Book Image

Practical Data Wrangling

By : Allan Visochek
Book Image

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 (16 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Outputting the modified data to a new file


A new file can be created by opening a file with write permission. If named file does not exist, it will be created so long as the directory specified in the file path exists.

As is done in the following continuation of process_data.py, open a new output file in the data folder called scf_output_data.json with write permission.

Note

In general, when you open a file with write permission, remember to be careful not to accidentally overwrite an existing file by using the name of a file you do not want to erase. Additionally remember to use double backslashes instead of forward slashes for file paths if you are using Windows

...
for issue in issues:
    new_issue = {}
    for variable in variables: ##
       new_issue[variable] = issue[variable] ##
    new_data.append(new_issue)

### OUTPUTTING THE NEW DATA TO A NEW FILE ###
outfile = open("data/scf_output_data.json","w")
outfile.close()

You can use the json.dump() method to write your data to the output...