A basic knowledge of Python is essential. Knowledge of importing packages, and experience of working with NumPy, Pandas, and DataFrames, will help the reader get the most out of this book.

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-on-Data-Visualization-with-Bokeh. If there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/Hands-onDataVisualizationwithBokeh_ColorImages.pdf.

Visit the following link to check out videos of the code being run:

http://bit.ly/2xZJdHt.

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We had to set the x-axis type to datetime in the figure function in order to render dates along the x-axis".

A block of code is set as follows:

#Output the plot

output_file('second_plot.html')

show(plot2)

Any command-line input or output is written as follows:

bokeh serve --show bokeh.py

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text in bold. Here is an example: "We will be working with the S&P 500 stock data found on Kaggle."