Book Image

Jupyter Cookbook

By : Dan Toomey
Book Image

Jupyter Cookbook

By: Dan Toomey

Overview of this book

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Reading text files

Text files, in contrast to those flat files, do not normally have column widths specified, nor do they have delimiters. The prototypical example would be programming logging files that are used by programmers to log the progress of their programs. The log files may have a consistent prefix to each record with a timestamp or such, but the rest of the record is completely up to the developer's needs.

Text files tend to be very large as well, easily running into many megabytes of storage.

An entirely new form of database has emerged for the storage and retrieval of text files, appropriately named text databases. Access to records in these databases is normally looking for strings that can be used to index the records. As before, log file entries normally have a consistent timestamp present, so you can order the results accordingly and file records occurring at particular times. You can just as easily look for any string in a text entry as well.

Getting ready

While it is interesting...