WEBVTT 00:00:00.001 --> 00:00:03.400 One of the hallmarks of successful developers is continuous learning. 00:00:03.400 --> 00:00:07.840 The best developers I know don't just keep learning, it's one of the things that drives them. 00:00:07.840 --> 00:00:12.900 That's why I'm excited to bring you this episode on 10 books Python developers should read. 00:00:12.900 --> 00:00:17.240 You'll meet Timo Kola, who is an avid reader and self-learner in the Python space. 00:00:17.240 --> 00:00:21.040 He's found 10 books from his experience that have had a huge effect on him, 00:00:21.040 --> 00:00:28.120 and we'll discuss those now. This is Talk Python To Me, episode 130, recorded July 18, 2017. 00:00:28.120 --> 00:00:49.480 Welcome to Talk Python To Me, a weekly podcast on Python, the language, the libraries, the ecosystem, and the personalities. 00:00:49.480 --> 00:00:53.600 This is your host, Michael Kennedy. Follow me on Twitter, where I'm @mkennedy. 00:00:53.600 --> 00:00:57.480 Keep up with the show and listen to past episodes at talkpython.fm, 00:00:57.480 --> 00:01:00.040 and follow the show on Twitter via at Talk Python. 00:01:00.040 --> 00:01:04.420 Talk Python To Me is partially supported by our training courses. 00:01:04.420 --> 00:01:07.940 Have you tried to learn Python but got stuck or lost focus? 00:01:07.940 --> 00:01:15.200 We know how it feels to try and jam fact after fact, loop construct after turn hair expression, into your head. 00:01:15.200 --> 00:01:19.320 At best, it's boring. At worst, it can turn you off programming altogether. 00:01:19.800 --> 00:01:23.340 That's why we built our course, Python Jumpstart by Building 10 Apps. 00:01:23.340 --> 00:01:26.580 This course guides you through carefully planned applications. 00:01:26.580 --> 00:01:30.100 It starts simple, but progresses to quite real apps. 00:01:30.100 --> 00:01:33.020 Best of all, you won't be learning dry facts. 00:01:33.020 --> 00:01:37.600 You'll be learning like the pros do, by building real applications and learning in context. 00:01:37.600 --> 00:01:43.480 If you want to start building with Python, just visit talkpython.fm/course to get started. 00:01:43.480 --> 00:01:46.100 Timo, welcome to Talk Python. 00:01:46.100 --> 00:01:46.780 Thank you. 00:01:47.060 --> 00:01:48.060 It's great to have you here. 00:01:48.060 --> 00:01:50.480 I'm really looking forward to talking about these books. 00:01:50.480 --> 00:01:56.140 You and I are both avid readers, and I think one of the best things you can do for your career, 00:01:56.140 --> 00:01:59.540 or just even as a person, is to keep reading. 00:01:59.540 --> 00:02:06.880 And so maybe we can use this episode to inspire a bunch of people to do some technical and some not-so-technical reading. 00:02:06.880 --> 00:02:08.320 Sure. Sounds great. 00:02:08.320 --> 00:02:09.000 Yeah, sounds fun. 00:02:09.000 --> 00:02:11.560 But before we get to that, let's start with your story. 00:02:11.560 --> 00:02:12.860 How did you get into programming in Python? 00:02:12.860 --> 00:02:15.820 I started with Python in 2005. 00:02:15.820 --> 00:02:20.120 I started programming professionally in like 97. 00:02:20.120 --> 00:02:29.920 I was a philosophy major, and I was thinking whether I should be starting an academic career or get a real job. 00:02:30.000 --> 00:02:32.080 Then I ended up getting a real job. 00:02:32.080 --> 00:02:44.680 Then I did something like CE and Perl and all sorts of stuff and slowly wandered into sort of a middle management in big companies. 00:02:44.680 --> 00:02:51.020 And 2005, I figured out that I don't want to do that. 00:02:51.020 --> 00:02:55.580 I want to be a programmer and kind of relearned programming back then. 00:02:55.580 --> 00:03:03.260 I had done something like, I always did something like Perl programming, very small throwaway scripts. 00:03:03.580 --> 00:03:12.200 And then I was given a task of writing an Excel for time reporting in a team. 00:03:12.200 --> 00:03:13.300 It was quite funny. 00:03:13.300 --> 00:03:18.940 It was a really big company, but they handled time bookkeeping with an Excel sheet. 00:03:18.940 --> 00:03:19.780 That's really funny. 00:03:19.780 --> 00:03:26.100 Isn't it weird how like some of these huge companies, like they'll waste crazy amounts of money on certain things. 00:03:26.100 --> 00:03:28.200 And then others like, no, we can't do anything. 00:03:28.200 --> 00:03:29.380 There's no system we're going to buy. 00:03:29.380 --> 00:03:30.640 We're not going to build one or anything. 00:03:30.640 --> 00:03:32.060 We're just going to like, here, use Excel. 00:03:32.060 --> 00:03:32.560 Yeah. 00:03:32.560 --> 00:03:38.560 It was like, here's a network drive and everyone writes down their own Excel sheet. 00:03:38.560 --> 00:03:41.660 And we need a summary report for the management. 00:03:41.660 --> 00:03:45.140 And it was like a 60,000 people company. 00:03:45.140 --> 00:03:49.280 And they didn't have like a centralized process for that. 00:03:49.280 --> 00:03:53.340 And then I ended up writing first that reporting system in Perl. 00:03:54.060 --> 00:03:56.780 Then I thought, well, my friend was using Python. 00:03:56.780 --> 00:03:58.680 And I thought, well, this looks neat. 00:03:58.680 --> 00:04:01.300 Although I hated, of course, the white space first. 00:04:01.300 --> 00:04:04.000 I got used to that in two weeks. 00:04:04.000 --> 00:04:06.440 Figured out that, hey, this is actually a feature. 00:04:06.440 --> 00:04:08.540 Reading is more important than writing. 00:04:08.540 --> 00:04:11.780 And then, well, I wrote that. 00:04:11.780 --> 00:04:17.400 And a couple of years later, my friend said, well, it's still reporting full hours for you. 00:04:17.400 --> 00:04:19.780 The system that you did back then. 00:04:19.780 --> 00:04:22.440 And that's how I got into Python. 00:04:22.680 --> 00:04:25.760 And I've done, after that, I've done web stuff. 00:04:25.760 --> 00:04:33.020 And previous project before this company that I'm now working in, I was two years doing Android programming. 00:04:33.020 --> 00:04:37.700 And all the time I've been doing something a little with Python. 00:04:37.700 --> 00:04:41.180 I've done quite a few of Twitter bots. 00:04:41.180 --> 00:04:46.040 That's a kind of a fun way to keep up your Python programming skill. 00:04:46.040 --> 00:04:46.620 Oh, cool. 00:04:46.620 --> 00:04:46.860 Yeah. 00:04:46.860 --> 00:04:48.460 So tell me about some of your Twitter bots. 00:04:48.460 --> 00:04:50.740 The most interesting ones are in Finnish. 00:04:50.740 --> 00:04:54.200 But I have two James Joyce readers. 00:04:54.200 --> 00:05:01.520 Ulysses Reader that reads Ulysses perpetually by James Joyce. 00:05:01.520 --> 00:05:03.100 And also Finnegan's Wake. 00:05:03.100 --> 00:05:09.460 These two kind of really hard books split to 140 characters at a time. 00:05:09.860 --> 00:05:12.040 And sometimes it's quite funny. 00:05:12.040 --> 00:05:15.360 And most of the time it's quite boring to follow them. 00:05:15.360 --> 00:05:15.900 Yeah. 00:05:15.900 --> 00:05:19.960 I'll put the two Twitter accounts in the show notes so people can follow them if they want. 00:05:19.960 --> 00:05:20.220 Yeah. 00:05:20.220 --> 00:05:22.060 Nice. 00:05:22.060 --> 00:05:26.240 And you said you also do some chatbots, like some AWS Lambda chatbots or something like that? 00:05:26.240 --> 00:05:26.680 Yeah. 00:05:26.720 --> 00:05:28.760 That's what I do at the work right now. 00:05:28.760 --> 00:05:32.840 We define it something like a conversational commerce. 00:05:32.840 --> 00:05:40.100 We take, let's say, a product catalog, any kind of a structural data associated with that. 00:05:40.100 --> 00:05:51.700 Add some like handmade rules and try to make it as automated as possible and generate a chatbot that you can talk to a product catalog with. 00:05:51.700 --> 00:05:52.880 Oh, that's really cool. 00:05:53.100 --> 00:05:58.620 So are you using like Alexa skills or like how are you understanding the words there? 00:05:58.620 --> 00:06:06.680 Well, we are both experimenting with like wit.ai, which I think is the state of the art at the moment. 00:06:06.680 --> 00:06:17.660 And then we need to have our own bits that understand Finnish or take the Finnish sentences and then take out the stuff that Finnish language adds there. 00:06:17.820 --> 00:06:24.860 Finnish language is quite different from many of the other European languages like English and Germany or even Swedish. 00:06:24.860 --> 00:06:25.780 Yeah, sure. 00:06:25.780 --> 00:06:27.280 Nice. 00:06:27.280 --> 00:06:29.240 So are you enjoying working on chatbots? 00:06:29.240 --> 00:06:29.740 Yeah. 00:06:29.740 --> 00:06:33.520 It's also, well, it's kind of interesting to see. 00:06:33.520 --> 00:06:42.120 We are quite aggressive in user testing and how to see what's like the expected level of intelligence that people want from them. 00:06:42.120 --> 00:06:43.920 And the bar is quite high. 00:06:44.720 --> 00:06:44.960 Yeah. 00:06:44.960 --> 00:06:54.880 I've heard people describe both the like the voice assistants and the chatbot text equivalents and having this problem of the uncanny valley. 00:06:54.880 --> 00:06:55.880 Yeah. 00:06:55.880 --> 00:07:00.400 And I think this problem originally it was stated in terms of like animated movies. 00:07:00.400 --> 00:07:06.780 So think Shrek or something like that, where if it clearly, the characters clearly look animated, people are fine. 00:07:06.780 --> 00:07:11.100 If they look perfectly real or are real, people are fine. 00:07:11.100 --> 00:07:15.880 But if they're really, really close to real, but not quite, it kind of creeps people out. 00:07:15.880 --> 00:07:22.300 And things are getting good enough that we're getting a little bit into this uncanny valley where it works, it works, it works. 00:07:22.300 --> 00:07:22.820 Wait a minute. 00:07:22.820 --> 00:07:23.840 You don't understand that? 00:07:23.840 --> 00:07:24.720 That doesn't make any sense. 00:07:24.720 --> 00:07:25.560 Why don't you understand that? 00:07:25.560 --> 00:07:25.760 Right? 00:07:25.760 --> 00:07:26.420 What do you think? 00:07:26.540 --> 00:07:27.220 Yeah, exactly. 00:07:27.220 --> 00:07:29.560 I think that's exactly what's going on there. 00:07:29.560 --> 00:07:38.060 And also, it's kind of a, even if the system is quite smart, it makes different mistakes than human would do. 00:07:38.820 --> 00:07:42.620 What we found out in our user studies is like that. 00:07:42.620 --> 00:07:48.860 You really need to concentrate on a task, satisfy the user's need on a single task. 00:07:48.860 --> 00:07:54.180 That's how you get the, and then concentrate on one specific domain. 00:07:54.180 --> 00:07:57.740 That's how you get the most satisfactory results. 00:07:57.740 --> 00:08:04.200 When you open it up, people start expecting it to be able to answer everything. 00:08:04.200 --> 00:08:06.740 And that's when it goes sour. 00:08:06.960 --> 00:08:08.480 Yeah, but it's going to get better, right? 00:08:08.480 --> 00:08:11.460 I mean, it's already in the last five years gotten so good. 00:08:11.460 --> 00:08:19.600 And all of these Amazon Echoes and Siri and HomePod and all these things are really going to push these companies to make it legitimate. 00:08:19.600 --> 00:08:20.600 Google Home, for example. 00:08:20.600 --> 00:08:21.700 Yeah, exactly. 00:08:21.700 --> 00:08:27.720 I have an Echo and I, you quickly forget how magical it would have been five years ago. 00:08:27.860 --> 00:08:32.740 It's like, it can pick up your speech on another room. 00:08:32.740 --> 00:08:36.740 And usually it's quite, at least it understands what you said. 00:08:36.740 --> 00:08:40.740 It doesn't necessarily do anything smart with it, but it understands you. 00:08:40.740 --> 00:08:41.160 Yeah. 00:08:41.160 --> 00:08:43.420 What are your three favorite things that you do with your Echo? 00:08:43.420 --> 00:08:47.460 Setting up timers and alarms. 00:08:47.460 --> 00:08:49.980 Like wake me up tomorrow at 6.30? 00:08:49.980 --> 00:08:51.540 Yeah, wake me up tomorrow. 00:08:51.540 --> 00:08:58.000 And I almost exclusively do my calendar appointments through Echo because it's quite natural. 00:08:58.000 --> 00:09:03.740 And you can have your shopping bags in your hand and start that I need to do laundry tomorrow. 00:09:03.740 --> 00:09:06.200 Add that to my calendar and stuff like that. 00:09:06.200 --> 00:09:06.480 Yeah. 00:09:06.580 --> 00:09:11.020 Then one of the favorite things is to watch my kids interact with that. 00:09:11.020 --> 00:09:18.440 I think even if I'm a bit skeptical that they will be as big as, let's say, mobile apps or something like that. 00:09:18.440 --> 00:09:26.840 But when you see kids interacting with them, it's just like they really genuinely try to have a grasp on what is going on there. 00:09:26.840 --> 00:09:30.080 And they are really entertained by something like Alexa. 00:09:30.080 --> 00:09:30.980 Oh, sorry. 00:09:30.980 --> 00:09:32.760 Probably someone else. 00:09:32.760 --> 00:09:34.040 Alexa went off there. 00:09:34.040 --> 00:09:35.480 But sing a song. 00:09:35.480 --> 00:09:37.540 And sing a song. 00:09:37.540 --> 00:09:39.060 Or tell a joke. 00:09:39.060 --> 00:09:43.580 And it's kind of funny how kids interact with that. 00:09:43.580 --> 00:09:46.200 And I think it's going to lead something like that. 00:09:46.200 --> 00:09:53.560 Voice is going to be something like a touchscreen in the sense that you expect that feature to be there. 00:09:53.560 --> 00:10:05.100 If you go to a vending machine and you could do something more quickly by saying something to it or be frustrated with it and you expect it to listen to you. 00:10:05.380 --> 00:10:06.680 It's quite the way. 00:10:06.680 --> 00:10:13.980 Like after iPhone, you started touching every screen even if they didn't work that way. 00:10:13.980 --> 00:10:13.980 Yeah. 00:10:13.980 --> 00:10:14.200 That's right. 00:10:14.200 --> 00:10:18.060 After the iPad came out, there were videos of kids touching real magazines. 00:10:18.060 --> 00:10:20.680 Like little two-year-olds going, this one is broken. 00:10:20.680 --> 00:10:21.940 Yeah, exactly. 00:10:21.940 --> 00:10:23.400 Voice will probably be like that. 00:10:23.400 --> 00:10:24.580 It won't listen to me. 00:10:24.580 --> 00:10:25.480 It's a refrigerator. 00:10:25.480 --> 00:10:26.360 It doesn't talk. 00:10:26.360 --> 00:10:26.860 I'm sorry. 00:10:26.860 --> 00:10:27.820 Yeah. 00:10:28.940 --> 00:10:29.380 Nice. 00:10:29.380 --> 00:10:29.600 All right. 00:10:29.600 --> 00:10:29.740 Yeah. 00:10:29.740 --> 00:10:31.800 I think it's definitely going to be an interesting wave. 00:10:31.800 --> 00:10:33.800 And I think it must be fun to work with it. 00:10:33.800 --> 00:10:34.360 Yeah. 00:10:34.360 --> 00:10:38.740 So that's probably a good place to switch over, start talking about the books. 00:10:38.740 --> 00:10:45.720 So you've chosen 10 plus 1 books for us that you feel are pretty essential reading for Python programmers. 00:10:45.720 --> 00:10:46.360 Yeah. 00:10:46.360 --> 00:10:50.700 I don't know if these are like the definitive 10 books to read. 00:10:51.040 --> 00:10:59.840 But I tried to pick up things that are relevant for a newcomer and relevant for someone intermediate. 00:10:59.840 --> 00:11:06.220 And then there are a couple of books that are totally outside of the box of a regular Python programmer. 00:11:06.220 --> 00:11:10.860 Everyone is going to read Python programming at least at some point. 00:11:10.860 --> 00:11:18.160 And everyone probably ends up seeing like starting with the learning Python. 00:11:18.160 --> 00:11:24.480 But I hope I picked a nicer alternative to learning Python in this system. 00:11:24.480 --> 00:11:24.820 Yeah. 00:11:24.820 --> 00:11:25.120 Nice. 00:11:25.120 --> 00:11:30.900 So your first book is Automate the Boring Stuff, Python Practical Programming for Total Beginners 00:11:30.900 --> 00:11:33.300 by friend of the show, Al Swigert. 00:11:33.300 --> 00:11:34.020 Yes. 00:11:34.020 --> 00:11:35.720 I've listened to that episode. 00:11:35.720 --> 00:11:40.660 And today when I was preparing for this and I was checking through the book and I realized, 00:11:40.660 --> 00:11:44.280 hey, this is actually really relevant still to me. 00:11:44.280 --> 00:11:46.460 It's a really good introductory book. 00:11:47.080 --> 00:11:51.420 But then there are chapters like Automating Excel Workflow. 00:11:51.420 --> 00:11:58.040 Or today I discovered that there's a chapter about image manipulation that I will almost 00:11:58.040 --> 00:12:03.360 immediately take into use after this when I go back and start writing stuff. 00:12:03.360 --> 00:12:03.720 Yeah. 00:12:03.720 --> 00:12:04.560 It's really cool. 00:12:04.560 --> 00:12:06.040 It's a very gentle introduction. 00:12:06.520 --> 00:12:11.640 And one of the things I like about it is it has some applied aspects. 00:12:11.640 --> 00:12:17.540 Because a lot of times I feel like the biggest mistake people, books, presentations, whatever, 00:12:17.540 --> 00:12:21.820 make when they're trying to teach programmers is they go, here are all the facts. 00:12:21.820 --> 00:12:28.140 And assume at the end of this, you know, semester course or this book or whatever, you'll be able 00:12:28.140 --> 00:12:29.140 to do something interesting. 00:12:29.260 --> 00:12:31.280 But for now, here's how you define a variable. 00:12:31.280 --> 00:12:32.340 Here's what a string is. 00:12:32.340 --> 00:12:33.960 Here's how you define a loop. 00:12:33.960 --> 00:12:35.300 Here's how you define a function. 00:12:35.300 --> 00:12:37.180 And it's just boring, boring, boring. 00:12:37.180 --> 00:12:37.460 Right? 00:12:37.460 --> 00:12:38.160 Yeah. 00:12:38.160 --> 00:12:41.320 And so this book actually comes with a bunch of little problems you can solve. 00:12:41.320 --> 00:12:46.340 Like one is automating workflows with Excel, which I'm sure resonated well with you, given 00:12:46.340 --> 00:12:47.000 your background. 00:12:47.000 --> 00:12:47.640 Yeah. 00:12:47.640 --> 00:12:47.720 Yeah. 00:12:47.720 --> 00:12:55.060 I think that kind of solve a problem is really essential for someone trying to learn a new 00:12:55.060 --> 00:12:55.460 skill. 00:12:55.460 --> 00:12:59.080 It's especially if you are not a programmer yourself. 00:12:59.080 --> 00:13:00.140 There's this book. 00:13:00.140 --> 00:13:05.800 And I think that many of the Raspberry Pi books are extremely good examples of those. 00:13:05.800 --> 00:13:09.820 Either they are funny or either they solve your real problem. 00:13:09.820 --> 00:13:16.680 And that's a good motivation to go through all that string and list and do all the stuff 00:13:16.680 --> 00:13:18.820 that you end up doing with Python. 00:13:18.820 --> 00:13:19.140 Yeah. 00:13:19.140 --> 00:13:19.980 It's really great. 00:13:19.980 --> 00:13:26.920 So I also had Al recently on the show on episode 106 for his Invent Your Own Computer Games with 00:13:26.920 --> 00:13:27.220 Python. 00:13:27.220 --> 00:13:33.040 So depending on your experience level and what makes you tick, if you want to actually get 00:13:33.040 --> 00:13:35.520 work done, check out the Automate the Boring Stuff to learn Python. 00:13:35.520 --> 00:13:41.100 If you want to write games, like maybe you're trying to get your younger kid who's in high 00:13:41.100 --> 00:13:44.980 school or something interested in Python, maybe the Invent Your Own Computer Games would be a 00:13:44.980 --> 00:13:45.580 good one. 00:13:45.580 --> 00:13:46.200 Yeah. 00:13:46.200 --> 00:13:50.100 You also pointed out that Al has this as a video course on Udemy. 00:13:50.100 --> 00:13:53.780 And so I feel a bit of a kinship to Al here. 00:13:53.780 --> 00:13:56.220 Like he and I both are living a similar life. 00:13:56.220 --> 00:13:57.980 Like he writes the books and has the courses. 00:13:57.980 --> 00:14:00.440 I do the podcasts and have the courses. 00:14:00.440 --> 00:14:04.020 And so, yeah, if you want to take this as a video course version, that's available as 00:14:04.020 --> 00:14:04.300 well. 00:14:04.300 --> 00:14:04.920 Yeah. 00:14:04.920 --> 00:14:05.280 Yeah. 00:14:05.280 --> 00:14:05.440 Nice. 00:14:05.440 --> 00:14:05.680 Okay. 00:14:05.680 --> 00:14:08.040 And then the next one is actually a really short book, right? 00:14:08.040 --> 00:14:08.420 Yeah. 00:14:08.420 --> 00:14:12.720 This is something that I, when I was learning, was on my desk. 00:14:12.720 --> 00:14:19.880 Also often I carried with it because it's kind of a, I had trouble wrapping my head around 00:14:19.880 --> 00:14:21.580 Python lists. 00:14:21.580 --> 00:14:29.160 There was something really, really, I couldn't tell when the method was mutable and when it's 00:14:29.160 --> 00:14:30.820 going to create me a new one. 00:14:30.820 --> 00:14:35.200 And then that kind of stuff is, and it's kind of a thing that you really need to be 00:14:35.200 --> 00:14:37.540 to just put it in your long-term memory. 00:14:37.540 --> 00:14:38.280 It doesn't. 00:14:38.280 --> 00:14:38.600 Right. 00:14:38.600 --> 00:14:41.380 Like sorted of list versus list.sorted. 00:14:41.380 --> 00:14:42.140 Things like that. 00:14:42.140 --> 00:14:42.640 Yeah. 00:14:42.640 --> 00:14:43.100 Exactly. 00:14:43.100 --> 00:14:49.020 So the book you're referencing is Python Pocket Reference, Python in Your Pocket by Mark Lutz. 00:14:49.180 --> 00:14:49.460 Yeah. 00:14:49.460 --> 00:14:49.500 Yeah. 00:14:49.500 --> 00:14:53.080 Sadly, it's like two or three years old already. 00:14:53.080 --> 00:14:55.960 And I think it's Python 3.3. 00:14:55.960 --> 00:15:01.420 If, oh no, no, actually it's Python, Python 3.6 is as well as covered. 00:15:01.420 --> 00:15:01.760 So. 00:15:01.760 --> 00:15:02.720 Oh, great. 00:15:02.720 --> 00:15:02.900 Yeah. 00:15:02.900 --> 00:15:03.340 Yeah. 00:15:03.340 --> 00:15:03.660 Yeah. 00:15:03.660 --> 00:15:07.340 And most of the stuff, like long as it's Python 3 hasn't actually changed. 00:15:07.340 --> 00:15:08.020 Yeah. 00:15:08.020 --> 00:15:08.540 That much. 00:15:08.540 --> 00:15:10.600 It's more additive rather than changes too. 00:15:10.600 --> 00:15:10.900 Yeah. 00:15:10.900 --> 00:15:11.320 Yeah. 00:15:11.320 --> 00:15:18.180 And it's always with this kind of a technical books, there's some sort of risk of 00:15:18.180 --> 00:15:22.660 obsolescence if it's tied to a certain version of something. 00:15:22.660 --> 00:15:30.880 And the books that I chose here should be so that they are either up to date or then are 00:15:30.880 --> 00:15:31.340 timeless. 00:15:31.340 --> 00:15:32.280 Yeah. 00:15:32.280 --> 00:15:32.680 Yeah. 00:15:32.680 --> 00:15:32.880 Yeah. 00:15:32.880 --> 00:15:37.580 There's a few books in this list that are definitely timeless and then more timeless than others. 00:15:37.580 --> 00:15:39.900 But yeah, this is really nice. 00:15:39.900 --> 00:15:45.700 Like I, I'm pretty good at memorizing these obscure details of programming languages and whatnot. 00:15:45.700 --> 00:15:49.480 But you know, the one that I really need a pocket reference for is JavaScript. 00:15:49.480 --> 00:15:54.480 I'm always like, God, how do I iterate an array correctly in JavaScript again? 00:15:54.480 --> 00:15:58.300 And this, like, it's always, I feel like I'm always Googling for JavaScript stuff, even though 00:15:58.300 --> 00:15:59.160 I've been doing it for years. 00:15:59.160 --> 00:16:01.900 But Python, yeah, this is cool for people learning Python. 00:16:01.900 --> 00:16:02.740 Yeah, exactly. 00:16:02.740 --> 00:16:05.820 I have the JavaScript one and CSS one. 00:16:05.820 --> 00:16:12.500 And I, the only thing is that JavaScript and CSS, they are moving, moving so fast that 00:16:12.500 --> 00:16:17.780 you're probably learning something that you don't need to learn when you're trying to 00:16:17.780 --> 00:16:18.020 learn. 00:16:18.020 --> 00:16:18.620 Yeah. 00:16:18.620 --> 00:16:19.080 Yeah. 00:16:19.080 --> 00:16:23.220 So then the next one you picked, I think, is a really interesting book as well. 00:16:23.220 --> 00:16:27.680 And that's the Python Cookbook by David Beasley and Brian Jones. 00:16:27.680 --> 00:16:28.280 Yeah. 00:16:28.280 --> 00:16:35.560 It's, I had it before and I don't know where I lost my copy, but it was really essential. 00:16:35.560 --> 00:16:40.960 So I think when I started learning Python, Stack Overflow didn't exist at all. 00:16:40.960 --> 00:16:46.260 So this kind of a ready-made recipe for your program that you can just drop in. 00:16:46.260 --> 00:16:46.480 Yeah. 00:16:46.480 --> 00:16:51.780 So the idea is that it's broken into general areas like stuff to do with strings, stuff to 00:16:51.780 --> 00:16:55.600 do with data structures, numbers, iteration, files. 00:16:55.600 --> 00:17:00.100 And then for each one of these, there's maybe 15 to 20, how do I do this? 00:17:00.100 --> 00:17:02.860 How do I iterate over a fixed set of records? 00:17:02.860 --> 00:17:05.040 How do I print a file to the console? 00:17:05.040 --> 00:17:06.960 How do I communicate with serial ports? 00:17:06.960 --> 00:17:09.620 How do I print bad file names? 00:17:09.620 --> 00:17:10.820 And so on and so on, right? 00:17:10.820 --> 00:17:11.200 Yeah. 00:17:11.200 --> 00:17:13.720 And this was really essential back then. 00:17:13.720 --> 00:17:21.540 Nowadays, it would be a good reference, but often you just type something into Google 00:17:21.540 --> 00:17:23.600 and end up in Stack Overflow. 00:17:23.600 --> 00:17:24.040 Sure. 00:17:24.040 --> 00:17:26.700 So I think this is interesting coming at it from two ways. 00:17:26.700 --> 00:17:32.960 Like the way that you're talking about where you type something into Stack Overflow is, I 00:17:32.960 --> 00:17:36.200 know I want to like make a temporary file in directory. 00:17:36.200 --> 00:17:40.780 So you type maybe that into Google and it probably finds something on Stack Overflow and you're 00:17:40.780 --> 00:17:41.280 kind of good. 00:17:41.280 --> 00:17:47.660 But it could be that you're coming from a place of less experience and you want to learn 00:17:47.660 --> 00:17:49.340 how, you kind of want to get a feel for it. 00:17:49.340 --> 00:17:52.260 What can I do with the file system and files in Python? 00:17:52.780 --> 00:17:52.920 Yeah. 00:17:52.920 --> 00:17:54.540 You could go through these 15 or 20 things. 00:17:54.540 --> 00:17:59.500 You're like, whoa, actually you would be pretty capable if you could almost treat each one 00:17:59.500 --> 00:18:00.560 of these as a project. 00:18:00.560 --> 00:18:01.320 You know what I mean? 00:18:01.320 --> 00:18:06.380 Like people who are new often have a hard time finding something concrete to practice. 00:18:06.380 --> 00:18:07.180 Yeah. 00:18:07.180 --> 00:18:11.600 And it seems like there's, you know, hundreds of little practices with the solution right 00:18:11.600 --> 00:18:11.820 here. 00:18:11.820 --> 00:18:12.800 And I think that's pretty nice. 00:18:12.800 --> 00:18:13.180 Yeah. 00:18:13.220 --> 00:18:16.900 And also kind of a guaranteed quality of these. 00:18:16.900 --> 00:18:21.820 When you Google and Stack Overflow stuff, you may end up. 00:18:21.820 --> 00:18:21.820 Yeah. 00:18:21.820 --> 00:18:25.720 Well, you also waste a lot of time on Stack Overflow because they're trying to do a thing, 00:18:25.720 --> 00:18:28.500 but they're trying to do it slightly differently than you were trying to do it. 00:18:28.500 --> 00:18:29.740 So it doesn't quite fit. 00:18:29.740 --> 00:18:30.260 You know what I mean? 00:18:30.260 --> 00:18:30.720 Yeah. 00:18:31.120 --> 00:18:36.780 And especially if you're new to technology, you really can't tell good answers from bad 00:18:36.780 --> 00:18:37.240 answers. 00:18:37.240 --> 00:18:41.880 And it doesn't mean that the most upvoted one is the best one. 00:18:41.880 --> 00:18:42.060 Yeah. 00:18:42.060 --> 00:18:47.160 Sometimes you're like, if I could just uncheck that accepted answer because you know the next 00:18:47.160 --> 00:18:47.580 one's better. 00:18:47.580 --> 00:18:48.560 Yeah, exactly. 00:18:48.680 --> 00:18:54.340 It's one of those first steps of understanding technology is to start understanding which 00:18:54.340 --> 00:18:57.380 of the Stack Overflow questions are bad and which ones are good. 00:18:57.380 --> 00:18:58.640 Yeah. 00:18:58.640 --> 00:19:02.980 I think, you know, looping it back to book one, I'm pretty sure Al Swigert shows you like 00:19:02.980 --> 00:19:08.500 how to take like a traceback error and use Google to find the answer for that. 00:19:08.500 --> 00:19:09.920 How to fix it. 00:19:09.920 --> 00:19:10.260 Yeah. 00:19:10.260 --> 00:19:13.520 And that's one of those things that has really changed in 20 years. 00:19:13.520 --> 00:19:20.000 It's like you were completely screwed 20 years ago if something like that happened and it 00:19:20.000 --> 00:19:22.500 didn't show up on Altavista or something. 00:19:22.500 --> 00:19:28.920 It's like the way of working has changed and I think it's really cool and it's sometimes 00:19:28.920 --> 00:19:34.440 you lose the sight of how much we have progressed and how much easier stuff is. 00:19:34.440 --> 00:19:35.620 Yeah, it's for sure. 00:19:35.620 --> 00:19:38.860 It's not only good, but it's mostly good. 00:19:38.860 --> 00:19:40.420 It's mostly good. 00:19:40.420 --> 00:19:40.760 I agree. 00:19:40.760 --> 00:19:41.420 It's mostly good. 00:19:42.220 --> 00:19:45.340 This portion of Talk Python To Me has been brought to you by Rollbar. 00:19:45.340 --> 00:19:49.040 One of the frustrating things about being a developer is dealing with errors. 00:19:49.040 --> 00:19:49.520 Ugh. 00:19:49.520 --> 00:19:54.960 Relying on users to report errors, digging through log files, trying to debug issues, or getting 00:19:54.960 --> 00:19:58.060 millions of alerts just flooding your inbox and ruining your day. 00:19:58.060 --> 00:20:02.840 With Rollbar's full stack error monitoring, you get the context, insight, and control you 00:20:02.840 --> 00:20:04.800 need to find and fix bugs faster. 00:20:04.800 --> 00:20:08.800 Adding Rollbar to your Python app is as easy as pip install Rollbar. 00:20:09.040 --> 00:20:13.040 You can start tracking production errors and deployments in eight minutes or less. 00:20:13.040 --> 00:20:17.220 Are you considering self-hosting tools for security or compliance reasons? 00:20:17.220 --> 00:20:20.800 Then you should really check out Rollbar's compliant SaaS option. 00:20:20.800 --> 00:20:25.940 Get advanced security features and meet compliance without the hassle of self-hosting, including 00:20:25.940 --> 00:20:30.160 HIPAA, ISO 27001, Privacy Shield, and more. 00:20:30.160 --> 00:20:31.540 They'd love to give you a demo. 00:20:32.080 --> 00:20:33.240 Give Rollbar a try today. 00:20:33.240 --> 00:20:36.920 Go to talkpython.fm/Rollbar and check them out. 00:20:36.920 --> 00:20:43.660 The next one is switching us over to working with data, mostly from a data science, not 00:20:43.660 --> 00:20:49.120 database perspective, with Python for data analysis, data wrinkling with Pandas, NumPy, and 00:20:49.120 --> 00:20:51.360 IPython with Wes McKinley. 00:20:51.360 --> 00:20:52.040 McKinney. 00:20:52.040 --> 00:20:52.620 Sorry, Wes. 00:20:52.700 --> 00:20:54.000 Yeah, exactly. 00:20:54.000 --> 00:20:56.740 And I thought this was something like that. 00:20:56.740 --> 00:21:02.080 It was already years old, but it seems to be that there's a new version coming in September. 00:21:02.080 --> 00:21:09.200 So IPython stuff is updated with also Jupyter stuff and all that kind of stuff. 00:21:09.700 --> 00:21:16.560 So this is very good for most of the machine learning, deep learning, and that kind of stuff 00:21:16.560 --> 00:21:21.540 is most really boring, cleaning up and finding data. 00:21:21.540 --> 00:21:23.900 And this covers that part. 00:21:23.900 --> 00:21:25.160 You need to know. 00:21:25.160 --> 00:21:25.360 Right. 00:21:25.360 --> 00:21:28.700 And that, to me, is the primary goal of Pandas, right? 00:21:28.700 --> 00:21:29.180 Yeah. 00:21:29.180 --> 00:21:35.560 Pandas is all about loading up semi-structured data, putting it into a consistent structure, 00:21:35.560 --> 00:21:41.980 maybe reorganizing it like a group by or something like that, fixing the bad data or filtering 00:21:41.980 --> 00:21:45.700 out the bad data and getting it ready to then go do a thing with, right? 00:21:45.700 --> 00:21:46.660 Yeah, exactly. 00:21:46.660 --> 00:21:54.120 And it's like filtering out noise and either filtering out the missing data or adding default 00:21:54.120 --> 00:21:55.100 values for that. 00:21:55.100 --> 00:22:01.400 And that kind of stuff that is, you really need to know is, I think if you come somewhere from 00:22:01.400 --> 00:22:09.680 like R, that's like the forte of R is cleaning up data and putting it up in a, let's say, 00:22:09.680 --> 00:22:10.620 table structure. 00:22:10.620 --> 00:22:17.720 And this is the same thing for Python and very important when getting into machine learning 00:22:17.720 --> 00:22:22.840 and all kinds of data analysis and maybe data visualization tasks. 00:22:22.840 --> 00:22:23.140 Yeah. 00:22:23.140 --> 00:22:23.400 Yeah. 00:22:23.400 --> 00:22:23.820 It's pretty cool. 00:22:23.820 --> 00:22:29.840 So it starts by introducing you to IPython and Jupyter and NumPy and Pandas. 00:22:29.840 --> 00:22:36.360 And then it really talks about like loading, cleaning, transforming, merging, reshaping data, 00:22:36.360 --> 00:22:42.640 which it's, you know, that is so, so powerful with that and even some visualization stuff. 00:22:42.640 --> 00:22:44.440 So yeah, it's pretty cool. 00:22:44.440 --> 00:22:49.640 If you got to do a lot of that kind of thing, then yeah, Pandas is a good place to look. 00:22:49.640 --> 00:22:51.060 And this book seems like a really good one. 00:22:51.060 --> 00:22:53.580 It's from Wes McKinney, who is the creator of Pandas. 00:22:53.580 --> 00:22:58.240 So that's a good, definitely a definitive source of knowledge for that library. 00:22:58.240 --> 00:22:59.040 Yeah. 00:22:59.040 --> 00:22:59.440 Nice. 00:22:59.440 --> 00:22:59.880 Yeah. 00:22:59.880 --> 00:23:00.060 Okay. 00:23:00.060 --> 00:23:00.720 Yeah. 00:23:00.720 --> 00:23:06.980 So the next one that we're going to talk about is not as new as the 2016 version of that last 00:23:06.980 --> 00:23:07.280 book. 00:23:07.280 --> 00:23:10.200 It's called Working Effectively with Legacy Code. 00:23:10.200 --> 00:23:11.780 What's the story of this one? 00:23:11.860 --> 00:23:18.060 This is kind of like a, more like a book that teaches you how to think about code. 00:23:18.060 --> 00:23:22.600 And in a realistic situation, you rarely get to start something new. 00:23:22.600 --> 00:23:29.560 Usually, if you are done consultation like me, it's like you end up somewhere that it's already 00:23:29.560 --> 00:23:33.300 late and already not delivering what should be there. 00:23:33.700 --> 00:23:37.020 And this is answering questions. 00:23:37.020 --> 00:23:38.720 What can you do about that? 00:23:38.720 --> 00:23:39.920 What can you do there? 00:23:39.920 --> 00:23:44.360 And it's like, comes with the definitions of what is legacy code? 00:23:44.360 --> 00:23:48.960 It's bluntly defines legacy code as something that doesn't have a test. 00:23:48.960 --> 00:23:58.140 And then it goes on like slowly teaching you how to move from that zero state to something 00:23:58.140 --> 00:24:04.460 that is almost enjoyable, building up, decoupling the system, building up unit tests. 00:24:04.460 --> 00:24:04.860 Yeah. 00:24:04.860 --> 00:24:10.600 I feel like this book is certainly required reading, even though it's not based on Python. 00:24:10.600 --> 00:24:13.140 I think it does some C++ and some Java. 00:24:13.140 --> 00:24:15.160 Maybe there's even a C# version. 00:24:15.160 --> 00:24:15.960 I can't recall. 00:24:15.960 --> 00:24:22.520 But this book is kind of part of a trifecta of the working with effectively legacy code, 00:24:22.520 --> 00:24:26.320 the original refactoring book by Martin Fowler. 00:24:26.320 --> 00:24:26.740 Yeah. 00:24:26.740 --> 00:24:27.860 That's an excellent book. 00:24:27.860 --> 00:24:28.180 Yes. 00:24:28.180 --> 00:24:28.700 Yeah. 00:24:28.700 --> 00:24:28.960 Yeah. 00:24:28.960 --> 00:24:33.080 And then the agile principles, patterns, and practices by Robert C. Martin. 00:24:33.080 --> 00:24:38.360 Like those three kind of come together to attack like the same problem from multiple directions. 00:24:38.360 --> 00:24:43.620 And the concept that I still remember, so I'm looking here at my Amazon page for this book, 00:24:43.620 --> 00:24:46.580 and it says, you purchased this item on June 10th, 2005. 00:24:46.580 --> 00:24:52.860 So it's not a new book, but it's a, it's really, like you said, does teach you to think in interesting 00:24:52.860 --> 00:24:59.500 ways about systems, especially systems, which they called brownfield systems, not greenfield, 00:24:59.500 --> 00:25:02.480 where it's like, you know, here's half a million lines of code. 00:25:02.480 --> 00:25:04.160 It's been working for 15 years. 00:25:04.400 --> 00:25:05.720 It's mission critical. 00:25:05.720 --> 00:25:10.460 Please, you do not break it, but you have to now take it over and add features to it. 00:25:10.460 --> 00:25:11.520 Like, what do you do? 00:25:11.520 --> 00:25:12.480 Yeah. 00:25:12.580 --> 00:25:16.620 So there's all these interesting ways of like, how do you get it under test? 00:25:16.620 --> 00:25:21.240 How do you create what, the biggest thing that comes from this one in my mind is this 00:25:21.240 --> 00:25:22.220 concept of seams. 00:25:22.220 --> 00:25:28.440 How do you create these seams where this huge monolithic brittle thing becomes flexible in 00:25:28.440 --> 00:25:31.080 the certain ways that you need to extend it? 00:25:31.080 --> 00:25:36.460 And how do you carve off these little subsystems that change frequently that are very maintainable 00:25:36.460 --> 00:25:38.120 without breaking the thing? 00:25:38.200 --> 00:25:40.520 I think that's just really very interesting. 00:25:40.520 --> 00:25:40.920 Yeah. 00:25:40.920 --> 00:25:43.480 I really like the notion of seam. 00:25:43.480 --> 00:25:47.320 It's like, oh, this, it has a name. 00:25:47.320 --> 00:25:52.660 I've been doing this 10 years, but I didn't know this, this technique had a name. 00:25:52.660 --> 00:25:53.120 It's cool. 00:25:53.120 --> 00:25:53.280 Yeah. 00:25:53.280 --> 00:25:58.340 Here's this thing that is inflexible, but in this one little axis, it is flexible and that's 00:25:58.340 --> 00:25:58.800 what I needed. 00:25:58.800 --> 00:25:59.540 So it's good. 00:25:59.540 --> 00:25:59.800 Right. 00:25:59.800 --> 00:26:01.160 Yeah. 00:26:01.160 --> 00:26:01.380 Yeah. 00:26:01.380 --> 00:26:05.400 So definitely recommend, I actually recommend all three of those books, but working effectively 00:26:05.400 --> 00:26:06.320 with legacy code. 00:26:06.320 --> 00:26:11.340 Well, it's definitely something that will change the way that you think about programming 00:26:11.340 --> 00:26:13.700 and especially large old systems. 00:26:13.700 --> 00:26:14.100 Yeah. 00:26:14.100 --> 00:26:14.480 Yeah. 00:26:14.480 --> 00:26:19.380 And that's where we all end up at some point doing old big systems. 00:26:19.380 --> 00:26:20.840 Yeah, that's right. 00:26:20.840 --> 00:26:22.220 We all, we all end up there somehow. 00:26:22.220 --> 00:26:23.060 All right. 00:26:23.060 --> 00:26:29.980 The next one is a perfect carry on from this concept of testing, but zooming forward in Python 00:26:29.980 --> 00:26:36.140 to something that's just now being released by my friend and Python Bytes co-host, Brian 00:26:36.140 --> 00:26:36.400 Hawken. 00:26:36.400 --> 00:26:36.760 Yeah. 00:26:36.760 --> 00:26:38.200 Python testing with pytest. 00:26:38.200 --> 00:26:38.540 Yeah. 00:26:38.540 --> 00:26:42.560 It's, I bought it and I started reading it. 00:26:42.560 --> 00:26:48.400 It is one of those books that after each chapter, you need to go and change something in your code. 00:26:48.400 --> 00:26:49.660 I've used pytest before. 00:26:49.660 --> 00:26:51.140 Like I've been doing it wrong for years. 00:26:51.140 --> 00:26:51.760 Oh my gosh. 00:26:51.760 --> 00:26:53.760 I got to put this down and go fix it right now. 00:26:53.760 --> 00:26:54.140 Yeah. 00:26:54.140 --> 00:26:59.580 I like the pytest philosophy compared to like a unit test and knows it's more PyTonic. 00:27:00.360 --> 00:27:06.400 And reading this book, I realized, oh, I've used like 10% of the possibilities there. 00:27:06.400 --> 00:27:08.160 It's like, and it's quick read. 00:27:08.160 --> 00:27:11.440 You probably will read it through in one evening or two. 00:27:11.440 --> 00:27:19.180 And it's, if you are not like a super familiar with pytest, it will probably make you change 00:27:19.180 --> 00:27:23.660 your project structure and make it more pytestable. 00:27:23.660 --> 00:27:24.200 Yeah. 00:27:24.200 --> 00:27:24.700 That's awesome. 00:27:24.700 --> 00:27:25.040 Yeah. 00:27:25.040 --> 00:27:26.400 Brian did a really good job with this. 00:27:26.640 --> 00:27:31.400 And so he talks about writing test functions, using the search stuff, all the standard things, 00:27:31.400 --> 00:27:38.940 you know, fixtures, but also things like the tempter fixture, the monkey patching stuff, 00:27:38.940 --> 00:27:46.000 the doc test namespaces and plugins, like finding and using the pytest plugins, even how to create 00:27:46.000 --> 00:27:46.440 your own. 00:27:46.440 --> 00:27:48.000 So yeah, very, very cool stuff. 00:27:48.000 --> 00:27:48.740 Yeah, exactly. 00:27:48.740 --> 00:27:49.400 All right. 00:27:49.400 --> 00:27:54.080 The next one we have is sort of the other side of the Panda story. 00:27:54.080 --> 00:27:56.280 Once you have the data, how do you get going? 00:27:56.480 --> 00:28:04.240 I would say this is one of the hottest developer software areas around just with machine learning. 00:28:04.240 --> 00:28:07.800 So the book is hands-on machine learning with scikit-learn and TensorFlow. 00:28:07.800 --> 00:28:08.560 Yeah. 00:28:08.560 --> 00:28:12.220 I had like half a dozen of these books. 00:28:12.220 --> 00:28:19.060 And I think this one was like the best companion to all those online courses that everyone probably 00:28:19.060 --> 00:28:23.260 go through, either the Coursera one or the Udacity one. 00:28:23.260 --> 00:28:27.640 Or one of my favorites is course.fast.ai. 00:28:27.640 --> 00:28:34.720 It's like you go through the course and then you have a like a vague understanding of the 00:28:34.720 --> 00:28:35.200 concepts. 00:28:35.200 --> 00:28:37.560 And then you need to read the book. 00:28:37.820 --> 00:28:45.640 There's like the definitive books like the deep learning by Ian Goodfellow or the elements of 00:28:45.640 --> 00:28:46.880 statistical learning. 00:28:46.880 --> 00:28:51.260 But they probably are too heavy to start with. 00:28:51.260 --> 00:28:56.840 This is kind of goes through like many of the algorithms quickly. 00:28:56.840 --> 00:29:10.700 It has like a practice questions and is all around good reference to accompany you with learning through trial and error and through perhaps some online course. 00:29:11.100 --> 00:29:18.200 And well, this is probably one of those books that wouldn't appear on this list maybe in a year or two. 00:29:18.200 --> 00:29:21.280 There probably are going to be many, many other books. 00:29:21.280 --> 00:29:26.180 But this one was like the books that have come out this year. 00:29:26.180 --> 00:29:29.200 I think this one was my favorite on this topic. 00:29:29.480 --> 00:29:29.960 It's really cool. 00:29:29.960 --> 00:29:33.120 So it has like the fundamentals of machine learning for the first part. 00:29:33.120 --> 00:29:42.660 And it just goes into like what is an end to end machine learning project look like, classification, models, or vector machines, decision trees, all that kind of stuff. 00:29:42.720 --> 00:29:52.460 And then it gets into actual neural networks and deep learning things with TensorFlow and training deep neural networks and stuff like that, which is really cool stuff. 00:29:52.460 --> 00:30:00.700 Even how to just like do distributed computation of TensorFlow across multiple machines and GPUs and all kinds of good stuff. 00:30:00.700 --> 00:30:01.800 It sounds really, really fun. 00:30:01.800 --> 00:30:02.800 All right. 00:30:02.800 --> 00:30:05.680 Do you get to do any of this kind of stuff with your chatbots? 00:30:05.680 --> 00:30:09.060 I've mostly done stuff with Finnish language. 00:30:09.060 --> 00:30:13.580 Like I have this huge data set of Finnish cursing. 00:30:13.580 --> 00:30:25.560 And I've been going through that with like Word2Weck and other kinds of machine learning and clustering algorithms to see what kind of patterns I can find. 00:30:25.560 --> 00:30:37.460 And it's kind of a funny way to look at data that makes you lose your belief in other human beings. 00:30:37.460 --> 00:30:44.960 But also it really, really gets you nice patterns around what kind of things make people curse. 00:30:44.960 --> 00:30:47.700 What are people really passionate about? 00:30:47.700 --> 00:30:54.120 Well, it's this kind of stuff that, well, probably would get moderated in a forum. 00:30:54.520 --> 00:30:58.180 It's like any type of day-to-day politics. 00:30:58.180 --> 00:31:04.560 But then there's sports and human relationships and stuff like that. 00:31:04.560 --> 00:31:07.860 Well, you could probably, well, this is Finnish. 00:31:07.860 --> 00:31:15.920 So you could probably follow Finnish sports team, how they are doing through how people are cursing. 00:31:15.920 --> 00:31:22.220 They are either cursing because we are losing or cursing that, well, we won this time. 00:31:22.220 --> 00:31:22.940 Yeah. 00:31:23.940 --> 00:31:29.020 Or how are they expressing their extreme emotion in a positive or negative way? 00:31:29.020 --> 00:31:32.140 Well, it's mostly about the same. 00:31:32.140 --> 00:31:33.780 Yeah, it's tough. 00:31:33.780 --> 00:31:38.540 I'm sure it's actually a really tricky natural language processing problem. 00:31:38.540 --> 00:31:45.300 Yeah, it's something that I have a data set of like 100 million Finnish tweets that contain a curse word. 00:31:45.300 --> 00:31:51.000 And I'm trying to figure out a way to make that somehow public and usable to everyone. 00:31:51.000 --> 00:31:52.580 It's really interesting data. 00:31:52.580 --> 00:31:53.760 That is actually pretty cool. 00:31:53.760 --> 00:31:55.820 Nice. 00:31:55.920 --> 00:31:56.920 All right. 00:31:56.920 --> 00:31:56.920 All right. 00:31:56.920 --> 00:32:04.600 So the next one that we're going to talk about is About Face, The Essentials of Interactive Design by Alan Cooper and some co-authors. 00:32:05.560 --> 00:32:09.560 And this one had been on my list, my reading list for a very long time. 00:32:09.560 --> 00:32:10.280 I haven't read it. 00:32:10.280 --> 00:32:19.060 And it's kind of grouped in there with, you know, Stephen Krug's Don't Make Me Think, 100 Things Every Designer Needs to Know About People and the Design of Everyday Things. 00:32:19.700 --> 00:32:23.940 A couple of those are about physical things, but this is about interface design, right? 00:32:23.940 --> 00:32:25.660 Yeah, this is interface design. 00:32:25.660 --> 00:32:31.960 And it's, well, it starts with like on the conceptual level, how to design systems. 00:32:31.960 --> 00:32:37.240 That's kind of probably only good to know if you're a Python programmer. 00:32:37.240 --> 00:32:47.020 But then it goes on like stuff like that are like immediately usable or that make you think stuff like that. 00:32:47.020 --> 00:32:48.880 Don't make stupid error messages. 00:32:49.560 --> 00:32:51.860 Make the software that you're creating. 00:32:51.860 --> 00:32:55.400 Think it as like a service person. 00:32:55.400 --> 00:32:59.460 You don't want to forget things that you have promised to a user. 00:32:59.460 --> 00:33:04.860 You don't want to pester the user with unnecessary questions all the time. 00:33:04.860 --> 00:33:07.860 And you don't lose your customer stuff. 00:33:07.860 --> 00:33:10.560 If they give you something, you don't lose that. 00:33:10.560 --> 00:33:18.920 It has really, really good metaphors of how your program should be that it solves the user problem. 00:33:19.420 --> 00:33:23.360 Not only your problem as a business or as a developer. 00:33:23.360 --> 00:33:23.800 Right. 00:33:23.800 --> 00:33:29.320 And it has a, I think this might be one of the first places where the concept of personas came from. 00:33:29.320 --> 00:33:34.020 I think this is the origin of that, but I'm not 100% sure. 00:33:34.200 --> 00:33:34.380 Yeah. 00:33:34.380 --> 00:33:46.660 So the idea is to break up the users into prototypical groups and then try to think through the interacting with your software, whether it's a website, it's an app, it's whatever. 00:33:46.660 --> 00:33:48.380 Think through using it. 00:33:48.380 --> 00:33:52.300 I guess you could even do it for an API like an open source library if you want. 00:33:52.300 --> 00:33:52.540 Yeah. 00:33:52.540 --> 00:33:53.060 Yeah. 00:33:53.140 --> 00:33:57.520 Think through like working with it with that persona, the various personas and like, what are their goals? 00:33:57.520 --> 00:33:58.740 What do they want out of this? 00:33:58.740 --> 00:34:00.480 What are they bringing to the table? 00:34:00.480 --> 00:34:01.820 How are they going to work with it? 00:34:01.820 --> 00:34:02.500 And things like this. 00:34:02.500 --> 00:34:02.640 Yeah. 00:34:02.640 --> 00:34:03.500 Yeah, exactly. 00:34:03.500 --> 00:34:07.500 Thinking your software through your users point of view. 00:34:07.660 --> 00:34:07.780 Yeah. 00:34:07.780 --> 00:34:08.940 That sounds really cool. 00:34:08.940 --> 00:34:09.320 Yeah. 00:34:09.320 --> 00:34:11.380 It's something that I revisited lately. 00:34:11.380 --> 00:34:16.480 I had read it like 15 years ago when it was first or second edition. 00:34:16.480 --> 00:34:18.320 And it's really good book. 00:34:18.320 --> 00:34:26.720 It's, it has really good, like a one sentence summarizations of what makes software good and not only the user interface good. 00:34:26.720 --> 00:34:26.920 Yep. 00:34:26.920 --> 00:34:31.120 And this one is from September, 2014, the fourth edition. 00:34:31.120 --> 00:34:32.080 So very nice. 00:34:32.080 --> 00:34:32.520 Yeah. 00:34:32.520 --> 00:34:37.460 It's up to date to iOS seven level or something like that. 00:34:37.560 --> 00:34:42.220 So it's, it has pretty recent UI screenshots in there. 00:34:42.220 --> 00:34:42.560 Nice. 00:34:42.560 --> 00:34:56.260 And then the next one that you chose is more of a high level thinking about stuff, not necessarily software in particular, but even politics and society and governments and whatnot. 00:34:56.260 --> 00:34:58.960 It's thinking in systems, Primer. 00:34:58.960 --> 00:35:00.420 Primer, take your pick. 00:35:00.420 --> 00:35:00.820 Yeah. 00:35:00.820 --> 00:35:02.460 By Donald L.H. 00:35:02.460 --> 00:35:02.820 Meadows. 00:35:02.820 --> 00:35:06.540 It's, I think it's one of the lesser known books, but it's one of those. 00:35:06.960 --> 00:35:12.280 I think the classic that everyone thinks is, is the fifth discipline by Peter Senge. 00:35:12.800 --> 00:35:21.140 And it's, I think it's kind of a, you step an abstraction level or two up from your code. 00:35:21.140 --> 00:35:24.540 And this is kind of a stuff that you end up worrying about. 00:35:24.540 --> 00:35:30.540 It's like a very simple systems that have like an actors and they have interfaces. 00:35:30.540 --> 00:35:36.020 Then there are stuff in the system that make it work as an entity. 00:35:36.020 --> 00:35:41.080 And it doesn't need to be anything complicated. 00:35:41.080 --> 00:35:47.380 It's it can be like a bathtub that has an incoming water flow and outgoing water flow. 00:35:47.380 --> 00:35:48.820 It can be a store inventory. 00:35:48.820 --> 00:35:51.520 You have stuff stolen from you. 00:35:51.520 --> 00:35:55.380 You have the stuff that is delivered from the factory. 00:35:55.380 --> 00:35:58.880 And then there are the customers that buy from you. 00:35:58.880 --> 00:36:05.560 Or if you have fresh produce, something may go bad before it's sold and stuff like that. 00:36:05.560 --> 00:36:09.480 And you end up in those systems. 00:36:09.480 --> 00:36:19.500 You easily end up in situations that you can't really predict or fix with any kind of a simple solution. 00:36:19.500 --> 00:36:22.120 You need to consider that. 00:36:22.120 --> 00:36:24.220 What are the delays? 00:36:24.220 --> 00:36:25.560 What are the feedback loops? 00:36:25.560 --> 00:36:27.680 What is the system doing itself? 00:36:27.680 --> 00:36:33.100 It's not just that someone who is making sure that store has everything. 00:36:33.100 --> 00:36:40.800 It's not their smartness or stupidity that makes the store run out of beer on a hot summer day. 00:36:40.800 --> 00:36:50.220 It's all kinds of interfaces that convenience store has to other people and even weather that affect everything. 00:36:50.220 --> 00:36:50.600 Yeah. 00:36:50.600 --> 00:36:58.320 It's really an interesting way to say, look, sometimes you need to go to a higher level and see all the things moving together. 00:36:58.320 --> 00:37:03.960 And you said you think this can apply to software systems or human systems, right? 00:37:04.080 --> 00:37:04.980 Yeah, exactly. 00:37:04.980 --> 00:37:06.460 It's kind of a... 00:37:06.460 --> 00:37:07.460 I've done like... 00:37:07.460 --> 00:37:11.360 I've mostly been in pretty small companies lately. 00:37:11.360 --> 00:37:18.080 But I've also gone to big, big IT departments to like cloud consultation and stuff like that. 00:37:18.080 --> 00:37:23.540 There are a thousand people that everyone is smart when you take them apart. 00:37:23.920 --> 00:37:33.300 But then when they work together with all the systems that have piled up during like 20 years, it's really slow. 00:37:33.300 --> 00:37:37.740 And you sometimes end up stuff that you really can't understand. 00:37:37.740 --> 00:37:38.040 Why? 00:37:38.040 --> 00:37:44.760 If you ask anyone, they will say that, well, this is not an optimal way of doing things. 00:37:44.760 --> 00:37:53.580 When I was young, I was like a gung-ho nerd going there and telling them, hey, you need to fix it like this and this and this. 00:37:53.580 --> 00:38:04.480 But you can't really, really do that unless you go like a step up or step two and ask, is there something that I don't know? 00:38:04.480 --> 00:38:12.640 If this ticketing system makes everything last two weeks, whenever I want something, it takes two weeks. 00:38:12.640 --> 00:38:14.200 These people are not stupid. 00:38:14.200 --> 00:38:15.300 You understand that. 00:38:15.300 --> 00:38:21.160 So what is it there you can do to perhaps make things faster? 00:38:21.160 --> 00:38:26.740 Why does it take two weeks for me to get a new keyboard when right across the street there's a store that sells the keyboard I want? 00:38:26.740 --> 00:38:27.920 Yeah, exactly. 00:38:27.920 --> 00:38:28.520 Yeah. 00:38:28.520 --> 00:38:33.060 When you have big organizers and those things start adding up. 00:38:33.060 --> 00:38:33.640 Yeah, yeah. 00:38:33.640 --> 00:38:35.240 There's a nice quote from the book that says, 00:38:35.240 --> 00:38:53.640 Pretty cool. 00:38:53.640 --> 00:38:54.160 Yeah. 00:38:54.160 --> 00:38:54.200 Yeah. 00:38:54.200 --> 00:38:55.220 I like the book. 00:38:55.220 --> 00:38:58.600 And it's one of those like one evening or two evening books. 00:38:58.600 --> 00:39:06.620 And then you can, if this is something you really like, there are a lot of books about systems thinking, this area in general. 00:39:06.620 --> 00:39:15.080 And also design thinking, which is kind of a perhaps the more fashionable choice nowadays compared to systems thinking. 00:39:15.220 --> 00:39:15.340 Yeah. 00:39:15.340 --> 00:39:21.080 The next one that you chose is somewhat related to this called Thinking Fast and Slow by Daniel Kahneman. 00:39:21.080 --> 00:39:21.920 Yeah. 00:39:21.920 --> 00:39:23.700 And this is a really cool book. 00:39:23.700 --> 00:39:24.400 I haven't read it. 00:39:24.400 --> 00:39:28.280 I've sort of put it on my list of things to read, but I haven't got to it. 00:39:28.280 --> 00:39:29.300 Tell us about it. 00:39:29.300 --> 00:39:29.480 Yeah. 00:39:29.480 --> 00:39:36.860 This is the word irrational is not the right word, but it's about people can't help themselves. 00:39:36.960 --> 00:39:48.320 They have like this fast thinking system, which is evolutionary creation of like you detect the danger and you need to react immediately. 00:39:48.320 --> 00:39:49.180 I heard a noise. 00:39:49.180 --> 00:39:50.620 Is that a lion or a squirrel? 00:39:50.620 --> 00:39:51.860 Yeah, exactly. 00:39:51.860 --> 00:39:52.740 Stuff like that. 00:39:52.740 --> 00:40:04.700 There's this slow deliberate side of you that can solve mathematical puzzles and can learn other languages and make programs and stuff like that. 00:40:04.700 --> 00:40:10.880 And it's kind of pairing with the other one is also the about face and thinking in systems. 00:40:10.880 --> 00:40:21.820 It's kind of you need to understand people and people aren't as like as left brain as our industry often thinks. 00:40:21.820 --> 00:40:22.220 Yeah. 00:40:22.220 --> 00:40:23.700 You are a philosophy major, right? 00:40:23.700 --> 00:40:32.500 A lot of the people in the like Hobbes and those guys, they talked about like the rational person versus the irrational. 00:40:32.860 --> 00:40:36.840 And they often treated people as just rational thinkers. 00:40:36.840 --> 00:40:48.340 A lot of the economic policy and capitalism and John Keene's and those types of guys, they always say, okay, people are going to act rationally for themselves, but maybe not, right? 00:40:48.340 --> 00:40:49.180 No. 00:40:49.180 --> 00:40:50.140 Yeah, it's exactly. 00:40:50.140 --> 00:40:55.220 And it's not like the rational means that you follow the same rules all the time. 00:40:55.220 --> 00:41:04.940 And it's this book is full of examples that show that no, it's really easy to trick people and it takes a conscious effort. 00:41:04.940 --> 00:41:07.480 And sometimes it doesn't even help you. 00:41:07.480 --> 00:41:16.040 It's just like you can't help yourself that if someone says that this is worth $1,000 and you know that it's not true. 00:41:16.120 --> 00:41:17.320 That's called a price anchor. 00:41:17.320 --> 00:41:20.880 You know that it's not even close to that. 00:41:20.880 --> 00:41:31.160 And even knowing that and even knowing the effect, you still have the $1,000 there affecting your evaluation of the price. 00:41:31.160 --> 00:41:31.660 Right. 00:41:31.660 --> 00:41:35.740 Like here, this thing is normally sold for $1,000, but it's on sale for $599. 00:41:35.740 --> 00:41:36.460 Yeah. 00:41:36.580 --> 00:41:41.180 You can trick people to make them think it's a good deal versus, well, it's just for $599. 00:41:41.180 --> 00:41:42.480 They might go, that's not a good deal. 00:41:42.480 --> 00:41:43.360 It's way too expensive. 00:41:43.360 --> 00:41:43.900 Yeah. 00:41:43.900 --> 00:41:48.700 And when you see the price $599, you start thinking through your needs and your values. 00:41:48.700 --> 00:41:57.020 But when you contrast it with the $1,000 going rate and it's like, oh, well, it's practically free. 00:41:57.020 --> 00:41:57.840 Can I buy it? 00:41:57.840 --> 00:41:58.480 Yeah. 00:41:58.480 --> 00:42:04.920 The craziest example that I can think of is Black Friday, the crazy shopping experience after Thanksgiving in the U.S. 00:42:04.920 --> 00:42:05.620 Yeah. 00:42:05.620 --> 00:42:05.760 Yeah. 00:42:05.760 --> 00:42:08.520 So bring this back to software. 00:42:08.520 --> 00:42:14.460 Like what lessons can we take from this for helping like users, helping designer software, things like that? 00:42:14.460 --> 00:42:23.260 All these books like build up like a toolbox of things that are outside the like immediate day-to-day programming. 00:42:23.260 --> 00:42:26.180 And it's really good to understand people. 00:42:26.180 --> 00:42:31.700 Even if you are writing like backend software, you are still writing software for people. 00:42:31.700 --> 00:42:35.120 And people are not, well, you are serving them. 00:42:35.300 --> 00:42:36.640 You are in a service business. 00:42:36.640 --> 00:42:46.520 You need to be the like really good waitress that serves them with the stuff that they need, with the stuff that solves their problem. 00:42:46.520 --> 00:42:47.000 Right. 00:42:47.000 --> 00:42:48.980 Even if they don't necessarily know they need it, maybe. 00:42:48.980 --> 00:42:49.500 Yeah. 00:42:49.500 --> 00:42:55.080 That's like a really good software stuff that solves a problem that user is not even aware of. 00:42:56.360 --> 00:42:59.080 This portion of Talk Python is brought to you by us. 00:42:59.080 --> 00:43:04.980 As many of you know, I have a growing set of courses to help you go from Python beginner to novice to Python expert. 00:43:04.980 --> 00:43:07.020 And there are many more courses in the works. 00:43:07.020 --> 00:43:11.280 So please consider Talk Python training for you and your team's training needs. 00:43:11.280 --> 00:43:17.700 If you're just getting started, I've built a course to teach you Python the way professional developers learn by building applications. 00:43:18.220 --> 00:43:22.780 Check out my Python jumpstart by building 10 apps at talkpython.fm/course. 00:43:22.780 --> 00:43:25.900 Are you looking to start adding services to your app? 00:43:25.900 --> 00:43:29.060 Try my brand new consuming HTTP services in Python. 00:43:29.440 --> 00:43:34.520 You'll learn to work with RESTful HTTP services as well as SOAP, JSON, and XML data formats. 00:43:34.520 --> 00:43:36.360 Do you want to launch an online business? 00:43:36.360 --> 00:43:40.560 Well, Matt Makai and I built an entrepreneur's playbook with Python for Entrepreneurs. 00:43:40.560 --> 00:43:45.800 This 16-hour course will teach you everything you need to launch your web-based business with Python. 00:43:45.800 --> 00:43:49.340 And finally, there's a couple of new course announcements coming really soon. 00:43:49.340 --> 00:43:54.800 So if you don't already have an account, be sure to create one at training.talkpython.fm to get notified. 00:43:54.920 --> 00:43:58.580 And for all of you who have bought my courses, thank you so much. 00:43:58.580 --> 00:44:00.520 It really, really helps support the show. 00:44:00.520 --> 00:44:02.740 Those were the 10 books. 00:44:02.740 --> 00:44:05.180 And then you gave us a plus one, right? 00:44:05.180 --> 00:44:11.300 Yeah, this plus one is like, I think everyone should have a fun book outside your... 00:44:11.300 --> 00:44:12.400 Outside your specialty, right? 00:44:12.400 --> 00:44:15.240 Yeah, outside your specialty, you should have one book. 00:44:15.240 --> 00:44:16.580 One fun book. 00:44:16.580 --> 00:44:19.120 And mine is Learn Your Haskell for Great Good. 00:44:19.120 --> 00:44:22.140 It's like, I'm fascinated by Haskell. 00:44:22.140 --> 00:44:24.480 I really don't understand anything about it. 00:44:24.560 --> 00:44:34.800 But it's this book, I occasionally pick up and read a chapter there, look at more dads and look at higher order functions, which is also... 00:44:34.800 --> 00:44:38.140 That is very applicable to any kind of a Python application. 00:44:38.140 --> 00:44:40.300 But this is kind of a... 00:44:40.300 --> 00:44:45.960 You probably have your own fun book that you go every now and back to every now and then. 00:44:45.960 --> 00:44:47.160 And this is mine. 00:44:47.160 --> 00:44:48.240 Yeah, this is really cool. 00:44:48.240 --> 00:44:50.300 It's illustrated. 00:44:50.300 --> 00:44:53.460 It's supposed to be funny, but it also is supposed to teach you Haskell. 00:44:53.460 --> 00:45:00.600 And I think from a programming perspective, there's real value in knowing a language that's really different from what you do day to day. 00:45:00.600 --> 00:45:01.040 Yeah. 00:45:01.040 --> 00:45:01.700 And it's... 00:45:01.700 --> 00:45:01.780 Right. 00:45:01.780 --> 00:45:06.640 I believe that you also need to like know pretty theoretical stuff. 00:45:07.040 --> 00:45:08.280 And I... 00:45:08.280 --> 00:45:13.480 Well, somebody might disagree with me, but I think Haskell is mostly a theoretical thing. 00:45:13.480 --> 00:45:20.980 It has a nice type system and it has nice abstractions on side effects and stuff like that. 00:45:20.980 --> 00:45:21.360 But it's... 00:45:22.360 --> 00:45:25.600 I probably wouldn't write production code with it. 00:45:25.600 --> 00:45:25.960 Sure. 00:45:25.960 --> 00:45:31.920 The closest thing that I learned and studied and know a little bit close to this is Lisp or Scheme. 00:45:31.920 --> 00:45:36.340 And I would not go write anything in that at all. 00:45:36.340 --> 00:45:40.980 There's like four languages I would choose before I would pick that language for a thing at least. 00:45:41.680 --> 00:45:51.540 But it really opened my eyes to, oh, the way we can write programs and think through solving problems algorithmically is way broader than I thought. 00:45:51.540 --> 00:45:51.900 Yeah. 00:45:51.900 --> 00:45:56.480 One of those Scheme, Lisp, or Haskell are really good at that. 00:45:56.480 --> 00:46:00.800 My programming languages 101 was an AFL. 00:46:01.040 --> 00:46:05.180 I don't know if you remember that language back from the early 90s. 00:46:05.180 --> 00:46:05.820 Which language? 00:46:05.820 --> 00:46:06.580 AFL. 00:46:06.580 --> 00:46:06.820 Yeah. 00:46:06.820 --> 00:46:09.240 I do remember the name, but I don't know anything about it. 00:46:09.240 --> 00:46:09.440 Yeah. 00:46:09.440 --> 00:46:14.200 It's like object-oriented programming due to the max. 00:46:14.200 --> 00:46:19.940 It's like all sorts of things that you have multiple inheritance there. 00:46:19.940 --> 00:46:25.000 One of the really cool things about that language was like a design by contract. 00:46:25.140 --> 00:46:37.980 It had like a language level support for assertions in classes like preconditions for functions and postconditions for functions and invariants for classes and all that kind of stuff. 00:46:37.980 --> 00:46:47.580 It's probably something you wouldn't use today, but it gives you a nice perspective on how other people think of the problems that we face as a programmer. 00:46:47.580 --> 00:46:48.120 Yeah. 00:46:48.120 --> 00:46:49.240 Absolutely. 00:46:49.240 --> 00:46:49.820 All right. 00:46:49.820 --> 00:46:51.840 So, learn you a Haskell for great good. 00:46:51.840 --> 00:46:53.200 Very nice. 00:46:53.200 --> 00:46:53.440 Okay. 00:46:53.440 --> 00:46:56.320 So, this has been a very interesting list. 00:46:56.320 --> 00:47:00.240 And everyone has their own list of things that they really like. 00:47:00.240 --> 00:47:04.220 And certainly, many of these books are on my list as well. 00:47:04.220 --> 00:47:10.660 I would say, if people are listening, check out the episode page and go to the bottom. 00:47:10.660 --> 00:47:11.980 There's a discussion section. 00:47:11.980 --> 00:47:18.820 Put your favorite book or two on there, and we can have a really nice list if everybody comes and contributes something that's not there yet. 00:47:18.820 --> 00:47:20.400 I think that'll be lots of fun. 00:47:20.400 --> 00:47:20.720 Yeah. 00:47:20.720 --> 00:47:21.200 Yeah, nice. 00:47:21.260 --> 00:47:21.420 Okay. 00:47:21.420 --> 00:47:24.640 So, before we get out of here, let me ask you the two questions. 00:47:24.640 --> 00:47:27.680 If you're going to write some Python code, what editor do you run? 00:47:27.680 --> 00:47:30.120 I used to be a solid Vim guy. 00:47:31.140 --> 00:47:39.880 When I'm not coding, I used to be like, this can handle a broken terminal after a nuclear war. 00:47:39.880 --> 00:47:42.000 I can really do anything with this. 00:47:42.000 --> 00:47:52.000 And then I realized maybe this is not a reason to choose a programming environment in 2010. 00:47:52.400 --> 00:47:54.780 And then I started learning things. 00:47:54.780 --> 00:47:57.320 When doing Java, I really enjoyed IntelliJ. 00:47:57.320 --> 00:48:03.280 I've also used IntelliJ stuff like resharper on .net. 00:48:03.280 --> 00:48:07.820 And it was quite easy to jump to PyCharm. 00:48:07.820 --> 00:48:11.200 And I'm now a PyCharm convert. 00:48:11.200 --> 00:48:13.200 I really use it. 00:48:13.200 --> 00:48:17.640 That's the only thing that I open up when doing Python. 00:48:17.640 --> 00:48:20.360 I've tried Visual Studio Code. 00:48:20.360 --> 00:48:22.200 I really liked it. 00:48:22.200 --> 00:48:25.980 But I already had familiarized myself with PyCharm. 00:48:25.980 --> 00:48:29.880 Cost of change was not justified, I would say. 00:48:29.880 --> 00:48:30.180 Yeah. 00:48:30.180 --> 00:48:30.740 I hear you. 00:48:30.740 --> 00:48:31.360 All right. 00:48:31.360 --> 00:48:31.940 Go PyCharm. 00:48:31.940 --> 00:48:33.440 That's my favorite as well. 00:48:33.840 --> 00:48:35.660 And notable PyPI package. 00:48:35.660 --> 00:48:36.200 Yeah. 00:48:36.200 --> 00:48:43.460 Well, I had to, in my mind, it's like the old one, the natural language toolkit for Python, 00:48:43.460 --> 00:48:49.680 which solves many problems, which solved many problems like 20 years ago that people are 00:48:49.680 --> 00:48:56.800 struggling today with any kind of text processing and splitting text to sentences or paragraphs 00:48:56.800 --> 00:48:59.720 and stuff like that and any multiple languages. 00:49:00.040 --> 00:49:04.460 But the one that I picked today is called Newspaper. 00:49:04.460 --> 00:49:08.080 I'm so fascinated with this one right now. 00:49:08.080 --> 00:49:13.600 It's just like I was writing one of those web scraper things a couple of months back. 00:49:13.600 --> 00:49:19.040 And then I just, there must be something that you could do with newspapers directly. 00:49:19.540 --> 00:49:29.280 And it's like a full-blown system for picking up newspaper, like articles from a newspaper page. 00:49:29.280 --> 00:49:36.220 And then when you have the article, pick up its title, its main text, pictures, and stuff like that. 00:49:36.220 --> 00:49:38.680 I'm flipping through the GitHub page here. 00:49:38.680 --> 00:49:39.800 This thing is incredible. 00:49:39.800 --> 00:49:42.460 There's some things I'm like, oh, that's pretty neat. 00:49:42.460 --> 00:49:43.520 I could probably use that someday. 00:49:43.520 --> 00:49:45.940 This just makes me want to try it, like straight up. 00:49:45.940 --> 00:49:50.960 Yeah, it's like one of those Python comes with that batteries included thingy. 00:49:50.960 --> 00:49:53.560 This just made me happy when I found this. 00:49:53.560 --> 00:50:01.600 This is exactly, it's like the batteries included and you don't need to write your own web scraper again for the thousandth time. 00:50:01.600 --> 00:50:02.560 Yeah, yeah. 00:50:02.560 --> 00:50:03.780 So let me give people a sense of this. 00:50:04.000 --> 00:50:06.500 So I'll put the link, of course, in the show notes. 00:50:06.500 --> 00:50:10.300 But you go and you install it and you say, from newspaper import article. 00:50:10.300 --> 00:50:12.880 And then you say, create an instance of the article. 00:50:12.880 --> 00:50:16.440 And you give it a URL, like some news article, literally. 00:50:16.440 --> 00:50:17.820 Give it like the page of that. 00:50:17.820 --> 00:50:18.340 Say download. 00:50:18.340 --> 00:50:19.260 You can get the HTML. 00:50:19.260 --> 00:50:20.640 You can parse it. 00:50:20.640 --> 00:50:22.420 And it has a list of authors. 00:50:22.420 --> 00:50:23.300 Publish date. 00:50:23.300 --> 00:50:24.200 It has the date. 00:50:24.200 --> 00:50:26.320 You can say, what's the top image? 00:50:26.800 --> 00:50:29.160 You can run an NLP processing on it. 00:50:29.160 --> 00:50:32.300 It has the keywords and a summary and all sorts of craziness. 00:50:32.300 --> 00:50:34.540 It's really, really fascinating. 00:50:34.540 --> 00:50:37.440 It doesn't work as well on all web pages. 00:50:37.440 --> 00:50:41.540 But it's really, really impressive that you even have something like this. 00:50:41.540 --> 00:50:41.760 Cool. 00:50:41.760 --> 00:50:42.140 All right. 00:50:42.140 --> 00:50:43.360 That's a really great recommendation. 00:50:43.360 --> 00:50:44.360 I've never heard of it. 00:50:44.360 --> 00:50:44.920 So thank you. 00:50:44.920 --> 00:50:45.260 Yeah. 00:50:45.500 --> 00:50:45.920 All right, Timo. 00:50:45.920 --> 00:50:49.760 It's really fun to talk about these books and reminisce on some old ones and learn about 00:50:49.760 --> 00:50:50.900 some new ones for me. 00:50:50.900 --> 00:50:51.980 So thanks for being on the show. 00:50:51.980 --> 00:50:52.320 Yeah. 00:50:52.320 --> 00:50:52.800 Thank you. 00:50:52.800 --> 00:50:53.280 You bet. 00:50:53.280 --> 00:50:53.500 Bye. 00:50:53.500 --> 00:50:57.640 This has been another episode of Talk Python To Me. 00:50:57.640 --> 00:51:00.000 Our guest has been Timo Kola. 00:51:00.000 --> 00:51:05.120 And this episode has been brought to you by Rollbar and us right here at Talk Python Training. 00:51:05.120 --> 00:51:07.900 Rollbar takes the pain out of errors. 00:51:07.900 --> 00:51:12.980 They give you the context and insight you need to quickly locate and fix errors that might have 00:51:12.980 --> 00:51:15.620 gone unnoticed until your users complain, of course. 00:51:15.620 --> 00:51:20.420 As Talk Python To Me listeners, track a ridiculous number of errors for free at 00:51:20.420 --> 00:51:22.760 rollbar.com slash Talk Python To Me. 00:51:22.760 --> 00:51:25.020 Are you or a colleague trying to learn Python? 00:51:25.020 --> 00:51:29.680 Have you tried books and videos that just left you bored by covering topics point by point? 00:51:29.680 --> 00:51:33.720 Well, check out my online course, Python Jumpstart by Building 10 Apps at 00:51:33.720 --> 00:51:38.320 Talk Python.fm slash course to experience a more engaging way to learn Python. 00:51:38.320 --> 00:51:41.300 And if you're looking for something a little more advanced, 00:51:41.460 --> 00:51:45.640 try my WritePythonic code course at talkpython.fm/Pythonic. 00:51:45.640 --> 00:51:48.360 Be sure to subscribe to the show. 00:51:48.360 --> 00:51:50.560 Open your favorite podcatcher and search for Python. 00:51:50.560 --> 00:51:51.800 We should be right at the top. 00:51:51.800 --> 00:51:55.140 You can also find the iTunes feed at /itunes, 00:51:55.140 --> 00:51:57.200 Google Play feed at /play, 00:51:57.200 --> 00:52:01.100 and direct RSS feed at /rss on talkpython.fm. 00:52:01.100 --> 00:52:03.000 This is your host, Michael Kennedy. 00:52:03.000 --> 00:52:04.360 Thanks so much for listening. 00:52:04.360 --> 00:52:05.420 I really appreciate it. 00:52:05.780 --> 00:52:07.380 Now get out there and write some Python code. 00:52:07.380 --> 00:52:07.380 Now get out there and write some Python code. 00:52:07.380 --> 00:52:07.940 Thank you. 00:52:07.940 --> 00:52:08.320 Thank you. 00:52:08.320 --> 00:52:08.820 Thank you. 00:52:08.820 --> 00:52:08.920 Thank you. 00:52:08.920 --> 00:52:09.320 Thank you. 00:52:09.320 --> 00:52:10.040 Thank you. 00:52:10.040 --> 00:52:10.060 Thank you. 00:52:10.060 --> 00:52:10.420 Thank you. 00:52:10.420 --> 00:52:11.420 Thank you. 00:52:11.420 --> 00:52:12.420 Thank you. 00:52:12.420 --> 00:52:13.420 Thank you. 00:52:13.420 --> 00:52:13.420 Thank you. 00:52:13.420 --> 00:52:13.420 Thank you. 00:52:13.420 --> 00:52:13.420 Thank you. 00:52:13.420 --> 00:52:14.420 Thank you. 00:52:14.420 --> 00:52:15.420 Thank you. 00:52:15.420 --> 00:52:16.420 Thank you. 00:52:16.420 --> 00:52:17.420 Thank you. 00:52:17.420 --> 00:52:18.420 Thank you. 00:52:18.420 --> 00:52:19.420 Thank you. 00:52:19.420 --> 00:52:20.420 Thank you. 00:52:20.420 --> 00:52:21.420 Thank you. 00:52:21.420 --> 00:52:22.420 Thank you. 00:52:22.420 --> 00:52:23.420 Thank you. 00:52:23.420 --> 00:52:24.420 Thank you. 00:52:24.420 --> 00:52:54.400 Thank you.