Graphics and Plotting
Vector graphics and plotting using PyQtGraph

One of the major strengths of Python is in data science and visualization, using tools such as Pandas, numpy and sklearn for data analysis. Buiding GUI applications with PyQt gives you access to all these Python tools directly from within your app, allowing you to build complex data-driven apps and interactive dashboards. We've already covered the model views, which allow us to show data in lists and tables. In this chapter we'll look at the final piece of that puzzle -- plotting data.

When building apps with PyQt you have two main choices -- matplotlib (which also gives access to Pandas plots) and PyQtGraph, which creates plots with Qt-native graphics. In this chapter we'll look at how you can use these libraries to visualize data in your applications.

Start with “Plotting with PyQtGraph”

Graphics and Plotting

Plotting with PyQtGraph

Create custom plots in PyQt with PyQtGraph

Plotting with Matplotlib

Create PyQt5 plots with the popular Python plotting library

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