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Python for Everybody

Python for Everybody : Exploring Data in Python 3

 Severance, Charles
 Ann Arbor, [2013] ISBN: 9781530051120
 Michigan : [200] p. : ill. ; 29 cm. English
Chương trình máy tínhLập trìnhPythonKhoa Quản trị kinh doanh và du lịch
Mô tả biểu ghi
ID:69664
DDC 005.133
Tác giả CN Severance, Charles
Nhan đề Python for Everybody : Exploring Data in Python 3 / Charles Severance
Thông tin xuất bản Michigan : Ann Arbor, [2013]
Mô tả vật lý [200] p. : ill. ; 29 cm.
Tóm tắt 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
Thuật ngữ chủ đề Chương trình máy tính-Lập trình
Từ khóa tự do Chương trình máy tính
Từ khóa tự do Lập trình
Từ khóa tự do Python
Môn học Khoa Quản trị kinh doanh và du lịch
Địa chỉ 100TK_Tài liệu môn học-MH516029(2): 000140956-7
Tệp tin điện tử http://lib.hanu.vn/kiposdata1/bookcover/tailieumonhoc/000140956thumbimage.jpg
MARC
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520[ ] |a 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
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