Transcript #302: The Blue Shirt Episode

00:00 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds.

00:05 This is episode 302, recorded September 20th, 2022.

00:11 I'm Michael Kennedy.

00:12 And I'm Brian Okken.

00:14 Hey, Brian. How are you doing?

00:15 I'm great. It's a nice day.

00:17 Yeah, it is a lovely fall day here in the Pacific Northwest.

00:20 Dry as can be. I just had a very nice walk with my dog.

00:24 Nice.

00:24 It's going to be hard to go back to work after this podcast, looking out the window.

00:29 I give myself 50-50 chances of making it.

00:31 Yeah, I got to go back to the other screen.

00:34 That's right. I'm going to be looking that way.

00:37 Awesome. Well, before we kick off the show, I also want to say thank you once again to Microsoft for Startups.

00:43 They're sponsoring this episode again.

00:45 And huge supporters of the show. Tell you more about that later.

00:48 Brian, could you just whisper to me about your next project here?

00:52 Code Whisperer.

00:54 So we've talked about, I think we've talked about GitHub Copilot before.

00:59 And I'm not sure if we talked about Amazon's Code Whisperer yet.

01:02 I don't think so.

01:04 Okay. So Code Whisperer is a similar kind of thing, I think.

01:07 I haven't tried it myself, actually.

01:09 But there's an article by Brian Tarbox that says, Can Amazon's Code Whisperer write better Python than you?

01:17 I brought this up because I've been thinking about it a lot, about these AI copilot sort of things and stuff.

01:24 So Amazon's offering looks like it's almost, I don't know if it's a similar sort of model.

01:31 In this example that he's giving, he has a bunch of examples.

01:36 He's going through, you write a description.

01:39 He's writing a description.

01:40 I don't know if this is the only way.

01:42 But basically, describe the function you want.

01:44 Like, function to open an S3 file.

01:46 And it writes one for you.

01:48 And even titles it.

01:50 So you give it a code comment and it, like, pops out some code.

01:54 Now, for this is kind of an interesting thing around, especially around Amazon services,

01:59 because there's a lot of Amazon services.

02:01 And, you know, you do a lot of API lookups and stuff.

02:03 So some help directly around APIs.

02:07 Actually, I think that that area makes kind of some sense.

02:11 Although, if you need an AI to figure out the API, maybe the API is a little complicated.

02:16 Just saying.

02:17 Exactly.

02:19 But the discussion is an interesting one through here about, basically, about the code that it gets out.

02:26 And it's really not talking about the morals of it or anything.

02:30 It's just really talking about using it and how good it is.

02:34 The punchline at the end.

02:37 So the author admits that the title was intended to be clickbaity.

02:44 And, you know, which is cool.

02:48 Because it's the internet.

02:49 Yeah.

02:49 But despite that, he, in walking through it, he thinks that it's actually, it's just making him a little bit better because it's more efficient.

03:01 And I'd like to quote a little bit.

03:03 It's a little bit better.

03:05 It's a little bit better.

03:06 It's a little bit better.

03:06 It's a little bit better.

03:07 But it's a little bit better.

03:07 It's a little bit better.

03:07 It's a little bit better.

03:07 But it's a little bit better.

03:08 spurs code is better or worse than mine is at the margins and not really important. What is

03:14 significant is that it has the potential to save me a ton of time and mental space to focus on

03:19 improving, refactoring, and testing. It makes me a better programmer by taking on some of the

03:24 undifferentiated heavy lifting. And I kind of like that idea of it kind of takes away the blank

03:31 canvas situation of like, you know, it might show you how it might one way to do it. And you can look

03:38 at it and go, Oh, no, I wouldn't do it that way. And then you can change it. But you've you now you're

03:42 on your second draft already, instead of so it's letting the AI do the first draft. It's kind of a

03:48 neat idea. I was looking he did this data class one, for instance, this kind of blew me away. He's got an

03:54 inventory item. And, and it's already any writes a description for a function that returns whether or

04:03 not an item costs more than $10. And, and it returns, it writes a function called expensive,

04:10 like he didn't say expensive in the title at all. But it's interesting. It said expensive,

04:16 and then it returns whether or not the unit price is greater than 10.

04:19 And it realized it was within a class. And so it used self dot unit price and not just some

04:25 unassociated function that returns greater than 10.

04:28 Yeah. So it is interesting. Yeah, yeah. Anyway, interesting discussion. And then also interesting

04:36 looking at the code, he tried it against test code, he said, I want to function the test the inventory

04:41 class. Well, one, I think it was probably maybe this was a prompting problem. You shouldn't have one

04:47 function to test an entire class. My, my, my druthers, but it did a decent job of at least

04:53 giving you a first start of like, one of the things to test is you need to test the expensive thing.

04:57 You need to function, you need to test the total cost. It just did it all in one function though. So

05:03 I mean, I guess that's what he asked for, but coming up with the total cost, which is computed.

05:06 That's kind of interesting. Yeah. Yeah. That is interesting. Yeah.

05:10 And the base item is a unit price of $10 and there's five of them. And so in the test,

05:16 it asserted the total cost is 50. Yeah, definitely. Interesting. Interesting to definitely look at

05:21 and good. And it might help you think about other test cases around it. So, so I guess cool. I wanted

05:28 to point out while I'm thinking about it, one of the reasons why I brought this up is I just listened

05:32 to a changelog episode with Simon Willison called stable diffusion breaks the internet. And this is

05:38 focused on AI driven artwork, which is definitely interesting and interesting conversation,

05:45 but in it they talk, since these are all programmers, they talk, talk about how this,

05:50 the same sort of argument applies around, around code generation of the morality of it. And,

05:56 and then aside more morals aside or in legal stuff aside, it's happening. So how do you,

06:02 Simon brings up the term of basically just you need to be one level of abstraction above the AI system. So

06:12 just to make sure that you were still adding value and the original author of this article talked about

06:18 this as well of it's, it's not about really not thinking it's about freeing up some of your brain

06:24 space to do other things. So in interesting. So, yeah, it is interesting. I mean, there's certain

06:29 things that you probably don't just don't need to remember. You know, I'm thinking of, do I really need to

06:35 remember all the steps in the connection string schema for connecting to SQLAlchemy? Probably not. I could

06:42 just say connect to SQL, you know, connect SQLAlchemy to a Postgres database and boom, it gives me,

06:47 you know, create the metadata, the metadata base class, and then create an engine and create a connection

06:55 and you're buying the engine, all those steps, right? Like if you could just kick that kind of

07:00 stuff out, that's something you want for a project and you just never do. It's not like, boy, I'm sure

07:05 I'm not good at connecting to SQLAlchemy. I'm just not a good programmer, I guess, right? You look it up,

07:09 you put it in there and you go. And so if you didn't have to take the step of looking up, that's kind of

07:13 cool. Yeah. I also like that. I didn't think about this before. And I think GitHub actually intended you

07:18 to think about it like this with naming it Copilot. It's not intended to take over your work, but it's

07:24 like sitting down with somebody that kind of knows what they're doing and being in pair programming with

07:29 them. Yeah. You can't turn off your brain, but maybe you can ease up a little bit. So anyway.

07:34 Wait, before you close this, scroll down to this black and white code editor. Boy, look at that.

07:39 If you check out this article, there is a, I don't even know what to make of it. Because to me,

07:43 it looks like a super retro early macOS, like macOS one type of UI, but then the file is c colon

07:51 backslash CD. It's just a mix of like beautiful retro. Yeah. Well, he was talking about the first

07:58 recorded code completion appears in the Pascal editor called Alice in 1985. So yeah. And I guess that's it.

08:07 Well, that's a, that's a heck of an editor. Super cool. All right. On to the next one.

08:12 Yeah. Two things real quick. I just want to point out or sort of make a comment. It's not pointed out

08:17 this morning. I had to make a new API because one thing I've learned about writing courses that depend

08:23 on other people's APIs, these other people suck at keeping their APIs running. They either decide,

08:29 you know what, this is costing me $10,000 a month and I'm going to have to charge for it. Boo hoo. No,

08:34 just kidding. That's a reasonable reason to change, but it changes like with the open weather API

08:37 or like this one for this Twilio course I was using. So I spent the morning a little bit of

08:44 yesterday and this morning, just doing a complete from scratch FastAPI API. And what a ton of fun

08:50 it is to just work with FastAPI and get to build out all sorts of neat, neat little things. And so,

08:56 you know, I just want to shout out if you're, if you're building something with FastAPI or you're

09:00 building an API, you can definitely give FastAPI. Look, there's a lot of, a lot of neat things you can do

09:04 to put together. Like here's a whole little website. It even does CSS and images and sort of,

09:09 sort of chameleon templates. I mean, it's basically static, but anyway, fun stuff and continues to be

09:14 fun. And so which, which, course is this for? Is it for the Python powered chat apps with Twilio

09:21 and SendGrid, which is actually a free course, but it sets up a chat bot that you order from like a

09:26 bakery type thing over WhatsApp. And the problem is if you go to the APIs that the WhatsApp thing was

09:33 using, they just 500 or 404 or one of those two things, neither of which is super useful for the

09:39 course. So I recreated it in FastAPI this morning, which is cool. Now it lives on the internet,

09:43 but that's not what I want to talk about as super as that is. I want to talk about Apache

09:48 superset. Okay. Have you heard of superset?

09:51 No. Well, the word I know.

09:53 Of course. But Apache superset is a modern data exploration and visualization platform. And I came

09:58 across that the other day and I'm like, what the heck is this? I haven't even heard of this. It has

10:02 almost 50,000 GitHub stars. Okay. That's insane. And is put together, by back, Max Bushman,

10:10 co also the creator of Apache airflow, which is pretty cool, right?

10:15 So this is, this turns out to be a really interesting program and it's written in Python

10:21 and TypeScript. It's like really front end heavy because it has a lot of visualizations and stuff,

10:25 right? But all the backend stuff, it's all the things that you would know. It's Flask, it's Redis,

10:30 Celery, many of the, you know, pandas and data science tools you would know, but it's not exactly

10:35 a tool for developers like Jupyter. So Jupyter would be a way that data scientists who know Python would sit

10:42 down and leverage their Python skills to check out data and explore things. This one is really almost

10:48 meant for like people who would say, I'm going to fire up Excel and see what's going on, or I'm going

10:53 to fire up some BI tool like Tableau. And I want to look at it a little bit and see what's going on.

10:58 And it's also open source and written in Python, which means it has APIs and extensions and plugins in

11:06 Python, which is pretty excellent. So it has a way to explore your data. Like Brian, look at this

11:10 picture. What do you think? It's, I don't know what it is, but it's pretty.

11:13 It's glorious, right? Like it's a fantastic way to visualize. You know, here's 25 contributors to a

11:19 stream over time. You can sort of see like the growth of their contributions or not. And so the way you

11:24 generate this is you just connect it to a database. It gives you the table. You say, make a chart out of

11:29 this database and you draggy, droppy, the pieces over and boom, there it goes. And it doesn't have to

11:35 just be the data in the database. It can be a computed field. So you could say, I want to graph

11:41 the sum of this join onto like the orders of each customer, or I want to see the max order for each

11:48 customer, you know, things like that. Right. So that's pretty cool. So you can explore data like

11:52 that. You can create these dashboards, these live dashboards to see what's the state of our

11:57 business today. And it even comes with a SQL IDE, all of this in the browser, very Jupyter-esque.

12:03 Pretty cool.

12:04 This is pretty neat. Yeah.

12:06 Yeah. Very, very neat. And it connects to, I told you it was Python. It connects to all of its databases

12:13 using SQLAlchemy. And so any database that can be a data source for SQLAlchemy, you know,

12:18 obviously Microsoft SQL server, Postgres, MySQL, but you know, things you might not think of like

12:24 Vertica or Druid or Amazon Redshift or Google BigQuery, all of these different data sources,

12:30 Databricks are available as a data source because SQLAlchemy knows how to talk to it. And this just

12:36 leverages SQLAlchemy. Yeah. Hey, hold it there for a sec. One of the things I learned recently,

12:40 which I don't know why I never got this before, but look at the SQLite logo. Yes. It's got a quill in

12:46 it. Did you, did you know that before that it's a quill for SQLite? Oh, quill. I did not put that

12:53 together now. How funny. Now we know. Cool. So anyway, yeah, people can check this out. It's

13:00 kind of a little bit intense to run, but you can pip install it, but probably the better way to do it,

13:06 you want to just try it out is to install it locally with Docker. So for me, for example, I just

13:13 put in the GitHub repo and then went in there and said Docker compose, gave it the YAML file and said,

13:17 pull and then up and off it goes.

13:19 So this is not a service. This is just something you can download and you run then.

13:24 It's something you can download and run, but it has a lot of infrastructure bits clicking together.

13:28 Okay. And so, when I interviewed Max Bushman, he actually is now the CEO and founder of preset,

13:37 which is superset as a service. So if you want to, if you want to have someone else host it for you,

13:42 you can go check it out with them. Right. But it's also a thing you can just run yourself,

13:47 but look how popular it is. Almost 50,000 get up stars, 10,000 forks. And I just learned of it.

13:51 That's nuts.

13:52 Well, I mean, you know, go figure. People actually want to know what's in their data.

13:56 I know.

13:57 Weirdos.

13:58 Yeah. It's so weird. What I think is cool about it is it lets you connect into like your live

14:04 operational data, not just like, Oh, I downloaded a CSV and now I can ask questions. Right. You can just

14:08 like whatever the current data is, let's get that and build a dashboard around it.

14:12 Pretty awesome. Yeah. Yeah. All right. Well, superset, if people need an alternative to Excel

14:17 or BI or Tableau or whatever, check out superset. It's very, very Python friendly and looks pretty

14:22 nice.

14:23 You know what else is nice?

14:24 Tell me.

14:24 Microsoft for startups.

14:27 Ah, they are. They are very nice. So yes, it's time to tell everyone about our sponsor,

14:33 isn't it, Brian?

14:34 Yeah.

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15:29 heart of so many startups. And it's not just, you know, the nice weather, if anything,

15:34 I don't encourage people to go outside and not work on their projects, right? It's the network

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16:18 Founders Hub. Check them out at pythonbytes.fm/foundershub 2022. Thanks again to Microsoft for

16:25 supporting our show. Yeah. Thank you. Yeah. Indeed. All right, Brian. Now what you got?

16:30 Well, I want to share something that Jeremy Page from the chat says, I thought SQL, always thought the

16:37 SQLite logo was an homage to TCL and I've got the logo for TCL. So maybe, I don't know.

16:44 Perhaps. Interesting. So, but I wanted to talk about recipes from Python SQLite again, recipes from Python

16:53 SQLite docs. So this is kind of a, there's a, this is an article by, I wrote it down, I promise I did.

17:00 Redowan Delaware, cool name. So this, he was going through the SQLite three docs on the Python docs.

17:09 And there's a, there's a lot of examples, but some of them don't have actual examples. It just talks

17:15 about the API. And so he decided to write out some of the examples as little code snippets. And I really

17:21 like this. If you're learning SQLite or even, you're just want to learn not SQLite in particular, but

17:27 databases. These are concepts that apply to a lot of things. So he's, he's got, of course, whether or not

17:33 you can execute individual statements or batch statements. So he's got little examples for that

17:38 goes into this is interesting. I thought was user defined callbacks. I thought this was really cool.

17:45 For instance, a scalar function, he defined a, and I knew that like you could put user defined functions

17:51 in databases, but I haven't ever done that really. He has a, a hash function, SHA256, that creates a hash

17:59 for passwords. And then he shows how to use that when he passes in a username and password into the

18:05 database, how it turns it into a hash, hashes it before it stores it.

18:10 That is cool. I never knew you could do that. Here's a Python function passed over as part of a

18:16 passed over to SQLite. And then the SQL statements can call it. That's, that's real cool.

18:21 Yeah. I mean, there's a special syntax. So that's good that there's these examples of like insert into

18:26 user values, users values, and then this question mark and SHA256 question mark.

18:32 Also, that's fantastic that that's being shown because that's the parametrized,

18:37 then the anti little Bobby tables version.

18:40 Okay.

18:41 Which is the best practice, right? The alternative is something worse.

18:46 Yeah. And then, you know, aggregate functions, which kind of got lost here, but there's a whole

18:53 bunch of really cool examples of using, using SQLite and, and they're really tiny examples. And so the,

19:00 one of the other things I wanted to share the reasons I wanted to share this article is I think

19:04 this is a really great way to learn an API or learn a service is to write these little example

19:11 things in little code snippets and try it out. Try it out with a table that you're creating that only

19:17 has two or three elements in it so that you can, you can play with it and, and you can get your head

19:22 around what you think the answer should be and what it does. The only thing I think I'd probably add,

19:27 of course, is if you're going to do little code snippets, these all have to be in separate files,

19:33 right? Unless you just write test functions. So this is a great use for pytest. I use it all the time.

19:38 If I'm learning something, I just do these little code snippets, but I do them within a test function.

19:43 And then it can be, it's not really testing anything except my own knowledge, but I can run

19:47 them just by right clicking on the, or clicking on the little arrow that the editor has for each

19:52 little function. So just rerun the failed test until, until I understand it. Yeah. Oh yeah. Very cool.

19:59 Anyway. All right. How about something we don't understand? Okay.

20:03 Let me take you over, let me take you over to a weird world of cascading consequences. So there's

20:08 this guy who is a assistant professor at NYU Tandon security and reverse engineering person named

20:16 Brandon Dolan Gavit. And there's this tweet here over to his blog post saying, a new blog post in which I

20:24 I download four terabytes of Python packages containing native X86 libraries, you know,

20:31 something that's done some C++ thing like G event or pandas, one of those numpy that then bundles

20:38 it into a wheel. And apparently there's a bug in one of the C compilers that if you pass dash F

20:45 fast dash math, it will potentially alter the floating point behavior of your program. If you

20:51 compile it with that. All right. So we're in Python, we don't compile things that often. What do we care?

20:56 Well, what this does is it reconfigures how the process uses like some low level registers,

21:04 but some feature of the CPU on how it does floating point math. And because when the library is loaded,

21:10 it changes that feature while it changes it for the entire program, AKA your program. That doesn't

21:16 sound great. Does it? No. So let's, let's dive in. So the article is called someone's been messing

21:22 with my sub normals, sub normals, I suppose being an aspect of floating point computations. So here he

21:28 is in Python 3.8 and he says from transformers import code gen for causal LM. And that's all they,

21:36 it's all he wanted. This is in IPython terminal. And it starts bumping out all these warnings.

21:41 NumPy core get limits. User warning, the value of the smallest subnormal for class

21:46 numpy.float32 type is zero. Over and over and over these start popping out. It's like,

21:52 hmm, well, warnings about floating point numbers sounds bad. What do you think?

21:56 Yeah.

21:57 So it turns out that something, not numpy, but something that is in this library was compiled with

22:05 this dash FF math dash fast flag. When it got imported, it changed how numpy was working. Okay.

22:13 So it says, what were the problems? It says, well, it changes the floating point unit behavior that's on

22:19 the CPU, the actual FPU. I remember when, by the way, CPUs didn't come with that. Like I was trying to

22:25 decide with my first computer to get a 486 SX or DX. And I got the DX because it came with a floating

22:31 point unit on the CPU. Anyway, that thing gets messed with and says for some algorithms that depend on the

22:39 behavior and will fail to converge if it's set to treat this as different. So it uses the FTZ DAZ flags

22:47 in the MX CSR register. That's part of the part that I don't understand. I don't, I don't work that low

22:53 level, but it turns out it's not ideal. So it said, well, what is actually going on here? And apparently

23:00 there's a way, there's a whole bunch of stuff, how you can search through Linux and whatnot to figure out

23:05 what processes are doing this weird stuff. And also apparently if you compile with the dash, oh fast, it

23:12 also like cascades over to having the same behavior. So there's some exploration, like you wrote some C code

23:18 and then imported it into Python. and it seemed all fine. And then did the same thing with oh fast and able to get

23:25 all these warnings. I've never seen this warning. So I guess that's good, but it turns out the culprit was

23:31 G event of all things, which is a event-based asyncio networking library. Yeah. But somehow something was

23:39 using it. And when it got imported, it freaked everything out. So then the question becomes, well,

23:43 if G event, G event can be causing these problems because somebody thought it was awesome to compile

23:49 the fast version, not the slow version. What else is out there? So, Brandon went through and decided

23:55 to download four terabytes of wheels for all the things that might have some kind of x86 binary in

24:02 them. And then there's a ton of analysis of trying to figure out like, well, how do you actually look for

24:08 and find whether or not this program has this feature or not? It turns out to be pretty tricky.

24:13 So there's a bunch of stuff about going through to just check to see like what, how do you test it for

24:19 this many packages? Cause the test he was using before was super slow. So anyway, it's, it's not ideal.

24:26 I think there was something like 49 different packages. Let's see. I wrote it down up here. I'll get this

24:32 number, right? Yeah. There's 49 packages, 49 packages on PyPI that were built with this flag.

24:38 However, thousands of packages use those libraries and hence were also subject to that behavior with

24:45 10 million downloads in the last 30 days. So that's pretty nuts, huh?

24:49 Well, I mean, you're kind of scaring me. So how do I know if I need to care? I guess,

24:55 you know, I, are you doing iterative floating point math that goes down to like very small things?

25:01 Probably, probably not. I don't think I need to care. I'm doing like, I need, I didn't need to

25:06 know what 33% of, you know, 69 is. It should be fine. Right. but if you're doing, well,

25:12 you got to test, got to test your code. And I guess we have to test our math as well. I just sort of

25:17 trust that a lot of that works. Yeah. I suppose you would see those warnings, right? That about the

25:23 floating point subnormal coming in. Okay. Yeah. so there's a great long list of here of packages,

25:31 let's see. I'll just read some out. People might know. So for example, G event, G event, G event, HTTP client, flask socket IO dagster, which is used in data science a lot for

25:43 like data engineering, web socket, G event, web socket, locust for a testing, interpret high Kafka

25:51 and on and locust plugins, parallel SSH, right? So it doesn't matter if you're using that library

25:56 for the math, just if it gets imported, it changes all the math of the program.

26:01 So anyway, there, there it is. People can check it out. The comments are pretty glowing about this

26:07 research. Matthew Adams, for example, says crazy, awesome work, bro. You should be knighted for this.

26:13 in our chat, Alvaro says, run your, run your test with a dash W error, which you should be

26:20 anyway. So cool. So warnings treated as errors basically. Yeah. Yeah. Or set that particular

26:26 one to be, warning. All right. Well, I guess that's it for our four items that we're

26:31 covering today. Am I right? Yeah. I was just, I was, I was giggling during part of that. Cause I,

26:36 the subnormal just cracking me up. Like, like why is, why is Brian talk like that? I don't understand

26:42 most of his words. Oh, don't worry about him. He's subnormal. I don't know. I also, I also like the

26:50 title of the overall blog, push the red button, push the red button for a research, malware

26:56 reverse engineering, pen testing on the blog. Yeah. Nice. Nice. All right. Well, how about some extras?

27:01 Yeah. I don't have anything I want to show, but, but I was just going to say a couple of

27:08 things I've been up to. I've been thinking about change logs a lot and for on test and code,

27:13 instead of doing like a one episode on change logs, I thought I would talk to several people and do an

27:19 NPR style combined. So it might be, it might end up being a series of episodes that I'll release

27:25 together or, or one long episode. I'm not sure yet, but, basically I'm thinking about change

27:30 logs a lot. the other thing I've been doing is, thinking about, so we had that pie test

27:36 course out, right? Last week. we did just awesome on, Talk Python Training. and,

27:43 I, I, I, it's cool. anyway, Talk Python Training. I always get to it by just

27:48 remembering that I switched that and just say training.talkpython.fm and you can get there.

27:53 But, but I've had some requests to take some of the content and, change it for individual

27:59 teams. So, and this is an interesting thing to me to, to, and to think about, to say, cause like in

28:05 this course we do a database and a command line interface, but we're mostly testing through the API.

28:09 So API API with the database application. so we're doing things, the, the layered things,

28:15 but some people are like, well, I don't use the database. So maybe we could swap that out with

28:19 an example that uses one of the resources we have. And more of our example, we don't do the API. We do

28:26 these little, we're testing something else. So like, okay, we can cover the concept. So it's a neat idea

28:31 to try to focus that towards people. So if it, I guess if you're interested in doing that,

28:35 check out, python test.com and under training, check me out. So yeah. Awesome. Yeah. There's a

28:42 lot of ideas in that course that can be applied to different industries, different ways. Yeah. Yeah.

28:46 Different ways for sure. Awesome. Yeah. So the PI test course is going super strong. People really

28:51 love it. great work on that, Brian. I have another course to announce cause it's been a week.

28:55 It's been a week. It's been a week. Python data visualization. So this is a course by Chris

29:02 Moffitt over at Talk Python Training. And the idea is there's all these different choices. I mean,

29:07 we just talked about superset today and throw, throw that in as another thing in the pile of general

29:12 visualization tools, right? So you might do matplotlib, or maybe you want to use something new

29:17 like Altair. So this course goes through and shows you what it's like to do visualizations

29:23 in these different frameworks, like matplotlib, Seaborn, even pandas and plot.ly and streamlet.

29:29 And then you can build out these different scenarios and say, well, in this case, it might make more

29:34 sense to use matplotlib, or I might choose Altair and it'll help you choose a visualization

29:38 framework, but also it'll show you how to use all of them. So it's a nice broad exposure to all these

29:43 different frameworks. So people can check that out. Talk by thumb.fm click on courses. Ooh, this,

29:48 this is definitely useful. I got a project that I need this for. So yeah, this is going to be a good

29:52 one. It is a good one. I've already seen it. I've seen it several times actually, but it's good.

29:57 Let me see. Do I have any more extras I want to give a shout out to? No, just those two things.

30:02 And then I have, I have two jokes for you this week because one is not enough.

30:05 No. Yeah.

30:06 The first one here has to do with people who maybe learned a different language, maybe are

30:13 hating a little on Python. So here's somebody says, me laughing at all the Python hate on this

30:19 sub Reddit as I study C#. Silly language. Come on. We all know C# is better. And then

30:26 that's like a smiling, laughing person. And then a more seriously, somewhat concerned starting a new

30:31 job and realizing on the job board, 95% of them are asking for Python. That's very fun.

30:37 Well, that now I want to go over to the, like the, the C# subreddit and see if I can find some

30:44 anti Python jokes.

30:45 I know.

30:45 Wouldn't that be good?

30:46 All right. Well, that one's pretty good. And then were you affected by the recent, we have,

30:52 for people who are not in our area in Pacific Northwest, there was a massive windstorm, like

30:57 30, 40 mile an hour wind, 25% humidity, a hundred degrees. It was like, if somebody threw a cigarette

31:04 out the window, the entire Pacific Northwest would just go instantly catch fire. It was like,

31:08 it was insanely bad. And so we had our power turned off in the West Hills here because the

31:14 trees were so likely to fall over and cause a fire from knocking over. So they just cut the power for

31:20 like a little bit. They also did that in California. There's like a big, it was a bit of an irony.

31:25 Like one day they said, we're going to only allow the sale of electric cars after 30, 30, 35 or something,

31:32 whatever the date is. I mean, I'm, I'm going to support that. I'm a fan of electric cars and all,

31:36 but like the next week they said, Oh, we're going to turn off your power. Cause actually I think the

31:41 electric cars might help balance it out. But anyway, bit of an irony. So this next joke has to do with

31:45 that. So I got ahold of this from Kylie codes and she highlighted this tweet that says the governor

31:52 has declared that for California, the governor has declared a state of emergency and ask all

31:56 Californians not to run NPM install between 4 PM and 9 PM today in an effort to save energy and fight

32:04 this wildfire danger. Oh, that's awesome. Isn't that good? Yeah. Yeah. I love it. So that's,

32:11 that's the two jokes I got for you. yeah, nothing too deep. Well then also you, me and missed one.

32:17 There was a, like the, the, the build on of that. The build. All right. Do tell us about it.

32:22 Okay. Governor declares the state of emergency and asked all Californians to not run,

32:25 a wasm pack build between 4 PM and 9 PM. Exactly. Nice. Cool. And John Sheehan says,

32:33 it's funny because it's true. Didn't you just talk about the other day, about rough and having

32:43 our Python tools faster, like the JavaScript community is being concerned about faster tools.

32:48 Maybe not everywhere. Maybe not a hundred percent. Yeah. Awesome. All right. All right. Well,

32:53 good episode as always. Thank you. Thank you. We'll talk to you next week. Yeah. See you next week.

32:59 Thanks everyone for listening. Bye. Bye.