Python for everybody : exploring data using Python 3 / Charles R. Severance

By:

Severance, Charles R

Contributor(s):

Material type:

Continuing resourceSeries: Open Textbook LibraryPublisher: [Place of publication not identified] : Charles Severance, 2009-Distributor: Minneapolis : Open Textbook Library Copyright date: ©2009-Description: 1 online resource : illustrations (some colour)Content type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781530051120
1530051126

Subject(s):

Python (Computer program language)

Genre/Form:

DDC classification:

005.133 SEV 2009

LOC classification:

QA76.73.P98 S483eb

Online resources:

Open Textbook Library (Open Access) Connect to resource

Contents:

Why should you learn to write programs? -- Variables, expressions, and statements -- Conditional execution -- Functions -- Iteration -- Strings -- Files -- Lists -- Dictionaries -- Tuples -- Regular expressions -- Networked programs -- Using Web Services -- Object-Oriented Programming -- Using databases and SQL -- Visualizing data

Summary: "I never seemed to find the perfect data-oriented Python book for my course, so I set out to write just such a book. Luckily at a faculty meeting three weeks before I was about to start my new book from scratch over the holiday break, Dr. Atul Prakash showed me the Think Python book which he had used to teach his Python course that semester. It is a well-written Computer Science text with a focus on short, direct explanations and ease of learning. The overall book structure has been changed to get to doing data analysis problems as quickly as possible and have a series of running examples and exercises about data analysis from the very beginning. Chapters 2--10 are similar to the Think Python book, but there have been major changes. Number-oriented examples and exercises have been replaced with data- oriented exercises. Topics are presented in the order needed to build increasingly sophisticated data analysis solutions. Some topics like try and except are pulled forward and presented as part of the chapter on conditionals. Functions are given very light treatment until they are needed to handle program complexity rather than introduced as an early lesson in abstraction. Nearly all user-defined functions have been removed from the example code and exercises outside of Chapter 4. The word "recursion"1 does not appear in the book at all. In chapters 1 and 11--16, all of the material is brand new, focusing on real-world uses and simple examples of Python for data analysis including regular expressions for searching and parsing, automating tasks on your computer, retrieving data across the network, scraping web pages for data, object-oriented programming, using web services, parsing XML and JSON data, creating and using databases using Structured Query Language, and visualizing data. The ultimate goal of all of these changes is a shift from a Computer Science to an Informatics focus is to only include topics into a first technology class that can be useful even if one chooses not to become a professional programmer."--Open Textbook Library

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Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Book	Symbiosis International University, Dubai	005.133 SEV 2009 (Browse shelf(Opens below))	1	Available		SIU01337

"This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License."--Title page verso

Includes index

Open Access Electronic Book

"I never seemed to find the perfect data-oriented Python book for my course, so I set out to write just such a book. Luckily at a faculty meeting three weeks before I was about to start my new book from scratch over the holiday break, Dr. Atul Prakash showed me the Think Python book which he had used to teach his Python course that semester. It is a well-written Computer Science text with a focus on short, direct explanations and ease of learning. The overall book structure has been changed to get to doing data analysis problems as quickly as possible and have a series of running examples and exercises about data analysis from the very beginning. Chapters 2--10 are similar to the Think Python book, but there have been major changes. Number-oriented examples and exercises have been replaced with data- oriented exercises. Topics are presented in the order needed to build increasingly sophisticated data analysis solutions. Some topics like try and except are pulled forward and presented as part of the chapter on conditionals. Functions are given very light treatment until they are needed to handle program complexity rather than introduced as an early lesson in abstraction. Nearly all user-defined functions have been removed from the example code and exercises outside of Chapter 4. The word "recursion"1 does not appear in the book at all. In chapters 1 and 11--16, all of the material is brand new, focusing on real-world uses and simple examples of Python for data analysis including regular expressions for searching and parsing, automating tasks on your computer, retrieving data across the network, scraping web pages for data, object-oriented programming, using web services, parsing XML and JSON data, creating and using databases using Structured Query Language, and visualizing data. The ultimate goal of all of these changes is a shift from a Computer Science to an Informatics focus is to only include topics into a first technology class that can be useful even if one chooses not to become a professional programmer."--Open Textbook Library

Mode of access: World Wide Web

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