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Python Data Structures and Algorithms

Python Data Structures and Algorithms

By : Benjamin Baka
2.7 (11)
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Python Data Structures and Algorithms

Python Data Structures and Algorithms

2.7 (11)
By: Benjamin Baka

Overview of this book

Data structures allow you to organize data in a particular way efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. In this book, you will learn the essential Python data structures and the most common algorithms. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. We will also discuss how to organize your code in a manageable, consistent, and extendable way. The book will explore in detail sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. By the end of the book, you will learn how to build components that are easy to understand, debug, and use in different applications.
Table of Contents (14 chapters)
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5
Stacks and Queues
7
Hashing and Symbol Tables

Tree nodes


Just as was the case with other data structures that we encountered, such as lists and stacks, trees are built up of nodes. But the nodes that make up a tree need to contain data about the parent-child relationship that we mentioned earlier.

Let us now look at how to build a binary tree node class in Python:

    class Node: 
        def __init__(self, data): 
            self.data = data 
            self.right_child = None 
            self.left_child = None 

Just like in our previous implementations, a node is a container for data and holds references to other nodes. Being a binary tree node, these references are to the left and the right children.

To test this class out, we first create a few nodes:

    n1 = Node("root node")  
    n2 = Node("left child node") 
    n3 = Node("right child node") 
    n4 = Node("left grandchild node") 

Next, we connect the nodes to each other. We let n1 be the root node with n2 and n3 as its children. Finally, we hook n4 as the left child to n2, so...

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