BUG: groupby.rank  results do not scale to 100% for dense
#### Code Sample

```python
In [61]: df_test = pd.DataFrame({"A":[1,1,2,2],"B":[1,1,1,1]})

In [62]: df_test.groupby("B").rank(method="dense", ascending=True, pct=False, na_option='top')
Out[62]:
     A
0  1.0
1  1.0
2  2.0
3  2.0

In [63]: df_test.groupby("B").rank(method="dense", ascending=True, pct=True, na_option='top')
Out[63]:
      A
0  0.25
1  0.25
2  0.50
3  0.50
```
#### Problem description
``pd.groupby.rank`` result does not scale to 100% when method is "dense". 
#### Expected Output
``` python
In [65]: df_test['A'].rank(method="dense", ascending=True, pct=True, na_option='top')
Out[65]:
0    0.5
1    0.5
2    1.0
3    1.0
Name: A, dtype: float64
```
#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: 448124c138dc39001638aacd68f253b1034d7f04
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: zh_CN.UTF-8
LOCALE: None.None

pandas: 0.23.0.dev0+743.g448124c13
pytest: 3.3.2
pip: 9.0.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.7
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.4.10
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: 0.7.11.None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
