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Data Wrangling with Python

You're reading from   Data Wrangling with Python Creating actionable data from raw sources

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789800111
Length 452 pages
Edition 1st Edition
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Tools
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Authors (2):
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Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Author Profile Icon Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Author Profile Icon Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
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Table of Contents (12) Chapters Close

Data Wrangling with Python
Preface
1. Introduction to Data Wrangling with Python FREE CHAPTER 2. Advanced Data Structures and File Handling 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. Application of Data Wrangling in Real Life 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 of data is not only useful for preparing beautiful reports – it is often very important to guarantee data integrity for the downstream system.

We ended the 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 time 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 the how to read web pages, XML files, and APIs.

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