Python graphics libraries for data visualization
Pinpoint Accuracy
© Lead Image © Sergii Gnatiuk, 123RF.com
Python's powerful Matplotlib, Bokeh, PyQtGraph, and Pandas libraries lend programmers a helping hand when visualizing complex data and their relationships.
Daily life is inundated by data collected, processed, and made available again in edited form. Examples include weather, temperature, precipitation, humidity, air pressure, wind, sales, and load measurements, as well as vehicle and services data.
A first step is to evaluate data in the simplest form as a table that provides an overview of individual values. Beyond this, a suitable graphical visualization helps you to understand the relationships at a glance. Python has excellent libraries to implement this visualization step, including Matplotlib [1], PyQtGraph [2], Bokeh [3], and Pandas [4]. Appropriate libraries for other languages are summarized the "Visualization in Other Languages" box.
In addition to showing data as two- or three-dimensional images, the respective API libraries contain interaction (PyQtGraph, Bokeh) and data analysis (Pandas) methods. With a short sample program, I can show you in each case how to take the initial hurdles in stride. (See the "Installation and Variants" box.)
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