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Python Data Structures and Algorithms
Python Data Structures and Algorithms

Python Data Structures and Algorithms: Improve application performance with graphs, stacks, and queues

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Python Data Structures and Algorithms

Python Data Types and Structures

In this chapter, we are going to examine the Python data types in detail. We have already been introduced to two data types, the string, str(), and list(). It is often the case where we want more specialized objects to represent our data. In addition to the built-in types, there are several internal modules that allow us to address common issues when working with data structures. First, we are going to review some operations and expressions that are common to all data types.

Operations and expressions

There are a number of operations that are common to all data types. For example, all data types, and generally all objects, can be tested for a truth value in some way. The following are values that Python considers False:

  • The None type
  • False
  • An integer, float, or complex zero
  • An empty sequence or mapping
  • An instance of a user-defined class that defines a __len__() or __bool__() method that returns zero or False

All other values are considered True.

Boolean operations

A Boolean operation returns a value of either True or False. Boolean operations are ordered in priority, so if more than one Boolean operation occurs in an expression, the operation with the highest priority will occur first...

Built-in data types

Python data types can be divided into three categories: numeric, sequence, and mapping. There is also the None object that represents a Null, or absence of a value. It should not be forgotten either that other objects such as classes, files, and exceptions can also properly be considered types; however, they will not be considered here.

Every value in Python has a data type. Unlike many programming languages, in Python you do not need to explicitly declare the type of a variable. Python keeps track of object types internally.

Python built-in data types are outlined in the following table:

Category

Name

Description

None

None

The null object.

Numeric

int

Integer.

float

Floating point number.

complex

Complex number.

bool

Boolean (True, False).

Sequences

str

String of characters.

list

List of arbitrary...

Sets

Sets are unordered collections of unique items. Sets are themselves mutable, we can add and remove items from them; however, the items themselves must be immutable. An important distinction with sets is that they cannot contain duplicate items. Sets are typically used to perform mathematical operations such as intersection, union, difference, and complement.

Unlike sequence types, set types do not provide any indexing or slicing operations. There are also no keys associated with values, as is the case with dictionaries. There are two types of set objects in Python, the mutable set object and the immutable frozenset object. Sets are created using comma-separated values within curly braces. By the way, we cannot create an empty set using a={}, because this will create a dictionary. To create an empty set, we write either a=set() or a=frozenset().

Methods and operations of sets...

Modules for data structures and algorithms

In addition to the built-in types, there are several Python modules that we can use to extend these built-in types and functions. In many cases, these Python modules may offer efficiency and programming advantages that allow us to simplify our code.

So far, we have looked at the built-in datatypes of strings, lists, sets, and dictionaries as well as the decimal and fractions modules. They are often described by the term abstract data types (ADTs). ADTs can be considered as mathematical specifications for the set of operations that can be performed on data. They are defined by their behavior rather than their implementation. In addition to the ADTs that we have looked at, there are several Python libraries that provide extensions to the built-in datatypes. These will be discussed in the following section.

...

Operations and expressions


There are a number of operations that are common to all data types. For example, all data types, and generally all objects, can be tested for a truth value in some way. The following are values that Python considers False:

  • The None type
  • False
  • An integer, float, or complex zero
  • An empty sequence or mapping
  • An instance of a user-defined class that defines a __len__() or __bool__() method that returns zero or False

All other values are consideredTrue.

Boolean operations

A Boolean operation returns a value of either True or False. Boolean operations are ordered in priority, so if more than one Boolean operation occurs in an expression, the operation with the highest priority will occur first. The following table outlines the three Boolean operators in descending order of priority:

Operator

Example

not x

Returns True if x is False; returns False otherwise.

x and y

Returns True if both x and y are True; returns False otherwise.

x or y

Returns True if either x or y is True; returns False...

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Key benefits

  • 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.

Description

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.

Who is this book 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.

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 30, 2017
Length: 310 pages
Edition : 1st
Language : English
ISBN-13 : 9781786467355
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Product Details

Publication date : May 30, 2017
Length: 310 pages
Edition : 1st
Language : English
ISBN-13 : 9781786467355
Category :
Languages :
Concepts :

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Table of Contents

13 Chapters
Python Objects, Types, and Expressions Chevron down icon Chevron up icon
Python Data Types and Structures Chevron down icon Chevron up icon
Principles of Algorithm Design Chevron down icon Chevron up icon
Lists and Pointer Structures Chevron down icon Chevron up icon
Stacks and Queues Chevron down icon Chevron up icon
Trees Chevron down icon Chevron up icon
Hashing and Symbol Tables Chevron down icon Chevron up icon
Graphs and Other Algorithms Chevron down icon Chevron up icon
Searching Chevron down icon Chevron up icon
Sorting Chevron down icon Chevron up icon
Selection Algorithms Chevron down icon Chevron up icon
Design Techniques and Strategies Chevron down icon Chevron up icon
Implementations, Applications, and Tools Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.7
(11 Ratings)
5 star 27.3%
4 star 9.1%
3 star 9.1%
2 star 18.2%
1 star 36.4%
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Top Reviews

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Chris Oct 31, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Pretty sure those other two reviews are fake. Also, if you complain about grammar and spelling then you don't know Packt Publishing.My opinion on this book is that so far it has great information and is easy to follow along. I'm taking AI courses online and stumbled across this by accident. It just so happens that some of the chapters in this book are perfect supplemental material for my program, namely the Graphs chapters. I haven't read then entire book but so far so great.
Amazon Verified review Amazon
eliekawerk Aug 09, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is concise and very well written. If you want to learn practical data structures and algorithms in Python, I recommend this resource. One of the nice features of this book is that code is presented hand in hand with theory.
Amazon Verified review Amazon
MOL May 02, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Concise, emphasis is on the principles. Other books get lost in details, mixing different levels of abstraction. Best text to learn about data structures using Python.
Amazon Verified review Amazon
volsbit Jun 24, 2017
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This books makes for a great read. I appreciate the conciseness and how most of the concepts are projected with relative ease in the book. Absolutely worth your salt. I will encourage anyone wanting a head-start in data structures and algorithms to take a stab at it. Highly comprehensible to say the least. That said, I will like to confess I don't ever regret purchasing the book.
Amazon Verified review Amazon
Elina Mayster Jan 11, 2021
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
More details would be better
Amazon Verified review Amazon
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