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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

Understanding the CSV format

CSV, which stands for comma-separated value, is a file format used to store tabular data. As you may have guessed, a CSV file consists of text values that are separated by commas.

In a CSV file, each data entry is represented by a single line. (Another way of thinking about this is that each line is separated by a newline '\n' character, though newline characters are invisible in most text editors.)

By convention, the first row in a CSV file contains the column headers, or the names attributed to each column. In each subsequent row, the position of each value corresponds to the data variable to which that value belongs. In other words, the first value in a row corresponds to the first column header, the second value in a row corresponds to the second column header, and so on. The following example demonstrates the syntax of a CSV file:

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