Learning Python: Learn to code like a professional with Python - an open source, versatile, and powerful programming language

Table of Contents

Learning Python

Credits

About the Author

Acknowledgements

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Introduction and First Steps – Take a Deep Breath

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 – the great debate

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

Object and classes

Guidelines on how to write good code

The Python culture

A note on the IDEs

Summary

2. Built-in Data Types

Everything is an object

Mutable or immutable? That is the question

Numbers

Integers

Booleans

Reals

Complex numbers

Fractions and decimals

Immutable sequences

Strings and bytes

Encoding and decoding strings

Indexing and slicing strings

Tuples

Mutable sequences

Lists

Byte arrays

Set types

Mapping types – dictionaries

The collections module

Named tuples

Defaultdict

ChainMap

Final considerations

Small values caching

How to choose data structures

About indexing and slicing

About the names

Summary

3. 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 this all together

Example 1 – a prime generator

Example 2 – applying discounts

A quick peek at the itertools module

Infinite iterators

Iterators terminating on the shortest input sequence

Combinatoric generators

Summary

4. Functions, the Building Blocks of Code

Why use functions?

Reduce code duplication

Splitting a complex task

Hide implementation details

Improve readability

Improve traceability

Scopes and name resolution

The global and nonlocal statements

Input parameters

Argument passing

Assignment to argument names don'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

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

5. Saving Time and Memory

map, zip, and filter

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

6. Advanced Concepts – OOP, Decorators, and Iterators

Decorators

A decorator factory

Object-oriented programming

The simplest Python class

Class and object namespaces

Attribute shadowing

I, me, and myself – using the self variable

Initializing an instance

OOP is about code reuse

Inheritance and composition

Accessing a base class

Multiple inheritance

Method resolution order

Static and class methods

Static methods

Class methods

Private methods and name mangling

The property decorator

Operator overloading

Polymorphism – a brief overview

Writing a custom iterator

Summary

7. 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

A classic unit test example

Making a test fail

Interface testing

Comparing tests with and without mocks

Boundaries and granularity

A more interesting example

Test-driven development

Exceptions

Profiling Python

When to profile?

Summary

8. The Edges – 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 to improve the application?

Where do we go from here?

The tkinter.tix module

The turtle module

wxPython, PyQt, and PyGTK

The principle of least astonishment

Threading considerations

Summary

9. Data Science

IPython and Jupyter 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

10. Web Development Done Right

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

11. 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

12. Summing Up – A Complete Example

The challenge

Our implementation

Implementing the Django interface

The setup

The model layer

A simple form

The view layer

Imports and home view

Listing all records

Creating records

Updating records

Deleting records

Setting up the URLs

The template layer

Home and footer templates

Listing all records

Creating and editing records

Talking to the API

Deleting records

Implementing the Falcon API

The main application

Writing the helpers

Coding the password validator

Coding the password generator

Writing the handlers

Coding the password validator handler

Coding the password generator handler

Running the API

Testing the API

Testing the helpers

Testing the handlers

Where do you go from here?

Summary

A word of farewell

Index

Learning Python

Learning Python

Copyright © 2015 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

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First published: December 2015

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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

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Cover Work

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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.

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: Open up a Python console, and type import this.

A block of code is set as follows:

# we define a function, called local

def local():

    m = 7

    print(m)

m = 5

print(m)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

# we define a function, called local

def local():

m = 7

    print(m)

m = 5

print(m)

Any command-line input or output is written as follows:

>>> from math import factorial >>> factorial(5) 120

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: To open the console on Windows, go to the Start menu, choose Run, and type cmd.

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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Errata

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Questions

If you have a problem with any aspect of this book, you can contact us at <[email protected]>, and we will do our best to address the problem.

Chapter 1. Introduction and First Steps – Take a Deep Breath

According to Wikipedia, computer programming is:

...a process that leads from an original formulation of a computing problem to executable computer programs. Programming involves activities such as analysis, developing understanding, generating algorithms, verification of requirements of algorithms including their correctness and resources consumption, and implementation (commonly referred to as coding) of algorithms in a target programming language.

In a nutshell, coding is telling a computer to do something using a language it understands.

Computers are very powerful tools, but unfortunately, they can't think for themselves. So they need to be told everything. They need to be told how to perform a task, how to evaluate a condition to decide which path to follow, how to handle data that comes from a device such as the network or a disk, and how to react when something unforeseen happens, say, something is broken or missing.

You can code in many different styles and languages. Is it hard? I would say yes and no. It's a bit like writing. Everybody can learn how to write, and you can too. But what if you wanted to become a poet? Then writing alone is not enough. You have to acquire a whole other set of skills and this will take a longer and greater effort.

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 the Python Package Index (PyPI). When you code Python and you realize that you need a certain feature, in most cases, there is at least one library where that feature has already been implemented for you.

Software quality

Python is heavily focused on readability, coherence, and quality. The language uniformity allows for high readability and this is crucial nowadays where code is more of a collective effort than a solo experience. Another important aspect of Python is its intrinsic multi-paradigm nature. You can use it as scripting language, but you also can exploit object-oriented, imperative, and functional programming styles. It is versatile.

Software integration

Another important aspect is that Python can be extended and integrated with many other languages, which means that even when a company is using a different language as their mainstream tool, Python can come in and act as a glue agent between complex applications that need to talk to each other in some way. This is kind of an advanced topic, but in the real world, this feature is very important.

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