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Practical Data Wrangling
Practical Data Wrangling

Practical Data Wrangling: Expert techniques for transforming your raw data into a valuable source for analytics

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Profile Icon Allan Visochek
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$24.29 $26.99
eBook Nov 2017 204 pages 1st Edition
eBook
$24.29 $26.99
Paperback
$34.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Allan Visochek
Arrow right icon
$24.29 $26.99
eBook Nov 2017 204 pages 1st Edition
eBook
$24.29 $26.99
Paperback
$34.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$24.29 $26.99
Paperback
$34.99
Subscription
Free Trial
Renews at $19.99p/m

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Practical Data Wrangling

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Key benefits

  • ? This easy-to-follow guide takes you through every step of the data wrangling process in the best possible way
  • ? Work with different types of datasets, and reshape the layout of your data to make it easier for analysis
  • ? Get simple examples and real-life data wrangling solutions for data pre-processing

Description

Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them. You’ll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You’ll work with different data structures and acquire and parse data from various locations. You’ll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases. The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the end of the book, you’ll have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way.

Who is this book for?

If you are a data scientist, data analyst, or a statistician who wants to learn how to wrangle your data for analysis in the best possible manner, this book is for you. As this book covers both R and Python, some understanding of them will be beneficial.

What you will learn

  • ? Read a csv file into python and R, and print out some statistics on the data
  • ? Gain knowledge of the data formats and programming structures involved in retrieving API data
  • ? Make effective use of regular expressions in the data wrangling process
  • ? Explore the tools and packages available to prepare numerical data for analysis
  • ? Find out how to have better control over manipulating the structure of the data
  • ? Create a dexterity to programmatically read, audit, correct, and shape data
  • ? Write and complete programs to take in, format, and output data sets

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 15, 2017
Length: 204 pages
Edition : 1st
Language : English
ISBN-13 : 9781787283671
Vendor :
RStudio
Category :
Languages :
Concepts :
Tools :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
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Product Details

Publication date : Nov 15, 2017
Length: 204 pages
Edition : 1st
Language : English
ISBN-13 : 9781787283671
Vendor :
RStudio
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Table of Contents

9 Chapters
Programming with Data Chevron down icon Chevron up icon
Introduction to Programming in Python Chevron down icon Chevron up icon
Reading, Exploring, and Modifying Data - Part I Chevron down icon Chevron up icon
Reading, Exploring, and Modifying Data - Part II Chevron down icon Chevron up icon
Manipulating Text Data - An Introduction to Regular Expressions Chevron down icon Chevron up icon
Cleaning Numerical Data - An Introduction to R and RStudio Chevron down icon Chevron up icon
Simplifying Data Manipulation with dplyr Chevron down icon Chevron up icon
Getting Data from the Web Chevron down icon Chevron up icon
Working with Large Datasets Chevron down icon Chevron up icon
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