Practical Web Scraping for Data Science : Best Practices and Examples with Python /: Best Practices and Examples with Python. (2018)
- Record Type:
- Book
- Title:
- Practical Web Scraping for Data Science : Best Practices and Examples with Python /: Best Practices and Examples with Python. (2018)
- Main Title:
- Practical Web Scraping for Data Science : Best Practices and Examples with Python
- Further Information:
- Note: Seppe vanden Broucke, Bart Baesens.
- Authors:
- Broucke, Seppe vanden
Baesens, Bart - Contents:
- Intro; Table of Contents; About the Authors; About the Technical Reviewer; Introduction; Part I: Web Scraping Basics; Chapter 1: Introduction; 1.1 What Is Web Scraping?; 1.1.1 Why Web Scraping for Data Science?; 1.1.2 Who Is Using Web Scraping?; 1.2 Getting Ready; 1.2.1 Setting Up; 1.2.2 A Quick Python Primer; Chapter 2: The Web Speaks HTTP; 2.1 The Magic of Networking; 2.2 The HyperText Transfer Protocol: HTTP; 2.3 HTTP in Python: The Requests Library; 2.4 Query Strings: URLs with Parameters; Chapter 3: Stirring the HTML and CSS Soup; 3.1 Hypertext Markup Language: HTML 3.2 Using Your Browser as a Development Tool3.3 Cascading Style Sheets: CSS; 3.4 The Beautiful Soup Library; 3.5 More on Beautiful Soup; Part II: Advanced Web Scraping; Chapter 4: Delving Deeper in HTTP; 4.1 Working with Forms and POST Requests; 4.2 Other HTTP Request Methods; 4.3 More on Headers; 4.4 Dealing with Cookies; 4.5 Using Sessions with Requests; 4.6 Binary, JSON, and Other Forms of Content; Chapter 5: Dealing with JavaScript; 5.1 What Is JavaScript?; 5.2 Scraping JavaScript; 5.3 Scraping with Selenium; 5.4 More on Selenium; Chapter 6: From Web Scraping to Web Crawling 6.1 What Is Web Crawling?6.2 Web Crawling in Python; 6.3 Storing Results in a Database; Part III: Managerial Concerns and Best Practices; Chapter 7: Managerial and Legal Concerns; 7.1 The Data Science Process; 7.2 Where Does Web Scraping Fit In?; 7.3 Legal Concerns; Chapter 8: Closing Topics; 8.1 Other Tools; 8.1.1 Alternative PythonIntro; Table of Contents; About the Authors; About the Technical Reviewer; Introduction; Part I: Web Scraping Basics; Chapter 1: Introduction; 1.1 What Is Web Scraping?; 1.1.1 Why Web Scraping for Data Science?; 1.1.2 Who Is Using Web Scraping?; 1.2 Getting Ready; 1.2.1 Setting Up; 1.2.2 A Quick Python Primer; Chapter 2: The Web Speaks HTTP; 2.1 The Magic of Networking; 2.2 The HyperText Transfer Protocol: HTTP; 2.3 HTTP in Python: The Requests Library; 2.4 Query Strings: URLs with Parameters; Chapter 3: Stirring the HTML and CSS Soup; 3.1 Hypertext Markup Language: HTML 3.2 Using Your Browser as a Development Tool3.3 Cascading Style Sheets: CSS; 3.4 The Beautiful Soup Library; 3.5 More on Beautiful Soup; Part II: Advanced Web Scraping; Chapter 4: Delving Deeper in HTTP; 4.1 Working with Forms and POST Requests; 4.2 Other HTTP Request Methods; 4.3 More on Headers; 4.4 Dealing with Cookies; 4.5 Using Sessions with Requests; 4.6 Binary, JSON, and Other Forms of Content; Chapter 5: Dealing with JavaScript; 5.1 What Is JavaScript?; 5.2 Scraping JavaScript; 5.3 Scraping with Selenium; 5.4 More on Selenium; Chapter 6: From Web Scraping to Web Crawling 6.1 What Is Web Crawling?6.2 Web Crawling in Python; 6.3 Storing Results in a Database; Part III: Managerial Concerns and Best Practices; Chapter 7: Managerial and Legal Concerns; 7.1 The Data Science Process; 7.2 Where Does Web Scraping Fit In?; 7.3 Legal Concerns; Chapter 8: Closing Topics; 8.1 Other Tools; 8.1.1 Alternative Python Libraries; 8.1.2 Scrapy; 8.1.3 Caching; 8.1.4 Proxy Servers; 8.1.5 Scraping in Other Programming Languages; 8.1.6 Command-Line Tools; 8.1.7 Graphical Scraping Tools; 8.2 Best Practices and Tips; Chapter 9: Examples; 9.1 Scraping Hacker News 9.2 Using the Hacker News API9.3 Quotes to Scrape; 9.4 Books to Scrape; 9.5 Scraping GitHub Stars; 9.6 Scraping Mortgage Rates; 9.7 Scraping and Visualizing IMDB Ratings; 9.8 Scraping IATA Airline Information; 9.9 Scraping and Analyzing Web Forum Interactions; 9.10 Collecting and Clustering a Fashion Data Set; 9.11 Sentiment Analysis of Scraped Amazon Reviews; 9.12 Scraping and Analyzing News Articles; 9.13 Scraping and Analyzing a Wikipedia Graph; 9.14 Scraping and Visualizing a Board Members Graph; 9.15 Breaking CAPTCHA's Using Deep Learning; Index … (more)
- Publisher Details:
- Place of publication not identified : Springer Science and Business Media Apress
- Publication Date:
- 2018
- Extent:
- 1 online resource
- Subjects:
- 005.133
Computer science
COMPUTERS / Programming / General
Python (Computer program language)
Data mining
Automatic data collection systems
Automatic data collection systems
Data mining
Python (Computer program language)
Computers -- Database Management -- General
Business & Economics -- Industries -- Computer Industry
Databases
Business mathematics & systems
Python (Computer program language)
Database management
Big data
Computers -- Programming Languages -- Python
Programming & scripting languages: general
Electronic books - Languages:
- English
- ISBNs:
- 9781484235829
1484235827 - Related ISBNs:
- 9781484235812
- Notes:
- Note: Includes bibliographical references and index.
Note: Online resource; title from PDF title page (EBSCO, viewed April 26, 2018). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.360070
- Ingest File:
- 01_323.xml