PyPI, there's 135,000 packages last week on that, and it's really hard to compete with that. 00:07:28.120 --> 00:07:29.000 That's super compelling. 00:07:29.000 --> 00:07:30.160 So, really cool. 00:07:30.160 --> 00:07:31.560 Yeah, it is super compelling. 00:07:31.560 --> 00:07:40.360 So, speaking of community, Brian, you know I love to, like, pull on the Stack Overflow developer survey and, like, try to dig out results from the community, right? 00:07:40.360 --> 00:07:40.840 Yes. 00:07:40.840 --> 00:07:41.760 Yeah, that's always fun. 00:07:42.040 --> 00:07:50.000 So, there's another one that just came out that gives us a different perspective and also is more Python-focused than that one, right? 00:07:50.000 --> 00:07:52.200 That's, like, broad software development. 00:07:52.200 --> 00:08:00.160 So, JetBrains, the PyCharm team, teamed up with the PSF just at the end of last year to do a Python developer survey. 00:08:00.160 --> 00:08:06.840 And so, the thing I'm linking to is Python developer survey 2017 results, but it's, like, December, right? 00:08:06.840 --> 00:08:08.300 So, it's pretty relevant still. 00:08:08.300 --> 00:08:09.180 It's pretty fresh. 00:08:09.180 --> 00:08:10.560 They were just talking about it on their blog. 00:08:11.300 --> 00:08:17.960 And so, this is a really nice piece of sort of almost journalism around data science, I think. 00:08:17.960 --> 00:08:19.620 It's actually, they've really written it up nice. 00:08:19.620 --> 00:08:23.180 They show you graphs, and they're like, here's the main takeaway from this section. 00:08:23.180 --> 00:08:24.700 Here's the main takeaway from that section. 00:08:24.700 --> 00:08:26.720 So, how about I share some takeaways with you? 00:08:26.720 --> 00:08:27.400 Yes. 00:08:27.400 --> 00:08:27.520 Yeah. 00:08:28.400 --> 00:08:28.860 All right. 00:08:28.860 --> 00:08:32.980 So, the first one is, says, of the people that they interviewed. 00:08:32.980 --> 00:08:36.880 Now, this is obviously a self-selecting crowd. 00:08:37.320 --> 00:08:39.940 But the question was, you're obviously doing Python. 00:08:39.940 --> 00:08:42.320 Is this your main language or a secondary language? 00:08:42.320 --> 00:08:46.380 And they said 80% of the people are, Python is their main language. 00:08:46.380 --> 00:08:47.020 That was pretty cool. 00:08:47.020 --> 00:08:54.500 They said data analysis is actually just as popular as web development, which is pretty cool. 00:08:55.140 --> 00:08:59.680 So, there's basically as many Python web developers as there are data scientists. 00:08:59.680 --> 00:09:00.840 Does that surprise either of you? 00:09:00.840 --> 00:09:03.640 To me, Python felt like a web thing for most folks. 00:09:04.040 --> 00:09:04.400 Yeah. 00:09:04.400 --> 00:09:06.640 I mean, recently, it wouldn't surprise me. 00:09:06.640 --> 00:09:10.720 But, you know, if you would have said that two years ago, it probably wasn't the case two years ago. 00:09:10.720 --> 00:09:12.940 But now, yeah, it's not surprising to me. 00:09:12.940 --> 00:09:13.300 Yeah. 00:09:13.300 --> 00:09:16.020 They also talk about the growth of Python. 00:09:16.020 --> 00:09:18.620 And Brian and I, you and I, we've touched on this a few times. 00:09:18.620 --> 00:09:24.120 But they're also confirming, like, we think that massive hockey stick growth is largely data science people coming in. 00:09:24.120 --> 00:09:25.200 Yeah, it could be. 00:09:25.200 --> 00:09:26.920 There's, I think there's a lot of room to grow. 00:09:26.920 --> 00:09:36.140 There's a lot of people who are using Excel and some of these tools that you mentioned who probably want to migrate to something like Python for the libraries and machine learning capabilities. 00:09:36.140 --> 00:09:36.700 Yeah. 00:09:36.700 --> 00:09:40.540 Another interesting one was Python versus legacy Python. 00:09:40.540 --> 00:09:48.220 So, Python is at 75% usage among this group and 25% for Python 2. 00:09:48.220 --> 00:09:54.760 And if you look at the curve, that's, like, increasing in time, like, the rate at which people are moving to Python 3. 00:09:54.760 --> 00:09:56.060 So, that's really cool. 00:09:56.420 --> 00:09:57.300 Yeah, that's cool. 00:09:57.300 --> 00:10:00.240 Yeah, you wonder how much self-selecting is there, right? 00:10:00.240 --> 00:10:03.000 The legacy laggards didn't want to participate in this. 00:10:03.000 --> 00:10:04.300 That's right. 00:10:04.300 --> 00:10:05.520 I don't need no stick-in surveys. 00:10:05.520 --> 00:10:08.300 I learned everything about Python I need to know in 2008. 00:10:08.300 --> 00:10:09.280 Okay. 00:10:09.280 --> 00:10:13.360 So, they also talked about where code runs, where people run their Python code. 00:10:13.360 --> 00:10:17.040 And this is, I don't think it clues, like, the hosted notebook type stuff. 00:10:17.040 --> 00:10:18.140 So, probably not that. 00:10:18.140 --> 00:10:20.380 But 67% AWS. 00:10:20.380 --> 00:10:21.860 Brian, does that surprise you? 00:10:21.860 --> 00:10:22.840 I'm going to plead. 00:10:22.960 --> 00:10:29.000 I'm not in the field, in the web sort of space to know, really, where it's running. 00:10:29.000 --> 00:10:39.100 My basis for sort of judging the use of AWS compared to other platforms is when AWS goes down, what parts of the internet are no longer accessible? 00:10:39.100 --> 00:10:40.160 And they're pretty broad. 00:10:40.380 --> 00:10:43.320 Yeah, I would think that it'd actually be a little bit higher than that. 00:10:43.320 --> 00:10:50.280 The ones that surprised me was you've got Google App Engine at 29, Heroku at 26, Digital Ocean at 23. 00:10:50.280 --> 00:10:53.760 And the last one they say is Microsoft Azure at 16. 00:10:54.200 --> 00:10:57.220 And I think that 16 is probably going to change a little bit. 00:10:57.220 --> 00:11:01.520 They've been doing a lot of hiring in the Python space and getting some prominent Python people. 00:11:01.520 --> 00:11:04.480 So, there's a big push from Microsoft on that. 00:11:04.480 --> 00:11:07.540 They're definitely focused on Python in a lot of important ways. 00:11:07.540 --> 00:11:09.040 They now have Azure Notebooks. 00:11:09.040 --> 00:11:14.900 They have Brett Cannon, Steve Dower, both Python Core devs, working there. 00:11:14.900 --> 00:11:18.720 They brought the guy who did the Python extension for Visual Studio Code in-house. 00:11:18.720 --> 00:11:19.940 Like, they're doing a bunch of cool stuff. 00:11:20.500 --> 00:11:21.720 All right, so a few more takeaways. 00:11:21.720 --> 00:11:23.200 Team size, right? 00:11:23.200 --> 00:11:29.160 Like, you think of how big of a group do you, you know, like how large of a team do you work on? 00:11:29.160 --> 00:11:34.980 And if you think about, like, one of the advantages of Python is you don't need a large set of people to build something interesting. 00:11:34.980 --> 00:11:36.420 And I think that's reflected here. 00:11:36.420 --> 00:11:43.020 So, it says, like, team size, 2 to 7 people, 75%, 74% of the respondents are in that 2 to 7 group. 00:11:43.020 --> 00:11:45.040 And then 8 to 12 is 16. 00:11:45.380 --> 00:11:54.480 And then, basically, above that, above 12 people, all the way up to, like, 40 or larger, is 9% of the balance, basically. 00:11:54.480 --> 00:11:55.880 So, really small teams. 00:11:55.880 --> 00:11:58.200 And then operating systems, Brian, you touched on this a lot. 00:11:58.200 --> 00:12:03.440 49% of the people are still using, are currently using Windows as their OS. 00:12:03.440 --> 00:12:05.960 Then 19% for Linux, 15% for Mac. 00:12:06.620 --> 00:12:12.560 And, like you said before, like, Windows often gets the short end of the stick and sort of testing and examples and stuff. 00:12:12.560 --> 00:12:13.660 But it probably shouldn't. 00:12:13.660 --> 00:12:18.780 Yeah, one of the things I want to go back to is the cloud platforms that we talked about. 00:12:18.780 --> 00:12:26.420 One of the things that's interesting to there is that, clearly, some people are running on multiple platforms because that's over 100%. 00:12:26.420 --> 00:12:27.480 Yeah, that's interesting. 00:12:27.680 --> 00:12:34.860 Like, I could tell you for sure that, like, if somebody asked me, which of these platforms do you use, I would definitely check the DigitalOcean and AWS boxes. 00:12:34.860 --> 00:12:35.380 Okay. 00:12:35.380 --> 00:12:41.540 Because, like, for example, the main server for our podcast and the database server runs on DigitalOcean droplets. 00:12:42.020 --> 00:12:51.120 But when you interact with it and you get, like, say, an email, especially around the training stuff, that goes through Amazon simple email notification service and things like that. 00:12:51.120 --> 00:12:53.100 So, like, there's this blend of them. 00:12:53.100 --> 00:12:54.380 Yeah, I'm similar. 00:12:54.380 --> 00:12:57.960 I've done Heroku and DigitalOcean and both had stuff in S3 as well. 00:12:57.960 --> 00:13:00.140 So, it's not an either or. 00:13:00.140 --> 00:13:03.560 One of the things I thought was interesting was the operating systems. 00:13:03.560 --> 00:13:10.800 I mean, like you said, Windows tends to get, you know, people have something in their heart against it or whatnot. 00:13:10.800 --> 00:13:13.360 But I was surprised that Mac was so low on this. 00:13:13.360 --> 00:13:14.140 Yeah. 00:13:14.140 --> 00:13:14.920 Interesting. 00:13:14.920 --> 00:13:16.400 I wouldn't have thought that at all. 00:13:16.400 --> 00:13:21.600 But you go to the, I'm telling you, Dark Matter developers, that's what it is. 00:13:21.600 --> 00:13:22.280 Yeah. 00:13:22.280 --> 00:13:23.560 It's interesting. 00:13:23.560 --> 00:13:26.580 I think the story on Windows is going to get better. 00:13:26.580 --> 00:13:36.840 I believe the new version of Python is going to use MS Build and not Visual Studio 2008 for its compilation stuff during install, 00:13:37.000 --> 00:13:45.280 which means, like, modern versions of Windows will be able to install stuff without, like, installing a 2008 version of Visual Studio, which will be real nice. 00:13:45.280 --> 00:13:45.960 All right. 00:13:45.960 --> 00:13:50.840 So, before we get to the next one, I want to just tell you both a little bit about Datadog. 00:13:50.840 --> 00:14:04.160 So, speaking of stuff hosted in the clouds and spanning multiple machines and things like that, Datadog is a monitoring solution that provides visibility and tracks down issues with distributed systems involving Python applications. 00:14:04.320 --> 00:14:09.580 So, within just a few minutes, you can find bottlenecks in your code by exploring graphs and rich dashboards. 00:14:09.580 --> 00:14:18.440 And you can visualize your whole performance across all of your apps, which, when you're doing distributed programming or distributed apps, microservice type things, that's a huge deal. 00:14:19.260 --> 00:14:28.420 And you can go to pythonbytes.fm/datadog, do a quick little trial there, and you'll get a free Datadog t-shirt, which is pretty cute. 00:14:28.420 --> 00:14:31.680 So, check them out and let them know you appreciate them supporting the show. 00:14:31.680 --> 00:14:32.400 All right, Brian. 00:14:32.400 --> 00:14:35.600 I'm a big fan of databases, especially shiny new ones. 00:14:35.600 --> 00:14:37.180 You've got a really new one yet. 00:14:37.180 --> 00:14:39.280 Like, I can't even get this one yet, but it's still pretty cool. 00:14:39.340 --> 00:14:40.380 Yeah, you can't get it. 00:14:40.380 --> 00:14:43.740 But one of our listeners, Arash, I think that's his name. 00:14:43.740 --> 00:14:50.680 Anyway, he let us know about EdgeDB, and EdgeDB has a blog post up. 00:14:50.680 --> 00:14:52.300 It says, EdgeDB, a new beginning. 00:14:52.300 --> 00:14:59.740 And at first, I thought, yeah, okay, we'll keep an eye on this, and maybe we'll cover it later when we can actually play with it. 00:14:59.740 --> 00:15:02.860 Because it's a new database that's not available to use yet. 00:15:02.860 --> 00:15:05.220 However, it's going to be open source. 00:15:05.560 --> 00:15:10.760 And the reason why I brought it up now is because it's coming from some fairly interesting people. 00:15:10.760 --> 00:15:13.340 It has some pretty powerful Python origins, right? 00:15:13.340 --> 00:15:14.000 Yeah. 00:15:14.000 --> 00:15:21.360 Well, so, like, for instance, Elvis and Yuri, and I'm not going to try to pronounce their last names, or they will flame me. 00:15:21.360 --> 00:15:25.760 Part of this, and they're the people that brought us AsyncIO and UVloop. 00:15:25.760 --> 00:15:27.080 So, that's pretty impressive. 00:15:27.080 --> 00:15:27.860 That's very impressive. 00:15:27.860 --> 00:15:32.260 One of the things that's interesting is looking at the kind of code that you get with this. 00:15:32.680 --> 00:15:41.540 So, they're trying to attack the problem they're addressing is that document databases have some issues with just scalability. 00:15:41.540 --> 00:15:49.200 After your project gets larger, the schemaless part of it sometimes can be hard to deal with. 00:15:49.200 --> 00:15:52.520 A lot of people deal with it fine, but they see it as part of a problem. 00:15:52.520 --> 00:15:58.640 And relational databases are growing a lot, and Postgres, for instance, is keeping up to date. 00:15:58.640 --> 00:16:07.700 But the interface, how you interact with the database, the schemas and the underlying API to the database, hasn't changed much in a long time. 00:16:07.700 --> 00:16:09.880 So, they're trying to change that. 00:16:09.880 --> 00:16:13.880 And I forget what they call it, an object relational? 00:16:13.880 --> 00:16:15.700 They call it an object database. 00:16:15.700 --> 00:16:17.300 Object relational database. 00:16:17.500 --> 00:16:18.260 Yeah, yeah, yeah. 00:16:18.260 --> 00:16:21.760 Yeah, not like the traditional ones, they say, from the 80s. 00:16:21.760 --> 00:16:28.060 Yeah, so one of the things to look at, if you're going to look at anything, is to go to the link and look at the example. 00:16:28.060 --> 00:16:30.280 They have a new query language called EdgeQL. 00:16:30.980 --> 00:16:40.080 So, they have a different way to write a schema that's fairly, doesn't really look like Python, but it's type-based, and it's fairly expressive. 00:16:40.080 --> 00:16:41.180 It's pretty interesting. 00:16:41.180 --> 00:16:47.580 So, instead of saying, like, let's have a class and map that to the database, like, say, SQLAlchemy or MongoEngine might, 00:16:48.140 --> 00:16:55.860 they said, we're going to define our own data definition language, our own DSL, but it's really incredibly simple. 00:16:55.860 --> 00:17:01.980 Like, doing the relationships with cascading this and that in SQLAlchemy, I always get that wrong. 00:17:01.980 --> 00:17:03.820 You know, I have to look it up. 00:17:03.820 --> 00:17:10.700 And this is like, you want a foreign key relationship, you say link assignees, goes to a user definition, and the cardinality is double star, 00:17:10.700 --> 00:17:13.500 so I'm guessing multiple, many-to-many sort of thing. 00:17:14.000 --> 00:17:17.120 And that's on, like, incredibly short there. 00:17:17.120 --> 00:17:23.940 So, my first impression was, like, really a new schema definition language, a new query language. 00:17:23.940 --> 00:17:29.420 Like, seriously, like, it's just like, okay, well, I'm tired of SQL, and I'm tired of the other ways of programming. 00:17:29.420 --> 00:17:31.960 So, we're going to invent, like, another thing that people are going to get tired of. 00:17:31.960 --> 00:17:33.740 But it's starting to grow on me. 00:17:33.740 --> 00:17:34.820 Matt, what do you think about this? 00:17:34.820 --> 00:17:43.980 Whenever you say, I'm going to invent something that's going to replace SQL, I think you hear a million developers cringe because they all know SQL, right? 00:17:43.980 --> 00:17:50.380 But I think if you can get the five-minute out-of-the-box presentation where it's like, this is a compelling reason to use it. 00:17:50.380 --> 00:17:57.360 And, you know, everyone, or at least most Python people I know, want to use, like, an ORM and interact with the database that way. 00:17:57.360 --> 00:17:59.420 But there is this impedance mismatch with those. 00:17:59.420 --> 00:18:08.960 So, if you can nail that down and have, like, a really smooth five-minute out-of-the-box experience with this, I think you could get a lot of people interested in them. 00:18:08.960 --> 00:18:09.980 Yeah, it's pretty interesting. 00:18:09.980 --> 00:18:11.200 I'm glad you brought it, Brian. 00:18:11.240 --> 00:18:11.480 Thanks. 00:18:11.480 --> 00:18:12.160 All right. 00:18:12.160 --> 00:18:16.520 Matt, so you're a fan of the Wizard of the Oz is what I'm to draw from this next one. 00:18:16.520 --> 00:18:17.580 Yeah, yeah. 00:18:17.580 --> 00:18:18.740 Follow the yellow brick road. 00:18:18.740 --> 00:18:21.740 So, I've been, I do corporate training and I do consulting. 00:18:21.740 --> 00:18:26.000 And one of the things that I do when I'm doing data stuff is visualization. 00:18:26.000 --> 00:18:27.960 Visualization is pretty important. 00:18:27.960 --> 00:18:35.160 I mean, I've literally found bugs by visualizing something that we couldn't have found just by looking at the data necessarily. 00:18:35.800 --> 00:18:39.940 And so, visualization is also important in the evaluation of machine learning projects. 00:18:39.940 --> 00:18:44.480 And one of the projects that I've been liking and using recently is a project called Yellow Brick. 00:18:44.480 --> 00:18:47.780 So, I guess this will take you to the Wizard of Oz if you follow it. 00:18:47.780 --> 00:18:54.540 It's not a new project necessarily, but it's a project that's alive and going and being worked on still. 00:18:55.060 --> 00:18:59.640 And what it does is it offers visualizations for various machine learning algorithms. 00:18:59.640 --> 00:19:05.060 So, if you use a tool like scikit-learn, you can go to their website and they'll have all these visualizations up there. 00:19:05.060 --> 00:19:08.400 But those aren't included in the library for scikit-learn. 00:19:08.400 --> 00:19:09.520 You either need to... 00:19:09.520 --> 00:19:11.380 You've got to go create them yourself, right? 00:19:11.380 --> 00:19:11.860 Yeah. 00:19:11.860 --> 00:19:15.280 You've got to either copy and paste their code or go find some Stack Overflow. 00:19:15.280 --> 00:19:17.100 So, what I've been doing... 00:19:17.100 --> 00:19:21.820 I mean, I have a project on my GitHub, MLViz, that I just have my own... 00:19:21.820 --> 00:19:24.000 Here's the visualizations that I commonly use. 00:19:24.000 --> 00:19:25.440 And then I use my little library. 00:19:25.440 --> 00:19:28.020 But I'm looking to replace it with this. 00:19:28.020 --> 00:19:30.900 And I've been using this for some of my training as well recently. 00:19:30.900 --> 00:19:36.220 So, it's got visualizations for classification, regression, clustering, and text. 00:19:36.220 --> 00:19:44.200 One of the cool things about it is if you're familiar with SK-learn or scikit-learn is that it has a similar API to that. 00:19:44.200 --> 00:19:45.000 So, there's a fit. 00:19:45.000 --> 00:19:46.600 You can fit your visualization. 00:19:46.600 --> 00:19:47.860 You can transform it. 00:19:47.860 --> 00:19:49.600 And then you call this method called POOF. 00:19:49.600 --> 00:19:52.720 And that will pull up a map plotlet for your POOF. 00:19:52.720 --> 00:19:54.200 That's the magic method that they have. 00:19:54.200 --> 00:19:55.060 How do you spell POOF? 00:19:55.060 --> 00:19:55.780 P-U-F-F? 00:19:55.780 --> 00:19:57.360 P-O-O-F. 00:19:57.360 --> 00:19:57.940 POOF. 00:19:57.940 --> 00:19:59.000 Yeah, POOF. 00:19:59.000 --> 00:19:59.260 Gotcha. 00:19:59.260 --> 00:20:00.060 Yeah. 00:20:00.060 --> 00:20:00.800 Perfect. 00:20:01.300 --> 00:20:01.660 Love it. 00:20:01.660 --> 00:20:09.080 So, just a nice little library to, you know, one of those things that can be annoying or that you always go and copy and paste that code. 00:20:09.080 --> 00:20:11.360 And if you can just pip install this and use it. 00:20:11.360 --> 00:20:12.820 And it has a great interface. 00:20:12.820 --> 00:20:14.260 It makes your life a little bit easier. 00:20:14.260 --> 00:20:15.120 Yeah, absolutely. 00:20:15.120 --> 00:20:22.180 So, Brian, the next one, the last one that I want to cover, comes from the whole Alexa thing. 00:20:22.180 --> 00:20:27.580 We've had a couple of people write us about interesting things with, like, say, Flask Ask and Alexa skills, right? 00:20:27.580 --> 00:20:28.020 Yeah. 00:20:28.020 --> 00:20:28.320 Yeah. 00:20:28.380 --> 00:20:34.000 So, this one is a little bit of a serious one or at least addressing a serious problem, right? 00:20:34.000 --> 00:20:37.420 It's not like putting mustaches on cats, but, like, it's actually trying to solve a problem. 00:20:38.160 --> 00:20:41.320 Although, that would be a hard thing to do audibly on Alexa. 00:20:41.320 --> 00:20:44.340 Nonetheless, so, this one. 00:20:45.400 --> 00:20:47.860 This one is called Depression AI. 00:20:47.860 --> 00:20:49.500 And it's an Alexa skill. 00:20:49.500 --> 00:20:50.320 I apologize. 00:20:50.320 --> 00:20:52.900 Everybody's little device is probably going off. 00:20:52.900 --> 00:20:58.460 It's a Amazon device skill for people who are suffering with depression. 00:20:58.460 --> 00:21:00.520 And it's open source. 00:21:00.520 --> 00:21:07.260 It's based on Flask Ask, which I covered pretty deeply on episode 146 of Talk Python. 00:21:07.960 --> 00:21:14.800 So, that's basically a way to use Flask to write these Amazon voice assistant skills, which is pretty cool. 00:21:14.800 --> 00:21:28.300 So, the idea is that if you are suffering from depression, one of the things that's really hard for people, apparently, who are suffering from depression is to sort of go about your normal daily routine, right? 00:21:28.300 --> 00:21:28.860 Get up. 00:21:28.860 --> 00:21:29.640 Make your bed. 00:21:29.640 --> 00:21:30.360 Take a shower. 00:21:30.360 --> 00:21:34.200 It's, like, easy to just sort of, like, stay sprawled out on the couch or the bed or whatever. 00:21:34.420 --> 00:21:39.220 And so, it sort of helps to encourage you to keep doing those things. 00:21:39.220 --> 00:21:44.220 And it's supposed to be able to detect your moods and kind of give you some feedback. 00:21:44.220 --> 00:21:44.700 What do you think? 00:21:44.700 --> 00:21:46.560 I think that's super impressive. 00:21:46.560 --> 00:21:50.480 I mean, I have relatives who have dealt with these sorts of issues. 00:21:50.480 --> 00:21:54.300 And I don't know that they're necessarily ones who would take to technology. 00:21:54.300 --> 00:22:03.340 But anytime you can get some help or get, you know, some feedback or someone other, you know, you're not listening to yourself, it can be a good thing. 00:22:03.340 --> 00:22:05.020 I think this is awesome to have. 00:22:05.020 --> 00:22:17.560 I think there's a lot of people that would aren't somebody that wants to go talk to somebody else, but having making the decision to put this in place when they're feeling good and then have it help them through the hard times. 00:22:17.560 --> 00:22:18.300 This would be great. 00:22:18.300 --> 00:22:19.200 Yeah, it's pretty cool. 00:22:19.200 --> 00:22:25.300 It won the Valley Hackathon, which I think is, I think that's in Modesto, sort of outside San Francisco. 00:22:25.300 --> 00:22:32.400 But this was apparently built like, what is that, a weekend or something, which is also a pretty, pretty big testament. 00:22:32.400 --> 00:22:35.740 So you can do things like it'll evaluate your mood. 00:22:35.740 --> 00:22:37.580 It actually has suicidal intervention. 00:22:37.580 --> 00:22:41.580 It has location-based recommendations and mostly helps you with small activities. 00:22:41.580 --> 00:22:47.260 So you can say things like, Alexa, check on me, or I feel down, or help me feel better. 00:22:47.260 --> 00:22:48.960 I haven't gotten out of bed today. 00:22:48.960 --> 00:22:51.620 It'll ask you things like, have you gotten out of bed yet? 00:22:51.620 --> 00:22:52.240 Things like that. 00:22:52.240 --> 00:22:53.500 So it's pretty cool. 00:22:54.040 --> 00:22:55.300 Yeah, that's also open source. 00:22:55.300 --> 00:22:58.260 Yeah, it's open source and on GitHub and based on Python. 00:22:58.260 --> 00:23:03.780 So if this is inspiring, even if it's a totally different subject area, take it and use it as an example. 00:23:03.780 --> 00:23:04.400 Nice. 00:23:04.400 --> 00:23:06.880 Well, that's it for our official news. 00:23:06.880 --> 00:23:09.100 Brian, you got anything you want to share with the world while we're here? 00:23:09.260 --> 00:23:12.000 I've got some good news and some bad news. 00:23:12.000 --> 00:23:20.820 So the good news is I went to an estate sale the other day and I bought a book called How to Be Interesting in 10 Simple Steps. 00:23:21.360 --> 00:23:22.440 So that's the good news. 00:23:22.440 --> 00:23:26.620 The bad news is I'm a really slow reader, so it might take a while to take effect. 00:23:26.620 --> 00:23:28.140 Step two. 00:23:28.140 --> 00:23:31.780 No, I've just skimmed it so far, so I haven't even started yet. 00:23:31.780 --> 00:23:32.600 Well, what's a book? 00:23:32.600 --> 00:23:33.240 Yeah, very quick. 00:23:33.240 --> 00:23:33.980 What's your interest? 00:23:33.980 --> 00:23:37.980 It was printed a long time ago before we had e-books. 00:23:37.980 --> 00:23:39.400 Is it one of those things on paper? 00:23:39.400 --> 00:23:42.080 It's like a tablet, but it doesn't run out of batteries? 00:23:42.080 --> 00:23:42.900 Is that what you're telling me? 00:23:42.900 --> 00:23:47.200 I've got another book author harassing me about physical books. 00:23:47.200 --> 00:23:47.880 How about you, Matt? 00:23:47.880 --> 00:23:49.520 You got any books lined up? 00:23:49.940 --> 00:23:52.160 I'm working on revamping my Pandas one. 00:23:52.160 --> 00:23:56.460 So big demand for Pandas and I want to update mine to the latest version. 00:23:56.460 --> 00:24:00.040 So it's .17 and that's a couple years out. 00:24:00.040 --> 00:24:01.400 Sure, very cool. 00:24:01.400 --> 00:24:05.680 Oh yeah, and we also have some news about maybe a course coming out from you. 00:24:05.680 --> 00:24:10.380 We'll leave that as a teaser, but I think a video course maybe in the near future. 00:24:10.380 --> 00:24:11.560 Yeah, yeah. 00:24:11.560 --> 00:24:12.420 Maybe. 00:24:12.420 --> 00:24:13.240 I don't know. 00:24:13.240 --> 00:24:13.920 We'll have to see. 00:24:13.920 --> 00:24:15.720 We'll have to see if we can get our act together. 00:24:15.720 --> 00:24:16.360 Awesome. 00:24:16.360 --> 00:24:16.720 All right. 00:24:16.720 --> 00:24:20.140 Well, Matt, thank you for joining us and dropping in on this podcast. 00:24:20.140 --> 00:24:21.520 And Brian, thank you as always. 00:24:21.520 --> 00:24:22.440 Yeah, thanks. 00:24:22.440 --> 00:24:22.900 My pleasure. 00:24:22.900 --> 00:24:23.360 Thank you. 00:24:23.360 --> 00:24:23.680 Yep. 00:24:23.680 --> 00:24:24.100 Bye, guys. 00:24:24.100 --> 00:24:24.520 Bye. 00:24:26.620 --> 00:24:28.400 Thank you for listening to Python Bytes. 00:24:28.400 --> 00:24:30.980 Follow the show on Twitter via at Python Bytes. 00:24:30.980 --> 00:24:33.860 That's Python Bytes as in B-Y-T-E-S. 00:24:33.860 --> 00:24:37.300 And get the full show notes at pythonbytes.fm. 00:24:37.300 --> 00:24:41.640 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:24:41.640 --> 00:24:44.320 We're always on the lookout for sharing something cool. 00:24:44.320 --> 00:24:47.720 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:24:47.720 --> 00:24:51.360 Thank you for listening and sharing this podcast with your friends and colleagues.