On balance, the figure-level functions add some additional complexity that can make things more confusing for beginners, but their distinct features give them additional power. Many parameters not in function signatureĬannot be part of a larger matplotlib figure Here is a summary of the pros and cons that we have discussed above: Relative merits of figure-level functions # A downside is that, when you do want to change the figure size, you’ll need to remember that things work a bit differently than they do in matplotlib. The upshot is that you can assign faceting variables without stopping to think about how you’ll need to adjust the total figure size. FacetGrid ( penguins, col = "sex", height = 3.5, aspect =. To illustrate the difference between these approaches, here is the default output of () with one subplot: Most importantly, the parameters correspond to the size of each subplot, rather than the size of the overall figure. Second, these parameters, height and aspect, parameterize the size slightly differently than the width, height parameterization in matplotlib (using the seaborn parameters, width = height * aspect). First, the functions themselves have parameters to control the figure size (although these are actually parameters of the underlying FacetGrid that manages the figure). When using a figure-level function, there are several key differences. When using an axes-level function in seaborn, the same rules apply: the size of the plot is determined by the size of the figure it is part of and the axes layout in that figure. with the figsize parameter of ()), or by calling a method on the figure object (e.g. To increase or decrease the size of a matplotlib plot, you set the width and height of the entire figure, either in the global rcParams, while setting up the plot (e.g. While convenient, this does add a bit of extra complexity, as you need to remember that this method is not part of the matplotlib API and exists only when using a figure-level function. set_axis_labels ( "Flipper length (mm)", "Bill length (mm)" ) relplot ( data = penguins, x = "flipper_length_mm", y = "bill_length_mm", col = "sex" ) g. But they additionally accept an ax= argument, which integrates with the object-oriented interface and lets you specify exactly where each plot should go: The axes-level functions call () internally, which hooks into the matplotlib state-machine interface so that they draw their plots on the “currently-active” axes. That means they can be composed into arbitrarily-complex matplotlib figures with predictable results. While they add axis labels and legends automatically, they don’t modify anything beyond the axes that they are drawn into. The axes-level functions are written to act like drop-in replacements for matplotlib functions. Axes-level functions make self-contained plots #
#Subplot figsize how to
Some of their features might be less discoverable, and you may need to look at two different pages of the documentation before understanding how to achieve a specific goal. That means they are no less flexible, but there is a downside: the kind-specific parameters don’t appear in the function signature or docstrings. The figure-level functions wrap their axes-level counterparts and pass the kind-specific keyword arguments (such as the bin size for a histogram) down to the underlying function. displot ( data = penguins, x = "flipper_length_mm", hue = "species", col = "species" )
#Subplot figsize code
Its default behavior is to draw a histogram, using the same code as histplot() behind the scenes:
The organization looks a bit like this:įor example, displot() is the figure-level function for the distributions module. Each module has a single figure-level function, which offers a unitary interface to its various axes-level functions. In contrast, figure-level functions interface with matplotlib through a seaborn object, usually a FacetGrid, that manages the figure. They plot data onto a single object, which is the return value of the function. The examples above are axes-level functions. In addition to the different modules, there is a cross-cutting classification of seaborn functions as “axes-level” or “figure-level”. They are designed to facilitate switching between different visual representations as you explore a dataset, because different representations often have complementary strengths and weaknesses. kdeplot ( data = penguins, x = "flipper_length_mm", hue = "species", multiple = "stack" )įunctions within a module share a lot of underlying code and offer similar features that may not be present in other components of the library (such as multiple="stack" in the examples above).
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