Categorical histplot not working properly with boolean data

[e-pet]

Hi,

while categorical histplots generally work, they don't seem to work (properly) for boolean data.

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

if __name__ == '__main__':
    df = pd.DataFrame({'y': [True, True, True, False, False]})

    plt.figure()
    # produces (undesired) float scale and the following message:
    # <__array_function__ internals>:180: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.
    sns.histplot(df, x='y')

    df['y'] = df['y'].map({True: 'True', False: 'False'})

    plt.figure()
    # desired output (categorical scale)
    sns.histplot(df, x='y')

    plt.show()

Produces the following two figures:

This is with seaborn 0.12.1.

(Unrelated, but - wouldn't it be nicer if the y axis showed integer, not float values, as these are count data?)

[mwaskom]

Numpy (and Python in general) upcasts boolean data to numeric dtypes where relevant (True/False are equivalent to 1/0).

[e-pet]

I am aware of this general behavior, but in this particular context it seems highly undesirable to me? Maybe I am overlooking some obvious use case, but who would ever want the current behavior?

It also seems inconsistent across seaborn; see e.g. this example from the documentation which doesn't print "Weekend=0.0/1.0".

tips = sns.load_dataset("tips")
tips["weekend"] = tips["day"].isin(["Sat", "Sun"])
sns.catplot(
    data=tips, x="day", y="total_bill", hue="weekend",
    kind="box", dodge=False,
)