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Data Analysis with Python

You're reading from   Data Analysis with Python A Modern Approach

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789950069
Length 490 pages
Edition 1st Edition
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Author (1):
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David Taieb David Taieb
Author Profile Icon David Taieb
David Taieb
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Table of Contents (14) Chapters Close

Preface 1. Programming and Data Science – A New Toolset FREE CHAPTER 2. Python and Jupyter Notebooks to Power your Data Analysis 3. Accelerate your Data Analysis with Python Libraries 4. Publish your Data Analysis to the Web - the PixieApp Tool 5. Python and PixieDust Best Practices and Advanced Concepts 6. Analytics Study: AI and Image Recognition with TensorFlow 7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis 8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting 9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis 10. The Future of Data Analysis and Where to Develop your Skills A. PixieApp Quick-Reference Other Books You May Enjoy Index

Run Node.js inside a Python Notebook

Even though I've clearly stated at the beginning of this book that Python has emerged as a clear leader in the field of data science, it is still only marginally used by the developer community where traditional languages, such as Node.js, are still preferred. Recognizing that, for some developers, learning a new language, such as Python, is a cost of entry to data science that may be too high, I partnered with my IBM colleague, Glynn Bird, to build an extension library to PixieDust called pixiedust_node (https://github.com/pixiedust/pixiedust_node) that would let developers run Node.js/JavaScript code inside cells in a Python Notebook. The goal of this library is to ease developers into the Python world by allowing them to reuse their favourite Node.js libraries, for example, to load and process data from existing data sources.

To install the pixiedust_node library, simply run the following command in its own cell:

!pip install pixiedust_node...
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