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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

Exploring the contents of a data file


Before writing code to process a dataset, you first need to know some information about the contents of the dataset. This is slightly different from exploratory data analysis, in which the goal is to draw insight from the data.

The details of your initial exploration generally depend on what you already know about a particular dataset and what you ultimately intend to do with the data. That being said, there are a few questions that are usually helpful to ask:

  • How is the data structured?
    • If the dataset is tabular, the answer to this question is rather simple. However, for a hierarchical dataset, there may be a relatively loose structure of the data.
  • What are the data variables?
  • For each available variable, what is the data type and what is the range of possible values?
  • Are there any errors, missing values, or outliers in the data that can be corrected?

It is not always necessary to do this exploration programmatically. However, often files are too big or messy...

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