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Matplotlib: Visualization with Python. Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Create publication quality plots. Make interactive figures that can zoom, pan, update
matplotlib.pyplot is a collection of functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc.
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations.
Check out Plot types to get an overview of the types of plots you can create with Matplotlib. Learn Matplotlib from the ground up in the Quick-start guide.
One convenient way to install Matplotlib with other useful Python software is to use the Anaconda Python scientific software collection, which includes Python itself and a wide range of libraries; if you need a library that is not available from the collection, you can install it yourself using standard methods such as pip. See the Anaconda web ...
Currently Matplotlib supports PyQt/PySide, PyGObject, Tkinter, and wxPython. When embedding Matplotlib in a GUI, you must use the Matplotlib API directly rather than the pylab/pyplot procedural interface, so take a look at the examples/api directory for some example code working with the API.
Each Axes also has a title (set via set_title()), an x-label (set via set_xlabel()), and a y-label set via set_ylabel()). The Axes methods are the primary interface for configuring most parts of your plot (adding data, controlling axis scales and limits, adding labels etc.).
Overview of many common plotting commands provided by Matplotlib. See the gallery for more examples and the tutorials page for longer examples. Pairwise data #
This page contains a few tutorials for using Matplotlib. For the old tutorials, see below. For shorter examples, see our examples page. You can also find external resources and a FAQ in our user guide. Pyplot tutorial. Image tutorial. The Lifecycle of a Plot. Artist tutorial.
matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs) [source] #. Plot y versus x as lines and/or markers. Call signatures: plot([x], y, [fmt], *, data=None, **kwargs) plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) The coordinates of the points or line nodes are given by x, y.
