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You're reading from  Python Data Visualization Cookbook

Product typeBook
Published inNov 2013
Reading LevelIntermediate
PublisherPackt
ISBN-139781782163367
Edition1st Edition
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Author (1)
Igor Milovanovic
Igor Milovanovic
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Igor Milovanovic

Igor Milovanović is an experienced developer, with strong background in Linux system knowledge and software engineering education. He is skilled in building scalable data-driven distributed software rich systems. An evangelist for high-quality systems design, he has a strong interest in software architecture and development methodologies. Igor is always committed to advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration. He also possesses solid knowledge of product development. With field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa. Igor is most grateful to his girlfriend for letting him spend hours on work instead with her and being an avid listener to his endless book monologues. He thanks his brother for being the strongest supporter. He is also thankful to his parents for letting him develop in various ways to become a person he is today.
Read more about Igor Milovanovic

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Setting ticks, labels, and grids


In this recipe we will continue with setting axis and line properties and adding more data to our figure and charts.

Getting ready

Let us learn a little about figures and subplots.

In matplotlib, figure() is used to explicitly create a figure, which represents a user interface window. Figures are created implicitly just by calling plot() or similar functions. This is fine for simple charts, but having the ability to explicitly create a figure and get a reference to its instance is very useful for more advanced use.

A figure contains one or more subplots. Subplots allow us to arrange plots in a regular grid. We already used subplot(), in which we specify the number of rows and columns and the number of the plot we are referring to.

If we want more control, we need to use axes instances from the matplotlib.axes.Axes class. They allow us to place plots at any location in the figure. An example of this would be to put a smaller plot inside a bigger one.

How to do...

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Python Data Visualization Cookbook
Published in: Nov 2013Publisher: PacktISBN-13: 9781782163367

Author (1)

author image
Igor Milovanovic

Igor Milovanović is an experienced developer, with strong background in Linux system knowledge and software engineering education. He is skilled in building scalable data-driven distributed software rich systems. An evangelist for high-quality systems design, he has a strong interest in software architecture and development methodologies. Igor is always committed to advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration. He also possesses solid knowledge of product development. With field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa. Igor is most grateful to his girlfriend for letting him spend hours on work instead with her and being an avid listener to his endless book monologues. He thanks his brother for being the strongest supporter. He is also thankful to his parents for letting him develop in various ways to become a person he is today.
Read more about Igor Milovanovic