This is Python Bytes. Python headlines and news delivered directly to your earbuds.
It's episode six, recorded Monday, December 12th. This episode is brought to you by Rollbar.
They help take the pain out of errors. Thank you so much, Rollbar, for sponsoring the show.
And this is Michael Kennedy, one of your hosts. I'm here with Brian Okken, my co-host. Hello,
Brian. How's it going, man? Hello. It's going really good. Good, good. This year's gone by
quickly, hasn't it? It's gone. It's super fast. Yeah, I feel like everyone was fixated on the
election because it was so unique. And then kind of nobody knows what to do with the rest of the year.
So I'm going to go on vacation. How about you? I'm going to start writing. So yeah, you got a book
to finish, man. Anyway, so the point is, this is actually our last episode of the year. So I want
to say thanks to everybody who subscribed and listened and shared this podcast with their friends.
We're taking a break and we will be back first thing in January, first week. We'll have even more
share, won't we? Yeah, that'll be the, I have like three weeks of stuff to catch up on.
Yeah. Hopefully no drama. Okay. So what's the first item you want to share with everyone? What'd you
find? Well, there's an article by Dan Bader, make your Python code more readable with custom
exception classes. This is great. He wrote up a little tutorial along with a five minute video where
he walks through stuff. He talks about the, including your own exception classes can make your errors
more readable within your code and also communicate to the users of your functions or whatever your
intent and what the errors are. And also you can add more context. I like it because it's one of those
things that I was afraid to add custom exceptions and it's really just pretty darn easy. The main
points I think is to remember to derive from either exception or one of the other built-in Python
exceptions. Like value error or something like that, right? Yeah. Whatever sort of makes sense.
And, and it's a, it's a good one. I also wanted to add that if you are, there's extra things you
should do if you're releasing a package and, or even within, within your company, I think it's good to
derive from your own, create a custom exception and then derive all your other exceptions from that one.
And I got that tip from another article that we'll link to in the show notes by Julian Danju.
Yeah. Yeah. Yeah. That's a really great point actually, because it would be great to do try,
accept all of the errors from this package. Like whatever's happening in here, I want to start by
handling those and maybe get more specific ones. Maybe not, but yeah, I hadn't really thought about
that. The make your own special base exception. Even the reverse, try to do specific first and then,
and then do a catch all of those. And then if it isn't one of your package ones, then you can also
still catch the general exception and do something different there. Yeah, that's cool. There's two,
there's a couple of things about this article. I like when Dan Bader is doing a bunch of cool videos
on YouTube, he really seems to be putting a lot of effort to frequently get those out. And so here's an
article that takes like five minutes to read, but there's also a five minute video screencast to go
along with it. So well done on that, Dan. And then more generally, this idea here, it makes it easier
for one of my favorite programming patterns, which is also quite Pythonic is in that it's, the pattern is
it's easier to ask for forgiveness than permission, as opposed to like the C++ style, look before you leap
programming model where you test the heck out of everything and then you try a thing. You just, here, you just do it.
If there was an exception, you know, you catch it and then accept block and it's all good.
Yeah. I definitely like a, at a high level, like for instance, if I were, as an example, if I were using
requests, I could have a special handler that dealt with exceptions from requests and know that, that I,
cause I understand what part of my system's dealing with that. I could handle it in one place.
Right. Absolutely. Yeah. Could not contact server. The web service seems to be down.
Something like this, right? Beautiful. Wouldn't it be awesome if like artificial intelligence could just
like sit there and just like help you code? I mean, we all know of AI for like Siri and silly things like
that. But I feel like actually 2016 is a little bit where the future kind of intersected with now.
Like things became the future came to us. There are so many things with AI that are really awesome.
Like there was several AIs that actually on their own invented encryption. But what I want is an AI to
help me as a developer. How about you? Sounds great. Yeah. That'd be super cool. So, so I actually ran
across this thing. one of the listeners, Gilberto Diaz was nice enough to send me a link and,
an email say, Hey, there's this thing that is so cool. You must check it out. It's not technically
built in Python, but it really supports Python developers in a crazy good way. So there's this
thing called kite at kite.com and it's in private beta. We were lucky enough to get some early access
to it. And what it is, is like you install it in your OS and it sits in kind of like a sidebar.
It takes up maybe like, I don't know, a fifth of your screen. If you have two screens and, you know,
stick it on the other one and it watches what you do in all sorts of different programming
environments in sublime text and Visual Studio Code in PyCharm, even Vim or Emacs has many,
many plugins. As you type, it helps you. All right. So, if you're writing Python code and you type
import space, it'll show you a list of the most popular packages. If you type R, it'll show you the
list of the most popular packages that start with R like request right at the top. Yeah. And then once
you import a thing, it'll start showing you documentation about it. Here's some examples
on how to use that thing you just imported as you start to like, you know, interact with it,
like request dot, it'll say, well, the most commonly called function here is get, you want to see some
examples, how to use request dot get or like the documentation. And it just, it just really is quite
cool how much it knows. And you know, it does auto completion. Like many editors do it'll, it'll like
show you on the left, like, here's the things you can complete. But what I like about it is it uses like
crowdsourcing for popularity. So there's like, you know, 10,000, developers working on this.
And they'll say, well, this is the most popular completion in this situation here.
Base, not just alphabetical or something. So I think it's cool. There's, there's a great video
you have to watch. If you go check this out at kite.com, it's like right there in the homepage.
I'm, I'm looking forward to playing with it. Yeah. You, you'd mentioned it seems like AI pair programming
and, does sure sound like that. It's, it's quite interesting. I feel like it's more like a
artificial intelligence mentor rather than paired programmer. Like it's there going, oh, by the way,
did you know that the, you actually can call this function? Oh, that package is really cool. Cause
it has this thing you might not know about. It's kind of like the wiser, wiser version rather than like,
you know, pair programmer, but I think it's great.
Oh, definitely looking forward to trying that.
Yeah, for sure. I hate it when my data gets messy.
Yeah, definitely. so there, there's an article, by, I'm going to even try this
Jean Nicholas hold, I think tidy data in Python. This article was pretty interesting. It caught my
attention because I often have data that's in a format that I can't use right away and I need to
transform it into something else. And I, I didn't ever think of it like, changing tables around and,
and completely changing things automatically. Anyway, this article gives some, some attributes
of what problems to look at in data sets. He based it on a paper named tidy data by Hadley Wickham.
He takes the idea of the paper and then transfers it to, into Python and how to, how to utilize it
in Python with the pandas library. And like, so this seems very abstract right now, but he has some
examples of, data that's, that's really data is in the column headers. He has some attributes to
look for, like each variable forms a column and contains a value and each observation forms a row
and the type of observation units form a table. And it still sounds kind of complex or kind of abstract,
but the examples really are pretty easy to follow. And I, it's just a neat idea of, of taking a look at
your data and making it more useful. I had never really thought about data in this sense,
but it seems to me like this talks about how you put your data together so that you'll be,
it's most natural to take advantage of it with things like pandas and the various data science tools
and all sorts of languages, but he's focused on Python, which is extra cool.
And so he's, he has several examples on, on, you know, do things this way, not that way. And your life will be
much easier. And I just never thought of systemizing that, I guess.
Yeah. And I also, looking at the different comparison of before and after tables, you can
definitely see how it's definitely the, the converted table is easier programmatically, but the from table,
the, the dirty one is, it's, it's more broken up, like how people look at stuff. I didn't think there
would be a difference, but it does appear to be, it's, it's easier for, to visualize smaller tables
as people, but, as computers, we need things a little bit more broken down.
Yeah, it's cool. So if you've got a lot of data, especially in tabular form, check that out.
We're going on break. Like I'm actually heading out on vacation somewhere away from home and,
you know, I'm still taking my laptop with me because that would be kind of insane. But you know,
our sponsor roll bar, they, they were going to let me just chill out and not worry about whether the
web app is working and not check it all the time. Like I might not use my computer for a few days.
And unless I get notifications on my phone or, you know, text messages or slack or something like
that saying, Hey, your website's down. There's this problem. You know, things are going to be good.
I'm not going to worry about it. That's because I got roll bar integrated with all the talk Python
stuff, which is really cool. So if there's a problem, you know, I can just read what the error is and
probably fix it pretty easily. So all the Python byte listeners, they can have the
same peace of mind. They just have to go to rollbar.com/pythonbytes and sign up for the
free tier. Sounds cool, huh? Sounds very good. Yeah. Yeah. It's good. It's good to be on vacation
and you know, not worry about things working. Just set up the error notifications and you're all good.
I'm really excited that there's a new version of Python. I'm, I think people are starting to move
on from legacy Python and there's just end of this week, there's going to be one more reason to do so.
So we actually have as our next item, a video, a conference presentation at PyCon Canada
2016 by Brett Cannon. He did a talk called what's new in Python three. And I think it's interesting.
You look at all the peps and all the stuff that's coming and you think, well, okay,
they're doing little tweaks here. I guess it has new string formatting and whatever. It turns out
there are over 16 peps in Python three, six, and that's more than any peps that have been in Python
three other than the actual creation of Python 3.0 itself. That's pretty cool, right?
Maybe we should have had another role before now then maybe, but I'm excited for all these.
Yeah, it's going to be great. It's going to be great. The reason I like this video and I want to
point it out is we could all go to, you know, what's new in Python three and we can read the list. Like,
okay, well here dictionaries will be ordered. there's now a private identifier or like a versioning
number on dictionaries. Like, okay, whatever, who cares? This gives you the story behind all the
changes coming to Python three, six, right? Why did the core developers of which Brett is one
think that this is a good idea? Like, how does it help? What, what really problem, what problem are
they really trying to solve deep down when they made this change? And it really is a cool look inside
what's coming with Python three, six. that sounds great.
I think it's, it's good. So Python three, six release candidates available right now if you want to
download it, but you know, if you can wait like five days, you should have the final version of
Python three, six. They did something with virtual environments, right?
Yeah. This is one of the things I, I was stumbled across. I think it was in a Reddit line somewhere,
the command command line command of pyvenv, which I can't remember which version that came in on one of
the three O's that's being deprecated. And the reason, and they want people to use the dash M V E N V
instead. And that's so that you specifically tell the virtual environment system, which Python to use.
And it makes the programmer know, need to know how to get to Python first before they create a virtual
environment. I think that it totally makes sense. It's just something to point out. I think it's
important. It I'm going to have to actually edit my book because of this, because, I think I, I
recommended pyvenv in mine. So time to un-recommend it. Yeah. that's cool. No, I do think it is good.
I mean, you can say Python three --version, you'll see what that is. And then you can say Python
three dash M V and V, and then that's the thing you're going to get. Right. So it is a little more
clear, I guess. Very cool. Yeah. It wouldn't be a week in Python without some form of testing or code
coverage, right? Yeah, definitely. And, Ned Batchelder, who we all owe some gratitude for,
for coming up with, or for taking over the coverage package. He's, I interviewed him
about that on episode 12 of testing code, but, he's planning a new feature for coverage up high,
um, which will tell us which, so when you, when you run a coverage report, also what functions cause
this, or, or how did you get this stuff covered? And, he covers it in an article. He taught,
he titled, who tests what this is a discussion about kind of, how I like the discussion because
those, those people new to coverage can kind of peek inside and see the different stages of how
coverage works. It goes through measurement and storing the data and then combining the data
and reporting because you can't have multiple tests, most multiple runs and combine them into
it to one report. This idea of, trying to figure out which code was covered by which tests
is how it started, but he comes up with some, some reasons why there may be more, more uses than just
in testing. And it's a, it's a good discussion, but part of one of the things he, why he wrote this
article is, is to try to get some help. He wants some feedback. So some of the questions are,
um, he discusses the memory, memory usage and, and the data model. And he wants to know if there's
something we need more sophisticated problem solving on this, or should we be more conservative with
memory? And then the output, the output right now is intended to be in Jason, but he wants to know if
somebody could use a different data format. I like the direction he's going with it. And I think
I'll use it and I encourage anybody that has sort of a non-standard usage of coverage to take a look
at this article and give Ned some feedback. Yeah. I really like it as well, because a lot of times,
unless you're actually a contributor to the project already, you feel like the fate of the thing is
already determined, but this was much more like, look, we have this challenging problem. these are the
ways and the trade-offs were thinking about the ways we're thinking about solving it. The trade-offs were
contemplating. We're not sure where to go. If you want to help, here's how to help. If you want to
give us feedback before we jump in and go the wrong direction, do it. So this is a really cool article
just on philosophy as well. Yeah. I think it's great. And being able to know which tests actually
triggered the covered line when you run a thousand tests with these three, like that's pretty interesting.
Yeah. And you know, before I talked to him, I didn't realize that there was, there were so many
features of coverage that I didn't realize, like being able to have multiple test runs and combine
that data and HTML output and things like that. It's pretty cool. Yeah. Yeah. Quite cool. Quite cool.
So what do you got for us, Michael? All right. I've got one, one final glorious topic for the rest of the
year to round out 2016. So here it is, threaded asynchronous magic and how to wield it. So this is a
really cool article by Christian Medina. You know, we had talked about the, I don't remember exactly
the title, but that asyncio tutorial, that was like the G event tutorial remade for asyncio and Python
three, four. And we're like, well, wouldn't it be cool if there's a great one on async and await,
like the really, the, the truly new cool features of Python for threading and parallelism. Well, Christian
Medina wrote it. Awesome. He wrote a really nice one and it has a bunch of bite-sized examples. You can,
you know, they're not too, they're not too large, but they don't seem entirely fake.
So he has one example of like sending email notifications, sort of asynchronously, but it's
like, you know, but you realize there's no awaitable asynchronous, like way to send mail in Python. So,
well, we're just going to block, but at least we can use this asyncio loop for that. That's cool.
And then also talked about the AIO HTTP library, which is an HTTP client server for asyncio and how to do a
bunch of parallel web requests. So we often think about requests being the primary way that we do
programming against web services and generally HTTP. But this is another option that's really simple,
that fits exactly into the asyncio. So you can easily block on the network calls, which will then free
up the thread to go do other stuff because it knows it's waiting on the network. That sounds cool.
Yeah, it's really cool. It covers things like tasks, scheduling tasks, scatter, gather styles of
programming, and then even moving the asyncio loop to a background thread. So you don't block up your
main thread, but you kind of have a place to put all the asynchronous work and let it run on that other
one thread, which is pretty cool. So yeah. So thanks, Christian, for doing that. Got to meet him at
PyCon last year. And so that was fun. And hopefully everyone else is going to PyCon. If you don't have your tickets yet,
you need to be getting it, right, Brian? Definitely. I missed out last year. So.
Don't be sad. Get tickets before they sell out because this, I'm sure it will sell out. It sold
out early last year. Plus it's in Portland and Portland rocks. Portland definitely rocks. It's
going to be weird. They're going to be keeping it weird. All right. Well, that's it for me. I don't
really have a whole lot of news to share other than just saying thanks to everyone for listening,
you know, for our new podcast. It's been fun to do for you and I appreciate all the feedback.
I appreciate it too. Speaking of feedback, we did get one person. I think it's Harry J.
I hope I didn't pronounce that wrong. On Twitter said that he listened to episode three and I had
mentioned that a library called Pianini that don't know if I'm pronouncing that right, but I assumed that
these were that maybe it was named after the knights who say knee. We thought that would be fun anyway.
Yeah, yeah, definitely. But Harry J. tells me that this is not true. It's named after a Sanskrit grammarian
with a P-A. I don't know how to pronounce that either. It's P-A-N-I-N-I. Anyway, thanks. I like fact checking.
Yeah, it's great to have fact checkers. Awesome. I'm still going to call it Pianini.
Knee, knee, knee. Yeah, definitely.
Just know it has a different origin. Okay. Well, thank you, Brian. Thank you everyone for listening.
Thank you, Rural Bar for sponsoring the show. We will see you all in January. Until then,
you know, enjoy the winter wonderland. Unless you're down in Australia, then have a nice summer.
Yeah, definitely. See you next year. Yeah. See you next year. Bye everyone. Bye.
Thank you for listening to Python Bytes. Follow the show on Twitter via @pythonbytes. That's
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