In addition to these single letter codes, you can also set more complex colours using hex notation eg. They're pretty unsurprising, except that 'k' is used for black. There are a number of simple colours available using single letters, based on the standard colours used in matplotlib. In the next section we'll look at what options we have available to us in PyQtGraph to improve the appearance and usability of our plots.Ĭhange PyQtGraph Plot Background to White The default plot style of PyQtGraph is quite bare - a black background with a thin (barely visible) white line. The custom PyQtGraph widget showing dummy data.
You an import and use it as import pyqtgraph if you prefer. This is a common convention in PyQtGraph examples to keep things tidy & reduce typing. In all our examples below we import PyQtGraph using import pyqtgraph as pg. Super(MainWindow, self)._init_(*args, **kwargs) Import sys # We need sys so that we can pass argv to QApplication
#MATPLOTLIB SCATTER MARKER SIZE CONSTANT INSTALL#
To be able to use PyQtGraph with PyQt you first need to install the package to your Python environment. In this tutorial we'll walk through the first steps of creating a plot widget with PyQtGraph and then demonstrate plot customization using line colours, line type, axis labels, background colour and plotting multiple lines. PyQtGraph is built on top of Qt's native QGraphicsScene giving better drawing performance, particularly for live data, as well as providing interactivity and the ability to easily customize plots with Qt graphics widgets.
For simple and highly interactive plots you may want to consider using PyQtGraph instead. While it is possible to embed matplotlib plots in PyQt the experience does not feel entirely native. 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. One of the major strengths of Python is in exploratory data science and visualization, using tools such as Pandas, numpy, sklearn for data analysis and matplotlib plotting.
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