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Learn Python Programming
Learn Python Programming

Learn Python Programming: The no-nonsense, beginner's guide to programming, data science, and web development with Python 3.7 , Second Edition

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Learn Python Programming

Built-in Data Types

"Data! Data! Data!" he cried impatiently. "I can't make bricks without clay."
– Sherlock Holmes – The Adventure of the Copper Beeches

Everything you do with a computer is managing data. Data comes in many different shapes and flavors. It's the music you listen to, the movies you stream, the PDFs you open. Even the source of the chapter you're reading at this very moment is just a file, which is data.

Data can be simple, an integer number to represent an age, or complex, like an order placed on a website. It can be about a single object or about a collection of them. Data can even be about data, that is, metadata. Data that describes the design of other data structures or data that describes application data or its context. In Python, objects are abstraction for data, and Python has an amazing variety of data...

Everything is an object

Before we delve into the specifics, I want you to be very clear about objects in Python, so let's talk a little bit more about them. As we already said, everything in Python is an object. But what really happens when you type an instruction like age = 42 in a Python module?

If you go to http://pythontutor.com/, you can type that instruction into a text box and get its visual representation. Keep this website in mind; it's very useful to consolidate your understanding of what goes on behind the scenes.

So, what happens is that an object is created. It gets an id, the type is set to int (integer number), and the value to 42. A name age is placed in the global namespace, pointing to that object. Therefore, whenever we are in the global namespace, after the execution of that line, we can retrieve that object by simply accessing it through its name...

Mutable or immutable? That is the question

A first fundamental distinction that Python makes on data is about whether or not the value of an object changes. If the value can change, the object is called mutable, while if the value cannot change, the object is called immutable.

It is very important that you understand the distinction between mutable and immutable because it affects the code you write, so here's a question:

>>> age = 42
>>> age
42
>>> age = 43 #A
>>> age
43

In the preceding code, on the line #A, have I changed the value of age? Well, no. But now it's 43 (I hear you say...). Yes, it's 43, but 42 was an integer number, of the type int, which is immutable. So, what happened is really that on the first line, age is a name that is set to point to an int object, whose value is 42. When we type age = 43, what happens is that...

Numbers

Let's start by exploring Python's built-in data types for numbers. Python was designed by a man with a master's degree in mathematics and computer science, so it's only logical that it has amazing support for numbers.

Numbers are immutable objects.

Integers

Python integers have an unlimited range, subject only to the available virtual memory. This means that it doesn't really matter how big a number you want to store is: as long as it can fit in your computer's memory, Python will take care of it. Integer numbers can be positive, negative, and 0 (zero). They support all the basic mathematical operations, as shown in the following example:

>>> a = 14
>>> b = 3
>>&gt...

Immutable sequences

Let's start with immutable sequences: strings, tuples, and bytes.

Strings and bytes

Textual data in Python is handled with str objects, more commonly known as strings. They are immutable sequences of Unicode code points. Unicode code points can represent a character, but can also have other meanings, such as formatting data, for example. Python, unlike other languages, doesn't have a char type, so a single character is rendered simply by a string of length 1.

Unicode is an excellent way to handle data, and should be used for the internals of any application. When it comes to storing textual data though, or sending it on the network, you may want to encode it, using an appropriate encoding for...

Mutable sequences

Mutable sequences differ from their immutable sisters in that they can be changed after creation. There are two mutable sequence types in Python: lists and byte arrays. I said before that the dictionary is the king of data structures in Python. I guess this makes the list its rightful queen.

Lists

Python lists are mutable sequences. They are very similar to tuples, but they don't have the restrictions of immutability. Lists are commonly used to storing collections of homogeneous objects, but there is nothing preventing you from store heterogeneous collections as well. Lists can be created in many different ways. Let's see an example:

>>> []  # empty list
[]
>>> list() # same as...

Set types

Python also provides two set types, set and frozenset. The set type is mutable, while frozenset is immutable. They are unordered collections of immutable objects. Hashability is a characteristic that allows an object to be used as a set member as well as a key for a dictionary, as we'll see very soon.

From the official documentation: An object is hashable if it has a hash value which never changes during its lifetime, and can be compared to other objects. Hashability makes an object usable as a dictionary key and a set member, because these data structures use the hash value internally. All of Python’s immutable built-in objects are hashable while mutable containers are not.

Objects that compare equally must have the same hash value. Sets are very commonly used to test for membership, so let's introduce the in operator in the following example:

>...

Mapping types – dictionaries

Of all the built-in Python data types, the dictionary is easily the most interesting one. It's the only standard mapping type, and it is the backbone of every Python object.

A dictionary maps keys to values. Keys need to be hashable objects, while values can be of any arbitrary type. Dictionaries are mutable objects. There are quite a few different ways to create a dictionary, so let me give you a simple example of how to create a dictionary equal to {'A': 1, 'Z': -1} in five different ways:

>>> a = dict(A=1, Z=-1)
>>> b = {'A': 1, 'Z': -1}
>>> c = dict(zip(['A', 'Z'], [1, -1]))
>>> d = dict([('A', 1), ('Z', -1)])
>>> e = dict({'Z': -1, 'A': 1})
>>> a == b == c == d == e # are they all the...

The collections module

When Python general purpose built-in containers (tuple, list, set, and dict) aren't enough, we can find specialized container datatypes in the collections module. They are:

Data type Description

namedtuple()

Factory function for creating tuple subclasses with named fields

deque

List-like container with fast appends and pops on either end

ChainMap

Dictionary-like class for creating a single view of multiple mappings

Counter

Dictionary subclass for counting hashable objects

OrderedDict

Dictionary subclass that remembers the order entries were added

defaultdict

Dictionary subclass that calls a factory function to supply missing values

UserDict

Wrapper around dictionary objects for easier dictionary subclassing

UserList

Wrapper around list objects for easier list subclassing

UserString

Wrapper around...

Enums

Technically not a built-in data type, as you have to import them from the enum module, but definitely worth mentioning, are enumerations. They were introduced in Python 3.4, and though it is not that common to see them in professional code (yet), I thought I'd give you an example anyway.

The official definition goes like this: "An enumeration is a set of symbolic names (members) bound to unique, constant values. Within an enumeration, the members can be compared by identity, and the enumeration itself can be iterated over."

Say you need to represent traffic lights. In your code, you might resort to doing this:

>>> GREEN = 1
>>> YELLOW = 2
>>> RED = 4
>>> TRAFFIC_LIGHTS = (GREEN, YELLOW, RED)
>>> # or with a dict

>>> traffic_lights = {'GREEN': 1, 'YELLOW': 2, 'RED': 4}

There&apos...

Final considerations

That's it. Now you have seen a very good proportion of the data structures that you will use in Python. I encourage you to take a dive into the Python documentation and experiment further with each and every data type we've seen in this chapter. It's worth it, believe me. Everything you'll write will be about handling data, so make sure your knowledge about it is rock solid.

Before we leap into Chapter 3, Iterating and Making Decisions, I'd like to share some final considerations about different aspects that to my mind are important and not to be neglected.

Small values caching

When we discussed objects at the beginning of this chapter, we saw that when we assigned a name to an...

Summary

In this chapter, we've explored the built-in data types of Python. We've seen how many there are and how much can be achieved by just using them in different combinations.

We've seen number types, sequences, sets, mappings, collections (and a special guest appearance by Enum), we've seen that everything is an object, we've learned the difference between mutable and immutable, and we've also learned about slicing and indexing (and, proudly, negative indexing as well).

We've presented simple examples, but there's much more that you can learn about this subject, so stick your nose into the official documentation and explore.

Most of all, I encourage you to try out all the exercises by yourself, get your fingers using that code, build some muscle memory, and experiment, experiment, experiment. Learn what happens when you divide by zero...

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Key benefits

  • Learn the fundamentals of Python programming with interactive projects
  • Apply Python to data science with tools such as IPython and Jupyter
  • Utilize Python for web development and build a real-world app using Django

Description

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.

Who is this book for?

Learn Python Programming is for individuals with relatively little experience in coding or Python. It's also ideal for aspiring programmers who need to write scripts or programs to accomplish tasks. The book shows you how to create a full-fledged application.

What you will learn

  • Get Python up and running on Windows, Mac, and Linux
  • Explore fundamental concepts of coding using data structures and control flow
  • Write elegant, reusable, and efficient code in any situation
  • Understand when to use the functional or OOP approach
  • Cover the basics of security and concurrent/asynchronous programming
  • Create bulletproof, reliable software by writing tests
  • Build a simple website in Django
  • Fetch, clean, and manipulate data
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Table of Contents

15 Chapters
A Gentle Introduction to Python Chevron down icon Chevron up icon
Built-in Data Types Chevron down icon Chevron up icon
Iterating and Making Decisions Chevron down icon Chevron up icon
Functions, the Building Blocks of Code Chevron down icon Chevron up icon
Saving Time and Memory Chevron down icon Chevron up icon
OOP, Decorators, and Iterators Chevron down icon Chevron up icon
Files and Data Persistence Chevron down icon Chevron up icon
Testing, Profiling, and Dealing with Exceptions Chevron down icon Chevron up icon
Cryptography and Tokens Chevron down icon Chevron up icon
Concurrent Execution Chevron down icon Chevron up icon
Debugging and Troubleshooting Chevron down icon Chevron up icon
GUIs and Scripts Chevron down icon Chevron up icon
Data Science Chevron down icon Chevron up icon
Web Development Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1
(28 Ratings)
5 star 60.7%
4 star 14.3%
3 star 7.1%
2 star 10.7%
1 star 7.1%
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DieChemie May 30, 2020
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I've been going through the book as a non-programmer beginner. I ran through another book on Python syntax first and this was my next step forward. I have a few thoughts on the book I'll share.1. The book is definitely oriented towards people with a computer science/programming background.2. However, if you have a high tolerance for not understanding everything it is a lovely introduction to a GREAT many concepts. I really think the author does a great job giving you an intro into many/most applications of Python.3. This is a book on professional Python programming. Which is good and bad depending on you. If you are interested in using this professionally then I think this book would be great for you. Fabrizio is clearly a professional and his insights into skills and habits needed to program effectively are very valuable and delivered in a memorable way. If you are just interested in writing some ugly scribble that gets the job done inefficiently, you may find these insights distracting/time-consuming from your quest of learning the basics.4. Lastly, to get the most out of the book you really need to take your time and practice the concepts. Learning anything requires remembering/testing your knowledge and if you just 'read' the book there are very few tests, only examples. I'll admit I have rushed through a lot of it. Likely I will now try to use the concepts to take on projects and review/study topics in more depth going forward.Final thought: I think the book has been very challenging for me. Likely I could have found a slightly more beginner friendly book to do first. However, I still think this has been fantastic and now that I have suffered through many of the chapters I feel MUCH MUCH more knowledgeable of thinking like a programmer, the fundamental philosophies of Python (Fabrizio's strongest teaching point), and moving forward to focus both on mastering needed concepts and studying the Python disciplines I'm most interested in to do projects.
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mindy19 Feb 21, 2020
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Bought this for my Grandson for his studies at University, loves it!
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Matthew May 08, 2019
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Setting up a IDE environment and learning Python, I use to use Perl so I hope this book works to re-fire my memory.
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Martin Smith Aug 11, 2019
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I find books by this publisher are very hit and miss but this one is definitely a hit for me.I've been programming for more than 10 years but totally new to Python and found for me the book was paced well and structured well and covered a good breadth of the topic. I also enjoyed the enthusiasm with which the subject matter was presented and occasional dashes of humour to keep things from getting too dry.
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Vignesh Jan 11, 2021
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Book print quality is good.
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