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Python 3 Object-Oriented Programming - Second Edition
Python 3 Object-Oriented Programming - Second Edition

Python 3 Object-Oriented Programming - Second Edition: Building robust and maintainable software with object oriented design patterns in Python

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Python 3 Object-Oriented Programming - Second Edition

Chapter 2. Objects in Python

So, we now have a design in hand and are ready to turn that design into a working program! Of course, it doesn't usually happen this way. We'll be seeing examples and hints for good software design throughout the book, but our focus is object-oriented programming. So, let's have a look at the Python syntax that allows us to create object-oriented software.

After completing this chapter, we will understand:

  • How to create classes and instantiate objects in Python
  • How to add attributes and behaviors to Python objects
  • How to organize classes into packages and modules
  • How to suggest people don't clobber our data

Creating Python classes

We don't have to write much Python code to realize that Python is a very "clean" language. When we want to do something, we just do it, without having to go through a lot of setup. The ubiquitous "hello world" in Python, as you've likely seen, is only one line.

Similarly, the simplest class in Python 3 looks like this:

class MyFirstClass:
    pass

There's our first object-oriented program! The class definition starts with the class keyword. This is followed by a name (of our choice) identifying the class, and is terminated with a colon.

Note

The class name must follow standard Python variable naming rules (it must start with a letter or underscore, and can only be comprised of letters, underscores, or numbers). In addition, the Python style guide (search the web for "PEP 8") recommends that classes should be named using CamelCase notation (start with a capital letter; any subsequent words should also start with a capital)...

Modules and packages

Now, we know how to create classes and instantiate objects, but how do we organize them? For small programs, we can just put all our classes into one file and add a little script at the end of the file to start them interacting. However, as our projects grow, it can become difficult to find the one class that needs to be edited among the many classes we've defined. This is where modules come in. Modules are simply Python files, nothing more. The single file in our small program is a module. Two Python files are two modules. If we have two files in the same folder, we can load a class from one module for use in the other module.

For example, if we are building an e-commerce system, we will likely be storing a lot of data in a database. We can put all the classes and functions related to database access into a separate file (we'll call it something sensible: database.py). Then, our other modules (for example, customer models, product information, and inventory...

Organizing module contents

Inside any one module, we can specify variables, classes, or functions. They can be a handy way to store the global state without namespace conflicts. For example, we have been importing the Database class into various modules and then instantiating it, but it might make more sense to have only one database object globally available from the database module. The database module might look like this:

class Database:
    # the database implementation
    pass

database = Database()

Then we can use any of the import methods we've discussed to access the database object, for example:

from ecommerce.database import database

A problem with the preceding class is that the database object is created immediately when the module is first imported, which is usually when the program starts up. This isn't always ideal since connecting to a database can take a while, slowing down startup, or the database connection information may not yet be available. We could delay...

Who can access my data?

Most object-oriented programming languages have a concept of access control. This is related to abstraction. Some attributes and methods on an object are marked private, meaning only that object can access them. Others are marked protected, meaning only that class and any subclasses have access. The rest are public, meaning any other object is allowed to access them.

Python doesn't do this. Python doesn't really believe in enforcing laws that might someday get in your way. Instead, it provides unenforced guidelines and best practices. Technically, all methods and attributes on a class are publicly available. If we want to suggest that a method should not be used publicly, we can put a note in docstrings indicating that the method is meant for internal use only (preferably, with an explanation of how the public-facing API works!).

By convention, we should also prefix an attribute or method with an underscore character, _. Python programmers will interpret...

Third-party libraries

Python ships with a lovely standard library, which is a collection of packages and modules that are available on every machine that runs Python. However, you'll soon find that it doesn't contain everything you need. When this happens, you have two options:

  • Write a supporting package yourself
  • Use somebody else's code

We won't be covering the details about turning your packages into libraries, but if you have a problem you need to solve and you don't feel like coding it (the best programmers are extremely lazy and prefer to reuse existing, proven code, rather than write their own), you can probably find the library you want on the Python Package Index (PyPI) at http://pypi.python.org/. Once you've identified a package that you want to install, you can use a tool called pip to install it. However, pip does not come with Python, but Python 3.4 contains a useful tool called ensurepip, which will install it:

python -m ensurepip

This may fail for...

Case study

To tie it all together, let's build a simple command-line notebook application. This is a fairly simple task, so we won't be experimenting with multiple packages. We will, however, see common usage of classes, functions, methods, and docstrings.

Let's start with a quick analysis: notes are short memos stored in a notebook. Each note should record the day it was written and can have tags added for easy querying. It should be possible to modify notes. We also need to be able to search for notes. All of these things should be done from the command line.

The obvious object is the Note object; less obvious one is a Notebook container object. Tags and dates also seem to be objects, but we can use dates from Python's standard library and a comma-separated string for tags. To avoid complexity, in the prototype, let's not define separate classes for these objects.

Note objects have attributes for memo itself, tags, and creation_date. Each note will also need a unique...

Creating Python classes


We don't have to write much Python code to realize that Python is a very "clean" language. When we want to do something, we just do it, without having to go through a lot of setup. The ubiquitous "hello world" in Python, as you've likely seen, is only one line.

Similarly, the simplest class in Python 3 looks like this:

class MyFirstClass:
    pass

There's our first object-oriented program! The class definition starts with the class keyword. This is followed by a name (of our choice) identifying the class, and is terminated with a colon.

Note

The class name must follow standard Python variable naming rules (it must start with a letter or underscore, and can only be comprised of letters, underscores, or numbers). In addition, the Python style guide (search the web for "PEP 8") recommends that classes should be named using CamelCase notation (start with a capital letter; any subsequent words should also start with a capital).

The class definition line is followed by the class...

Modules and packages


Now, we know how to create classes and instantiate objects, but how do we organize them? For small programs, we can just put all our classes into one file and add a little script at the end of the file to start them interacting. However, as our projects grow, it can become difficult to find the one class that needs to be edited among the many classes we've defined. This is where modules come in. Modules are simply Python files, nothing more. The single file in our small program is a module. Two Python files are two modules. If we have two files in the same folder, we can load a class from one module for use in the other module.

For example, if we are building an e-commerce system, we will likely be storing a lot of data in a database. We can put all the classes and functions related to database access into a separate file (we'll call it something sensible: database.py). Then, our other modules (for example, customer models, product information, and inventory) can import...

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Key benefits

  • Stop writing scripts and start architecting programs
  • Learn the latest Python syntax and libraries
  • A practical, hands-on tutorial that teaches you all about abstract design patterns and how to implement them in Python 3

Description

Python 3 is more versatile and easier to use than ever. It runs on all major platforms in a huge array of use cases. Coding in Python minimizes development time and increases productivity in comparison to other languages. Clean, maintainable code is easy to both read and write using Python's clear, concise syntax. Object-oriented programming is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. Many modern programming languages utilize the powerful concepts behind object-oriented programming and Python is no exception. Starting with a detailed analysis of object-oriented analysis and design, you will use the Python programming language to clearly grasp key concepts from the object-oriented paradigm. This book fully explains classes, data encapsulation, inheritance, polymorphism, abstraction, and exceptions with an emphasis on when you can use each principle to develop well-designed software. You'll get an in-depth analysis of many common object-oriented design patterns that are more suitable to Python's unique style. This book will not just teach Python syntax, but will also build your confidence in how to program. You will also learn how to create maintainable applications by studying higher level design patterns. Following this, you'll learn the complexities of string and file manipulation, and how Python distinguishes between binary and textual data. Not one, but two very powerful automated testing systems will be introduced in the book. After you discover the joy of unit testing and just how easy it can be, you'll study higher level libraries such as database connectors and GUI toolkits and learn how they uniquely apply object-oriented principles. You'll learn how these principles will allow you to make greater use of key members of the Python eco-system such as Django and Kivy. This new edition includes all the topics that made Python 3 Object-oriented Programming an instant Packt classic. It's also packed with updated content to reflect recent changes in the core Python library and covers modern third-party packages that were not available on the Python 3 platform when the book was first published.  

Who is this book for?

If you're new to object-oriented programming techniques, or if you have basic Python skills and wish to learn in depth how and when to correctly apply object-oriented programming in Python to design software, this is the book for you.

What you will learn

  • Implement objects in Python by creating classes and defining methods
  • Separate related objects into a taxonomy of classes and describe the properties and behaviors of those objects via the class interface
  • Extend class functionality using inheritance
  • Understand when to use object-oriented features, and more importantly when not to use them
  • Discover what design patterns are and why they are different in Python
  • Uncover the simplicity of unit testing and why it s so important in Python
  • Grasp common concurrency techniques and pitfalls in Python 3
  • Exploit object-oriented programming in key Python technologies such as Kivy and Django.
  • Object-oriented programming concurrently with asyncio

Product Details

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Publication date : Aug 20, 2015
Length: 460 pages
Edition : 1st
Language : English
ISBN-13 : 9781784395957
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Product Details

Publication date : Aug 20, 2015
Length: 460 pages
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Language : English
ISBN-13 : 9781784395957
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Table of Contents

14 Chapters
1. Object-oriented Design Chevron down icon Chevron up icon
2. Objects in Python Chevron down icon Chevron up icon
3. When Objects Are Alike Chevron down icon Chevron up icon
4. Expecting the Unexpected Chevron down icon Chevron up icon
5. When to Use Object-oriented Programming Chevron down icon Chevron up icon
6. Python Data Structures Chevron down icon Chevron up icon
7. Python Object-oriented Shortcuts Chevron down icon Chevron up icon
8. Strings and Serialization Chevron down icon Chevron up icon
9. The Iterator Pattern Chevron down icon Chevron up icon
10. Python Design Patterns I Chevron down icon Chevron up icon
11. Python Design Patterns II Chevron down icon Chevron up icon
12. Testing Object-oriented Programs Chevron down icon Chevron up icon
13. Concurrency Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.9
(36 Ratings)
5 star 47.2%
4 star 22.2%
3 star 13.9%
2 star 5.6%
1 star 11.1%
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D. Thomson May 29, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Certainly one of the better python books out there. Especially so if you are someone wishing to break out from writing only basic scripts.
Amazon Verified review Amazon
cosmos May 15, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Thanks!
Amazon Verified review Amazon
Santhosh Apr 26, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book for Object oriented python
Amazon Verified review Amazon
Ralf Korff Nov 22, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I instantly liked the introduction which is short, informative and personal. For me this can already be half the battle for a buying decision. The table of contents delivered the other half by reminding me that the Gang of Four’s Design Patterns was sitting on my shelf untouched for long. (It has been written in 1994, how time flies!) Dusty’s book seemed to be a fit occasion for having a fresh look at design patterns and see whether Python makes any difference. Furthermore, as the author freely admits in the introduction he has added a chapter about unit testing and one about concurrency which are not directly related to OOP, but which have nevertheless attracted my interest in this book.About 20% of the book’s pages (after 50% on OOP with Python) are dedicated to design patterns (chapters 9-11). Chapter 9 elaborates on the iterator pattern while the following two chapters cover 6 design patterns each. That’s roughly half of the design patterns described in Gamma et al. but I’m fine with the selection made. Chapters 1 (on OOD), 12 (on unit testing) and 13 (Concurrency) frame the book’s core.When reading I have had already a pretty good grasp about OOP but I did not know Python. Having said this I cannot judge how comprehensible this book might be for readers without programming experience but it looks for me like a smooth intro into object-oriented programming. Yet the author states “This is a practical book, not a textbook.“. The author is likely hinting at the absence of exercises (though the book contains several short think-about sections which have been titled ‘exercises’). According to my conception, this book is a practical textbook indeed.Soon after starting reading I got convinced that this book works for me as a great introduction to Python. Whether you are interested in learning Python, refreshing design patterns or whether you are curious where “pythonic OOP thinking” differs, this book is expository in a readable and concise fashion. Clearly, it should not be your first programming book.I don’t hesitate to give this book 5 stars.
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Ecki Dec 13, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Das Buch ist für Leser mit Basiskenntnissen in Python geeignet.
Amazon Verified review Amazon
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