Pandas version checks

  • [X] I have checked that this issue has not already been reported.

  • [X] I have confirmed this bug exists on the latest version of pandas.

  • [X] I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

example_df = pd.DataFrame(
{'event_name': ['a', 'b'],
'event_time': [pd.Timestamp("2020-01-01T03:00:00-07:00"), pd.Timestamp("2020-01-01T04:00-07:00")]})
example_df.head()

 event_name                event_time
0          a 2020-01-01 03:00:00-07:00
1          b 2020-01-01 04:00:00-07:00

example_df[['event_time']].values # fine when DataFrame preserved

array([[Timestamp('2020-01-01 03:00:00-0700', tz='pytz.FixedOffset(-420)')],
       [Timestamp('2020-01-01 04:00:00-0700', tz='pytz.FixedOffset(-420)')]],
      dtype=object)

example_df['event_time'].values # strips timezone when accessed as a Series

array(['2020-01-01T10:00:00.000000000', '2020-01-01T11:00:00.000000000'],
      dtype='datetime64[ns]')



### Issue Description

When a column of timezone-aware timestamps are accessed as a Series (i.e. pulled out with single brackets) the timezone information is stripped. This doesn't happen when the column is accessed as a DataFrame (i.e. pulled out with double brackets).

### Expected Behavior

`example_df['event_time'].values`

`array([[Timestamp('2020-01-01 03:00:00-0700', tz='pytz.FixedOffset(-420)')],
       [Timestamp('2020-01-01 04:00:00-0700', tz='pytz.FixedOffset(-420)')]],
      dtype=object)`


### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.8.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.15.0-1022-aws
Version          : #26~20.04.1-Ubuntu SMP Sat Oct 15 03:22:07 UTC 2022
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : C.UTF-8
LOCALE           : en_US.UTF-8
pandas           : 1.2.3
numpy            : 1.19.2
pytz             : 2022.2
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 52.0.0.post20211006
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.6 (dt dec pq3 ext lo64)
jinja2           : 3.0.3
IPython          : 8.3.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.4
numexpr          : None
odfpy            : None
openpyxl         : 3.0.6
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : 2.0.1
xlwt             : None
numba            : None


</details>


**Comment From: jreback**

you have a really old version

try with 1.5.2

**Comment From: MarcoGorelli**

thanks for the report - if you use `to_numpy()` you'll get

In [3]: example_df['event_time'].to_numpy() Out[3]: array([Timestamp('2020-01-01 03:00:00-0700', tz='pytz.FixedOffset(-420)'), Timestamp('2020-01-01 04:00:00-0700', tz='pytz.FixedOffset(-420)')], dtype=object) ```

closing for now then

.values is discouraged now, check https://pandas.pydata.org/docs/reference/api/pandas.Series.values.html

Comment From: ericbarnhill

Problem replicated with Pandas 1.5.2 . However best resolution as Marco indicates is to switch to .array rather than .values . I will update my code to start using this attribute. Thank you!!