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Practical Data Wrangling

Practical Data Wrangling

By : Allan Visochek
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Practical Data Wrangling

Practical Data Wrangling

By: Allan Visochek

Overview of this book

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.
Table of Contents (10 chapters)
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Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The next lines of code read the link and assign it to the open function."

A block of code is set as follows:

fin = open('data/fake_weather_data.csv','r',newline='')
reader = csv.reader(fin)
for row in reader:
myData.append(row)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

new_scf_data = []
for old_entry in scf_data["issues"]:
new_entry={}

Any command-line input or output is written as follows:

$ mongoimport --file fake_weather_data.csv

New terms and important words are shown in bold.

Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter."

Warnings or important notes appear like this.
Tips and tricks appear like this.
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Practical Data Wrangling
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