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Everyday data structures
Everyday data structures

Everyday data structures: A practical guide to learning data structures simply and easily

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Paperback Mar 2017 344 pages 1st Edition
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Paperback Mar 2017 344 pages 1st Edition
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Everyday data structures

Chapter 2.  Arrays: Foundational Collections

Quite often, our applications need to store multiple pieces of user data or objects in memory at runtime. One solution is to define multiple fields (properties) in our various classes to store each of our required data points. Unfortunately, even when working with the simplest workflows, this approach quickly becomes ineffective. We will either have too many fields to work with, or we simply won't have any way of anticipating all of the dynamic requirements for our project at compile time.

One solution to this problem is to use an array. Arrays are simple collections of data, and they are one of the most common data structures you will encounter in your day-to-day programming experience due to the fact that many other data structures are built on top of them.

Arrays are containers that hold a fixed number of items of a particular type. The size of an array in C and its descendant languages are determined when the array is created...

Mutable versus immutable arrays

Typically, languages that are based on the C language share many of the same fundamental characteristics. For example, in C the size of a plain array cannot be changed once the array has been created. Since the four languages we are examining here are all based on C, the arrays we will be working with also have a fixed length in. However, although the size of an array cannot be changed, the contents of the structure can change after the array is created.

So, are arrays mutable or immutable? In terms of mutability, we say that plain C arrays are  immutable because the structure itself cannot change once it has been created. For this reason, it is typically a bad idea to use a plain C array for anything other than static datasets. This is because, whenever the dataset changes, your program will need to copy the modified data into a new array object and dispose of the old one, which are both costly operations.

Most of the array objects you will be working...

Advanced topics

Now that we have seen how arrays are used in common practice, let's examine a few advanced topics relating to arrays: search patterns and variations on the basic types of objects that can be stored in an array.

Linear search

When learning about data structures, it is impossible to avoid discussing the subjects of searching and sorting. Without the ability to search a data structure, the data would be fairly useless to us. Without the ability to sort the data set for use in a particular application, the data would be extremely tedious to manage.

The steps or process we follow to perform a search or a sort of a particular data structure are called an algorithm. The performance or the complexity of an algorithm in computer science is measured using the big O notation, which is derived from the function f(n) = O (g(n)), read as f of n equals big oh of g of n. In the simplest terms, big O is the terminology we use to describe the worst case for how long an algorithm takes...

Summary

In this chapter, you learned the basic definition of an array structure, how arrays look in memory, and how each of the four languages we are discussing implement some form of the plain C array structure. Next, we discussed the difference between mutable and immutable arrays. Using examples, we looked at how each of the four languages we are discussing implements arrays and array functionality. In the remainder of the chapter, we examined the linear search algorithm and introduced the big O notation, including how this notation is applied to arrays with examples of simple iteration. We discussed the difference between primitive arrays, object arrays, and mixed arrays. Finally, we examined multidimensional arrays and their counterpart, jagged arrays.

As a final note, it is important to know when to use an array. Arrays are great for containing small lists of constant data or data that changes very little to not at all. If you find yourself constantly manipulating the data in your...

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Key benefits

  • • This book is a very practical, friendly, and useful guide that will help you analyze problems and choose the right data structures for your solution
  • • Learn to recognize data patterns for determining which structures apply to a given problem
  • • Explore the unique rules or "gotchas" that will help you become an excellent programmer

Description

Explore a new world of data structures and their applications easily with this data structures book. Written by software expert William Smith, you?ll learn how to master basic and advanced data structure concepts. ? Fully understand data structures using Java, C and other common languages ? Work through practical examples and learn real-world applications ? Get to grips with data structure problem solving using case studies

Who is this book for?

Who is this book for? úÿÿNew or self-taught programmers (in any language) who want to gain a solid understanding of data structures & how to use them to solve problems

What you will learn

  • Learn new data structures and their uses, including arrays, lists, stacks and queues, along with their applications and language-specific concerns. Once you?ve nailed the basics, expert software engineer William Smith takes you through advanced concepts associated with data structures using case studies.

Product Details

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Publication date : Mar 14, 2017
Length: 344 pages
Edition : 1st
Language : English
ISBN-13 : 9781787121041
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Length: 344 pages
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Language : English
ISBN-13 : 9781787121041
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Table of Contents

13 Chapters
1. Data Types: Foundational Structures Chevron down icon Chevron up icon
2. Arrays: Foundational Collections Chevron down icon Chevron up icon
3. Lists: Linear Collections Chevron down icon Chevron up icon
4. Stacks: LIFO Collections Chevron down icon Chevron up icon
5. Queues: FIFO Collections Chevron down icon Chevron up icon
6. Dictionaries: Keyed Collections Chevron down icon Chevron up icon
7. Sets: No Duplicates Chevron down icon Chevron up icon
8. Structs: Complex Types Chevron down icon Chevron up icon
9. Trees: Non-Linear Structures Chevron down icon Chevron up icon
10. Heaps: Ordered Trees Chevron down icon Chevron up icon
11. Graphs: Values with Relationships Chevron down icon Chevron up icon
12. Sorting: Bringing Order Out Of Chaos Chevron down icon Chevron up icon
13. Searching: Finding What You Need Chevron down icon Chevron up icon
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