考虑以下几点：

import matplotlib as mpl
import seaborn as sns
import numpy as np

A = np.random.randint(0,100, size=(20,20))
mask_array = np.zeros((20, 20), dtype=bool)
mask_array[:, :5] = True
cmap = mpl.colormaps["viridis"]

newcolors = cmap(np.linspace(0, 1, 100))
newcolors[:10] = np.array([1,1,1,1])
newcolors[90:] = np.array([0,0,0,1])
newcolors[20] = mpl.colors.to_rgb('tab:orange') + (1,)

newcmap = mpl.colors.ListedColormap(newcolors)

g = sns.heatmap(A, cmap=newcmap, mask=mask_array)
# Set color of masked region
g.set_facecolor('lightgrey')

样品结果：

下面的代码（附加到上面的脚本中）将使颜色条具有可见的轮廓。

cbar_ax = g.figure.axes[-1]

for spine in cbar_ax.spines.values():
    spine.set(visible=True)

带大纲的示例结果：

要遮罩热图的颜色，但不显示更新的颜色条，请设置cbar=False，然后附加自定义颜色条，如Standalone colorbar所示。

g = sns.heatmap(A, cmap=newcmap, mask=mask_array, cbar=False)

# add a new axes of the desired shape
cb = g.figure.add_axes([0.93, 0.11, 0.025, 0.77])

# attach a new colorbar to the axes
mpl.colorbar.ColorbarBase(cb, cmap='viridis', norm=mpl.colors.Normalize(10, 90),  # vmax and vmin
                          label=None, ticks=range(10, 91, 10))

从this answer中提取，这里有一个使用遮罩而不是自定义颜色条的解决方案：

import matplotlib
import seaborn as sns
import numpy as np
from matplotlib.colors import ListedColormap

np.random.seed(7)
A = np.random.randint(0,100, size=(20,20))
mask_array = np.zeros((20, 20), dtype=bool)
mask_array[:, :5] = True
# cmap = matplotlib.colormaps["viridis"]
cmap = matplotlib.cm.get_cmap('viridis')

# Set the under color to white
cmap.set_under("white")

# Set the voer color to white
cmap.set_over("black")

# Set the background color

g = sns.heatmap(A, vmin=10, vmax=90, cmap=cmap, mask=mask_array)
# Set color of masked region
g.set_facecolor('lightgrey')

special_data = np.ma.masked_where(A==20, A)
sns.heatmap(special_data, cmap=ListedColormap(['orange']), 
            mask=(special_data != 1), cbar=False)

• 困难来自于需要使用多于一个掩模。
• mask_array，如果数组仅为int，则掩码为A == 20，如果数组为float，则掩码为20 <= A < 21。
• matplotlib.colors.Colormap仅提供3种设置颜色的方法。

1. set_under
2. set_over
3. set_bad-设置已用于mask_array**的掩码值的颜色。

• with_extremes-将三者合并。
• cmap = mpl.colormaps['viridis'].with_extremes(bad='orange', under='w', over='k')

• 使用这些方法不会影响颜色条的外观。

导入和样本数据

import numpy as np
import seaborn as sns
import matplotlib as mpl

# set for repeatable sample
np.random.seed(2023)

# random 20 x 20 array
# A = np.random.randint(0, 100, size=(20, 20))  # random integers
A = np.random.random(size=(20, 20)) * 100  # random floats

1.不显示切片列

• 这是最简单的选择，因为它释放了mask

# remove the unneeded columns
A = A[:, 5:]

# create the value mask
mask = np.logical_and(A >= 20, A < 21)

# create the colormap with extremes
cmap = mpl.colormaps["viridis"].with_extremes(bad='orange', under='w', over='k')

# plot
g = sns.heatmap(A, vmin=10, vmax=90, cmap=cmap, mask=mask)

# reset the xticklabels to show the correct column labels
_ = g.set_xticks(ticks=g.get_xticks(), labels=range(5, 20))

2.显示所有列

• 这是一个比较麻烦的选项，因为像Ben提供的answer一样，这需要手动添加颜色到cmap，并添加自定义颜色条。
• 使用mask，或数组中的任何np.nan值，都用set_bad着色。
• 这重用了我之前添加到Ben的答案中的颜色条创建方法，该方法来自Standalone colorbar。
• 如Creating Colormaps in Matplotlib中所示，可以将新颜色添加到重采样色图中的颜色切片中。
• 如果使用了vmin和vmax，则不会获得正确的值，因为这些选项会更改颜色条的范围。

# create the column mask
mask = np.zeros((20, 20), dtype=bool)
mask[:, :5] = True  # A[:, :5] = np.nan has the same effect

# slice the colors into the range of values in the array
colors = mpl.colormaps["viridis"].resampled(100).colors

# map a specific value range to a color; use a range for floats, and / or set a hight number to resampled
colors[19:21] = mpl.colors.to_rgba('tab:orange')

# create the new colormap with extremes
cmap = mpl.colors.ListedColormap(colors).with_extremes(bad='lightgray', under='w', over='k')

# draw the heatmap
g = sns.heatmap(A, cmap=cmap, mask=mask, cbar=False)

# add a new axes of the desired shape
cb_ax = g.figure.add_axes([0.93, 0.11, 0.025, 0.77])

# attach a new colorbar to the axes without an outline
cb = mpl.colorbar.ColorbarBase(cb_ax, cmap='viridis', norm=mpl.colors.Normalize(10, 90),  # vmax and vmin
                               label=None, ticks=range(10, 91, 10)).outline.set_visible(False)

3.让列和坏值共享颜色

# create the value mask
mask = np.logical_and(A >= 20, A < 21)

# add the unwanted column to the mask as np.nan
mask[:, :5] = np.nan

# create the colormap with extremes
cmap = mpl.colormaps["viridis"].with_extremes(bad='orchid', under='w', over='k')

# plot
g = sns.heatmap(A, vmin=10, vmax=90, cmap=cmap, mask=mask)

创建自定义颜色Mapmatplotlib.colors.LinearSegmentedColormap.from_list .当使用连续颜色Map（如“viridis”）时，这里没有一种直接方法来将特定值（如20）设置为特定颜色（如橙子）。

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import seaborn as sns
import numpy as np

np.random.seed(7)
A = np.random.randint(0,100, size=(20,20))
mask_array = np.zeros((20, 20), dtype=bool)
mask_array[:, :5] = True

# Create a colormap for each part
colormap_under_10 = plt.cm.viridis(np.linspace(0, 0.1, 10))
colormap_10_30 = plt.cm.Oranges(np.linspace(0.5, 1, 20))
colormap_above_30 = plt.cm.viridis(np.linspace(0.1, 1, 70))

# Combine them and build a new colormap
colors_combined = np.vstack((colormap_under_10, colormap_10_30, colormap_above_30))
mymap = colors.LinearSegmentedColormap.from_list('colormap_combined', colors_combined)

ax = sns.heatmap(A, vmin=0, vmax=100, cmap=mymap, mask=mask_array)

# Set color of masked region
ax.set_facecolor('lightgrey')

plt.show()