ENH: gb.is_monotonic_increasing
#### Code Sample, a copy-pastable example if possible

```python
source_dict = {
    'A': ['foo', 'bar', 'baz', 'foo', 'bar', 'baz'],
    'B': ['cat_a', 'cat_a', 'cat_a', 'cat_b', 'cat_b', 'cat_b'],
    'C': [1, 2, 3, 2, 2, 0]
}

example = pd.DataFrame(source_dict)
example.groupby(['B']).C.apply(lambda x: x.is_monotonic_decreasing)

# Output:
# B
# cat_a    False
# cat_b     True
# Name: C, dtype: bool
```
#### Problem description

In order to check which groups are monotonically increasing or decreasing, you can currently use gb.apply(lambda x: x.is_monotonic_increasing). 

What I'm suggesting is that Series.is_monotonic_increasing be extended to groupby, so there would be a groupby.is_monotonic_increasing() method, which would return the same output as the apply.

I'm not sure if extending a Series property to a Groupby method is something that makes sense for Pandas, and I haven't contributed to Pandas before, but I wanted to ask.

#### Expected Output

```
example.groupby(['B']).C.is_monotonic_decreasing()
# Output:
# B
# cat_a    False
# cat_b     True
# Name: C, dtype: bool
```

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 16.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.20.2
pytest: 2.9.2
pip: 9.0.1
setuptools: 28.3.0
Cython: 0.24.1
numpy: 1.13.0
scipy: 0.19.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_gbq: None
pandas_datareader: None

</details>
