![]() ![]() The FacetGrid class is useful when you want to visualize the distribution of a variable or the relationship between multiple variables separately within subsets of your dataset. This chapter explains how the underlying objects work, which may be useful for advanced applications. They take care of some important bookkeeping that synchronizes the multiple plots in each grid. In most cases, you will want to work with those functions. The figure-level functions are built on top of the objects discussed in this chapter of the tutorial. Matplotlib offers good support for making figures with multiple axes seaborn builds on top of this to directly link the structure of the plot to the structure of your dataset. It allows a viewer to quickly extract a large amount of information about a complex dataset. This technique is sometimes called either “lattice” or “trellis” plotting, and it is related to the idea of “small multiples”. Plt.When exploring multi-dimensional data, a useful approach is to draw multiple instances of the same plot on different subsets of your dataset. The key is to map the scatter PathCollection to a HandlerPathCollection with an updating function being set to it. This has the advantage that it would not use any "private" methods and works even with other objects than scatters present in the legend. Plt.legend(,, loc="lower left", markerscale=2, ![]() The only real downside is that you have to construct the legend explicitly from lists of objects and labels, but this is a well-documented matplotlib feature so it feels pretty safe to use. This is nice because it doesn't require placing an object in your axes (potentially triggering a resize event), and it doesn't require use of any hidden attributes. You can make a Line2D object that resembles your chosen markers, except with a different marker size of your choosing, and use that to construct the legend. But now you can use everything scatter offers. No need to touch the source, even though this is quite a hack. Now the _sizes (another underscore property) does the trick. Lgnd = plt.legend(loc="lower left", scatterpoints=1, fontsize=10) A better hack: import matplotlib.pyplot as plt It may break down at any update in matplotlib.
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