BUG: Inconsistent behavior of Series.values with Series.array or Series.to_numpy() #55128
Description
Pandas version checks
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I have checked that this issue has not already been reported.
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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
import pandas series = pandas.Series([pandas.Timestamp("2016-01-01", tz="US/Eastern") for _ in range(3)]) print(series.values) # ['2016-01-01T05:00:00.000000000' '2016-01-01T05:00:00.000000000' # '2016-01-01T05:00:00.000000000'] print(series.array) # <DatetimeArray> # ['2016-01-01 00:00:00-05:00', '2016-01-01 00:00:00-05:00', # '2016-01-01 00:00:00-05:00'] # Length: 3, dtype: datetime64[ns, US/Eastern] print(series.to_numpy()) # [Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern') # Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern') # Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')]Issue Description
.values output does not match .array output neither .to_numpy() output
Expected Behavior
In this case, the expected behavior is when the output of .value matches the output of .array (my guess).
Installed Versions
INSTALLED VERSIONS ------------------ commit : ba1cccd19da778f0c3a7d6a885685da16a072870 python : 3.9.17.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.22621 machine : AMD64 processor : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : English_United States.1252 pandas : 2.1.0 numpy : 1.25.2 pytz : 2023.3 dateutil : 2.8.2 setuptools : 68.0.0 pip : 23.2.1 Cython : None pytest : 7.4.0 hypothesis : None sphinx : 7.1.1 blosc : None feather : None xlsxwriter : None lxml.etree : 4.9.3 html5lib : None pymysql : None psycopg2 : 2.9.6 jinja2 : 3.1.2 IPython : 8.12.2 pandas_datareader : None bs4 : 4.12.2 bottleneck : None dataframe-api-compat: None fastparquet : 2023.7.0 fsspec : 2023.6.0 gcsfs : None matplotlib : 3.7.2 numba : None numexpr : 2.8.4 odfpy : None openpyxl : 3.1.2 pandas_gbq : 0.15.0 pyarrow : 11.0.0 pyreadstat : None pyxlsb : None s3fs : 2023.6.0 scipy : 1.11.2 sqlalchemy : 2.0.20 tables : 3.8.0 tabulate : None xarray : None xlrd : 2.0.1 zstandard : None tzdata : 2023.3 qtpy : 2.3.1 pyqt5 : None