Pocket Primer Series Read Description

Regular Expressions

Pocket Primer

Mixed media product
June 2018
9781683922278
More details
  • Publisher
    Mercury Learning and Information
  • Published
    21st June 2018
  • ISBN 9781683922278
  • Language English
  • Pages 190 pp.
  • Size 6" x 9"
  •    Request Exam Copy
$34.95
E-Book (ePub)
June 2018
9781683922285
More details
  • Publisher
    Mercury Learning and Information
  • Published
    4th June 2018
  • ISBN 9781683922285
  • Language English
  • Pages 190 pp.
  • Size 6" x 9"
  •    Request E-Exam Copy
$19.95
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June 2018
9781683923114
More details
  • Publisher
    Mercury Learning and Information
  • Published
    4th June 2018
  • ISBN 9781683923114
  • Language English
  • Pages 190 pp.
  • Size 6" x 9"
$124.95

As part of the bestselling Pocket Primer series, the goal of this book is to introduce readers to regular expressions in several technologies. It is intended for data scientists, data analysts, and others who want to understand regular expressions to perform various tasks. You will acquire an understanding of how to create an assortment of regular expressions, such as filtering data for strings containing uppercase or lowercase letters; matching integers, decimals, hexadecimal, and scientific numbers; and context-dependent pattern matching expressions. It includes REs with Python, R, bash, Perl, Java, and more. Companion files with source code are available for downloading from the publisher.

Features:
• Uses REs with Python, R, bash, Java, and more
• Packed with realistic examples and numerous commands
• Assumes the reader has no prior experience, but the topic is covered comprehensively enough to teach a pro some new tricks
• Includes companion files with all of the source code examples (download from the publisher)


ON THE COMPANION FILES
(available from the publisher for downloading)
• Source code samples

1: Introduction to Regular Expressions.
2: Common Regex Tasks.
3: REs in Python.
4: Working with REs in R.
5: Working with REs in bash.
Appendix A. REs in Perl.
Appendix B. REs in Java.
Index.

Oswald Campesato

Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, Java, Android, and TensorFlow. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Python 3 for Machine Learning, and the NLP Using R Pocket Primer (all Mercury Learning).