Pandas version checks
-
[x] I have checked that this issue has not already been reported.
-
[x] I have confirmed this bug exists on the latest version of pandas.
-
[ ] I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
series1 = pd.Series([1,2,3,4,5])
series2 = pd.Series([2,3,5,1,2])
pd.eval(
"(a / b).cumsum()",
local_dict={"a": series1, "b": series2}
)
Issue Description
AttributeError: 'BinOp' object has no attribute 'value'
raised.
Expected Behavior
0 0.500000
1 1.166667
2 1.766667
3 5.766667
4 8.266667
dtype: float64
should yield this result.
Installed Versions
INSTALLED VERSIONS
------------------
commit : c888af6d0bb674932007623c0867e1fbd4bdc2c6
python : 3.11.10
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 183 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 2.3.1
numpy : 2.3.1
pytz : 2025.2
dateutil : 2.9.0.post0
pip : 25.1.1
Cython : None
sphinx : None
IPython : 9.4.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : None
numexpr : 2.11.0
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None
Comment From: ryantam626
Also an issue for
import pandas as pd
series1 = pd.Series([0,1,0,2,3,4,5],dtype=bool)
pd.eval(
"(~a).cumsum()",
local_dict={"a": series1}
)
AttributeError: 'UnaryOp' object has no attribute 'value'
raised
Excepted
0 1
1 1
2 2
3 2
4 2
5 2
6 2
dtype: int32
Comment From: ryantam626
Actually fixed in a82307f15630690fc288afeed27c549a028d94e9 - sorry for the noise.