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Are you fluent in Python, or do you speak the language with an accent?

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Python's ease of learning can also lead to non-Pythonic patterns for even experienced developers.

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Luciano Romalo is here to give us a deeper understanding of this language we love.

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It's episode number 24, recorded Thursday, August 6, 2015.

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I'm a developer in many senses of the word, because I make these applications, but I also use these verbs to make this music.

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I construct it line by line, just like when I'm coding another software design.

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In both cases, it's about design patterns. Anyone can get the job done, it's the execution that matters.

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I have many interests, sometimes conflict.

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Welcome to Talk Python To Me, a weekly podcast on Python.

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The language, the libraries, the ecosystem, and the personalities.

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This is your host, Michael Kennedy. Follow me on Twitter where I'm @mkennedy.

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Keep up with the show and listen to past episodes at talkpython.fm.

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And follow the show on Twitter via at Talk Python.

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This episode is brought to you by Hired and CodeChip.

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Thank them for supporting the show on Twitter via at Hired underscore HQ and at CodeChip.

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Before we get to the conversation with Luciano, he generously agreed to give away an electronic copy of his book, Fluent Python.

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All you have to do to be eligible to win is to be a friend of the show.

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Visit talkpython.fm, choose friend of the show in the nav bar, and sign up.

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Now, let me introduce Luciano.

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Luciano Romalo is the author of Fluent Python.

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Romalo was a web developer before the Netscape IPO in 1995 and switched from Perl to Java and finally to Python in 1998.

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Since then, he's worked on some of the largest news portals in Brazil using Python.

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He's taught Python web development in the Brazilian media, banking, and government sectors.

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He's spoken multiple times at OSCON, PyCon, Python Brazil, FISL, and Rupy.

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Romalo is a fellow at the Python Software Foundation and a co-founder at the Garoa Hacker Club, the first hackerspace in Brazil.

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Finally, he's the managing partner at python.pro.br at Python Training Company.

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Luciano, welcome to the show.

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Thanks, Mike.

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Thanks for having me.

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Yeah, I'm really excited to talk to you about your new book, Fluent Python.

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Yeah, I'm excited about it too.

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I bet.

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You've been working on it for a while.

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Before we get into talking about your book, which is really an amazing book, I'll have more to say on that in a minute.

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Thanks.

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You're welcome.

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Let's talk about how you got started in the programming and Python at all.

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You know so much.

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I can tell from your book.

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But at some point, you had to start at the beginning, like we all do, right?

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Where did you get started?

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Okay.

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Now, I'm 51 years old.

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So when I was a teenager in Brazil, there were no microcomputers.

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But I got a hand on a programmable calculator, a TA-58.

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And I learned to program by programming it.

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And actually, my first interesting program was a port of a game from the HP-25 calculator to the TI-59.

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It was the lunar landing game that was famous.

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Oh, that was a great game.

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Yeah.

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So that was my first significant program.

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And then I was an exchange student in 81 in the US.

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I lived in Little Harrisburg, Illinois, population 10,000.

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And there I got – there were – in the library, in the school, there were a few Apple II computers that had just arrived when I arrived.

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And nobody knew how to use them.

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The teachers who were supposed to teach about them hadn't yet taken the course to do it.

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So they were basically free for anyone to do whatever they wanted.

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And I learned – I taught myself basic programming in them.

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Anyway, so that's a long time.

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And then I went through lots of languages after basic, Pascal.

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And Pascal was one of the most important ones for me.

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And then Delphi.

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And then Visual Basic.

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And I studied a little bit of small talk.

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And then when the web started here in Brazil, again, we had a kind of a different situation because there was – all telecom companies in Brazil were state-owned at that time.

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Later they were privatized, but they were state-owned in 94.

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And so I was hired by a large publishing company here to start developing their strategy for online.

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And I learned – I taught myself pro programming because everybody did everything on the web that was automated using Perl at the time.

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Right?

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Right.

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And I loved Perl.

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I thought it was awesome.

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But I also found it – well, I'm saying I just said I love it.

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But I loved it, but I also hated it at the same time because it was very powerful and quick.

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But it was also full of traps.

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And it was difficult to read afterwards and so on.

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Then I tried Java and I probably was one of the first people in Brazil to do server-side Java because when Java was first released, Sun was trying to market it as a tool for client-side programming, right, with the applets and so on.

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It took a while for them to realize that server-side was where Java was going to thrive.

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But I didn't enjoy the verbosity of Java.

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I liked the fact that it was really object-oriented, which Perl was not at the time.

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But it was too verbose.

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And then after a while, I went back to Perl again.

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And Perl had acquired object-oriented features in around Perl 5.

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And then something happened.

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As the Perl community was discussing how to do things in a dynamic language, but also in an object-oriented way,

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they kept repeating the sentence.

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Here's how they do it in Python.

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In Python, this is like that.

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And that's how I first heard of Python.

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It was discussions on the Perl mailing list about how to do things in an object-oriented way,

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but in a language that was a scripting language, in a dynamic language.

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So after reading mentions of Python a few times, I decided to go study it.

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And then I read the tutorial, which was shorter at the time than it is now.

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And I just fell in love immediately because for me, it had the best features of Python, of Java and Perl.

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It was really object-oriented with an object-oriented standard library like Java and had exceptions and other things that I liked about Java,

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but was not verbose.

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So it was, you know, very concise and to the point and agile like Perl.

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And so it was for me the combination of the best features of both languages.

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And then I basically never looked back.

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That's a great story.

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I think we've all had those sorts of feelings about some of our first programming languages.

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We love them because we really got into programming, but, you know, they're not necessarily the best.

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So you have those words as well, right?

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Exactly.

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But Python's where you've sort of settled into.

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I can tell you've been doing it for a long time from your book.

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Yeah.

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So I started doing it in 98.

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That was when I found the tutorial that I mentioned.

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And then I immediately pitched a Python solution to a client at the time for an online news site.

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And then I discovered Zope, which was just then released as open source.

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And I started using it like a couple weeks after it was released as open source.

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And then a couple months later, we actually launched in Brazil a new site that was based on Zope.

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And it was one of the first big Zope cases around the world.

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Oh, that's excellent.

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Yeah.

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And so I owe a lot to Zope.

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I don't use Zope anymore these days, but I owe a lot to it because it was because of Zope that I was able to deliver the kinds of products that the clients were looking for me to deliver, like content management systems.

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And so I was able to get paid for writing Python pretty early, at least in the context of Brazil, since 98.

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That's a great feeling, right?

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Yeah.

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You find this thing you love and, wow, people pay me to do it.

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Exactly.

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Exactly.

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It's a good time to be a programmer indeed.

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So let's talk a little bit about your book.

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The title is Fluent Python.

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And it's coming out under, being published by O'Reilly.

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And it's coming out, I think, September 4th is what Amazon.com tells me anyway.

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Is that right?

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Yeah.

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To be honest, I don't know exactly when the print book is going to come out because different pages that I look at give different dates.

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I've seen August.

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I've seen September.

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And I've seen October all in the last two weeks.

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So, yeah.

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But soon, right?

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Pretty soon.

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Just about the same time that this podcast comes out, I think, should be the time that the book is released.

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Wow.

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That's excellent.

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But let me say, the e-book is now complete in the final form.

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So people who buy the e-book or who have bought it previously can now download the final version of, you know, first edition, first revision.

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And it's also available from Amazon.com for the Kindle in the final form.

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So we are only waiting for the print book at this time.

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Right.

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Okay.

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Excellent.

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Yeah.

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I think having the electronic book works really well.

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I was reading it on my Kindle and it, on my Kindle, paper white, and it was really nice.

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You know, the code samples came through really well.

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Like sometimes that doesn't work so well.

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Wow.

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That's cool.

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Nice to know.

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Yeah.

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Yeah.

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It came out really, really well.

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And, you know, so I've only had the book for a few days.

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So I've only been able to read maybe the first five chapters.

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But my impression of the first five chapters, if it keeps going like this, I'm sure it does, is this book is a masterpiece.

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It is really, really good.

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Well, thank you very much.

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I think it's going to go down as a classic book in the Python community.

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Really.

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Thank you very much.

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Yeah.

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I'm not just saying that because you're on the show.

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But I was reading it and it's a little hard for me to put in words, I think, for people to really understand.

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But it seemed like everything that you covered, and we'll talk a bit more about that.

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But it seemed like everything you covered, I'm kind of like, I mostly know this.

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Oh, wait.

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There's this really cool piece of information or motivation that I was never really aware of.

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And just underneath the surface of the knowledge that I do have.

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Right.

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And that was like all the time happening to me while I was reading your book.

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And so I think it's a great contribution.

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Thank you.

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Yeah.

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So let me read a quick quote right from the beginning to give people a sense of, I think, what you're going for with this book.

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So in the introduction or one of the first chapters, you say, Python is an easy-to-learn, powerful programming language.

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Those are the first words of the official Python tutorial.

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That's true, but there's a catch.

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Because the language is so easy to learn and put to use, many practicing Python programmers leverage only a fraction of its powerful features.

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Does that kind of sum up the problem you're trying to solve with this book?

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Exactly.

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And that's why I called it Fluent Python.

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You know, as somebody who is fluent in two languages, in Portuguese and English, and that knows how difficult it is to become fluent in a language,

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because it's easy to become fluent in the language in your native language, right?

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You grow with it, and that's basically people don't even notice.

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But getting fluent in another language is hard.

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And so I've been teaching Python to lots of people.

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When I had a company, whenever we hired people, we never could hire people that knew Python because nobody knew Python at the time.

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So we hired people who knew Java and then taught them Python.

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But I also worked as a Python instructor, and that's what I do now these days.

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So I realized that this thing that I say over there, that the language is easy to use, it's practical.

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So people start solving real problems real fast.

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But that means that sometimes they are already using it for several years and don't know why some things are the way they are.

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And another thing that I say right after that in the second paragraph is that when you're learning a new language,

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you look for things that are familiar to you.

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Like, for instance, maybe this language, probably this language has a way to use a regular expression.

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Let's see how that works.

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But if there is something in the language that is not matched by something that you know from another language,

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like, for instance, the concept of generators that many languages don't have,

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then you're unlikely to go look for it, right?

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Because you don't even know the thing exists.

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Another simpler example than generators is like tuple unpacking.

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It's just a nice synthetic sugar.

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But if you've never seen that in a language, you won't look for it.

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And maybe you're going to spend years working with Python without ever using it.

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But it's a really nice thing to use because it makes code more readable, even safer in some ways.

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Python tuple unpacking has this characteristic because it actually forces the interpreter to count the number of items.

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So if there's one more or one less, an error will be raised.

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So it's a very nice feature, but it's something that people who come from other languages may never use because they won't think about it.

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Right.

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If you came from something like C#, for example, that doesn't have this concept of tuple unpacking,

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and so you just wouldn't even, you might not ever find it.

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And that kind of gets at the broader issue or concept of Pythonic versus non-Pythonic code, right?

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Yes.

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Yes.

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It's pretty easy to come over to Python and learn it really quickly so that you can do the same thing that you do.

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But it's not always the best, is it?

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Some of your mental models don't really carry over so well.

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Exactly.

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Yeah.

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Yeah.

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It's very, it's, of course, it's a very elusive thing to define what is Pythonic.

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I actually mentioned it several times in the book and tried to address it afterwards.

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But basically, I just send people links to other people that I respect a lot in the Python community who have attempted to define it directly.

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Right.

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But it's like, it's about a good definition which doesn't really explain what is Pythonic is to say it's idiomatic Python, right?

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Right.

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But okay, so what is idiomatic Python?

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Right?

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It's hard to define.

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Yeah.

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I think your best bet is to show a bunch of people a bunch of examples, say, all of these things are Pythonic.

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All of these things are not Pythonic because they carry over concepts that work well in other languages but are not the proper way to do them here.

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And once you get enough experience of going, bad example, good example, then you kind of get this sense, like almost a smell for like, yeah, it seems like this is right.

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It seems like that's not right.

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But it's very hard to be very concrete.

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Exactly.

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Yeah.

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Yeah.

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I tried to do that in the book.

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And actually, a decision that I made early on was to be as Pythonic as I could in the program examples, even when being Pythonic might not have provided the clearest source code from something that's not familiar with the language.

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You know?

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Like, for instance, right in the first example in the book, I use named tuple, right?

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Yep.

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And then I give it just a couple sentences to explain.

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It's probably not enough if the person has never seen it, but it's actually something useful that I start using all over the book.

00:16:41.700 --> 00:16:48.380

And if that picks up the person's curiosity, then the person can go and look for it.

00:16:48.380 --> 00:16:50.480

So this is what I decided.

00:16:50.480 --> 00:16:59.900

Like, for instance, before I actually have a chapter that formally discusses generated expressions, I use them in the book whenever it was natural to do so.

00:16:59.900 --> 00:17:02.220

Even before I actually cover it.

00:17:02.220 --> 00:17:03.740

Because of what I just said.

00:17:03.740 --> 00:17:09.440

You know, I wanted to write idiomatic Python whenever I could, even if that made the...

00:17:09.440 --> 00:17:21.420

Because the thing is, when you're speaking to a native speaker of English, for example, the person won't dumb down his or her speech for you, right?

00:17:21.420 --> 00:17:22.020

That's right.

00:17:22.260 --> 00:17:23.520

And that's cool.

00:17:23.520 --> 00:17:33.460

Sometimes maybe you don't understand everything, but you're catching, you know, there's something new about the way this person is expressing herself.

00:17:33.460 --> 00:17:37.060

And then you're going to try and find out what that means.

00:17:37.720 --> 00:17:49.240

That's, you know, if you always talk to people that spoke English, easy English, you would only learn easy English and not real English, fluent English.

00:17:49.240 --> 00:17:52.420

You wouldn't get that chance to grow into a fluent speaker, kind of like you said.

00:17:53.080 --> 00:17:58.560

So your example with the name tuple kind of hits on the point that I was talking about.

00:17:58.560 --> 00:18:00.640

Like, I've used name tuples.

00:18:00.640 --> 00:18:01.660

I'm familiar with them.

00:18:01.660 --> 00:18:02.440

I think they're great.

00:18:02.440 --> 00:18:15.340

But, you know, you go a little bit deeper and you're like, hey, it's really cool to use a name tuple because the actual dictionary that stores the attributes is stored in the class object, which is a singleton in the runtime.

00:18:16.300 --> 00:18:17.860

Not in the individual instances.

00:18:17.860 --> 00:18:21.320

So storage is actually cheaper than regular custom classes.

00:18:21.320 --> 00:18:21.900

Yes.

00:18:21.900 --> 00:18:31.160

So if all you want to have is just a bunch of pieces of data that have names in an object, but not functionality, maybe you should use these name tuples.

00:18:31.160 --> 00:18:43.760

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00:19:31.960 --> 00:19:41.920

Exactly.

00:19:41.920 --> 00:19:42.780

Yeah.

00:19:42.780 --> 00:19:43.880

And another thing.

00:19:43.880 --> 00:19:47.240

They are, because they are inexpensive, you can use it.

00:19:47.240 --> 00:19:52.700

And they are inexpensive and they are completely compatible with tuples, right?

00:19:52.800 --> 00:19:55.980

You can unpack them, you can iterate over the items and so on.

00:19:55.980 --> 00:20:03.580

So, for instance, whenever you need to return more than one value from a function, I recommend you return a name tuple.

00:20:03.580 --> 00:20:11.300

Because it will make, for instance, understanding what the function does easier, because there's going to be the definition of this name tuple.

00:20:11.480 --> 00:20:23.520

If the person is looking at the result of the function in the console or in the debugger, the person is going to actually see the names of the parts of the tuple that the function is returning.

00:20:24.400 --> 00:20:26.120

So, this is a recommendation.

00:20:26.120 --> 00:20:34.080

Like, for instance, if you're going to return more than one value from a function, usually people do it with a tuple, and that's fine.

00:20:34.080 --> 00:20:36.680

But with the name tuple is even better.

00:20:36.680 --> 00:20:40.560

And it's cheap, like you said, because of the way it's implemented.

00:20:40.560 --> 00:20:44.760

It doesn't require more memory.

00:20:44.760 --> 00:20:45.000

Yeah.

00:20:45.000 --> 00:20:49.060

You don't have to store all the keys, copies of the keys for every instance, right?

00:20:49.060 --> 00:20:52.380

Like you would with regular classes or objects.

00:20:52.380 --> 00:20:52.920

Yeah.

00:20:53.180 --> 00:21:02.820

And, of course, when those come back, if you're unsure what's coming back from that method, you could print it out and you get a nice listing of actually not just what's in there, but the meaning of them by the name.

00:21:02.820 --> 00:21:03.280

Exactly.

00:21:03.280 --> 00:21:03.900

Exactly.

00:21:03.900 --> 00:21:10.200

Or if you're in a debugger like PyCharm, you could probably hover over it and actually see it pop up in the watch window.

00:21:10.200 --> 00:21:11.060

Yeah, it's excellent.

00:21:11.060 --> 00:21:11.320

Yeah.

00:21:11.320 --> 00:21:12.000

Yeah.

00:21:12.000 --> 00:21:18.820

So, one thing that you said you used in your book is something called Doctest to check the code in the console listings.

00:21:18.820 --> 00:21:20.360

Can you talk about that a little bit?

00:21:20.360 --> 00:21:20.920

Yeah.

00:21:20.920 --> 00:21:28.620

So, that's something that I think was created by the people in the ZOOP community.

00:21:28.620 --> 00:21:32.340

And it's not...

00:21:32.340 --> 00:21:35.100

So, in the ZOOP community, this was always very big.

00:21:35.220 --> 00:21:56.100

So, the main idea of Doctest is to make sure that whatever code snippets appear in documentation, particularly listings of console sessions demonstrating stuff, that those actually match the behavior of the program at that time.

00:21:56.100 --> 00:22:00.840

So, this was the starting point for Doctest.

00:22:01.360 --> 00:22:07.800

In the ZOOP community, which is still active, there's a lot of development going on still in ZOOP.

00:22:07.800 --> 00:22:17.500

There's a lot of also files that are testing files that are not strictly documentation that are written in the Doctest format.

00:22:18.180 --> 00:22:19.440

Because it's really easy to...

00:22:19.440 --> 00:22:20.440

Because it's really easy to...

00:22:20.440 --> 00:22:22.220

I find it excellent to...

00:22:22.220 --> 00:22:24.800

When I have to design a new API.

00:22:24.800 --> 00:22:27.440

Because I...

00:22:27.440 --> 00:22:30.460

It's sort of a BDD thing, you know?

00:22:30.800 --> 00:22:41.100

You start typing those fake console sessions where you're thinking how it would feel to interact with this API.

00:22:41.100 --> 00:22:44.900

And then the result would look like that and so on.

00:22:44.900 --> 00:22:46.360

And then you...

00:22:46.360 --> 00:22:48.520

It's really good to do TDD.

00:22:49.960 --> 00:22:59.640

Because you write a couple of stanzas of Doctest and then you implement the code that makes it pass and so on and on and on.

00:22:59.640 --> 00:23:10.400

You know, the whole idea of trying to envision what your API looks like from the outside and then implement it generates much cleaner APIs in the end.

00:23:10.400 --> 00:23:12.280

And this is, you know, one flavor of that, right?