Gropuby using pd.Series.mode throws error when `by` column contains values with same starting value
#### Groupby with pd.Series.mode throws error, if `by` values starts with a value from previous row.

```python
df2 = pd.DataFrame({
    "Name" : ['Thomas', 'Thomas', 'Thomas John'],
    "Credit" : [1200, 1300, 900],
    "Mood" : ['sad', 'happy', 'happy']
})
aggrFDColumnDetails = {
   'Mood':pd.Series.mode,
   'Credit':'sum'
}
df2.groupby(['Name']).agg(aggrFDColumnDetails)

```
#### Problem description
When I execute the above code I got error 
```
Exception: Must produce aggregated value
.....
....
and a lot of stack traces..
```
If I change the third name to John instead of Thomas John, it works as expected. ie The following code works.
```python
df2 = pd.DataFrame({
    "Name" : ['Thomas', 'Thomas', 'John'],
    "Credit" : [1200, 1300, 900],
    "Mood" : ['sad', 'happy', 'happy']
})
aggrFDColumnDetails = {
   'Mood':pd.Series.mode,
   'Credit':'sum'
}
df2.groupby(['Name']).agg(aggrFDColumnDetails)
```

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#### Expected Output
Groupby should work, even if first letters of column values are same
#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.16.final.0
python-bits: 64
OS: Darwin
OS-release: 19.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.23.3
pytest: None
pip: 10.0.1
setuptools: 40.0.0
Cython: None
numpy: 1.14.5
scipy: 0.13.0b1
pyarrow: None
xarray: None
IPython: 5.7.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
