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Practical Data Wrangling

Author: Allan Visochek
Publisher: Packt Publishing, 2017.
Edition/Format:   eBook : Document : English : 1st editionView all editions and formats
Summary:
Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and R About This Book 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  Read more...
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Allan Visochek
ISBN: 9781787286139 1787286134
OCLC Number: 1019734006
Description: 1 online resource (204 pages)
Contents: Cover; Title Page; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Programming with Data; Understanding data wrangling; Getting and reading data; Cleaning data; Shaping and structuring data; Storing data; The tools for data wrangling; Python; R; Summary; Chapter 2: Introduction to Programming in Python; External resources; Logistical overview; Installation requirements; Using other learning resources; Python 2 versus Python 3; Running programs in python; Using text editors to write and manage programs. Writing the hello world programUsing the terminal to run programs; Running the Hello World program; What if it didn't work?; Data types, variables, and the Python shell; Numbers --
integers and floats; Why integers? ; Strings; Booleans; The print function; Variables; Adding to a variable; Subtracting from a variable; Multiplication; Division; Naming variables; Arrays (lists, if you ask Python); Dictionaries ; Compound statements; Compound statement syntax and indentation level; For statements and iterables; If statements; Else and elif clauses; Functions; Passing arguments to a function. Returning values from a functionMaking annotations within programs; A programmer's resources; Documentation; Online forums and mailing lists; Summary; Chapter 3: Reading, Exploring, and Modifying Data --
Part I; External resources; Logistical overview; Installation requirements; Data; File system setup; Introducing a basic data wrangling work flow; Introducing the JSON file format; Opening and closing a file in Python using file I/O; The open function and file objects; File structure --
best practices to store your data; Opening a file; Reading the contents of a file; Modules in Python. Parsing a JSON file using the json moduleExploring the contents of a data file; Extracting the core content of the data; Listing out all of the variables in the data; Modifying a dataset; Extracting data variables from the original dataset; Using a for loop to iterate over the data; Using a nested for loop to iterate over the data variables; Outputting the modified data to a new file; Specifying input and output file names in the Terminal; Specifying the filenames from the Terminal; Summary; Chapter 4: Reading, Exploring, and Modifying Data --
Part II; Logistical overview; File system setup. DataInstalling pandas; Understanding the CSV format; Introducing the CSV module; Using the CSV module to read CSV data; Using the CSV module to write CSV data; Using the pandas module to read and process data; Counting the total road length in 2011 revisited; Handling non-standard CSV encoding and dialect; Understanding XML; XML versus JSON; Using the XML module to parse XML data; XPath; Summary; Chapter 5: Manipulating Text Data --
An Introduction to Regular Expressions; Logistical overview; Data; File structure setup; Understanding the need for pattern recognition.
Responsibility: Visochek, Allan.

Abstract:

Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and R About This Book 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 Who This Book Is 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 In Detail 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 en ...

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