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BUG: Sparse[bool] type allows any fill_value #45004

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@bdrum

Description

@bdrum

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Reproducible Example

a = pd.arrays.SparseArray([1, 0, 0, 1], dtype=pd.SparseDtype(np.bool8, 12))
a
#[True, False, False, True]
#Fill: 12
#IntIndex
#Indices: array([0, 1, 2, 3])

Issue Description

According to discussion in #44955 I think that Sparse[bool] type should have only three available value: True, False or nan.

Actually I've already tried to implement deprecation of setting any value instead of mentioned list, but plenty of test was failed. So I've decided to get approval from community.

What do you think?

BTW we can just use implicit conversion of any fill_value to bool type

Expected Behavior

a = pd.arrays.SparseArray([1, 0, 0, 1], dtype=pd.SparseDtype(np.bool8, 12))
ValueError
fill_value must be True, False or nan for boolean type. Got 12 instead

Installed Versions

INSTALLED VERSIONS ------------------ commit : 2cc9ab3 python : 3.8.12.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19044 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : Russian_Russia.1252

pandas : 1.4.0.dev0+1436.g2cc9ab3ef7
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3
setuptools : 58.0.4
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.23.3
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.28.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.10.1
fastparquet : None
gcsfs : 2021.10.1
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : 2021.10.1
scipy : 1.7.1
sqlalchemy : 1.4.25
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1

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    API DesignBugNeeds TriageIssue that has not been reviewed by a pandas team memberSparseSparse Data Type

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