Prefer values over NaN when adding DataFrames
#### Code Sample, a copy-pastable example if possible

```python
import numpy as np
import pandas as pd

# Create DataFrames to play with
dates = pd.date_range("20170101", periods=10, freq="S")
df_a = pd.DataFrame(np.random.randn(10,2), index=dates, columns=list("AB"))
df_a = df_a.abs() * np.random.randint(0, 1000)
dates = pd.date_range("20170101", periods=5, freq="S")
df_b = pd.DataFrame(np.random.randn(5,2), index=dates, columns=list("AB"))
df_b = df_b.abs() * np.random.randint(0, 1000)
df_c = pd.DataFrame(np.random.randn(5,1), index=dates, columns=list("A"))
df_c = df_c.abs() * np.random.randint(0, 1000)
df_c["B"] = np.nan

# Have a look-see
print df_a; print 
print df_b

# Shorter index wins, everything else is NaN'd.
print df_a.add(df_b)

# NaNs wins.
print df_b.add(df_c)

# Concat works.
print pd.concat([df_a, df_b, df_c], axis=1)["B"].sum(axis=1)

df = pd.concat([df_a, df_b, df_c], axis=1)
series_dict = {}
for col in df.columns.unique():
  series_dict[col] = df[col].sum(axis=1)
df = pd.DataFrame(series_dict)

print df

```
#### Problem description

Adding DFs of varying lengths and with missing data results in missing data.
1. The shorter index is used and all values beyond are simply NaN'd.
2. If one of the two series values is NaN, NaN wins.

#### Expected Output
1. The longer index DF should be used, filling the shorter DF with NaN.
2. If a value is present, it should be used instead of NaN.

I've found DataFrame.concat to be a useful workaround.  It will result in a DF with multiple columns having the same key.  The unique columns can be iterated over, building a dictionary of series.  This can then be used to create a new DataFrame.  There's probably more elegant solutions, but it works.

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 32.2.0
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None

</details>
