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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 databases


In addition to using plenty of memory, large amounts of data can also take a long time to process. In some cases, it may make sense to process a large dataset from one input file to another output file. Data wrangling, however, is often an iterative process, involving back and forth between data analysis and modification. It can be hard to iterate with a large dataset using a Python script. This is where database management systems can be helpful. 

A database simply refers to an organized collection of data. In contrast to files, databases are typically organized structurally to index each of the documents (another word for data entry) making it faster to retrieve specific documents or groups of documents. A database management system is software for interfacing with a database to do the following:

  • Retrieve data from a database
  • Modify data in a database
  • Write data to a database

Database management systems define a language for analyzing and modifying data that does not...

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