#444 Begone Python of Yore!

WEBVTT
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Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to
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your earbuds. This is episode 444, recorded August 11th, 2025. I am Michael Kennedy.
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And I'm Brian Okken.
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And this episode is brought to you by DigitalOcean and their generative AI features and tools,
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and DigitalOcean more generally, but specifically by their gen AI tools,
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which we're going to talk about. Brian's going to tell you about them.
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Links are in the show notes at the top.
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And I highly recommend that you subscribe to our newsletter, which Brian sends out a really nice email pretty soon after the show.
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is out. Diving further into the stuff that we talked about, maybe some additional resources
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and links and insights and tools and all that. It's not just a email version of the show notes.
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It's really cool. I enjoy getting it as well. Thanks, Ryan. I enjoy putting it together.
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I enjoy hearing about whatever you've got to cover first. So let's, yeah, let's jump in there.
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Speaking of coverage, let's talk about coverage. Ned Batchelder released,
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I guess this was in July, but just recently,
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a blog post called coverage.py regex pragmas.
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So that seems like a mouthful.
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And probably, please don't run away because this is powerful.
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I know that pragmas and regex isn't everybody's favorite topic.
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I'm feeling like C++ is back again.
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So what does pragma mean?
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It's the pound thing.
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So let's say if you say like pragma exclude or something like that to say or no cov to say, hey, for coverage for this particular line of code, don't worry about measuring the coverage on that.
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However, so this is a great article and I did not realize I've been using coverage for years and I did not realize it was this powerful.
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So, coverage uses regexes to define pragma syntax.
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And you don't really have to care about it if you just want to just for each individual thing you want to pop in.
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And there is a way to say particular files, just ignore the whole file.
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However, this is pretty cool.
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So, what is he talking about?
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He's talking about a couple things around it that allow you can extend it using regular expressions.
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And you don't necessarily have to figure this out.
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other people have figured it out for you. For instance, there are plugins for coverage that do
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a lot of this. So there's cov defaults. And this one's brought to us by, oh, Anthony Satili. Cool.
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So Anthony has cov defaults. And so there's a whole bunch of defaults already in there to do
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things like exclude your dundermain.py file. Because if you're distributing a package,
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that might be just so you can test things.
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You're probably not going,
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it's okay if coverage doesn't hit it.
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A lot of stuff like that.
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And then a whole bunch of regular expressions
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for things like type relating,
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typing relating code and excluding that.
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That's pretty cool.
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I didn't know that was there.
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That's neat.
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Another one is coverage conditional plugin,
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which does things like if you've got certain,
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what does it do?
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He talks about it.
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It gives you a way to create common syntaxes
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for entire files or entire packages,
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whether a package is installed
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or different operating systems, things like that.
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That's pretty neat also.
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But a change happened apparently recently,
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this was, well, recently for me, last year,
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but a change to coverage that allowed multi-line,
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and this was contributed by Daniel Diniz,
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multi-line fragments or multi-line regular expressions
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so that it like compares it for the whole file.
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So one of the neat things about this is you can do things like,
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with this regular expression that you can put in your no coverage stuff,
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you can pop in pragma exclude file and define a file there.
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Instead of trying to figure out how to find the file in your directory structure,
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you can just pop this in and say,
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this particular file, I don't want to delete it yet,
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but don't worry about this for testing.
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That's pretty cool.
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Right. My boss has been on me about getting that code coverage percentage up.
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So I wouldn't do it for that.
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And that's not what it's for. Okay. No, I like that. I'm just teasing. I like the idea.
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I actually like, I mean, I've heard a lot of people say hitting a hundred percent isn't,
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isn't worthwhile, but I think it is because coverage is so powerful. You can, with all of
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this stuff, you can say, I want to hit a hundred percent of the code that I care about, not the
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stuff I don't care about but the things that the problem area is you can define this so that you
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can just focus on the files and areas where you really want coverage and that needs to be 100%
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so that's where that's where that's my caveat for why I like 100% a couple other cool pragmas
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start and stop so you can just say at the start and stop like add a comment to the top of
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a code block to say yeah this junk we're not I'm leaving it in here but it's not it's not running
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during testing so don't cover it um so really powerful i really love a couple all of these um
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they're pretty great oh this is a neat one so have you ever had like um these uh uh default like a
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placeholder method where you just uh use three dots or something i'm using three dots a lot better
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more than pass because um it kind of does the same thing um and you can just say yeah this is here
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because it has to for some structural reason,
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but it's never going to run.
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So don't worry about trying to cover it.
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So that's pretty neat.
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Yeah, that's cool.
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Yeah, very cool.
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Anyway, thanks, Ned,
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for teaching us how to use regular expressions for coverage.
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Yeah, that's very neat.
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All right.
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I want to take you back to the times of knights, maidens,
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to the land of yore.
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No, this is totally different.
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This comes to us from Matthias.
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It is a package called yore, however.
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Okay.
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It's quite new.
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It's like an undiscovered gem sort of thing.
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So when I first saw this, I'm like, huh, do I care about this?
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I don't know if I care about this.
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And then I looked more carefully.
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That is kind of interesting.
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It starts with a quote from ChatGPT, the sage, which is always good.
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But it's more productive to just jump down to some examples.
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So basically what this is, is it is a tool a little bit like Ruff or Flint
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or one of those things that works on--
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basically, it looks at your code, tells you if there are issues.
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But this one is focused specifically on have you previously supported unsupported versions of Python by doing stuff the old way, you know, be that typing or example is the abstract, the ASTune parser.
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I don't know what that is.
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Or the AST parser.
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Yeah, abstracts and text tree.
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If you want to use that and you're using 3.8 or before, you had to use the astune parse, I guess, from unparse.
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Or if you're in 3.9 or above, you can use AST from unparse.
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So, right, you probably have this comment, like, if the version of Python that I'm running on is less than this, I'm going to do this one thing.
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Otherwise, I'm going to do the other.
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And so what you do here is you still write that code because that's how it is.
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However, you put a comment kind of like, you know, PyCharm and VS Code, if you put a capital
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to-do colon as a comment, I don't know if you've noticed this, but probably.
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If you say capital to-do colon and you write something, that shows up in a separate window,
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at least in PyCharm, you say, show me my to-dos.
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And when you try to do a commit, it'll say, hey, you added some new to-dos that are not
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done.
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You sure you want to commit?
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There's a whole integration with the tooling like that.
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So this is the same type of deal.
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So you say, put a comment that is capital your colon, and then you say EOL, in this case, EOL 3.8, and you put a comment.
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Oh.
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And then a thing like replace block with line four.
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Okay.
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Okay.
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So you say, once 3.8 is gone and we stop supporting it, just do the straight import.
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We don't have to do this test.
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We don't have to do anything.
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And then later, you can run in your code, you say, your check EOL within five months.
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And it says, whenever you run it at the right time,
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Python 3, it will reach its end of life approximately four months.
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So it uses the comments to say,
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you've got sections of your code that depend,
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are complicated because of Python versioning breaking changes.
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Oh, cool.
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Here they are, right?
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And you can look for them much like you can use ruff check to say,
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show me issues with my code.
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But what's even better, ruff check has a --fix.
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And so does yours.
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You can say your fix, EOL, within five months,
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It'll actually rewrite that code and replace the block with line four and just go back to the straight import.
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Remove the your comment, remove the version checks, all that.
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Hmm.
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What do you think?
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Okay, I like it.
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For such a simple idea, I like it too.
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Now, I'll have to play with this a little bit because it totally makes sense for Python deprecating Python versions and stuff.
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And the reason one of the cool things about like looking into the future is you might be working on a you might be working on a release that you're not planning on releasing for another month.
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So you're getting started cleaning those up.
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But that might be your own stuff.
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You might be like you might be deprecating your own features.
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So you might I'm wondering if you can put like your for your own features or is it just a Python EOL thing?
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Yeah, that's what I've been thinking about as well.
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I was like, I would like it not just necessarily for my features, but hey,
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Pydantic went from V1 to V2 and that had super big deprecations for
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certain really common functions, right?
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Like dumping to JSON and so on.
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Yeah.
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Is it just the Python release cycle?
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I don't know, I'll have to look into that.
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Yeah, I think it's just Python version itself, but who knows?
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Okay.
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Neat, yeah, very, very neat.
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All right, also neat is DigitalOcean.
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Yeah, let me tell you about DigitalOcean.
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Please use our link when checking out their offer.
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You'll find it in the podcast player show notes.
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And it's a clickable chapter URL as you're hearing this segment.
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And it's also at the top of the episode page in pythonbytes.fm.
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And why wouldn't you?
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It's $200 credit.
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Thank you to DigitalOcean for supporting Python Bytes.
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Indeed, indeed.
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Thank you.
00:12:14.280 --> 00:12:14.660
Over to you.
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What's next?
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Wait, no, over to me.
00:12:16.840 --> 00:12:17.420
Over to me.
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Sorry, I'm retracting that statement.
00:12:19.650 --> 00:12:20.700
I got the order wrong, Brian.
00:12:21.060 --> 00:12:23.780
So we've talked about Nox before, right?
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Yeah.
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You're a fan of Nox, aren't you?
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Yeah.
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So Nox is a command line tool like pytest or Tox that allows you to test against multiple
00:12:32.780 --> 00:12:34.280
versions of Python, right?
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So super cool.
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You can run it.
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You can say like this might depend on whatever particular version of Python or whatever, right?
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So the thing I want to tell you about is something called uv.
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No, sorry, Knox-UV.
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And this comes to us from John Hagen.
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And this facilitates Knox integration with uv for Python projects.
00:12:56.810 --> 00:13:02.040
So remember, uv is awesome at installing different versions of Python rapidly.
00:13:02.480 --> 00:13:19.080
Like you can install, go to a machine that has no Python whatsoever, and you can pick your version of Python and have it installed and ready to use in under two seconds versus PyMV, which builds it and takes minutes or downloading the installer and making sure you have all the versions you're going to test with or whatever.
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Right.
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Yeah.
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So that's really awesome.
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And then also installing the dependencies for these different versions of Python, uv is ultra fast at that as well.
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So there's huge advantages to speeding up testing and CI with uv.
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And if you're using Knox to create a combinatorial matrix
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of all the different versions of Python you might work with,
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well, then uv speed is only going to multiply across that, right?
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Yeah.
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Yeah, so super cool.
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So you can just say uv add group Knox and Knox uv,
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and it'll add that in there.
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And then within your Knox file, you import the session.
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Instead of from Knox, you do it from Knox uv,
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set the backend to be uv.
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And then for your session, you specify which versions of Python, which groups, et cetera.
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Off it goes.
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I feel like this should be your topic, honestly, Brian.
00:14:03.480 --> 00:14:05.640
I think I stole it from you somehow, effectively.
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Why is that?
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Just because you're so into testing.
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And this is like right at the heart of it.
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Okay.
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What do you think?
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Cool?
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I think this is neat.
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Yeah, I'm using like uv almost everywhere now.
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And I actually, I used to recommend people like just average folks that are just starting Python
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or they just need it for work,
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I would recommend them just go to python.org and download it.
00:14:30.260 --> 00:14:31.720
And I'm still kind of doing that,
00:14:31.790 --> 00:14:34.860
but I'm leaning towards trying to teach people
00:14:34.890 --> 00:14:36.500
how to use uv in virtual environments
00:14:36.850 --> 00:14:38.920
because it's not going to be that long
00:14:39.250 --> 00:14:40.700
after they start using Python
00:14:40.920 --> 00:14:42.200
that they're going to want to use that anyway.
00:14:42.520 --> 00:14:43.260
So, yeah.
00:14:44.180 --> 00:14:46.240
Yeah, and recently we just covered this,
00:14:46.320 --> 00:14:47.840
that one of the main things uv did
00:14:48.100 --> 00:14:52.259
is now there's a Python version in your path
00:14:52.280 --> 00:14:54.700
if you install a Python via uv,
00:14:54.970 --> 00:14:57.340
which it used to just be in like some obscure place
00:14:57.570 --> 00:14:59.600
hidden in like your user profile sort of thing.
00:14:59.600 --> 00:15:01.940
It was hard to run it outside of a virtual environment.
00:15:02.300 --> 00:15:02.400
Yeah.
00:15:02.940 --> 00:15:03.040
Yeah.
00:15:04.160 --> 00:15:04.340
Yeah.
00:15:04.720 --> 00:15:04.780
Anyway.
00:15:05.190 --> 00:15:05.280
Okay.
00:15:05.720 --> 00:15:05.780
Cool.
00:15:05.960 --> 00:15:06.080
Yeah.
00:15:06.360 --> 00:15:06.600
Very cool.
00:15:06.860 --> 00:15:07.720
Thanks, John, for sending that in.
00:15:08.400 --> 00:15:09.140
Now over to you, Brian.
00:15:09.920 --> 00:15:10.260
All right.
00:15:10.640 --> 00:15:12.520
So let's take a look.
00:15:12.640 --> 00:15:16.020
I've got a couple short-ish Django items.
00:15:16.090 --> 00:15:18.200
So I thought I'd cover them together.
00:15:18.600 --> 00:15:21.440
So the first comes from Johnny Metz.
00:15:22.240 --> 00:15:25.800
And it's stop using Django's squash migrations.
00:15:26.220 --> 00:15:27.040
There's a better way.
00:15:27.280 --> 00:15:29.260
And actually, I didn't know about squash migrations.
00:15:29.380 --> 00:15:32.260
I'm kind of new to Django, the Django world, new-ish.
00:15:33.360 --> 00:15:38.720
But I like this article because I watched a pro, kind of an old hat,
00:15:39.380 --> 00:15:45.180
when I was doing a session with somebody that knows Django way better than I do.
00:15:45.760 --> 00:15:50.299
And I had a whole bunch of migrations because I was fiddling with
00:15:50.320 --> 00:15:53.080
the database structure and everything.
00:15:53.200 --> 00:15:54.780
And I was going back and forth, changing things.
00:15:54.940 --> 00:15:56.220
And I had a bunch of migrations.
00:15:56.980 --> 00:15:59.060
And he said, wait a second, just a second.
00:15:59.220 --> 00:16:00.040
Let me do something.
00:16:00.100 --> 00:16:01.760
And he just like, he did this.
00:16:01.860 --> 00:16:02.720
So what am I doing?
00:16:02.900 --> 00:16:03.680
What am I talking about?
00:16:05.019 --> 00:16:06.980
So this is Johnny Metz.
00:16:07.080 --> 00:16:09.580
He was recommending doing a clean reset.
00:16:10.260 --> 00:16:13.060
So you fully migrate all your environments,
00:16:13.440 --> 00:16:14.620
make sure everything's running well.
00:16:14.900 --> 00:16:17.360
And then you delete all your migration files.
00:16:17.780 --> 00:16:18.380
Seems crazy.
00:16:18.800 --> 00:16:22.260
but you delete them all and generate a fresh migrations.
00:16:22.840 --> 00:16:25.720
And then you've got just a new one.
00:16:26.320 --> 00:16:31.500
So this creates a new 0001 initial for each app,
00:16:31.820 --> 00:16:37.800
and then some apps might get a 002 or higher.
00:16:37.900 --> 00:16:41.300
So you do this on different, it's going to do it on different apps.
00:16:41.600 --> 00:16:44.340
But then you, so, okay, I'll just walk through it.
00:16:44.580 --> 00:16:47.460
Fully migrate all your environments, delete all your migrations,
00:16:47.840 --> 00:16:52.420
generate fresh migrations, then add data migrations,
00:16:53.080 --> 00:16:57.100
temporarily disable automatic migrations during deployment,
00:16:57.540 --> 00:16:58.220
reset migrations.
00:16:58.620 --> 00:16:59.500
Okay, so it's not trivial.
00:16:59.700 --> 00:17:01.360
Then re-enable automatic migrations.
00:17:01.900 --> 00:17:04.199
So why would you do all of this?
00:17:04.199 --> 00:17:05.780
This seems more complicated.
00:17:06.120 --> 00:17:10.800
However, you end up with a really clean set.
00:17:11.020 --> 00:17:14.939
So you've got a very clean migrations set at the end.
00:17:15.780 --> 00:17:31.480
And I think, I don't know if this is, I'm not a Django pro, but so I'm not, I'm not sure if this is appropriate later on, but especially after you've like hashed through the initial development, this is a great time to start clean and then, and then work from there.
00:17:31.690 --> 00:17:33.580
So interesting Django article.
00:17:33.840 --> 00:17:34.920
It is an interesting idea.
00:17:35.140 --> 00:17:40.180
And certainly, I think if you've not pushed it to production, you know, you just like,
00:17:40.630 --> 00:17:44.640
instead of having the 100 migrations you may have built over time as you built up the app
00:17:44.860 --> 00:17:49.280
before you push it out live, like just get that down to a single migration, get the database
00:17:49.520 --> 00:17:50.480
up and running, go from there.
00:17:50.840 --> 00:17:50.940
Yeah.
00:17:52.000 --> 00:17:57.600
I'm not sure if it mentions it in here too, but I have been, guess, yeah, I think it sets
00:17:57.680 --> 00:18:00.660
it up in here, but having migrations per application.
00:18:00.940 --> 00:18:01.040
Anyway.
00:18:02.180 --> 00:18:06.840
The other one is this comes from Adam Hill is DJ light.
00:18:06.840 --> 00:18:08.700
And this is a fairly new project.
00:18:09.760 --> 00:18:14.740
And it is how to use SQLite in production with Django.
00:18:15.140 --> 00:18:19.280
So yes, you can use SQLite with Django, but there's some tricks.
00:18:19.440 --> 00:18:27.120
So he wrote an article called the definitive guide to using Django with SQLite in production.
00:18:27.560 --> 00:18:33.000
And then he decided to go ahead and he's still recommending that,
00:18:33.320 --> 00:18:35.180
but there's this plugin to make it easier.
00:18:35.580 --> 00:18:39.820
So with this plugin, you got a couple settings that you can change,
00:18:39.860 --> 00:18:41.020
and it just runs with it.
00:18:41.200 --> 00:18:45.860
So it's way easier setup if you want to use SQLite with Django.
00:18:47.239 --> 00:18:50.980
And this little blurb here at the top says that
00:18:52.820 --> 00:18:55.540
simplify deploying and maintaining production Django websites
00:18:56.000 --> 00:18:57.280
using SQLite in production.
00:18:57.680 --> 00:19:03.280
DJ Lite helps enable the best performance for SQLite for small to medium-sized projects.
00:19:03.680 --> 00:19:11.900
It requires Django 5.1 or above, but I think SQLite is appropriate for a lot of Django.
00:19:12.180 --> 00:19:15.400
A lot of people just start with Postgres, but anyway.
00:19:15.690 --> 00:19:16.500
Yeah, definitely.
00:19:17.030 --> 00:19:24.100
I think there's certainly defaults you can change or behaviors you can change about SQLite to make it way more high performance,
00:19:24.760 --> 00:19:27.000
especially with concurrent read-write sort of things.
00:19:27.320 --> 00:19:27.440
Yeah.
00:19:27.540 --> 00:19:28.600
So, yeah, very cool.
00:19:29.100 --> 00:19:32.820
And following up on the squash migrations,
00:19:33.320 --> 00:19:33.960
Pat Decker says,
00:19:34.330 --> 00:19:36.400
sounds like a similar familiar to Git Rebase maybe.
00:19:36.740 --> 00:19:37.780
Yeah, it does to me for sure.
00:19:38.120 --> 00:19:38.280
Yeah.
00:19:38.420 --> 00:19:38.820
Squashing.
00:19:39.130 --> 00:19:39.960
Yeah, very similar.
00:19:40.740 --> 00:19:40.900
Okay.
00:19:41.320 --> 00:19:42.080
While you have the stage,
00:19:42.260 --> 00:19:43.060
how extra do you feel?
00:19:43.700 --> 00:19:44.560
I got a couple extras,
00:19:44.980 --> 00:19:46.040
so we can just cover them.
00:19:46.360 --> 00:19:51.060
So the first up is I've got a testing code episode
00:19:51.290 --> 00:19:51.760
in the queue.
00:19:52.020 --> 00:19:53.200
It's in draft mode currently.
00:19:53.940 --> 00:19:56.620
FastAPI cloud with Sebastian Ramirez.
00:19:57.080 --> 00:19:59.260
and it was a real fun talk with Sebastian.
00:19:59.390 --> 00:20:00.280
This was recorded.
00:20:00.540 --> 00:20:01.740
I can't even remember when it was recorded.
00:20:01.940 --> 00:20:03.520
Maybe it was a few weeks ago at least.
00:20:04.090 --> 00:20:06.300
I feel bad getting so slow to get it out.
00:20:06.560 --> 00:20:10.000
But a really interesting article about FastAPI
00:20:10.140 --> 00:20:11.880
and about FastAPI Cloud.
00:20:12.100 --> 00:20:14.380
And Sebastian's just a joy to talk with.
00:20:14.560 --> 00:20:16.140
So that'll be out later today.
00:20:16.580 --> 00:20:18.880
I only have to rerecord the ad for it,
00:20:19.060 --> 00:20:20.720
but not much left to do.
00:20:21.800 --> 00:20:25.060
And then next up, pythontest.com,
00:20:25.090 --> 00:20:25.740
a couple things.
00:20:26.320 --> 00:20:29.360
I released a, I had a fixture.
00:20:29.980 --> 00:20:30.360
When was this?
00:20:30.500 --> 00:20:31.980
2013 that I started.
00:20:32.520 --> 00:20:34.240
So this is what, over 10 years ago?
00:20:34.960 --> 00:20:37.980
That I wrote a four-part series on pytest fixtures.
00:20:38.640 --> 00:20:43.980
And I had been talking that I was looking at analytics.
00:20:44.560 --> 00:20:46.180
And some of this stuff is still getting hit.
00:20:46.400 --> 00:20:47.960
So I went and reviewed it.
00:20:48.180 --> 00:20:50.000
And this series was a mess.
00:20:50.300 --> 00:20:54.580
And I went ahead and like re, I didn't rewrite it,
00:20:54.880 --> 00:20:59.860
But I went through everything and split it up into a more multi-part series.
00:21:00.050 --> 00:21:01.100
A lot of these are really short.
00:21:01.270 --> 00:21:06.600
I just wanted to keep one topic per blog post and make sure that it runs.
00:21:06.800 --> 00:21:09.820
A lot of the stuff didn't even run on current pytest and current Python.
00:21:10.180 --> 00:21:12.120
Now it all runs with current pytest and Python.
00:21:12.440 --> 00:21:14.740
So there's a nice fixture tutorial there.
00:21:15.440 --> 00:21:20.920
The other thing I wanted to bring up with Python Test is there is a bunch of stuff.
00:21:21.380 --> 00:21:26.100
So 2013 up through this year, there was a bunch of stuff that was like formatted badly.
00:21:26.500 --> 00:21:30.480
And I have gone through and made sure everything is mostly correct.
00:21:31.000 --> 00:21:33.300
So if you see any, I think everything's working now.
00:21:33.660 --> 00:21:35.360
So if you see anything weird, let me know.
00:21:35.570 --> 00:21:40.660
I just, as I was reviewing this morning, I realized that in this particular post, I
00:21:40.850 --> 00:21:45.380
refer to a fixture as fixture B and it's really resource B.
00:21:45.600 --> 00:21:46.380
So I'll have to fix that.
00:21:46.590 --> 00:21:48.640
But, you know, things like that shouldn't be too bad.
00:21:48.860 --> 00:21:49.920
Anyway, that's all up to date.
00:21:50.110 --> 00:21:50.700
Do you have any updates?
00:21:51.260 --> 00:21:54.720
I have three extras, and I think they're really exciting.
00:21:54.880 --> 00:21:56.180
They're going to be really cool for people.
00:21:56.480 --> 00:21:57.380
So let me tell you about it.
00:21:57.480 --> 00:21:59.520
First, brand new course over at Talk Python.
00:21:59.760 --> 00:22:01.180
I'm really excited about this one.
00:22:01.340 --> 00:22:05.840
Some of the most impactful courses that we create there are ones that take people from
00:22:06.000 --> 00:22:11.780
like zero to one or one and 1.5 to two, like the first sort of steps, right?
00:22:12.180 --> 00:22:15.980
And so this new course is called Just Enough Python for Data Scientists.
00:22:16.460 --> 00:22:16.740
Oh, nice.
00:22:16.940 --> 00:22:20.200
Python is kind of a stand-in for software engineering with Python, right?
00:22:20.240 --> 00:22:33.400
So the idea of this course is if you've done work in Jupyter notebooks, but you kind of feel like, well, I just learned notebooks and a little bit of Python because I was at this research lab or in this course I took.
00:22:33.510 --> 00:22:36.060
But I don't really know Git very well.
00:22:36.400 --> 00:22:38.220
I don't know refactoring super well.
00:22:38.500 --> 00:22:39.880
Reproducibility is still an issue.
00:22:40.490 --> 00:22:41.200
Those kinds of things.
00:22:41.660 --> 00:22:47.660
So it covers like what do you need to pay attention to in the dev world or the software world?
00:22:47.730 --> 00:22:49.120
And what can you safely ignore?
00:22:49.680 --> 00:22:49.760
Right.
00:22:49.940 --> 00:22:52.320
Like don't try to boil the ocean when you're getting started.
00:22:52.560 --> 00:22:54.040
What can you just not pay attention to?
00:22:54.890 --> 00:22:55.880
How do you write functions?
00:22:56.340 --> 00:22:59.820
Create importable modules and packages to split up notebooks
00:23:00.300 --> 00:23:05.080
from like one large notebook cell into a bunch of usable, testable, more readable pieces.
00:23:06.160 --> 00:23:08.380
Debugging, reproducibility with like Docker and uv,
00:23:08.590 --> 00:23:13.060
and even a little bit of agentic AI for like data analysis to like jumpstart your work.
00:23:13.200 --> 00:23:16.880
So if people are interested in this, I highly recommend it.
00:23:16.920 --> 00:23:19.680
It's an awesome course that I wrote over the last three or four weeks.
00:23:20.040 --> 00:23:21.320
and I'm really happy to have it out.
00:23:21.700 --> 00:23:22.480
That's pretty exciting.
00:23:22.840 --> 00:23:23.480
I like the topic.
00:23:23.920 --> 00:23:24.260
Yeah, thanks.
00:23:24.720 --> 00:23:24.780
Yeah.
00:23:25.340 --> 00:23:25.580
All right.
00:23:26.200 --> 00:23:26.780
Speaking of uv,
00:23:27.000 --> 00:23:29.880
I was just on with Will Vincent from Django Chat.
00:23:30.920 --> 00:23:31.420
When was this?
00:23:31.600 --> 00:23:34.240
This was live streamed four days ago.
00:23:34.660 --> 00:23:40.220
And we did a whole one hour paired presenting sort of thing on uv,
00:23:40.480 --> 00:23:43.020
why people might use it, compatibility, what's new.
00:23:44.020 --> 00:23:45.860
I even did a thing like how to make your Docker builds
00:23:46.140 --> 00:23:47.620
five times faster sort of thing,
00:23:47.900 --> 00:23:49.900
or 30 times faster, depending on what you're using.
00:23:49.950 --> 00:23:51.720
If you're using pip, it'll be 30 times faster.
00:23:52.070 --> 00:23:55.320
So super neat, super neat stuff that people can check out.
00:23:55.370 --> 00:23:56.060
And that's just on YouTube.
00:23:56.190 --> 00:23:56.820
So I'm going to link to that.
00:23:57.340 --> 00:24:00.400
And then last is I'm a fan of cursor
00:24:00.720 --> 00:24:03.380
when I'm doing like super heavy AI programming.
00:24:04.060 --> 00:24:05.160
Obviously a big fan of PyCharm
00:24:05.280 --> 00:24:07.400
when I'm doing like focused development work, right?
00:24:07.560 --> 00:24:10.600
But if I'm just like, I just need some heavy duty AI tools,
00:24:11.160 --> 00:24:14.840
cursor and Claude Code has been getting a lot of traction lately
00:24:14.990 --> 00:24:16.560
because it's like this terminal based thing.
00:24:16.590 --> 00:24:17.240
You can use it anywhere.
00:24:17.700 --> 00:24:22.460
Well, Cursor just came out with a Cursor CLI that you could set up.
00:24:22.580 --> 00:24:23.540
That's pretty neat.
00:24:23.680 --> 00:24:25.620
So use it in your IDE or any terminal.
00:24:25.920 --> 00:24:28.960
Cursor, JetBrains, Android Studio, Xcode, whatever.
00:24:29.420 --> 00:24:30.260
So pretty neat.
00:24:31.530 --> 00:24:36.840
If you kind of like Cursor, but you also kind of prefer the terminal side of things, check this out.
00:24:36.890 --> 00:24:39.160
It's in beta, but so far it seems pretty neat.
00:24:39.210 --> 00:24:41.620
If you have a subscription, it just integrates with that.
00:24:41.800 --> 00:24:42.020
Neat.
00:24:42.290 --> 00:24:43.260
Yeah, those are my extras.
00:24:43.660 --> 00:24:44.760
I have a joke for us as well.
00:24:45.280 --> 00:24:45.700
Oh, good.
00:24:45.960 --> 00:24:46.060
Yes.
00:24:46.580 --> 00:24:51.480
And I think this AI thing I just gave a shout out to perfectly, perfectly blends into this.
00:24:51.630 --> 00:24:51.740
Okay.
00:24:52.400 --> 00:24:54.300
Control C, Control V remains eternal.
00:24:54.600 --> 00:24:56.680
But what kind of dev are you?
00:24:56.800 --> 00:24:57.860
This means something different, Brian.
00:24:58.320 --> 00:25:01.620
If you're a new school developer, you copy and paste from ChatGPT.
00:25:01.960 --> 00:25:06.160
If you're old school, you copy and paste from Stack Overflow.
00:25:06.660 --> 00:25:10.060
And if you're ancient like you and me, you copy and paste from the documentation.
00:25:11.460 --> 00:25:13.900
Wait, how do I copy and paste from a book?
00:25:14.260 --> 00:25:17.060
Okay, I guess I'm so ancient that I'm not even on the chart.
00:25:17.260 --> 00:25:20.620
Oh, that one takes a while because that's when you remember people talk about,
00:25:20.880 --> 00:25:24.480
oh, I saw that code in the magazine that I typed it in.
00:25:24.800 --> 00:25:25.660
Yeah, that was me.
00:25:26.120 --> 00:25:26.900
That's how you do it.
00:25:27.120 --> 00:25:27.740
That's how you do it.
00:25:28.520 --> 00:25:31.120
You copy and paste with the fingers one character at a time.
00:25:32.100 --> 00:25:32.300
Yeah.
00:25:32.780 --> 00:25:32.920
Yeah.
00:25:33.260 --> 00:25:37.400
Anyway, copy, control Z, control V, still important, but changing over time.
00:25:37.780 --> 00:25:37.960
Yeah.
00:25:38.700 --> 00:25:38.980
All right.
00:25:39.140 --> 00:25:40.740
Well, thanks for the episode.
00:25:41.120 --> 00:25:41.700
Thanks for being here.
00:25:41.900 --> 00:25:43.180
And thanks everyone else for listening.
00:25:43.500 --> 00:25:43.700
Thanks.
00:25:43.820 --> 00:25:43.960
Bye.