WEBVTT 00:00:00.001 --> 00:00:05.340 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:05.340 --> 00:00:09.820 This is episode 57, recorded December 19th, 2017. 00:00:09.820 --> 00:00:10.900 I'm Michael Kennedy. 00:00:10.900 --> 00:00:11.820 And I'm Brian Okken. 00:00:11.820 --> 00:00:14.200 And we have a bunch of really cool stuff to share with you. 00:00:14.200 --> 00:00:20.580 As always, we've been looking through all of the news sources and finding all the Python goodies for you. 00:00:20.580 --> 00:00:22.540 Brian, what's the first goodie that you found? 00:00:22.540 --> 00:00:27.440 Anthony Shaw, or I know him as Tony, has at work on GitHub. 00:00:27.440 --> 00:00:29.740 I like his handle, Tony Maloney. 00:00:30.380 --> 00:00:33.660 But he has been working on a tool called Retox. 00:00:33.660 --> 00:00:38.120 And this is a tool you can really run anything with it. 00:00:38.120 --> 00:00:41.940 But the original intent is to run your tests. 00:00:41.940 --> 00:00:48.640 What Tox does is a tool that will run your setup if you have packages. 00:00:48.640 --> 00:00:52.320 And you can turn that off if you don't want the setup to get tested. 00:00:52.320 --> 00:00:55.800 But it'll test your setup and then run it in different environments. 00:00:55.800 --> 00:00:59.480 And the typical example is running multiple Python versions. 00:00:59.480 --> 00:01:05.080 Running all your tests in multiple Python or multiple library versions to make sure all your tests pass. 00:01:05.840 --> 00:01:11.940 And there is a Detox version that can distribute that across processors. 00:01:11.940 --> 00:01:18.500 Can speed it up two to three times or two to four times faster or more if you run it on distributed processors. 00:01:18.500 --> 00:01:23.760 Or if you spent like, what, $15,000 on a new iMac Pro and got 18 cores? 00:01:23.760 --> 00:01:24.980 Yeah, yeah. 00:01:24.980 --> 00:01:27.940 You could be 18 times faster or 16 or something. 00:01:27.940 --> 00:01:38.920 Anthony put together Retox, which does all this, but also does it with a nice GUI so you can watch your tests run, which is really kind of cool. 00:01:38.920 --> 00:01:48.500 And he did it for Python 2 at first and then very quickly within a week, ported it to Python 3 with some problems along the way. 00:01:48.500 --> 00:01:50.060 But he worked through them, which is nice. 00:01:50.060 --> 00:01:50.700 Yeah, well done. 00:01:50.700 --> 00:01:53.880 It also has the cool capability of watching a directory. 00:01:53.880 --> 00:02:01.000 So you can have this GUI sitting up on a monitor somewhere if you've got a couple monitors or in the corner of your window. 00:02:01.280 --> 00:02:12.160 And then it'll watch, you can have it watch your source code and when you, as you, as you save changes, it tests everything on multiple versions of Python or multiple hardware or whatever you want to do. 00:02:12.160 --> 00:02:13.380 Yeah, that's really, really cool. 00:02:13.380 --> 00:02:13.680 Very cool. 00:02:13.680 --> 00:02:15.660 You know, I'll just describe kind of what it looks like. 00:02:15.660 --> 00:02:24.140 So you run it in terminal or command prompt and you get like an ANSI art type of interactive thing. 00:02:24.180 --> 00:02:33.840 And it shows you different columns for the different tests that are all running like Python 2.7, Python 3.6, Lint, PyLint, all these things running in parallel and their status. 00:02:33.840 --> 00:02:34.580 It's pretty cool, right? 00:02:34.580 --> 00:02:34.960 Yeah. 00:02:34.960 --> 00:02:41.320 And one of the things I was trying to do this morning, getting a chance to quite get it done with meetings and all. 00:02:41.320 --> 00:02:51.280 But instead of running Python 2 versus 3 and stuff, what I'm going to use it for is running the same set of tests with the same Python, but against different instruments. 00:02:51.640 --> 00:02:54.720 So I can run across multiple hardware. 00:02:54.720 --> 00:02:55.280 That's cool. 00:02:55.280 --> 00:03:02.920 So you're parallelizing the same code running, but against, you're parallelizing the hardware, not the versions of Python. 00:03:02.920 --> 00:03:03.400 Yeah. 00:03:03.400 --> 00:03:04.280 Yeah, that's pretty awesome. 00:03:04.280 --> 00:03:04.800 That'll be good. 00:03:04.800 --> 00:03:05.380 Yeah, that'll be good. 00:03:05.380 --> 00:03:14.340 So I thought this episode, I'd talk about some of the tools that I've been using for a little while that I really enjoy, but just haven't thought to bring up on the show. 00:03:14.340 --> 00:03:20.460 So the first one I want to talk about is a database management tool for MongoDB called, it used to be called Robo Mongo. 00:03:20.460 --> 00:03:25.540 It got bought by a company, which is an interesting story in and of itself, by a company called 3T. 00:03:25.540 --> 00:03:29.640 And so now it's Robo 3T, but in my heart, it will always be Robo Mongo. 00:03:29.900 --> 00:03:34.920 Anyway, what this thing is, is a shell for MongoDB that you type. 00:03:34.920 --> 00:03:40.680 So a command line type of interaction with it, but all the results appear as a GUI result. 00:03:40.680 --> 00:03:51.600 So you have this kind of part where you type in and you run commands against Mongo, but all the results show up in maybe a tree view, or you can right click and interact with them once the results come back and stuff. 00:03:51.660 --> 00:03:57.780 So it's really, really nice if you're doing anything with MongoDB, runs on all the platforms, check it out, Robo 3T. 00:03:57.780 --> 00:03:59.580 Nice way to visualize your database. 00:03:59.580 --> 00:04:00.120 That's cool. 00:04:00.120 --> 00:04:07.680 Yeah, it's one of the best, like simple visual interactions with databases because the majority of what you do is still the CLI. 00:04:07.680 --> 00:04:09.720 It's still the straight interface for working with it. 00:04:09.720 --> 00:04:13.780 But all the results that come out of that interaction are GUI based, which I think is cool. 00:04:14.060 --> 00:04:16.980 It's also interesting in an open source way. 00:04:16.980 --> 00:04:22.680 On one hand, it's number 34 most popular repository on GitHub, which is cool. 00:04:22.680 --> 00:04:31.220 It's built with QT, which makes it look like a really nice cross-platform app, which you could build out with PySide or PyQt, I think, and get the same looking app. 00:04:31.220 --> 00:04:32.920 So that's also a really interesting example. 00:04:32.920 --> 00:04:37.700 And it was an open source project that was started somewhere in Eastern Europe. 00:04:37.700 --> 00:04:38.720 I don't remember where. 00:04:38.720 --> 00:04:39.940 It became really successful. 00:04:39.940 --> 00:04:41.320 And this other company bought it. 00:04:41.320 --> 00:04:46.540 And so here's also an interesting business model around open source stuff. 00:04:46.540 --> 00:04:46.840 Yeah. 00:04:46.840 --> 00:04:47.200 Cool. 00:04:47.200 --> 00:04:50.440 So anyway, if you're doing anything with MongoDB, check out Robo Mongo. 00:04:50.440 --> 00:04:51.000 Cool. 00:04:51.000 --> 00:04:58.600 Sometimes, we talked about in the new version of Django, you don't have to do regular expressions anymore for... 00:04:58.600 --> 00:04:59.540 For the routes, right? 00:04:59.540 --> 00:05:01.960 For the route definitions, which I think is a positive thing for sure. 00:05:01.960 --> 00:05:02.740 Yeah, definitely. 00:05:02.740 --> 00:05:08.160 But there's definitely some times where I've been using regular expressions for as long as I've been programming, almost. 00:05:08.160 --> 00:05:09.480 So programming's always been hard. 00:05:09.480 --> 00:05:11.020 Yeah. 00:05:11.020 --> 00:05:11.140 Yeah. 00:05:11.140 --> 00:05:11.820 Yeah. 00:05:11.820 --> 00:05:18.220 Shortly into my CS program, I got thrown a Perl book and said, learn this. 00:05:18.220 --> 00:05:19.320 So yeah. 00:05:19.320 --> 00:05:27.480 Anyway, there's a couple articles that came out recently that I thought were really good for people that need to get a handle on regular expressions quickly. 00:05:27.960 --> 00:05:31.540 And one of them is regular expressions practical guide. 00:05:31.540 --> 00:05:40.900 And it's a kind of a nice article that talks through using the RE package to do things like parse email addresses and phone numbers and URLs. 00:05:41.800 --> 00:05:44.000 And those are good examples. 00:05:44.000 --> 00:05:47.220 Even if you don't have to do that, everybody knows what those look like. 00:05:47.220 --> 00:05:51.440 So it's good to sort of learn some of the regular expressions with that. 00:05:51.440 --> 00:05:51.700 Yeah. 00:05:51.760 --> 00:05:54.040 I really like the example driven approach as well. 00:05:54.040 --> 00:05:57.620 Like, here's how you match a bunch of different things you might want to. 00:05:57.620 --> 00:06:06.220 And also the fact that it's using the Python libraries and not just here's a random regular expression means it's really quick to just drop in directly, which is cool. 00:06:06.360 --> 00:06:11.340 And then there's another article called Regular Expressions for Data Scientists that do some of this. 00:06:11.340 --> 00:06:18.980 But then also it's mostly focused around parsing text, of course, with regular expressions. 00:06:18.980 --> 00:06:26.040 But it's also a good intro and it dives a little bit deeper into find all and search. 00:06:26.040 --> 00:06:30.260 And so I think check both those out if you want to beef up on regular expressions. 00:06:30.260 --> 00:06:31.120 Yeah, that's really cool. 00:06:31.120 --> 00:06:31.480 Nice. 00:06:31.480 --> 00:06:35.900 I like the motivational introduction for the data science one. 00:06:36.400 --> 00:06:40.960 Sometimes, you know, this might include searching massive corpus of text. 00:06:40.960 --> 00:06:46.560 For example, suppose you're asked to figure out who's been emailing whom in a scandal of the Panama Papers. 00:06:46.560 --> 00:06:49.240 That's 11.5 million documents. 00:06:49.240 --> 00:06:50.320 We need regular expressions. 00:06:50.320 --> 00:06:50.860 Let's go. 00:06:50.860 --> 00:06:51.720 Yeah, definitely. 00:06:51.720 --> 00:06:53.140 Yeah, very exciting. 00:06:53.140 --> 00:06:53.700 Very exciting. 00:06:53.700 --> 00:06:54.500 Cool. 00:06:54.500 --> 00:06:58.400 So before we go on, let me just tell you about the sponsor for this week's episode. 00:06:58.400 --> 00:07:00.000 That is DigitalOcean. 00:07:00.000 --> 00:07:06.100 Thank you, DigitalOcean, for sponsoring the show as they are, I would say, the major sponsor for the show. 00:07:06.100 --> 00:07:07.660 They definitely sponsor it more than anyone else. 00:07:07.660 --> 00:07:12.080 And with good reason, they have really, really cool things going on over there. 00:07:12.080 --> 00:07:19.680 You know, there's a lot of places you can get your cloud computing resources, but a lot of them are overly complicated, overly expensive, and so on. 00:07:19.680 --> 00:07:25.220 With DigitalOcean, go over there, quickly set up a server, set up some storage, link them together. 00:07:25.440 --> 00:07:26.380 It's really wonderful. 00:07:26.380 --> 00:07:32.700 So this site's website's running on DigitalOcean, among a bunch of other things that I'm running as well. 00:07:32.700 --> 00:07:35.020 So definitely a great place to check out. 00:07:35.020 --> 00:07:40.120 And you can get started by going to pythonbytes.fm/DigitalOcean. 00:07:40.120 --> 00:07:47.760 So keeping with my theme of things that I want to talk about that I've been using and really enjoying but haven't bothered to bring up on the show, 00:07:47.760 --> 00:07:53.360 to complement the other side of the story with the MongoDB thing is MongoEngine. 00:07:53.360 --> 00:07:55.420 So you've heard of SQLAlchemy, right, Brian? 00:07:55.420 --> 00:07:56.140 Yeah, definitely. 00:07:56.140 --> 00:08:01.160 Yeah, so that's probably the most popular way in Python to talk to relational databases. 00:08:01.160 --> 00:08:05.880 Well, the MongoDB equivalent of SQLAlchemy is this thing called MongoEngine. 00:08:05.880 --> 00:08:12.540 There's a handful of these so-called document, object document mappers, because you don't have a relational. 00:08:12.540 --> 00:08:14.460 So it's not an ORM, it's an ODM. 00:08:15.000 --> 00:08:20.800 And anyway, there's a handful of these for MongoDB, but this one really is quite needed. 00:08:20.800 --> 00:08:24.720 Let's you take a class, derive from a base class, and map it into MongoDB. 00:08:24.720 --> 00:08:28.100 And the way you work with it is very similar to Django's ORM, actually. 00:08:28.100 --> 00:08:32.900 You map a class, you create an instance of it, you call save, you can do queries on it, all sorts of stuff. 00:08:32.900 --> 00:08:38.680 But leveraging the hierarchical document TV nature of MongoDB, it also adds new features. 00:08:38.680 --> 00:08:44.320 Like, MongoDB doesn't really have schemas in general, like, they're starting to add some of these features. 00:08:44.320 --> 00:08:47.620 But there's no schema definition, it's just whatever you put in there. 00:08:47.620 --> 00:08:53.200 With MongoEngine, you have a schema that matches your classes, so you have some reliable data structure. 00:08:53.200 --> 00:08:59.520 MongoDB has no concept of required fields or constraints, like this number must be between 10 and 1,000. 00:08:59.520 --> 00:09:03.300 But MongoEngine does, relationships, all sorts of cool stuff. 00:09:03.300 --> 00:09:08.240 So it really provides a lot of nice structure around working with a NoSQL schema-less database. 00:09:08.420 --> 00:09:09.040 Oh, that's nice. 00:09:09.040 --> 00:09:10.540 Yeah, definitely check that out. 00:09:10.540 --> 00:09:12.160 Yeah, so I've found this to be super helpful. 00:09:12.160 --> 00:09:15.960 Like, actually, a lot of my sites are implemented, including Python Bytes, for example. 00:09:15.960 --> 00:09:23.640 The one thing that's a little bit of a pain is the deserialization speed is not awesome. 00:09:23.640 --> 00:09:28.300 So if you retrieve 100,000 records from MongoDB, that's really fast, probably. 00:09:28.300 --> 00:09:31.760 But then it'll drag on, turning those into MongoEngine documents. 00:09:31.760 --> 00:09:34.520 But if you're getting 50 or 100, it's totally fine. 00:09:34.520 --> 00:09:37.540 For whatever reason, it's just not that fast deserialized and stuff. 00:09:37.660 --> 00:09:38.840 And usually it's plenty fast. 00:09:38.840 --> 00:09:42.100 But if you do it 100,000 times, it turns out to start to show off. 00:09:42.100 --> 00:09:42.360 Okay. 00:09:42.360 --> 00:09:42.640 Yeah. 00:09:42.640 --> 00:09:43.080 All right. 00:09:43.080 --> 00:09:43.800 What you got first, next? 00:09:43.800 --> 00:09:45.020 Another Pretty Printer. 00:09:45.020 --> 00:09:48.080 So this is, I guess it's called Pretty Printer. 00:09:48.080 --> 00:09:48.740 Yeah. 00:09:48.740 --> 00:09:51.660 An article called Introducing Pretty Printer for Python. 00:09:52.280 --> 00:09:53.760 I think it's an extensible one. 00:09:53.760 --> 00:09:54.520 It's kind of nice. 00:09:54.520 --> 00:09:58.800 So somebody took, it's 3.6, or three, six and above only. 00:09:58.800 --> 00:10:04.720 But somebody was taking a look at all the different ways to Pretty Printer their output for debugging your code. 00:10:04.720 --> 00:10:06.640 And it wasn't really happy with them. 00:10:06.640 --> 00:10:08.020 So added yet another. 00:10:08.500 --> 00:10:10.280 But this is kind of extensible and cool. 00:10:10.280 --> 00:10:20.040 So you can add on, if you have a particular way you want to print your classes or your calls to different functions, you can customize that. 00:10:20.420 --> 00:10:22.520 And it's a pretty simple interface. 00:10:22.520 --> 00:10:23.040 And I like it. 00:10:23.040 --> 00:10:23.200 Yeah. 00:10:23.200 --> 00:10:23.720 It's really nice. 00:10:23.720 --> 00:10:28.600 So if you're in, say, the Python REPL, you can ask it to print out some stuff. 00:10:28.600 --> 00:10:35.920 And it'll actually do things like format the dictionaries according to PEP 8 with the line breaks and indentation. 00:10:35.920 --> 00:10:38.340 It'll colorize all the values. 00:10:38.560 --> 00:10:39.840 Like the keys will be one color. 00:10:39.840 --> 00:10:42.060 The values will be another. 00:10:42.060 --> 00:10:43.200 Things like that. 00:10:43.200 --> 00:10:44.600 So it really does. 00:10:44.600 --> 00:10:45.880 It is really nice like that. 00:10:45.880 --> 00:10:47.360 The colorizing is very nice. 00:10:47.360 --> 00:10:48.100 That's pretty cool. 00:10:48.100 --> 00:10:48.380 Yeah. 00:10:48.380 --> 00:10:48.880 I would say that. 00:10:48.880 --> 00:10:56.100 The colorizing and also the reformatting for human readable parts is quite nice as well. 00:10:56.100 --> 00:10:59.860 So it's very nice and has a nice declarative API as well. 00:10:59.860 --> 00:11:01.680 So you can even extend it, right? 00:11:01.680 --> 00:11:07.080 There's an example in there to be able to extend your call with comments even. 00:11:08.020 --> 00:11:11.200 So let me ask you a question. 00:11:11.200 --> 00:11:14.100 What do you think the most popular database in the world is? 00:11:14.100 --> 00:11:14.600 MySQL. 00:11:14.600 --> 00:11:16.040 MySQL is definitely a good one. 00:11:16.040 --> 00:11:17.480 I'm going to say Excel. 00:11:17.480 --> 00:11:20.260 At least it turns them out of data. 00:11:20.260 --> 00:11:23.500 The number of people that like create new quote databases. 00:11:23.500 --> 00:11:32.580 Like Excel is this weird thing that people who don't quite know programming or technology use it to more or less play the same role, right? 00:11:32.580 --> 00:11:33.320 You've got relationships. 00:11:33.320 --> 00:11:35.120 You've got formulas and whatnot. 00:11:35.820 --> 00:11:40.980 So Excel is really super important in the business world all over the place. 00:11:40.980 --> 00:11:51.700 Well, it so happens that Microsoft is considering replacing the macro scripting language for Excel that is built into it, VBA, with Python. 00:11:51.700 --> 00:11:52.040 Yes. 00:11:52.040 --> 00:11:53.100 That would be very nice. 00:11:53.220 --> 00:11:56.840 So on one hand, I'm like, whee, I don't really want to program Excel, right? 00:11:56.840 --> 00:11:58.880 It doesn't make me super excited or super happy. 00:11:59.040 --> 00:12:06.960 On the other, look at what the adoption of Python in the data science space has done for Python in general. 00:12:06.960 --> 00:12:10.160 It's really, really complemented it and made it a better place. 00:12:10.160 --> 00:12:14.880 I think that's what would happen if Python became the main scripting language of Excel as well. 00:12:14.880 --> 00:12:17.940 There's all these people who are not really working in this space now. 00:12:18.020 --> 00:12:22.540 All of a sudden, they'd be super interested and contributing things that who knows what they would actually come up with. 00:12:22.540 --> 00:12:25.240 I'm imagining this is actually pretty cool. 00:12:25.500 --> 00:12:40.580 I'm imagining a manager working with a spreadsheet and wanting to add some macro capabilities and getting a little bit too deep into it and getting a little lost, grabbing a developer and saying, hey, can you help me with this? 00:12:40.580 --> 00:12:45.060 And right now, if it was me, I'd say, no, you're on your own. 00:12:45.060 --> 00:12:45.960 I don't want to do that. 00:12:46.320 --> 00:12:48.420 I would hide under a desk like, no, Michael's busy. 00:12:48.420 --> 00:12:49.920 Michael can't talk right now. 00:12:49.920 --> 00:12:51.520 He's looking for someone to work on PVA. 00:12:51.520 --> 00:12:52.080 Stay away. 00:12:52.080 --> 00:12:55.500 But if it's a Python interface there, yeah, I'd help out. 00:12:55.500 --> 00:13:01.940 So I think the interaction between developers and managers would increase dramatically if Python was in Excel. 00:13:01.940 --> 00:13:03.300 Yeah, I think it would be super cool. 00:13:03.300 --> 00:13:05.980 Who knows what the chances of this actually happening are. 00:13:05.980 --> 00:13:10.840 I tried to get Python into Windows because, you know, it doesn't ship with Windows. 00:13:12.060 --> 00:13:22.300 So Microsoft has this place called User Voice where you can like, but maybe it's not even a Microsoft thing, but they use User Voice, which lets you vote on features and requests and things like that. 00:13:22.300 --> 00:13:29.260 So I actually got over a thousand people to vote for shipping Python 3 with Windows 10 before it actually came out, but no dice. 00:13:29.260 --> 00:13:36.340 However, they did put Python into SQL Server so you can do in-process machine learning against your data. 00:13:36.340 --> 00:13:41.840 So there's one example of them doing this, and if they'll do it for SQL Server, they may well do it for Excel, which would be awesome. 00:13:42.360 --> 00:13:45.680 So everyone listening can have a voice on this. 00:13:45.680 --> 00:13:54.560 If you go there, click the link, upvote the item, and there's a survey you can fill out and tell them, Python 3, please, we'll take more of that. 00:13:54.560 --> 00:13:55.400 That'd be awesome. 00:13:55.400 --> 00:13:55.820 Yeah. 00:13:55.820 --> 00:14:01.940 So if this sounds cool to you, be sure to go there, at least upvote it so that they know this is something we all want. 00:14:01.940 --> 00:14:03.000 And call your congressman. 00:14:03.000 --> 00:14:03.620 That's right. 00:14:03.760 --> 00:14:07.580 No, that was net neutrality, which didn't really help so much. 00:14:07.580 --> 00:14:09.360 But this might, this might, so that's awesome. 00:14:09.360 --> 00:14:12.480 All right, well, that's our items for this week. 00:14:12.480 --> 00:14:13.700 How about you? 00:14:13.700 --> 00:14:14.440 Any personal news? 00:14:14.440 --> 00:14:16.960 We've been getting over, still getting over being sick. 00:14:16.960 --> 00:14:20.160 So we have a tree up now, but we haven't decorated it. 00:14:20.160 --> 00:14:22.280 So that's our decorating for Christmas. 00:14:22.280 --> 00:14:22.860 That's awesome. 00:14:22.860 --> 00:14:24.080 That'll be a fun, fun thing. 00:14:24.080 --> 00:14:26.420 Kids going to be home from school pretty soon? 00:14:26.420 --> 00:14:27.320 They're all home now. 00:14:27.320 --> 00:14:28.700 So that's fun. 00:14:28.840 --> 00:14:30.720 So work is super productive these days. 00:14:30.720 --> 00:14:33.840 Yeah, I can actually get to work at a decent time. 00:14:33.840 --> 00:14:34.740 So that's good. 00:14:34.740 --> 00:14:35.100 Cool. 00:14:35.100 --> 00:14:36.100 All right. 00:14:36.100 --> 00:14:39.680 Well, for me, I have a webcast that I'm doing. 00:14:39.680 --> 00:14:41.680 And I guess one more MongoDB thing. 00:14:41.680 --> 00:14:46.480 So I'm doing a webcast that I call Let's Build Something in MongoDB and Python. 00:14:46.480 --> 00:14:48.040 I don't have to click the link. 00:14:48.040 --> 00:14:48.400 I don't remember. 00:14:48.400 --> 00:14:49.260 I think it's February. 00:14:49.260 --> 00:14:52.300 It is February 22nd. 00:14:52.300 --> 00:14:56.880 So there's a link at the bottom of the show notes that if you're interested, you can come attend the webcast. 00:14:56.880 --> 00:14:58.060 It's just free. 00:14:58.060 --> 00:14:58.720 It might be fun. 00:14:58.840 --> 00:14:59.400 I'll definitely go. 00:14:59.400 --> 00:15:03.060 So do you have like an intro to Mongo also? 00:15:03.060 --> 00:15:04.360 I do have an intro to Mongo. 00:15:04.360 --> 00:15:05.460 A free one, actually. 00:15:05.460 --> 00:15:07.760 It's at freemongodbcourse.com. 00:15:07.760 --> 00:15:08.080 Okay. 00:15:08.080 --> 00:15:09.200 I just thought I brought that. 00:15:09.200 --> 00:15:09.820 Yeah. 00:15:09.820 --> 00:15:13.180 And it uses MongoEngine and it uses RoboMongo. 00:15:13.180 --> 00:15:14.020 How about that? 00:15:14.020 --> 00:15:14.420 Nice. 00:15:14.420 --> 00:15:15.580 But it doesn't use Excel. 00:15:15.580 --> 00:15:16.200 Good. 00:15:16.200 --> 00:15:18.380 All right. 00:15:18.380 --> 00:15:21.820 Well, Brian, thanks so much for finding all these things and sharing with everyone. 00:15:21.820 --> 00:15:22.600 Yeah, thanks. 00:15:22.600 --> 00:15:25.600 Thank you for listening to Python Bytes. 00:15:25.600 --> 00:15:28.160 Follow the show on Twitter via at Python Bytes. 00:15:28.280 --> 00:15:31.060 That's Python Bytes as in B-Y-T-E-S. 00:15:31.060 --> 00:15:34.460 And get the full show notes at pythonbytes.fm. 00:15:34.460 --> 00:15:38.820 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:15:38.820 --> 00:15:41.520 We're always on the lookout for sharing something cool. 00:15:42.020 --> 00:15:44.920 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:15:44.920 --> 00:15:48.520 Thank you for listening and sharing this podcast with your friends and colleagues.