BUG: Groupby.transform on datetime with nunique giving incorrect result

[joelowj]

• 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.

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Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

# Your code here
>>> import pandas as pd
>>> bdates = pd.bdate_range('2008-12-31', '2011-12-30')
>>> df = pd.DataFrame(bdates, columns=['date'])
>>> df['year'] = df['date'].dt.year
>>> df.groupby('year')['date'].transform('nunique')
0     1970-01-01 00:00:00.000000001
1     1970-01-01 00:00:00.000000261
2     1970-01-01 00:00:00.000000261
3     1970-01-01 00:00:00.000000261
4     1970-01-01 00:00:00.000000261
                   ...             
778   1970-01-01 00:00:00.000000260
779   1970-01-01 00:00:00.000000260
780   1970-01-01 00:00:00.000000260
781   1970-01-01 00:00:00.000000260
782   1970-01-01 00:00:00.000000260
Name: date, Length: 783, dtype: datetime64[ns]
>>> 

Problem description

The current behaviour returns the sum of default timestamp with the number of unique count of dates in each year. The expected output should have been the number of unique counts for the date in each year which is consistent with the behavior of other Groupby.transform functions.

Expected Output

# Your code here
>>> import pandas as pd
>>> bdates = pd.bdate_range('2008-12-31', '2011-12-30')
>>> df = pd.DataFrame(bdates, columns=['date'])
>>> df['year'] = df['date'].dt.year
>>> df.groupby('year')['date'].transform('nunique')
0     1
1     261
2     261
3     261
4     261
                   ...             
778   260
779   260
780   260
781   260
782   260
Name: date, Length: 783, dtype: int
>>> 

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.181-140.257.amzn2.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1.post20200604
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None

[erfannariman]

Tested this on master and can reproduce.