Apply 2 different masks to a seaborn heatmap or manually change the color of a cell

Question

I have a dataframe and I want to plot the seaborn heatmap:

import seaborn as sns

res = sns.heatmap(df, cmap='flare',xticklabels=1, yticklabels=1,linecolor='white',linewidths=0.5,
                 cbar=True,mask=df.isnull(),cbar_kws={'shrink': 0.6},vmin=vmin, vmax=vmax)

I have applied a mask for NaN cells. Now, I want to change the color of few cells by a customized color which is not in the colormap, for example blue, to show that those cells belong to another category.

My question is: Is it possible to apply 2 masks or more with different colors to a seaborn heatmap or manually change the color of a cell to totally another color?

You can't use two makes in one call but you can call it twice and mask everything but the cells you want to show differently the second time (masking just sets the cells to transparent. — mwaskom, Apr 12 '21 at 23:04

JohanC · Accepted Answer · 2021-04-13T04:59:28.760

It is unclear how the blue squares are represented. The following solution supposes they are represented as ones in a second matrix. The first heatmap is drawn as before. Then the second heatmap uses a special colormap (in this case with one color, but also a full range is possible), masking out all places where nothing should be drawn.

Note that masks can be combined via the logical or (symbol: |).

from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn as sns
import pandas as pd
import numpy as np

N = 10
data = np.random.uniform(0, 45, size=(N, N))
for x, y in np.random.randint(0, N, 50).reshape(-1, 2):
    data[x, y] = np.nan  # fill in some nans at random places
df = pd.DataFrame(data)
up_triang = np.triu(np.ones_like(data)).astype(bool)
ax = sns.heatmap(df, cmap='flare', xticklabels=True, yticklabels=True, square=True,
                 linecolor='white', linewidths=0.5,
                 cbar=True, mask=df.isnull() | up_triang, cbar_kws={'shrink': 0.6, 'pad': 0}, vmin=0, vmax=45)

data_special = np.random.randint(0, 5, size=(N, N)) // 4
sns.heatmap(data_special, cmap=ListedColormap(['cornflowerblue']), linecolor='white', linewidths=0.5,
            square=True, cbar=False, mask=(data_special != 1) | up_triang, ax=ax)
ax.plot([0, N, 0, 0], [0, N, N, 0], clip_on=False, color='black', lw=2)
ax.tick_params(left=False, bottom=False)
plt.show()

An alternative approach, when there is only one color for the special cells, is to use an "under" color for the colormap, and give these cells negative values. An additional benefit is that the color can be shown in the colorbar. Here is some sample code:

N = 10
data = np.random.uniform(0, 45, size=(N, N))
for x, y in np.random.randint(0, N, 50).reshape(-1, 2):
    data[x, y] = np.nan
data_special = np.random.randint(0, 5, size=(N, N)) // 4
data[data_special == 1] = -1
df = pd.DataFrame(data)

up_triang = np.triu(np.ones_like(data)).astype(bool)
cmap =  sns.color_palette('mako', as_cmap=True).copy()
cmap.set_under('crimson ')
ax = sns.heatmap(df, cmap=cmap, xticklabels=True, yticklabels=True, square=True,
                 linecolor='white', linewidths=0.5, cbar=True, mask=df.isnull() | up_triang,
                 cbar_kws={'shrink': 0.6, 'pad': 0, 'extend': 'min', 'extendrect': True}, vmin=0, vmax=45)
ax.plot([0, N, 0, 0], [0, N, N, 0], clip_on=False, color='black', lw=2)
ax.tick_params(left=False, bottom=False)
plt.show()

Is the "under" color mismatched between the code (cornflowerblue) and plot (red), or am I misunderstanding what "under" does? — , Apr 13 '21 at 00:08
would you check related question [here](https://stackoverflow.com/questions/73581384/plotting-a-fancy-diagonal-correlation-matrix-in-python-within-dataframe) — Mario, Sep 03 '22 at 14:05

Apply 2 different masks to a seaborn heatmap or manually change the color of a cell

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