How to put the legend outside the plot in Matplotlib

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Placing the legend (bbox_to_anchor)

A legend is positioned inside the bounding box of the axes using the loc argument to plt.legend.
E.g. loc="upper right" places the legend in the upper right corner of the bounding box, which by default extents from (0,0) to (1,1) in axes coordinates (or in bounding box notation (x0,y0, width, height)=(0,0,1,1)).

To place the legend outside of the axes bounding box, one may specify a tuple (x0,y0) of axes coordinates of the lower left corner of the legend.

plt.legend(loc=(1.04,0))

A more versatile approach is to manually specify the bounding box into which the legend should be placed, using the bbox_to_anchor argument. One can restrict oneself to supply only the (x0, y0) part of the bbox. This creates a zero span box, out of which the legend will expand in the direction given by the loc argument. E.g.

plt.legend(bbox_to_anchor=(1.04,1), loc="upper left")

places the legend outside the axes, such that the upper left corner of the legend is at position (1.04,1) in axes coordinates.

Further examples are given below, where additionally the interplay between different arguments like mode and ncols are shown.

enter image description here

l1 = plt.legend(bbox_to_anchor=(1.04,1), borderaxespad=0)
l2 = plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
l3 = plt.legend(bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
l4 = plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc="lower left",
                mode="expand", borderaxespad=0, ncol=3)
l5 = plt.legend(bbox_to_anchor=(1,0), loc="lower right", 
                bbox_transform=fig.transFigure, ncol=3)
l6 = plt.legend(bbox_to_anchor=(0.4,0.8), loc="upper right")

Details about how to interpret the 4-tuple argument to bbox_to_anchor, as in l4, can be found in this question. The mode="expand" expands the legend horizontally inside the bounding box given by the 4-tuple. For a vertically expanded legend, see this question.

Sometimes it may be useful to specify the bounding box in figure coordinates instead of axes coordinates. This is shown in the example l5 from above, where the bbox_transform argument is used to put the legend in the lower left corner of the figure.

Postprocessing

Having placed the legend outside the axes often leads to the undesired situation that it is completely or partially outside the figure canvas.

Solutions to this problem are:

  • Adjust the subplot parameters
    One can adjust the subplot parameters such, that the axes take less space inside the figure (and thereby leave more space to the legend) by using plt.subplots_adjust. E.g.

    plt.subplots_adjust(right=0.7)

    leaves 30% space on the right-hand side of the figure, where one could place the legend.

  • Tight layout
    Using plt.tight_layout Allows to automatically adjust the subplot parameters such that the elements in the figure sit tight against the figure edges. Unfortunately, the legend is not taken into account in this automatism, but we can supply a rectangle box that the whole subplots area (including labels) will fit into.

    plt.tight_layout(rect=[0,0,0.75,1])

  • Saving the figure with bbox_inches = "tight"
    The argument bbox_inches = "tight" to plt.savefig can be used to save the figure such that all artist on the canvas (including the legend) are fit into the saved area. If needed, the figure size is automatically adjusted.

    plt.savefig("output.png", bbox_inches="tight")

  • automatically adjusting the subplot params
    A way to automatically adjust the subplot position such that the legend fits inside the canvas without changing the figure size can be found in this answer: Creating figure with exact size and no padding (and legend outside the axes)

Comparison between the cases discussed above:

enter image description here

Alternatives

A figure legend

One may use a legend to the figure instead of the axes, matplotlib.figure.Figure.legend. This has become especially useful for matplotlib version >=2.1, where no special arguments are needed

fig.legend(loc=7)

to create a legend for all artists in the different axes of the figure. The legend is placed using the loc argument, similar to how it is placed inside an axes, but in reference to the whole figure – hence it will be outside the axes somewhat automatically. What remains is to adjust the subplots such that there is no overlap between the legend and the axes. Here the point “Adjust the subplot parameters” from above will be helpful. An example:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0,2*np.pi)
colors=["#7aa0c4","#ca82e1" ,"#8bcd50","#e18882"]
fig, axes = plt.subplots(ncols=2)
for i in range(4):
    axes[i//2].plot(x,np.sin(x+i), color=colors[i],label="y=sin(x+{})".format(i))

fig.legend(loc=7)
fig.tight_layout()
fig.subplots_adjust(right=0.75)   
plt.show()

enter image description here

Legend inside dedicated subplot axes

An alternative to using bbox_to_anchor would be to place the legend in its dedicated subplot axes (lax).
Since the legend subplot should be smaller than the plot, we may use gridspec_kw={"width_ratios":[4,1]} at axes creation.
We can hide the axes lax.axis("off") but still put a legend in. The legend handles and labels need to obtained from the real plot via h,l = ax.get_legend_handles_labels(), and can then be supplied to the legend in the lax subplot, lax.legend(h,l). A complete example is below.

import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = 6,2

fig, (ax,lax) = plt.subplots(ncols=2, gridspec_kw={"width_ratios":[4,1]})
ax.plot(x,y, label="y=sin(x)")
....

h,l = ax.get_legend_handles_labels()
lax.legend(h,l, borderaxespad=0)
lax.axis("off")

plt.tight_layout()
plt.show()

This produces a plot, which is visually pretty similar to the plot from above:

enter image description here

We could also use the first axes to place the legend, but use the bbox_transform of the legend axes,

ax.legend(bbox_to_anchor=(0,0,1,1), bbox_transform=lax.transAxes)
lax.axis("off")

In this approach, we do not need to obtain the legend handles externally, but we need to specify the bbox_to_anchor argument.

Further reading and notes:

  • Consider the matplotlib legend guide with some examples of other stuff you want to do with legends.
  • Some example code for placing legends for pie charts may directly be found in answer to this question: Python – Legend overlaps with the pie chart
  • The loc argument can take numbers instead of strings, which make calls shorter, however, they are not very intuitively mapped to each other. Here is the mapping for reference:

enter image description here

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