Book Image

Getting Started with Python

By : Fabrizio Romano, Benjamin Baka, Dusty Phillips
Book Image

Getting Started with Python

By: Fabrizio Romano, Benjamin Baka, Dusty Phillips

Overview of this book

This Learning Path helps you get comfortable with the world of Python. It starts with a thorough and practical introduction to Python. You’ll quickly start writing programs, building websites, and working with data by harnessing Python's renowned data science libraries. 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 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: • Learn Python Programming - Second Edition by Fabrizio Romano • Python Data Structures and Algorithms by Benjamin Baka • Python 3 Object-Oriented Programming by Dusty Phillips
Table of Contents (31 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
8
Stacks and Queues
10
Hashing and Symbol Tables
Index

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 []
[]
>>> [1, 2, 3]  # as with tuples, items are comma separated
[1, 2, 3]
>>> [x + 5 for x in [2, 3, 4]]  # Python is magic
[7, 8, 9]
>>> list((1, 3, 5, 7, 9))  # list from a tuple
[1, 3, 5, 7, 9]
>>> list('hello')  # list from a string
['h', ...