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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 an insightful visualization

Now that we have a fundamental idea of what our data contains, we can proceed to making the visualization. The first step is to ensure we have the foundation of the visualization ready.

Creating the base plot

The foundation consists of the base plot that you want to visualize. In our case, we want to see how the volume of stocks traded over a period of time correlates with the high prices. In order to build this application, we use the code shown here:

#Import the required packages

from bokeh.io import curdoc
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
import pandas as pd

#Read the data into the notebook

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

#Convert...
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