BUG: eval and query ignore empty local_dict and global_dict · Issue #47084 · pandas-dev/pandas (original) (raw)
Pandas version checks
- I have checked that this issue has not already been reported.
- 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 df = pd.DataFrame([[1,2], [1,2]], columns=["a", "b"]) df.eval("c = @df.a + @df.b", local_dict={}, global_dict={})
a b c
0 1 2 3
1 1 2 3
Issue Description
If someone was to pass empty dictionaries into local_dict
and global_dict
kwargs of pd.eval
, df.eval
or df.query
, it would work as if None
was passed instead (i.e. actual scope would be populated with current frame locals and globals). This is an unexpected behavior and a security concern.
The reason behind this is an incorrect check of argument None
equality there and there. If user passes global_dict={}
, the following happens: global_dict or frame.f_globals => {} or frame.f_globals => False or frame.f_globals => frame.f_globals
and the same with local_dict
later on.
Expected Behavior
import pandas as pd df = pd.DataFrame([[1,2], [1,2]], columns=["a", "b"]) df.eval("c = @df.a + @df.b", local_dict={}, global_dict={})
UndefinedVariableError: Undefined variable 'df'
Or a similar error
Installed Versions
INSTALLED VERSIONS
commit : 1be9d38
python : 3.9.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.17.5-76051705-generic
Version : #202204271406165150484021.10~63e51bd SMP PREEMPT Mon May 2 15:
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.5.0.dev0+798.g1be9d3868f
numpy : 1.21.3
pytz : 2021.1
dateutil : 2.8.2
pip : 20.3.4
setuptools : 52.0.0
Cython : 0.29.30
pytest : 6.2.5
hypothesis : 6.46.7
sphinx : 4.5.0
blosc : 1.10.6
feather : None
xlsxwriter : 3.0.3
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.1
IPython : 7.33.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.4
brotli : None
fastparquet : 0.7.2
fsspec : 2022.5.0
gcsfs : 2022.5.0
matplotlib : 3.4.3
numba : 0.53.1
numexpr : 2.8.1
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : 7.0.0
pyreadstat : 1.1.6
pyxlsb : None
s3fs : 0.6.0
scipy : 1.7.3
snappy :
sqlalchemy : 1.4.36
tables : 3.7.0
tabulate : 0.8.9
xarray : 2022.3.0
xlrd : 2.0.1
xlwt : 1.3.0
zstandard : None