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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 import numpy as np a = pd.Series(np.zeros(1000000), dtype="float32") + np.float32(1) b = pd.Series(np.zeros(1000001), dtype="float32") + np.float32(1) print(a.dtype, b.dtype)
Issue Description
Performing binary operations on larger Series with dtype == 'float32' leads to unexpected upcasts to float64.
Above example prints float32 float64.
Using to_numpy() on the series before addition inhibits the implicit upcast.
Expected Behavior
I expect above snippet to print float32 float32.
Installed Versions
pandas : 2.3.1
numpy : 2.1.3
pytz : 2025.2
dateutil : 2.9.0.post0
pip : 25.1.1
Cython : 3.1.2
sphinx : None
IPython : 9.4.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.13.4
blosc : None
bottleneck : 1.5.0
dataframe-api-compat : None
fastparquet : None
fsspec : 202570
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.10.3
numba : 0.61.2
numexpr : 2.11.0
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : 2.9.10
pymysql : None
pyarrow : 20.0.0
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.16.0
sqlalchemy : 2.0.41
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
xlsxwriter : None
zstandard : 0.23.0
tzdata : 2025.2
qtpy : None
pyqt5 : None