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The Data Wrangling Workshop

You're reading from   The Data Wrangling Workshop Create your own actionable insights using data from multiple raw sources

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839215001
Length 576 pages
Edition 2nd Edition
Languages
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Authors (3):
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Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Author Profile Icon Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Author Profile Icon Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
Brian Lipp Brian Lipp
Author Profile Icon Brian Lipp
Brian Lipp
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Toc

Table of Contents (11) Chapters Close

Preface
1. Introduction to Data Wrangling with Python 2. Advanced Operations on Built-In Data Structures FREE CHAPTER 3. Introduction to NumPy, Pandas, and Matplotlib 4. A Deep Dive into Data Wrangling with Python 5. Getting Comfortable with Different Kinds of Data Sources 6. Learning the Hidden Secrets of Data Wrangling 7. Advanced Web Scraping and Data Gathering 8. RDBMS and SQL 9. Applications in Business Use Cases and Conclusion of the Course Appendix

Summary

In this chapter, we learned about interesting ways to deal with list data by using a generator expression. They are easy and elegant and, once mastered, they give us a powerful trick that we can use repeatedly to simplify several common data wrangling tasks. We also examined different ways to format data. Formatting data is not only useful for preparing beautiful reports – it is often very important to guarantee data integrity for the downstream system.

We ended this chapter by checking out some methods to identify and remove outliers. This is important for us because we want our data to be properly prepared and ready for all our fancy downstream analysis jobs. We also observed how important it is to take the time to and use domain expertise to set up rules for identifying outliers, as doing this incorrectly can do more harm than good.

In the next chapter, we will cover how to read web pages, XML files, and APIs.

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