adding columns to a dataframe fills in with null values
#### Should adding dataframes yield a dataframe with all the columns of the added dataframes?
First I simply create two dataframes:
```python
import pandas as pd
import numpy as np
df1 = pd.DataFrame({'A':[4,5,6,7],'B':[10,20,30,40], 'C':[100,50,-30,-50]})
new_data = np.random.rand(4,3)
df2 = pd.DataFrame(new_data, columns=['I','II','III'], index=df1.index)
df_new = df1 + df2
```
I thought I would get a nice new dataframe ```df_new```, when adding ``` df1 + df2``` but it is filled with null values:
```python
In [63]: df_new
Out[63]: 
    A   B   C   I  II  III
0 NaN NaN NaN NaN NaN  NaN
1 NaN NaN NaN NaN NaN  NaN
2 NaN NaN NaN NaN NaN  NaN
3 NaN NaN NaN NaN NaN  NaN
```
#### Problem description
Is this a problem? The add `df1.add(df2)` method also gives the _same wrong_ result.

#### Expected Output
I thought I would get the same as using ```.assign()``` , and ```pd.concat()``` namely when:
```python
dfnew=df1.assign(I=new_data[:,0])
dfnew=dfnew.assign(II=new_data[:,1])
dfnew=dfnew.assign(III=new_data[:,2])
```
or 

```python
dfnew =pd.concat((df1, df2), axis=1)
```
**then the output is correct** as far as I understand the workings of pandas:
```python
In [107]: dfnew
Out[107]: 
   A   B    C         I        II       III
0  4  10  100  0.980611  0.492488  0.260895
1  5  20   50  0.607885  0.884491  0.689950
2  6  30  -30  0.068171  0.173972  0.890535
3  7  40  -50  0.348082  0.827598  0.081198
```

#### other things I don't get
Why this output:
```python
In [79]: df1 + df2['I']
Out[79]: 
    A   B   C   0   1   2   3
0 NaN NaN NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN NaN
```

I tried also ```.append()``` method, but I get this:
```python
In [86]: df1.append(df2)
Out[86]: 
     A     B      C         I        II       III
0  4.0  10.0  100.0       NaN       NaN       NaN
1  5.0  20.0   50.0       NaN       NaN       NaN
2  6.0  30.0  -30.0       NaN       NaN       NaN
3  7.0  40.0  -50.0       NaN       NaN       NaN
0  NaN   NaN    NaN  0.166225  0.883279  0.520274
1  NaN   NaN    NaN  0.358485  0.689133  0.757827
2  NaN   NaN    NaN  0.311398  0.015805  0.079101
3  NaN   NaN    NaN  0.948523  0.315923  0.888804
```

although the index is the same!?
```python
In [89]: df1.index==df2.index
Out[89]: array([ True,  True,  True,  True], dtype=bool)
```
#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Darwin
OS-release: 16.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.19.0
statsmodels: 0.8.0
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
boto: 2.46.1
pandas_datareader: 0.3.0.post
</details>
