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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Learn Python Programming is a quick, thorough, and practical introduction to Python - an extremely flexible and powerful programming language that can be applied to many disciplines.
Unlike other books, it doesn't bore you with elaborate explanations of the basics but gets you up-and-running, using the language. You will begin by learning the fundamentals of Python so that you have a rock-solid foundation to build upon.
You will explore the foundations of Python programming and learn how Python can be manipulated to achieve results. Explore different programming paradigms and find the best approach to a situation; understand how to carry out performance optimization and effective debugging; control the flow of a program; and utilize an interchange format to exchange data. You'll also walk through cryptographic services in Python and understand secure tokens.
Learn Python Programming will give you a thorough understanding of the Python language. You'll learn how to write programs, build websites, and work with data by harnessing Python's renowned data science libraries. Filled with real-world examples and projects, the book covers various types of applications, and concludes by building real-world projects based on the concepts you have learned.
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Seitenzahl: 733
Veröffentlichungsjahr: 2018
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Learn Python Programming Second Edition
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First published: December 2015 Second edition: June 2018
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Foreword
I first got to know Fabrizio when he became our lead developer a few years ago. It was quickly apparent that he was one of those rare people who combine rigorous technical expertise with a genuine care for the people around him and a true passion to mentor and teach. Whether it was designing a system, pairing to write code, doing code reviews, or even organizing team card games at lunch, Fab was always thinking not only about the best way to do the job, but also about how to make sure that the entire team had the skills and motivation to do their best.
You'll meet the same wise and caring guide in this book. Every chapter, every example, every explanation has been carefully thought out, driven by a desire to impart the best and most accurate understanding of the technology, and to do it with kindness. Fab takes you under his wing to teach you both Python's syntax and its best practices.
I'm also impressed with the scope of this book. Python has grown and evolved over the years, and it now spans an enormous ecosystem, being used for web development, routine data handling, and ETL, and increasingly for data science. If you are new to the Python ecosystem, it's often hard to know what to study to achieve your goals. In this book, you will find useful examples exposing you to many different uses of Python, which will help guide you as you move through the breadth that Python offers.
I hope you will enjoy learning Python and become a member of our global community. I'm proud to have been asked to write this, but above all, I'm pleased that Fab will be your guide.
Naomi Ceder
Python Software Foundation Fellow
Contributors
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, Reiki master and teacher, and a member of CNHC.
He moved to London in 2011 to work for companies such as Glasses Direct, TBG/Sprinklr, and student.com. He now works at Sohonet as a Principal Engineer/Team Lead.
He has given talks on Teaching Python and TDD at two editions of EuroPython, and at Skillsmatter and ProgSCon, in London.
About the reviewers
Heinrich Kruger was born in South Africa in 1981. He obtained a bachelor's degree with honors from the University of the Witwatersrand in South Africa in 2005 and a master's degree in computer science from Utrecht University in the Netherlands in 2008.
He worked as a research assistant at Utrecht University from 2009 until 2013 and has been working as a professional software developer developer since 2014. He has been using Python for personal and projects and in his studies since 2004, and professionally since 2014.
Julio Vicente Trigo Guijarro is a computer science engineer with over a decade of experience in software development. He completed his studies at the University of Alicante, Spain, in 2007 and moved to London in 2010.
He has been using Python since 2012 and currently works as a senior software developer and team lead at Sohonet, developing real-time collaboration applications for the media industry.
He is also a certified ScrumMaster and was one of the technical reviewers of the first edition of this book.
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Table of Contents
Title Page
Copyright and Credits
Learn Python Programming Second Edition
Dedication
Packt Upsell
Why subscribe?
PacktPub.com
Foreword
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Reviews
A Gentle Introduction to Python
A proper introduction
Enter the Python
About Python
Portability
Coherence
Developer productivity
An extensive library
Software quality
Software integration
Satisfaction and enjoyment
What are the drawbacks?
Who is using Python today?
Setting up the environment
Python 2 versus Python 3
Installing Python
Setting up the Python interpreter
About virtualenv
Your first virtual environment
Your friend, the console
How you can run a Python program
Running Python scripts
Running the Python interactive shell
Running Python as a service
Running Python as a GUI application
How is Python code organized?
How do we use modules and packages?
Python's execution model
Names and namespaces
Scopes
Objects and classes
Guidelines on how to write good code
The Python culture
A note on IDEs
Summary
Built-in Data Types
Everything is an object
Mutable or immutable? That is the question
Numbers
Integers
Booleans
Real numbers
Complex numbers
Fractions and decimals
Immutable sequences
Strings and bytes
Encoding and decoding strings
Indexing and slicing strings
String formatting
Tuples
Mutable sequences
Lists
Byte arrays
Set types
Mapping types – dictionaries
The collections module
namedtuple
defaultdict
ChainMap
Enums
Final considerations
Small values caching
How to choose data structures
About indexing and slicing
About the names
Summary
Iterating and Making Decisions
Conditional programming
A specialized else – elif
The ternary operator
Looping
The for loop
Iterating over a range
Iterating over a sequence
Iterators and iterables
Iterating over multiple sequences
The while loop
The break and continue statements
A special else clause
Putting all this together
A prime generator
Applying discounts
A quick peek at the itertools module
Infinite iterators
Iterators terminating on the shortest input sequence
Combinatoric generators
Summary
Functions, the Building Blocks of Code
Why use functions?
Reducing code duplication
Splitting a complex task
Hiding implementation details
Improving readability
Improving traceability
Scopes and name resolution
The global and nonlocal statements
Input parameters
Argument-passing
Assignment to argument names doesn't affect the caller
Changing a mutable affects the caller
How to specify input parameters
Positional arguments
Keyword arguments and default values
Variable positional arguments
Variable keyword arguments
Keyword-only arguments
Combining input parameters
Additional unpacking generalizations
Avoid the trap! Mutable defaults
Return values
Returning multiple values
A few useful tips
Recursive functions
Anonymous functions
Function attributes
Built-in functions
One final example
Documenting your code
Importing objects
Relative imports
Summary
Saving Time and Memory
The map, zip, and filter functions
map
zip
filter
Comprehensions
Nested comprehensions
Filtering a comprehension
dict comprehensions
set comprehensions
Generators
Generator functions
Going beyond next
The yield from expression
Generator expressions
Some performance considerations
Don't overdo comprehensions and generators
Name localization
Generation behavior in built-ins
One last example
Summary
OOP, Decorators, and Iterators
Decorators
A decorator factory
Object-oriented programming (OOP)
The simplest Python class
Class and object namespaces
Attribute shadowing
Me, myself, and I – using the self variable
Initializing an instance
OOP is about code reuse
Inheritance and composition
Accessing a base class
Multiple inheritance
Method resolution order
Class and static methods
Static methods
Class methods
Private methods and name mangling
The property decorator
Operator overloading
Polymorphism – a brief overview
Data classes
Writing a custom iterator
Summary
Files and Data Persistence
Working with files and directories
Opening files
Using a context manager to open a file
Reading and writing to a file
Reading and writing in binary mode
Protecting against overriding an existing file
Checking for file and directory existence
Manipulating files and directories
Manipulating pathnames
Temporary files and directories
Directory content
File and directory compression
Data interchange formats
Working with JSON
Custom encoding/decoding with JSON
IO, streams, and requests
Using an in-memory stream
Making HTTP requests
Persisting data on disk
Serializing data with pickle
Saving data with shelve
Saving data to a database
Summary
Testing, Profiling, and Dealing with Exceptions
Testing your application
The anatomy of a test
Testing guidelines
Unit testing 
Writing a unit test
Mock objects and patching
Assertions
Testing a CSV generator
Boundaries and granularity
Testing the export function
Final considerations
Test-driven development
Exceptions
Profiling Python
When to profile?
Summary
Cryptography and Tokens
The need for cryptography
Useful guidelines
Hashlib
Secrets
Random numbers
Token generation
Digest comparison
HMAC
JSON Web Tokens
Registered claims
Time-related claims
Auth-related claims
Using asymmetric (public-key) algorithms
Useful references
Summary
Concurrent Execution
Concurrency versus parallelism
Threads and processes – an overview
Quick anatomy of a thread
Killing threads
Context-switching
The Global Interpreter Lock
Race conditions and deadlocks
Race conditions
Scenario A – race condition not happening
Scenario B – race condition happening
Locks to the rescue
Scenario C – using a lock
Deadlocks
Quick anatomy of a process
Properties of a process
Multithreading or multiprocessing?
Concurrent execution in Python
Starting a thread
Starting a process
Stopping threads and processes
Stopping a process
Spawning multiple threads
Dealing with race conditions
A thread's local data
Thread and process communication
Thread communication
Sending events
Inter-process communication with queues
Thread and process pools
Using a process to add a timeout to a function
Case examples
Example one – concurrent mergesort
Single-thread mergesort
Single-thread multipart mergesort
Multithreaded mergesort
Multiprocess mergesort
Example two – batch sudoku-solver
What is Sudoku?
Implementing a sudoku-solver in Python
Solving sudoku with multiprocessing
Example three – downloading random pictures
Downloading random pictures with asyncio
Summary
Debugging and Troubleshooting
Debugging techniques
Debugging with print
Debugging with a custom function
Inspecting the traceback
Using the Python debugger
Inspecting log files
Other techniques
Profiling
Assertions
Where to find information
Troubleshooting guidelines
Using console editors
Where to inspect
Using tests to debug
Monitoring
Summary
GUIs and Scripts
First approach – scripting
The imports
Parsing arguments
The business logic
Second approach – a GUI application
The imports
The layout logic
The business logic
Fetching the web page
Saving the images
Alerting the user
How can we improve the application?
Where do we go from here?
The turtle module
wxPython, PyQt, and PyGTK
The principle of least astonishment
Threading considerations
Summary
Data Science
IPython and Jupyter Notebook
Installing the required libraries
Using Anaconda
Starting a Notebook
Dealing with data
Setting up the Notebook
Preparing the data
Cleaning the data
Creating the DataFrame
Unpacking the campaign name
Unpacking the user data
Cleaning everything up
Saving the DataFrame to a file
Visualizing the results
Where do we go from here?
Summary
Web Development
What is the web?
How does the web work?
The Django web framework
Django design philosophy
The model layer
The view layer
The template layer
The Django URL dispatcher
Regular expressions
A regex website
Setting up Django
Starting the project
Creating users
Adding the Entry model
Customizing the admin panel
Creating the form
Writing the views
The home view
The entry list view
The form view
Tying up URLs and views
Writing the templates
The future of web development
Writing a Flask view
Building a JSON quote server in Falcon
Summary
Farewell
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
When I started writing the first edition of this book, I knew very little about what was expected. Gradually, I learned how to convert each topic into a story. I wanted to talk about Python by offering useful, simple, easy-to-grasp examples, but, at the same time, I wanted to pour my own experience into the pages, anything I've learned over the years that I thought would be valuable for the reader—something to think about, reflect upon, and hopefully assimilate. Readers may disagree and come up with a different way of doing things, but hopefully a better way.
I wanted this book to not just be about the language but about programming. The art of programming, in fact, comprises many aspects, and language is just one of them.
Another crucial aspect of programming is independence. The ability to unblock yourself when you hit a wall and don't know what to do to solve the problem you're facing. There is no book that can teach it, so I thought, instead of trying to teach that aspect, I will try and train the reader in it. Therefore, I left comments, questions, and remarks scattered throughout the whole book, hoping to inspire the reader. I hoped that they would take the time to browse the Web or the official documentation, to dig deeper, learn more, and discover the pleasure of finding things out by themselves.
Finally, I wanted to write a book that, even in its presentation, would be slightly different. So, I decided, with my editor, to write the first part in a theoretical way, presenting topics that would describe the characteristics of Python, and to have a second part made up of various real-life projects, to show the reader how much can be achieved with this language.
With all these goals in mind, I then had to face the hardest challenge: take all the content I wanted to write and make it fit in the amount of pages that were allowed. It has been tough, and sacrifices were made.
My efforts have been rewarded though: to this day, after almost 3 years, I still receive lovely messages from readers, every now and then, who thank me and tell me things like your book has empowered me. To me, it is the most beautiful compliment. I know that the language might change and pass, but I have managed to share some of my knowledge with the reader, and that piece of knowledge will stick with them.
And now, I have written the second edition of this book, and this time, I had a little more space. So I decided to add a chapter about IO, which was desperately needed, and I even had the opportunity to add two more chapters, one about secrets and one about concurrent execution. The latter is definitely the most challenging chapter in the whole book, and its purpose is that of stimulating the reader to reach a level where they will be able to easily digest the code in it and understand its concepts.
I have kept all the original chapters, except for the last one that was slightly redundant. They have all been refreshed and updated to the latest version of Python, which is 3.7 at the time of writing.
When I look at this book, I see a much more mature product. There are more chapters, and the content has been reorganized to better fit the narrative, but the soul of the book is still there. The main and most important point, empowering the reader, is still very much intact.
I hope that this edition will be even more successful than the previous one, and that it will help the readers become great programmers. I hope to help them develop critical thinking, great skills, and the ability to adapt over time, thanks to the solid foundation they have acquired from the book.
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 to 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 for providing a wide range of considerations and suggestions collected over two decades of experience.
What this book covers
Chapter 1, A Gentle Introduction to Python, introduces you to fundamental programming concepts. It guides you through 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, 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, Files and Data Persistence, teaches you how to deal with files, streams, data interchange formats, and databases, among other things.
Chapter 8, 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 9, Cryptography and Tokens, touches upon the concepts of security, hashes, encryption, and tokens, which are part of day-to-day programming at present.
Chapter 10, Concurrent Execution, is a challenging chapter that describes how to do many things at the same time. It provides an introduction to the theoretical aspects of this subject and then presents three nice exercises that are developed with different techniques, thereby enabling the reader to understand the differences between the paradigms presented.
Chapter 11, Debugging and Troubleshooting, shows you the main methods for debugging your code and some examples on how to apply them.
Chapter 12, GUIs and Scripts, guides you through an example from two different points of view. They are at opposite ends of the spectrum: one implementation is a script, and another one is a proper graphical user interface application.
Chapter 13, Data Science, introduces a few key concepts and a very special tool, the Jupyter Notebook.
Chapter 14, Web Development, introduces the fundamentals of web development and delivers a project using the Django web framework. The example will be based on regular expressions.
To get the most out of 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 3.7, but it should also work, for the most part, with any recent Python 3.* version. I have given guidelines on how to install Python on your operating system. The procedures to do that change all the time, so you will need to refer to the most up-to-date guide on the Web to find precise setup instructions. 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.
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A Gentle Introduction to Python
According to Wikipedia, computer programming is:
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. They need to be told everything: 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 and that 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 concerning them (the kettle is plugged in, the mug is clean, and 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 to 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).
In this chapter, we are going to cover the following:
Python's characteristics and ecosystem
Guidelines on how to get up and running with Python and virtual environments
How to run Python programs
How to organize Python code and Python's execution model
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 about the things it has to do. Where does the action happen? In many places: the computer memory, hard drives, network cables, the 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 email, 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 heights and weights 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 among 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 represent 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 (https://docs.python.org/3/reference/datamodel.html):
We'll take a closer look at Python objects in Chapter6, 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 ID 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 creation 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. Its 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 did I finally have 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, 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 epitomizes 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 specific operating system (OS) 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, as well as less skimming through the documentation, and less need for mappings 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 for 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 coding is more of a collective effort than a solo endeavor. Another important aspect of Python is its intrinsic multiparadigm nature. You can use it as a 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, there is 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 of why I would recommend Python to everyone. 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 its 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. Moreover, many programs spend a great proportion of the time waiting for IO operations to complete; therefore, the raw execution speed is often a secondary factor to the overall performance. When it comes to number crunching though, one can switch to faster Python implementations, such as PyPy, which provides an average five-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 has been used to drive the backend of services such as Spotify and Instagram, where performance is a concern. Nonetheless, Python has done 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, Blender 3D, Pixar, NASA, the NSA, Red Hat, Nokia, IBM, Netflix, Yelp, Intel, Cisco, HP, Qualcomm, and JPMorgan Chase, to name just a few.
Even games such as Battlefield 2, Civilization IV, 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. Several prestigious universities have also adopted Python as their main language in computer science courses.
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
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 in some respects.
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 from Version 2 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 them, 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 has hindered 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, though, 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 library (the name comes from the multiplication 2 x 3, due to the porting from Version 2 to 3), which helps introspecting and adapting the behavior according to the version used. According to PEP 373 (https://legacy.python.org/dev/peps/pep-0373/), the end of life (EOL) of Python 2.7 has been set to 2020, and there won't be a Python 2.8, so this is the time when companies that have projects running in Python 2 need to start devising an upgrade strategy to move to Python 3 before it's too late.
On my box (MacBook Pro), this is the latest Python version I have:
>>> import sys
>>> print(sys.version)
3.7.0a3 (default, Jan 27 2018, 00:46:45)
[Clang 9.0.0 (clang-900.0.39.2)]
So you can see that the version is an alpha release of Python 3.7, which will be released in June 2018. 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 Python 3.7. Even though at the moment the final version might still be slightly different than what I have, I will make sure that all the code and examples are up to date with 3.7 by the time the book is published.
Some of the code can also run in Python 2.7, either as it is or with minor tweaks, but at this point in time, 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 instructions 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 in the way that 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.
I am conscious that the majority of readers would probably have preferred to have guidelines in the book. I doubt it would have made their life much easier, as I strongly believe that if you want to get started with Python you have to put in that initial effort in order to get familiar with the ecosystem. It is very important, and it will boost your confidence to face the material in the chapters ahead. If you get stuck, remember that 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 macOS, 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 and macOS seem to be the most user-friendly OSes for Python programmers; Windows, on the other hand, is the one that requires the biggest effort.
My current system is a MacBook Pro, and this is what I will use throughout the book, along with Python 3.7.
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.
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.
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 in this book, please make sure you are running the console with administrator rights.
On the macOS 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 Macintosh Terminal. I will also indicate the command-line prompt with the Linux default format, like this:
$ sudo apt-get update
If you're not familiar with that, please take some time to learn the basics on how a console works. In a nutshell, after the $ sign, you normally find an instruction that you have to type. Pay attention to capitalization and spaces, as they are very important.
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 roughly what you should see when you run Python (it will change in some details according to the version and OS):
$ python3.7
Python 3.7.0a3 (default, Jan 27 2018, 00:46:45)
[Clang 9.0.0 (clang-900.0.39.2)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>
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 14, Web Development. 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. She 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.
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 follow the instructions you can find on the virtualenv official website: https://virtualenv.pypa.io.
You will find that one of the most common ways to install virtualenv is by using pip, a package management system used to install and manage software packages written in Python.
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 virtualenv, when you want to work with it, is to activate it. Activating 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 my Macintosh console. We will:
Create a folder named
learn.pp
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.
Within the
learn.pp
folder, we will create a virtual environment called
learnpp
.
After creating the virtual environment, we will activate it. The methods are slightly different between Linux, macOS, and Windows.
Then, we'll make sure that we are running the desired Python version (3.7.*) by running the Python interactive shell.
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 (note that you might get a slightly different result, according to your OS, Python version, and so on) on the macOS (commands that start with a # are comments, spaces have been introduced for readability, and ⇢ indicates where the line has wrapped around due to lack of space):
fabmp:srv fab$ # step 1 - create folder
fabmp:srv fab$ mkdir learn.pp
fabmp:srv fab$ cd learn.pp
fabmp:learn.pp fab$ # step 2 - create virtual environment
fabmp:learn.pp fab$ which python3.7
/Users/fab/.pyenv/shims/python3.7
fabmp:learn.pp fab$
virtualenv -p
⇢ /Users/fab/.pyenv/shims/python3.7 learnpp
Running virtualenv with interpreter /Users/fab/.pyenv/shims/python3.7
Using base prefix '/Users/fab/.pyenv/versions/3.7.0a3'
New python executable in /Users/fab/srv/learn.pp/learnpp/bin/python3.7
Also creating executable in /Users/fab/srv/learn.pp/learnpp/bin/python
Installing setuptools, pip, wheel...done.
fabmp:learn.pp fab$ # step 3 - activate virtual environment
fabmp:learn.pp fab$ source learnpp/bin/activate
(learnpp) fabmp:learn.pp fab$ # step 4 - verify which python
(learnpp) fabmp:learn.pp fab$ which python
/Users/fab/srv/learn.pp/learnpp/bin/python
(learnpp) fabmp:learn.pp fab$ python
Python 3.7.0a3 (default, Jan 27 2018, 00:46:45)
[Clang 9.0.0 (clang-900.0.39.2)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> exit()
(learnpp) fabmp:learn.pp fab$ # step 5 - deactivate
(learnpp) fabmp:learn.pp fab$ deactivate
fabmp:learn.pp fab$
Notice that I had to tell virtualenv explicitly
