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BUG: concat losing column dtype for extension arrays and object dtype

See original GitHub issue

Pandas version checks

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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

df1 = pd.DataFrame([[0, 1, 1]], columns=pd.Index([1, 2, 3], dtype="Int64"))
df2 = pd.DataFrame([[0, 1]], columns=pd.Index([1, 2], dtype="Int64"))
result = pd.concat([df1, df2], ignore_index=True, join="outer", sort=True)
expected = pd.DataFrame([[0, 1, 1.0], [0, 1, np.nan]], columns=pd.Index([1, 2, 3], dtype="Int64"))
tm.assert_frame_equal(result, expected)

Issue Description

this raises

Expected Behavior

The above code should not return an error

Installed Versions

INSTALLED VERSIONS ------------------ commit : https://github.com/pandas-dev/pandas/commit/5465f54f7640cbb4be9b223846823d4935142431 python : 3.8.12.final.0 python-bits : 64 OS : Linux OS-release : 5.13.0-48-generic Version : https://github.com/pandas-dev/pandas/issues/54~20.04.1-Ubuntu SMP Thu Jun 2 23:37:17 UTC 2022 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 1.5.0.dev0+939.g5465f54f76.dirty numpy : 1.22.0 pytz : 2021.1 dateutil : 2.8.2 setuptools : 59.8.0 pip : 21.3.1 Cython : 0.29.30 pytest : 7.1.1 hypothesis : 6.23.1 sphinx : 4.2.0 blosc : None feather : None xlsxwriter : 3.0.1 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.28.0 pandas_datareader: None bs4 : 4.10.0 bottleneck : 1.3.2 brotli : fastparquet : 0.7.1 fsspec : 2021.11.0 gcsfs : 2021.05.0 matplotlib : 3.4.3 numba : 0.53.1 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : 5.0.0 pyreadstat : 1.1.2 pyxlsb : None s3fs : 2021.11.0 scipy : 1.7.2 snappy : sqlalchemy : 1.4.25 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.18.0 xlrd : 2.0.1 xlwt : 1.3.0 zstandard : None

Issue Analytics

State:
Created a year ago
Comments:11 (11 by maintainers)

Top GitHub Comments

1reaction

phoflcommented, Jun 13, 2022

We don’t pass codes, so this is not an issue in our case

0reactions

simonjayhawkinscommented, Jul 4, 2022

example of this occuring for object dtype

df1 = pd.DataFrame([[0, 1, 1]], columns=pd.Index([1, 2, 3], dtype="object"))
df2 = pd.DataFrame([[0, 1]], columns=pd.Index([1, 2], dtype="object"))
result = pd.concat([df1, df2], ignore_index=True, join="outer", sort=True)
print(result.columns)
# Int64Index([1, 2, 3], dtype='int64')

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