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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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Color Palettes

Color is a very important factor for your visualization. Color can reveal patterns in data if used effectively or hide patterns if used poorly. Seaborn makes it easy to select and use color palettes that are suited to your task. The color_palette() function provides an interface for many of the possible ways to generate color palettes.

The seaborn.color_palette([palette], [n_colors], [desat]) command returns a list of colors, thus defining a color palette.

The parameters are as follows:

  • palette (optional): Name of palette or None to return the current palette.
  • n_colors (optional): Number of colors in the palette. If the specified number of colors is larger than the number of colors in the palette, the colors will be cycled.
  • desat (optional): The proportion to desaturate each color by.

You can set the palette for all plots with set_palette(). This function accepts the same arguments as color_palette(). In the following sections, we will explain...

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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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