DataFrame.multiply(Series, axis=1) with 10k+ rows raises AttributeError

[JoElfner]

Code Sample raising the error:

df = pd.DataFrame(data=np.random.rand(15000, 5), columns=['x', 'a', 'ab', 'b', 'c'])
ser = pd.Series(data=[1.1, 9, 17.8], index=['a', 'b', 'c'])
df.multiply(ser, axis=1)

not raising the error if up to 10000 rows are taken:

# NO error:
df.iloc[:10000].multiply(ser, axis=1)
# error:
df.iloc[:10001].multiply(ser, axis=1)

Problem description

Multiplying a DataFrame with more than 10k rows with a Series with the keyword axis=1 raises the following error:

AttributeError: 'numpy.dtype' object has no attribute 'value_counts'
It seems like another path is taken, if more than 10k rows are multiplied.
Version 0.24.2 did NOT reproduce the error. The error occurs since 0.25.0

Omitting the axis keyword seems to solve the problem, but is it safe to do so in all cases?

Any ideas for a workaround except for solving it in chunks?

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : None python : 3.7.4.final.0 python-bits : 64 OS : Windows OS-release : 7 machine : AMD64 processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel byteorder : little LC_ALL : None LANG : de LOCALE : de_DE.ISO8859-1

pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.0
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8

[TomAugspurger]

Fixed in 0.25.1: #27636

[JoElfner]

ouch, must have missed all issues/pr/changes from 0.25.0 to 0.25.1 when looking for existing issues/prs...