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03305nam a2200493 4500 |
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978-1-4842-3582-9 |
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20191220131237.0 |
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180418s2018 xxu| s |||| 0|eng d |
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|a 9781484235829
|9 978-1-4842-3582-9
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|a 10.1007/978-1-4842-3582-9
|2 doi
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|d GrThAP
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|a QA76.73.P98
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|a UMX
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|a COM051360
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|a UMX
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|a 005.133
|2 23
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|a vanden Broucke, Seppe.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Practical Web Scraping for Data Science
|h [electronic resource] :
|b Best Practices and Examples with Python /
|c by Seppe vanden Broucke, Bart Baesens.
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|a 1st ed. 2018.
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|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2018.
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|a XVI, 306 p. 35 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a Part I: Web Scraping Basics -- 1. Introduction -- 2. The Web Speaks HTTP -- 3. Stirring the HTML and CSS Soup -- Part II: Advanced Web Scraping -- 4. Delving Deeper in HTTP -- 5. Dealing with JavaScript -- 6. From Web Scraping to Web Crawling -- Part III: Managerial Concerns and Best Practices -- 7. Managerial and Legal Concerns -- 8. Closing Topics -- 9. Examples.
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|a This book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Written with a data science audience in mind, the book explores both scraping and the larger context of web technologies in which it operates, to ensure full understanding. The authors recommend web scraping as a powerful tool for any data scientist's arsenal, as many data science projects start by obtaining an appropriate data set. Starting with a brief overview on scraping and real-life use cases, the authors explore the core concepts of HTTP, HTML, and CSS to provide a solid foundation. Along with a quick Python primer, they cover requests and Beautiful Soup, Selenium for JavaScript-heavy sites, and web crawling in detail. The book finishes with a recap of best practices and a collection of examples that bring together everything you've learned and illustrate various data science use cases.
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650 |
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|a Python (Computer program language).
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|a Database management.
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|a Big data.
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|a Python.
|0 http://scigraph.springernature.com/things/product-market-codes/I29080
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|a Database Management.
|0 http://scigraph.springernature.com/things/product-market-codes/I18024
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|a Big Data/Analytics.
|0 http://scigraph.springernature.com/things/product-market-codes/522070
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|a Baesens, Bart.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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2 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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776 |
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|i Printed edition:
|z 9781484235812
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776 |
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|i Printed edition:
|z 9781484235836
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|i Printed edition:
|z 9781484248195
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|u https://doi.org/10.1007/978-1-4842-3582-9
|z Full Text via HEAL-Link
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|a ZDB-2-CWD
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|a Professional and Applied Computing (Springer-12059)
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