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Python Data Structures and Algorithms

You're reading from   Python Data Structures and Algorithms Improve application performance with graphs, stacks, and queues

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781786467355
Length 310 pages
Edition 1st Edition
Languages
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Author (1):
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Benjamin Baka Benjamin Baka
Author Profile Icon Benjamin Baka
Benjamin Baka
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Table of Contents (14) Chapters Close

Preface 1. Python Objects, Types, and Expressions FREE CHAPTER 2. Python Data Types and Structures 3. Principles of Algorithm Design 4. Lists and Pointer Structures 5. Stacks and Queues 6. Trees 7. Hashing and Symbol Tables 8. Graphs and Other Algorithms 9. Searching 10. Sorting 11. Selection Algorithms 12. Design Techniques and Strategies 13. Implementations, Applications, and Tools

Graph representation


Graphs can be represented in two main forms. One way is to use an adjacency matrix and the other is to use an adjacency list.

We shall be working with the following figure to develop both types of representation for graphs:

Adjacency list

A simple list can be used to present a graph. The indices of the list will represent the nodes or vertices in the graph. At each index, the adjacent nodes to that vertex can be stored:

The numbers in the box represent the vertices. Index 0 represents vertex A, with its adjacent nodes being B and C.

Using a list for the representation is quite restrictive because we lack the ability to directly use the vertex labels. A dictionary is therefore more suited. To represent the graph in the diagram, we can use the following statements:

    graph = dict() 
    graph['A'] = ['B', 'C'] 
    graph['B'] = ['E','A'] 
    graph['C'] = ['A', 'B', 'E','F'] 
    graph['E'] = ['B', 'C'] 
    graph['F'] = ['C'] 

Now we easy establish that vertex A has the adjacent...

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