Pandas version checks
Reproducible Example
import pandas as pd
df = pd.DataFrame(
pd.Series(
["2026-01-01", "2026-06-17", "2026-12-31"],
dtype="date32[day][pyarrow]",
index=["a", "b", "c"]
),
columns =["paDate"]
)
print(df)
print(df['paDate'].dt.isocalendar()) # produces index of 0, 1, 2
df['failed_assign'] = df['paDate'].dt.isocalendar().week.astype(int)
print(df['failed_assign'])
# the below works as intended
df = pd.DataFrame(
pd.Series(
["2026-01-01", "2026-06-17", "2026-12-31"],
dtype=object,
index=["a", "b", "c"]
),
columns =["strDate"]
)
df['pdDate'] = pd.to_datetime(df['strDate'])
print(df)
print(df['pdDate'].dt.isocalendar()) # produces index of a, b, cIssue Description
When parsing a Series of pyarrow date32 values using Series.dt.isocalendar(), the resulting dataframe has an index of 0, 1, 2, ... onwards, instead of using the source Series object's index.
This was encountered when using pd.read_parquet() to generate the DataFrame from a parquet file containing datetime columns.
The code
df = pd.DataFrame( pd.Series( ["2026-01-01", "2026-06-17", "2026-12-31"], dtype="date32[day][pyarrow]", index=["a", "b", "c"] ), columns =["paDate"] )
produces an output of
However, df['paDate'].dt.isocalendar() produces an output of
And the resulting index misalignment causes the following assignment issue:
Expected Behavior
df['paDate'].dt.isocalendar() to result in a DataFrame with the same index as the Series df['paDate']
Installed Versions
Details
INSTALLED VERSIONS
commit : 72f2fea
python : 3.13.14
python-bits : 64
OS : Windows
OS-release : 11
Version : 10.0.26100
machine : AMD64
processor : Intel64 Family 6 Model 151 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_New Zealand.1252
pandas : 3.0.3
numpy : 2.4.6
dateutil : 2.9.0.post0
pip : 26.1.2
Cython : None
sphinx : None
IPython : 9.14.1
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.15.0
bottleneck : None
fastparquet : None
fsspec : 2026.6.0
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.11.0
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
psycopg2 : 2.9.12
pymysql : None
pyarrow : 24.0.0
pyiceberg : None
pyreadstat : None
pytest : None
python-calamine : None
pytz : None
pyxlsb : None
s3fs : None
scipy : 1.17.1
sqlalchemy : 2.0.51
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
qtpy : None
pyqt5 : None
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
Issue Description
When parsing a Series of pyarrow date32 values using Series.dt.isocalendar(), the resulting dataframe has an index of 0, 1, 2, ... onwards, instead of using the source Series object's index.
This was encountered when using pd.read_parquet() to generate the DataFrame from a parquet file containing datetime columns.
The code
df = pd.DataFrame( pd.Series( ["2026-01-01", "2026-06-17", "2026-12-31"], dtype="date32[day][pyarrow]", index=["a", "b", "c"] ), columns =["paDate"] )produces an output of
However,
df['paDate'].dt.isocalendar()produces an output ofAnd the resulting index misalignment causes the following assignment issue:
Expected Behavior
df['paDate'].dt.isocalendar() to result in a DataFrame with the same index as the Series df['paDate']
Installed Versions
Details
INSTALLED VERSIONS
commit : 72f2fea
python : 3.13.14
python-bits : 64
OS : Windows
OS-release : 11
Version : 10.0.26100
machine : AMD64
processor : Intel64 Family 6 Model 151 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_New Zealand.1252
pandas : 3.0.3
numpy : 2.4.6
dateutil : 2.9.0.post0
pip : 26.1.2
Cython : None
sphinx : None
IPython : 9.14.1
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.15.0
bottleneck : None
fastparquet : None
fsspec : 2026.6.0
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.11.0
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
psycopg2 : 2.9.12
pymysql : None
pyarrow : 24.0.0
pyiceberg : None
pyreadstat : None
pytest : None
python-calamine : None
pytz : None
pyxlsb : None
s3fs : None
scipy : 1.17.1
sqlalchemy : 2.0.51
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
qtpy : None
pyqt5 : None