Dataframe column filled with .sum() of int-series has dtype not int64, but object
#### MnWE

```python
import pandas as pd

the_int_column = pd.Series([1, 2, 3])
foobar = pd.DataFrame(columns=("ints",))
foobar.loc["First"] = [the_int_column.sum()]

print(foobar.dtypes)

the_float_column = pd.Series([1.0, 2.0, 3.0])
floatbar = pd.DataFrame(columns=("floats",))
floatbar.loc["First"] = [the_float_column.sum()]

print(floatbar.dtypes)
```
This yields
````python
ints    object
dtype: object
floats    float64
dtype: object
````

#### Problem description
Assigning the sum of a Series of (vanilla Python) `int`s to a DataFrame leads to not specific enough type inference on the frame: it keeps the object type. Vanilla `float`s work fine, as the second example demonstrates.

This is an issue for some testing I'm doing, where I want to ensure a column of these sums contains only numeric data, and specifically integers.

Might be related to #20635. 

#### Expected Output
Expected output:
````python
ints       int64
dtype: object
floats    float64
dtype: object
````

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.22.0
pytest: 3.3.2
pip: 9.0.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.4.10
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
