BUG: python crashes on filtering with `.loc` on boolean Series with `dtype_backend=pyarrow` on some dataframes.

[pstorozenko]

This bug is related to pandas 2.0. On 1.5.3 (with numpy as dtype backend) everything works.

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

Using parquet from wiki100.zip.

import pandas as pd

wiki = pd.read_parquet("wiki100.parquet", engine="pyarrow",  dtype_backend="pyarrow")

wiki.loc[wiki['curr'] == "Warsaw", :]

Issue Description

After running such script I get

➜  python 02_pandas20.py
[1]    24955 floating point exception (core dumped)  python 02_pandas20.py

Expected Behavior

I should get the result of this query:

                                                       prev    curr  type    n
13488247                                            Trumpet  Warsaw  link   10
13488399                                Bronislava_Nijinska  Warsaw  link   33
13488166  List_of_European_cities_by_population_within_c...  Warsaw  link  480
13488365                               Warsaw_pogrom_(1881)  Warsaw  link   10
13488408                                     Witold_Pilecki  Warsaw  link   19

Installed Versions

INSTALLED VERSIONS

commit : 23c3dc2
python : 3.10.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.19.0-35-generic
Version : #36~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Feb 17 15:17:25 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.0.dev0+246.g23c3dc2c37
numpy : 1.25.0.dev0+918.g28bce82c8
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 65.6.3
pip : 23.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2022.7
qtpy : None
pyqt5 : None
None

Context

The file is a subset subset of wiki clickstream data.

This code works well if I don't set dtype_backend="pyarrow".
Tested in both 2.0.0rc1 and on nightly.

The operation wiki['curr'] == "Warsaw" executes correctly so it's an issue with filtering on boolean array.
Sorry for not providing a simpler example, as all those handcrafted worked all the time.

[eloyfelix]

I have found what I believe is the same bug.

Tested it in 2.0 RC1 and in master branch 2.1.0.dev0+265.gd8d1a474c7

Using a single string column csv file bug.csv.gz

import pandas as pd
df = pd.read_csv("bug.csv", dtype={"data_validity_comment": "string[pyarrow]"})
outside = df[df["data_validity_comment"] == "Outside typical range"]
# python crashes

Dataframe shape is (145000, 1). It stops crashing if I cut it exactly at 143116.

import pandas as pd
df = pd.read_csv("bug.csv", dtype={"data_validity_comment": "string[pyarrow]"})
df = df[0:143116].copy()
outside = df[df["data_validity_comment"] == "Outside typical range"]

This is also the simpler example I could come with...

[phofl]

cc @mroeschke

Looks like this is coming from pc.replace_with_mask(values, pa.array(mask), replacements) in _replace_with_mask. There is a comment that this caused segfaults for earlier version e.g. pyarrow < 8. Looks like this is not completely solved. Thoughts here?

The following reproduces for me:

x = pa.scalar(False, type=pa.bool_(), from_pandas=True)
arr = pa.chunked_array([np.array([True] * 5)])
mask = pa.array([False] * 5)
pc.replace_with_mask(arr, mask, pa.array([False] * 5))

[lukemanley]

Calling arr.combine_chunks() before passing to pc.replace_with_mask seems to work. Probably a good idea to report upstream since this is happening with the latest version of pyarrow.

[pstorozenko]

Unfortunately I never used pyarrow directly, could you report it, as you know much better what is really bugged here?

[lukemanley]

I opened apache/arrow#34634 upstream in the arrow repo.

[char101]

I also experienced this, pyarrow backed series contains <NA> in comparison result.

pd.Series([1, pd.NA, 2]) > 0
Out[24]: 
0     True
1    False
2     True
dtype: bool

pd.Series([1, pd.NA, 2], dtype='int32[pyarrow]') > 0
Out[25]: 
0    True
1    <NA>
2    True
dtype: bool[pyarrow]

[pstorozenko]

@char101
I think that's by design and that's how it should work. The problem with numpy array is that it cannot differentiate between NA and NaN for float, and don't have any NA for ints (so int arrays with NA are converted to floats as in your case).
With arrow backend, we can finally differentiate between NA and NaN for floats, and introduce 'proper' na for other types, like here for ints.
You don't know whether NA is greater or smaller than 0, so you get NA in the result.