WEBVTT 00:00:00.001 --> 00:00:05.260 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to 00:00:05.260 --> 00:00:12.240 your earbuds. This is episode 115, recorded January 29th, 2019. I'm Michael Kennedy. 00:00:12.240 --> 00:00:13.160 And I'm Brian Okken. 00:00:13.160 --> 00:00:15.000 And Brian, we got a special guest, don't we? 00:00:15.000 --> 00:00:15.340 Yeah. 00:00:15.340 --> 00:00:18.580 Nina Zagarenko. Say hello, Nina. How you doing? 00:00:18.580 --> 00:00:22.520 Hey, everyone. I'm very proud of you for pronouncing my last name correctly. 00:00:22.520 --> 00:00:27.920 Thank you. Brian and I, we specialize in mangling people's last names on this show, 00:00:27.920 --> 00:00:29.300 but we try. 00:00:29.520 --> 00:00:30.700 This time you nailed it. 00:00:30.700 --> 00:00:31.580 Thank you. 00:00:31.580 --> 00:00:35.320 For those who don't know me, I'm a senior cloud developer advocate at Microsoft, 00:00:35.320 --> 00:00:42.080 and my focus is on Python. You can find me on Twitter at NNJA. That's like ninja, but without 00:00:42.080 --> 00:00:42.400 the I. 00:00:42.400 --> 00:00:46.400 Yeah, that's a pretty cool Twitter handle. Awesome. And also, this episode is brought to you by 00:00:46.400 --> 00:00:51.240 Datadog. Check them out at pythonbytes.fm/Datadog. Tell you more about that. You know, 00:00:51.240 --> 00:00:56.580 Brian, I expect quite a bit of stuff out of Datadog. I would say I have great expectations 00:00:56.580 --> 00:00:59.300 when I go use their stuff. What do you think? How about you? 00:00:59.300 --> 00:01:06.620 Yeah, yeah. I do too. Yes. For our first item, we have great expectations. And it is a package 00:01:06.620 --> 00:01:11.240 that was shared by a listener. And I don't know the listener because they shared it with you, 00:01:11.240 --> 00:01:12.740 Michael, and you didn't tell me who it was. 00:01:12.820 --> 00:01:16.220 I just sent you over the link and I forgot. But thank you for sending it in. Next time, 00:01:16.220 --> 00:01:17.020 we'll do a better job. 00:01:17.020 --> 00:01:22.800 Well, we're just also trying to make it so that sharing information with everybody else is not 00:01:22.800 --> 00:01:26.080 an ego-boosting exercise because we won't remember your name. 00:01:26.080 --> 00:01:27.240 Or we'll mispronounce it. 00:01:27.240 --> 00:01:28.760 Unless it's important to you, then let us know. 00:01:28.760 --> 00:01:29.440 Yeah. 00:01:29.440 --> 00:01:29.660 Yeah. 00:01:29.660 --> 00:01:35.560 No, great expectations. It's kind of cool. It's this idea that we have a lot of tools out, 00:01:35.760 --> 00:01:42.700 for instance, pytest, to test your code. But there's in a lot of stuff, the data that you're 00:01:42.700 --> 00:01:50.220 running your code through, like in data science or a lot of data pipelines, the data is important too. 00:01:50.380 --> 00:01:56.540 And being able to check to make sure your data fits what you expect it to fit, what look like, is important. 00:01:56.540 --> 00:02:00.580 So these are some really cool expect calls. 00:02:00.580 --> 00:02:02.980 They're a bunch of functions that start with expect. 00:02:02.980 --> 00:02:07.280 They aren't assertions, so they aren't going to throw an exception. 00:02:07.280 --> 00:02:11.860 But what happens is you, like maybe on some of them, you pass them a data frame. 00:02:11.860 --> 00:02:16.360 Like I'll just give one as an example. You expect a column to exist. 00:02:16.660 --> 00:02:21.780 So you give it a data frame and you give it a specific column, and you want that column to actually be there. 00:02:21.780 --> 00:02:28.600 And if it's not, you get, actually, regardless of whether the answer, it comes back in the form of a JSON object. 00:02:28.600 --> 00:02:35.120 And it has like a valid, and you can say whether or not, you know, if it failed or passed the test. 00:02:35.120 --> 00:02:37.680 But it also shows you the parts where it didn't. 00:02:37.680 --> 00:02:43.940 So this is, there's a little video, actually, demo video that they have, where you can see it in action using a Jupyter notebook. 00:02:44.560 --> 00:02:48.020 And it's kind of cool. It shows you exactly where it's failing. 00:02:48.020 --> 00:02:52.920 So I imagine doing this interactively to look at your data would be helpful. 00:02:52.920 --> 00:02:58.500 But also you could probably put this in place in some data cleaning steps to make sure things are around. 00:02:58.500 --> 00:03:00.940 Like making sure there aren't any nulls in a column. 00:03:00.940 --> 00:03:05.460 There's a whole bunch of different things you can assert on or expect on your data. 00:03:05.460 --> 00:03:06.300 It's pretty fun. 00:03:06.300 --> 00:03:07.060 Yeah, it's pretty cool. 00:03:07.060 --> 00:03:08.400 Some of them are totally straightforward. 00:03:08.400 --> 00:03:10.420 Like expect these values to be in the set. 00:03:11.020 --> 00:03:20.640 Others, a little more data science, mathematical focused, like expect the chi-squared test p-value to be greater than such and such, right? 00:03:20.640 --> 00:03:26.580 I honestly haven't done a lot of chi-squared lately, but I don't do that much data science, you know, on the web. 00:03:26.580 --> 00:03:29.840 It's more we use addition and like stuff like that. 00:03:30.920 --> 00:03:42.080 Yeah, but some of the fun things, like it was in the video example, he expected some data from a column to be in a particular set. 00:03:42.080 --> 00:03:44.360 And it was either male or female. 00:03:45.200 --> 00:03:47.840 And first off, like how binary of this. 00:03:47.840 --> 00:03:51.800 But anyway, the males, a lot of them came back with spaces in them. 00:03:51.800 --> 00:03:59.340 So doing some of this exploratory thing might tell you where you need to add some cleaning steps or dealing with nulls or things like that. 00:03:59.340 --> 00:04:06.060 The follow-up, I'd like to hear how people might be able to use this, how it's used exploratory-wise. 00:04:06.220 --> 00:04:12.040 But how, I'd like to see somebody using it in their pipeline stages and how that works. 00:04:12.040 --> 00:04:21.080 Whether you, I guess I imagine if you had it in production, you'd have some code that would, if it failed an expectation, you'd write a log entry or something? 00:04:21.080 --> 00:04:22.620 Or I don't know what you'd do. 00:04:22.640 --> 00:04:26.700 Yeah, or maybe return, if you were accepting data over a web method, right? 00:04:26.700 --> 00:04:30.640 Somebody's doing ETL and they're like, here, we're submitting this new set of data. 00:04:30.640 --> 00:04:35.740 You could return like 400 bad requests to indicate, no, no, something's wrong with the data you sent me. 00:04:35.740 --> 00:04:36.740 Things like that, possible. 00:04:36.740 --> 00:04:37.100 Yeah. 00:04:37.100 --> 00:04:38.360 You know, what do you think about this? 00:04:38.360 --> 00:04:42.100 I think it's very cool and is probably going to be pretty helpful. 00:04:42.100 --> 00:04:43.080 I like it as well. 00:04:43.080 --> 00:04:52.880 What I like about it is it lets you take what would be a little algorithm you'd have to write to, say, go through and test all, say, the chi-square values and then compare them and then assert on that. 00:04:52.880 --> 00:04:56.080 Now you just do one line and it does it on the whole data set, the whole data frame. 00:04:56.080 --> 00:04:56.580 That's pretty sweet. 00:04:56.580 --> 00:04:59.000 Yeah, I'm going to be watching the video demo after the show. 00:04:59.000 --> 00:04:59.960 Yeah, right on. 00:04:59.960 --> 00:05:04.700 So the thing that you are going to cover next, the timing is incredible, Nina. 00:05:04.700 --> 00:05:11.180 I literally got a notification that I had ordered one of these and it shipped today and it's on its way to my house. 00:05:11.180 --> 00:05:12.080 So I'm very excited. 00:05:12.320 --> 00:05:14.120 Tell people what is on its way. 00:05:14.120 --> 00:05:17.740 Would that be the Circuit Playground Express? 00:05:17.740 --> 00:05:18.920 It would be. 00:05:18.920 --> 00:05:19.300 Yeah. 00:05:19.300 --> 00:05:19.840 Awesome. 00:05:19.840 --> 00:05:20.620 Yeah. 00:05:20.620 --> 00:05:28.800 So I wanted to chat a little bit about using CircuitPython and MicroPython to write Python for wearable electronics and embedded platforms. 00:05:28.800 --> 00:05:34.040 I've been playing with electronics projects as a hobby for probably about the past two years now. 00:05:34.040 --> 00:05:39.200 In the past few months, I've been focusing my attention on Python for microcontrollers. 00:05:39.200 --> 00:05:39.640 Right on. 00:05:39.640 --> 00:05:42.400 And what kind of little things are you making with your projects? 00:05:42.400 --> 00:05:45.760 My last one was Python powered LED earrings. 00:05:45.760 --> 00:05:49.720 And I dropped a link to the repo for the code as well as a photo. 00:05:49.720 --> 00:05:50.660 So you can see that. 00:05:50.660 --> 00:05:51.340 Okay. 00:05:51.340 --> 00:05:53.780 So what I know I've heard of MicroPython. 00:05:53.780 --> 00:05:55.260 And I think I've heard of CircuitPython. 00:05:55.260 --> 00:05:56.320 Are they the same? 00:05:56.320 --> 00:05:56.820 Are they different? 00:05:56.820 --> 00:05:57.520 Tell everyone about it. 00:05:57.580 --> 00:06:00.600 Yeah, there's definitely a little bit of confusion about that. 00:06:00.600 --> 00:06:02.920 So MicroPython is the original. 00:06:02.920 --> 00:06:09.320 It's a lean and efficient implementation of Python 3 that can run on these tiny little microcontrollers. 00:06:09.320 --> 00:06:15.060 And all it needs is 256 kilobytes of code space and 16 kilobytes of RAM. 00:06:15.060 --> 00:06:16.040 It's incredible. 00:06:16.040 --> 00:06:17.120 It's truly awesome. 00:06:17.120 --> 00:06:17.780 Yeah. 00:06:17.800 --> 00:06:19.500 It's so super low level too. 00:06:19.500 --> 00:06:19.700 Yeah. 00:06:19.700 --> 00:06:23.600 And CircuitPython is a port of MicroPython. 00:06:23.600 --> 00:06:26.240 And it's optimized for Adafruit devices. 00:06:26.240 --> 00:06:26.700 Cool. 00:06:26.700 --> 00:06:29.060 So I guess that's the one I'm going to be learning about. 00:06:29.060 --> 00:06:29.260 Yeah. 00:06:29.260 --> 00:06:30.160 Yeah. 00:06:30.560 --> 00:06:34.800 And some of these, the devices that Adafruit sells, they're as small as a quarter. 00:06:34.800 --> 00:06:36.260 That would be the trinket. 00:06:36.260 --> 00:06:42.360 But of course, my favorite Python hardware platform for beginners is that the Adafruit Circuit Playground 00:06:42.360 --> 00:06:42.660 Express. 00:06:42.660 --> 00:06:47.900 It has everything you need to get started with programming hardware without even needing to 00:06:47.900 --> 00:06:48.560 learn how to solder. 00:06:48.560 --> 00:06:52.120 All you need is some alligator clips for the conductive pads. 00:06:52.120 --> 00:06:55.760 And the board has a ring of neopixel LEDs. 00:06:56.160 --> 00:07:02.640 It has buttons, switches, temperature sensors, motion detectors, sound sensors, a tiny little 00:07:02.640 --> 00:07:04.820 speaker, and a lot more stuff. 00:07:04.820 --> 00:07:09.540 Like you can even use it to control servos, which are those tiny little motor arms. 00:07:09.540 --> 00:07:10.020 Wow. 00:07:10.020 --> 00:07:11.060 That's really awesome. 00:07:11.060 --> 00:07:11.500 Yeah. 00:07:11.500 --> 00:07:13.020 It's a tiny little thing. 00:07:13.020 --> 00:07:13.320 Yeah. 00:07:13.320 --> 00:07:17.960 What I really like about this and the reason that I ordered it was I can go to Adafruit and 00:07:17.960 --> 00:07:20.900 look around and I'm just like, this is too low level for me. 00:07:20.900 --> 00:07:22.340 I don't really know what I need. 00:07:22.340 --> 00:07:25.860 I don't even know if I have a power supply, if I get this little chip or that. 00:07:25.980 --> 00:07:26.840 Like how to put together. 00:07:26.840 --> 00:07:31.440 I'm like, okay, give me like one thing that has all the stuff to do little projects like 00:07:31.440 --> 00:07:31.900 you're describing. 00:07:31.900 --> 00:07:33.200 And I think it's awesome. 00:07:33.200 --> 00:07:35.400 And doing it in Python is super cool. 00:07:35.400 --> 00:07:36.600 It only costs $25. 00:07:36.600 --> 00:07:37.320 Yeah. 00:07:37.320 --> 00:07:38.120 It's not too expensive. 00:07:38.120 --> 00:07:38.560 No. 00:07:38.560 --> 00:07:44.780 And then if you don't want to use Python to program this, there's a tool that you can use 00:07:44.780 --> 00:07:46.280 called Microsoft MakeCode. 00:07:46.280 --> 00:07:51.740 And it lets you program these little devices with a drag and drop style kind of scratch 00:07:51.740 --> 00:07:52.580 like interface. 00:07:52.580 --> 00:07:54.900 So that's perfect for kids. 00:07:54.900 --> 00:07:57.000 And you'll find a lot of examples on their site. 00:07:57.000 --> 00:08:01.420 Oh, that's awesome that you point that out because I might get my daughter to do some kind 00:08:01.420 --> 00:08:04.320 of little game, you know, like a Simon Says or something with the LEDs. 00:08:04.320 --> 00:08:05.080 Who knows? 00:08:05.080 --> 00:08:05.660 That'd be fun. 00:08:05.660 --> 00:08:06.000 Yeah. 00:08:06.080 --> 00:08:09.280 The Mu editor has some tutorials on using these as well. 00:08:09.280 --> 00:08:09.620 Right. 00:08:09.620 --> 00:08:10.340 That's awesome. 00:08:10.340 --> 00:08:10.940 Super cool. 00:08:10.940 --> 00:08:11.560 All right. 00:08:11.560 --> 00:08:12.900 And I like you threw in the link. 00:08:12.900 --> 00:08:13.340 Yeah. 00:08:13.340 --> 00:08:14.320 To your code. 00:08:14.320 --> 00:08:16.300 So people can do the earrings as well. 00:08:16.300 --> 00:08:16.680 Yeah. 00:08:16.800 --> 00:08:20.680 And like you said earlier, there are tons of guides for Python projects on the Adafruit 00:08:20.680 --> 00:08:21.180 website. 00:08:21.180 --> 00:08:26.720 Stuff from making your own synthesizers to jewelry to silly little robots. 00:08:26.720 --> 00:08:28.560 So definitely make sure to check that out. 00:08:28.560 --> 00:08:28.760 Cool. 00:08:28.760 --> 00:08:28.960 Yeah. 00:08:28.960 --> 00:08:31.280 Well, I can almost recommend it. 00:08:31.280 --> 00:08:32.300 It looks really, really good. 00:08:32.300 --> 00:08:35.320 And when I get it, I'm going to play with it and let everyone know what I think. 00:08:35.320 --> 00:08:38.340 But yeah, it seems like a great little package that you can get. 00:08:38.340 --> 00:08:39.060 25 bucks. 00:08:39.060 --> 00:08:42.800 You know, it's like, you know, as software developers, you could just try it, right? 00:08:42.800 --> 00:08:43.700 It's not that big of a risk. 00:08:43.700 --> 00:08:45.500 It's not like you're getting a new MacBook or something. 00:08:45.500 --> 00:08:45.920 Mm-hmm. 00:08:45.920 --> 00:08:51.040 And Adafruit has a Python for microcontrollers mailing list. 00:08:51.040 --> 00:08:54.640 And they always drop kind of hot news and interesting new things. 00:08:54.640 --> 00:08:57.220 So there's a link to sign up for that in the show notes. 00:08:57.220 --> 00:08:57.600 Awesome. 00:08:57.600 --> 00:08:58.200 That's a great one. 00:08:58.200 --> 00:09:04.400 So this next one, I think, is a really interesting use of Python 3.7. 00:09:04.400 --> 00:09:10.600 So when you think of the main features of Python 3.7, certainly data classes has to be 00:09:10.600 --> 00:09:11.100 one of them. 00:09:11.100 --> 00:09:13.360 Have either of you found any use for data classes yet? 00:09:13.360 --> 00:09:14.280 Yeah, I just love them. 00:09:14.280 --> 00:09:15.900 I use them like named tuples. 00:09:15.900 --> 00:09:16.120 Yeah. 00:09:16.120 --> 00:09:16.420 Yeah. 00:09:16.420 --> 00:09:17.440 They're awesome. 00:09:17.440 --> 00:09:20.260 Trey Hunter has a great talk on data classes. 00:09:20.260 --> 00:09:20.740 Definitely. 00:09:20.740 --> 00:09:24.820 So this is a library that's derived from data classes. 00:09:24.820 --> 00:09:26.840 And it's a CSV file reader. 00:09:26.840 --> 00:09:29.780 Now, Python comes with pretty good support for CSV readers. 00:09:29.780 --> 00:09:33.780 You import CSV and then you create a dict reader based on a file stream. 00:09:33.780 --> 00:09:36.040 And it'll read the header and figure out the columns. 00:09:36.040 --> 00:09:38.620 And then it gives you little dictionaries based on the column names. 00:09:38.620 --> 00:09:39.480 And that's pretty sweet. 00:09:39.480 --> 00:09:44.560 But what you get back is a whole bunch of strings corresponding to those values. 00:09:44.560 --> 00:09:44.880 Right? 00:09:45.340 --> 00:09:51.520 And with this, what you can do is you can actually define a data class that maps the schema of 00:09:51.520 --> 00:09:53.560 your CSV file. 00:09:53.560 --> 00:09:54.220 Right? 00:09:54.220 --> 00:09:59.580 So I could define a data class that maybe has an ID and a name and a value or price. 00:09:59.580 --> 00:09:59.800 Right? 00:09:59.800 --> 00:10:00.700 Maybe it's like products. 00:10:00.700 --> 00:10:04.220 So like the ID could be an integer defined in the data class. 00:10:04.220 --> 00:10:05.160 The name could be a string. 00:10:05.160 --> 00:10:07.780 And the value, the price could be, say, a float. 00:10:07.880 --> 00:10:13.020 And it'll actually do all those conversions for you and give you meaningful errors if like 00:10:13.020 --> 00:10:15.320 it's a non-parsable float or something like that. 00:10:15.320 --> 00:10:15.880 Isn't that nice? 00:10:15.880 --> 00:10:16.560 That's incredible. 00:10:16.560 --> 00:10:19.580 Working with CSV is the bane of my existence. 00:10:19.580 --> 00:10:20.020 Yeah. 00:10:20.020 --> 00:10:21.460 So you can just define these things. 00:10:21.460 --> 00:10:26.640 You get autocomplete in, you know, PyCharm or Visual Studio Code for your types, for your 00:10:26.640 --> 00:10:28.660 rows, because they come back as these data classes. 00:10:28.660 --> 00:10:31.200 And you get validation. 00:10:31.200 --> 00:10:33.420 And on top of that, you can actually do cool stuff. 00:10:33.520 --> 00:10:39.280 Like you can say, with data classes, you can specify either just the type or the type and 00:10:39.280 --> 00:10:40.200 a value. 00:10:40.200 --> 00:10:42.060 And that value becomes the default value. 00:10:42.060 --> 00:10:45.740 So if like only sometimes the price is there, you could put zero or minus one. 00:10:45.740 --> 00:10:48.400 And it'll just go through and substitute that value. 00:10:48.400 --> 00:10:50.120 So a lot of cool little nice touches here. 00:10:50.120 --> 00:10:50.420 Nice. 00:10:50.420 --> 00:10:50.660 Yeah. 00:10:50.660 --> 00:10:54.440 I'm definitely going to check this out the next time that I have to do some sort of CSV parsing 00:10:54.440 --> 00:10:59.060 because it's way better to let this thing do the validation and the type conversion and 00:10:59.060 --> 00:11:00.760 all that and just, you know, not worry. 00:11:00.760 --> 00:11:03.880 I don't think I've been this excited about CSV files in a long time. 00:11:03.880 --> 00:11:04.200 I know. 00:11:04.200 --> 00:11:05.860 They are. 00:11:05.860 --> 00:11:07.180 They are pretty amazing. 00:11:07.180 --> 00:11:09.980 No, this really makes working with them nice. 00:11:09.980 --> 00:11:11.340 And I'm excited, too. 00:11:11.340 --> 00:11:12.320 All right. 00:11:12.320 --> 00:11:14.380 Speaking of excited, I do want to tell you about Datadog. 00:11:14.380 --> 00:11:15.980 They're helping make this show possible. 00:11:15.980 --> 00:11:18.680 So before we get to our next item, let me tell you about them. 00:11:18.680 --> 00:11:21.320 This show is brought to you by Datadog. 00:11:21.320 --> 00:11:25.880 They're a cloud scale monitoring platform that brings like metrics and logs, distributed traces 00:11:25.880 --> 00:11:26.460 all together. 00:11:26.760 --> 00:11:32.580 You can do auto instrumenting of frameworks like Django and Flask and PostgreSQL, which 00:11:32.580 --> 00:11:37.980 means you can track requests across service boundaries, across machines, things like that, which is 00:11:37.980 --> 00:11:38.300 awesome. 00:11:38.300 --> 00:11:43.140 It makes it really easy to troubleshoot your slow Python apps and figure out overall where 00:11:43.140 --> 00:11:44.000 the time is being spent. 00:11:44.000 --> 00:11:48.260 So you can get started for free at pythonbytes.fm/Datadog. 00:11:48.340 --> 00:11:53.240 And they'll also give you a cool t-shirt with a Datadog character on it, which is nice and 00:11:53.240 --> 00:11:53.420 cute. 00:11:53.420 --> 00:11:54.700 So check them out. 00:11:54.700 --> 00:11:56.160 It helps keep the show going. 00:11:56.160 --> 00:12:01.780 Now, Brian, I want to come to a topic that we haven't really covered very much on the show, 00:12:01.780 --> 00:12:05.180 but maybe I think a while ago we did talk about packaging ones, right? 00:12:05.180 --> 00:12:06.940 Yeah. 00:12:06.940 --> 00:12:08.640 You want to catch us up on it? 00:12:08.640 --> 00:12:11.140 I think it's only second to GUIs so far. 00:12:11.140 --> 00:12:12.160 That's right. 00:12:12.160 --> 00:12:17.780 There's a fun article called How to Rock Python Packaging with Poetry in Briefcase. 00:12:17.780 --> 00:12:22.220 Plus, it has the phrase how to rock something, and I'm a sucker for that. 00:12:22.220 --> 00:12:25.360 It's actually kind of a nice tutorial on packaging. 00:12:25.360 --> 00:12:30.820 So for those of you that just joined us and haven't learned all of our discussions on packaging, 00:12:30.820 --> 00:12:36.440 this is a nice introduction to packaging and how it fits in Python and also kind of how 00:12:36.440 --> 00:12:43.760 the changing of things, changing of packaging, like where Flit, Pipenv, and Poetry sort of fit in 00:12:43.760 --> 00:12:44.940 with all of this. 00:12:44.940 --> 00:12:46.780 It's kind of a nice run through of that. 00:12:46.780 --> 00:12:53.380 Poetry is one of those things for packaging, and it uses the pyproject.toml file. 00:12:53.380 --> 00:12:57.220 And of course, we talked about poetry in episode 100. 00:12:57.220 --> 00:13:04.940 And the nitty-gritty details of pyproject.toml was in testing code 52 with Brett Cannon. 00:13:04.940 --> 00:13:09.980 But the neat thing, one of the neat things why I picked this is it also talks about Briefcase. 00:13:09.980 --> 00:13:12.720 And we haven't talked about Briefcase yet. 00:13:12.720 --> 00:13:17.860 And Briefcase is one of those tools from Py, the Beware project. 00:13:17.860 --> 00:13:24.560 And it's something that you can, it packages your, a Python application as a standalone native 00:13:24.560 --> 00:13:27.080 application for lots of stuff. 00:13:27.080 --> 00:13:31.640 It claims Mac, Windows, Linux, even iOS and Android, which is interesting. 00:13:31.640 --> 00:13:33.140 I haven't tried any of this. 00:13:33.140 --> 00:13:38.760 The tutorial talks about desktop distribution of code through Briefcase. 00:13:38.760 --> 00:13:40.040 And it's kind of cool. 00:13:40.040 --> 00:13:42.020 And how to get that done with poetry. 00:13:42.020 --> 00:13:43.920 I think poetry is definitely nice. 00:13:43.920 --> 00:13:49.920 It's, you know, sort of an alternative philosophy to PipInv, which is, we talked about that as well. 00:13:49.920 --> 00:13:56.880 And Briefcase is part of that whole set of small independent tools from Beware, which also is 00:13:56.880 --> 00:13:57.220 pretty cool. 00:13:57.220 --> 00:13:58.700 And it's nice to see them working together. 00:13:58.820 --> 00:14:02.620 Also, we got Cookie Cutter doing some magic in here as well. 00:14:02.620 --> 00:14:03.040 Definitely. 00:14:03.040 --> 00:14:08.120 And then one of the things that I like at the end of this, there's several tutorials on publishing, 00:14:08.120 --> 00:14:12.120 like how to push your new package to PyPI. 00:14:12.560 --> 00:14:17.420 But this one I really like because instead of telling you exactly how to push it to PyPI, 00:14:17.420 --> 00:14:19.940 they tell you how to push it to the test server. 00:14:19.940 --> 00:14:23.040 And I think that's an important step for people to do. 00:14:23.040 --> 00:14:28.120 Before you subject the world to your code, try it out at the test server first. 00:14:28.120 --> 00:14:29.220 So it's nice. 00:14:29.220 --> 00:14:29.960 Of course. 00:14:29.960 --> 00:14:31.340 Yeah, of course. 00:14:31.400 --> 00:14:32.420 Why don't more of them do that? 00:14:32.420 --> 00:14:34.660 I don't know. 00:14:34.660 --> 00:14:39.500 But please, everybody, like save the world and push it here first. 00:14:39.500 --> 00:14:39.740 Yeah. 00:14:39.740 --> 00:14:42.820 The packages on PyPI cannot be changed when they're uploaded. 00:14:42.820 --> 00:14:44.340 You can only add newer ones. 00:14:44.340 --> 00:14:50.700 So although you've got to admire the rate at which you'll increase your version of your package 00:14:50.700 --> 00:14:52.860 if you screw it up a few times trying to publish it. 00:14:52.860 --> 00:14:56.060 Yeah. 00:14:56.140 --> 00:14:57.160 That's productivity, right? 00:14:57.160 --> 00:14:57.640 That's right. 00:14:57.640 --> 00:15:04.100 So Nina, this next one that you found for us is one of these awesome lists. 00:15:04.100 --> 00:15:08.480 And I think these awesome lists are coming along really faster and faster these days, right? 00:15:08.480 --> 00:15:10.840 We've got awesome Python, awesome Python applications. 00:15:10.840 --> 00:15:13.660 What's the next one that's in that category? 00:15:13.660 --> 00:15:17.280 And this is a new one I came across called Awesome Python Security. 00:15:17.280 --> 00:15:22.340 It's a collection of tools, techniques, and resources to make your Python more secure. 00:15:22.340 --> 00:15:23.120 Oh, that's cool. 00:15:23.120 --> 00:15:23.640 Yeah. 00:15:23.700 --> 00:15:27.880 And it's got a lot of stuff for web apps like the secure.py, which is awesome. 00:15:27.880 --> 00:15:28.600 We've covered that. 00:15:28.600 --> 00:15:32.920 And the Flask, Flask Talesman, Django sessions, all kinds of stuff, right? 00:15:32.920 --> 00:15:34.340 But not just the web. 00:15:34.340 --> 00:15:35.300 There's a whole bunch of other ones. 00:15:35.300 --> 00:15:39.640 I think, and hopefully all of you agree, that all of your production and client-facing code 00:15:39.640 --> 00:15:41.280 should be written with security in mind. 00:15:42.280 --> 00:15:47.940 And this list features a few resources that I'd come across before, like Anthony Shaw's excellent 00:15:47.940 --> 00:15:54.060 10 common security gotchas article that highlights problems like input ejection and depending on 00:15:54.060 --> 00:15:55.360 assert statements in production. 00:15:56.100 --> 00:15:58.600 And I also came across a few that were new to me. 00:15:58.600 --> 00:16:01.360 So the OWASP Python resources. 00:16:01.360 --> 00:16:04.980 OWASP stands for Open Web Application Security Project. 00:16:04.980 --> 00:16:08.380 And there's tons of OWASP resources out there. 00:16:08.380 --> 00:16:10.740 I didn't know that there was a Python-specific one. 00:16:10.740 --> 00:16:13.440 You can find that one at pythonsecurity.org. 00:16:13.680 --> 00:16:17.980 I came across Bandit, which is a tool to find common security issues in Python. 00:16:17.980 --> 00:16:24.180 And now Bandit has a lot of really useful plugins that test for some issues like hard-coded password 00:16:24.180 --> 00:16:31.060 strings in production, leaving Flask debug on in production, using exec in your code, and 00:16:31.060 --> 00:16:31.820 a lot more. 00:16:31.820 --> 00:16:33.960 I linked to the full list in the show notes. 00:16:33.960 --> 00:16:40.020 And then a few other cool ones like Detect Secrets, which is a tool to detect secrets that 00:16:40.020 --> 00:16:42.120 were accidentally left in your Python code base. 00:16:42.120 --> 00:16:42.700 That's cool. 00:16:42.700 --> 00:16:42.980 Yeah. 00:16:42.980 --> 00:16:43.280 Yeah. 00:16:43.420 --> 00:16:44.400 Let's open source that. 00:16:44.400 --> 00:16:44.940 Oh, wait. 00:16:44.940 --> 00:16:48.480 Was our full access AWS or Azure key in there? 00:16:48.480 --> 00:16:48.940 Whoopsie. 00:16:48.940 --> 00:16:50.660 Oops. 00:16:50.660 --> 00:16:52.500 Oopsie doops. 00:16:52.500 --> 00:16:54.540 We'll just check it in again without that in there. 00:16:54.540 --> 00:16:55.400 I'm sure it'll be fine. 00:16:55.400 --> 00:16:56.140 There's no history. 00:16:56.140 --> 00:17:00.840 And something I really like about this list in particular is it also includes resources 00:17:00.840 --> 00:17:03.720 for learning about security concepts like cryptography. 00:17:03.720 --> 00:17:04.140 Yeah. 00:17:04.140 --> 00:17:09.060 You know, out of that cryptography section, they listed one of my all-time favorite packages, 00:17:09.060 --> 00:17:09.980 which is PassLib. 00:17:10.120 --> 00:17:16.200 So if you're going to store user secrets and you want to hash them, like passwords are 00:17:16.200 --> 00:17:19.500 probably the most common, but there could be other things as well that you don't want 00:17:19.500 --> 00:17:23.120 to store directly, but you want to accept from user and see if you have it. 00:17:23.120 --> 00:17:23.820 All right. 00:17:23.820 --> 00:17:26.220 Like you can hash it and that's a good idea. 00:17:26.220 --> 00:17:30.920 But what you really should do is like take that result, add some salt, then hash it again, 00:17:30.920 --> 00:17:33.940 take that, do it again, maybe a hundred thousand times, right? 00:17:33.940 --> 00:17:39.620 PassLib, that's like one function, like dot encrypt rounds equal 150,000, 200,000, whatever. 00:17:39.620 --> 00:17:40.700 It's really nice. 00:17:40.800 --> 00:17:41.320 That's awesome. 00:17:41.320 --> 00:17:41.660 Yep. 00:17:41.660 --> 00:17:44.380 To make sure your password doesn't end up on have I been pwned. 00:17:44.380 --> 00:17:44.860 Exactly. 00:17:44.860 --> 00:17:45.820 Exactly. 00:17:45.820 --> 00:17:51.500 So basically you can say, I want it to take, you know, 0.2 seconds to determine, to brute 00:17:51.500 --> 00:17:53.500 force or to check each version of the password. 00:17:53.500 --> 00:17:57.960 And it automatically, because of that, will slow down dictionary attacks against your site 00:17:57.960 --> 00:18:00.960 because you can only do them point, you know, it'd take only five per second, right? 00:18:00.960 --> 00:18:03.640 So, and then there's another one called let's be bad guys. 00:18:03.640 --> 00:18:04.280 That's interesting. 00:18:04.280 --> 00:18:05.560 So yeah, a lot of cool stuff here. 00:18:07.100 --> 00:18:08.420 It's a great project name. 00:18:08.420 --> 00:18:08.780 I know. 00:18:08.780 --> 00:18:10.200 It's like a hacker playground. 00:18:10.200 --> 00:18:10.920 Yeah. 00:18:10.920 --> 00:18:12.460 So check out the full list on GitHub. 00:18:12.460 --> 00:18:16.740 And then if there's something missing that you think should be there, maybe open a pull 00:18:16.740 --> 00:18:17.120 request. 00:18:17.120 --> 00:18:17.720 Yeah, absolutely. 00:18:17.720 --> 00:18:18.720 That's a cool one. 00:18:18.720 --> 00:18:19.480 I'm glad you found it. 00:18:19.480 --> 00:18:20.360 All right. 00:18:20.360 --> 00:18:27.340 The last official item I want to cover is PyDBG, which is the implementation of a, of a 00:18:27.340 --> 00:18:29.760 Rust macro called DBG. 00:18:29.760 --> 00:18:31.540 So just put the pie on the front. 00:18:31.540 --> 00:18:34.140 Now I haven't done that much Rust. 00:18:34.140 --> 00:18:35.520 I've actually been wanting to learn Rust. 00:18:35.600 --> 00:18:41.200 It looks pretty interesting to me, but the basic idea of this DBG macro is instead of 00:18:41.200 --> 00:18:47.560 just printing out, like I'm here, I'm here, the value is, you know, printing X as the value, 00:18:47.560 --> 00:18:51.600 it'll actually give you a higher level statement without doing more work. 00:18:51.600 --> 00:18:54.940 So if you're trying to debug something through print statements and that kind of thing, this 00:18:54.940 --> 00:18:55.740 makes it a lot easier. 00:18:56.160 --> 00:19:00.080 So you can go and say, like, I have a equals two, b equals three. 00:19:00.080 --> 00:19:06.840 If I could say DBG of a plus b, the output is the file of the line a plus b equals five. 00:19:06.840 --> 00:19:08.200 Things like that. 00:19:08.200 --> 00:19:09.600 Really, really nice. 00:19:09.600 --> 00:19:14.400 It sort of shows you in your message where you are in the file, what thing is you're actually 00:19:14.400 --> 00:19:17.840 printing without having to like come up with elaborate print statements. 00:19:18.160 --> 00:19:19.100 So pretty cool. 00:19:19.100 --> 00:19:20.940 Oh man, I'm going to use this like every day. 00:19:20.940 --> 00:19:25.740 Do you still use print statements to debug brain? 00:19:25.740 --> 00:19:26.560 Yes, I do. 00:19:26.560 --> 00:19:27.800 I love print statements. 00:19:29.000 --> 00:19:32.840 I use the debugger a lot, but every now and then I'm just like, you know, I just want 00:19:32.840 --> 00:19:35.160 to print this out and just see what is happening. 00:19:35.160 --> 00:19:41.500 Like I don't primarily use print statements for debugging, but sometimes I do when I'm kind 00:19:41.500 --> 00:19:45.040 of exploring that I want it to run, but I kind of want to see what's happening. 00:19:45.040 --> 00:19:46.640 I'm like, Oh, what am I getting back from that API? 00:19:46.640 --> 00:19:48.040 What is this value? 00:19:48.040 --> 00:19:49.180 Things like that. 00:19:49.180 --> 00:19:51.400 Have either of you used watch statements? 00:19:51.400 --> 00:19:52.240 No, tell us about it. 00:19:52.240 --> 00:19:55.280 You can just set up a variable or an expression to watch. 00:19:55.740 --> 00:19:59.740 And, you know, every time you hit a break point, you're like, Oh, I see what's in there. 00:19:59.740 --> 00:20:00.800 I don't have to type it again. 00:20:00.800 --> 00:20:02.080 With VS Code or PyCharm? 00:20:02.080 --> 00:20:02.540 Yeah. 00:20:02.540 --> 00:20:06.560 I believe you can set up watches with PDB2, but I don't know. 00:20:06.560 --> 00:20:08.460 I usually do those in a graphical debugger. 00:20:08.460 --> 00:20:09.320 Yeah, me too. 00:20:09.320 --> 00:20:10.600 Yeah, I definitely have used those. 00:20:10.600 --> 00:20:14.320 I was, I was thinking something different, but so what, where, where I'm going to use this 00:20:14.320 --> 00:20:20.040 DBG, PyDBG thing is a lot of times I've got, we've got test code that generates 00:20:20.040 --> 00:20:23.160 huge amounts of data, like trace data. 00:20:23.820 --> 00:20:30.660 And, these are stored and the test runs are really long and throwing a couple 00:20:30.660 --> 00:20:33.660 of these extra ones for intermediate values. 00:20:33.660 --> 00:20:39.940 So the failing tests or failing test runs, we can take a look at those postmortem, things 00:20:39.940 --> 00:20:40.280 like that. 00:20:40.280 --> 00:20:41.640 It'd be just save. 00:20:41.640 --> 00:20:44.080 It's an elegant way to, to have that be done. 00:20:44.080 --> 00:20:44.380 Yeah. 00:20:44.380 --> 00:20:44.700 Yeah. 00:20:44.700 --> 00:20:45.200 It's pretty cool. 00:20:45.200 --> 00:20:47.060 What I like about it is this like so simple, right? 00:20:47.060 --> 00:20:50.720 Like you place the word print with DBG and you, you kind of got something going on here. 00:20:50.720 --> 00:20:51.960 It also kind of like that. 00:20:51.960 --> 00:20:52.680 It's more explicit. 00:20:52.780 --> 00:20:54.880 You're like, this is not really supposed to be a print statement. 00:20:54.880 --> 00:20:57.860 This is just here till I figure out what's going on. 00:20:57.860 --> 00:20:59.080 And then we're going to stop this. 00:20:59.080 --> 00:20:59.480 Yeah. 00:20:59.480 --> 00:21:00.500 But cool. 00:21:00.500 --> 00:21:01.300 People can check it out. 00:21:01.300 --> 00:21:04.460 Thanks for sending that in to our listeners. 00:21:04.460 --> 00:21:05.020 All right. 00:21:05.020 --> 00:21:09.400 So I guess that's it for all of our main topics, but looking at our show notes here, Brian, 00:21:09.400 --> 00:21:12.020 we all like kind of had a second round in the extra. 00:21:12.020 --> 00:21:14.260 So maybe we'll do like a lightning round one more time. 00:21:14.260 --> 00:21:14.960 What do you got for us? 00:21:14.960 --> 00:21:24.040 This is just a quickie that pytest has temporary directories and temperature factory fixtures for dealing with temporary files. 00:21:24.040 --> 00:21:33.020 But they've added, as of pytest 3.9, there's path versions that return pathlib path objects. 00:21:33.020 --> 00:21:35.780 And those are just quite fun. 00:21:35.780 --> 00:21:37.200 And I'll drop a link in the show notes. 00:21:37.400 --> 00:21:37.620 Okay. 00:21:37.620 --> 00:21:38.140 That's great. 00:21:38.140 --> 00:21:44.520 So I want to bring your attention to something, let's say, non-standard in terms of conference presentations. 00:21:44.520 --> 00:21:46.160 So this is something at PyCon US. 00:21:46.680 --> 00:22:01.120 So in May, Cleveland, 2019, there's a project called The Art of Python, which is a miniature arts festival focusing on narrative performance and visual art around programming and Python. 00:22:01.120 --> 00:22:08.300 And basically showcase novel art that helps us share our emotionally charged programming experiences, particularly to do with Python. 00:22:08.300 --> 00:22:12.160 So it's like five to 20 minute presentations in a separate little track. 00:22:12.160 --> 00:22:13.500 And the call for papers are open. 00:22:13.680 --> 00:22:17.960 So if you've always been a theater fan and you program, here you go. 00:22:17.960 --> 00:22:19.260 Oh, this looks very cool. 00:22:19.260 --> 00:22:20.100 Yeah. 00:22:20.100 --> 00:22:20.560 Yeah. 00:22:20.560 --> 00:22:21.140 That's pretty interesting. 00:22:21.140 --> 00:22:23.180 So people can check that out if that connects with them. 00:22:23.180 --> 00:22:31.140 The other one is one of my favorite surveys and sort of put your finger on the pulse of the community items is the Stack Overflow survey. 00:22:31.140 --> 00:22:33.740 Well, the 2019 one is open. 00:22:33.740 --> 00:22:39.180 So everyone should go out there and represent for Python and fill out the 2019 Stack Overflow survey. 00:22:39.180 --> 00:22:40.520 So that's good. 00:22:40.840 --> 00:22:44.160 And then finally, this gets a little bit back to your pick, Nina. 00:22:44.160 --> 00:22:50.940 NumPy is awaiting a fix for a critical remote, remote code execution bug. 00:22:50.940 --> 00:22:52.440 That's bad. 00:22:52.440 --> 00:22:55.300 That doesn't sound super good. 00:22:55.300 --> 00:22:59.360 So, yeah, I don't know if it has been entirely fixed yet. 00:22:59.440 --> 00:23:01.660 I don't, you know, this is a couple days ago. 00:23:01.660 --> 00:23:02.320 It was not. 00:23:02.320 --> 00:23:06.260 So the idea is basically there's a problem with the pickle module. 00:23:06.260 --> 00:23:11.700 Have you ever, could you imagine there'd be a problem with accepting user input straight in pickle form? 00:23:11.700 --> 00:23:12.900 I can't imagine. 00:23:13.580 --> 00:23:19.000 So the idea is there's some part of NumPy that you can load pickled data. 00:23:19.000 --> 00:23:28.980 And, you know, for those who don't know, like part of the pickle statement is here's some Python code as a module and here's arbitrary code to run as part of deserializing that. 00:23:28.980 --> 00:23:30.320 So good luck. 00:23:30.320 --> 00:23:30.820 Oh, boy. 00:23:31.080 --> 00:23:33.000 Yeah, yeah, that's not so, that's not so good. 00:23:33.000 --> 00:23:39.980 So this goes up to at least version 110 through 116, which at least is January 14th. 00:23:39.980 --> 00:23:40.620 This hadn't been fixed. 00:23:40.620 --> 00:23:42.260 So hopefully it's been fixed. 00:23:42.340 --> 00:23:50.980 But more importantly, if you're using NumPy and you're accepting user input through the load function, you want to upgrade and you want to be a little careful around that. 00:23:50.980 --> 00:23:51.920 All right. 00:23:51.920 --> 00:23:53.780 Then last one I just want to throw out there really quick. 00:23:53.780 --> 00:23:58.500 I ran across this and I've known about it for a long time, but it turns out to be more useful than I thought. 00:23:58.500 --> 00:24:06.780 So I use Google Docs a lot and I have like sheets in there and I've got Word, like documents and stuff. 00:24:07.080 --> 00:24:13.400 But the problem is if you use like Google Drive, what ends up on your hard drive is like a hyperlink back to the actual sheet, right? 00:24:13.400 --> 00:24:14.760 So how do you back that stuff up? 00:24:14.760 --> 00:24:20.280 It turns out if you go to takeout.google.com slash settings slash takeout, that's a lot of repetition. 00:24:20.280 --> 00:24:26.400 Anyway, you go there, you can say, give me all my document format, all my documents, and it'll give them to you in Microsoft Office format. 00:24:26.400 --> 00:24:33.320 Like it'll convert the sheets to Excel, it'll convert the docs to Word docs, and then let you download them so you have like a sort of permanent version. 00:24:33.320 --> 00:24:36.060 Anyway, I thought that was cool and people might find that useful too. 00:24:36.060 --> 00:24:36.360 Cool. 00:24:36.360 --> 00:24:36.680 Yeah. 00:24:36.680 --> 00:24:38.800 All right, Nina, you got some as well. 00:24:38.800 --> 00:24:40.380 You're teaching a class that looks really interesting. 00:24:40.380 --> 00:24:41.120 That's right. 00:24:41.120 --> 00:24:47.260 Yeah, I'm teaching a two-day introduction to an intermediate Python course on March 19th and 20th. 00:24:47.260 --> 00:24:52.440 And that class is going to live stream for free at Front End Masters on each day. 00:24:52.440 --> 00:24:55.760 And all the course materials I'm going to release for free as well. 00:24:55.760 --> 00:24:56.540 That's really excellent. 00:24:56.540 --> 00:25:00.160 And it has an in-person component if you happen to be, where is it, Minneapolis? 00:25:00.160 --> 00:25:01.580 In Minneapolis, that's right. 00:25:01.580 --> 00:25:02.600 You can come to the class. 00:25:02.600 --> 00:25:04.320 Will Minneapolis be thought out by then? 00:25:04.320 --> 00:25:06.120 The class size is about 20 people. 00:25:06.180 --> 00:25:08.520 That I cannot promise. 00:25:08.520 --> 00:25:10.220 Hopefully in March it's a little warmer. 00:25:10.220 --> 00:25:12.740 Yeah, so up to 20 people could drop in in person. 00:25:12.740 --> 00:25:13.500 That'd be really cool. 00:25:13.500 --> 00:25:17.620 The next thing, I recently recorded a series of videos with Carlton Gibson. 00:25:17.620 --> 00:25:32.920 He's a Django maintainer, maintains a lot of other projects on developing Django web apps with VS Code, deploying them to Azure with just a few clicks, setting up continuous integration and continuous delivery, as well as creating serverless applications. 00:25:33.300 --> 00:25:36.760 You can watch that video series at aka.ms.com.au. 00:25:36.760 --> 00:25:40.220 Python-virus.com.au. 00:25:40.220 --> 00:25:40.720 Check that out, too. 00:25:40.720 --> 00:25:41.540 Yeah, it's great. 00:25:41.540 --> 00:25:44.540 We got to film in the Microsoft Channel 9 studio. 00:25:44.540 --> 00:25:47.740 And it's a very well-done series. 00:25:47.740 --> 00:25:49.420 All the bright lights and everything, huh? 00:25:49.420 --> 00:25:50.520 Not just screencasts. 00:25:50.520 --> 00:25:51.040 That's cool. 00:25:51.040 --> 00:25:52.440 Yeah, we feel like newscasters. 00:25:53.300 --> 00:25:59.260 I'm also planning on being a mentor at a brand new hatchery event at PyCon US 2019. 00:25:59.260 --> 00:26:04.800 That's going to be mentored sprints for diverse beginners organized by Tanya Allard. 00:26:04.800 --> 00:26:10.400 The goal is to help underrepresented folks at PyCon contribute to open source in a supportive environment. 00:26:10.760 --> 00:26:15.240 The details aren't out yet, but I dropped a link to where they'll be when they're finalized. 00:26:15.240 --> 00:26:16.180 Oh, that's super cool. 00:26:16.180 --> 00:26:25.520 And there's also things like scholarships or something like that to help folks get actually physically to the event if they need some help as well, right? 00:26:25.520 --> 00:26:27.700 They can apply for that at the PyCon site. 00:26:27.700 --> 00:26:30.380 Yeah, PyCon US offers a lot of financial aid. 00:26:30.380 --> 00:26:42.320 Lastly, if you're interested in Python for hardware, like we talked about earlier, you can catch my talk about electronics projects in Python with LEDs at PyCascades in Seattle on February 24th. 00:26:42.320 --> 00:26:44.640 Currently, tickets for that are still on sale. 00:26:44.640 --> 00:26:44.960 Excellent. 00:26:44.960 --> 00:26:46.900 Yeah, and Brian and I are definitely going to be there. 00:26:46.900 --> 00:26:50.080 We're all going to PyCascades, so we're going to catch it. 00:26:50.080 --> 00:26:51.240 Hopefully, everyone else does as well. 00:26:51.240 --> 00:26:51.600 Great. 00:26:51.600 --> 00:26:52.900 I'm excited to see you there. 00:26:52.900 --> 00:27:00.080 And I do have one last thing to sneak in, and that is if you haven't tried the Python extension for VS Code yet, 00:27:00.500 --> 00:27:01.900 now is a really good time. 00:27:01.900 --> 00:27:10.140 The December release included some really killer features like remote Jupyter support and exporting Python files as Jupyter notebooks. 00:27:10.140 --> 00:27:15.960 And if you're interested in keeping up with future releases, I dropped a link to the Python at Microsoft blog. 00:27:15.960 --> 00:27:16.300 Nice. 00:27:16.300 --> 00:27:21.160 And didn't you as a group, I'm speaking to you as a Microsoft, as VS Code, 00:27:21.160 --> 00:27:28.280 didn't you guys just release like an AI-powered autocomplete backend for Python as well? 00:27:28.400 --> 00:27:30.200 That's been around for a few months. 00:27:30.200 --> 00:27:34.040 I believe it's still in preview mode, but it works really well. 00:27:34.040 --> 00:27:37.540 The dataset was trained on a bunch of open source projects. 00:27:37.540 --> 00:27:38.740 Yeah, it looks super cool. 00:27:38.740 --> 00:27:40.160 So I definitely want to check that. 00:27:40.160 --> 00:27:42.940 I think I installed it just the other day, so it should be fun. 00:27:42.940 --> 00:27:43.320 Yeah. 00:27:43.320 --> 00:27:44.280 Try it. 00:27:44.280 --> 00:27:45.100 Let me know what you think. 00:27:45.100 --> 00:27:45.560 Absolutely. 00:27:45.560 --> 00:27:47.580 Brian, we've come to our joke section, right? 00:27:47.580 --> 00:27:47.880 Yeah. 00:27:47.880 --> 00:27:50.620 Nina, you want to kick us off? 00:27:50.620 --> 00:27:51.320 Yeah. 00:27:51.580 --> 00:27:55.160 I found a bunch of really cheesy snake jokes, so here they go. 00:27:55.160 --> 00:27:58.340 What do you call a snake that only eats dessert? 00:27:58.340 --> 00:27:58.840 I don't know. 00:27:58.840 --> 00:28:00.800 It's a pie-thon. 00:28:00.800 --> 00:28:02.940 Nice. 00:28:02.940 --> 00:28:03.900 I'll do the next one. 00:28:03.900 --> 00:28:05.540 How do you measure a Python? 00:28:05.540 --> 00:28:06.680 In inches. 00:28:06.680 --> 00:28:07.600 They don't have any feet. 00:28:07.600 --> 00:28:10.980 Brian, what's the last one? 00:28:10.980 --> 00:28:13.420 What is a Python's favorite subject? 00:28:13.420 --> 00:28:13.940 I don't know. 00:28:13.940 --> 00:28:14.320 What is it? 00:28:14.320 --> 00:28:14.800 History? 00:28:14.800 --> 00:28:15.560 That's bad. 00:28:15.560 --> 00:28:16.600 These are all bad. 00:28:16.600 --> 00:28:17.400 History. 00:28:17.400 --> 00:28:17.920 Lovely. 00:28:19.740 --> 00:28:21.120 And I will not apologize. 00:28:21.120 --> 00:28:22.060 No, those are great. 00:28:22.060 --> 00:28:23.480 Thank you for finding those, Nina. 00:28:23.480 --> 00:28:24.380 We're coming up with them. 00:28:24.380 --> 00:28:25.180 Either way, they're great. 00:28:25.180 --> 00:28:26.240 All right, folks. 00:28:26.240 --> 00:28:27.960 Well, thank you for listening. 00:28:27.960 --> 00:28:30.500 And Nina and Brian, thank you for being here today, of course. 00:28:30.500 --> 00:28:30.960 Thank you. 00:28:30.960 --> 00:28:31.860 Thanks for having me. 00:28:31.860 --> 00:28:32.120 You bet. 00:28:32.120 --> 00:28:32.500 Bye. 00:28:32.500 --> 00:28:32.740 Bye. 00:28:32.740 --> 00:28:33.140 Bye. 00:28:33.140 --> 00:28:35.120 Thank you for listening to Python Bytes. 00:28:35.120 --> 00:28:37.640 Follow the show on Twitter via at Python Bytes. 00:28:37.640 --> 00:28:40.540 That's Python Bytes as in B-Y-T-E-S. 00:28:40.540 --> 00:28:43.940 And get the full show notes at pythonbytes.fm. 00:28:43.940 --> 00:28:48.300 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:28:48.300 --> 00:28:51.020 We're always on the lookout for sharing something cool. 00:28:51.020 --> 00:28:54.400 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:28:54.400 --> 00:28:57.900 Thank you for listening and sharing this podcast with your friends and colleagues.