Description
Code Sample, a copy-pastable example if possible
import pandas as pd
df = pd.DataFrame({'name': ['A', 'A', 'A', 'B', 'B'], 'a':[1,2,3,4,5], 'date': ['2016', '2017', '2018', '2016', '2017']}, )
df['date'] = pd.to_datetime(df['date'])
#current behaviour
df.groupby('name').idxmin()
>> a date
>> name
>> A 0 1970-01-01 00:00:00.000000000
>> B 3 1970-01-01 00:00:00.000000003Problem description
The returning df's dtypes would be the same as that of index.
Thanks.
Expected Output
# expected behaviour
df.groupby('name').idxmin().astype('int')
>> a date
>> name
>> A 0 0
>> B 3 3Output of pd.show_versions()
[paste the output of pd.show_versions() here below this line]
INSTALLED VERSIONS
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Darwin
OS-release: 18.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: ja_JP.UTF-8
LOCALE: ja_JP.UTF-8
pandas: 0.23.4
pytest: 4.0.1
pip: 18.1
setuptools: 40.6.2
Cython: 0.29
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.11
xlrd: 1.1.0
xlwt: 1.2.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None