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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
Languages
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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

The exploratory data analysis

Since we have worked extensively with the S&P 500 stock data from Kaggle, we are going to be using that dataset in order to create our application. The dataset can be found here: https://www.kaggle.com/camnugent/sandp500/data.

The first step is to read the data into Jupyter Notebook and understand what the data looks like. This can be done using the code shown here:

#Import packages

import pandas as pd

#Read the data into the notebook

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

#Extract information about the data

df.info()

This renders the output shown in this screenshot:

This sheds information on the number of rows the dataset has, the data types of each column, the number of variables, and any missing values.

The next step is to understand the kind of information contained in all the columns of your dataset. We can do this by using the...

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