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Matplotlib for Python Developers

You're reading from   Matplotlib for Python Developers Effective techniques for data visualization with Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788625173
Length 300 pages
Edition 2nd Edition
Languages
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Authors (3):
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Claire Chung Claire Chung
Author Profile Icon Claire Chung
Claire Chung
Aldrin Yim Aldrin Yim
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Aldrin Yim
Allen Yu Allen Yu
Author Profile Icon Allen Yu
Allen Yu
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Toc

Table of Contents (16) Chapters Close

Title Page
Dedication
Packt Upsell
Contributors
Preface
1. Introduction to Matplotlib FREE CHAPTER 2. Getting Started with Matplotlib 3. Decorating Graphs with Plot Styles and Types 4. Advanced Matplotlib 5. Embedding Matplotlib in GTK+3 6. Embedding Matplotlib in Qt 5 7. Embedding Matplotlib in wxWidgets Using wxPython 8. Integrating Matplotlib with Web Applications 9. Matplotlib in the Real World 10. Integrating Data Visualization into the Workflow Index

Chapter 10. Integrating Data Visualization into the Workflow

We have now come to the concluding chapter of this book. Throughout the course of this book, you have mastered the techniques to create and customize static and animated plots using real-world data in different formats scraped from the web. To wrap up, we will start a mini-project in this chapter to combine the skills of data analytics with the visualization techniques you've learned. We will demonstrate how to integrate visualization techniques in your current workflow.

In the era of big data, machine learning becomes fundamental to ease analytic work by replacing huge amounts of manual curation with automatic prediction. Yet, before we enter model building, Exploratory Data Analysis (EDA) is always essential to get a good grasp of what the data is like. Constant review during the optimization process also helps improve our training strategy and results.

High-dimensional data typically requires special processing techniques to be...

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