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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.
Read more about Tim Großmann

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Multi-Plots in Seaborn

In the previous topic, we introduced a multi-plot, namely, the pair plot. In this topic, we want to talk about a different way to create flexible multi-plots.

FacetGrid

The FacetGrid is useful for visualizing a certain plot for multiple variables separately. A FacetGrid can be drawn with up to three dimensions: row, col, and hue. The first two have the obvious relationship with the rows and columns of an array. The hue is the third dimension and is shown in different colors. The FacetGrid class has to be initialized with a DataFrame, and the names of the variables that will form the row, column, or hue dimensions of the grid. These variables should be categorical or discrete.

The seaborn.FacetGrid(data, row, col, hue, …) command initializes a multi-plot grid for plotting conditional relationships.

Here are some interesting parameters:

  • data: A tidy ("long-form") DataFrame where each column corresponds to a variable, and each...
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You have been reading a chapter from
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