Pandas groupby sum misbehaves when one of the columns has string objects.
#### Code Sample, a copy-pastable example if possible

```python
df = pd.DataFrame({'A': 'a b c'.split(), 'B': [1,2,3], 'C': [4,6, 5]})
df['B'] = np.nan
df.groupby(lambda x:x, axis=1).sum(min_count=1)
```
#### Problem description
The output for the above code should return NaN for the column 'B' of the DataFrame when min_count=1 as mentioned in [DataFrame.sum doc.](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sum.html), but it is returning 0 instead. 

#### Actual Output
```
	A	B	C
0	a	0.0	4
1	b	0.0	6
2	c	0.0	5
```
#### Expected Ouput
```
	A	B	 C
0	a	NaN	 4
1	b	NaN	 6
2	c	NaN	 5
```
So, I decided to remove the column with strings, then it returns NaN for the column 'B'. However, I think the columns in the Dataframe should be independent to each other. It seems like a bug.

```python
del df['A']
df.groupby(lambda x:x, axis=1).sum(min_count=1)
```
#### Output
```
	B	C
0	NaN	4.0
1	NaN	6.0
2	NaN	5.0
```
#### Output of ``pd.show_versions()``

<details>

pandas: 0.23.4
pytest: 4.0.1
pip: 8.1.1
setuptools: 40.6.2
Cython: 0.29.1
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.11.1
xarray: 0.11.0
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: 1.6.2
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 0.999
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.2.0
pandas_gbq: None
pandas_datareader: None

</details>
