Pandas version checks
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[X] I have checked that this issue has not already been reported.
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[X] I have confirmed this bug exists on the latest version of pandas.
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[ ] I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
(Pdb) data
[136457654736252, 134736784364431, 245345345545332, 223432411, 2343241, 3634548734, 23234]
(Pdb) dtype
'timedelta64[s]'
(Pdb) np.array(data, dtype=dtype)
array([136457654736252, 134736784364431, 245345345545332, 223432411,
2343241, 3634548734, 23234],
dtype='timedelta64[s]')
(Pdb) pd.Series(np.array(data, dtype=dtype))
0 1579371003 days 21:24:12
1 1559453522 days 17:40:31
2 2839645203 days 01:42:12
3 2586 days 00:33:31
4 27 days 02:54:01
5 42066 days 12:52:14
6 0 days 06:27:14
dtype: timedelta64[s]
(Pdb) psr = pd.Series(np.array(data, dtype=dtype))
(Pdb) psr.dt.days
0 1579371003
1 1559453522
2 -1455322093 # Overflowing because of `int32` output dtype.
3 2586
4 27
5 42066
6 0
dtype: int32
(Pdb) pd.Series([1579371003, 1559453522, 1455322093, 2839645203])
0 1579371003
1 1559453522
2 1455322093
3 2839645203 # This value can be fit in `int64` dtype easily.
dtype: int64
(Pdb) pd.Series([1579371003, 1559453522, 1455322093, 2839645203]).astype('int32')
0 1579371003
1 1559453522
2 1455322093
3 -1455322093
dtype: int32
Issue Description
Series.dt.day returns days output in int32 dtype. However, values can go into int64 territory and we should likely be returning int64 if there is an overflow detected or always.
Expected Behavior
(Pdb) psr.dt.days
0 1579371003
1 1559453522
2 2839645203
3 2586
4 27
5 42066
6 0
dtype: int36
Installed Versions
INSTALLED VERSIONS
------------------
commit : c2a7f1ae753737e589617ebaaff673070036d653
python : 3.10.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.0.0rc1
numpy : 1.23.5
pytz : 2023.3
dateutil : 2.8.2
setuptools : 67.6.1
pip : 23.0.1
Cython : 0.29.33
pytest : 7.2.2
hypothesis : 6.70.1
sphinx : 5.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.12.0
pandas_datareader: None
bs4 : 4.12.0
bottleneck : None
brotli :
fastparquet : None
fsspec : 2023.3.0
gcsfs : None
matplotlib : None
numba : 0.56.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : 2023.3.0
scipy : 1.10.1
snappy :
sqlalchemy : 1.4.46
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : None
qtpy : None
pyqt5 : None
Comment From: mroeschke
Thanks for the report. Yeah with non-nanosecond support look like int32 is probably no longer sufficient for the return types of these attributes