这个解决方案不需要手动调整轴位置或颜色条大小，适用于多行和单行布局，并适用于tight_layout()。它改编自一个图库示例，使用了matplotlib的AxesGrid工具箱中的ImageGrid。

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid

# Set up figure and image grid
fig = plt.figure(figsize=(9.75, 3))

grid = ImageGrid(fig, 111,          # as in plt.subplot(111)
                 nrows_ncols=(1,3),
                 axes_pad=0.15,
                 share_all=True,
                 cbar_location="right",
                 cbar_mode="single",
                 cbar_size="7%",
                 cbar_pad=0.15,
                 )

# Add data to image grid
for ax in grid:
    im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)

# Colorbar
ax.cax.colorbar(im)
ax.cax.toggle_label(True)

#plt.tight_layout()    # Works, but may still require rect paramater to keep colorbar labels visible
plt.show()

作为一个无意中发现这条线程的初学者，我想添加一个python for dummies对abevieiramota非常简洁的回答的改编(因为我的水平是，我必须查找“ravel”来了解他们的代码在做什么):

import numpy as np
import matplotlib.pyplot as plt

fig, ((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(2,3)

axlist = [ax1,ax2,ax3,ax4,ax5,ax6]

first = ax1.imshow(np.random.random((10,10)), vmin=0, vmax=1)
third = ax3.imshow(np.random.random((12,12)), vmin=0, vmax=1)

fig.colorbar(first, ax=axlist)

plt.show()

更少的python，更容易让像我这样的新手看到这里到底发生了什么。

为了补充@abevieiramota的精彩答案，你可以用constrained_layout得到等价的tight_layout。如果你使用imshow而不是pcolormesh，你仍然会得到很大的水平间隙，因为imshow施加了1:1的纵横比。

import numpy as np
import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=2, ncols=2, constrained_layout=True)
for ax in axes.flat:
    im = ax.pcolormesh(np.random.random((10,10)), vmin=0, vmax=1)

fig.colorbar(im, ax=axes.flat)
plt.show()

这个主题已经被很好地涵盖了，但我仍然想提出另一种略有不同的哲学方法。

它的设置有点复杂，但它允许(在我看来)更多的灵活性。例如，我们可以处理每个子图/色条的比例:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.gridspec import GridSpec

# Define number of rows and columns you want in your figure
nrow = 2
ncol = 3

# Make a new figure
fig = plt.figure(constrained_layout=True)

# Design your figure properties
widths = [3,4,5,1]
gs = GridSpec(nrow, ncol + 1, figure=fig, width_ratios=widths)

# Fill your figure with desired plots
axes = []
for i in range(nrow):
    for j in range(ncol):
        axes.append(fig.add_subplot(gs[i, j]))
        im = axes[-1].pcolormesh(np.random.random((10,10)))

# Shared colorbar    
axes.append(fig.add_subplot(gs[:, ncol]))
fig.colorbar(im, cax=axes[-1])

plt.show()

I noticed that almost every solution posted involved ax.imshow(im, ...) and did not normalize the colors displayed to the colorbar for the multiple subfigures. The im mappable is taken from the last instance, but what if the values of the multiple im-s are different? (I'm assuming these mappables are treated in the same way that the contour-sets and surface-sets are treated.) I have an example using a 3d surface plot below that creates two colorbars for a 2x2 subplot (one colorbar per one row). Although the question asks explicitly for a different arrangement, I think the example helps clarify some things. I haven't found a way to do this using plt.subplots(...) yet because of the 3D axes unfortunately.

如果我能以更好的方式定位颜色条就好了……(可能有更好的方法来做到这一点，但至少它应该不会太难遵循。)

import matplotlib
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D

cmap = 'plasma'
ncontours = 5

def get_data(row, col):
    """ get X, Y, Z, and plot number of subplot
        Z > 0 for top row, Z < 0 for bottom row """
    if row == 0:
        x = np.linspace(1, 10, 10, dtype=int)
        X, Y = np.meshgrid(x, x)
        Z = np.sqrt(X**2 + Y**2)
        if col == 0:
            pnum = 1
        else:
            pnum = 2
    elif row == 1:
        x = np.linspace(1, 10, 10, dtype=int)
        X, Y = np.meshgrid(x, x)
        Z = -np.sqrt(X**2 + Y**2)
        if col == 0:
            pnum = 3
        else:
            pnum = 4
    print("\nPNUM: {}, Zmin = {}, Zmax = {}\n".format(pnum, np.min(Z), np.max(Z)))
    return X, Y, Z, pnum

fig = plt.figure()
nrows, ncols = 2, 2
zz = []
axes = []
for row in range(nrows):
    for col in range(ncols):
        X, Y, Z, pnum = get_data(row, col)
        ax = fig.add_subplot(nrows, ncols, pnum, projection='3d')
        ax.set_title('row = {}, col = {}'.format(row, col))
        fhandle = ax.plot_surface(X, Y, Z, cmap=cmap)
        zz.append(Z)
        axes.append(ax)

## get full range of Z data as flat list for top and bottom rows
zz_top = zz[0].reshape(-1).tolist() + zz[1].reshape(-1).tolist()
zz_btm = zz[2].reshape(-1).tolist() + zz[3].reshape(-1).tolist()
## get top and bottom axes
ax_top = [axes[0], axes[1]]
ax_btm = [axes[2], axes[3]]
## normalize colors to minimum and maximum values of dataset
norm_top = matplotlib.colors.Normalize(vmin=min(zz_top), vmax=max(zz_top))
norm_btm = matplotlib.colors.Normalize(vmin=min(zz_btm), vmax=max(zz_btm))
cmap = cm.get_cmap(cmap, ncontours) # number of colors on colorbar
mtop = cm.ScalarMappable(cmap=cmap, norm=norm_top)
mbtm = cm.ScalarMappable(cmap=cmap, norm=norm_btm)
for m in (mtop, mbtm):
    m.set_array([])

# ## create cax to draw colorbar in
# cax_top = fig.add_axes([0.9, 0.55, 0.05, 0.4])
# cax_btm = fig.add_axes([0.9, 0.05, 0.05, 0.4])
cbar_top = fig.colorbar(mtop, ax=ax_top, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_top)
cbar_top.set_ticks(np.linspace(min(zz_top), max(zz_top), ncontours))
cbar_btm = fig.colorbar(mbtm, ax=ax_btm, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_btm)
cbar_btm.set_ticks(np.linspace(min(zz_btm), max(zz_btm), ncontours))

plt.show()
plt.close(fig)
## orientation of colorbar = 'horizontal' if done by column

您可以使用带有坐标轴列表的figure.colorbar()的axparameter简化Joe Kington的代码。 从文档中可以看到:

斧头 没有|父轴对象，其中新的色条轴的空间将被窃取。如果给出了坐标轴列表，它们将全部调整大小，以便为颜色条坐标轴腾出空间。

import numpy as np
import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
    im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)

fig.colorbar(im, ax=axes.ravel().tolist())

plt.show()