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Reproducible Example

In [2]: A = pd.array(["a1", "a2"], dtype="string")

In [3]: A
Out[3]:
<StringArray>
['a1', 'a2']
Length: 2, dtype: string

In [4]: isinstance(A, pd.arrays.StringArray)
Out[4]: True

In [5]: isinstance(A, pd.arrays.PandasArray)
Out[5]: True

Issue Description

PandasArray is described as a thin wrapper around Numpy values for internal compatibility. However, StringArray is a subclass of PandasArray for some reason (other ExtensionArrays are not). This inconsistency causes issues for unboxing the values for Bodo JIT (and I assume other systems consuming Pandas data).

https://pandas.pydata.org/docs/reference/api/pandas.Series.array.html https://pandas.pydata.org/docs/reference/api/pandas.arrays.PandasArray.html

Expected Behavior

Output of isinstance(pd.array(["a1", "a2"], dtype="string"), pd.arrays.PandasArray) should be False.

Installed Versions

INSTALLED VERSIONS ------------------ commit : bf856a85a3b769821809ff456e288542af16310c python : 3.9.6.final.0 python-bits : 64 OS : Darwin OS-release : 21.2.0 Version : Darwin Kernel Version 21.2.0: Sun Nov 28 20:28:54 PST 2021; root:xnu-8019.61.5~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.6.0.dev0+145.gbf856a85a3 numpy : 1.21.1 pytz : 2021.1 dateutil : 2.8.2 setuptools : 49.6.0.post20210108 pip : 21.2.2 Cython : 0.29.32 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : 7.26.0 pandas_datareader: None bs4 : None bottleneck : None brotli : fastparquet : None fsspec : None gcsfs : None matplotlib : None numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : None snappy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None zstandard : None tzdata : None

Comment From: jbrockmendel

This inconsistency causes issues for unboxing the values for Bodo JIT

Can you elaborate on this?

Comment From: datapythonista

Thanks for the report. I don't think the API for extension arrays is very clear. Did you try to simply remove the subclass here and see what fails? I guess there may be some generic functionality that lives in the parent classes meant for numpy backed data. It'd surely make sense to move things to the right place if that's the case.

Comment From: ehsantn

This inconsistency causes issues for unboxing the values for Bodo JIT

Can you elaborate on this?

The code paths for unboxing each array type is different. For PandasArray, Bodo just needs to unbox _ndarray since PandasArray is just a wrapper for Numpy arrays. StringArray is a different code path with slight differences such as handling pd.NA. This led to an error in Bodo so I noticed this class hierarchy issue.

Comment From: ehsantn

Thanks for the report. I don't think the API for extension arrays is very clear. Did you try to simply remove the subclass here and see what fails? I guess there may be some generic functionality that lives in the parent classes meant for numpy backed data. It'd surely make sense to move things to the right place if that's the case.

Yes, the APIs should be clarified to enable more robust integration with Pandas in general. Removing the subclass causes an error somewhere else so it may be a bit of work. Not a big issue for us right now since the workaround is just an extra type check. Opened the issue as feedback about extension array APIs and what other users may expect.