从matplotlib 2.1版本开始，您可以使用图形图例。我们可以创建一个图形图例，而不是ax.legend()，它使用斧头ax的句柄生成一个图例

fig.legend(loc="upper right")

它将收集图中所有子图的所有句柄。因为它是一个图形图例，所以它将被放置在图形的角落，loc参数是相对于图形的。

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0,10)
y = np.linspace(0,10)
z = np.sin(x/3)**2*98

fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y, '-', label = 'Quantity 1')

ax2 = ax.twinx()
ax2.plot(x,z, '-r', label = 'Quantity 2')
fig.legend(loc="upper right")

ax.set_xlabel("x [units]")
ax.set_ylabel(r"Quantity 1")
ax2.set_ylabel(r"Quantity 2")

plt.show()

为了将图例放回坐标轴，需要提供bbox_to_anchor和bbox_transform。后者将是图例应驻留的轴的轴变换。前者可以是坐标轴坐标中给定的由loc定义的边的坐标。

fig.legend(loc="upper right", bbox_to_anchor=(1,1), bbox_transform=ax.transAxes)

我不确定这个功能是否是新的，但你也可以使用get_legend_handles_labels()方法，而不是自己跟踪行和标签:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')

pi = np.pi

# fake data
time = np.linspace (0, 25, 50)
temp = 50 / np.sqrt (2 * pi * 3**2) \
        * np.exp (-((time - 13)**2 / (3**2))**2) + 15
Swdown = 400 / np.sqrt (2 * pi * 3**2) * np.exp (-((time - 13)**2 / (3**2))**2)
Rn = Swdown - 10

fig = plt.figure()
ax = fig.add_subplot(111)

ax.plot(time, Swdown, '-', label = 'Swdown')
ax.plot(time, Rn, '-', label = 'Rn')
ax2 = ax.twinx()
ax2.plot(time, temp, '-r', label = 'temp')

# ask matplotlib for the plotted objects and their labels
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc=0)

ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()

准备

import numpy as np
from matplotlib import pyplot as plt

fig, ax1 = plt.subplots( figsize=(15,6) )

Y1, Y2 = np.random.random((2,100))

ax2 = ax1.twinx()

内容

我很惊讶它没有显示到目前为止，但最简单的方法是手动收集它们到一个轴objs(躺在彼此的顶部)

l1 = ax1.plot( range(len(Y1)), Y1, label='Label 1' )
l2 = ax2.plot( range(len(Y2)), Y2, label='Label 2', color='orange' )

ax1.legend( handles=l1+l2 )

或者通过fig.legend()将它们自动收集到周围的图形中，并摆弄bbox_to_anchor参数:

ax1.plot( range(len(Y1)), Y1, label='Label 1' )
ax2.plot( range(len(Y2)), Y2, label='Label 2', color='orange' )

fig.legend( bbox_to_anchor=(.97, .97) )

终结

fig.tight_layout()
fig.savefig('stackoverflow.png', bbox_inches='tight')

从matplotlib 2.1版本开始，您可以使用图形图例。我们可以创建一个图形图例，而不是ax.legend()，它使用斧头ax的句柄生成一个图例

fig.legend(loc="upper right")

它将收集图中所有子图的所有句柄。因为它是一个图形图例，所以它将被放置在图形的角落，loc参数是相对于图形的。

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0,10)
y = np.linspace(0,10)
z = np.sin(x/3)**2*98

fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y, '-', label = 'Quantity 1')

ax2 = ax.twinx()
ax2.plot(x,z, '-r', label = 'Quantity 2')
fig.legend(loc="upper right")

ax.set_xlabel("x [units]")
ax.set_ylabel(r"Quantity 1")
ax2.set_ylabel(r"Quantity 2")

plt.show()

为了将图例放回坐标轴，需要提供bbox_to_anchor和bbox_transform。后者将是图例应驻留的轴的轴变换。前者可以是坐标轴坐标中给定的由loc定义的边的坐标。

fig.legend(loc="upper right", bbox_to_anchor=(1,1), bbox_transform=ax.transAxes)

您可以通过添加以下行轻松添加第二个图例:

ax2.legend(loc=0)

你会得到这个:

但是如果你想要所有的标签都在一个图例上，那么你应该这样做:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')

time = np.arange(10)
temp = np.random.random(10)*30
Swdown = np.random.random(10)*100-10
Rn = np.random.random(10)*100-10

fig = plt.figure()
ax = fig.add_subplot(111)

lns1 = ax.plot(time, Swdown, '-', label = 'Swdown')
lns2 = ax.plot(time, Rn, '-', label = 'Rn')
ax2 = ax.twinx()
lns3 = ax2.plot(time, temp, '-r', label = 'temp')

# added these three lines
lns = lns1+lns2+lns3
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=0)

ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()

它会给你这个:

正如matplotlib.org的例子所提供的，从多个轴实现单个图例的干净方法是使用plot句柄:

import matplotlib.pyplot as plt


fig, ax = plt.subplots()
fig.subplots_adjust(right=0.75)

twin1 = ax.twinx()
twin2 = ax.twinx()

# Offset the right spine of twin2.  The ticks and label have already been
# placed on the right by twinx above.
twin2.spines.right.set_position(("axes", 1.2))

p1, = ax.plot([0, 1, 2], [0, 1, 2], "b-", label="Density")
p2, = twin1.plot([0, 1, 2], [0, 3, 2], "r-", label="Temperature")
p3, = twin2.plot([0, 1, 2], [50, 30, 15], "g-", label="Velocity")

ax.set_xlim(0, 2)
ax.set_ylim(0, 2)
twin1.set_ylim(0, 4)
twin2.set_ylim(1, 65)

ax.set_xlabel("Distance")
ax.set_ylabel("Density")
twin1.set_ylabel("Temperature")
twin2.set_ylabel("Velocity")

ax.yaxis.label.set_color(p1.get_color())
twin1.yaxis.label.set_color(p2.get_color())
twin2.yaxis.label.set_color(p3.get_color())

tkw = dict(size=4, width=1.5)
ax.tick_params(axis='y', colors=p1.get_color(), **tkw)
twin1.tick_params(axis='y', colors=p2.get_color(), **tkw)
twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)
ax.tick_params(axis='x', **tkw)

ax.legend(handles=[p1, p2, p3])

plt.show()