WEBVTT 00:00:00.001 --> 00:00:03.900 If you're doing any sort of data exploration, you've likely heard about Jupyter Notebooks. 00:00:03.900 --> 00:00:08.060 In fact, there are quite a few options for running and hosting your Jupyter Notebooks. 00:00:08.060 --> 00:00:12.520 You may have also heard me rave about PyCharm as an editor. Well, on this episode, 00:00:12.520 --> 00:00:17.200 you'll meet Adam Hood from the Datalore team at JetBrains. That's a new project that tries to 00:00:17.200 --> 00:00:22.420 bring the power of PyCharm to notebooks and more. This is Talk Python To Me, episode 196, 00:00:22.420 --> 00:00:24.960 recorded January 9, 2019. 00:00:24.960 --> 00:00:43.300 Welcome to Talk Python To Me, a weekly podcast on Python, the language, the libraries, the ecosystem, 00:00:43.300 --> 00:00:48.380 and the personalities. This is your host, Michael Kennedy. Follow me on Twitter where I'm @mkennedy. 00:00:48.380 --> 00:00:53.740 Keep up with the show and listen to past episodes at talkpython.fm and follow the show on Twitter via 00:00:53.740 --> 00:00:59.900 at talkpython. This episode is sponsored by Linode and brilliant.org. Please check out what they're 00:00:59.900 --> 00:01:04.100 offering during their segments. It really helps support the show. Adam, welcome to Talk Python. 00:01:04.100 --> 00:01:06.740 Let's jump right in. How'd you get into programming? 00:01:06.740 --> 00:01:11.040 How did I get into programming? I guess, well, originally my dad is a programmer, 00:01:11.040 --> 00:01:16.960 so he tried to teach me when I was, I don't remember, 10 or 11. It didn't really take then, 00:01:16.960 --> 00:01:23.580 but then I started taking classes in high school and college. Python, I didn't know anything 00:01:23.580 --> 00:01:28.520 about Python or start learning Python until college. I took a few classes, but even then, 00:01:28.520 --> 00:01:34.340 the bulk of my exposure to Python has been working on this project specifically. Before that, 00:01:34.340 --> 00:01:36.480 it was just a couple of classes that I took. 00:01:36.480 --> 00:01:41.680 Okay. So mostly college experience, you'd say, with getting into programming or like what, 00:01:41.680 --> 00:01:44.640 did you go to college for computer science? What was the start there? 00:01:44.640 --> 00:01:46.540 No, my degree is actually in math. 00:01:46.680 --> 00:01:46.860 Okay. 00:01:46.940 --> 00:01:52.940 I ended up going into programming partly to put off graduate school. And it was, I mean, 00:01:52.940 --> 00:01:58.140 I had taken a number of programming classes and I also worked in a lab for two years and the bulk 00:01:58.140 --> 00:02:02.140 of my work was programming in MATLAB granted, but still programming. 00:02:02.140 --> 00:02:06.320 Yeah. I think that's how a lot of math people get into programming. They start there and they're 00:02:06.320 --> 00:02:10.380 like, yeah, this is kind of crummy. What can I do other than this? What can I do other than MATLAB? 00:02:10.380 --> 00:02:11.620 There you go. 00:02:11.620 --> 00:02:18.800 Yeah. That's actually a real similar to my story. Actually, I started in math and got into programming. 00:02:18.800 --> 00:02:24.180 And I spent some time writing MATLAB stuff as well, but bailed on it as soon as I could, 00:02:24.180 --> 00:02:24.580 basically. 00:02:24.580 --> 00:02:24.940 Right. 00:02:25.180 --> 00:02:30.420 Nice. So when you joined the Datalore team or the team that's working on the project that became 00:02:30.420 --> 00:02:33.000 Datalore, that's more or less when you got into Python. 00:02:33.000 --> 00:02:41.060 I guess it was at first we weren't really working on a Python editor. So it was probably another year 00:02:41.060 --> 00:02:43.080 or so before I actually got into Python. 00:02:43.080 --> 00:02:46.860 Right. So you got to build the foundation of all the tooling and whatnot. 00:02:46.860 --> 00:02:47.240 Right. 00:02:47.240 --> 00:02:48.520 Before it matters. Yeah. 00:02:48.520 --> 00:02:52.400 Working on this project, that's when I really started getting into Python, especially when I started, 00:02:53.060 --> 00:02:58.360 I've went to a couple of conferences. I've demoed the tool a fair amount. So I've 00:02:58.360 --> 00:03:02.520 certainly had some exposure to at least simple Python from that. 00:03:02.520 --> 00:03:06.060 Yeah, sure. That's cool. So what do you do day to day at JetBrains? 00:03:06.060 --> 00:03:13.500 I'm a developer. I'm working on building features, fixing bugs, and so on. I can say I've worked 00:03:13.500 --> 00:03:18.820 around various different parts of the tool at various points in time. Maybe we could get into that later 00:03:18.820 --> 00:03:21.400 once we talk more about the features that we actually have, I guess. 00:03:21.620 --> 00:03:26.380 Yeah, yeah, sure. Well, let's just start there. Let's set the stage a little bit with what this 00:03:26.920 --> 00:03:32.080 guys have created and you got here. And then we'll maybe take a step back. So what is Datalore? 00:03:32.080 --> 00:03:35.880 Like what else is out there that's comparable or is it something totally new? 00:03:35.880 --> 00:03:41.300 Well, it's a little bit new. Things that are comparable would be Jupyter Notebooks, especially 00:03:41.300 --> 00:03:48.280 if you talk about Google Colaboratory, I guess. It's this computational notebook. In our case, 00:03:48.320 --> 00:03:54.060 it's hosted in the cloud, it's hosted in the cloud, also completely collaborative. You can share with 00:03:54.060 --> 00:04:00.300 coworkers, collaborators, whoever. In my limited experience working with Jupyter and also based on 00:04:00.300 --> 00:04:05.060 talking to a lot of people, I get the impression that a lot of people, they'll use Jupyter, but they'll 00:04:05.060 --> 00:04:11.220 use something else as well sometimes to write the code and then they execute in Jupyter. A lot of people 00:04:11.220 --> 00:04:16.640 have come to our booth at a conference talking about how they use PyCharm and Jupyter together and so on. 00:04:16.640 --> 00:04:22.980 So maybe one idea here is that this is a single unified tool, which kind of takes the ideas from 00:04:22.980 --> 00:04:29.220 Jupyter as the computational notebook, but also from PyCharm as an IDE. Also the collaboration that you 00:04:29.220 --> 00:04:31.700 might find in Google Docs are now in Collaboratory. 00:04:31.960 --> 00:04:35.520 Okay. So let's come back to that collaboration stuff because I think that's super interesting, 00:04:35.520 --> 00:04:41.000 but maybe let's just take a step back and just talk about notebooks in general, not the computers 00:04:41.000 --> 00:04:46.760 with keyboards, but, you know, Jupyter and other computational notebook type things. There was a 00:04:46.760 --> 00:04:52.120 recent article and I forget what it was. It was in the Atlantic and it's, I've maybe mentioned it at a 00:04:52.120 --> 00:04:58.540 time or two on the show. And basically the title is that the scientific paper is obsolete. And it's 00:04:58.540 --> 00:05:05.220 really kind of provocative. It has like this picture of like a scientific paper with 15 authors on it. 00:05:05.220 --> 00:05:11.240 And it's literally on fire, right? Like it's crazy. And it's actually a super interesting look into the 00:05:11.240 --> 00:05:19.700 history of where notebooks came from. And it looks a little bit at Mathematica and Wolfram and why that 00:05:19.700 --> 00:05:25.160 really didn't take off in the scientific space in the same way. You know how a Jupyter and things like 00:05:25.160 --> 00:05:32.020 DataLore and Google Colaboratory and whatnot are really redefining what it means for really to 00:05:32.020 --> 00:05:36.760 present like computational work, like science, you know, hard science type stuff. Let me get your take 00:05:36.760 --> 00:05:40.840 on that. What do you think about this positioning that like notebooks are the new scientific paper 00:05:40.840 --> 00:05:45.680 for data science or for, you know, real science, like hard science, you know, chemistry, whatever. 00:05:45.680 --> 00:05:49.060 Oh, it might take some doing to convince all the journals of that. 00:05:49.820 --> 00:05:58.260 To be fair, they have a very strong financial vested interest in keeping their position. I think there's 00:05:58.260 --> 00:06:03.860 actually something a little bit fairly wrong about these whole scientific journals because, 00:06:03.860 --> 00:06:11.700 you know, so much of this research is funded by taxpayers and then is published in these private, 00:06:11.700 --> 00:06:16.440 ultra expensive journals. Like, isn't that our research? Didn't we pay for that? So that's a whole 00:06:16.440 --> 00:06:20.360 other angle, right? We mean, we shouldn't go down that too far, but there's also a lot of politics 00:06:20.360 --> 00:06:26.600 involved in my experience, at least from what I've seen. But if we go come back to the idea of the 00:06:26.600 --> 00:06:31.960 notebooks, I mean, I remember seeing Mathematica for the first time and I thought it was a really awesome 00:06:31.960 --> 00:06:38.440 tool. Just and it's exactly like as pretty much what you were saying about the article, it was pretty 00:06:38.440 --> 00:06:44.040 much exactly that. Everything that you have in the article, like you have this idea that you can have 00:06:44.040 --> 00:06:52.280 the code and these nice visualizations or outputs, you can print out the mathematical formula and so on, 00:06:52.280 --> 00:06:57.640 all just coming together in one thing. That was really, I thought that was really awesome when I 00:06:57.640 --> 00:07:03.540 first was introduced to it. And yeah, in a lot of ways you can start to, I don't know if I want to say 00:07:03.540 --> 00:07:09.380 replace papers because that's hard to do, but you do get to see the scientific process 00:07:09.380 --> 00:07:12.800 when you look at a nice, well-organized notebook. 00:07:12.800 --> 00:07:17.520 I mean, that does assume that it is put together for presentation, right? I mean, 00:07:17.520 --> 00:07:21.920 you've probably seen a ton of notebooks that are just like the equivalent of spaghetti code, 00:07:21.920 --> 00:07:25.580 just scrambled all through there. And you're like, what is this thing? And that doesn't actually help, 00:07:25.580 --> 00:07:29.840 but like, at least it's the canvas upon which that could be built, right? 00:07:29.940 --> 00:07:36.080 Right, exactly. And I think there's nothing wrong with this spaghetti code type notebook in the sense that, 00:07:36.080 --> 00:07:40.020 you know, the first stage you have to do a lot of exploration or you're not going to get anywhere. 00:07:40.020 --> 00:07:44.060 And it's not until you have to present it that you need to really think about it. 00:07:44.060 --> 00:07:49.820 Yeah. So I agree that you look at the way people work with the notebooks and after people who don't, 00:07:49.820 --> 00:07:55.040 you know, say like web developers that work in something like PyCharm or Visual Studio Code, 00:07:55.040 --> 00:08:00.460 like this, that sort of iterative exploratory type of working with your tool. 00:08:00.460 --> 00:08:04.980 You don't get that in these other tools, really. Not in the same way, right? 00:08:04.980 --> 00:08:09.700 You've got to rerun them all from scratch. You can't just like, let me tweak this formula and rerun that cell 00:08:09.700 --> 00:08:14.520 and see how that looks. Right? So I definitely think there's this interesting way of working 00:08:14.520 --> 00:08:21.340 in this exploring data with notebooks. Yeah. Let's dig into data lore. 00:08:21.340 --> 00:08:21.700 Sure. 00:08:21.700 --> 00:08:27.360 Tell people what it is. Like, how do you guys, if you're at a conference, you know, be at PyCon or whatever, 00:08:27.360 --> 00:08:30.560 and someone comes up and says, data lore, what's that? What do you tell them? 00:08:30.560 --> 00:08:33.460 I've had a lot of different lines throughout the years, actually. 00:08:34.240 --> 00:08:37.960 It's an online computational notebook in Python. 00:08:37.960 --> 00:08:43.540 To me, it kind of looks like something that is really familiar to people that use Jupyter, 00:08:43.540 --> 00:08:47.280 but has a lot of the features of, say, PyCharm. 00:08:47.280 --> 00:08:49.720 That's definitely one of the ideas. Yeah. 00:08:49.720 --> 00:08:55.360 A lot of the code smarts around understanding the code, suggesting fixes and improvements and whatnot, 00:08:55.360 --> 00:08:59.260 like PyCharm does, but in notebook form, right? 00:08:59.460 --> 00:09:04.720 Yes. And I mean, that's always been one of the big trademarks of the whole company, 00:09:04.720 --> 00:09:09.180 this idea of understanding the code, helping you with your code. So it would be 00:09:09.180 --> 00:09:12.520 kind of remiss on our part if we didn't include that. 00:09:12.520 --> 00:09:15.360 Yeah. It definitely would look out of place if it wasn't there, right? 00:09:15.360 --> 00:09:15.580 Yeah. 00:09:15.580 --> 00:09:19.240 You have all these cool, like, code assistant, code intention tools. Like, 00:09:19.240 --> 00:09:23.520 why are they not being brought over here? When you talk about building it, 00:09:23.520 --> 00:09:26.180 who do you imagine your users are? 00:09:26.180 --> 00:09:31.760 I can't really speak to specifics. I would just say, like, the whole realm of data scientists, 00:09:31.760 --> 00:09:37.520 data analysts, anyone who works with data and works with data in Python in particular, 00:09:37.520 --> 00:09:39.460 hopefully could use this tool. 00:09:39.460 --> 00:09:42.440 Sure. Like, pretty similar to the folks that might choose Jupyter? 00:09:42.440 --> 00:09:43.160 Yes. 00:09:43.160 --> 00:09:48.920 Okay. Maybe it's worth doing, like, a compare and contrast between Jupyter and JupyterLab. 00:09:48.920 --> 00:09:54.320 So how are they different? What does Jupyter have that you guys don't? What do you have 00:09:54.320 --> 00:09:57.040 that Jupyter doesn't? Maybe position those two things for us. 00:09:57.040 --> 00:10:01.600 Sure. I guess it's easier for me to talk about what we have that Jupyter doesn't. 00:10:01.600 --> 00:10:01.860 Sure. 00:10:01.860 --> 00:10:06.580 Not that I want to say Jupyter is awful. I mean, obviously, Jupyter is doing a great job of filling 00:10:06.580 --> 00:10:13.960 the space right now. One of the big differences is the way that we work with the environment that 00:10:13.960 --> 00:10:20.300 you have. In Jupyter, you have this one global environment. You execute the code in some cell, 00:10:20.440 --> 00:10:22.360 and it executes on top of that environment. 00:10:22.360 --> 00:10:27.460 So, like, basically, if I declare, like, a variable in a cell, it's just global to all the other cells, 00:10:27.460 --> 00:10:29.400 and it's that kind of thing? 00:10:29.400 --> 00:10:33.180 At least that's my understanding of Jupyter up to this point. 00:10:33.180 --> 00:10:33.380 Okay. 00:10:33.380 --> 00:10:38.160 Data lore is a bit different. You can almost think of it as after every cell, 00:10:38.160 --> 00:10:43.640 you have a different environment. So you have whatever you write into the first cell, 00:10:43.720 --> 00:10:49.960 you execute that, or by default, it executes automatically, and you have that environment. 00:10:49.960 --> 00:10:55.040 But then you start adding to another cell. That'll execute on top of the environment from the previous 00:10:55.040 --> 00:11:01.880 cell. Basically, the idea here is that, at least for me working with Jupyter, one thing that gets 00:11:01.880 --> 00:11:07.220 confusing is if I start having to re-execute previous cells, the environment gets a little bit 00:11:07.220 --> 00:11:12.420 confusing for me to keep track of just because, you know, I see this sequential code, 00:11:12.420 --> 00:11:15.600 but that doesn't represent what we actually have in the environment. 00:11:15.600 --> 00:11:21.060 Right. Like, maybe there's 10 cells, and you went back and you fiddled with number three and re-ran it, 00:11:21.060 --> 00:11:24.700 but you didn't run four, which would have actually taken the output of three to make a difference. 00:11:24.700 --> 00:11:25.960 You know, like that kind of thing, right? 00:11:25.960 --> 00:11:30.100 Right. And for us, that's not the reality. For us, like, 00:11:30.540 --> 00:11:34.440 if you go back and fiddle with three, you don't necessarily have to execute four, 00:11:34.440 --> 00:11:40.760 five, and six right away. But if you do, that will, they will reflect what you had from three. 00:11:40.760 --> 00:11:41.140 Okay. 00:11:41.140 --> 00:11:45.800 So I guess that's one pretty big difference. I touched on it already, but we have 00:11:45.800 --> 00:11:51.180 two different computation modes. By default, we have basically live computation. Like, 00:11:51.180 --> 00:11:53.640 as you write the code, we're executing it. 00:11:53.640 --> 00:11:56.360 Even before you say execute cell, like, just like if you write, 00:11:56.360 --> 00:11:59.680 as you type lines out, it just, if it's valid syntax, it runs? 00:11:59.800 --> 00:12:00.500 Yeah, pretty much. 00:12:00.500 --> 00:12:00.880 Okay. 00:12:00.880 --> 00:12:05.140 It's useful if you're doing lightweight stuff. The idea is, like, you're getting the feedback 00:12:05.140 --> 00:12:10.120 right away. If you write something and you try to reference a variable that doesn't exist or 00:12:10.120 --> 00:12:15.900 something, you know that right there. Of course, if you have something that's really long running, 00:12:15.900 --> 00:12:23.400 you might not want to run it a million times, but we do have a more Jupyter-like computation mode 00:12:23.400 --> 00:12:27.540 where you actually execute the cell. So I would say that's another kind of key difference. 00:12:27.920 --> 00:12:33.180 Yeah. Probably the other one, I guess you might say, it's like, it's in the cloud, period, right? 00:12:33.180 --> 00:12:34.680 It's in the cloud. Yes. 00:12:34.680 --> 00:12:37.260 You can't just locally pip install Datalore. 00:12:37.260 --> 00:12:37.960 Nope. 00:12:37.960 --> 00:12:42.980 Datalore notebook sort of thing. So for both what the good and bad that comes with that, 00:12:42.980 --> 00:12:45.340 right? But it's definitely a difference between the two. 00:12:45.460 --> 00:12:50.860 Yeah. It's in the cloud. Some of the things that come with being in the cloud is, like, for one thing, 00:12:50.860 --> 00:12:52.320 it's collaborative. 00:12:52.320 --> 00:12:52.900 Right. 00:12:53.100 --> 00:13:00.960 A lot like a Google Docs. I mean, you can share with coworkers, whoever, that you can have them just view it, 00:13:00.960 --> 00:13:03.440 or they could also edit your workbooks. 00:13:03.440 --> 00:13:10.000 On the collaborative bit, that sounds super interesting. And it's really cool. I think this is actually a trend 00:13:10.000 --> 00:13:16.640 in a decent amount of the tooling to have this sort of collaborative live coding, right? 00:13:16.780 --> 00:13:24.360 I know there's been so many folks who've done all sorts of tricks to get some sort of, like, real-time collaboration, 00:13:24.360 --> 00:13:28.600 even so much as, like, going to Amazon, spinning up a virtual machine, 00:13:28.600 --> 00:13:34.720 that you can both log into over remote desktop or something, and actually, like, we're going to both type on this. 00:13:34.720 --> 00:13:36.920 And, like, that's probably not the right way to do it. 00:13:36.920 --> 00:13:39.160 But, like, people were forced to do this, right? 00:13:39.160 --> 00:13:45.180 Because we got Google Docs and cool stuff like that, but PyCharm doesn't do it. The other tooling doesn't do it. 00:13:45.180 --> 00:13:51.440 But, for example, Microsoft announced this with Visual Studio, like, this year, where you can remotely share a session, 00:13:51.440 --> 00:13:56.160 and you can even, like, remotely debug the other person's code on their machine. 00:13:56.160 --> 00:14:02.100 I mean, it's pretty interesting. I think this is, you know, tapping into a trend that is making a lot of sense, yeah. 00:14:02.100 --> 00:14:08.260 Anytime you have a project where you need multiple people to work on it together, you need some sort of tooling for collaboration. 00:14:08.620 --> 00:14:12.900 Alternative has been check it into Git and merge it. 00:14:12.900 --> 00:14:19.640 Yeah. I've only interacted with Git with Jupyter Notebooks once, and it was a painful process. 00:14:19.640 --> 00:14:24.660 I can imagine. Well, and also the exploratory nature doesn't make a lot of sense. 00:14:24.660 --> 00:14:28.080 Like, you're like, oh, I changed a variable, I checked it in, do a pull, right? 00:14:28.080 --> 00:14:31.500 Like, it doesn't, I mean, that's, like, too quick of an iteration, right? 00:14:31.500 --> 00:14:35.300 Like, it's, I added a feature, check it out. That's a different type of thing. 00:14:35.300 --> 00:14:41.400 But Notebooks, like you said, sort of tend to be more iterative and exploratory, and I think that makes a lot of sense. 00:14:41.400 --> 00:14:43.220 What does the collaboration look like? 00:14:43.220 --> 00:14:46.260 So I've played with Datalore, but I haven't played with it with other people. 00:14:46.260 --> 00:14:49.060 So how real time is it? 00:14:49.060 --> 00:14:52.260 You know, is it every word, every character, every cell? 00:14:52.460 --> 00:14:54.740 Pretty much every character that you type. 00:14:54.740 --> 00:14:57.520 You could end up sending several characters at once. 00:14:57.520 --> 00:14:59.360 I don't know exactly how that works. 00:14:59.360 --> 00:15:05.240 But more or less every character, as you type it, it's getting synchronized to your collaborators and so on. 00:15:05.240 --> 00:15:09.300 We have this mode where you can actually follow what your collaborator is doing. 00:15:09.300 --> 00:15:12.680 Like, they have a little avatar in the top right corner. 00:15:12.680 --> 00:15:14.960 If you click on it, you'll start tracking them. 00:15:14.960 --> 00:15:18.060 So as they make edits, you'll see them real time. 00:15:18.060 --> 00:15:18.740 That's pretty awesome. 00:15:18.740 --> 00:15:34.640 Have you seen people do anything or heard of people, like, using this for teaching or, say, like, live presentations where everyone else can, like, come in and, like, I hear we're all sharing the notebook and all the students follow along or the participants follow along? 00:15:34.640 --> 00:15:36.520 It's definitely something that you can do. 00:15:36.520 --> 00:15:39.340 And that I think some people have done that. 00:15:39.680 --> 00:15:42.940 It sounds so much better than slides over WebEx. 00:15:42.940 --> 00:15:43.560 Right. 00:15:43.560 --> 00:15:44.260 You know? 00:15:44.260 --> 00:15:47.480 Like, here, open this in your browser and follow along as we do it. 00:15:47.480 --> 00:15:47.780 Yeah. 00:15:47.780 --> 00:15:52.700 I mean, one thing that was kind of cool was we had this little, like, internal workshop. 00:15:52.700 --> 00:15:58.040 One of our data scientists did a presentation on data science, just, like, some overview stuff. 00:15:58.040 --> 00:15:59.840 And he was doing it in data lore. 00:15:59.840 --> 00:16:09.340 And there is, like, a big difference between just, like, watching what he's doing if he's screen sharing versus just being able to follow him as he moves along. 00:16:09.540 --> 00:16:10.400 And that was really cool. 00:16:10.400 --> 00:16:10.620 Yeah. 00:16:10.620 --> 00:16:11.900 It seems like there's a lot of possibilities. 00:16:11.900 --> 00:16:12.160 Yeah. 00:16:12.160 --> 00:16:12.380 Yeah. 00:16:12.380 --> 00:16:13.060 I think definitely. 00:16:13.060 --> 00:16:13.460 Nice. 00:16:13.460 --> 00:16:19.280 So I guess this puts it kind of in the realm of Google Colaboratory, which I actually haven't used very much. 00:16:19.280 --> 00:16:23.140 I don't know if that has live collaboration, but I'm guessing by the name that it does. 00:16:23.140 --> 00:16:24.200 Yeah. 00:16:24.200 --> 00:16:25.460 I would assume so. 00:16:25.460 --> 00:16:26.960 I haven't used it. 00:16:26.960 --> 00:16:28.180 I have opened it. 00:16:28.180 --> 00:16:37.620 It looks to have just a new format for Google Docs or Sheets or Slides, except it's a notebook instead of one of those things. 00:16:37.620 --> 00:16:39.460 Pretty nice thing to have. 00:16:39.460 --> 00:16:40.060 Yeah, that's cool. 00:16:40.060 --> 00:16:46.080 So I haven't done much with Google Colaboratory, but I haven't heard a ton of people talking about it, but it does seem pretty nice. 00:16:46.080 --> 00:16:46.460 Yeah. 00:16:46.460 --> 00:16:46.760 Cool. 00:16:46.760 --> 00:16:53.840 So maybe we could dig into some of the features that makes data lore cool. 00:16:53.840 --> 00:16:56.680 So there's a few things that caught my attention. 00:16:56.680 --> 00:17:01.580 Maybe I'll bring them up and then you can describe what they are and how they work and why they're cool or whatever. 00:17:02.460 --> 00:17:06.020 So one of them is smart code completion. 00:17:06.020 --> 00:17:09.440 Sort of context-aware code completion. 00:17:10.080 --> 00:17:14.720 I mean, basically all the code completion you have in PyCharm, you also have over data lore, right? 00:17:14.720 --> 00:17:20.860 In some ways, we could have even more because we're aware of everything that's in the environment already. 00:17:20.860 --> 00:17:21.620 But yes. 00:17:21.620 --> 00:17:22.180 Okay. 00:17:22.680 --> 00:17:27.660 It feels like it's more of a real editor when you're working in the cells, to me. 00:17:27.660 --> 00:17:28.700 That's the idea, at least. 00:17:28.700 --> 00:17:29.280 That's the hope. 00:17:29.280 --> 00:17:29.480 Yeah. 00:17:29.600 --> 00:17:40.760 I think one of the cool things, just going between data lore and PyCharm for me, though, was in PyCharm, you're going to get all of the static inspections, completion, everything. 00:17:40.760 --> 00:17:48.460 But you can even get runtime information as you go along and you edit in data lore because it is running as you go along. 00:17:48.460 --> 00:17:49.140 Oh, I see. 00:17:49.140 --> 00:17:52.900 So maybe a field was added to some object at runtime. 00:17:52.900 --> 00:17:57.740 And because it's just hanging out there in the environment, you could get completion on that. 00:17:57.840 --> 00:18:01.700 Whereas in PyCharm, it's got to be static analysis, right? 00:18:01.700 --> 00:18:02.200 That's what you're saying? 00:18:02.200 --> 00:18:02.600 Yeah. 00:18:02.600 --> 00:18:03.600 I think so. 00:18:03.600 --> 00:18:03.980 Yeah, yeah. 00:18:03.980 --> 00:18:04.440 That's pretty awesome. 00:18:04.440 --> 00:18:13.420 So then you have all the code inspection, like this thing has a type annotation that takes a list, but you're passing a string. 00:18:13.420 --> 00:18:14.360 What gives here? 00:18:14.360 --> 00:18:15.160 That kind of stuff, right? 00:18:15.160 --> 00:18:18.540 As far as I know, we're still working on adding a bunch of those inspections. 00:18:18.540 --> 00:18:19.540 So we only have a few. 00:18:19.540 --> 00:18:24.320 I think one example is we have an inspection for unused variables. 00:18:24.580 --> 00:18:29.480 If we see a variable that's just sitting around completely unused, we'll highlight it. 00:18:29.480 --> 00:18:31.500 We probably have a couple of others. 00:18:31.500 --> 00:18:32.760 I don't know all of them. 00:18:32.760 --> 00:18:33.120 Sure. 00:18:33.120 --> 00:18:34.480 Navigation. 00:18:34.480 --> 00:18:42.360 So you can navigate, turn your parts of your notebook into hyperlinks that take you to like where the function is defined or the variable and things like that? 00:18:42.360 --> 00:18:42.860 Yeah, yeah. 00:18:42.860 --> 00:18:43.280 You can. 00:18:43.440 --> 00:18:45.920 I've only used it a few times, but you definitely can do that. 00:18:45.920 --> 00:18:48.300 I don't remember the shortcut even. 00:18:48.300 --> 00:18:54.860 When I'm developing software, that's one of the most useful things that I use. 00:18:54.860 --> 00:18:56.360 Like I always want to see the function. 00:18:56.360 --> 00:18:56.840 Absolutely. 00:18:56.840 --> 00:19:09.360 I mean, that's one of the things that drives me crazy about some of the more basic or, you know, not multifile type of editors that you can't just say, go to definition on this. 00:19:09.500 --> 00:19:13.220 I don't care where it is, go find it and show me what it is. 00:19:13.220 --> 00:19:13.820 You know what I mean? 00:19:13.820 --> 00:19:14.240 Yeah. 00:19:16.480 --> 00:19:19.320 This portion of Talk Python To Me is brought to you by Linode. 00:19:19.320 --> 00:19:23.040 Are you looking for hosting that's fast, simple, and incredibly affordable? 00:19:23.040 --> 00:19:28.160 Well, look past that bookstore and check out Linode at talkpython.fm/Linode. 00:19:28.160 --> 00:19:30.040 That's L-I-N-O-D-E. 00:19:30.040 --> 00:19:34.480 Plans start at just $5 a month for a dedicated server with a gig of RAM. 00:19:34.480 --> 00:19:36.680 They have 10 data centers across the globe. 00:19:36.680 --> 00:19:40.500 So no matter where you are or where your users are, there's a data center for you. 00:19:40.500 --> 00:19:54.920 Whether you want to run a Python web app, host a private Git server, or just a file server, you'll get native SSDs on all the machines, a newly upgraded 200 gigabit network, 24-7 friendly support, even on holidays, and a seven-day money-back guarantee. 00:19:54.920 --> 00:19:56.560 Need a little help with your infrastructure? 00:19:56.560 --> 00:20:01.280 They even offer professional services to help you with architecture, migrations, and more. 00:20:01.280 --> 00:20:04.240 Do you want a dedicated server for free for the next four months? 00:20:04.240 --> 00:20:07.300 Just visit talkpython.fm/Linode. 00:20:09.140 --> 00:20:21.340 The other thing that we can do in terms of navigation is we have a find, basically like Command F or Control F in your browser, except it just searches the code that you have. 00:20:21.340 --> 00:20:22.300 Okay, that's pretty cool. 00:20:22.300 --> 00:20:25.660 You also have this code, or just intentions. 00:20:25.660 --> 00:20:26.720 Tell us about that. 00:20:26.720 --> 00:20:29.160 That looks pretty cool with like data sets and all sorts of things. 00:20:29.160 --> 00:20:29.440 Right. 00:20:29.440 --> 00:20:35.120 There are these context-aware suggestions for actions that you can take with your code. 00:20:35.120 --> 00:20:39.060 By and large, they're trying to focus on the data science workflow. 00:20:39.060 --> 00:20:50.680 Like if you have a completely empty workbook you just started, you can start out by adding some standard imports like pandas, numpy, matplotlib. 00:20:50.680 --> 00:20:53.300 We also have this internal plotting library, I think. 00:20:53.300 --> 00:20:56.600 You can also start by loading a data set. 00:20:56.600 --> 00:21:04.900 You can upload something, or you can work with, we provide like 10 or 12 example data sets or something like that. 00:21:04.900 --> 00:21:08.860 Well, we don't provide all of them, but you can load using this attention. 00:21:08.860 --> 00:21:09.700 You can be loaded. 00:21:09.700 --> 00:21:14.120 So like some of them might go and like suck it off of like a website or something, or off an API. 00:21:14.120 --> 00:21:14.620 Right. 00:21:14.620 --> 00:21:15.480 You can do that. 00:21:15.480 --> 00:21:17.780 We do have this file uploader as well. 00:21:17.780 --> 00:21:21.100 You can upload files from disk, or if you know... 00:21:21.100 --> 00:21:21.240 Sure. 00:21:21.240 --> 00:21:22.500 What format do they have to be in? 00:21:22.500 --> 00:21:24.860 Can you just put whatever and you just read it? 00:21:24.860 --> 00:21:27.980 Or is it like, does it get understood when you upload it? 00:21:27.980 --> 00:21:29.860 You're going to end up reading it in the code. 00:21:29.860 --> 00:21:34.720 So if you know how to read it, like programmatically, pretty much anything. 00:21:34.720 --> 00:21:35.220 I see. 00:21:35.220 --> 00:21:39.460 But it's not like you send it a CSV file and it like pre-parses it for you. 00:21:39.460 --> 00:21:40.680 No, it won't do that. 00:21:40.680 --> 00:21:41.140 Yeah. 00:21:41.140 --> 00:21:41.360 Okay. 00:21:41.360 --> 00:21:47.520 If you do upload a CSV or TSV file, it will get suggested by the intention. 00:21:47.520 --> 00:21:51.700 So you said it has all these different data sources and some data sets. 00:21:51.700 --> 00:21:54.380 Like what are some of the data sets that I could work with? 00:21:54.380 --> 00:21:56.720 Well, we have a number of examples. 00:21:56.720 --> 00:21:58.840 I can even give a full list. 00:21:58.840 --> 00:22:03.460 So a lot of these are like just examples that are used, you know, teaching examples. 00:22:03.460 --> 00:22:07.660 Iris, Boston Housing, Breast Cancer. 00:22:07.660 --> 00:22:10.760 We have two different digits data sets. 00:22:10.760 --> 00:22:11.660 One of them is small. 00:22:11.660 --> 00:22:12.640 One of them is large. 00:22:12.640 --> 00:22:15.320 Like the handwriting, you're trying to recognize handwriting digits. 00:22:15.320 --> 00:22:15.840 Right. 00:22:15.840 --> 00:22:19.200 So that's like an OCR, like machine learning one. 00:22:19.200 --> 00:22:26.460 The Iris one is also like an optical image processing data set that's pretty commonly used, I think. 00:22:26.460 --> 00:22:26.960 Yeah. 00:22:26.960 --> 00:22:27.500 Sounds cool. 00:22:27.500 --> 00:22:34.340 If you wanted to do like demonstrate some form of say machine learning around OCR, you could just go. 00:22:34.340 --> 00:22:37.120 And we're just going to use the well-known data set from here, right? 00:22:37.120 --> 00:22:37.480 Yeah. 00:22:37.480 --> 00:22:37.860 Yeah. 00:22:37.860 --> 00:22:38.900 Are you planning on adding more? 00:22:38.900 --> 00:22:39.580 I'm not sure. 00:22:39.680 --> 00:22:42.480 I think for now we're satisfied with it. 00:22:42.480 --> 00:22:44.900 So basically you have the ability to upload. 00:22:44.900 --> 00:22:46.780 So you just like upload your files. 00:22:46.780 --> 00:22:51.160 I think for the most part, we're not even, we don't even have these uploaded internally. 00:22:51.160 --> 00:22:54.800 It's just, we know how to call the functions that are, that will load these. 00:22:54.800 --> 00:23:00.260 Like with Iris, you can get it directly from scikit-learn and same with a few others. 00:23:00.260 --> 00:23:01.300 That sounds pretty cool. 00:23:02.140 --> 00:23:04.060 What else happens with the intentions? 00:23:04.060 --> 00:23:06.140 So it's not just about data sets, right? 00:23:06.140 --> 00:23:06.500 Right. 00:23:06.500 --> 00:23:07.840 It's not just about data sets. 00:23:07.840 --> 00:23:20.000 So you start out by loading and then after you load the data set, you can, I guess, split it, extract out like X and Y where X is all the, what is it called? 00:23:20.000 --> 00:23:21.820 The feature matrix or something like that. 00:23:21.820 --> 00:23:23.520 And Y is your target. 00:23:24.120 --> 00:23:26.140 We will do the train test split for me. 00:23:26.140 --> 00:23:30.380 I remember when I always screw up the syntax for train test split. 00:23:30.380 --> 00:23:33.060 Like is it X test, X train? 00:23:33.060 --> 00:23:34.580 Or is it X test then Y test? 00:23:34.580 --> 00:23:35.500 Or I don't know. 00:23:35.500 --> 00:23:36.260 But data lore knows? 00:23:36.260 --> 00:23:36.980 Data lore knows. 00:23:36.980 --> 00:23:38.140 So I use the intention. 00:23:38.560 --> 00:23:40.200 It'll split the data for me. 00:23:40.200 --> 00:23:43.120 It'll create some set of models. 00:23:43.120 --> 00:23:49.780 Like it knows how it'll create the object for various scikit-learn estimators. 00:23:49.780 --> 00:23:53.020 It can also do a few pre-processing steps if you need to. 00:23:53.020 --> 00:24:04.400 Like if you loaded Titanic, there's some missing values and some columns that like you have a couple of features that need to be transformed in some way. 00:24:04.400 --> 00:24:10.900 So you can do like a one-hot encoding, you can impute, and there are intentions that will do these things for you. 00:24:10.900 --> 00:24:11.200 Nice. 00:24:11.200 --> 00:24:12.460 So how does that show up? 00:24:12.460 --> 00:24:17.420 Does it show up like you type a word and hit tab and then it gives you like some big expanded thing? 00:24:17.420 --> 00:24:18.960 Or what does it do? 00:24:18.960 --> 00:24:23.780 Actually, they just manifest as buttons at the bottom of the cell. 00:24:23.780 --> 00:24:29.580 Well, we call them blocks, but for the sake of Jupyter users, we can talk about cells. 00:24:29.580 --> 00:24:32.560 There's just all these buttons that you have at the bottom. 00:24:32.960 --> 00:24:36.220 And the buttons that you see exactly will depend on the context. 00:24:36.220 --> 00:24:38.340 Like what variable do you have focused? 00:24:38.340 --> 00:24:43.340 Or it could just depend on the context of like how far along are you in this machine learning process? 00:24:43.340 --> 00:24:44.860 Like what have you done so far? 00:24:44.860 --> 00:24:45.980 What would be the next step? 00:24:45.980 --> 00:24:46.360 Okay. 00:24:46.360 --> 00:24:47.660 You can just click the button. 00:24:47.660 --> 00:24:50.760 You might have a dialogue with some options or it'll just do something. 00:24:50.760 --> 00:24:51.580 And then it'll... 00:24:51.580 --> 00:24:52.380 Yeah, that sounds really cool. 00:24:52.380 --> 00:24:55.420 What machine learning libraries does it support? 00:24:55.420 --> 00:24:56.620 Is it just scikit-learn? 00:24:56.620 --> 00:24:58.340 Is it do like Keras or any others? 00:24:58.340 --> 00:25:00.880 The intention specifically or Datalore as a whole? 00:25:00.880 --> 00:25:01.320 Yeah. 00:25:01.320 --> 00:25:04.480 When it suggests to do like some training, what does it, you know, where does that work with? 00:25:04.480 --> 00:25:07.300 It's going to work by and large with scikit-learn. 00:25:07.300 --> 00:25:07.620 Okay. 00:25:07.620 --> 00:25:12.240 And I mean, it'll use like Pandas or NumPy to support the process. 00:25:12.240 --> 00:25:16.320 Like I think it generally prefers to work with, we prefer to work with Pandas data frames. 00:25:16.720 --> 00:25:26.980 So another thing that caught my attention, and I think you were touching on it before, is this idea of dependencies between the cells or blocks and the variables and like incremental computation. 00:25:26.980 --> 00:25:42.900 So if you have something that defines like X somewhere and then something else that maybe passes X to a function, Datalore is aware of that relationship and can do like a partial re-execution to make it go back and sync. 00:25:42.940 --> 00:25:43.280 Is that right? 00:25:43.280 --> 00:25:43.640 Yeah. 00:25:43.640 --> 00:25:49.080 Like if you update X way above, it'll know that you need to reevaluate that. 00:25:49.080 --> 00:25:50.400 I think that's a pretty cool feature. 00:25:50.400 --> 00:25:57.380 Like before you were talking about, you know, running the cells and say a Jupyter Notebook in different orders and getting different results. 00:25:57.380 --> 00:25:57.660 Right. 00:25:57.700 --> 00:26:00.280 Which it's not at all obvious how that happened. 00:26:00.280 --> 00:26:04.580 I know it has like a little number along the side of like order of execution. 00:26:04.580 --> 00:26:10.920 But like if you've got 20 cells, like do you really have that held in memory, in your memory of which order it is? 00:26:10.920 --> 00:26:11.720 Not really, right? 00:26:11.720 --> 00:26:15.080 I know from talking to people that for some people it isn't really a problem. 00:26:15.080 --> 00:26:22.200 But I think like if you have a lot of experience working with Jupyter Notebooks, you're probably used to it and you know how to work with it well. 00:26:22.200 --> 00:26:23.760 You know what trouble you can run into. 00:26:23.760 --> 00:26:24.720 So you just don't do it, right? 00:26:24.800 --> 00:26:26.700 Yeah, for me, it's always a problem. 00:26:26.700 --> 00:26:27.320 Yeah. 00:26:27.320 --> 00:26:29.200 Well, I'm not that experienced with Notebooks. 00:26:29.200 --> 00:26:34.240 And I think I imagine for a lot of people trying to break in, it is, you know, a hurdle you have to get through. 00:26:34.240 --> 00:26:34.560 Sure. 00:26:34.560 --> 00:26:43.280 So I guess once you kind of built up your data, maybe train some models with these intentions, things like that, you probably want to look at it, right? 00:26:43.280 --> 00:26:46.120 And so you talked about some libraries, you know, Matplotlib, for example. 00:26:46.120 --> 00:26:54.680 But you also have some built-in plotting libraries, one for like two-dimensional plots and one for geographical ones. 00:26:54.680 --> 00:26:56.840 So I guess what are those? 00:26:56.840 --> 00:27:02.900 And then like why not just import Altair or something, you know, like some other thing, right? 00:27:02.900 --> 00:27:04.320 Like why do you have your own? 00:27:04.320 --> 00:27:05.520 What are the benefits? 00:27:05.520 --> 00:27:09.960 I can't really compare and contrast with a lot of different libraries because I don't know them that well. 00:27:10.600 --> 00:27:14.540 But yeah, we do have, I guess, datalore.plot. 00:27:14.540 --> 00:27:17.040 That's the prefix for the library. 00:27:17.040 --> 00:27:19.120 It's our internal graphics library. 00:27:19.120 --> 00:27:20.920 It's based on the grammar of graphics. 00:27:20.920 --> 00:27:25.220 So it's going to be very similar to like ggplot2 and R. 00:27:25.740 --> 00:27:30.260 And I think there are some Python libraries that might also be based on the grammar of graphics. 00:27:30.260 --> 00:27:30.960 Sure. 00:27:30.960 --> 00:27:34.680 That I don't know about or I don't know the names of off the top of my head. 00:27:34.680 --> 00:27:41.060 My impression is that matplotlib is definitely the most popular library in Python for graphics or plotting. 00:27:41.060 --> 00:27:41.740 Yeah. 00:27:41.740 --> 00:27:48.900 But I have, well, me personally, and I guess enough other people that we decided to develop this library 00:27:48.900 --> 00:27:54.100 have this preference for the grammar of graphics for that different syntax. 00:27:54.680 --> 00:28:02.760 I remember in my days of working with MATLAB, the general torture that was working with graphics in MATLAB. 00:28:02.760 --> 00:28:07.440 And matplotlib is, as far as I understand, based on that or very similar. 00:28:07.440 --> 00:28:14.020 So yeah, I guess the motivation originally was to bring the grammar of graphics to Python, at least within our tool. 00:28:14.020 --> 00:28:14.380 Okay. 00:28:14.380 --> 00:28:16.220 You can still use the others? 00:28:16.220 --> 00:28:22.040 Like if I wanted to use some other library that typically works in Jupyter, I could install it, import it, run it? 00:28:22.040 --> 00:28:22.940 Yeah, it should work. 00:28:23.100 --> 00:28:25.480 If it doesn't, we appreciate the bug report. 00:28:25.480 --> 00:28:27.040 Sure. 00:28:27.040 --> 00:28:28.920 But the intention is that you can, right? 00:28:28.920 --> 00:28:29.420 Yeah. 00:28:29.420 --> 00:28:29.800 Yeah. 00:28:29.800 --> 00:28:30.100 Okay. 00:28:30.100 --> 00:28:35.300 So I guess another question that makes me start to think of is, what about dependencies? 00:28:35.300 --> 00:28:40.500 Like how do I specify my Python dependencies, the other libraries I'm going to use? 00:28:40.500 --> 00:28:41.740 And then how do I get them? 00:28:41.740 --> 00:28:48.780 Like if it's running on your cloud, how do I get the five libraries off PyPI that I actually need to do my thing? 00:28:48.900 --> 00:28:51.040 So we have a library manager. 00:28:51.040 --> 00:28:54.820 Like we have a tool that lets you set up a custom environment. 00:28:54.820 --> 00:29:03.440 We have some default environment and includes a lot of the basics that you probably want, like NumPy and Pandas and scikit-learn and matplotlib. 00:29:03.440 --> 00:29:03.800 Right. 00:29:03.800 --> 00:29:06.160 You might as well just start with those installed, right? 00:29:06.160 --> 00:29:06.880 Right. 00:29:06.880 --> 00:29:07.380 Exactly. 00:29:07.380 --> 00:29:16.880 But then you have the option to use this library manager to add whichever libraries you like from Conda, from pip. 00:29:16.880 --> 00:29:21.420 Even if you have access to some GitHub repo. 00:29:21.420 --> 00:29:22.060 Right. 00:29:22.060 --> 00:29:25.540 Where you pip with the git command just to the repo base. 00:29:25.540 --> 00:29:25.960 Yeah. 00:29:25.960 --> 00:29:26.300 Yeah. 00:29:26.300 --> 00:29:27.520 So you can install from GitHub. 00:29:27.880 --> 00:29:33.260 Yeah, but basically you can install whatever you really need to to do your work, right? 00:29:33.260 --> 00:29:33.600 Yeah. 00:29:33.600 --> 00:29:38.100 For now, as we sort of started at the beginning, Datalore is a cloud thing. 00:29:38.100 --> 00:29:45.960 I know there's a ton of people out there listening going, that's great, but my company won't let me upload my data to a cloud thing. 00:29:47.260 --> 00:29:49.180 Is there a way to run it on premise? 00:29:49.180 --> 00:29:57.740 Maybe even if that's not like pip installed Datalore, but maybe it's like, here's a virtual machine you can license from us internally. 00:29:57.740 --> 00:30:01.020 Unfortunately, no, it's not possible right now. 00:30:01.020 --> 00:30:09.060 It's something we're very much aware of because we've heard this feedback, this exact feedback from a number of users through various channels. 00:30:09.060 --> 00:30:13.360 So it's very much something that's like on our radar, something that we could consider. 00:30:13.360 --> 00:30:16.380 But unfortunately, right now it is not supported, no. 00:30:16.380 --> 00:30:18.800 And if it's not, maybe that's fine, right? 00:30:18.800 --> 00:30:22.300 Like Google Docs doesn't have an on-prem Google Docs. 00:30:22.300 --> 00:30:22.560 Yeah. 00:30:22.560 --> 00:30:23.520 Just don't do it, right? 00:30:23.520 --> 00:30:31.600 I do think that a lot of the data science folks are analyzing data that's pretty closely guarded, right? 00:30:31.600 --> 00:30:31.980 Yeah. 00:30:31.980 --> 00:30:33.520 It would be good, but who knows? 00:30:33.520 --> 00:30:36.860 As I said, it's not the first time we've heard it. 00:30:36.860 --> 00:30:37.580 Yeah. 00:30:37.580 --> 00:30:41.980 Is the source code for Datalore available or is it private? 00:30:41.980 --> 00:30:42.880 Right now it's private. 00:30:42.880 --> 00:30:43.400 Okay. 00:30:43.400 --> 00:30:44.520 It doesn't surprise me. 00:30:44.700 --> 00:30:49.140 Even for public, it's probably like deeply bound to your infrastructure. 00:30:49.140 --> 00:30:50.240 You know what I mean? 00:30:50.240 --> 00:30:50.680 Yeah. 00:30:50.680 --> 00:30:53.800 I mean, I can't say if there are any plans to open source it or not. 00:30:53.800 --> 00:30:57.700 We have open sourced some projects and other projects we've never open sourced. 00:30:57.700 --> 00:31:00.180 It's not really my decision in the end either. 00:31:00.180 --> 00:31:00.540 Sure. 00:31:00.540 --> 00:31:08.140 So there's different ways, like different machines or computational units, whatever you can buy to like do your analysis. 00:31:08.140 --> 00:31:21.500 So you start out, you can just play around with like some moderate virtual machine that it's running on, but you can get like a higher end computation or can you run it on, run your stuff on GPUs as well? 00:31:21.740 --> 00:31:22.740 Yes, you can. 00:31:22.740 --> 00:31:23.460 How's that work? 00:31:23.460 --> 00:31:25.540 The only thing that's available. 00:31:25.540 --> 00:31:27.840 So we have two different plans right now. 00:31:27.840 --> 00:31:29.940 We have a free plan, a paid plan. 00:31:29.940 --> 00:31:39.100 The only thing that's available on the free plan is just these medium, like these moderate instances, which work fine for just some general all-purpose stuff. 00:31:39.520 --> 00:31:46.200 But if you upgrade, you have the opportunity to upgrade to larger, more powerful machines. 00:31:46.200 --> 00:31:51.700 All of these right now are posted on AWS, so you can even look them up on AWS. 00:31:51.700 --> 00:31:55.500 But one of them does include GPUs. 00:31:55.500 --> 00:31:56.020 Right. 00:31:56.020 --> 00:32:06.920 One of them is, I guess, in EC2 parlance, a P2X large, which would be a four CPU and single GPU machine with 61 gigs of RAM. 00:32:07.380 --> 00:32:10.080 That's a pretty serious machine with 12 gigs of GPU RAM. 00:32:10.080 --> 00:32:10.460 Yeah. 00:32:10.460 --> 00:32:12.300 Maybe we talk about pricing as well. 00:32:12.300 --> 00:32:19.220 So this is both a thing you can use for free and also something you can pay to get more for, right? 00:32:19.220 --> 00:32:19.760 Right. 00:32:19.760 --> 00:32:22.240 What do you get for free and then what do you have to pay for? 00:32:22.240 --> 00:32:28.940 What you get for free is a monthly allowance on these moderate machines. 00:32:28.940 --> 00:32:33.920 I think 120 hours per month across all your various computations. 00:32:33.920 --> 00:32:38.440 Is that actual execution time or just like I'm working with my notebook? 00:32:38.440 --> 00:32:40.940 The second one, like when you're working on the notebook. 00:32:40.940 --> 00:32:43.920 Like if I have it open and I'm interacting with it rather than... 00:32:43.920 --> 00:32:47.520 So like what if I leave my browser open and I go to lunch? 00:32:47.520 --> 00:32:48.640 Does that count towards my hour? 00:32:48.640 --> 00:32:50.200 It does. 00:32:50.200 --> 00:33:00.560 Now, eventually we will shut it down if it's been idle for a really long time because we don't want you to just like run through your full allowance by accident without realizing it. 00:33:00.660 --> 00:33:04.080 As long as it's open, we keep the instance running. 00:33:04.080 --> 00:33:04.660 Right. 00:33:04.660 --> 00:33:09.780 So 120 hours is like if you do that eight hours a day, that's 15 days a month. 00:33:09.780 --> 00:33:12.300 I got still, you know, that's a lot of time for free. 00:33:12.300 --> 00:33:18.580 So I just want to make sure people understand like what it is when you say hour, what you're actually talking about, right? 00:33:18.580 --> 00:33:19.140 That's fair. 00:33:19.500 --> 00:33:26.680 Then if you, so if you upgrade, you unlock the ability to use some of these larger, more powerful machines. 00:33:26.680 --> 00:33:28.800 You still have to pay for them, I guess. 00:33:28.800 --> 00:33:32.560 But like we have various different machines depending on what you need. 00:33:32.560 --> 00:33:38.080 But you basically pay like the EC2 price of the machines if you select them? 00:33:38.240 --> 00:33:39.620 It's marked up. 00:33:39.620 --> 00:33:43.100 I mean, you can look up the EC2 prices, so it's not going to be exactly the same. 00:33:43.100 --> 00:33:43.560 Sure. 00:33:43.560 --> 00:33:47.720 But it's going to be based roughly on the EC2 prices. 00:33:47.720 --> 00:33:50.480 The more expensive machines will obviously cost more. 00:33:50.480 --> 00:33:52.240 The less expensive machines will cost less. 00:33:52.240 --> 00:33:53.980 Well, that sounds pretty cool. 00:33:54.100 --> 00:34:02.620 I mean, the fact that there's quite a bit of free space and computation and whatnot, I think that's what it takes to be interesting to everybody, right? 00:34:02.620 --> 00:34:03.120 Yeah. 00:34:03.120 --> 00:34:08.300 At the end of the day, we are competing with an open source thing, which will always be open source. 00:34:08.300 --> 00:34:10.440 So we'd better do something more. 00:34:10.440 --> 00:34:12.060 Yeah. 00:34:12.060 --> 00:34:12.320 Yeah. 00:34:12.320 --> 00:34:13.540 I think it makes sense. 00:34:13.540 --> 00:34:14.920 It seems pretty fair. 00:34:14.920 --> 00:34:20.340 This portion of Talk Python To Me is brought to you by Brilliant.org. 00:34:20.940 --> 00:34:27.560 Many of you have come to software development and data science through paths that did not include a full-on computer science or mathematic degree. 00:34:27.560 --> 00:34:32.160 Yet, in our technical field, you may find you need to learn exactly these topics. 00:34:32.160 --> 00:34:33.820 You could go back to university. 00:34:33.820 --> 00:34:37.740 But then again, this is the 21st century, and we do have the internet. 00:34:37.740 --> 00:34:42.440 Why not take some engaging online courses to quickly get just the skills that you need? 00:34:42.440 --> 00:34:44.680 That's where Brilliant.org comes in. 00:34:44.680 --> 00:34:47.140 They believe that effective learning is active. 00:34:47.700 --> 00:34:51.860 So master the concepts you need by solving fun, challenging problems yourself. 00:34:51.860 --> 00:34:53.500 Get started today. 00:34:53.500 --> 00:34:57.540 Just visit talkpython.fm/brilliant and sign up for free. 00:34:57.540 --> 00:34:58.720 And don't wait either. 00:34:58.720 --> 00:35:03.060 If you decide to upgrade to a paid account for guided courses and more practice exercises, 00:35:03.060 --> 00:35:09.500 the first 200 people that sign up from Talk Python will get an extra 20% off an annual premium subscription. 00:35:09.500 --> 00:35:12.540 That's talkpython.fm/brilliant. 00:35:14.420 --> 00:35:18.120 I ask if it will run outside of your cloud. 00:35:18.120 --> 00:35:19.900 And right now you say, no, it's hosted at EC2. 00:35:19.900 --> 00:35:22.540 Can I pick where it runs in EC2? 00:35:22.540 --> 00:35:30.600 Like, suppose I work on healthcare and it has to stay, you know, within Europe or has to stay within France or something. 00:35:30.600 --> 00:35:31.600 At the moment, no. 00:35:31.600 --> 00:35:34.180 You can't pick which cloud it runs in. 00:35:34.180 --> 00:35:37.080 But basically, right now, we don't have like a dropdown. 00:35:37.080 --> 00:35:41.780 Like, I want to do my work in Europe or I want to do my work in the US or something like that. 00:35:41.780 --> 00:35:43.120 No, there's nothing like that. 00:35:43.120 --> 00:35:43.560 Yeah. 00:35:43.560 --> 00:35:45.760 Do you know if there's any plans for something like that? 00:35:45.760 --> 00:35:47.240 I'm not sure, actually. 00:35:47.240 --> 00:35:57.300 It's, I mean, one thing we always have to keep our eye on is all the different privacy laws and make sure that we're not doing something untoward with anyone's data. 00:35:57.300 --> 00:35:59.820 Making sure that we're always complying. 00:36:00.280 --> 00:36:05.880 Yeah, I mean, there's certainly value to the privacy laws, but man, they can be hard to keep straight. 00:36:05.880 --> 00:36:08.140 Because it's not just one country, right? 00:36:08.140 --> 00:36:08.980 It's all the countries. 00:36:08.980 --> 00:36:09.340 Right. 00:36:09.340 --> 00:36:13.920 And if you have users in one country, you have to comply with the laws for that country, for that user. 00:36:14.740 --> 00:36:15.500 Yes, exactly. 00:36:15.500 --> 00:36:17.660 It's, you know, welcome to the internet, right? 00:36:17.660 --> 00:36:18.060 Yeah. 00:36:19.520 --> 00:36:29.480 So if people want to go and play around or explore data lore, you have a set of pre-built notebooks that they can go play with. 00:36:29.480 --> 00:36:32.100 You want to maybe talk us through some of those that they can explore? 00:36:32.340 --> 00:36:32.740 Right. 00:36:32.740 --> 00:36:34.280 We do have some sample workbooks. 00:36:34.280 --> 00:36:41.840 I am not too up to date on what the exact sample workbooks are on, but we actually do have some set here, which is kind of nice. 00:36:41.840 --> 00:36:45.200 So the first one, these links will be somewhere. 00:36:45.200 --> 00:36:48.880 So yeah, the links are super long and crazy, and I'll put them in the show notes. 00:36:48.880 --> 00:36:50.760 So don't worry about trying to say them. 00:36:50.760 --> 00:36:53.120 They're like, you know, UUID type of things. 00:36:53.120 --> 00:36:53.360 Yeah. 00:36:53.360 --> 00:36:53.620 Yeah. 00:36:53.620 --> 00:37:01.180 In any case, if I'm understanding correctly, I think if you create an account, these workbooks will be automatically created for you. 00:37:01.280 --> 00:37:03.000 So you can see them for yourself. 00:37:03.000 --> 00:37:03.440 Exactly. 00:37:03.440 --> 00:37:07.860 Under your home, you've got like, when I log in, at least in my account, I haven't done really much. 00:37:07.860 --> 00:37:09.580 So like, this has got to be what's coming with it. 00:37:09.580 --> 00:37:12.560 You know, there's like a scikit learn section. 00:37:12.560 --> 00:37:16.160 There's a map section and there's a plotting section. 00:37:16.160 --> 00:37:17.600 And the plotting has the most actually. 00:37:17.600 --> 00:37:18.020 Yeah. 00:37:18.020 --> 00:37:19.500 I guess we're kind of proud of it. 00:37:19.500 --> 00:37:20.600 We want to show it off. 00:37:20.600 --> 00:37:21.420 Yeah, sure. 00:37:21.420 --> 00:37:28.800 In any case, I guess the first one, we're basically showing, first of all, in pandas, you have this scatter matrix function. 00:37:28.960 --> 00:37:31.120 It's very useful for like some exploration. 00:37:31.120 --> 00:37:39.180 You can see distributions of the different features as well as their distributions with each other, whatever the right terminology is. 00:37:39.180 --> 00:37:42.640 I feel like I'm butchering data science terminology as we go along. 00:37:42.640 --> 00:37:47.780 But in any case, so first we're showing how you can generate the scatter matrix in pandas. 00:37:47.780 --> 00:37:52.540 But then we also want to show how you can do it using data lore plot, using the internal plotting. 00:37:52.540 --> 00:37:58.860 So first of all, you can create a scatter pretty easily for any pair of features that you have. 00:37:58.860 --> 00:38:04.020 You can also create a histogram using the geom point and geom histogram functions. 00:38:04.020 --> 00:38:10.020 You can update it to show density instead of just the counts of the different bins. 00:38:10.420 --> 00:38:10.860 Yeah, nice. 00:38:10.860 --> 00:38:19.420 So basically, like all the plotting that you, most plotting you want to do with like ggplot or the data lore plotting stuff, right? 00:38:19.420 --> 00:38:19.740 Yeah. 00:38:19.740 --> 00:38:20.080 Yeah. 00:38:20.080 --> 00:38:20.640 So these are nice. 00:38:20.640 --> 00:38:22.400 These graphs are pretty interactive. 00:38:22.400 --> 00:38:22.940 Yeah. 00:38:22.940 --> 00:38:24.820 I guess that's something I didn't even talk about yet. 00:38:24.820 --> 00:38:26.280 They are interactive. 00:38:26.280 --> 00:38:32.280 Like if you hover over them, you can see the different points on them, like the different coordinates, which is kind of cool. 00:38:32.280 --> 00:38:32.720 I like it. 00:38:32.720 --> 00:38:33.380 It's quite nice. 00:38:33.380 --> 00:38:38.680 So you have another one on like Bayesian inference, which is pretty cool. 00:38:38.680 --> 00:38:46.420 So one thing here that I'm seeing is, gosh, I'm not entirely sure what the syntax is. 00:38:46.420 --> 00:38:48.360 I think it might be LaTeX. 00:38:48.360 --> 00:38:57.380 So at the top, you've got basically really symbolically nice mathematics in here, right? 00:38:57.380 --> 00:38:57.780 Yeah. 00:38:57.780 --> 00:39:08.200 That's pretty cool that you can actually write the formulas in mathematical notation, not just trying to put, you know, ASCII characters to make like the integral sign look like an integral sign or something. 00:39:08.200 --> 00:39:13.300 Yeah, basically we have support, we have these markdown cells or blocks as I guess we call them. 00:39:13.300 --> 00:39:19.940 And you can include LaTeX into these cells as you can see there, which is always kind of fun. 00:39:19.940 --> 00:39:21.940 It does make for a nice presentation. 00:39:21.940 --> 00:39:22.500 Yeah. 00:39:22.500 --> 00:39:26.140 I have totally mixed feelings about LaTeX coming from a math background. 00:39:26.140 --> 00:39:28.420 Like I love the outcome. 00:39:28.420 --> 00:39:31.080 I hate the creation of it. 00:39:31.080 --> 00:39:31.900 It's so painful. 00:39:31.900 --> 00:39:32.820 It's so not obvious. 00:39:32.820 --> 00:39:34.020 I can fully understand. 00:39:34.020 --> 00:39:44.320 But see, when I was in college, what made LaTeX bearable for me was I started discovering tools like, I don't remember if it was called Overleaf then and it's write LaTeX now or vice versa. 00:39:44.320 --> 00:39:49.340 But it was basically this real-time LaTeX editor, which was collaborative. 00:39:49.340 --> 00:39:50.360 Oh, interesting. 00:39:50.360 --> 00:39:55.780 You could switch between modes like compile incrementally or just you press a button to compile. 00:39:55.780 --> 00:40:02.400 But it gave you like the real-time feedback about exactly what you were writing, which was always really cool. 00:40:02.400 --> 00:40:03.320 Yeah, that's pretty cool. 00:40:03.400 --> 00:40:06.560 And for people who don't know, LaTeX is basically, it's been around for a while. 00:40:06.560 --> 00:40:11.180 It's kind of like a markdown syntax, but for symbolic mathematics. 00:40:11.180 --> 00:40:17.260 Like I want the sum from n equals 0 to 1, or for 100, you know, of like it'll actually do all the symbols, right? 00:40:17.260 --> 00:40:19.540 And it used a lot in like math papers and whatnot. 00:40:19.540 --> 00:40:20.980 So let's see, what else? 00:40:20.980 --> 00:40:27.140 We got a lot of like a whole bunch of the data lore plot stuff, the geometry and whatnot. 00:40:28.140 --> 00:40:35.700 And you have some California housing data as well graphed out, which has got some cool geometry stuff. 00:40:35.700 --> 00:40:38.700 And it pulls in open street map and then like overlays it, right? 00:40:38.700 --> 00:40:39.860 Something like that. 00:40:39.860 --> 00:40:40.700 That's pretty nice. 00:40:40.700 --> 00:40:46.660 So I guess that's just using the California housing data from scikit-learn datasets. 00:40:46.660 --> 00:40:47.420 Exactly. 00:40:47.920 --> 00:40:51.080 It conveniently has the latitude and longitude coordinates. 00:40:51.080 --> 00:40:54.960 So we can just plot every point on the map as we see it, which is kind of neat. 00:40:54.960 --> 00:40:55.380 That's cool. 00:40:55.380 --> 00:41:03.320 So one thing at the bottom that's pretty interesting is it's got a picture of airplanes flying along the Hudson. 00:41:04.080 --> 00:41:11.320 And it overlays like animated flying airplanes using the geography plotting stuff, which is, that's pretty awesome. 00:41:11.320 --> 00:41:15.340 I don't work on this myself, but I'm always amazed at the new features that keep coming out. 00:41:15.340 --> 00:41:18.580 Like all the different things you can do with the maps and the plots. 00:41:18.580 --> 00:41:22.920 I think this is one of my favorite little animations is showing the different routes. 00:41:22.920 --> 00:41:24.640 You sit there and watch them fly along. 00:41:24.640 --> 00:41:25.520 That's pretty cool. 00:41:25.520 --> 00:41:25.880 Yeah. 00:41:25.880 --> 00:41:29.660 So people can check out those shared notebooks on data lore and play with them. 00:41:29.660 --> 00:41:33.440 Basically, like you said, if you create an account, you get all those like in your account. 00:41:33.440 --> 00:41:33.800 Yep. 00:41:33.800 --> 00:41:34.180 Yeah. 00:41:34.180 --> 00:41:36.880 What about a version control, right? 00:41:36.880 --> 00:41:47.460 Like if I have Jupyter Notebook local or specially if I have Python files locally, it's super easy to check that into GitHub, do a diff, do a branch, do a PR. 00:41:47.460 --> 00:41:49.820 Like what does a version control look like over here? 00:41:49.820 --> 00:41:53.240 We have our own integrated version control. 00:41:53.240 --> 00:41:55.380 Basically, we have this thing called history. 00:41:55.380 --> 00:42:02.100 Every now and then, if there's some inactivity, we'll just keep a checkpoint, kind of like a commit or something. 00:42:02.100 --> 00:42:03.020 You can think of it. 00:42:03.020 --> 00:42:03.260 Okay. 00:42:03.260 --> 00:42:05.820 Or you can make your own as you need to. 00:42:05.820 --> 00:42:07.520 And then you can compare the diffs. 00:42:07.520 --> 00:42:09.940 You can revert to the previous version. 00:42:09.940 --> 00:42:14.060 I think you can even revert specific changes, but you'd have to check me on that one. 00:42:14.060 --> 00:42:15.920 Basically, like this internal version control. 00:42:15.920 --> 00:42:16.540 That's pretty cool. 00:42:16.540 --> 00:42:19.000 And how do I, like, can I branch? 00:42:19.000 --> 00:42:20.580 Can I do stuff like that? 00:42:20.580 --> 00:42:21.720 No, it's completely linear. 00:42:21.720 --> 00:42:23.880 So no branching involved here. 00:42:23.880 --> 00:42:28.800 So right now, basically, it just, I can go forward or backwards in time for now, right? 00:42:29.000 --> 00:42:29.200 Yeah. 00:42:29.200 --> 00:42:29.240 Yeah. 00:42:29.240 --> 00:42:32.040 You can't really do branches or merges or anything like that. 00:42:32.040 --> 00:42:38.680 So one thing I'm noticing is I can export a Datalore file, which is kind of cool, right? 00:42:38.720 --> 00:42:43.300 So I theoretically could export the Datalore file and then check that into GitHub. 00:42:43.300 --> 00:42:44.860 That's one possibility. 00:42:44.860 --> 00:42:45.220 Yeah. 00:42:45.220 --> 00:42:45.480 Right. 00:42:45.480 --> 00:42:46.840 And then I do save points there. 00:42:46.840 --> 00:42:53.040 You can also export Jupyter Notebook format, the IPYNV format. 00:42:53.040 --> 00:42:53.540 Yeah. 00:42:53.720 --> 00:42:54.620 Can you go the other way? 00:42:54.620 --> 00:42:55.440 Can I import? 00:42:55.440 --> 00:42:56.000 Yes. 00:42:56.000 --> 00:43:02.000 Like, if I create a new notebook, can I say from this Jupyter Notebook or from someone else's Datalore notebook? 00:43:02.000 --> 00:43:03.220 Yeah, I'm pretty sure you can. 00:43:03.220 --> 00:43:07.960 If you go to the, if you're in the file system view, there should be a button. 00:43:07.960 --> 00:43:14.660 It looks kind of like the button to create a workbook, but basically it lets you import a workbook. 00:43:14.780 --> 00:43:26.260 It could have that Datalore format or it could have the IPNB, IPYNB, IPYNB, IPYNB, IPYNB, IPYNB. 00:43:26.260 --> 00:43:30.260 So yeah, we can import Jupyter Notebooks into Datalore. 00:43:30.260 --> 00:43:36.320 I think that would be a good place, a good way for people who are doing now, doing Jupyter and Explorer, 00:43:36.320 --> 00:43:39.620 just like, hey, what does it look like if I were to do the same work, but over here? 00:43:39.620 --> 00:43:40.060 Right. 00:43:40.060 --> 00:43:40.320 Yeah. 00:43:40.320 --> 00:43:44.480 I think that could be an interesting idea to try instead of just trying to start from scratch. 00:43:44.480 --> 00:43:49.940 You try to take some established work from Jupyter and see what it looks like working with it in Datalore. 00:43:49.940 --> 00:43:50.320 Right. 00:43:50.320 --> 00:43:53.660 Grab some interesting notebook off of GitHub and throw it in there. 00:43:53.660 --> 00:43:54.280 Okay. 00:43:54.280 --> 00:43:55.580 That sounds pretty good. 00:43:55.580 --> 00:44:01.440 I guess the final thing around Datalore would be, where is it going? 00:44:01.440 --> 00:44:03.700 It's pretty new, right? 00:44:03.700 --> 00:44:06.620 It just went 1.0 not long ago, right? 00:44:06.620 --> 00:44:08.280 Yeah, I think in October. 00:44:08.280 --> 00:44:09.000 What is that? 00:44:09.000 --> 00:44:10.460 Four months, something like that. 00:44:10.460 --> 00:44:11.540 So it's pretty new. 00:44:11.540 --> 00:44:16.800 And now that your app has met users, I'm sure you got some feedback. 00:44:16.800 --> 00:44:17.920 Yeah. 00:44:17.920 --> 00:44:20.200 What are you thinking on working on next? 00:44:20.200 --> 00:44:27.020 I can't really talk too much about the big features that we're working on, or big new features that we're working on. 00:44:27.020 --> 00:44:32.100 Certainly one of the big things that we try to do is, as we get feedback, we want to act on that feedback. 00:44:32.280 --> 00:44:35.260 We want users to get the sense that we actually care about what they're saying. 00:44:35.260 --> 00:44:38.060 I can't think of specific features that were suggested by users. 00:44:38.060 --> 00:44:47.220 I can certainly think of a lot of bugs that were reported, for example, and that they get that extra urgency because they're coming from the users and we want to fix it so that they can continue to work with the tool. 00:44:47.220 --> 00:44:52.000 So that's certainly a big part of the, I guess, philosophy or something. 00:44:52.000 --> 00:44:52.320 Okay. 00:44:52.320 --> 00:45:00.720 I did see in the blog post that announced the release of 1.0, there was a bunch of feedback in the comments around there. 00:45:00.720 --> 00:45:09.500 And one thing, I don't know how much my audience would care, but I know that they were asking, people were asking for Kotlin support, which Kotlin is a language JetBrains created. 00:45:09.500 --> 00:45:11.360 That's like a better Java. 00:45:11.360 --> 00:45:12.600 That's kind of the way I think of it. 00:45:12.600 --> 00:45:13.620 I don't know what happened. 00:45:13.740 --> 00:45:14.560 That's the idea. 00:45:14.560 --> 00:45:21.580 So are you planning on adding other languages like Kotlin or JavaScript or R or whatever? 00:45:21.580 --> 00:45:23.420 Or is it really focused on Python? 00:45:23.420 --> 00:45:25.060 For now, the focus is on Python. 00:45:25.060 --> 00:45:29.340 But the idea of supporting other languages, it's definitely something on our radar. 00:45:29.340 --> 00:45:36.000 We've been asked about, I think, R, Scala, Kotlin, probably a few others. 00:45:36.000 --> 00:45:39.460 So it's definitely something that's on our radar. 00:45:39.460 --> 00:45:42.240 I can't say whether or not we're working directly on it right now. 00:45:42.240 --> 00:45:42.660 Cool. 00:45:42.840 --> 00:45:47.280 Well, it's definitely an interesting entry in this whole computational notebook space. 00:45:47.280 --> 00:45:52.640 I think the smart editor features sort of borrowed from PyCharm is really cool. 00:45:52.640 --> 00:45:53.980 I think the collaboration is really cool. 00:45:53.980 --> 00:45:57.500 So hopefully people find it an interesting thing to consider. 00:45:57.500 --> 00:45:58.400 I hope so, yeah. 00:45:58.400 --> 00:45:58.680 All right. 00:45:58.680 --> 00:46:03.960 So I guess before I let you out of here, let me ask you the final two questions I always ask folks. 00:46:03.960 --> 00:46:07.800 So if you're going to write some Python code, what editor would you use? 00:46:07.800 --> 00:46:11.380 I feel like I only have one correct answer. 00:46:12.260 --> 00:46:14.700 I'm going to go ahead and say PyCharm. 00:46:14.700 --> 00:46:25.320 Though realistically, technically, I'm using IntelliJ IDEA for all my Python editing, but it has the Python plugin, which gives me PyCharm, basically. 00:46:25.320 --> 00:46:25.880 Yeah, exactly. 00:46:26.480 --> 00:46:28.680 So effectively, more or less the same thing. 00:46:28.680 --> 00:46:35.060 And then notable Python package that people might find interesting out there that maybe they don't know about. 00:46:35.060 --> 00:46:38.820 I don't know if I can really name something that a lot of people won't know about. 00:46:39.120 --> 00:46:41.680 I think my personal favorite. 00:46:41.680 --> 00:46:43.740 I really like SymPy. 00:46:43.740 --> 00:46:49.160 At least just the concept of SymPy is really neat to me, like the symbolic mathematics in Python. 00:46:49.160 --> 00:46:50.200 Yeah, that's really cool. 00:46:50.200 --> 00:46:51.340 I haven't used it. 00:46:51.340 --> 00:46:52.580 I've heard of it before. 00:46:52.580 --> 00:46:55.040 Maybe just tell really quick what it is. 00:46:55.280 --> 00:46:58.660 It's bringing symbolic mathematics into Python. 00:46:58.660 --> 00:47:04.200 Like you can define variables and build mathematical formulas, equations in SymPy. 00:47:04.200 --> 00:47:04.440 Right. 00:47:04.440 --> 00:47:13.940 So I could give it like a quadratic equation and say solve for Y and it'll or solve for X and it'll like express it in terms of, you know, things like that. 00:47:14.020 --> 00:47:15.400 It will be able to do that. 00:47:15.400 --> 00:47:26.200 I saw a demo, which was pretty neat, in which basically it was, you would define this partial differential equation and it would build a numeric solver for it. 00:47:26.200 --> 00:47:26.760 Oh, wow. 00:47:26.760 --> 00:47:27.060 Okay. 00:47:27.060 --> 00:47:32.780 I don't remember how that happened, but I remember seeing that tutorial and I thought it was really neat. 00:47:32.780 --> 00:47:33.540 That's pretty cool. 00:47:33.540 --> 00:47:39.400 Basically, all I remember from partial differential equations is that they were hard and I would love a library to solve them for me. 00:47:39.400 --> 00:47:39.760 Yeah. 00:47:39.760 --> 00:47:47.200 So, yeah, I think for me, the appeal is mostly just like the mathematician in me likes the idea. 00:47:47.200 --> 00:47:51.740 It sounds really cool and it ties in well with like all the other stuff that we're talking about here. 00:47:51.740 --> 00:47:52.520 Great. 00:47:52.520 --> 00:47:52.840 All right. 00:47:52.840 --> 00:47:54.180 Well, final call to action. 00:47:54.180 --> 00:47:56.200 People maybe are interested in Datalore. 00:47:56.200 --> 00:47:56.920 What do they do? 00:47:56.920 --> 00:48:02.820 Go to datalore.io and create an account and check it out if it sounds at all interesting. 00:48:02.820 --> 00:48:05.360 You can play around with the samples that we have. 00:48:05.360 --> 00:48:10.120 You can even upload your own notebooks, see what it's like to play with them in Datalore. 00:48:10.120 --> 00:48:10.620 Awesome. 00:48:10.620 --> 00:48:15.020 Well, Adam, thanks so much for being on the show and sharing your part of Datalore. 00:48:15.020 --> 00:48:15.420 It's cool. 00:48:15.420 --> 00:48:16.480 Thank you for having me. 00:48:16.480 --> 00:48:16.860 You bet. 00:48:16.860 --> 00:48:17.080 Bye. 00:48:17.080 --> 00:48:17.320 Bye. 00:48:17.320 --> 00:48:20.680 This has been another episode of Talk Python To Me. 00:48:20.680 --> 00:48:26.320 Our guest on this episode was Adam Hood and it's been brought to you by Linode and Brilliant.org. 00:48:26.320 --> 00:48:30.260 Linode is your go-to hosting for whatever you're building with Python. 00:48:30.260 --> 00:48:33.800 Get four months free at talkpython.fm/Linode. 00:48:33.800 --> 00:48:35.740 That's L-I-N-O-D-E. 00:48:35.740 --> 00:48:41.280 Brilliant.org wants to help you level up your math and science through fun, guided problem solving. 00:48:41.280 --> 00:48:45.160 Get started for free at talkpython.fm/brilliant. 00:48:45.160 --> 00:48:47.840 Want to level up your Python? 00:48:47.840 --> 00:48:52.680 If you're just getting started, try my Python Jumpstart by Building 10 Apps course. 00:48:52.800 --> 00:49:00.840 Or if you're looking for something more advanced, check out our new async course that digs into all the different types of async programming you can do in Python. 00:49:00.840 --> 00:49:05.520 And of course, if you're interested in more than one of these, be sure to check out our Everything Bundle. 00:49:05.520 --> 00:49:07.400 It's like a subscription that never expires. 00:49:07.400 --> 00:49:09.700 Be sure to subscribe to the show. 00:49:09.700 --> 00:49:12.200 Open your favorite podcatcher and search for Python. 00:49:12.200 --> 00:49:13.320 We should be right at the top. 00:49:13.320 --> 00:49:22.320 You can also find the iTunes feed at /itunes, the Google Play feed at /play, and the direct RSS feed at /rss on talkpython.fm. 00:49:22.420 --> 00:49:24.420 This is your host, Michael Kennedy. 00:49:24.420 --> 00:49:25.920 Thanks so much for listening. 00:49:25.920 --> 00:49:27.000 I really appreciate it. 00:49:27.000 --> 00:49:28.740 Now get out there and write some Python code. 00:49:28.740 --> 00:49:29.320 I'll see you next time. 00:49:29.320 --> 00:49:29.420 Bye. 00:49:29.420 --> 00:49:29.440 Bye. 00:49:29.440 --> 00:49:29.440 Bye. 00:49:29.440 --> 00:49:29.560 Bye. 00:49:29.560 --> 00:49:59.540 Thank you.