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You're reading from  Hands-On Data Preprocessing in Python

Product typeBook
Published inJan 2022
PublisherPackt
ISBN-139781801072137
Edition1st Edition
Tools
PyTorchAzure Functions
Concepts
Big Data
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Author (1)
Roy Jafari
Roy Jafari
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Roy Jafari

Roy Jafari, Ph.D. is an assistant professor of business analytics at the University of Redlands. Roy has taught and developed college-level courses that cover data cleaning, decision making, data science, machine learning, and optimization. Roy's style of teaching is hands-on and he believes the best way to learn is to learn by doing. He uses active learning teaching philosophy and readers will get to experience active learning in this book. Roy believes that successful data preprocessing only happens when you are equipped with the most efficient tools, have an appropriate understanding of data analytic goals, are aware of data preprocessing steps, and can compare a variety of methods. This belief has shaped the structure of this book.
Read more about Roy Jafari

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Resizing visuals and saving them

It is very simple to save Matplotlib visuals with any resolution that you would like. However, before adjusting the resolution and saving the visuals, you might want to resize the visual. Let's first take a look at how we can resize the visuals and then see how we can save the visuals with specific resolutions.

Resizing

Matplotlib uses a default visual size (6x4 inches) for all its visual output, and from time to time, you may want to adjust the size of the visuals (especially if you have subplots as you may need a larger output). To adjust the visual size, the easiest way is to run plt.figure(figsize=(6,4)) before starting to request any visuals. Of course, adding the mentioned code will not change the size as the inputted values are the same as the Matplotlib default size. To observe the difference, add plt.figure(figsize=(9,6)) to the code in the previous screenshot and run it to study the differences. Also, change the values a few times...

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Hands-On Data Preprocessing in Python
Published in: Jan 2022Publisher: PacktISBN-13: 9781801072137
© 2022 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Roy Jafari

Roy Jafari, Ph.D. is an assistant professor of business analytics at the University of Redlands. Roy has taught and developed college-level courses that cover data cleaning, decision making, data science, machine learning, and optimization. Roy's style of teaching is hands-on and he believes the best way to learn is to learn by doing. He uses active learning teaching philosophy and readers will get to experience active learning in this book. Roy believes that successful data preprocessing only happens when you are equipped with the most efficient tools, have an appropriate understanding of data analytic goals, are aware of data preprocessing steps, and can compare a variety of methods. This belief has shaped the structure of this book.
Read more about Roy Jafari

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