Python data structures and algorithms : improve the performance and speed of your applications / Benjamin Baka.

Format:

Book

Author/Creator:

Baka, Benjamin, author.

Language:

English

Subjects (All):

Python (Computer program language).

Data structures (Computer science).

Computer algorithms.

Physical Description:

1 online resource (303 pages) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham : Packt, 2017.

System Details:

text file

Summary:

Implement classic and functional data structures and algorithms using Python About This Book A step by step guide, which will provide you with a thorough discussion on the analysis and design of fundamental Python data structures. Get a better understanding of advanced Python concepts such as big-o notation, dynamic programming, and functional data structures. Explore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Who This Book Is For The book will appeal to Python developers. A basic knowledge of Python is expected. What You Will Learn Gain a solid understanding of Python data structures. Build sophisticated data applications. Understand the common programming patterns and algorithms used in Python data science. Write efficient robust code. In Detail 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. Style and Approach The easy-to-read book with its fast-paced nature will improve the productivity of Python programmers and improve the performance of Python applications. Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

Contents:

Cover

Title Page

Copyright

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Python Objects, Types, and Expressions

Understanding data structures and algorithms

Python for data

The Python environment

Variables and expressions

Variable scope

Flow control and iteration

Overview of data types and objects

Strings

Lists

Functions as first class objects

Higher order functions

Recursive functions

Generators and co-routines

Classes and object programming

Special methods

Inheritance

Data encapsulation and properties

Summary

Chapter 2: Python Data Types and Structures

Operations and expressions

Boolean operations

Comparison and Arithmetic operators

Membership, identity, and logical operations

Built-in data types

None type

Numeric Types

Representation error

Sequences

Tuples

Dictionaries

Sorting dictionaries

Dictionaries for text analysis

Sets

Immutable sets

Modules for data structures and algorithms

Collections

Deques

ChainMaps

Counter objects

Ordered dictionaries

defaultdict

Named tuples

Arrays

Chapter 3: Principles of Algorithm Design

Algorithm design paradigms

Recursion and backtracking

Backtracking

Divide and conquer - long multiplication

Can we do better? A recursive approach

Runtime analysis

Asymptotic analysis

Big O notation

Composing complexity classes

Omega notation (Ω)

Theta notation (ϴ)

Amortized analysis

Chapter 4: Lists and Pointer Structures

Pointer structures

Nodes

Finding endpoints

Node

Other node types

Singly linked lists

Singly linked list class

Append operation

A faster append operation

Getting the size of the list.

Improving list traversal

Deleting nodes

List search

Clearing a list

Doubly linked lists

A doubly linked list node

Doubly linked list

Delete operation

Circular lists

Appending elements

Deleting an element

Iterating through a circular list

Chapter 5: Stacks and Queues

Stacks

Stack implementation

Push operation

Pop operation

Peek

Bracket-matching application

Queues

List-based queue

Enqueue operation

Dequeue operation

Stack-based queue

Node-based queue

Queue class

Application of queues

Media player queue

Chapter 6: Trees

Terminology

Tree nodes

Binary trees

Binary search trees

Binary search tree implementation

Binary search tree operations

Finding the minimum and maximum nodes

Inserting nodes

Searching the tree

Tree traversal

Depth-first traversal

In-order traversal and infix notation

Pre-order traversal and prefix notation

Post-order traversal and postfix notation.

Breadth-first traversal

Benefits of a binary search tree

Expression trees

Parsing a reverse Polish expression

Balancing trees

Heaps

Chapter 7: Hashing and Symbol Tables

Hashing

Perfect hashing functions

Hash table

Putting elements

Getting elements

Testing the hash table

Using [] with the hash table

Non-string keys

Growing a hash table

Open addressing

Chaining

Symbol tables

Chapter 8: Graphs and Other Algorithms

Graphs

Directed and undirected graphs

Weighted graphs

Graph representation

Adjacency list

Adjacency matrix

Graph traversal

Breadth-first search

Depth-first search

Other useful graph methods.

Priority queues and heaps

Inserting

Pop

Testing the heap

Selection algorithms

Chapter 9: Searching

Linear Search

Unordered linear search

Ordered linear search

Binary search

Interpolation search

Choosing a search algorithm

Chapter 10: Sorting

Sorting algorithms

Bubble sort

Insertion sort

Selection sort

Quick sort

List partitioning

Pivot selection

Implementation

Heap sort

Chapter 11: Selection Algorithms

Selection by sorting

Randomized selection

Quick select

Partition step

Deterministic selection

Median of medians

Partitioning step

Chapter 12: Design Techniques and Strategies

Classification of algorithms

Classification by implementation

Recursion

Logical

Serial or parallel

Deterministic versus nondeterministic algorithms

Classification by complexity

Complexity curves

Classification by design

Divide and conquer

Dynamic programming

Greedy algorithms

Technical implementation

Memoization

Tabulation

The Fibonacci series

The Memoization technique

The tabulation technique

Divide

Conquer

Merge

Merge sort

Coin-counting problem

Dijkstra's shortest path algorithm

Complexity classes

P versus NP

NP-Hard

NP-Complete

Chapter 13: Implementations, Applications, and Tools

Tools of the trade

Data preprocessing

Why process raw data?

Missing data

Feature scaling

Min-max scalar

Standard scalar

Binarizing data

Machine learning

Types of machine learning

Hello classifier

A supervised learning example

Gathering data

Bag of words

Prediction

An unsupervised learning example

K-means algorithm

Prediction.

Data visualization

Bar chart

Multiple bar charts

Box plot

Pie chart

Bubble chart

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed June 15, 2017).

OCLC:

990784724