BUG: `isna` doesn't detect `pyarrow.NA` produced by 0/0

[vkhodygo]

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

#%%
df = pd.DataFrame({"A": [0, 1, 2, pd.NA, 1, 2], "B": [0, pd.NA, 2, pd.NA, 2, 4]}, dtype="float[pyarrow]")

df["rate"] = df["A"]/df["B"]

df.info()
df["rate"].isna()

#%%
df = pd.DataFrame([1, 2, np.nan])
df.info()
df.isna()

#%%
pd.Series([pa.NA], dtype="float[pyarrow]").isna()

Issue Description

I deal with tables with a lot of missing data, and using pyarrow backend is a must. However, I recently noticed that some intermediate results are not processed as they should be: applying isna to the rate column above results in pyarrow.NaN being see as a regular number, whereas numpy.nan is processed like expected. Creating a dataframe from scratch though behaves like it should.

Expected Behavior

The documentation says the following:

pandas/pandas/core/dtypes/missing.py

Lines 101 to 178 in 0691c5c

	def isna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
	"""
	Detect missing values for an array-like object.
	This function takes a scalar or array-like object and indicates
	whether values are missing (``NaN`` in numeric arrays, ``None`` or ``NaN``
	in object arrays, ``NaT`` in datetimelike).
	Parameters
	----------
	obj : scalar or array-like
	Object to check for null or missing values.
	Returns
	-------
	bool or array-like of bool
	For scalar input, returns a scalar boolean.
	For array input, returns an array of boolean indicating whether each
	corresponding element is missing.
	See Also
	--------
	notna : Boolean inverse of pandas.isna.
	Series.isna : Detect missing values in a Series.
	DataFrame.isna : Detect missing values in a DataFrame.
	Index.isna : Detect missing values in an Index.
	Examples
	--------
	Scalar arguments (including strings) result in a scalar boolean.
	>>> pd.isna('dog')
	False
	>>> pd.isna(pd.NA)
	True
	>>> pd.isna(np.nan)
	True
	ndarrays result in an ndarray of booleans.
	>>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
	>>> array
	array([[ 1., nan, 3.],
	[ 4., 5., nan]])
	>>> pd.isna(array)
	array([[False, True, False],
	[False, False, True]])
	For indexes, an ndarray of booleans is returned.
	>>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None,
	... "2017-07-08"])
	>>> index
	DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
	dtype='datetime64[ns]', freq=None)
	>>> pd.isna(index)
	array([False, False, True, False])
	For Series and DataFrame, the same type is returned, containing booleans.
	>>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
	>>> df
	0 1 2
	0 ant bee cat
	1 dog None fly
	>>> pd.isna(df)
	0 1 2
	0 False False False
	1 False True False
	>>> pd.isna(df[1])
	0 False
	1 True
	Name: 1, dtype: bool
	"""
	return _isna(obj)

and I'd expect NaN to be seen the same regardless of the actual dtype; this is a well-defined IEEE 754 object. There is a hint to what might be causing it here: apache/arrow#35535 (comment)

That's because pyarrow does not set that the NaN is the missing value indicator, and thus NaNs in the input are preserved.

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.12.6
python-bits : 64
OS : Linux
OS-release : 6.10.10-arch1-1
Version : #1 SMP PREEMPT_DYNAMIC Thu, 12 Sep 2024 17:21:02 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.3
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
pip : 24.1.2
Cython : None
sphinx : None
IPython : 8.26.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.3.1
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.4
lxml.etree : None
matplotlib : 3.9.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 17.0.0
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None

[WillAyd]

Thanks for the report - we already have a few open issues about this (see #32265 and #53887) so going to close this one and try to keep the discussions consolidated in those.

Appreciate you bringing this up!

[vkhodygo]

Thanks, I did search for open issues, but for some reason didn't see these ones.