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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 multiple plots along the same row

In order to create multiple plots along the same row, let's first create three unique plots. We will be working with the S&P 500 stock data found on Kaggle (https://www.kaggle.com/camnugent/sandp500/data).

The first step is to read the data and filter it so that we only use the data related to Apple as shown here:

#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 datetime data type

df['date'] = pd.to_datetime(df['date'])

#Filter the data for Apple stocks only

df_apple = df[df['Name'] == 'AAL']

Next, let's construct three unique plots using the code as shown here:

#Import the required packages

from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.plotting import ColumnDataSource...
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