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You're reading from  Matplotlib 2.x By Example

Product typeBook
Published inAug 2017
PublisherPackt
ISBN-139781788295260
Edition1st Edition
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Authors (3):
Allen Yu
Allen Yu
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Allen Yu

Allen Yu, PhD, is a Chevening Scholar, 2017-18, and an MSC student in computer science at the University of Oxford. He holds a PhD degree in Biochemistry from the Chinese University of Hong Kong, and he has used Python and Matplotlib extensively during his 10 years of bioinformatics experience.
Read more about Allen Yu

Claire Chung
Claire Chung
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Claire Chung

Claire Chung is pursuing her PhD degree as a Bioinformatician at the Chinese University of Hong Kong. She enjoys using Python daily for work and lifehack. While passionate in science, her challenge-loving character motivates her to go beyond data analytics. She has participated in web development projects, as well as developed skills in graphic design and multilingual translation. She led the Campus Network Support Team in college, and shared her experience in data visualization in PyCon HK 2017.
Read more about Claire Chung

Aldrin Yim
Aldrin Yim
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Aldrin Yim

Aldrin Yim is a PhD candidate and Markey Scholar in the Computation and System Biology program at Washington University, School of Medicine. His research focuses on applying big data analytics and machine learning approaches in studying neurological diseases and cancer. He is also the founding CEO of Codex Genetics Limited, which provides precision medicine solutions to patients and hospitals in Asia.
Read more about Aldrin Yim

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Adjusting the layout


Figure layout, including the size and location of plots, directs the focus of our readers. A figure with good layout facilitates data presentation in a logical flow. It is thus important to familiarize ourselves with layout settings when plotting. Let's learn how to assign proper sizes, positions, and spacing to our plots.

Adjusting the size of the figure

Depending on where you want to put your figure, you may want to adjust the size and layout. Instead of manually stretching your image output afterward, which takes extra effort and can distort the text. You can set the height and width directly by calling pyplot.figure(figsize=()). As briefly mentioned in Chapter 2, Figure Aesthetics, the figure module contains all the plot elements. The figsize setting controls the overall size in inches and aspect ratio of the figure. This is also useful when we want to show a larger plot directly in a notebook, without having to export it in a high-resolution image.

Here is an example...

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Matplotlib 2.x By Example
Published in: Aug 2017Publisher: PacktISBN-13: 9781788295260

Authors (3)

author image
Allen Yu

Allen Yu, PhD, is a Chevening Scholar, 2017-18, and an MSC student in computer science at the University of Oxford. He holds a PhD degree in Biochemistry from the Chinese University of Hong Kong, and he has used Python and Matplotlib extensively during his 10 years of bioinformatics experience.
Read more about Allen Yu

author image
Claire Chung

Claire Chung is pursuing her PhD degree as a Bioinformatician at the Chinese University of Hong Kong. She enjoys using Python daily for work and lifehack. While passionate in science, her challenge-loving character motivates her to go beyond data analytics. She has participated in web development projects, as well as developed skills in graphic design and multilingual translation. She led the Campus Network Support Team in college, and shared her experience in data visualization in PyCon HK 2017.
Read more about Claire Chung

author image
Aldrin Yim

Aldrin Yim is a PhD candidate and Markey Scholar in the Computation and System Biology program at Washington University, School of Medicine. His research focuses on applying big data analytics and machine learning approaches in studying neurological diseases and cancer. He is also the founding CEO of Codex Genetics Limited, which provides precision medicine solutions to patients and hospitals in Asia.
Read more about Aldrin Yim