BUG: `.str.contains()` regex lookbehind and lookahead fail for data type `string[pyarrow]`

[noahblakesmith]

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Reproducible Example

import pandas as pd

df = pd.DataFrame({"c": ["a f", "c d", "e f g", "hij"]})

print(df["c"].astype(str).str.contains("(?<=\s)f(?=\s)"))
print(df["c"].astype("string[python]").str.contains("(?<=\s)f(?=\s)"))
print(df["c"].astype("string[pyarrow]").str.contains("(?<=\s)f(?=\s)"))

Issue Description

.str.contains() regex lookbehind and lookahead fail for data type string[pyarrow].

Expected Behavior

I believe string[pyarrow] should produce the same results as string[python] and str.

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.10.16
python-bits : 64
OS : Linux
OS-release : 6.8.0-1020-azure
Version : #23-Ubuntu SMP Mon Dec 9 16:58:58 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.3
numpy : 2.2.2
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 24.2
Cython : None
sphinx : None
IPython : 8.31.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.12.0
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.5
lxml.etree : None
matplotlib : 3.10.0
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 19.0.0
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report! This also fails when infer_string=True and you have PyArrow, so tagging as 2.3.

cc @jorisvandenbossche @WillAyd

[WillAyd]

The Arrow library does not use the Python standard library for regular expressions, but rather re2. It looks like re2 does not support lookahead and lookbehinds, and doesn't really plan on doing so given the time complexity of it.

See apache/arrow#40220 and the re2 documentation https://github.com/google/re2/wiki/Syntax

[rhshadrach]

Thanks @WillAyd - I think the conclusion is therefore that pandas needs to implement something internally to achieve feature parity, would you agree?

[WillAyd]

This is probably something worth documenting in the conversion guide. Something to the effect of "for X list of regular expressions, users are advised to do ser.astype("object").str.contains(...) to force usage of the Python regular expression engine"

[rhshadrach]

Thanks @WillAyd - I am personally of the opinion that documenting differences in behavior here is not sufficient, but do not feel strongly so.

[WillAyd]

Looks like we replied at the same time :-)

A fallback to the object dtype internally is also an option. The downside to that is it could be a rather expensive operation to go from Arrow -> Python -> Arrow to compute a result. It might be faster to work with object storage in this very specific case, and by forcing end users down that path it helps signal performance implications.

Not a strong feeling either way though. There are merits to both approaches

[WillAyd]

This is also a good conversation for PDEP-13 #58455

[rhshadrach]

Looks like we replied at the same time :-)

I dunno, it appears to me my comment is above yours 😆

Agreed relying on conversion to object is not great, my personal preference would still be to do this rather than require users to do it on their own. This would be a case where #55385 could be useful.

[jorisvandenbossche]

It should be possible for us to reliably detect if the user is passing a regex using lookbehind and lookahead? (not very familiar here, quick google says that this is with ?! / ?= and ?<! / ?<=, but just looking for those strings might give false positives?)