BUG: unique() casts its types' elements from pd.Timestamp to numpy.datetime64

[SebastianoX]

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

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

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Code Sample

import pandas as pd 
 
 
df = pd.DataFrame({"date": ["2019-02-10", "2019-02-11"]}) 
df["date"] = pd.to_datetime(df["date"]) 
 
print("Date Types in column date:") 
for day in df["date"]: 
    print(type(day))  # pandas._libs.tslibs.timestamps.Timestamp 
 
print("Unique date Types in column date:") 
for day in df["date"].unique():  
    print(type(day))  # np.datetime64 

The code above results in:

Date Types in column date:
<class 'pandas._libs.tslibs.timestamps.Timestamp'>
<class 'pandas._libs.tslibs.timestamps.Timestamp'>

Unique date Types in column date:
<class 'numpy.datetime64'>
<class 'numpy.datetime64'>

Problem description

The class method unique() should not change the type of the elements in the original column.

Expected Output

Date Types in column date:
<class 'pandas._libs.tslibs.timestamps.Timestamp'>
<class 'pandas._libs.tslibs.timestamps.Timestamp'>

Unique date Types in column date:
<class 'pandas._libs.tslibs.timestamps.Timestamp'>
<class 'pandas._libs.tslibs.timestamps.Timestamp'>

INSTALLED VERSIONS

commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8

pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None

Note

Issue related to #35448, where in the thread I have been asked to re-create it with a clearer OP.

[TomAugspurger]

I'm not sure there's anything to do here. The array type for datetime64[ns] is an ndarray[datetime64[ns]].

In [23]: df['date'].values
Out[23]:
array(['2019-02-10T00:00:00.000000000', '2019-02-11T00:00:00.000000000'],
      dtype='datetime64[ns]')

[TomAugspurger]

I see from #35448 there's some discussion around making Series.unique always return an extension array (at least for datetimes?). I'm not sure about that either.

[mroeschke]

Yeah it appears that this is the intended behavior and not a bug as storing Timestamp objects in an ndarray would be a large change and inefficient (especially for a tz-naive datetime). Thanks for the report but closing as the expected behavior