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Legend guide

Generating legends flexibly in Matplotlib.

This legend guide is an extension of the documentation available at legend() - please ensure you are familiar with contents of that documentation before proceeding with this guide.

This guide makes use of some common terms, which are documented here for clarity:

legend entry

legend key

legend label

legend handle

Controlling the legend entries

Calling legend() with no arguments automatically fetches the legend handles and their associated labels. This functionality is equivalent to:

  1. handles, labels = ax.get_legend_handles_labels()
  2. ax.legend(handles, labels)

The get_legend_handles_labels() function returns a list of handles/artists which exist on the Axes which can be used to generate entries for the resulting legend - it is worth noting however that not all artists can be added to a legend, at which point a “proxy” will have to be created (see Creating artists specifically for adding to the legend (aka. Proxy artists) for further details).

For full control of what is being added to the legend, it is common to pass the appropriate handles directly to legend():

  1. line_up, = plt.plot([1,2,3], label='Line 2')
  2. line_down, = plt.plot([3,2,1], label='Line 1')
  3. plt.legend(handles=[line_up, line_down])

In some cases, it is not possible to set the label of the handle, so it is possible to pass through the list of labels to legend():

  1. line_up, = plt.plot([1,2,3], label='Line 2')
  2. line_down, = plt.plot([3,2,1], label='Line 1')
  3. plt.legend([line_up, line_down], ['Line Up', 'Line Down'])

Creating artists specifically for adding to the legend (aka. Proxy artists)

Not all handles can be turned into legend entries automatically, so it is often necessary to create an artist which can. Legend handles don’t have to exists on the Figure or Axes in order to be used.

Suppose we wanted to create a legend which has an entry for some data which is represented by a red color:

  1. import matplotlib.patches as mpatches
  2. import matplotlib.pyplot as plt
  4. red_patch = mpatches.Patch(color='red', label='The red data')
  5. plt.legend(handles=[red_patch])
  7. plt.show()

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There are many supported legend handles, instead of creating a patch of color we could have created a line with a marker:

  1. import matplotlib.lines as mlines
  3. blue_line = mlines.Line2D([], [], color='blue', marker='*',
  4.                           markersize=15, label='Blue stars')
  5. plt.legend(handles=[blue_line])
  7. plt.show()

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Legend location

The location of the legend can be specified by the keyword argument loc. Please see the documentation at legend() for more details.

The bbox_to_anchor keyword gives a great degree of control for manual legend placement. For example, if you want your axes legend located at the figure’s top right-hand corner instead of the axes’ corner, simply specify the corner’s location, and the coordinate system of that location:

  1. plt.legend(bbox_to_anchor=(1, 1),
  2.            bbox_transform=plt.gcf().transFigure)

More examples of custom legend placement:

  1. plt.subplot(211)
  2. plt.plot([1, 2, 3], label="test1")
  3. plt.plot([3, 2, 1], label="test2")
  5. # Place a legend above this subplot, expanding itself to
  6. # fully use the given bounding box.
  7. plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc='lower left',
  8.            ncol=2, mode="expand", borderaxespad=0.)
  10. plt.subplot(223)
  11. plt.plot([1, 2, 3], label="test1")
  12. plt.plot([3, 2, 1], label="test2")
  13. # Place a legend to the right of this smaller subplot.
  14. plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
  16. plt.show()

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Multiple legends on the same Axes

Sometimes it is more clear to split legend entries across multiple legends. Whilst the instinctive approach to doing this might be to call the legend() function multiple times, you will find that only one legend ever exists on the Axes. This has been done so that it is possible to call legend() repeatedly to update the legend to the latest handles on the Axes, so to persist old legend instances, we must add them manually to the Axes:

  1. line1, = plt.plot([1, 2, 3], label="Line 1", linestyle='--')
  2. line2, = plt.plot([3, 2, 1], label="Line 2", linewidth=4)
  4. # Create a legend for the first line.
  5. first_legend = plt.legend(handles=[line1], loc='upper right')
  7. # Add the legend manually to the current Axes.
  8. ax = plt.gca().add_artist(first_legend)
  10. # Create another legend for the second line.
  11. plt.legend(handles=[line2], loc='lower right')
  13. plt.show()

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Legend Handlers

In order to create legend entries, handles are given as an argument to an appropriate HandlerBase subclass. The choice of handler subclass is determined by the following rules:

  1. Update get_legend_handler_map() with the value in the handler_map keyword.
  2. Check if the handle is in the newly created handler_map.
  3. Check if the type of handle is in the newly created handler_map.
  4. Check if any of the types in the handle‘s mro is in the newly created handler_map.

For completeness, this logic is mostly implemented in get_legend_handler().

All of this flexibility means that we have the necessary hooks to implement custom handlers for our own type of legend key.

The simplest example of using custom handlers is to instantiate one of the existing HandlerBase subclasses. For the sake of simplicity, let’s choose matplotlib.legend_handler.HandlerLine2D which accepts a numpoints argument (note numpoints is a keyword on the legend() function for convenience). We can then pass the mapping of instance to Handler as a keyword to legend.

  1. from matplotlib.legend_handler import HandlerLine2D
  3. line1, = plt.plot([3, 2, 1], marker='o', label='Line 1')
  4. line2, = plt.plot([1, 2, 3], marker='o', label='Line 2')
  6. plt.legend(handler_map={line1: HandlerLine2D(numpoints=4)})

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As you can see, “Line 1” now has 4 marker points, where “Line 2” has 2 (the default). Try the above code, only change the map’s key from line1 to type(line1). Notice how now both Line2D instances get 4 markers.

Along with handlers for complex plot types such as errorbars, stem plots and histograms, the default handler_map has a special tuple handler (HandlerTuple) which simply plots the handles on top of one another for each item in the given tuple. The following example demonstrates combining two legend keys on top of one another:

  1. from numpy.random import randn
  3. z = randn(10)
  5. red_dot, = plt.plot(z, "ro", markersize=15)
  6. # Put a white cross over some of the data.
  7. white_cross, = plt.plot(z[:5], "w+", markeredgewidth=3, markersize=15)
  9. plt.legend([red_dot, (red_dot, white_cross)], ["Attr A", "Attr A+B"])

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The HandlerTuple class can also be used to assign several legend keys to the same entry:

  1. from matplotlib.legend_handler import HandlerLine2D, HandlerTuple
  3. p1, = plt.plot([1, 2.5, 3], 'r-d')
  4. p2, = plt.plot([3, 2, 1], 'k-o')
  6. l = plt.legend([(p1, p2)], ['Two keys'], numpoints=1,
  7.                handler_map={tuple: HandlerTuple(ndivide=None)})

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Implementing a custom legend handler

A custom handler can be implemented to turn any handle into a legend key (handles don’t necessarily need to be matplotlib artists). The handler must implement a “legend_artist” method which returns a single artist for the legend to use. Signature details about the “legend_artist” are documented at legend_artist().

  1. import matplotlib.patches as mpatches
  4. class AnyObject(object):
  5.     pass
  8. class AnyObjectHandler(object):
  9.     def legend_artist(self, legend, orig_handle, fontsize, handlebox):
  10.         x0, y0 = handlebox.xdescent, handlebox.ydescent
  11.         width, height = handlebox.width, handlebox.height
  12.         patch = mpatches.Rectangle([x0, y0], width, height, facecolor='red',
  13.                                    edgecolor='black', hatch='xx', lw=3,
  14.                                    transform=handlebox.get_transform())
  15.         handlebox.add_artist(patch)
  16.         return patch
  19. plt.legend([AnyObject()], ['My first handler'],
  20.            handler_map={AnyObject: AnyObjectHandler()})

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Alternatively, had we wanted to globally accept AnyObject instances without needing to manually set the handler_map keyword all the time, we could have registered the new handler with:

  1. from matplotlib.legend import Legend
  2. Legend.update_default_handler_map({AnyObject: AnyObjectHandler()})

Whilst the power here is clear, remember that there are already many handlers implemented and what you want to achieve may already be easily possible with existing classes. For example, to produce elliptical legend keys, rather than rectangular ones:

  1. from matplotlib.legend_handler import HandlerPatch
  4. class HandlerEllipse(HandlerPatch):
  5.     def create_artists(self, legend, orig_handle,
  6.                        xdescent, ydescent, width, height, fontsize, trans):
  7.         center = 0.5 * width - 0.5 * xdescent, 0.5 * height - 0.5 * ydescent
  8.         p = mpatches.Ellipse(xy=center, width=width + xdescent,
  9.                              height=height + ydescent)
  10.         self.update_prop(p, orig_handle, legend)
  11.         p.set_transform(trans)
  12.         return [p]
  15. c = mpatches.Circle((0.5, 0.5), 0.25, facecolor="green",
  16.                     edgecolor="red", linewidth=3)
  17. plt.gca().add_patch(c)
  19. plt.legend([c], ["An ellipse, not a rectangle"],
  20.            handler_map={mpatches.Circle: HandlerEllipse()})

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