BUG: Wrong inferred type in case of a mixture of boolean, float and integers #62033
Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
>>> import pandas as pd
>>> idx = pd.Index(pd.array([1., True, 2., 3., 4]))
>>> idx.inferred_type # Wrong, should be mixed
'mixed-integer'
>>> idx = pd.Index(pd.array([1., True, 2., 3., 4.]))
>>> idx.inferred_type # Correct
'mixed'
>>> idx = pd.Index(pd.array([1, True, 2, 3, 4]))
>>> idx.inferred_type # Correct
'mixed-integer'Issue Description
While exploring #61709, I noticed this strange behavior: In case of a mixture of boolean, float and integers, the inferred type is "mixed-integer" and not "mixed"
Expected Behavior
"mixed" inferred type when there are floats, integers and booleans.
Installed Versions
INSTALLED VERSIONS
commit : 2cc9b21
python : 3.10.16
python-bits : 64
OS : Linux
OS-release : 6.6.87.2-microsoft-standard-WSL2
Version : #1 SMP PREEMPT_DYNAMIC Thu Jun 5 18:30:46 UTC 2025
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 3.0.0.dev0+1904.g2cc9b21c9a
numpy : 1.26.4
dateutil : 2.9.0.post0
pip : 25.0
Cython : 3.0.11
sphinx : 8.1.3
IPython : 8.32.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.13.3
bottleneck : 1.4.2
fastparquet : 2024.11.0
fsspec : 2025.2.0
html5lib : 1.1
hypothesis : 6.125.1
gcsfs : 2025.2.0
jinja2 : 3.1.5
lxml.etree : 5.3.0
matplotlib : 3.10.0
numba : 0.61.0
numexpr : 2.10.2
odfpy : None
openpyxl : 3.1.5
psycopg2 : 2.9.9
pymysql : 1.4.6
pyarrow : 19.0.0
pyiceberg : None
pyreadstat : 1.2.8
pytest : 8.3.4
python-calamine : None
pytz : 2025.1
pyxlsb : 1.0.10
s3fs : 2025.2.0
scipy : 1.15.1
sqlalchemy : 2.0.37
tables : 3.10.1
tabulate : 0.9.0
xarray : 2024.9.0
xlrd : 2.0.1
xlsxwriter : 3.2.2
zstandard : 0.23.0
qtpy : None
pyqt5 : None