Date type output changes when using df.column.unique()[i]
Apologies if this is somehow expected. I admit I don't know the intricacies of all date types and what each of these is designed to return, but I found the result surprising.

#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
dates = [pd.Timestamp(year=2019, month=m, d=1) for m in range(1, 6)]
df = pd.DataFrame({'date': dates})

>>> df.iloc[2].date
Timestamp('2019-03-01 00:00:00')

>>> df.iloc[2].date.year
2019

>>> df.date.unique()[2]
numpy.datetime64('2019-03-01T00:00:00.000000000')

>>> df.date.unique()[2].year
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'numpy.datetime64' object has no attribute 'year'
```
#### Problem description

I was subsetting data based on unique dates in a data frame. Part of my code used a string formatter, assuming I could access `x.year` when I got the `AttributeError` above.

#### Expected Output

Intuitively, I would expect that any incantation of obtaining values from a data frame should give me the type that's there, not a new type I don't expect.

Again, not being familiar with the intricacies of the various types and what's going on behind the curtains, this could easily be a false assumption and somehow `pd.Timestamp` and `np.datetime64` are more closely related than I understand and the call to `.unique()` is expected to cast one to the other?

#### Output of ``pd.show_versions()``

<details>

>>> pd.show_versions()

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.3.1-arch1-1-ARCH
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.1
numpy            : 1.17.1
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.0.3
setuptools       : 41.2.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.8.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : 2.6.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
</details>
