Any way to change color bar (cbar) in seaborn to a legend (for a binary heatmap)?

Question

I'm using the heatmap in seaborn (sns.heatmap) to show a matrix of binary values true/false. It works just fine but the color bar as expected is showing a range of values from 0-1 (when in reality there are only two colors).

Is there any way to change this to a legend showing the true/false colors? I couldn't find anything in the documentation

https://seaborn.pydata.org/generated/seaborn.heatmap.html

Example:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df = pd.DataFrame({'a':[False,True,False,True,True,True],
                   'b':[False,False,False,False,True,False],
                   'c':[False,True,True,False,True,True],
                   'd':[False,True,False,True,True,True],
                   'e':[False,True,True,False,False,True],
                   'f':[False,True,False,False,True,True]})


# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(13, 13))

# Generate a custom diverging colormap
cmap = sns.diverging_palette(300, 180, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio
_ = sns.heatmap(df, cmap=cmap, center=0, square=True, linewidths=.5, cbar_kws={"shrink": .5})

Answer 1

Here is a solution using some random data (working on this before you included data in your post). The solution was adapted to work for your problem from this link.

from matplotlib.colors import LinearSegmentedColormap
import numpy as np
import random
import seaborn as sns
sns.set()

# Generate data
uniform_data = np.array([random.randint(0, 1) for _ in range(120)]).reshape((10, 12))

# Define colors
colors = ((1.0, 1.0, 0.0), (1, 0.0, 1.0))
cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))

ax = sns.heatmap(uniform_data, cmap=cmap)

# Set the colorbar labels
colorbar = ax.collections[0].colorbar
colorbar.set_ticks([0.25,0.75])
colorbar.set_ticklabels(['0', '1'])

Output

Applying on your problem:

# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(8, 8))

colors = ["gray", "lightgray"] 
cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))


# Draw the heatmap with the mask and correct aspect ratio
_ = sns.heatmap(df, cmap=cmap,square=True,  linewidths=.5, cbar_kws={"shrink": .5})

# Set the colorbar labels
colorbar = ax.collections[0].colorbar
colorbar.set_ticks([0.25,0.75])
colorbar.set_ticklabels(['0', '1'])

Output