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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

Interpolation search


There is another variant of the binary search algorithm that may closely be said to mimic more, how humans perform search on any list of items. It is still based off trying to make a good guess of where in a sorted list of items, a search item is likely to be found.

Examine the following list of items for example:

To find 120, we know to look at the right hand portion of the list. Our initial treatment of binary search would typically examine the middle element first in order to determine if it matches the search term.

A more human thing would be to pick a middle element in a such a way as to not only split the array in half but to get as close as possible to the search term. The middle position was calculated for using the following rule:

mid_point = (index_of_first_element + index_of_last_element)/2 

We shall replace this formula with a better one that brings us close to the search term. mid_point will receive the return value of the nearest_mid function.

def nearest_mid...
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Python Data Structures and Algorithms
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