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Mastering Matplotlib 2.x : effective data visualization techniques with Python

Author: Benjamin Walter Keller
Publisher: Birmingham, UK : Packt Publishing, 2018.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Mastering Matplotlib covers the use cases and encounter unusual cases that requires more powerful tools. With the easy to follow examples and high end components of matplotlib, this book will enable to develop advanced and interactive plots using Python scripting and Matplotlib.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Keller, Benjamin Walter
Mastering Matplotlib 2. x : Effective Data Visualization Techniques with Python
Birmingham : Packt Publishing Ltd,c2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Benjamin Walter Keller
ISBN: 1789618177 9781789618174
OCLC Number: 1085904882
Description: 1 online resource (1 volume) : illustrations
Contents: Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Heavy Customization; Customizing PyLab using style; How to use styles to change the appearance of our plots; Different Matplotlib styles; Creating your own styles; Deep diving into color; Questions to ask when choosing a color map; Using color maps; Working on non-trivial layouts; The Matplotlib configuration files; Matplotlibrc --
where does it live?; Summary; Chapter 2: Drawing on Plots; Putting lines in place; Adding horizontal and vertical lines; Adding spans that cover whole regions Adding and tweaking a background gridAdding text on your plots; Adding text to both axis and figure objects; Adding text in multi-panel figures; Playing with polygons and shapes; Adding polygons and shapes to our plots; The built-in shapes that Matplotlib provides; Building your own polygons; Versatile annotating; Adding arrows to our plots with the annotate method; Adding some text to the arrows; Customizing the appearance of the annotations; Summary; Chapter 3: Special Purpose Plots; Non-Cartesian plots; Creating polar axes; Applying log, symmetric log, and logistic scales to your axes Plotting vector fieldsMaking vector plots with quiver; Customizing the appearance of vector plots; Annotating vector plots with a quiver key; Making stream plots; Statistics with boxes and violins; Making box plots to show the interquartile ranges and the outliers; Making violin plots show different distributions; Customizing the appearance of plots; Visualizing ordinal and tabular data; Pie charts; Tables; Customizing the appearance of plots; Summary; Chapter 4: 3D and Geospatial Plots; Plotting with 3D axes; How to add 3D axes to a figure How to use the interactive backend to manipulate the 3D plotsHow to plot on the 3D axes; Looking at various 3D plot types; How to rotate the camera in 3D plots; How to add line and scatter plots; How to add wireframe, surface, and triangular surface plots; How to add 3D contour types; The basemap methods; How to create map projections; How to choose between different kinds of map projections; Further reading; Plotting on map projections; How to add simple points and lines to our plots; How to draw great circles; How to draw a day/night terminator; Adding geography How to add coastline and water featuresHow to add political boundaries for countries, states, and provinces; Summary; Chapter 5: Interactive Plotting; Interactive plots in the Jupyter Notebook; How to install and enable the ipywidgets module; How to use the interact method to make basic widgets; How to view the different kinds of widgets that ipywidgets provides; How to customize widgets; Event handling with plot callbacks; How to add interactivity by capturing mouse events; How to capture keyboard clicks; How to use the picker to manipulate plots; GUI neutral widgets
Other Titles: Mastering Matplotlib two point x
Responsibility: Benjamin Walter Keller.

Abstract:

Mastering Matplotlib covers the use cases and encounter unusual cases that requires more powerful tools. With the easy to follow examples and high end components of matplotlib, this book will enable  Read more...

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