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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 do a demonstration of the steps used to retrieve Seeclickfix data from the Seeclickfix API. While the resulting datasets are not exactly the same as those used in the previous chapters, they are quite similar. In this chapter, I will demonstrate two Python programs. The first, get_recent_issues.py, is used to access the Seeclickfix API from Python, retrieve data, and output a JSON file with the data.

The second script, get_scf_date_range.py, is used to retrieve data from the issue reports that occurred on the first day of January 2017. The get_scf_date_range.py script will output a CSV file with the data.

This chapter will be different from the other chapters in a couple of ways. First, because the data for the chapter is retrieved over the internet as a part of the demonstrations, there are no input data files that are used with the demonstrations.

Second, because APIs are outside of my control, and subject to change, I've made a version of this...

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