BUG: Converting Index/Series to numpy array does not convert pyarrow datetime/timedelta types.

[randolf-scholz]

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

import pandas as pd
import numpy as np

data = pd.date_range("2020-01-01", "2020-01-04", freq="1D")
data = data.astype("timestamp[us][pyarrow]")
print(np.array(data))

returns

[Timestamp('2020-01-01 00:00:00') Timestamp('2020-01-02 00:00:00')
 Timestamp('2020-01-03 00:00:00') Timestamp('2020-01-04 00:00:00')]

Issue Description

Given that other pyarrow types like int64[pyarrow]/float64[pyarrow] get converted to corresponding numpy types, one would assume the same happens for datetime/timedelta types.

Also, notably, pd.date_range creates an instance of DatetimeIndex, but casting to "timestamp[us][pyarrow]" converts it to a regular Index instance.

Expected Behavior

Converting a "timestamp[pyarrow]"-Index/Series to a numpy array should cast it to numpy.datetime64[ns].

Installed Versions

INSTALLED VERSIONS

commit : 2a953cf
python : 3.11.6.final.0
python-bits : 64
OS : Linux
OS-release : 6.2.0-36-generic
Version : #37~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon Oct 9 15:34:04 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.3
numpy : 1.26.2
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.2.2
pip : 23.3.1
Cython : None
pytest : 7.4.3
hypothesis : None
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.17.2
pandas_datareader : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : 2023.10.0
gcsfs : None
matplotlib : 3.8.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 14.0.1
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.11.3
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None