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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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Allan Visochek Allan Visochek
Author Profile Icon Allan Visochek
Allan Visochek
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Table of Contents (10) Chapters Close

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

Logistical overview


In this chapter, I will demonstrate four projects, using four programs respectively. These are as follows:

  • csv_intro.pyAn introduction to Python's built-in csv module
  • pandas_intro.py: An introduction to the pandas module
  • json_to_csv.py: An exercise in working with CSV data
  • xml_to_json.py: An introduction to the xml.etree.ElementTree module and an exercise in working with XML data

The finished product for each of these projects can be obtained from the code folder in the external resources. All of the external resources are available in one folder at the following link: https://goo.gl/8S58ra.

File system setup

To follow along with the exercises, you should create a project folder called ch4 to contain all of the code and data. There are a number of different programs, input datasets, and output datasets involved in the projects and exercises in this chapter. To keep things organized, I've created an additional folder to contain the code for the chapter. I've also fragmented...

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