BUG: Pandas does not validate some parameters properly when reading CSVs and it causes segmentation faults · Issue #59059 · pandas-dev/pandas (original) (raw)

Pandas version checks

Reproducible Example

#######################################################################

Example 1

#######################################################################

import pandas import io

string_i_o_0 = io.StringIO('"To98odN"\n-40542\n-5\n-4\n')

pandas.read_csv(string_i_o_0, encoding_errors=string_i_o_0)

#######################################################################

Example 2

#######################################################################

import pandas import io

string_i_o_0 = io.StringIO('"To98odN"\n-40542\n-5\n-4\n') pandas.read_table(string_i_o_0, encoding_errors=string_i_o_0)

#######################################################################

Example 3

#######################################################################

import io import pandas

string_i_o_0 = io.StringIO('"H45"\n6\n-1\n8\n') var_0 = pandas.read_csv(string_i_o_0) var_1 = pandas.read_csv( string_i_o_0, skipinitialspace=var_0, )

Issue Description

I was working on Pynguin, an automated unit test generation tool for Python, and the tool found that Pandas does not validate some parameters properly and the generated tests produce the following output:

Segmentation fault (core dumped)

First, there is the encoding_errors parameter in the pandas.read_csv and pandas.read_table functions. I don't know why it happens for this one.

Second, there is the skipinitialspace parameter in the pandas.read_csv function. I think that this one comes from the fact that Pandas does not support that an exception is raised in the __bool__ method of the var_0 dataframe.

Traceback (most recent call last):
  File "/home/lucas/Documents/GitHub/pynguin-for-ML-libraries/err13.py", line 7, in <module>
    print(bool(var_0))
  File "/home/lucas/.conda/envs/pynguin-for-ML-libraries/lib/python3.10/site-packages/pandas/core/generic.py", line 1495, in __nonzero__
    raise ValueError(
ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Expected Behavior

Pandas should raise an exception instead of causing a segmentation fault when a wrong value is passed as an argument.

Installed Versions

commit : c46fb76
python : 3.10.14.final.0
python-bits : 64
OS : Linux
OS-release : 6.8.11-200.fc39.x86_64
Version : #1 SMP PREEMPT_DYNAMIC Sun May 26 20:05:41 UTC 2024
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 3.0.0.dev0+1125.gc46fb76afa
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 68.2.2
pip : 23.3.1
Cython : None
pytest : 8.2.0
hypothesis : 6.103.2
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
fastparquet : None
fsspec : 2024.2.0
gcsfs : None
matplotlib : 3.8.4
numba : None
numexpr : None
odfpy : None
openpyxl : None
pyarrow : 10.0.1
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.13.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None