使用Matplotlib 2.2.2，可以使用gridspec特性来实现这一点。

在下面的例子中，目标是以2x2的方式排列四个子情节，并在底部显示图例。在底部创建一个“人造”轴，将图例放置在固定的位置。“人造”轴然后关闭，所以只有传说显示。结果:

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

# Gridspec demo
fig = plt.figure()
fig.set_size_inches(8, 9)
fig.set_dpi(100)

rows   = 17 # The larger the number here, the smaller the spacing around the legend
start1 = 0
end1   = int((rows-1)/2)
start2 = end1
end2   = int(rows-1)

gspec = gridspec.GridSpec(ncols=4, nrows=rows)

axes = []
axes.append(fig.add_subplot(gspec[start1:end1, 0:2]))
axes.append(fig.add_subplot(gspec[start2:end2, 0:2]))
axes.append(fig.add_subplot(gspec[start1:end1, 2:4]))
axes.append(fig.add_subplot(gspec[start2:end2, 2:4]))
axes.append(fig.add_subplot(gspec[end2, 0:4]))

line, = axes[0].plot([0, 1], [0, 1], 'b')         # Add some data
axes[-1].legend((line,), ('Test',), loc='center') # Create legend on bottommost axis
axes[-1].set_axis_off()                           # Don't show the bottom-most axis

fig.tight_layout()
plt.show()

如果您正在使用柱状图的子图，每个柱状图都有不同的颜色，那么使用mpatch自己创建工件可能会更快。

假设你有四个不同颜色的条，分别是r、m、c和k，你可以这样设置图例:

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
labels = ['Red Bar', 'Magenta Bar', 'Cyan Bar', 'Black Bar']


#####################################
# Insert code for the subplots here #
#####################################


# Now, create an artist for each color
red_patch = mpatches.Patch(facecolor='r', edgecolor='#000000') # This will create a red bar with black borders, you can leave out edgecolor if you do not want the borders
black_patch = mpatches.Patch(facecolor='k', edgecolor='#000000')
magenta_patch = mpatches.Patch(facecolor='m', edgecolor='#000000')
cyan_patch = mpatches.Patch(facecolor='c', edgecolor='#000000')
fig.legend(handles = [red_patch, magenta_patch, cyan_patch, black_patch], labels=labels,
       loc="center right",
       borderaxespad=0.1)
plt.subplots_adjust(right=0.85) # Adjust the subplot to the right for the legend

Figlegend可能就是您要找的:matplotlib.pyplot.figlegend

一个例子是在图图例演示。

另一个例子:

plt.figlegend(lines, labels, loc = 'lower center', ncol=5, labelspacing=0.)

Or:

fig.legend(lines, labels, loc = (0.5, 0), ncol=5)

你只需要在循环之外请求一次图例。

例如，在这种情况下，我有4个子情节，具有相同的线，和一个图例。

from matplotlib.pyplot import *

ficheiros = ['120318.nc', '120319.nc', '120320.nc', '120321.nc']

fig = figure()
fig.suptitle('concentration profile analysis')

for a in range(len(ficheiros)):
    # dados is here defined
    level = dados.variables['level'][:]

    ax = fig.add_subplot(2,2,a+1)
    xticks(range(8), ['0h','3h','6h','9h','12h','15h','18h','21h']) 
    ax.set_xlabel('time (hours)')
    ax.set_ylabel('CONC ($\mu g. m^{-3}$)')

    for index in range(len(level)):
        conc = dados.variables['CONC'][4:12,index] * 1e9
        ax.plot(conc,label=str(level[index])+'m')

    dados.close()

ax.legend(bbox_to_anchor=(1.05, 0), loc='lower left', borderaxespad=0.)
         # it will place the legend on the outer right-hand side of the last axes

show()

博士TL;

lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]
lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
fig.legend(lines, labels)

我注意到，其他答案都没有显示一个图像，其中一个图例引用了不同子图中的许多曲线，所以我必须给你看一个……为了让你好奇……

现在，如果我已经戏弄你们够多了，这就是代码

from numpy import linspace
import matplotlib.pyplot as plt

# each Axes has a brand new prop_cycle, so to have differently
# colored curves in different Axes, we need our own prop_cycle
# Note: we CALL the axes.prop_cycle to get an itertoools.cycle
color_cycle = plt.rcParams['axes.prop_cycle']()

# I need some curves to plot
x = linspace(0, 1, 51)
functs = [x*(1-x), x**2*(1-x),
          0.25-x*(1-x), 0.25-x**2*(1-x)] 
labels = ['$x-x²$', '$x²-x³$',
          '$\\frac{1}{4} - (x-x²)$', '$\\frac{1}{4} - (x²-x³)$']

# the plot, 
fig, (a1,a2) = plt.subplots(2)
for ax, f, l, cc in zip((a1,a1,a2,a2), functs, labels, color_cycle): 
    ax.plot(x, f, label=l, **cc)
    ax.set_aspect(2) # superfluos, but nice

# So far, nothing special except the managed prop_cycle. Now the trick:
lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]
lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]

# Finally, the legend (that maybe you'll customize differently)
fig.legend(lines, labels, loc='upper center', ncol=4)
plt.show()

If you want to stick with the official Matplotlib API, this is perfect, otherwise see note no.1 below (there is a private method...) The two lines lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes] lines, labels = [sum(lol, []) for lol in zip(*lines_labels)] deserve an explanation, see note 2 below. I tried the method proposed by the most up-voted and accepted answer, # fig.legend(lines, labels, loc='upper center', ncol=4) fig.legend(*a2.get_legend_handles_labels(), loc='upper center', ncol=4) and this is what I've got

注1 如果您不介意使用matplotlib的私有方法。传说模块…这真的非常非常简单

from matplotlib.legend import _get_legend_handles_labels
...

fig.legend(*_get_legend_handles_and_labels(fig.axes), ...)

注2

我把这两行棘手的代码封装在一个函数中，只有四行代码，但是注释了很多

def fig_legend(fig, **kwdargs):

    # Generate a sequence of tuples, each contains
    #  - a list of handles (lohand) and
    #  - a list of labels (lolbl)
    tuples_lohand_lolbl = (ax.get_legend_handles_labels() for ax in fig.axes)
    # E.g., a figure with two axes, ax0 with two curves, ax1 with one curve
    # yields:   ([ax0h0, ax0h1], [ax0l0, ax0l1]) and ([ax1h0], [ax1l0])

    # The legend needs a list of handles and a list of labels,
    # so our first step is to transpose our data,
    # generating two tuples of lists of homogeneous stuff(tolohs), i.e.,
    # we yield ([ax0h0, ax0h1], [ax1h0]) and ([ax0l0, ax0l1], [ax1l0])
    tolohs = zip(*tuples_lohand_lolbl)

    # Finally, we need to concatenate the individual lists in the two
    # lists of lists: [ax0h0, ax0h1, ax1h0] and [ax0l0, ax0l1, ax1l0]
    # a possible solution is to sum the sublists - we use unpacking
    handles, labels = (sum(list_of_lists, []) for list_of_lists in tolohs)

    # Call fig.legend with the keyword arguments, return the legend object

    return fig.legend(handles, labels, **kwdargs)

我认识到sum(list_of_lists，[])是一个非常低效的方法来扁平化列表的列表，但①我喜欢它的紧凑性，②通常是几个子图中的几条曲线，③Matplotlib和效率?: -)

对于在具有多个轴的图形中自动定位单个图例，例如使用subplots()获得的图例，以下解决方案非常有效:

plt.legend(lines, labels, loc = 'lower center', bbox_to_anchor = (0, -0.1, 1, 1),
           bbox_transform = plt.gcf().transFigure)

使用bbox_to_anchor和bbox_transform=plt.gcf()。transFigure，你正在定义一个新的边界框的大小你的数字作为loc的参考。使用(0，-0.1,1,1)将这个边界框略微向下移动，以防止图例被放置在其他艺术家之上。

OBS:在使用fig.set_size_inch()之后和使用fig.tight_layout()之前使用这个解决方案