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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
Languages
Tools
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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

Anatomy of a PixieApp

Note

Note: The PixieApp programming model doesn't require any prior experience with JavaScript, however, it is expected that the reader is familiar with the following:

The term PixieApp stands for Pixie Application, and is meant to emphasize its tight integration with the PixieDust capabilities, especially the display() API. Its main goal is to make it easy for developers to build a user interface that can invoke the data analytics implemented in the Jupyter Notebook.

A PixieApp follows the single-page application (SPA) design pattern (https://en.wikipedia.org/wiki/Single-page_application), where the user is presented with a welcome screen that is dynamically updated to respond to a user interaction. An update can be a partial refresh, such as updating a graph after the user clicks on a control or a full refresh, such as a new screen in a multistep...

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