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Practical Data Wrangling

You're reading from   Practical Data Wrangling Expert techniques for transforming your raw data into a valuable source for analytics

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286139
Length 204 pages
Edition 1st Edition
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Author (1):
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 Visochek Visochek
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Visochek
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Toc

Table of Contents (16) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Programming with Data FREE CHAPTER 2. Introduction to Programming in Python 3. Reading, Exploring, and Modifying Data - Part I 4. Reading, Exploring, and Modifying Data - Part II 5. Manipulating Text Data - An Introduction to Regular Expressions 6. Cleaning Numerical Data - An Introduction to R and RStudio 7. Simplifying Data Manipulation with dplyr 8. Getting Data from the Web 9. Working with Large Datasets

Summary


In summary this chapter was an introduction to the XML and CSV data formats. In Python, CSV data can be processed using the Python csv module or using the pandas module depending on personal preference and the nature of the task. The csv module can also be used to write output CSV data. (While it was not covered here, it is also possible to use the pandas module to output data in CSV and JSON formats.) Finally, XML data can be parsed using the Python xml.etree.ElementTree module.

In the next chapter, you will have the chance to work on a much more applied project--extracting street names from addresses. In the next chapter, I will introduce regular expressions, a tool for matching and extracting patterns in text data. 

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