A common-sense guide to data structures and algorithms in python. Volume 1 : level up your core programming skills. / Jay Wengrow; edited by Katharine Dvorak.

Format:

Book

Author/Creator:

Wengrow, Jay, author.

Contributor:

Dvorak, Katharine, editor.

Language:

English

Subjects (All):

JavaScript (Computer program language).

Computer programming.

Physical Description:

1 online resource (489 pages)

Edition:

First edition.

Place of Publication:

[Raleigh, North Carolina] : The Pragmatic Programmers, LLC, [2023]

Summary:

A Common-Sense Guide to Data Structures and Algorithms in Python, Volume 1 by Jay Wengrow provides an accessible introduction to the fundamental concepts of data structures and algorithms using Python. The book aims to demystify complex algorithmic content with a direct and engaging writing style, making it an ideal resource for beginners and software engineers seeking to enhance their programming skills. It offers clear explanations, practical examples, and clever illustrations to make the subject matter approachable, even for those without a strong mathematical background. The book covers key topics such as arrays, sorting algorithms, recursion, binary search trees, and graph algorithms, focusing on practical application and optimization techniques. Designed as a comprehensive reference, it encourages readers to improve their coding efficiency and understand the importance of algorithmic thinking in software development. Generated by AI.

Contents:

Cover

Table of Contents

Preface

Who Is This Book For?

The Python Edition

A Note About the Code

What's in This Book?

How to Read This Book

Online Resources

Connecting

Acknowledgments

1. Why Data Structures Matter

Data Structures

The Array: The Foundational Data Structure

Measuring Speed

Reading

Searching

Insertion

Deletion

Sets: How a Single Rule Can Affect Efficiency

Wrapping Up

Exercises

2. Why Algorithms Matter

Ordered Arrays

Searching an Ordered Array

Binary Search

Binary Search vs. Linear Search

3. O Yes! Big O Notation

Big O: How Many Steps Relative to N Elements?

The Soul of Big O

An Algorithm of the Third Kind

Logarithms

O(log N) Explained

Practical Examples

4. Speeding Up Your Code with Big O

Bubble Sort

Bubble Sort in Action

The Efficiency of Bubble Sort

A Quadratic Problem

A Linear Solution

5. Optimizing Code With and Without Big O

Selection Sort

Selection Sort in Action

The Efficiency of Selection Sort

Ignoring Constants

Big O Categories

6. Optimizing for Optimistic Scenarios

Insertion Sort

Insertion Sort in Action

The Efficiency of Insertion Sort

The Average Case

A Practical Example

7. Big O in Everyday Code

Mean Average of Even Numbers

Word Builder

Array Sample

Average Celsius Reading

Clothing Labels

Count the Ones

Palindrome Checker

Get All the Products

Password Cracker

8. Blazing Fast Lookup with Hash Tables

Hash Tables

Hashing with Hash Functions

Building a Thesaurus for Fun and Profit, but Mainly Profit

Hash Table Lookups

Dealing with Collisions.

Making an Efficient Hash Table

Hash Tables for Organization

Hash Tables for Speed

9. Crafting Elegant Code with Stacks and Queues

Stacks

Abstract Data Types

Stacks in Action

The Importance of Constrained Data Structures

Queues

Queues in Action

10. Recursively Recurse with Recursion

Recurse Instead of Loop

The Base Case

Reading Recursive Code

Recursion in the Eyes of the Computer

Filesystem Traversal

11. Learning to Write in Recursive

Recursive Category: Repeatedly Execute

Recursive Category: Calculations

Top-Down Recursion: A New Way of Thinking

The Staircase Problem

Anagram Generation

12. Dynamic Programming

Unnecessary Recursive Calls

The Little Fix for Big O

The Efficiency of Recursion

Overlapping Subproblems

Dynamic Programming Through Memoization

Dynamic Programming Through Going Bottom-Up

13. Recursive Algorithms for Speed

Partitioning

Quicksort

The Efficiency of Quicksort

Quicksort in the Worst-Case Scenario

Quickselect

Sorting as a Key to Other Algorithms

14. Node-Based Data Structures

Linked Lists

Implementing a Linked List

Efficiency of Linked List Operations

Linked Lists in Action

Doubly Linked Lists

Queues as Doubly Linked Lists

15. Speeding Up All the Things with Binary Search Trees

Trees

Binary Search Trees

Binary Search Trees in Action

Binary Search Tree Traversal

16. Keeping Your Priorities Straight with Heaps

Priority Queues

Heaps

Heap Properties

Heap Insertion.

Looking for the Last Node

Heap Deletion

Heaps vs. Ordered Arrays

The Problem of the Last Node…Again

Arrays as Heaps

Heaps as Priority Queues

17. It Doesn't Hurt to Trie

Tries

Storing Words

Trie Search

The Efficiency of Trie Search

Trie Insertion

Building Autocomplete

Completing Autocomplete

Tries with Values: A Better Autocomplete

18. Connecting Everything with Graphs

Graphs

Directed Graphs

Object-Oriented Graph Implementation

Graph Search

Depth-First Search

Breadth-First Search

The Efficiency of Graph Search

Weighted Graphs

Dijkstra's Algorithm

19. Dealing with Space Constraints

Big O of Space Complexity

Trade-Offs Between Time and Space

The Hidden Cost of Recursion

20. Techniques for Code Optimization

Prerequisite: Determine Your Current Big O

Start Here: The Best-Imaginable Big O

Magical Lookups

Recognizing Patterns

Greedy Algorithms

Change the Data Structure

Parting Thoughts

A1. Exercise Solutions

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Chapter 15

Chapter 16

Chapter 17

Chapter 18

Chapter 19

Chapter 20

Index

- SYMBOLS -

- A -

- B -

- C -

- D -

- E -

- F -

- G -

- H -

- I -

- J -

- K -

- L -

- M -

- N -

- O -

- P -

- Q -

- R -

- S -

- T -

- U -

- V -

- W -.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Description based on print version record.

Description based on print record.

ISBN:

979-88-88650-78-3

979-88-88650-79-0

OCLC:

1439602811