Book Image

Jupyter for Data Science

By : Dan Toomey
Book Image

Jupyter for Data Science

By: Dan Toomey

Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks. By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.
Table of Contents (17 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Monitoring Jupyter


As with the earlier discussions in this chapter on optimization, you can also use programming tools to monitor the overall interactions of your notebook. The predominant tool for Linux/Mac environments is memory_profiler. If you start this tool then your notebook, the profiler will keep track of memory use of your notebook.

With this record of information points you may be able to adjust your programmatic memory allocation to be smaller in profile if you find a large memory use occurring. For example, the profiler may highlight that you are creating (and dropping) a large memory item continuously inside of a loop. When you go back to your coding you realize this memory access could be pulled out of the loop and just done once or that size of the allocation could be minimized easily.