Learning Python PDF

Learning Python Learn to code like a professional with Python – an open source, versatile, and powerful programming language Fabrizio Romano BIRMINGHAM - MUMBAI Learning Python Copyright © 2015 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: December 2015 Production reference: 1171215 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78355-171-2 www.packtpub.com Credits Author Project Coordinator Fabrizio Romano Kinjal Bari Reviewers Proofreader Simone Burol Safis Editing Julio Vicente Trigo Guijarro Veit Heller Indexer Priya Sane Commissioning Editor Akram Hussain Graphics Kirk D'Penha Acquisition Editor Abhinash Sahu Indrajit Das Production Coordinator Content Development Editors Melwyn D'sa Samantha Gonsalves Adrian Raposo Cover Work Melwyn D'sa Technical Editor Siddhi Rane Copy Editors Janbal Dharmaraj Kevin McGowan About the Author Fabrizio Romano was born in Italy in 1975. He holds a master's degree in computer science engineering from the University of Padova. He is also a certified Scrum master. Before Python, he has worked with several other languages, such as C/C++, Java, PHP, and C#. In 2011, he moved to London and started working as a Python developer for Glasses Direct, one of Europe's leading online prescription glasses retailers. He then worked as a senior Python developer for TBG (now Sprinklr), one of the world's leading companies in social media advertising. At TBG, he and his team collaborated with Facebook and Twitter. They were the first in the world to get access to the Twitter advertising API. He wrote the code that published the first geo-narrowcasted promoted tweet in the world using the API. He currently works as a senior platform developer at Student.com, a company that is revolutionizing the way international students find their perfect home all around the world. He has delivered talks on Teaching Python and TDD with Python at the last two editions of EuroPython and at Skillsmatter in London. Acknowledgements I would like to thank Adrian Raposo and Indrajit Das from Packt Publishing for their help and support and giving me the opportunity to live this adventure. I would also like to thank everyone at Packt Publishing who have contributed to the realization of this book. Special thanks go to Siddhi Rane, my technical editor. Thank you for your kindness, for working very hard, and for going the extra mile just to make me happy. I would like to express my deepest gratitude to Simone Burol and Julio Trigo, who have gifted me with some of their precious free time. They have reviewed the book and provided me with invaluable feedback. A big thank you to my teammates, Matt Bennett and Jakub Kuba Borys, for their interest in this book and for their support and feedback that makes me a better coder every day. A heartfelt thank you to Marco "Tex" Beri, who introduced me to Python with an enthusiasm second to none. A special thanks to Dr. Naomi Ceder, from whom I learned so much over the last year. She has given me precious suggestions and has encouraged me to embrace this opportunity. Finally, I would like to thank all my friends who have supported me in any way. About the Reviewers Simone Burol is an Italian software developer who was born in Treviso (Italy) in 1978. He obtained a master's degree in computer science engineering from the University of Padua (Italy), and since then worked in banking for 5 years in Venice (Italy). In 2010, he moved to London (United Kingdom), where he worked in warehouse automation for Ocado Technology and then in banking for Algomi. Julio Vicente Trigo Guijarro is a computer scientist and software engineer with almost a decade of experience in software development. He is also a certified Scrum master, who enjoys the benefits of using agile software development (Scrum and XP). He completed his studies in computer science and software engineering from the University of Alicante, Spain, in 2007. Since then, he has worked with several technologies and languages, including Microsoft Dynamics NAV, Java, JavaScript, and Python. Some of the applications covered by Julio during his career include RESTful APIs, ERPs, billing platforms, payment gateways, and e-commerce websites. He has been using Python on both personal and professional projects since 2012, and he is passionate about software design, software quality, and coding standards. I would like to thank my parents for their love, good advice, and continuous support. I would also like to thank all my friends that I met along the way, who enriched my life, for motivating me and helping me progress. Veit Heller is a full stack developer, mostly working on the backend side of web projects. He currently resides in Berlin and works for a prototypical Pythonista company named Bright. In his free time, he writes interpreters for various programming languages. I would like to thank the people at Bright for being a welcoming company that supports me in all my endeavors, my friends and my family for coping with my strangeness, and manufacturers of caffeinated drinks worldwide. www.PacktPub.com Support files, eBooks, discount offers, and more For support files and downloads related to your book, please visit www.PacktPub.com. Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.PacktPub.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at [email protected] for more details. At www.PacktPub.com, you can also read a collection of free technical articles, sign up for a range of free newsletters and receive exclusive discounts and offers on Packt books and eBooks. https://www2.packtpub.com/books/subscription/packtlib Do you need instant solutions to your IT questions? PacktLib is Packt's online digital book library. Here, you can search, access, and read Packt's entire library of books. Why subscribe? Fully searchable across every book published by Packt Copy and paste, print, and bookmark content On demand and accessible via a web browser Free access for Packt account holders If you have an account with Packt at www.PacktPub.com, you can use this to access PacktLib today and view 9 entirely free books. Simply use your login credentials for immediate access. To Alan Turing, the father of Computer Science. To Guido Van Rossum, the father of Python. To Adriano Romano, my father, my biggest fan. Table of Contents Preface ix Chapter 1: Introduction and First Steps – Take a Deep Breath 1 A proper introduction 2 Enter the Python 4 About Python 5 Portability 5 Coherence 5 Developer productivity 6 An extensive library 6 Software quality 6 Software integration 6 Satisfaction and enjoyment 7 What are the drawbacks? 7 Who is using Python today? 8 Setting up the environment 8 Python 2 versus Python 3 – the great debate 8 Installing Python 9 Setting up the Python interpreter 10 About virtualenv 12 Your first virtual environment 14 Your friend, the console 17 How you can run a Python program 17 Running Python scripts 18 Running the Python interactive shell 18 Running Python as a service 20 Running Python as a GUI application 20 [i] Table of Contents How is Python code organized 21 How do we use modules and packages 22 Python's execution model 25 Names and namespaces 25 Scopes 27 Object and classes 30 Guidelines on how to write good code 33 The Python culture 34 A note on the IDEs 35 Summary 36 Chapter 2: Built-in Data Types 37 Everything is an object 37 Mutable or immutable? That is the question 38 Numbers 40 Integers 40 Booleans 42 Reals 43 Complex numbers 44 Fractions and decimals 45 Immutable sequences 46 Strings and bytes 46 Encoding and decoding strings 47 Indexing and slicing strings 48 Tuples 49 Mutable sequences 50 Lists 50 Byte arrays 54 Set types 55 Mapping types – dictionaries 57 The collections module 62 Named tuples 62 Defaultdict 64 ChainMap 65 Final considerations 66 Small values caching 66 How to choose data structures 67 About indexing and slicing 68 About the names 70 Summary 70 [ ii ] Table of Contents Chapter 3: Iterating and Making Decisions 73 Conditional programming 74 A specialized else: elif 75 The ternary operator 77 Looping 78 The for loop 78 Iterating over a range 79 Iterating over a sequence 80 Iterators and iterables 81 Iterating over multiple sequences 83 The while loop 85 The break and continue statements 88 A special else clause 90 Putting this all together 91 Example 1 – a prime generator 92 Example 2 – applying discounts 94 A quick peek at the itertools module 97 Infinite iterators 98 Iterators terminating on the shortest input sequence 98 Combinatoric generators 99 Summary 100 Chapter 4: Functions, the Building Blocks of Code 101 Why use functions? 102 Reduce code duplication 103 Splitting a complex task 103 Hide implementation details 104 Improve readability 105 Improve traceability 106 Scopes and name resolution 107 The global and nonlocal statements 108 Input parameters 110 Argument passing 111 Assignment to argument names don't affect the caller 112 Changing a mutable affects the caller 112 How to specify input parameters 113 Positional arguments 113 Keyword arguments and default values 114 Variable positional arguments 115 Variable keyword arguments 116 Keyword-only arguments 117 Combining input parameters 118 Avoid the trap! Mutable defaults 120 [ iii ] Table of Contents Return values 121 Returning multiple values 123 A few useful tips 124 Recursive functions 125 Anonymous functions 126 Function attributes 127 Built-in functions 128 One final example 129 Documenting your code 130 Importing objects 131 Relative imports 133 Summary 134 Chapter 5: Saving Time and Memory 135 map, zip, and filter 137 map 137 zip 140 filter 141 Comprehensions 142 Nested comprehensions 143 Filtering a comprehension 144 dict comprehensions 146 set comprehensions 147 Generators 148 Generator functions 148 Going beyond next 151 The yield from expression 155 Generator expressions 156 Some performance considerations 159 Don't overdo comprehensions and generators 162 Name localization 167 Generation behavior in built-ins 168 One last example 169 Summary 171 Chapter 6: Advanced Concepts – OOP, Decorators, and Iterators 173 Decorators 173 A decorator factory 180 Object-oriented programming 182 The simplest Python class 182 Class and object namespaces 183 [ iv ] Table of Contents Attribute shadowing 184 I, me, and myself – using the self variable 186 Initializing an instance 187 OOP is about code reuse 187 Inheritance and composition 188 Accessing a base class 192 Multiple inheritance 194 Method resolution order 197 Static and class methods 200 Static methods 200 Class methods 202 Private methods and name mangling 204 The property decorator 206 Operator overloading 208 Polymorphism – a brief overview 209 Writing a custom iterator 210 Summary 211 Chapter 7: Testing, Profiling, and Dealing with Exceptions 213 Testing your application 214 The anatomy of a test 216 Testing guidelines 217 Unit testing 218 Writing a unit test 219 Mock objects and patching 220 Assertions 221 A classic unit test example 221 Making a test fail 224 Interface testing 225 Comparing tests with and without mocks 225 Boundaries and granularity 228 A more interesting example 229 Test-driven development 233 Exceptions 235 Profiling Python 241 When to profile? 244 Summary 245 Chapter 8: The Edges – GUIs and Scripts 247 First approach – scripting 250 The imports 250 Parsing arguments 251 The business logic 253 [v] Table of Contents Second approach – a GUI application 258 The imports 260 The layout logic 261 The business logic 265 Fetching the web page 265 Saving the images 267 Alerting the user 271 How to improve the application? 272 Where do we go from here? 273 The tkinter.tix module 273 The turtle module 274 wxPython, PyQt, and PyGTK 274 The principle of least astonishment 275 Threading considerations 275 Summary 276 Chapter 9: Data Science 277 IPython and Jupyter notebook 278 Dealing with data 281 Setting up the notebook 282 Preparing the data 283 Cleaning the data 287 Creating the DataFrame 289 Unpacking the campaign name 291 Unpacking the user data 293 Cleaning everything up 297 Saving the DataFrame to a file 298 Visualizing the results 299 Where do we go from here? 307 Summary 308 Chapter 10: Web Development Done Right 309 What is the Web? 309 How does the Web work? 310 The Django web framework 311 Django design philosophy 311 The model layer 312 The view layer 312 The template layer 313 The Django URL dispatcher 313 Regular expressions 314 [ vi ] Table of Contents A regex website 314 Setting up Django 315 Starting the project 315 Creating users 317 Adding the Entry model 318 Customizing the admin panel 320 Creating the form 322 Writing the views 323 The home view 325 The entry list view 326 The form view 326 Tying up URLs and views 328 Writing the templates 329 The future of web development 336 Writing a Flask view 336 Building a JSON quote server in Falcon 338 Summary 340 Chapter 11: Debugging and Troubleshooting 341 Debugging techniques 342 Debugging with print 342 Debugging with a custom function 343 Inspecting the traceback 345 Using the Python debugger 348 Inspecting log files 351 Other techniques 353 Profiling 354 Assertions 354 Where to find information 354 Troubleshooting guidelines 355 Using console editors 355 Where to inspect 355 Using tests to debug 356 Monitoring 356 Summary 356 Chapter 12: Summing Up – A Complete Example 357 The challenge 357 Our implementation 358 Implementing the Django interface 358 The setup 358 The model layer 360 A simple form 364 [ vii ] Table of Contents The view layer 364 Imports and home view 364 Listing all records 365 Creating records 365 Updating records 367 Deleting records 369 Setting up the URLs 369 The template layer 370 Home and footer templates 372 Listing all records 372 Creating and editing records 377 Talking to the API 379 Deleting records 383 Implementing the Falcon API 385 The main application 385 Writing the helpers 386 Coding the password validator 387 Coding the password generator 390 Writing the handlers 391 Coding the password validator handler 392 Coding the password generator handler 393 Running the API 394 Testing the API 395 Testing the helpers 395 Testing the handlers 400 Where do you go from here? 402 Summary 403 Index 405 [ viii ] Preface Shortly after I started writing, a friend asked me if there really was a need of another Learning Python book. An excellent question that we could also express in another form: What has this book to offer? What makes this book different from the average introductory book on Python? I think there are two main differences and many good reasons why you would want to read it. Firstly, we start with introducing some important programming concepts. We build a solid foundation by covering the critical aspects of this wonderful language. The pace gradually increases, along with the difficulty of the subjects presented. By the end of Chapter 7, Testing, Profiling, and Dealing with Exceptions, we will cover all the fundamentals. From Chapter 8, The Edges – GUIs and Scripts, onward, the book takes a steep turn, which brings us to difference number two. To consolidate the knowledge acquired, there is nothing like working on a small project. So, in the second part of the book, each chapter delivers a project on a different subject. We explore scripting, graphical interfaces, data science, and web programming. Each project is small enough to fit within a chapter and yet big enough to be relevant. Each chapter is interesting, conveys a message, and teaches something valuable. After a short section on debugging, the book ends with a complete example that wraps things up. I tried to craft it so that you will be able to expand it in several ways. [ ix ] Preface So, this is definitely not the usual Learning Python book. Its approach is much more "hands-on" and practical. I wanted to empower you to help you become a true Python ninja. But I also did my best to entertain you and foster your logical thinking and creativity along the way. Now, have I answered the question? What this book covers Chapter 1, Introduction and First Steps – Take a Deep Breath, introduces you to fundamental programming concepts. It guides you to getting Python up and running on your computer and introduces you to some of its constructs. Chapter 2, Built-in Data Types, introduces you to Python built-in data types. Python has a very rich set of native data types and this chapter will give you a description and a short example for each of them. Chapter 3, Iterating and Making Decisions, teaches you how to control the flow of your code by inspecting conditions, applying logic, and performing loops. Chapter 4, Functions, the Building Blocks of Code, teaches you how to write functions. Functions are the keys to reusing code, to reducing debugging time, and in general, to writing better code. Chapter 5, Saving Time and Memory, introduces you to the functional aspects of Python programming. This chapter teaches you how to write comprehensions and generators, which are powerful tools that you can use to speed up your code and save memory. Chapter 6, Advanced Concepts – OOP, Decorators, and Iterators, teaches you the basics of object-oriented programming with Python. It shows you the key concepts and all the potentials of this paradigm. It also shows you one of the most beloved characteristics of Python: decorators. Finally, it also covers the concept of iterators. Chapter 7, Testing, Profiling, and Dealing with Exceptions, teaches you how to make your code more robust, fast, and stable using techniques such as testing and profiling. It also formally defines the concept of exceptions. Chapter 8, The Edges – GUIs and Scripts, guides you through an example from two different points of view. They are at the extremities of a spectrum: one implementation is a script and the other one a proper graphical user interface application. [x] Preface Chapter 9, Data Science, introduces a few key concepts and a very special tool, the Jupyter Notebook. Chapter 10, Web Development Done Right, introduces the fundamentals of web development and delivers a project using the Django web framework. The example will be based on regular expressions. Chapter 11, Debugging and Troubleshooting, shows you the main methods to debug your code and some examples on how to apply them. Chapter 12, Summing Up – A Complete Example, presents a Django website that acts as an interface to an underlying slim API written with the Falcon web framework. This chapter takes all the concepts covered in the book to the next level and suggests where to go to dig deeper and take the next steps. What you need for this book You are encouraged to follow the examples in this book. In order to do so, you will need a computer, an Internet connection, and a browser. The book is written in Python 3.4, but it should also work with any Python 3.* version. I have written instructions on how to install Python on the three main operating systems used today: Windows, Mac, and Linux. I have also explained how to install all the extra libraries used in the various examples and provided suggestions if the reader finds any issues during the installation of any of them. No particular editor is required to type the code; however, I suggest that those who are interested in following the examples should consider adopting a proper coding environment. I have given suggestions on this matter in the first chapter. Who this book is for Python is the most popular introductory teaching language in the top computer science universities in the US, so if you are new to software development or if you have little experience and would like to start off on the right foot, then this language and this book are what you need. Its amazing design and portability will help you become productive regardless of the environment you choose to work with. If you have already worked with Python or any other language, this book can still be useful to you both as a reference to Python's fundamentals and to provide a wide range of considerations and suggestions collected over two decades of experience. [ xi ] Preface Conventions In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Open up a Python console, and type import this." A block of code is set as follows: # we define a function, called local def local(): m = 7 print(m) m = 5 print(m) When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold: # we define a function, called local def local(): m = 7 print(m) m = 5 print(m) Any command-line input or output is written as follows: >>> from math import factorial >>> factorial(5) 120 [ xii ] Preface New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "To open the console on Windows, go to the Start menu, choose Run, and type cmd." Warnings or important notes appear in a box like this. Tips and tricks appear like this. Reader feedback Feedback from our readers is always welcome. Let us know what you think about this book—what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of. To send us general feedback, simply e-mail [email protected], and mention the book's title in the subject of your message. If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors. Customer support Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase. 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Questions If you have a problem with any aspect of this book, you can contact us at [email protected], and we will do our best to address the problem. [ xiv ] Introduction and First Steps – Take a Deep Breath "Give a man a fish and you feed him for a day. Teach a man to fish and you feed him for a lifetime." – Chinese proverb According to Wikipedia, computer programming is: "...a process that leads from an original formulation of a computing problem to executable computer programs. Programming involves activities such as analysis, developing understanding, generating algorithms, verification of requirements of algorithms including their correctness and resources consumption, and implementation (commonly referred to as coding) of algorithms in a target programming language". In a nutshell, coding is telling a computer to do something using a language it understands. Computers are very powerful tools, but unfortunately, they can't think for themselves. So they need to be told everything. They need to be told how to perform a task, how to evaluate a condition to decide which path to follow, how to handle data that comes from a device such as the network or a disk, and how to react when something unforeseen happens, say, something is broken or missing. You can code in many different styles and languages. Is it hard? I would say "yes" and "no". It's a bit like writing. Everybody can learn how to write, and you can too. But what if you wanted to become a poet? Then writing alone is not enough. You have to acquire a whole other set of skills and this will take a longer and greater effort. Introduction and First Steps – Take a Deep Breath In the end, it all comes down to how far you want to go down the road. Coding is not just putting together some instructions that work. It is so much more! Good code is short, fast, elegant, easy to read and understand, simple, easy to modify and extend, easy to scale and refactor, and easy to test. It takes time to be able to write code that has all these qualities at the same time, but the good news is that you're taking the first step towards it at this very moment by reading this book. And I have no doubt you can do it. Anyone can, in fact, we all program all the time, only we aren't aware of it. Would you like an example? Say you want to make instant coffee. You have to get a mug, the instant coffee jar, a teaspoon, water, and the kettle. Even if you're not aware of it, you're evaluating a lot of data. You're making sure that there is water in the kettle as well as the kettle is plugged-in, that the mug is clean, and that there is enough coffee in the jar. Then, you boil the water and maybe in the meantime you put some coffee in the mug. When the water is ready, you pour it into the cup, and stir. So, how is this programming? Well, we gathered resources (the kettle, coffee, water, teaspoon, and mug) and we verified some conditions on them (kettle is plugged-in, mug is clean, there is enough coffee). Then we started two actions (boiling the water and putting coffee in the mug), and when both of them were completed, we finally ended the procedure by pouring water in the mug and stirring. Can you see it? I have just described the high-level functionality of a coffee program. It wasn't that hard because this is what the brain does all day long: evaluate conditions, decide to take actions, carry out tasks, repeat some of them, and stop at some point. Clean objects, put them back, and so on. All you need now is to learn how to deconstruct all those actions you do automatically in real life so that a computer can actually make some sense of them. And you need to learn a language as well, to instruct it. So this is what this book is for. I'll tell you how to do it and I'll try to do that by means of many simple but focused examples (my favorite kind). A proper introduction I love to make references to the real world when I teach coding; I believe they help people retain the concepts better. However, now is the time to be a bit more rigorous and see what coding is from a more technical perspective. Chapter 1 When we write code, we're instructing a computer on what are the things it has to do. Where does the action happen? In many places: the computer memory, hard drives, network cables, CPU, and so on. It's a whole "world", which most of the time is the representation of a subset of the real world. If you write a piece of software that allows people to buy clothes online, you will have to represent real people, real clothes, real brands, sizes, and so on and so forth, within the boundaries of a program. In order to do so, you will need to create and handle objects in the program you're writing. A person can be an object. A car is an object. A pair of socks is an object. Luckily, Python understands objects very well. The two main features any object has are properties and methods. Let's take a person object as an example. Typically in a computer program, you'll represent people as customers or employees. The properties that you store against them are things like the name, the SSN, the age, if they have a driving license, their e-mail, gender, and so on. In a computer program, you store all the data you need in order to use an object for the purpose you're serving. If you are coding a website to sell clothes, you probably want to store the height and weight as well as other measures of your customers so that you can suggest the appropriate clothes for them. So, properties are characteristics of an object. We use them all the time: "Could you pass me that pen?" – "Which one?" – "The black one." Here, we used the "black" property of a pen to identify it (most likely amongst a blue and a red one). Methods are things that an object can do. As a person, I have methods such as speak, walk, sleep, wake-up, eat, dream, write, read, and so on. All the things that I can do could be seen as methods of the objects that represents me. So, now that you know what objects are and that they expose methods that you can run and properties that you can inspect, you're ready to start coding. Coding in fact is simply about managing those objects that live in the subset of the world that we're reproducing in our software. You can create, use, reuse, and delete objects as you please. According to the Data Model chapter on the official Python documentation: "Objects are Python's abstraction for data. All data in a Python program is represented by objects or by relations between objects." We'll take a closer look at Python objects in Chapter 6, Advanced Concepts – OOP, Decorators, and Iterators. For now, all we need to know is that every object in Python has an ID (or identity), a type, and a value. Introduction and First Steps – Take a Deep Breath Once created, the identity of an object is never changed. It's a unique identifier for it, and it's used behind the scenes by Python to retrieve the object when we want to use it. The type as well, never changes. The type tells what operations are supported by the object and the possible values that can be assigned to it. We'll see Python's most important data types in Chapter 2, Built-in Data Types. The value can either change or not. If it can, the object is said to be mutable, while when it cannot, the object is said to be immutable. How do we use an object? We give it a name of course! When you give an object a name, then you can use the name to retrieve the object and use it. In a more generic sense, objects such as numbers, strings (text), collections, and so on are associated with a name. Usually, we say that this name is the name of a variable. You can see the variable as being like a box, which you can use to hold data. So, you have all the objects you need: what now? Well, we need to use them, right? We may want to send them over a network connection or store them in a database. Maybe display them on a web page or write them into a file. In order to do so, we need to react to a user filling in a form, or pressing a button, or opening a web page and performing a search. We react by running our code, evaluating conditions to choose which parts to execute, how many times, and under which circumstances. And to do all this, basically we need a language. That's what Python is for. Python is the language we'll use together throughout this book to instruct the computer to do something for us. Now, enough of this theoretical stuff, let's get started. Enter the Python Python is the marvelous creature of Guido Van Rossum, a Dutch computer scientist and mathematician who decided to gift the world with a project he was playing around with over Christmas 1989. The language appeared to the public somewhere around 1991, and since then has evolved to be one of the leading programming languages used worldwide today. Chapter 1 I started programming when I was 7 years old, on a Commodore VIC 20, which was later replaced by its bigger brother, the Commodore 64. The language was BASIC. Later on, I landed on Pascal, Assembly, C, C++, Java, JavaScript, Visual Basic, PHP, ASP, ASP.NET, C#, and other minor languages I cannot even remember, but only when I landed on Python, I finally had that feeling that you have when you find the right couch in the shop. When all of your body parts are yelling, "Buy this one! This one is perfect for us!" It took me about a day to get used to it. Its syntax is a bit different from what I was used to, and in general, I very rarely worked with a language that defines scoping with indentation. But after getting past that initial feeling of discomfort (like having new shoes), I just fell in love with it. Deeply. Let's see why. About Python Before we get into the gory details, let's get a sense of why someone would want to use Python (I would recommend you to read the Python page on Wikipedia to get a more detailed introduction). To my mind, Python exposes the following qualities. Portability Python runs everywhere, and porting a program from Linux to Windows or Mac is usually just a matter of fixing paths and settings. Python is designed for portability and it takes care of operating system (OS) specific quirks behind interfaces that shield you from the pain of having to write code tailored to a specific platform. Coherence Python is extremely logical and coherent. You can see it was designed by a brilliant computer scientist. Most of the time you can just guess how a method is called, if you don't know it. You may not realize how important this is right now, especially if you are at the beginning, but this is a major feature. It means less cluttering in your head, less skimming through the documentation, and less need for mapping in your brain when you code. Introduction and First Steps – Take a Deep Breath Developer productivity According to Mark Lutz (Learning Python, 5th Edition, O'Reilly Media), a Python program is typically one-fifth to one-third the size of equivalent Java or C++ code. This means the job gets done faster. And faster is good. Faster means a faster response on the market. Less code not only means less code to write, but also less code to read (and professional coders read much more than they write), less code to maintain, to debug, and to refactor. Another important aspect is that Python runs without the need of lengthy and time consuming compilation and linkage steps, so you don't have to wait to see the results of your work. An extensive library Python has an incredibly wide standard library (it's said to come with "batteries included"). If that wasn't enough, the Python community all over the world maintains a body of third party libraries, tailored to specific needs, which you can access freely at the Python Package Index (PyPI). When you code Python and you realize that you need a certain feature, in most cases, there is at least one library where that feature has already been implemented for you. Software quality Python is heavily focused on readability, coherence, and quality. The language uniformity allows for high readability and this is crucial nowadays where code is more of a collective effort than a solo experience. Another important aspect of Python is its intrinsic multi-paradigm nature. You can use it as scripting language, but you also can exploit object-oriented, imperative, and functional programming styles. It is versatile. Software integration Another important aspect is that Python can be extended and integrated with many other languages, which means that even when a company is using a different language as their mainstream tool, Python can come in and act as a glue agent between complex applications that need to talk to each other in some way. This is kind of an advanced topic, but in the real world, this feature is very important. Chapter 1 Satisfaction and enjoyment Last but not least, the fun of it! Working with Python is fun. I can code for 8 hours and leave the office happy and satisfied, alien to the struggle other coders have to endure because they use languages that don't provide them with the same amount of well-designed data structures and constructs. Python makes coding fun, no doubt about it. And fun promotes motivation and productivity. These are the major aspects why I would recommend Python to everyone for. Of course, there are many other technical and advanced features that I could have talked about, but they don't really pertain to an introductory section like this one. They will come up naturally, chapter after chapter, in this book. What are the drawbacks? Probably, the only drawback that one could find in Python, which is not due to personal preferences, is the execution speed. Typically, Python is slower than its compiled brothers. The standard implementation of Python produces, when you run an application, a compiled version of the source code called byte code (with the extension.pyc), which is then run by the Python interpreter. The advantage of this approach is portability, which we pay for with a slowdown due to the fact that Python is not compiled down to machine level as are other languages. However, Python speed is rarely a problem today, hence its wide use regardless of this suboptimal feature. What happens is that in real life, hardware cost is no longer a problem, and usually it's easy enough to gain speed by parallelizing tasks. When it comes to number crunching though, one can switch to faster Python implementations, such as PyPy, which provides an average 7-fold speedup by implementing advanced compilation techniques (check http://pypy.org/ for reference). When doing data science, you'll most likely find that the libraries that you use with Python, such as Pandas and Numpy, achieve native speed due to the way they are implemented. If that wasn't a good enough argument, you can always consider that Python is driving the backend of services such as Spotify and Instagram, where performance is a concern. Nonetheless, Python does its job perfectly adequately. Introduction and First Steps – Take a Deep Breath Who is using Python today? Not yet convinced? Let's take a very brief look at the companies that are using Python today: Google, YouTube, Dropbox, Yahoo, Zope Corporation, Industrial Light & Magic, Walt Disney Feature Animation, Pixar, NASA, NSA, Red Hat, Nokia, IBM, Netflix, Yelp, Intel, Cisco, HP, Qualcomm, and JPMorgan Chase, just to name a few. Even games such as Battlefield 2, Civilization 4, and QuArK are implemented using Python. Python is used in many different contexts, such as system programming, web programming, GUI applications, gaming and robotics, rapid prototyping, system integration, data science, database applications, and much more. Setting up the environment Before we talk about installing Python on your system, let me tell you about which Python version I'll be using in this book. Python 2 versus Python 3 – the great debate Python comes in two main versions—Python 2, which is the past—and Python 3, which is the present. The two versions, though very similar, are incompatible on some aspects. In the real world, Python 2 is actually quite far from being the past. In short, even though Python 3 has been out since 2008, the transition phase is still far from being over. This is mostly due to the fact that Python 2 is widely used in the industry, and of course, companies aren't so keen on updating their systems just for the sake of updating, following the if it ain't broke, don't fix it philosophy. You can read all about the transition between the two versions on the Web. Another issue that was hindering the transition is the availability of third-party libraries. Usually, a Python project relies on tens of external libraries, and of course, when you start a new project, you need to be sure that there is already a version 3 compatible library for any business requirement that may come up. If that's not the case, starting a brand new project in Python 3 means introducing a potential risk, which many companies are not happy to take. Chapter 1 At the time of writing, the majority of the most widely used libraries have been ported to Python 3, and it's quite safe to start a project in Python 3 for most cases. Many of the libraries have been rewritten so that they are compatible with both versions, mostly harnessing the power of the six (2 x 3) library, which helps introspecting and adapting the behavior according to the version used. On my Linux box (Ubuntu 14.04), I have the following Python version: >>> import sys >>> print(sys.version) 3.4.0 (default, Apr 11 2014, 13:05:11) [GCC 4.8.2] So you can see that my Python version is 3.4.0. The preceding text is a little bit of Python code that I typed into my console. We'll talk about it in a moment. All the examples in this book will be run using this Python version. Most of them will run also in Python 2 (I have version 2.7.6 installed as well), and those that won't will just require some minor adjustments to cater for the small incompatibilities between the two versions. Another reason behind this choice is that I think it's better to learn Python 3, and then, if you need to, learn the differences it has with Python 2, rather than going the other way around. Don't worry about this version thing though: it's not that big an issue in practice. Installing Python I never really got the point of having a setup section in a book, regardless of what it is that you have to set up. Most of the time, between the time the author writes the instruction and the time you actually try them out, months have passed. That is, if you're lucky. One version change and things may not work the way it is described in the book. Luckily, we have the Web now, so in order to help you get up and running, I'll just give you pointers and objectives. If any of the URLs or resources I'll point you to are no longer there by the time you read this book, just remember: Google is your friend. Introduction and First Steps – Take a Deep Breath Setting up the Python interpreter First of all, let's talk about your OS. Python is fully integrated and most likely already installed in basically almost every Linux distribution. If you have a Mac, it's likely that Python is already there as well (however, possibly only Python 2.7), whereas if you're using Windows, you probably need to install it. Getting Python and the libraries you need up and running requires a bit of handiwork. Linux happens to be the most user friendly OS for Python programmers, Windows on the other hand is the one that requires the biggest effort, Mac being somewhere in between. For this reason, if you can choose, I suggest you to use Linux. If you can't, and you have a Mac, then go for it anyway. If you use Windows, you'll be fine for the examples in this book, but in general working with Python will require you a bit more tweaking. My OS is Ubuntu 14.04, and this is what I will use throughout the book, along with Python 3.4.0. The place you want to start is the official Python website: https://www.python.org. This website hosts the official Python documentation and many other resources that you will find very useful. Take the time to explore it. Another excellent, resourceful website on Python and its ecosystem is http://docs.python-guide.org. Find the download section and choose the installer for your OS. If you are on Windows, make sure that when you run the installer, you check the option install pip (actually, I would suggest to make a complete installation, just to be safe, of all the components the installer holds). We'll talk about pip later. Now that Python is installed in your system, the objective is to be able to open a console and run the Python interactive shell by typing python. Please note that I usually refer to the Python interactive shell simply as Python console. To open the console in Windows, go to the Start menu, choose Run, and type cmd. If you encounter anything that looks like a permission problem while working on the examples of this book, please make sure you are running the console with administrator rights. [ 10 ] Chapter 1 On the Mac OS X, you can start a terminal by going to Applications | Utilities | Terminal. If you are on Linux, you know all that there is to know about the console. I will use the term console interchangeably to indicate the Linux console, the Windows command prompt, and the Mac terminal. I will also indicate the command-line prompt with the Linux default format, like this: $ sudo apt-get update Whatever console you open, type python at the prompt, and make sure the Python interactive shell shows up. Type exit() to quit. Keep in mind that you may have to specify python3 if your OS comes with Python 2.* preinstalled. This is how it should look on Windows 7: [ 11 ] Introduction and First Steps – Take a Deep Breath And this is how it should look on Linux: Now that Python is set up and you can run it, it's time to make sure you have the other tool that will be indispensable to follow the examples in the book: virtualenv. About virtualenv As you probably have guessed by its name, virtualenv is all about virtual environments. Let me explain what they are and why we need them and let me do it by means of a simple example. You install Python on your system and you start working on a website for client X. You create a project folder and start coding. Along the way you also install some libraries, for example the Django framework, which we'll see in depth in Chapter 10, Web Development Done Right. Let's say the Django version you install for project X is 1.7.1. Now, your website is so good that you get another client, Y. He wants you to build another website, so you start project Y and, along the way, you need to install Django again. The only issue is that now the Django version is 1.8 and you cannot install it on your system because this would replace the version you installed for project X. You don't want to risk introducing incompatibility issues, so you have two choices: either you stick with the version you have currently on your machine, or you upgrade it and make sure the first project is still fully working correctly with the new version. Let's be honest, neither of these options is very appealing, right? Definitely not. So, here's the solution: virtualenv! [ 12 ] Chapter 1 virtualenv is a tool that allows you to create a virtual environment. In other words, it is a tool to create isolated Python environments, each of which is a folder that contains all the necessary executables to use the packages that a Python project would need (think of packages as libraries for the time being). So you create a virtual environment for project X, install all the dependencies, and then you create a virtual environment for project Y, installing all its dependencies without the slightest worry because every library you install ends up within the boundaries of the appropriate virtual environment. In our example, project X will hold Django 1.7.1, while project Y will hold Django 1.8. It is of vital importance that you never install libraries directly at the system level. Linux for example relies on Python for many different tasks and operations, and if you fiddle with the system installation of Python, you risk compromising the integrity of the whole system (guess to whom this happened…). So take this as a rule, such as brushing your teeth before going to bed: always, always create a virtual environment when you start a new project. To install virtualenv on your system, there are a few different ways. On a Debian-based distribution of Linux for example, you can install it with the following command: $ sudo apt-get install python-virtualenv Probably, the easiest way is to use pip though, with the following command: $ sudo pip install virtualenv # sudo may by optional pip is a package management system used to install and manage software packages written in Python. Python 3 has built-in support for virtual environments, but in practice, the external libraries are still the default on production systems. If you have trouble getting virtualenv up and running, please refer to the virtualenv official website: https://virtualenv.pypa.io. [ 13 ] Introduction and First Steps – Take a Deep Breath Your first virtual environment It is very easy to create a virtual environment, but according to how your system is configured and which Python version you want the virtual environment to run, you need to run the command properly. Another thing you will need to do with a virtualenv, when you want to work with it, is to activate it. Activating a virtualenv basically produces some path juggling behind the scenes so that when you call the Python interpreter, you're actually calling the active virtual environment one, instead of the mere system one. I'll show you a full example on both Linux and Windows. We will: 1. Create a folder named learning.python under your project root (which in my case is a folder called srv, in my home folder). Please adapt the paths according to the setup you fancy on your box. 2. Within the learning.python folder, we will create a virtual environment called.lpvenv. Some developers prefer to call all virtual environments using the same name (for example,.venv). This way they can run scripts against any virtualenv by just knowing the name of the project they dwell in. This is a very common technique that I use as well. The dot in.venv is because in Linux/Mac prepending a name with a dot makes that file or folder invisible. 3. After creating the virtual environment, we will activate it (this is slightly different between Linux, Mac, and Windows). 4. Then, we'll make sure that we are running the desired Python version (3.4.*) by running the Python interactive shell. 5. Finally, we will deactivate the virtual environment using the deactivate command. These five simple steps will show you all you have to do to start and use a project. Here's an example of how those steps might look like on Linux (commands that start with a # are comments): [ 14 ] Chapter 1 Notice that I had to explicitly tell virtualenv to use the Python 3.4 interpreter because on my box Python 2.7 is the default one. Had I not done that, I would have had a virtual environment with Python 2.7 instead of Python 3.4. You can combine the two instructions for step 2 in one single command like this: $ virtualenv -p $( which python3.4 ).lpvenv I preferred to be explicitly verbose in this instance, to help you understand each bit of the procedure. Another thing to notice is that in order to activate a virtual environment, we need to run the /bin/activate script, which needs to be sourced (when a script is "sourced", it means that its effects stick around when it's done running). This is very important. Also notice how the prompt changes after we activate the virtual environment, showing its name on the left (and how it disappears when we deactivate). In Mac OS, the steps are the same so I won't repeat them here. [ 15 ] Introduction and First Steps – Take a Deep Breath Now let's have a look at how we can achieve the same result in Windows. You will probably have to play around a bit, especially if you have a different Windows or Python version than I'm using here. This is all good experience though, so try and think positively at the initial struggle that every coder has to go through in order to get things going. Here's how it should look on Windows (commands that start with :: are comments): Notice there are a few small differences from the Linux version. Apart from the commands to create and navigate the folders, one important difference is how you activate your virtualenv. Also, in Windows there is no which command, so we used the where command. [ 16 ] Chapter 1 At this point, you should be able to create and activate a virtual environment. Please try and create another one without me guiding you, get acquainted to this procedure because it's something that you will always be doing: we never work system-wide with Python, remember? It's extremely important. So, with the scaffolding out of the way, we're ready to talk a bit more about Python and how you can use it. Before we do it though, allow me to spend a few words about the console. Your friend, the console In this era of GUIs and touchscreen devices, it seems a little ridiculous to have to resort to a tool such as the console, when everything is just about one click away. But the truth is every time you remove your right hand from the keyboard (or the left one, if you're a lefty) to grab your mouse and move the cursor over to the spot you want to click, you're losing time. Getting things done with the console, counter- intuitively as it may be, results in higher productivity and speed. I know, you have to trust me on this. Speed and productivity are important and personally, I have nothing against the mouse, but there is another very good reason for which you may want to get well acquainted with the console: when you develop code that ends up on some server, the console might be the only available tool. If you make friends with it, I promise you, you will never get lost when it's of utmost importance that you don't (typically, when the website is down and you have to investigate very quickly what's going on). So it's really up to you. If you're in doubt, please grant me the benefit of the doubt and give it a try. It's easier than you think, and you'll never regret it. There is nothing more pitiful than a good developer who gets lost within an SSH connection to a server because they are used to their own custom set of tools, and only to that. Now, let's get back to Python. How you can run a Python program There are a few different ways in which you can run a Python program. [ 17 ] Introduction and First Steps – Take a Deep Breath Running Python scripts Python can be used as a scripting language. In fact, it always proves itself very useful. Scripts are files (usually of small dimensions) that you normally execute to do something like a task. Many developers end up having their own arsenal of tools that they fire when they need to perform a task. For example, you can have scripts to parse data in a format and render it into another different format. Or you can use a script to work with files and folders. You can create or modify configuration files, and much more. Technically, there is not much that cannot be done in a script. It's quite common to have scripts running at a precise time on a server. For example, if your website database needs cleaning every 24 hours (for example, the table that stores the user sessions, which expire pretty quickly but aren't cleaned automatically), you could set up a cron job that fires your script at 3:00 A.M. every day. According to Wikipedia, the software utility Cron is a time-based job scheduler in Unix-like computer operating systems. People who set up and maintain software environments use cron to schedule jobs (commands or shell scripts) to run periodically at fixed times, dates, or intervals. I have Python scripts to do all the menial tasks that would take me minutes or more to do manually, and at some point, I decided to automate. For example, I have a laptop that doesn't have a Fn key to toggle the touchpad on and off. I find this very annoying, and I don't want to go clicking about through several menus when I need to do it, so I wrote a small script that is smart enough to tell my system to toggle the touchpad active state, and now I can do it with one simple click from my launcher. Priceless. We'll devote half of Chapter 8, The Edges – GUIs and Scripts on scripting with Python. Running the Python interactive shell Another way of running Python is by calling the interactive shell. This is something we already saw when we typed python on the command line of our console. So open a console, activate your virtual environment (which by now should be second nature to you, right?), and type python. You will be presented with a couple of lines that should look like this (if you are on Linux): Python 3.4.0 (default, Apr 11 2014, 13:05:11) [GCC 4.8.2] on linux Type "help", "copyright", "credits" or "license" for more information. [ 18 ] Chapter 1 Those >>> are the prompt of the shell. They tell you that Python is waiting for you to type something. If you type a simple instruction, something that fits in one line, that's all you'll see. However, if you type something that requires more than one line of code, the shell will change the prompt to..., giving you a visual clue that you're typing a multiline statement (or anything that would require more than one line of code). Go on, try it out, let's do some basic maths: >>> 2 + 4 6 >>> 10 / 4 2.5 >>> 2 ** 1024 1797693134862315907729305190789024733617976978942306572734300811577326758 0550096313270847732240753602112011387987139335765878976881441662249284743 0639474124377767893424865485276302219601246094119453082952085005768838150 6823424628814739131105408272371633505106845862982399472459384797163048353 56329624224137216 The last operation is showing you something incredible. We raise 2 to the power of 1024, and Python is handling this task with no trouble at all. Try to do it in Java, C++, or C#. It won't work, unless you use special libraries to handle such big numbers. I use the interactive shell every day. It's extremely useful to debug very quickly, for example, to check if a data structure supports an operation. Or maybe to inspect or run a piece of code. When you use Django (a web framework), the interactive shell is coupled with it and allows you to work your way through the framework tools, to inspect the data in the database, and many more things. You will find that the interactive shell will soon become one of your dearest friends on the journey you are embarking on. Another solution, which comes in a much nicer graphic layout, is to use IDLE (Integrated DeveLopment Environment). It's quite a simple IDE, which is intended mostly for beginners. It has a slightly larger set of capabilities than the naked interactive shell you get in the console, so you may want to explore it. It comes for free in the Windows Python installer and you can easily install it in any other system. You can find information about it on the Python website. Guido Van Rossum named Python after the British comedy group Monty Python, so it's rumored that the name IDLE has been chosen in honor of Erik Idle, one of Monty Python's founding members. [ 19 ] Introduction and First Steps – Take a Deep Breath Running Python as a service Apart from being run as a script, and within the boundaries of a shell, Python can be coded and run as proper software. We'll see many examples throughout the book about this mode. And we'll understand more about it in a moment, when we'll talk about how Python code is organized and run. Running Python as a GUI application Python can also be run as a GUI (Graphical User Interface). There are several frameworks available, some of which are cross-platform and some others are platform-specific. In Chapter 8, The Edges – GUIs and Scripts, we'll see an example of a GUI application created using Tkinter, which is an object-oriented layer that lives on top of Tk (Tkinter means Tk Interface). Tk is a graphical user interface toolkit that takes desktop application development to a higher level than the conventional approach. It is the standard GUI for Tcl (Tool Command Language), but also for many other dynamic languages and can produce rich native applications that run seamlessly under Windows, Linux, Mac OS X, and more. Tkinter comes bundled with Python, therefore it gives the programmer easy access to the GUI world, and for these reasons, I have chosen it to be the framework for the GUI examples that I'll present in this book. Among the other GUI frameworks, we find that the following are the most widely used: PyQt wxPython PyGtk Describing them in detail is outside the scope of this book, but you can find all the information you need on the Python website in the GUI Programming section. If GUIs are what you're looking for, remember to choose the one you want according to some principles. Make sure they: Offer all the features you may need to develop your project Run on all the platforms you may need to support Rely on a community that is as wide and active as possible Wrap graphic drivers/tools that you can easily install/access [ 20 ] Chapter 1 How is Python code organized Let's talk a little bit about how Python code is organized. In this paragraph, we'll start going down the rabbit hole a little bit more and introduce a bit more technical names and concepts. Starting with the basics, how is Python code organized? Of course, you write your code into files. When you save a file with the extension.py, that file is said to be a Python module. If you're on Windows or Mac, which typically hide file extensions to the user, please make sure you change the configuration so that you can see the complete name of the files. This is not strictly a requirement, but a hearty suggestion. It would be impractical to save all the code that it is required for software to work within one single file. That solution works for scripts, which are usually not longer than a few hundred lines (and often they are quite shorter than that). A complete Python application can be made of hundreds of thousands of lines of code, so you will have to scatter it through different modules. Better, but not nearly good enough. It turns out that even like this it would still be impractical to work with the code. So Python gives you another structure, called package, which allows you to group modules together. A package is nothing more than a folder, which must contain a special file, __init__.py that doesn't need to hold any code but whose presence is required to tell Python that the folder is not just some folder, but it's actually a package (note that as of Python 3.3 __init__.py is not strictly required any more). As always, an example will make all of this much clearer. I have created an example structure in my book project, and when I type in my Linux console: $ tree -v example I get a tree representation of the contents of the ch1/example folder, which holds the code for the examples of this chapter. Here's how a structure of a real simple application could look like: example/ ├── core.py ├── run.py └── util ├── __init__.py ├── db.py ├── math.py └── network.py [ 21 ] Introduction and First Steps – Take a Deep Breath You can see that within the root of this example, we have two modules, core.py and run.py, and one package: util. Within core.py, there may be the core logic of our application. On the other hand, within the run.py module, we can probably find the logic to start the application. Within the util package, I expect to find various utility tools, and in fact, we can guess that the modules there are called by the type of tools they hold: db.py would hold tools to work with databases, math.py would of course hold mathematical tools (maybe our application deals with financial data), and network.py would probably hold tools to send/receive data on networks. As explained before, the __init__.py file is there just to tell Python that util is a package and not just a mere folder. Had this software been organized within modules only, it would have been much harder to infer its structure. I put a module only example under the ch1/files_only folder, see it for yourself: $ tree -v files_only This shows us a completely different picture: files_only/ ├── core.py ├── db.py ├── math.py ├── network.py └── run.py It is a little harder to guess what each module does, right? Now, consider that this is just a simple example, so you can guess how much harder it would be to understand a real application if we couldn't organize the code in packages and modules. How do we use modules and packages When a developer is writing an application, it is very likely that they will need to apply the same piece of logic in different parts of it. For example, when writing a parser for the data that comes from a form that a user can fill in a web page, the application will have to validate whether a certain field is holding a number or not. Regardless of how the logic for this kind of validation is written, it's very likely that it will be needed in more than one place. For example in a poll application, where the user is asked many question, it's likely that several of them will require a numeric answer. For example: [ 22 ] Chapter 1 What is your age How many pets do you own How many children do you have How many times have you been married It would be very bad practice to copy paste (or, more properly said: duplicate) the validation logic in every place where we expect a numeric answer. This would violate the DRY (Don't Repeat Yourself) principle, which states that you should never repeat the same piece of code more than once in your application. I feel the need to stress the importance of this principle: you should never repeat the same piece of code more than once in your application (got the irony?). There are several reasons why repeating the same piece of logic can be very bad, the most important ones being: There could be a bug in the logic, and therefore, you would have to correct it in every place that logic is applied. You may want to amend the way you carry out the validation, and again you would have to change it in every place it is applied. You may forget to fix/amend a piece of logic because you missed it when searching for all its occurrences. This would leave wrong/inconsistent behavior in your application. Your code would be longer than needed, for no good reason. Python is a wonderful language and provides you with all the tools you need to apply all the coding best practices. For this particular example, we need to be able to reuse a piece of code. To be able to reuse a piece of code, we need to have a construct that will hold the code for us so that we can call that construct every time we need to repeat the logic inside it. That construct exists, and it's called function. I'm not going too deep into the specifics here, so please just remember that a function is a block of organized, reusable code which is used to perform a task. Functions can assume many forms and names, according to what kind of environment they belong to, but for now this is not important. We'll see the details when we are able to appreciate them, later on, in the book. Functions are the building blocks of modularity in your application, and they are almost indispensable (unless you're writing a super simple script, you'll use functions all the time). We'll explore functions in Chapter 4, Functions, the Building Blocks of Code. Python comes with a very extensive library, as I already said a few pages ago. Now, maybe it's a good time to define what a library is: a library is a collection of functions and objects that provide functionalities that enrich the abilities of a language. [ 23 ] Introduction and First Steps – Take a Deep Breath For example, within Python's math library we can find a plethora of functions, one of which is the factorial function, which of course calculates the factorial of a number. In mathematics, the factorial of a non-negative integer number N, denoted as N!, is defined as the product of all positive integers less than or equal to N. For example, the factorial of 5 is calculated as: 5! = 5 * 4 * 3 * 2 * 1 = 120 The factorial of 0 is 0! = 1, to respect the convention for an empty product. So, if you wanted to use this function in your code, all you would have to do is to import it and call it with the right input values. Don't worry too much if input values and the concept of calling is not very clear for now, please just concentrate on the import part. We use a library by importing what we need from it, and then we use it. In Python, to calculate the factorial of number 5, we just need the following code: >>> from math import factorial >>> factorial(5) 120 Whatever we type in the shell, if it has a printable representation, will be printed on the console for us (in this case, the result of the function call: 120). So, let's go back to our example, the one with core.py, run.py, util, and so on. In our example, the package util is our utility library. Our custom utility belt that holds all those reusable tools (that is, functions), which we need in our application. Some of them will deal with databases (db.py), some with the network (network.py), and some will perform mathematical calculations (math.py) that are outside the scope of Python's standard math library and therefore, we had to code them for ourselves. We will see in detail how to import functions and use them in their dedicated chapter. Let's now talk about another very important concept: Python's execution model. [ 24 ] Chapter 1 Python's execution model In this paragraph, I would like to introduce you to a few very important concepts, such as scope, names, and namespaces. You can read all about Python's execution model in the official Language reference, of course, but I would argue that it is quite technical and abstract, so let me give you a less formal explanation first. Names and namespaces Say you are looking for a book, so you go to the library and ask someone for the book you want to fetch. They tell you something like "second floor, section X, row three". So you go up the stairs, look for section X, and so on. It would be very different to enter a library where all the books are piled together in random order in one big room. No floors, no sections, no rows, no order. Fetching a book would be extremely hard. When we write code we have the same issue: we have to try and organize it so that it will be easy for someone who has no prior knowledge about it to find what they're looking for. When software is structured correctly, it also promotes code reuse. On the other hand, disorganized software is more likely to expose scattered pieces of duplicated logic. First of all, let's start with the book. We refer to a book by its title and in Python lingo, that would be a name. Python names are the closest abstraction to what other languages call variables. Names basically refer to objects and are introduced by name binding operations. Let's make a quick example (notice that anything that follows a # is a comment): >>> n = 3 # integer number >>> address = "221b Baker Street, NW1 6XE, London" # S. Holmes >>> employee = {... 'age': 45,... 'role': 'CTO',... 'SSN': 'AB1234567',... } >>> # let's print them >>> n 3 >>> address '221b Baker Street, NW1 6XE, London' >>> employee [ 25 ] Introduction and First Steps – Take a Deep Breath {'role': 'CTO', 'SSN': 'AB1234567', 'age': 45} >>> # what if I try to print a name I didn't define? >>> other_name Traceback (most recent call last): File "", line 1, in NameError: name 'other_name' is not defined We defined three objects in the preceding code (do you remember what are the three features every Python object has?): An integer number n (type: int, value: 3) A string address (type: str, value: Sherlock Holmes' address) A dictionary employee (type: dict, value: a dictionary which holds three key/value pairs) Don't worry, I know you're not supposed to know what a dictionary is. We'll see in the next chapter that it's the king of Python data structures. Have you noticed that the prompt changed from >>> to... when I typed in the definition of employee? That's because the definition spans over multiple lines. So, what are n, address and employee? They are names. Names that we can use to retrieve data within our code. They need to be kept somewhere so that whenever we need to retrieve those objects, we can use their names to fetch them. We need some space to hold them, hence: namespaces! A namespace is therefore a mapping from names to objects. Examples are the set of built-in names (containing functions that are always accessible for free in any Python program), the global names in a module, and the local names in a function. Even the set of attributes of an object can be considered a namespace. The beauty of namespaces is that they allow you to define and organize your names with clarity, without overlapping or interference. For example, the namespace associated with that book we were looking for in the library can be used to import the book itself, like this: from library.second_floor.section_x.row_three import book We start from the library namespace, and by means of the dot (.) operator, we walk into that namespace. Within this namespace, we look for second_floor, and again we walk into it with the. operator. We then walk into section_x, and finally within the last namespace, row_tree, we find the name we were looking for: book. [ 26 ] Chapter 1 Walking through a namespace will be clearer when we'll be dealing with real code examples. For now, just keep in mind that namespaces are places where names are associated to objects. There is another concept, which is closely related to that of a namespace, which I'd like to briefly talk about: the scope. Scopes According to Python's documentation, a scope is a textual region of a Python program, where a namespace is directly accessible. Directly accessible means that when you're looking for an unqualified reference to a name, Python tries to find it in the namespace. Scopes are determined statically, but actually during runtime they are used dynamically. This means that by inspecting the source code you can tell what the scope of an object is, but this doesn't prevent the software to alter that during runtime. There are four different scopes that Python makes accessible (not necessarily all of them present at the same time, of course): The local scope, which is the innermost one and contains the local names. The enclosing scope, that is, the scope of any enclosing function. It contains non-local names and also non-global names. The global scope contains the global names. The built-in scope contains the built-in names. Python comes with a set of functions that you can use in a off-the-shelf fashion, such as print, all, abs, and so on. They live in the built-in scope. The rule is the following: when we refer to a name, Python starts looking for it in the current namespace. If the name is not found, Python continues the search to the enclosing scope and this continue until the built-in scope is searched. If a name hasn't been found after searching the built-in scope, then Python raises a NameError exception, which basically means that the name hasn't been defined (you saw this in the preceding example). The order in which the namespaces are scanned when looking for a name is therefore: local, enclosing, global, built-in (LEGB). [ 27 ] Introduction and First Steps – Take a Deep Breath This is all very theoretical, so let's see an example. In order to show you Local and Enclosing namespaces, I will have to define a few functions. Don't worry if you are not familiar with their syntax for the moment, we'll study functions in Chapter 4, Functions, the Building Blocks of Code. Just remember that in the following code, when you see def, it means I'm defining a function. scopes1.py # Local versus Global # we define a function, called local def local(): m = 7 print(m) m = 5 print(m) # we call, or `execute` the function local local() In the preceding example, we define the same name m, both in the global scope and in the local one (the one defined by the function local). When we execute this program with the following command (have you activated your virtualenv?): $ python scopes1.py We see two numbers printed on the console: 5 and 7. What happens is that the Python interpreter parses the file, top to bottom. First, it finds a couple of comment lines, which are skipped, then it parses the definition of the function local. When called, this function does two things: it sets up a name to an object representing number 7 and prints it. The Python interpreter keeps going and it finds another name binding. This time the binding happens in the global scope and the value is 5. The next line is a call to the print function, which is executed (and so we get the first value printed on the console: 5). After this, there is a call to the function local. At this point, Python executes the function, so at this time, the binding m = 7 happens and it's printed. One very important thing to notice is that the part of the code that belongs to the definition of the function local is indented by four spaces on the right. Python in fact defines scopes by indenting the code. You walk into a scope by indenting and walk out of it by unindenting. Some coders use two spaces, others three, but the suggested number of spaces to use is four. It's a good measure to maximize readability. We'll talk more about all the conventions you should embrace when writing Python code later. [ 28 ] Chapter 1 What would happen if we removed that m = 7 line? Remember the LEGB rule. Python would start looking for m in the local scope (function local), and, not finding it, it would go to the next enclosing scope. The next one in this case is the global one because there is no enclosing function wrapped around local. Therefore, we would see two number 5 printed on the console. Let's actually see how the code would look like: scopes2.py # Local versus Global def local(): # m doesn't belong to the scope defined by the local function # so Python will keep looking into the next enclosing scope. # m is finally found in the global scope print(m, 'printing from the local scope') m = 5 print(m, 'printing from the global scope') local() Running scopes2.py will print this: (.lpvenv)fab@xps:ch1$ python scopes2.py 5 printing from the global scope 5 printing from the local scope As expected, Python prints m the first time, then when the function local is called, m isn't found in its scope, so Python looks for it following the LEGB chain until m is found in the global scope. Let's see an example with an extra layer, the enclosing scope: scopes3.py # Local, Enclosing and Global def enclosing_func(): m = 13 def local(): # m doesn't belong to the scope defined by the local # function so Python will keep looking into the next # enclosing scope. This time m is found in the enclosing # scope print(m, 'printing from the local scope') # calling the function local local() [ 29 ] Introduction and First Steps – Take a Deep Breath m = 5 print(m, 'printing from the global scope') enclosing_func() Running scopes3.py will print on the console: (.lpvenv)fab@xps:ch1$ python scopes3.py 5 printing from the global scope 13 printing from the local scope As you can see, the print instruction from the function local is referring to m as before. m is still not defined within the function itself, so Python starts walking scopes following the LEGB order. This time m is found in the enclosing scope. Don't worry if this is still not perfectly clear for now. It will come to you as we go through the examples in the book. The Classes section of the Python tutorial (official documentation) has an interesting paragraph about scopes and namespaces. Make sure you read it at some point if you wish for a deeper understanding of the subject. Before we finish off this chapter, I would like to talk a bit more about objects. After all, basically everything in Python is an object, so I think they deserve a bit more attention. Downloading the example code You can download the example code files from your account at http://www.packtpub.com for all the Packt Publishing books you have purchased. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you. Object and classes When I introduced objects in the A proper introduction section, I said that we use them to represent real-life objects. For example, we sell goods of any kind on the Web nowadays and we need to be able to handle, store, and represent them properly. But objects are actually so much more than that. Most of what you will ever do, in Python, has to do with manipulating objects. So, without going too much into detail (we'll do that in Chapter 6, Advanced Concepts – OOP, Decorators, and Iterators), I want to give you the in a nutshell kind of explanation about classes and objects. [ 30 ] Chapter 1 We've already seen that objects are Python's abstraction for data. In fact, everything in Python is an object. Numbers, strings (data structures that hold text), containers, collections, even functions. You can think of them as if they were boxes with at least three features: an ID (unique), a type, and a value. But how do they come to life? How do we create them? How to we write our own custom objects? The answer lies in one simple word: classes. Objects are, in fact, instances of classes. The beaut

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