BUG: DataFrame constructor incorrect with Series input depending on name

[JohnNapier]

DataFrame constructor example:

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.

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:

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

[TomAugspurger]

Looks like it's more of an issue w/ the call to the DataFrame constructor. With

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

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

we get

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

[JohnNapier]

@TomAugspurger, thanks for narrowing the issue to the constructor.

[TomAugspurger]

@JohnNapier, I changed the title and added an example to the original post. Let me know if you still have trouble with the join. FYI, the Series method .to_frame() should work. I'll work on a fix for the bug.

[JohnNapier]

@TomAugspurger, thanks for the help and the tip!