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You're reading from  The Data Visualization Workshop

Product typeBook
Published inJul 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781800568846
Edition1st Edition
Languages
Python
Tools
Jupyter
Concepts
Data Visualization
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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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Layouts

There are multiple ways to define a visualization layout in Matplotlib. By layout, we mean the arrangement of multiple Axes within a Figure. We will start with subplots and how to use the tight layout to create visually appealing plots and then cover GridSpec, which offers a more flexible way to create multi-plots.

Subplots

It is often useful to display several plots next to one another. Matplotlib offers the concept of subplots, which are multiple Axes within a Figure. These plots can be grids of plots, nested plots, and so on.

Explore the following options to create subplots:

  • The plt.subplots(, ncols) function creates a Figure and a set of subplots. nrows, ncols define the number of rows and columns of the subplots, respectively.
  • The plt.subplot(nrows, ncols, index) function or, equivalently, plt.subplot(pos) adds a subplot to the current Figure. The index starts at 1. The plt.subplot(2, 2, 1) function is equivalent to plt.subplot(221).
  • The Figure...
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The Data Visualization Workshop
Published in: Jul 2020Publisher: PacktISBN-13: 9781800568846
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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

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