This learning path helps you get comfortable in the world of Python. It starts with a thorough and practical introduction to Python. You'll quickly start writing programs in the first part of the learning path. With the power of linked lists, binary searches, and sorting algorithms, you'll easily create complex data structures, such as graphs, stacks, and queues. After understanding cooperative inheritance, you'll expertly raise, handle, and manipulate exceptions. You will effortlessly integrate the object-oriented and not-so-object-oriented aspects of Python, and create maintainable applications using higher level design patterns. Once you've covered the core topics, you’ll understand the joy of unit testing and just how easy it is to create unit tests.
By the end of this learning path, you will have built components that are easy to understand, debug, and can be used across different applications.
This learning path includes content from the following Packt products:
If you are relatively new to coding and want to write scripts or programs to accomplish tasks using Python, or if you are an object-oriented programmer for other languages and seeking a leg up in the world of Python, then this learning path is for you. Though not essential, it will help you to have basic knowledge of programming and OOP.
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, Files and Data Persistence, teaches you how to deal with files, streams, data interchange formats, and databases, among other things.
Chapter 6, Principles of Algorithm Design, covers how we can build structures with specific capabilities using the existing Python data structures. In general, the data structures we create need to conform to a number of principles. These principles include robustness, adaptability, reusability, and separating the structure from a function. We look at the role iteration plays and introduce recursive data structures.
Chapter 7, Lists and Pointer Structures, covers linked lists, which are one of the most common data structures and are often used to implement other structures, such as stacks and queues. In this chapter, we describe their operation and implementation. We compare their behavior to arrays and discuss the relative advantages and disadvantages of each.
Chapter 8, Stacks and Queues, discusses the behavior and demonstrates some implementations of these linear data structures. We give examples of typical applications.
Chapter 9, Trees, will look at how to implement a binary tree. Trees form the basis of many of the most important advanced data structures. We will examine how to traverse trees and retrieve and insert values. We will also look at how to create structures such as heaps.
Chapter 10, Hashing and Symbol Tables, describes symbol tables, gives some typical implementations, and discusses various applications. We will look at the process of hashing, give an implementation of a hash table, and discuss the various design considerations.
Chapter 11, Graphs and Other Algorithms, looks at some of the more specialized structures, including graphs and spatial structures. Representing data as a set of nodes and vertices is convenient in a number of applications, and from this, we can create structures such as directed and undirected graphs. We will also introduce some other structures and concepts such as priority queues, heaps, and selection algorithms.
Chapter 12, Searching, discusses the most common searching algorithms and gives examples of their use for various data structures. Searching a data structure is a fundamental task and there are a number of approaches.
Chapter 13, Sorting, looks at the most common approaches to sorting. This will include bubble sort, insertion sort, and selection sort.
Chapter 14, Selection Algorithms, covers algorithms that involve finding statistics, such as the minimum, maximum, or median elements in a list. There are a number of approaches and one of the most common approaches is to first apply a sort operation. Other approaches include partition and linear selection.
Chapter 15, Object-Oriented Design, covers important object-oriented concepts. It deals mainly with terminology such as abstraction, classes, encapsulation, and inheritance. We also briefly look at UML to model our classes and objects.
Chapter 16, Objects in Python, discusses classes and objects as they are used in Python. We will learn about attributes and behaviors of Python objects, and the organization of classes into packages and modules. Lastly, we will see how to protect our data.
Chapter 17, When Objects Are Alike, gives us a more in-depth look into inheritance. It covers multiple inheritance and shows us how to extend built-in. This chapter also covers how polymorphism and duck typing work in Python.
Chapter 18, Expecting the Unexpected, looks into exceptions and exception handling. We will learn how to create our own exceptions and how to use exceptions for program flow control.
Chapter 19, When to Use Object-Oriented Programming, deals with creating and using objects. We will see how to wrap data using properties and restrict data access. This chapter also discusses the DRY principle and how not to repeat code.
Chapter 20, Python Object-Oriented Shortcuts, as the name suggests, deals with time-savers in Python. We will look at many useful built-in functions, such as method overloading using default arguments. We'll also see that functions themselves are objects and how this is useful.
Chapter 21, The Iterator Pattern, introduces the concept of design patterns and covers Python's iconic implementation of the iterator pattern. We'll learn about list, set, and dictionary comprehensions. We'll also demystify generators and coroutines.
Chapter 22, Python Design Patterns I, covers several design patterns, including the decorator, observer, strategy, state, singleton, and template patterns. Each pattern is discussed with suitable examples and programs implemented in Python.
Chapter 23, Python Design Patterns II, wraps up our discussion of design patterns with coverage of the adapter, facade, flyweight, command, abstract, and composite patterns. More examples of how idiomatic Python code differs from canonical implementations are provided.
Chapter 24, Testing Object-Oriented Programs, opens with why testing is so important in Python applications. It focuses on test-driven development and introduces two different testing suites: unittest and py.test. Finally, it discusses mocking test objects and code coverage.
The code in this book will require you to run Python 2.7.x or higher. Python's default interactive environment can also be used to run the snippets of code.
Some of the examples in this book rely on third-party libraries that do not ship with Python. They are introduced within the book at the time they are used, so you do not need to install them in advance.
You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Getting-Started-with-Python. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The if, else, and elif statements control the conditional execution of statements."
A block of code is set as follows:
a=10; b=20 def my_function():
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
if "WARNING" in l:
yield l.replace("\tWARNING", "")Any command-line input or output is written as follows:
>>> print(warnings_filter([]))
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Then you have to manually click Yes or No if the label matches the color."
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