You can use a LinearSegmentedColormap from matplotlib.colors. You first have to find the largest value, in this case 10, then use that to create a colors variable that starts with green, then goes to the standard 'Reds' colorset. Also, set the colorbar to False when making the heatmap with seaborn, and separately make one with matplotlib.

This code was adapted from here:

import matplotlib.pyplot as plt
import matplotlib.colors as cl
import seaborn as sns
import pandas as pd
import numpy as np
import random

data = []
for i in range(10):
    data.append([random.randrange(0, 11, 1) for _ in range(10)])
df = pd.DataFrame(data)

fig, ax = plt.subplots(figsize = (12, 10)) 
cmap_reds = plt.get_cmap('Reds')
num_colors = 11
colors = ['#009933'] + [cmap_reds(i / num_colors) for i in range(1, num_colors)]
cmap = cl.LinearSegmentedColormap.from_list('', colors, num_colors)
ax = sns.heatmap(df, cmap=cmap, vmin=0, vmax=num_colors, square=True, cbar=False, annot = True)
cbar = plt.colorbar(ax.collections[0], ticks=range(num_colors + 1))
cbar.set_ticks(np.linspace(0, num_colors, 2*num_colors+1)[1::2])
cbar.ax.set_yticklabels(range(num_colors))
plt.show()

Here is the output:

• The difficulty arises from the requirement to use more than a single mask.
  • mask_array, and a mask for A == 20 if the array is only int, or 20 <= A < 21 if the array is float.
• matplotlib.colors.Colormap offers only 3 methods for setting colors.
  1. set_under
  2. set_over
  3. set_bad - Set the color for masked values, which is already used for mask_array.
  • with_extremes - Does the three combined.
    • cmap = mpl.colormaps['viridis'].with_extremes(bad='orange', under='w', over='k')
    • 
  • Using these methods doesn't effect the look of the colorbar.

Imports and Sample Data

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. Don't show the sliced columns

• This is the easiest option, because it frees up 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. Show all the columns

• This is the more cumbersome option because, like the answer provided by Ben, this requires manually adding a color to cmap, and adding a custom colorbar.
  • Using mask, or any np.nan values in the array, are colored by set_bad.
• This reuses the colorbar creation method I previously added to Ben's answer, which came from Standalone colorbar.
• As demonstrated in Creating Colormaps in Matplotlib, a new color can be added into a slice of colors from a resampled colormap.
  • This does not get the correct value if vmin and vmax are used, because those options change the range of the colorbar.

# 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. Let the columns and bad values share the color

# 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)