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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.

00:13:27.460 --> 00:13:32.120

Another simpler example than generators is like tuple unpacking.

00:13:32.540 --> 00:13:35.120

It's just a nice synthetic sugar.

00:13:35.120 --> 00:13:39.160

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.

00:13:43.460 --> 00:13:49.200

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

00:13:49.200 --> 00:13:59.600

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

00:13:59.800 --> 00:14:03.260

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

00:14:03.260 --> 00:14:10.340

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.

00:14:10.340 --> 00:14:10.940

Right.

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

00:14:15.820 --> 00:14:19.700

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

00:14:19.700 --> 00:14:29.080

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

00:14:29.080 --> 00:14:29.260

Yes.

00:14:29.260 --> 00:14:30.220

Yes.

00:14:30.220 --> 00:14:37.020

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.

00:14:37.020 --> 00:14:39.280

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

00:14:39.280 --> 00:14:42.280

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

00:14:42.280 --> 00:14:43.320

Exactly.

00:14:43.320 --> 00:14:43.880

Yeah.

00:14:43.880 --> 00:14:44.900

Yeah.

00:14:44.900 --> 00:14:51.280

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

00:14:51.280 --> 00:14:58.620

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

00:14:58.620 --> 00:15:11.100

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.

00:15:11.460 --> 00:15:11.760

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?

00:15:20.720 --> 00:15:21.120

Right.

00:15:21.220 --> 00:15:23.060

But okay, so what is idiomatic Python?

00:15:23.060 --> 00:15:24.800

Right?

00:15:24.800 --> 00:15:26.940

It's hard to define.

00:15:27.980 --> 00:15:28.560

Yeah.

00:15:28.560 --> 00:15:34.940

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

00:15:34.940 --> 00:15:44.420

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.

00:15:44.420 --> 00:15:54.380

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.

00:15:54.380 --> 00:15:55.400

It seems like that's not right.

00:15:55.400 --> 00:15:57.560

But it's very hard to be very concrete.

00:15:57.560 --> 00:15:58.540

Exactly.

00:15:58.540 --> 00:15:59.320

Yeah.

00:15:59.320 --> 00:15:59.800

Yeah.

00:15:59.800 --> 00:16:01.620

I tried to do that in the book.

00:16:01.620 --> 00:16:20.160

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.

00:16:20.160 --> 00:16:21.120

You know?

00:16:21.120 --> 00:16:29.460

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

00:16:29.460 --> 00:16:29.920

Yep.

00:16:29.920 --> 00:16:34.460

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

00:16:34.460 --> 00:16:41.700

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.

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

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So this is what I decided.

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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.

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Even before I actually cover it.

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Because of what I just said.

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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.

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And that's cool.

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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.

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

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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.

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

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

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Like, I've used name tuples.

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I'm familiar with them.

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I think they're great.

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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.

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Not in the individual instances.

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So storage is actually cheaper than regular custom classes.

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Yes.

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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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