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You're reading from  The Data Visualization Workshop

Product typeBook
Published inJul 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781800568846
Edition1st Edition
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Authors (2):
Mario Döbler
Mario Döbler
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Mario Döbler

Mario Döbler is a Ph.D. student with a focus on deep learning at the University of Stuttgart. He previously interned at the Bosch Center for artificial intelligence in the Silicon Valley in the field of deep learning. He used state-of-the-art algorithms to develop cutting-edge products. In his master thesis, he dedicated himself to applying deep learning to medical data to drive medical applications.
Read more about Mario Döbler

Tim Großmann
Tim Großmann
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Tim Großmann

Tim Großmann is a computer scientist with interest in diverse topics, ranging from AI and IoT to Security. He previously worked in the field of big data engineering at the Bosch Center for Artificial Intelligence in Silicon Valley. In addition to that, he worked on an Eclipse project for IoT device abstractions in Singapore. He's highly involved in several open-source projects and actively speaks at tech meetups and conferences about his projects and experiences.
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Basic Plotting

As mentioned before, the plotting interface of Bokeh gives us a higher-level abstraction, which allows us to quickly visualize data points on a grid.

To create a new plot, we have to define our imports to load the necessary dependencies:

# importing the necessary dependencies
import pandas as pd
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
output_notebook()

Before we can create a plot, we need to import the dataset. In the examples in this chapter, we will work with a computer hardware dataset. It can be imported by using pandas' read_csv method.

# loading the Dataset with pandas
dataset = pd.read_csv('../../Datasets/computer_hardware.csv')

The basic flow when using the plotting interface is comparable to that of Matplotlib. We first create a figure. This figure is then used as a container to define elements and call methods on:

# adding an index column to use it for the x-axis
dataset['index&apos...
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The Data Visualization Workshop
Published in: Jul 2020Publisher: PacktISBN-13: 9781800568846

Authors (2)

author image
Mario Döbler

Mario Döbler is a Ph.D. student with a focus on deep learning at the University of Stuttgart. He previously interned at the Bosch Center for artificial intelligence in the Silicon Valley in the field of deep learning. He used state-of-the-art algorithms to develop cutting-edge products. In his master thesis, he dedicated himself to applying deep learning to medical data to drive medical applications.
Read more about Mario Döbler

author image
Tim Großmann

Tim Großmann is a computer scientist with interest in diverse topics, ranging from AI and IoT to Security. He previously worked in the field of big data engineering at the Bosch Center for Artificial Intelligence in Silicon Valley. In addition to that, he worked on an Eclipse project for IoT device abstractions in Singapore. He's highly involved in several open-source projects and actively speaks at tech meetups and conferences about his projects and experiences.
Read more about Tim Großmann