Pandas version checks

  • [x] I have checked that this issue has not already been reported.

  • [x] I have confirmed this bug exists on the latest version of pandas.

  • [x] I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

### Using pandas 2.2.3
import pandas as pd

pd.DataFrame([['a', 'b'], ['c', 'd']]).astype('string').to_pickle('G:/temp/test2.pkl')

### Using pandas 2.3.0

import pandas as pd

df = pd.read_pickle('G:/temp/test2.pkl') # looks ok

df.dtypes # raises AttributeError: 'StringDtype' object has no attribute '_na_value'

df[0] + df[1] # also raises AttributeError

Issue Description

The code in a StringDtype object in 2.3 refers to an internal _na_value representation that appears not to have existed prior to 2.3.0. Pickled objects containing StringDtype columns pickled in earlier versions, including 2.2.3, may initially appear to unpickle successfully. However, listing the dtypes or even implicitly checking the dtypes by doing an operation, raises an AttributeError.

Expected Behavior

The documentation at read_pickle indicates backward compatibility to version 0.20.3, so a pickle from 2.2.3 should be readable and usable in 2.3.0.

A current workaround is something like this, to wrap the object in a freshly created 2.3.0-compatible dtype:

def unpickle_wrap(fn):
   df = pd.read_pickle(fn)
   for col, dtype in df.dtypes.items():
       if pd.api.types.is_string_dtype(dtype):
           df[col] = df[col].astype(object).astype('string')
   return df

Installed Versions

In [55]: pd.show_versions() INSTALLED VERSIONS ------------------ commit : 2cc37625532045f4ac55b27176454bbbc9baf213 python : 3.11.12 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19045 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 2.3.0 numpy : 2.2.6 pytz : 2025.2 dateutil : 2.9.0.post0 pip : 25.1.1 Cython : None sphinx : None IPython : 9.3.0 adbc-driver-postgresql: None adbc-driver-sqlite : None bs4 : 4.13.4 blosc : None bottleneck : 1.5.0 dataframe-api-compat : None fastparquet : None fsspec : 2025.5.1 html5lib : None hypothesis : None gcsfs : None jinja2 : 3.1.6 lxml.etree : 5.4.0 matplotlib : 3.10.3 numba : 0.61.2+0.g1e70d8ceb.dirty numexpr : 2.10.2 odfpy : None openpyxl : 3.1.5 pandas_gbq : None psycopg2 : None pymysql : None pyarrow : 19.0.1 pyreadstat : None pytest : 8.4.1 python-calamine : None pyxlsb : None s3fs : 2025.5.1 scipy : 1.15.2 sqlalchemy : 2.0.41 tables : None tabulate : 0.9.0 xarray : 2025.6.1 xlrd : None xlsxwriter : 3.2.5 zstandard : 0.23.0 tzdata : 2025.2 qtpy : None pyqt5 : None

(Edit: fixed example to make copy-pastable, and confirmed on main)

Comment From: Liam3851

take