Python Tricks - A Buffet of Awesome Python Features (Dan Bader)（Z-Library）
Author: Dan Bader
Python
"I don't even feel like I've scratched the surface of what I can do with Python" With Python Tricks: The Book you'll discover Python's best practices and the power of beautiful & Pythonic code with simple examples and a step-by-step narrative. You'll get one step closer to mastering Python, so you can write beautiful and idiomatic code that comes to you naturally. Learning the ins and outs of Python is difficult-and with this book you'll be able to focus on the practical skills that really matter. Discover the "hidden gold" in Python's standard library and start writing clean and Pythonic code today. Who Should Read This Book: If you're wondering which lesser known parts in Python you should know about, you'll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers' minds in your next code review. If you've got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. If you've worked with other programming languages and you want to get up to speed with Python, you'll pick up the idioms and practical tips you need to become a confident and effective Pythonista. If you want to make Python your own and learn how to write clean and Pythonic code, you'll discover best practices and little-known tricks to round out your knowledge. What Python Developers Say About The Book: "I kept thinking that I wished I had access to a book like this when I started learning Python many years ago." - Mariatta Wijaya, Python Core Developer "This book makes you write better Python code!" - Bob Belderbos, Software Developer at Oracle "Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty." - Ben Felder, Pythonista "It's like having a seasoned tutor explaining, well, tricks!" - Daniel Meyer, Sr. Deskt
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Python Tricks: The Book Dan Bader
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For online information and ordering of this and other books by Dan Bader, please visit dbader.org. For more information, please contact Dan Bader at mail@dbader.org. Copyright © Dan Bader (dbader.org), 2016–2017 ISBN: 9781775093305 (paperback) ISBN: 9781775093312 (electronic) Cover design by Anja Pircher Design (anjapircher.com) “Python” and the Python logos are trademarks or registered trade- marks of the Python Software Foundation, used by Dan Bader with permission from the Foundation. Thank you for downloading this ebook. This ebook is licensed for your personal enjoyment only. This ebookmaynot be re-sold or given away to other people. If you would like to share this book with another per- son, please purchase an additional copy for each recipient. If you’re reading this book and did not purchase it, or it was not purchased for your use only, then please return to dbader.org/pytricks-book and purchase your own copy. Thank you for respecting the hard work be- hind this book. Updated 2017-10-27 I would like to thankMichaelHowitz, Johnathan Willitts, Julian Orbach, Johnny Giorgis, Bob White, Daniel Meyer, Michael Stueben, Smital Desai, Andreas Kreisig, David Perkins, Jay Prakash Singh, and Ben Felder for their excellent feedback.
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What Pythonistas Say About Python Tricks: The Book ”I love love love the book. It’s like having a seasoned tutor explaining, well, tricks! I’m learning Python on the job and I’m coming from pow- ershell, which I learned on the job—so lots of new, great stuff. When- ever I get stuck in Python (usuallywith flask blueprints or I feel likemy code could be more Pythonic) I post questions in our internal Python chat room. I’m often amazed at some of the answers coworkers giveme. Dict com- prehensions, lambdas, and generators often pepper their feedback. I am always impressed and yet flabbergasted at how powerful Python is when you know these tricks and can implement them correctly. Your bookwas exactlywhat Iwanted to help getme fromabewildered powershell scripter to someonewho knows how andwhen to use these Pythonic ‘tricks’ everyone has been talking about. As someone who doesn’t have my degree in CS it’s nice to have the text to explain things that others might have learned when they were clas- sically educated. I am really enjoying the book and am subscribed to the emails as well, which is how I found out about the book.” —Daniel Meyer, Sr. Desktop Administrator at Tesla Inc.
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”I first heard about your book from a co-worker who wanted to trick me with your example of how dictionaries are built. I was almost 100% sure about the reason why the end product was a much smaller/simpler dictionary but I must confess that I did not expect the outcome :) He showed me the book via video conferencing and I sort of skimmed through it as he flipped the pages for me, and I was immediately curi- ous to read more. That same afternoon I purchasedmy own copy and proceeded to read your explanation for the way dictionaries are created in Python and later that day, as Imet a different co-worker for coffee, I used the same trick on him :) He then sprung a different question on the same principle, and be- cause of the way you explained things in your book, I was able tonot* guess the result but correctly answerwhat the outcomewould be. That means that you did a great job at explaining things :)* I am not new in Python and some of the concepts in some of the chap- ters are not new to me, but I must say that I do get something out of every chapter so far, so kudos for writing a very nice book and for do- ing a fantastic job at explaining concepts behind the tricks! I’m very much looking forward to the updates and Iwill certainly letmy friends and co-workers know about your book.” —OgMaciel, Python Developer at Red Hat
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”I really enjoyed reading Dan’s book. He explains important Python aspects with clear examples (using two twin cats to explain ‘is‘ vs ‘==‘ for example). It is not just code samples, it discusses relevant implementation details comprehensibly. What really matters though is that this book makes you write better Python code! The book is actually responsible for recent new good Python habits I picked up, for example: using custom exceptions and ABC’s (I found Dan’s blog searching for abstract classes.) These new learnings alone are worth the price.” — Bob Belderbos, Engineer at Oracle & Co-Founder of PyBites
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Contents Contents 6 Foreword 9 1 Introduction 11 1.1 What’s a Python Trick? . . . . . . . . . . . . . . . . 11 1.2 What This Book Will Do for You . . . . . . . . . . . 13 1.3 How to Read This Book . . . . . . . . . . . . . . . . 14 2 Patterns for Cleaner Python 15 2.1 Covering Your A** With Assertions . . . . . . . . . . 16 2.2 Complacent Comma Placement . . . . . . . . . . . . 25 2.3 Context Managers and the with Statement . . . . . . 29 2.4 Underscores, Dunders, and More . . . . . . . . . . . 36 2.5 A Shocking Truth About String Formatting . . . . . . 48 2.6 “The Zen of Python” Easter Egg . . . . . . . . . . . . 56 3 Effective Functions 57 3.1 Python’s Functions Are First-Class . . . . . . . . . . 58 3.2 Lambdas Are Single-Expression Functions . . . . . . 68 3.3 The Power of Decorators . . . . . . . . . . . . . . . 73 3.4 Fun With *args and **kwargs . . . . . . . . . . . . 86 3.5 Function Argument Unpacking . . . . . . . . . . . . 91 3.6 Nothing to Return Here . . . . . . . . . . . . . . . . 94 6
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Contents 4 Classes & OOP 97 4.1 Object Comparisons: “is” vs “==” . . . . . . . . . . . 98 4.2 String Conversion (Every Class Needs a __repr__) . 101 4.3 Defining Your Own Exception Classes . . . . . . . . 111 4.4 Cloning Objects for Fun and Profit . . . . . . . . . . 116 4.5 Abstract Base Classes Keep Inheritance in Check . . . 124 4.6 What Namedtuples Are Good For . . . . . . . . . . . 128 4.7 Class vs Instance Variable Pitfalls . . . . . . . . . . . 136 4.8 Instance, Class, and Static Methods Demystified . . . 143 5 Common Data Structures in Python 153 5.1 Dictionaries, Maps, and Hashtables . . . . . . . . . 156 5.2 Array Data Structures . . . . . . . . . . . . . . . . . 163 5.3 Records, Structs, and Data Transfer Objects . . . . . 173 5.4 Sets and Multisets . . . . . . . . . . . . . . . . . . . 185 5.5 Stacks (LIFOs) . . . . . . . . . . . . . . . . . . . . 189 5.6 Queues (FIFOs) . . . . . . . . . . . . . . . . . . . . 195 5.7 Priority Queues . . . . . . . . . . . . . . . . . . . . 201 6 Looping & Iteration 205 6.1 Writing Pythonic Loops . . . . . . . . . . . . . . . . 206 6.2 Comprehending Comprehensions . . . . . . . . . . 210 6.3 List Slicing Tricks and the Sushi Operator . . . . . . 214 6.4 Beautiful Iterators . . . . . . . . . . . . . . . . . . . 218 6.5 Generators Are Simplified Iterators . . . . . . . . . 231 6.6 Generator Expressions . . . . . . . . . . . . . . . . 239 6.7 Iterator Chains . . . . . . . . . . . . . . . . . . . . 246 7 Dictionary Tricks 250 7.1 Dictionary Default Values . . . . . . . . . . . . . . . 251 7.2 Sorting Dictionaries for Fun and Profit . . . . . . . . 255 7.3 Emulating Switch/Case Statements With Dicts . . . . 259 7.4 The Craziest Dict Expression in the West . . . . . . . 264 7.5 So Many Ways to Merge Dictionaries . . . . . . . . . 271 7.6 Dictionary Pretty-Printing . . . . . . . . . . . . . . 274 7
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Contents 8 Pythonic Productivity Techniques 277 8.1 Exploring Python Modules and Objects . . . . . . . . 278 8.2 Isolating Project Dependencies With Virtualenv . . . 282 8.3 Peeking Behind the Bytecode Curtain . . . . . . . . . 288 9 Closing Thoughts 293 9.1 Free Weekly Tips for Python Developers . . . . . . . 295 9.2 PythonistaCafe: A Community for Python Developers 296 8
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Foreword It’s been almost ten years since I first got acquainted with Python as a programming language. When I first learned Python many years ago, it was with a little reluctance. I had been programming in a different language before, and all of the sudden at work, I was assigned to a different team where everyone used Python. That was the beginning of my own Python journey. When I was first introduced to Python, I was told that it was going to be easy, that I should be able to pick it up quickly. When I asked my colleagues for resources for learning Python, all they gave me was a link to Python’s official documentation. Reading the documentation was confusing at first, and it really took me a while before I even felt comfortable navigating through it. Often I found myself needing to look for answers in StackOverflow. Coming from a different programming language, I wasn’t looking for just any resource for learning how to program or what classes and objects are. I was looking for specific resources that would teach me the features of Python, what sets it apart, and how writing in Python is different than writing code in another language. It really has takenmemany years to fully appreciate this language. As I read Dan’s book, I kept thinking that I wished I had access to a book like this when I started learning Python many years ago. For example, one of the many unique Python features that surprised me at first were list comprehensions. As Dan mentions in the book, 9
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Contents a tell of someone who just came to Python from a different language is the way they use for-loops. I recall one of the earliest code review comments I got when I started programming in Python was, “Why not use list comprehension here?” Dan explains this concept clearly in section 6, starting by showing how to loop the Pythonic way and building it all the way up to iterators and generators. In chapter 2.5, Dan discusses the different ways to do string format- ting in Python. String formatting is one of those things that defy the Zen of Python, that there should only be one obvious way to do things. Dan shows us the different ways, including my favorite new addition to the language, the f-strings, and he also explains the pros and cons of each method. The Pythonic Productivity Techniques section is another great re- source. It covers aspects beyond the Python programming language, and also includes tips on how to debug your programs, how tomanage the dependencies, and gives you a peek inside Python bytecode. It truly is an honor and my pleasure to introduce this book, Python Tricks, by my friend, Dan Bader. By contributing to Python as a CPython core developer, I get con- nected to many members of the community. In my journey, I found mentors, allies, and made many new friends. They remind me that Python is not just about the code, Python is a community. Mastering Python programming isn’t just about grasping the theoreti- cal aspects of the language. It’s just asmuch about understanding and adopting the conventions and best practices used by its community. Dan’s book will help you on this journey. I’m convinced that you’ll be more confident when writing Python programs after reading it. —Mariatta Wijaya, Python Core Developer (mariatta.ca) 10
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Chapter 1 Introduction 1.1 What’s a Python Trick? Python Trick: A short Python code snippet meant as a teaching tool. A Python Trick either teaches an aspect of Python with a simple illustration, or it serves as a moti- vating example, enabling you to dig deeper and develop an intuitive understanding. Python Tricks started out as a short series of code screenshots that I shared on Twitter for a week. To my surprise, they got rave responses and were shared and retweeted for days on end. More and more developers started asking me for a way to “get the whole series.” Actually, I only had a few of these tricks lined up, span- ning a variety of Python-related topics. There wasn’t a master plan behind them. They were just a fun little Twitter experiment. But from these inquiries I got the sense that my short-and-sweet code exampleswould beworth exploring as a teaching tool. Eventually I set out to create a few more Python Tricks and shared them in an email series. Within a few days, several hundred Python developers had signed up and I was just blown away by that response. 11
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1.1. What’s a Python Trick? Over the following days and weeks, a steady stream of Python devel- opers reached out to me. They thanked me for making a part of the language they were struggling to understand click for them. Hearing this feedback felt awesome. I thought these Python Tricks were just code screenshots, but so many developers were getting a lot of value out of them. That’s when I decided to double down on my Python Tricks experi- ment and expanded it into a series of around 30 emails. Each of these was still just a a headline and a code screenshot, and I soon realized the limits of that format. Around this time, a blind Python developer emailed me, disappointed to find that these Python Tricks were deliv- ered as images he couldn’t read with his screen reader. Clearly, I needed to invest more time into this project to make it more appealing and more accessible to a wider audience. So, I sat down to re-create the whole series of Python Tricks emails in plain text and with proper HTML-based syntax highlighting. That new iteration of Python Tricks chugged along nicely for a while. Based on the responses I got, developers seemed happy they could finally copy and paste the code samples in order to play around with them. As more and more developers signed up for the email series, I started noticing a pattern in the replies and questions I received. Some Tricks worked well as motivational examples by themselves. However, for the more complex ones there was no narrator to guide readers or to give them additional resources to develop a deeper understanding. Let’s just say this was another big area of improvement. My mission statement for dbader.org is to help Python developers become more awesome—and this was clearly an opportunity to get closer to that goal. I decided to take the best and most valuable Python Tricks from the email course, and I started writing a new kind of Python book around them: 12
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1.2. What This Book Will Do for You • A book that teaches the coolest aspects of the language with short and easy-to-digest examples. • A book that works like a buffet of awesome Python features (yum!) and keeps motivation levels high. • A book that takes you by the hand to guide you and help you deepen your understanding of Python. This book is really a labor of love for me and also a huge experiment. I hope you’ll enjoy reading it and learn something about Python in the process! — Dan Bader 1.2 What This BookWill Do for You My goal for this book is to make you a better—more effective, more knowledgeable, more practical—Python developer. You might be wondering, How will reading this book help me achieve all that? Python Tricks is not a step-by-step Python tutorial. It is not an entry-level Python course. If you’re in the beginning stages of learn- ing Python, the book alone won’t transform you into a professional Python developer. Reading it will still be beneficial to you, but you need to make sure you’re working with some other resources to build up your foundational Python skills. You’ll get the most out of this book if you already have some knowl- edge of Python, and youwant to get to the next level. It will work great for you if you’ve been coding Python for a while and you’re ready to go deeper, to round out your knowledge, and to make your code more Pythonic. Reading Python Tricks will also be great for you if you already have experience with other programming languages and you’re looking to get up to speed with Python. You’ll discover a ton of practical tips and design patterns that’ll make you a more effective and skilled Python coder. 13
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1.3. How to Read This Book 1.3 How to Read This Book The best way to read Python Tricks: The Book is to treat it like a buffet of awesome Python features. Each Python Trick in the book is self- contained, so it’s completely okay to jump straight to the ones that look the most interesting. In fact, I would encourage you to do just that. Of course, you can also read through all the Python Tricks in the order they’re laid out in the book. That way you won’t miss any of them, and you’ll know you’ve seen it all when you arrive at the final page. Some of these tricks will be easy to understand right away, and you’ll have no trouble incorporating them into your day to day work just by reading the chapter. Other tricks might require a bit more time to crack. If you’re having trouble making a particular trick work in your own programs, it helps to play through each of the code examples in a Python interpreter session. If that doesn’t make things click, then please feel free to reach out to me, so I can help you out and improve the explanation in this book. In the long run, that benefits not just you but all Pythonistas reading this book. 14
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Chapter 2 Patterns for Cleaner Python 15
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2.1. Covering Your A** With Assertions 2.1 Covering Your A** With Assertions Sometimes a genuinely helpful language feature gets less attention than it deserves. For some reason, this is what happened to Python’s built-in assert statement. In this chapter I’m going to give you an introduction to using asser- tions in Python. You’ll learn how to use them to help automatically detect errors in your Python programs. This will make your programs more reliable and easier to debug. At this point, you might be wondering “What are assertions and what are they good for?” Let’s get you some answers for that. At its core, Python’s assert statement is a debugging aid that tests a condition. If the assert condition is true, nothing happens, and your program continues to execute as normal. But if the condition evalu- ates to false, an AssertionError exception is raised with an optional error message. Assert in Python — An Example Here’s a simple example so you can see where assertions might come in handy. I tried to give this some semblance of a real-world problem you might actually encounter in one of your programs. Suppose you were building an online store with Python. You’re work- ing to add a discount coupon functionality to the system, and eventu- ally you write the following apply_discount function: def apply_discount(product, discount): price = int(product['price'] * (1.0 - discount)) assert 0 <= price <= product['price'] return price Notice the assert statement in there? It will guarantee that, no mat- terwhat, discounted prices calculated by this function cannot be lower 16
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2.1. Covering Your A** With Assertions than $0 and they cannot be higher than the original price of the prod- uct. Let’s make sure this actually works as intended if we call this function to apply a valid discount. In this example, products for our store will be represented as plain dictionaries. This is probably not what you’d do for a real application, but it’ll work nicely for demonstrating asser- tions. Let’s create an example product—a pair of nice shoes at a price of $149.00: >>> shoes = {'name': 'Fancy Shoes', 'price': 14900} By the way, did you notice how I avoided currency rounding issues by using an integer to represent the price amount in cents? That’s generally a good idea… But I digress. Now, if we apply a 25% discount to these shoes, we would expect to arrive at a sale price of $111.75: >>> apply_discount(shoes, 0.25) 11175 Alright, this worked nicely. Now, let’s try to apply some invalid dis- counts. For example, a 200% “discount” that would lead to us giving money to the customer: >>> apply_discount(shoes, 2.0) Traceback (most recent call last): File "<input>", line 1, in <module> apply_discount(prod, 2.0) File "<input>", line 4, in apply_discount assert 0 <= price <= product['price'] AssertionError As you can see, when we try to apply this invalid discount, our program halts with an AssertionError. This happens because a discount of 200% violated the assertion condition we placed in the apply_discount function. 17
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2.1. Covering Your A** With Assertions You can also see how the exception stacktrace points out the exact line of code containing the failed assertion. If you (or another developer on your team) ever encounter one of these errors while testing the online store, it will be easy to find out what happened just by looking at the exception traceback. This speeds up debugging efforts considerably, and it will make your programs more maintainable in the long-run. And that, my friend, is the power of assertions. Why Not Just Use a Regular Exception? Now, you’re probably wondering why I didn’t just use an if-statement and an exception in the previous example… You see, the proper use of assertions is to inform developers about unrecoverable errors in a program. Assertions are not intended to signal expected error conditions, like a File-Not-Found error, where a user can take corrective actions or just try again. Assertions aremeant to be internal self-checks for your program. They work by declaring some conditions as impossible in your code. If one of these conditions doesn’t hold, that means there’s a bug in the pro- gram. If your program is bug-free, these conditions will never occur. But if they do occur, the program will crash with an assertion error telling you exactly which “impossible” condition was triggered. This makes it much easier to track down and fix bugs in your programs. And I like anything that makes life easier—don’t you? For now, keep in mind that Python’s assert statement is a debugging aid, not a mechanism for handling run-time errors. The goal of using assertions is to let developers find the likely root cause of a bug more quickly. An assertion error should never be raised unless there’s a bug in your program. Let’s take a closer look at some other things we can do with assertions, 18
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2.1. Covering Your A** With Assertions and then I’ll cover two commonpitfalls when using them in real-world scenarios. Python’s Assert Syntax It’s always a good idea to study up on how a language feature is ac- tually implemented in Python before you start using it. So let’s take a quick look at the syntax for the assert statement, according to the Python docs:1 assert_stmt ::= "assert" expression1 ["," expression2] In this case, expression1 is the condition we test, and the optional expression2 is an errormessage that’s displayed if the assertion fails. At execution time, the Python interpreter transforms each assert state- ment into roughly the following sequence of statements: if __debug__: if not expression1: raise AssertionError(expression2) Two interesting things about this code snippet: Before the assert condition is checked, there’s an additional check for the __debug__ global variable. It’s a built-in boolean flag that’s true under normal circumstances and false if optimizations are requested. We’ll talk somemore about later that in the “common pitfalls” section. Also, you can use expression2 to pass an optional error message that will be displayed with the AssertionError in the traceback. This can simplify debugging even further. For example, I’ve seen code like this: >>> if cond == 'x': ... do_x() 1cf. Python Docs: “The Assert Statement” 19
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