str.extract output assignment behavior inconsisent and broken in some cases
```python
import pandas as pd
aval = ['A','B','C']
bval = ['[0-10)','[10-20)','[20-30)']

df = pd.DataFrame({'A':aval, 'B':bval})

#case1
#df.loc[:,'num'] = df['B'].str.extract( r'^\[([0-9]{1,2})' )#fails

#case2
#df['num'] = df['B'].str.extract( r'^\[([0-9]{1,2})' )#works

#case3  
df.loc[:,'num']=''
df.loc[:,'num'] = df['B'].str.extract( r'^\[([0-9]{1,2})' ) #works

print(df)

```
#### Problem description
Assigning the extraction of groups to a column fails if using the .loc method. The column is filled with NaN, regardless of if there is a match or not. Pre-filling the column with empty strings 'fixes' it.

This behavior is strange, and I spent a long time trying to figure out what is wrong, as it does not raise an error or anything.

### case 1 output
```
   A        B  num
0  A   [0-10)  NaN
1  B  [10-20)  NaN
2  C  [20-30)  NaN
```
#### Expected case 1 Output
```
   A        B num
0  A   [0-10)   0
1  B  [10-20)  10
2  C  [20-30)  20
```
#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None

pandas: 0.24.1
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.0
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
