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Matplotlib 3.0 Cookbook

You're reading from   Matplotlib 3.0 Cookbook Over 150 recipes to create highly detailed interactive visualizations using Python

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789135718
Length 676 pages
Edition 1st Edition
Languages
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Authors (2):
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Nikhil Borkar Nikhil Borkar
Author Profile Icon Nikhil Borkar
Nikhil Borkar
Srinivasa Rao Poladi Srinivasa Rao Poladi
Author Profile Icon Srinivasa Rao Poladi
Srinivasa Rao Poladi
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Toc

Table of Contents (17) Chapters Close

Preface 1. Anatomy of Matplotlib FREE CHAPTER 2. Getting Started with Basic Plots 3. Plotting Multiple Charts, Subplots, and Figures 4. Developing Visualizations for Publishing Quality 5. Plotting with Object-Oriented API 6. Plotting with Advanced Features 7. Embedding Text and Expressions 8. Saving the Figure in Different Formats 9. Developing Interactive Plots 10. Embedding Plots in a Graphical User Interface 11. Plotting 3D Graphs Using the mplot3d Toolkit 12. Using the axisartist Toolkit 13. Using the axes_grid1 Toolkit 14. Plotting Geographical Maps Using Cartopy Toolkit 15. Exploratory Data Analysis Using the Seaborn Toolkit 16. Other Books You May Enjoy

Regression plots

Regression plots help in fit two-dimensional data in to a linear or polynomial curve. This helps in visualizing the relationship between two variables to understand how closely it fits a linear or polynomial of order n. Seaborn provides three functions for this purpose: regplot(), residplot(), and lmplot(). Both regplot() as well as lmplot() serve the same purpose of fitting two-dimensional data to a linear or polynomial of order n, but regplot() is an axes-level function, whereas lmplot() is a figure-level function, which enables it to use the row and col semantic variables to draw multiple regression plots in a single figure. residplot() helps fitting the curve and plotting the residuals to understand the quality of the fit.

regplot() and residplot()

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