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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 CSV module


The csv module is designed to process CSV data in an iterative manner. Rather than reading the data and then processing it, the csv module is designed to simultaneously read and process the data line by line.

This makes sense from an efficiency perspective. It takes less memory and less time to read and operate on the data entry by entry than it does to read the data as whole. For this reason, the csv module is well suited to process very large data files that are too big to read into memory.

Additionally, because the csv module is built-in, it will work with any Python installation, making it transferable. This is worth mentioning because it is a slight advantage of the csv module over the pandas module.

In the following exercise, I will demonstrate how to use the csv module to read and process data. 

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