Pandas version checks
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[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.
-
[ ] I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
schema = {
"id" : "int64[pyarrow]",
"time" : "timestamp[s][pyarrow]",
"value" : "float[pyarrow]",
} # fmt: skip
dfA = (
pd.DataFrame(
[
(0, "2021-01-01 00:00:00", 5.3),
(1, "2021-01-01 00:01:00", 5.4),
(2, "2021-01-01 00:01:00", 5.4),
(3, "2021-01-01 00:02:00", 5.5),
],
columns=schema,
)
.astype(schema)
.set_index(["id", "time"])
)
dfB = (
pd.DataFrame(
[
(1, "2022-01-01 08:00:00", 6.3),
(2, "2022-01-01 08:01:00", 6.4),
(3, "2022-01-01 08:02:00", 6.5),
],
columns=schema,
)
.astype(schema)
.set_index(["id", "time"])
)
df = pd.concat([dfA, dfB], keys=[0, 1], names=["run"])
print(df.index.dtypes) # ❌ time: object
Issue Description
The time index type gets coerced to object.
Expected Behavior
concat should maintain the dtype, since all concatenated tables have the same schema.
Installed Versions
INSTALLED VERSIONS
------------------
commit : d9cdd2ee5a58015ef6f4d15c7226110c9aab8140
python : 3.11.7.final.0
python-bits : 64
OS : Linux
OS-release : 6.5.0-28-generic
Version : #29~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Apr 4 14:39:20 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 69.5.1
pip : 24.0
Cython : None
pytest : 8.1.1
hypothesis : 6.100.1
sphinx : 7.3.7
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.23.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.3.1
gcsfs : None
matplotlib : 3.8.4
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 16.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.13.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
Comment From: mroeschke
Thanks for the report. I've trace the issue to this operation:
In [3]: import pyarrow as pa, pandas as pd
In [4]: pd.Index(["2020-01-01"], dtype=pd.ArrowDtype(pa.timestamp("s"))).union(pd.Index(["2020-01-02"], dtype=pd.ArrowDtype(pa.timestamp("s"))))
Out[4]: Index([2020-01-01 00:00:00, 2020-01-02 00:00:00], dtype='object')
Comment From: afonso-antunes
take