BUG: DataFrame constructor incorrect with Series input depending on name
DataFrame constructor example:

``` python
In [55]: x = pd.Series(range(5), name=1)

In [56]: y = pd.Series(range(5), name=0)

In [57]: pd.DataFrame(x, columns=[0])
Out[57]:
Empty DataFrame
Columns: [0]
Index: []

In [58]: pd.DataFrame(x, columns=[1])
Out[58]:
   1
0  0
1  1
2  2
3  3
4  4

```

and for y, the name and columns don't have to match for it to construct.

```
In [59]: pd.DataFrame(y, columns=[0])
Out[59]:
   0
0  0
1  1
2  2
3  3
4  4

In [60]: pd.DataFrame(y, columns=[1])
Out[60]:
   1
0  0
1  1
2  2
3  3
4  4
```

---

Using Pandas 0.15.2-1. After renaming a column in a dataframe, a .join operation fails.

``` python
import numpy as np,pandas as pd
df1=pd.DataFrame(range(1,1001)).rename(columns={0:1})
a=np.log(df1[1])
df2=df1.join(pd.DataFrame(a,columns=[0]))
```

The second column contains all NaN, instead of the values from _a_. The same can be accomplished by other means, but I believe the above should work. For example, the following works:

``` python
df1=pd.DataFrame(range(1,1001))
a=np.log(df1[0])
df2=df1.join(pd.DataFrame(a,columns=[2]))
```
