您可以创建一个涵盖这两个子图的大子图，然后设置公共标签。

import random
import matplotlib.pyplot as plt

x = range(1, 101)
y1 = [random.randint(1, 100) for _ in range(len(x))]
y2 = [random.randint(1, 100) for _ in range(len(x))]

fig = plt.figure()
ax = fig.add_subplot(111)    # The big subplot
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)

# Turn off axis lines and ticks of the big subplot
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.tick_params(labelcolor='w', top=False, bottom=False, left=False, right=False)

ax1.loglog(x, y1)
ax2.loglog(x, y2)

# Set common labels
ax.set_xlabel('common xlabel')
ax.set_ylabel('common ylabel')

ax1.set_title('ax1 title')
ax2.set_title('ax2 title')

plt.savefig('common_labels.png', dpi=300)

另一种方法是使用fig.text()直接设置公共标签的位置。

import random
import matplotlib.pyplot as plt

x = range(1, 101)
y1 = [random.randint(1, 100) for _ in range(len(x))]
y2 = [random.randint(1, 100) for _ in range(len(x))]

fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)

ax1.loglog(x, y1)
ax2.loglog(x, y2)

# Set common labels
fig.text(0.5, 0.04, 'common xlabel', ha='center', va='center')
fig.text(0.06, 0.5, 'common ylabel', ha='center', va='center', rotation='vertical')

ax1.set_title('ax1 title')
ax2.set_title('ax2 title')

plt.savefig('common_labels_text.png', dpi=300)

如果你不打算导出矢量图形，或者你已经设置了matplotlib后端来忽略无色轴，Wen-wei Liao的答案是很好的;否则隐藏的轴将显示在导出的图形中。

我的答案suplabel在这里类似于fig.suptitle，它使用fig.text函数。因此，没有斧头艺术家被创造和被制作成无色的。 然而，如果你尝试多次调用它，你会得到文本添加在对方的顶部(fig.suptitle也一样)。Wen-wei Liao的答案不是，因为fig.add_subplot(111)将返回相同的Axes对象，如果它已经创建。

我的函数也可以在创建图之后调用。

def suplabel(axis,label,label_prop=None,
             labelpad=5,
             ha='center',va='center'):
    ''' Add super ylabel or xlabel to the figure
    Similar to matplotlib.suptitle
    axis       - string: "x" or "y"
    label      - string
    label_prop - keyword dictionary for Text
    labelpad   - padding from the axis (default: 5)
    ha         - horizontal alignment (default: "center")
    va         - vertical alignment (default: "center")
    '''
    fig = pylab.gcf()
    xmin = []
    ymin = []
    for ax in fig.axes:
        xmin.append(ax.get_position().xmin)
        ymin.append(ax.get_position().ymin)
    xmin,ymin = min(xmin),min(ymin)
    dpi = fig.dpi
    if axis.lower() == "y":
        rotation=90.
        x = xmin-float(labelpad)/dpi
        y = 0.5
    elif axis.lower() == 'x':
        rotation = 0.
        x = 0.5
        y = ymin - float(labelpad)/dpi
    else:
        raise Exception("Unexpected axis: x or y")
    if label_prop is None: 
        label_prop = dict()
    pylab.text(x,y,label,rotation=rotation,
               transform=fig.transFigure,
               ha=ha,va=va,
               **label_prop)

其他答案中的方法在符号较大时将无法正常工作。ylabel将与刻度重叠，在左侧被剪切或完全不可见/在图形之外。

我修改了Hagne的答案，使它适用于超过一列的子图，对于xlabel和ylabel，并且它移动了图以保持ylabel在图中可见。

def set_shared_ylabel(a, xlabel, ylabel, labelpad = 0.01, figleftpad=0.05):
    """Set a y label shared by multiple axes
    Parameters
    ----------
    a: list of axes
    ylabel: string
    labelpad: float
        Sets the padding between ticklabels and axis label"""

    f = a[0,0].get_figure()
    f.canvas.draw() #sets f.canvas.renderer needed below

    # get the center position for all plots
    top = a[0,0].get_position().y1
    bottom = a[-1,-1].get_position().y0

    # get the coordinates of the left side of the tick labels
    x0 = 1
    x1 = 1
    for at_row in a:
        at = at_row[0]
        at.set_ylabel('') # just to make sure we don't and up with multiple labels
        bboxes, _ = at.yaxis.get_ticklabel_extents(f.canvas.renderer)
        bboxes = bboxes.inverse_transformed(f.transFigure)
        xt = bboxes.x0
        if xt < x0:
            x0 = xt
            x1 = bboxes.x1
    tick_label_left = x0

    # shrink plot on left to prevent ylabel clipping
    # (x1 - tick_label_left) is the x coordinate of right end of tick label,
    # basically how much padding is needed to fit tick labels in the figure
    # figleftpad is additional padding to fit the ylabel
    plt.subplots_adjust(left=(x1 - tick_label_left) + figleftpad)

    # set position of label, 
    # note that (figleftpad-labelpad) refers to the middle of the ylabel
    a[-1,-1].set_ylabel(ylabel)
    a[-1,-1].yaxis.set_label_coords(figleftpad-labelpad,(bottom + top)/2, transform=f.transFigure)

    # set xlabel
    y0 = 1
    for at in axes[-1]:
        at.set_xlabel('')  # just to make sure we don't and up with multiple labels
        bboxes, _ = at.xaxis.get_ticklabel_extents(fig.canvas.renderer)
        bboxes = bboxes.inverse_transformed(fig.transFigure)
        yt = bboxes.y0
        if yt < y0:
            y0 = yt
    tick_label_bottom = y0

    axes[-1, -1].set_xlabel(xlabel)
    axes[-1, -1].xaxis.set_label_coords((left + right) / 2, tick_label_bottom - labelpad, transform=fig.transFigure)

它适用于以下示例，而Hagne的回答不会绘制ylabel(因为它在画布之外)，KYC的ylabel与tick标签重叠:

import matplotlib.pyplot as plt
import itertools

fig, axes = plt.subplots(3, 4, sharey='row', sharex=True, squeeze=False)
fig.subplots_adjust(hspace=.5)
for i, a in enumerate(itertools.chain(*axes)):
    a.plot([0,4**i], [0,4**i])
    a.set_title(i)
set_shared_ylabel(axes, 'common X', 'common Y')
plt.show()

或者，如果您对无色轴满意，我修改了Julian Chen的解决方案，使ylabel不会与tick标签重叠。

基本上，我们只需要设置无色的ylim，这样它就能匹配子图中最大的ylim，所以无色的tick labels为ylabel设置了正确的位置。

同样，我们必须缩小情节以防止剪辑。这里我已经硬编码了要收缩的数量，但你可以找到一个适合你的数字，或者像上面的方法一样计算它。

import matplotlib.pyplot as plt
import itertools

fig, axes = plt.subplots(3, 4, sharey='row', sharex=True, squeeze=False)
fig.subplots_adjust(hspace=.5)
miny = maxy = 0
for i, a in enumerate(itertools.chain(*axes)):
    a.plot([0,4**i], [0,4**i])
    a.set_title(i)
    miny = min(miny, a.get_ylim()[0])
    maxy = max(maxy, a.get_ylim()[1])

# add a big axes, hide frame
# set ylim to match the largest range of any subplot
ax_invis = fig.add_subplot(111, frameon=False)
ax_invis.set_ylim([miny, maxy])

# hide tick and tick label of the big axis
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.xlabel("common X")
plt.ylabel("common Y")

# shrink plot to prevent clipping
plt.subplots_adjust(left=0.15)
plt.show()

matplotlib 3.4.0新增功能

现在有内置的方法来设置公共轴标签:

supxlabel Fig.supxlabel ('common xlabel ') supylabel fig supylabel('common ylabel ')

要重现OP的日志图(通用标签但独立标题):

x = np.arange(0.01, 10.01, 0.01)
y = 2 ** x

fig, (ax1, ax2) = plt.subplots(2, 1, constrained_layout=True)
ax1.loglog(y, x)
ax2.loglog(x, y)

# separate subplot titles
ax1.set_title('ax1.title')
ax2.set_title('ax2.title')

# common axis labels
fig.supxlabel('fig.supxlabel')
fig.supylabel('fig.supylabel')

下面是一种解决方案，您可以设置其中一个图的ylabel并调整它的位置，使其垂直居中。这样可以避免KYC提到的问题。

import numpy as np
import matplotlib.pyplot as plt

def set_shared_ylabel(a, ylabel, labelpad = 0.01):
    """Set a y label shared by multiple axes
    Parameters
    ----------
    a: list of axes
    ylabel: string
    labelpad: float
        Sets the padding between ticklabels and axis label"""

    f = a[0].get_figure()
    f.canvas.draw() #sets f.canvas.renderer needed below

    # get the center position for all plots
    top = a[0].get_position().y1
    bottom = a[-1].get_position().y0

    # get the coordinates of the left side of the tick labels 
    x0 = 1
    for at in a:
        at.set_ylabel('') # just to make sure we don't and up with multiple labels
        bboxes, _ = at.yaxis.get_ticklabel_extents(f.canvas.renderer)
        bboxes = bboxes.inverse_transformed(f.transFigure)
        xt = bboxes.x0
        if xt < x0:
            x0 = xt
    tick_label_left = x0

    # set position of label
    a[-1].set_ylabel(ylabel)
    a[-1].yaxis.set_label_coords(tick_label_left - labelpad,(bottom + top)/2, transform=f.transFigure)

length = 100
x = np.linspace(0,100, length)
y1 = np.random.random(length) * 1000
y2 = np.random.random(length)

f,a = plt.subplots(2, sharex=True, gridspec_kw={'hspace':0})
a[0].plot(x, y1)
a[1].plot(x, y2)
set_shared_ylabel(a, 'shared y label (a. u.)')

# list loss and acc are your data
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)

ax1.plot(iteration1, loss)
ax2.plot(iteration2, acc)

ax1.set_title('Training Loss')
ax2.set_title('Training Accuracy')

ax1.set_xlabel('Iteration')
ax1.set_ylabel('Loss')

ax2.set_xlabel('Iteration')
ax2.set_ylabel('Accuracy')