DataFrame groupby.first() is much slower than groupby.nth(0)
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd, numpy as np
df1 = pd.DataFrame({'c1':np.concatenate([np.arange(0,10000),np.arange(0,10000)]),'c2':1,'c3':'a'})

# groupby.nth(0) runs in ~11ms
%timeit x1 =df1.groupby(['c1']).nth(0, dropna='all') 
# groupby.first() runs in ~700ms
%timeit  x2 = df1.groupby(['c1']).first()

```
#### Problem description

groupby.first() takes much longer to run than groupby.nth(0), even though both operations should be equivalent. Is there any reason why the current implementation of first() shouldn't just be replaced with a call to nth()?

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

pandas: 0.22.0
pytest: None
pip: 9.0.1
setuptools: 36.5.0
Cython: 0.23.4
numpy: 1.14.0
scipy: 1.0.0
pyarrow: 0.8.0
xarray: None
IPython: 5.5.0
sphinx: 1.5.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.5
feather: 0.4.0
matplotlib: 2.1.2
openpyxl: 2.4.1
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: None
html5lib: 1.0b10
sqlalchemy: 1.1.2
pymysql: None
psycopg2: 2.7.3.2 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
