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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

Introducing the JSON file format

If you have worked with an Excel spreadsheet, you are already familiar with one type of data file. A spreadsheet is an example of data with a tabular format. In a tabular dataset, the entries are arranged as a series of rows and columns, where each column represents a data variable and each row represents a data entry. In Chapter 4, Reading, Exploring, and Modifying Data - Part II, you will work with CSV files, a form of tabular data.

The dataset for this chapter uses the JSON file format. JSON is an example of a hierarchical data format. Hierarchical data is more free-form than tabular data, though usually a JSON dataset will contain a series of data entries with a fixed structure.

JSON has two structures that have the same form as the Python dictionary and the Python array. The first of these structures is a collection of key value pairs, while...

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