BUG: .dt.microsecond for pyarrow-backed Series returns 0

[MarcoGorelli]

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

In [22]: s = pd.Series(pd.date_range('2000', periods=3, freq='15ms'))

In [23]: s.dt.microsecond
Out[23]:
0        0
1    15000
2    30000
dtype: int32

In [24]: s.convert_dtypes(dtype_backend='pyarrow')
Out[24]:
0           2000-01-01 00:00:00
1    2000-01-01 00:00:00.015000
2    2000-01-01 00:00:00.030000
dtype: timestamp[ns][pyarrow]

In [25]: s.convert_dtypes(dtype_backend='pyarrow').dt.microsecond
Out[25]:
0    0
1    0
2    0
dtype: int64[pyarrow]

Issue Description

For non-pyarrow backed dtype, it returns the total number of microseconds since the last second

For pyarrow-backed, it just returns 0

Expected Behavior

In [25]: s.convert_dtypes(dtype_backend='pyarrow').dt.microsecond
Out[25]:
0        0
1    15000
2    30000
dtype: int64[pyarrow]

Installed Versions

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.11.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.153.1-microsoft-standard-WSL2
Version : #1 SMP Fri Mar 29 23:14:13 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.2
numpy : 2.0.0
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 65.5.0
pip : 24.0
Cython : None
pytest : 8.1.1
hypothesis : 6.100.1
sphinx : None
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 : None
pandas_gbq : None
pyarrow : 16.1.0
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

[Aloqeely]

This looks like an issue with pyarrow.

In [2]: import pyarrow as pa

In [3]: s = pd.Series(pd.date_range('2000', periods=3, freq='15ms'))

In [4]: pa.Array.from_pandas(s)
Out[4]:
<pyarrow.lib.TimestampArray object at 0x00000183A8E793C0>
[
  2000-01-01 00:00:00.000000000,
  2000-01-01 00:00:00.015000000,
  2000-01-01 00:00:00.030000000
]

In [5]: pa.compute.microsecond(pa.Array.from_pandas(s))
Out[5]:
<pyarrow.lib.Int64Array object at 0x00000183A8E7BDC0>
[
  0,
  0,
  0
]

[MarcoGorelli]

thanks for checking - looks like it's not a bug in pyarrow, but just that pyarrow's method does something different

https://arrow.apache.org/docs/python/generated/pyarrow.compute.microsecond.html

Millisecond returns number of microseconds since the last full millisecond

So, I think this needs working around in pandas