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Hands-On Data Visualization with Bokeh

You're reading from   Hands-On Data Visualization with Bokeh Interactive web plotting for Python using Bokeh

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781789135404
Length 174 pages
Edition 1st Edition
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Author (1):
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Kevin Jolly Kevin Jolly
Author Profile Icon Kevin Jolly
Kevin Jolly
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Table of Contents (10) Chapters Close

Preface 1. Bokeh Installation and Key Concepts FREE CHAPTER 2. Plotting using Glyphs 3. Plotting with different Data Structures 4. Using Layouts for Effective Presentation 5. Using Annotations, Widgets, and Visual Attributes for Visual Enhancement 6. Building and Hosting Applications Using the Bokeh Server 7. Advanced Plotting with Networks, Geo Data, WebGL, and Exporting Plots 8. The Bokeh Workflow – A Case Study 9. Other Books You May Enjoy

Creating annotations to convey supplemental information

When creating plots it's fundamental to get across the story that the information in the plot is trying to convey. This can be done by adding titles, legends, and color maps to your plot.

Adding titles to plots

Titles are used to tell the reader about the overall story of the plot.

For the purposes of this chapter, we will use the S&P 500 stock data found on Kaggle. (https://www.kaggle.com/camnugent/sandp500/data).

We will also filter the data to just information about Apple stocks, as illustrated in the following code:

#Import the required packages

import pandas as pd

#Read in the data

df = pd.read_csv('all_stocks_5yr.csv')

#Convert the date column into...
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