BUG: ValueError in pandas.DataFrame.replace with regex on single-row DataFrame with None/NaN

[martinandrovich]

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({"ticker": ["#1234#"], "name": [None]})

df.replace({col: {r"^#": "$"} for col in df.columns}, regex=True)  # raises
df.fillna("").replace({col: {r"^#": "$"} for col in df.columns}, regex=True)  # works
df.astype(str).replace({col: {r"^#": "$"} for col in df.columns}, regex=True)  # works
df.astype(pd.StringDtype()).replace({col: {r"^#": "$"} for col in df.columns}, regex=True)  # works

Issue Description

Using replace with a regex pattern on a single-row DataFrame containing None values raises the following error:

ValueError: cannot call `vectorize` on size 0 inputs unless `otypes` is set

Expected Behavior

The replace function should handle None values gracefully without requiring a manual fill or type conversion.

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.11.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.27766
machine : AMD64
processor : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Denmark.1252

pandas : 2.2.3
numpy : 2.0.2
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 24.3.1
Cython : None
sphinx : None
IPython : 8.30.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : 1.4.2
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : 1.1
hypothesis : None
gcsfs : None
jinja2 : 3.1.4
lxml.etree : 5.3.0
matplotlib : 3.9.2
numba : 0.60.0
numexpr : 2.10.2
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 18.1.0
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.1
sqlalchemy : 2.0.36
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report! Confirmed on main. It looks like pandas.core.array_algos.replace.compare_or_regex_search does not properly handle the case of all NA values. Further investigations and PRs to fix are welcome!

[snitish]

take

[snitish]

Upon further investigation, I found that this is indeed a bug in pandas.core.array_algos.replace.compare_or_regex_search as @rhshadrach mentioned earlier. It uses np.vectorize to call a vectorized operation on non-NA elements in the array which, in this case, would be an empty array. This apparently works only if the otypes argument is set when calling np.vectorize. Created PR #60691 to fix this.