pd.core.groupby.groupby.DataFrameGroupBy.nth yielding false values with Interval MultiIndex Interval
Hi,

using a solution for my problem posted on [SO](https://stackoverflow.com/questions/53667685/categorize-data-preserving-all-values), I stumbled upon this bug. Thanks @jorisvandenbossche for looking into that matter and confirming this to be a bug.

```python
np.random.seed(123)
df = pd.DataFrame({'temp_a': np.random.rand(50) * 50 + 20,
                   'temp_b': np.random.rand(50) * 30 + 40,
                   'power_deg': np.random.rand(50),
                   'eta': 1 - np.random.rand(50) / 5},
                  index=pd.date_range(start='20181201', freq='T', periods=50))

df_grpd = df.groupby(
    [pd.cut(df.temp_a, np.arange(0, 100, 5)),  # categorical for temp_a
     pd.cut(df.temp_b, np.arange(0, 100, 5)),   # categorical for temp_b
     pd.cut(df.power_deg, np.arange(0, 1, 1 / 20))  # categorical for power_deg
    ])

df_grpd.nth(1).T
# Out:
temp_a       (65, 70]
temp_b       (50, 55]
power_deg (0.8, 0.85]
temp_a      36.147946
temp_b      57.832956
power_deg    0.983631
eta          0.974274

df_grpd.nth(0).loc[(pd.Interval(65, 70), pd.Interval(50, 55), pd.Interval(0.8, 0.85))].T

# Out:
temp_a       (65, 70]            
temp_b       (50, 55]            
power_deg (0.8, 0.85] (0.8, 0.85]
temp_a      69.038210   46.591379
temp_b      52.510666   57.173709
power_deg    0.846506    0.988345
eta          0.867026    0.885187
```

#### Problem description
When using `groupby` with `df.cut` it seems that taking the n-th row of a group with `df_grpd.nth(n)` with `n > 1` results in values which lie out of the group boundaries, as shown with `df_grpd.nth(1).T` in my code example. Furthermore sometimes there are multiple rows per group for `n=0`, as shown with `
df_grpd.nth(0).loc[(pd.Interval(65, 70), pd.Interval(50, 55), pd.Interval(0.8, 0.85))].T`, also with values outside of the interval.

#### Expected Output
I guess the expected output is quite clear in this case... 
For `df_grpd.nth(1).T`:
```
temp_a       (65, 70]
temp_b       (50, 55]
power_deg (0.8, 0.85]
temp_a      66.147946  # some values within the interval...
temp_b      53.832956  # some values within the interval...
power_deg    0.833631  # some values within the interval...
eta          0.974274
```

#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None

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: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.1
openpyxl: 2.5.11
xlrd: 1.1.0
xlwt: 1.3.0
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
</details>

Is there currently any workaround for this problem? Can I somehow access the group values in any other way?
Thanks in advance!