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Home> Data> Data Analysis> Python Data Structures and Algorithms
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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Profile Icon Benjamin Baka
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$48.99
Full star iconFull star iconHalf star iconEmpty star iconEmpty star icon2.7(11 Ratings)
PaperbackMay 2017310 pages1st Edition
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Python Data Structures and Algorithms

Built-in data types

Python data types can be divided into three categories: numeric, sequence, and mapping. There is also theNone object that represents aNull, or absence of a value. It should not be forgotten either that other objects such as classes, files, and exceptions can also properly be consideredtypes; 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 mutableset object and the immutablefrozenset object. Sets are created using comma-separated values within curly braces. By the way, we cannot create an empty set usinga={}, because this will create a dictionary. To create an empty set, we write eithera=set() ora=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 thedecimal andfractions modules. They are often described by the termabstract 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 considersFalse:

  • TheNone 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 orFalse

All other values are consideredTrue.

Boolean operations

A Boolean operation returns a value of either True orFalse. 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

notx

ReturnsTrue ifx isFalse; returnsFalse otherwise.

x andy

ReturnsTrue if bothx andy areTrue; returnsFalse otherwise.

x ory

ReturnsTrue if eitherx ory isTrue; returnsFalse...

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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.
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Publication date :May 30, 2017
Length:310 pages
Edition :1st
Language :English
ISBN-13 :9781786467355
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Length:310 pages
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ISBN-13 :9781786467355
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Table of Contents

13 Chapters
Python Objects, Types, and ExpressionsChevron down iconChevron up icon
Python Objects, Types, and Expressions
Understanding data structures and algorithms
Python for data
Summary
Python Data Types and StructuresChevron down iconChevron up icon
Python Data Types and Structures
Operations and expressions
Built-in data types
Sets
Modules for data structures and algorithms
Summary
Principles of Algorithm DesignChevron down iconChevron up icon
Principles of Algorithm Design
Algorithm design paradigms
Recursion and backtracking
Runtime analysis
Amortized analysis
Summary
Lists and Pointer StructuresChevron down iconChevron up icon
Lists and Pointer Structures
Arrays
Pointer structures
Nodes
Finding endpoints
Singly linked lists
A faster append operation
Getting the size of the list
Improving list traversal
Deleting nodes
Clearing a list
Doubly linked lists
Circular lists
Summary
Stacks and QueuesChevron down iconChevron up icon
Stacks and Queues
Stacks
Queues
Summary
TreesChevron down iconChevron up icon
Trees
Terminology
Tree nodes
Binary trees
Summary
Hashing and Symbol TablesChevron down iconChevron up icon
Hashing and Symbol Tables
Hashing
Hash table
Summary
Graphs and Other AlgorithmsChevron down iconChevron up icon
Graphs and Other Algorithms
Graphs
Directed and undirected graphs
Weighted graphs
Graph representation
Graph traversal
Other useful graph methods
Priority queues and heaps
Selection algorithms
Summary
SearchingChevron down iconChevron up icon
Searching
Linear Search
Binary search
Interpolation search
Summary
SortingChevron down iconChevron up icon
Sorting
Sorting algorithms
Bubble sort
Insertion sort
Selection sort
Quick sort
Summary
Selection AlgorithmsChevron down iconChevron up icon
Selection Algorithms
Selection by sorting
Randomized selection
Deterministic selection
Summary
Design Techniques and StrategiesChevron down iconChevron up icon
Design Techniques and Strategies
Classification of algorithms
Technical implementation
Complexity classes
Summary
Implementations, Applications, and ToolsChevron down iconChevron up icon
Implementations, Applications, and Tools
Tools of the trade
Data preprocessing
Machine learning
Data visualization
Summary

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Customer reviews

Top Reviews
Rating distribution
Full star iconFull star iconHalf star iconEmpty star iconEmpty star icon2.7
(11 Ratings)
5 star27.3%
4 star9.1%
3 star9.1%
2 star18.2%
1 star36.4%
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Top Reviews

Filter reviews by




ChrisOct 31, 2017
Full star iconFull star iconFull star iconFull star iconFull star icon5
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 reviewAmazon
eliekawerkAug 09, 2017
Full star iconFull star iconFull star iconFull star iconFull star icon5
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 reviewAmazon
MOLMay 02, 2020
Full star iconFull star iconFull star iconFull star iconFull star icon5
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 reviewAmazon
volsbitJun 24, 2017
Full star iconFull star iconFull star iconFull star iconEmpty star icon4
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 reviewAmazon
Elina MaysterJan 11, 2021
Full star iconFull star iconFull star iconEmpty star iconEmpty star icon3
More details would be better
Amazon Verified reviewAmazon
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About the author

Profile icon Benjamin Baka
Benjamin Baka
Benjamin Baka is a full-stack software developer and is passionate about cutting-edge technologies and elegant programming techniques. He has 10 years in different technologies, from C++, Java, Ruby, Python to Qt. Some of the projects he's working on can be found on his GitHub page. He is currently working on exciting technologies all from the camp of mPedigree Network.
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