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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application.
The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned.
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Veröffentlichungsjahr: 2015
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Table of Contents
Learning Python
Learning Python
Copyright © 2015 Packt Publishing
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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.
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Credits
Author
Fabrizio Romano
Reviewers
Simone Burol
Julio Vicente Trigo Guijarro
Veit Heller
Commissioning Editor
Akram Hussain
Acquisition Editor
Indrajit Das
Content Development Editors
Samantha Gonsalves
Adrian Raposo
Technical Editor
Siddhi Rane
Copy Editors
Janbal Dharmaraj
Kevin McGowan
Project Coordinator
Kinjal Bari
Proofreader
Safis Editing
Indexer
Priya Sane
Graphics
Kirk D'Penha
Abhinash Sahu
Production Coordinator
Melwyn D'sa
Cover Work
Melwyn D'sa
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.
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To Alan Turing, the father of Computer Science.
To Guido Van Rossum, the father of Python.
To Adriano Romano, my father, my biggest fan.
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.
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.
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.
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Chapter 1. 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 proverbAccording 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.
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.
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.
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.
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.
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 thePython 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.
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.
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.
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:
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.
Tip
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.
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.
Tip
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.
Note
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.
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.
Note
I will use the term console interchangeably to indicate the Linux console, the Windows command prompt, and the Macterminal. I will also indicate the command-line prompt with the Linux default format, like this:
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:
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, virtualenvis 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!
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.
Note
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:
Probably, the easiest way is to use pip though, with the following command:
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.
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:
Note
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.
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):
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:
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.
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.
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.
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.
Note
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):
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:
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.
