Bug: Bizarre DataFrame printing inconsistency (REPL only)
Pandas 0.12.0 fails to print a column change in this specific example:

``` python
Python 2.7.3 (default, Apr 10 2013, 05:13:16) 
[GCC 4.7.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> import pandas
>>> length = 5
>>> xx = pandas.DataFrame({'a':np.random.randn(length),'b':np.random.randn(length),'c':np.random.randn(length)})
>>> xx['d']=xx['a']
>>> np.may_share_memory(xx['d'],xx['a'])
False
>>> print xx
          a         b         c         d
0  0.370188  0.437854 -0.020475  0.370188
1  1.839874  0.539278  0.954254  1.839874
2  0.183890 -0.924539 -0.943913  0.183890
3 -2.047403  1.213499 -0.742084 -2.047403
4 -1.003271  0.839525  0.154962 -1.003271
>>> xx['d'][1] = 42
>>> print xx
          a         b         c         d
0  0.370188  0.437854 -0.020475  0.370188
1  1.839874  0.539278  0.954254  1.839874
2  0.183890 -0.924539 -0.943913  0.183890
3 -2.047403  1.213499 -0.742084 -2.047403
4 -1.003271  0.839525  0.154962 -1.003271
>>> quit()
```

Cell D1 should be 42 in the last print statement.

After poking at this a bit, I discovered the following additional required conditions:
1) You must enter this in the Python REPL (as in typing "python" at the command line and entering them manually).  If you put the commands in a file and run the file, it will suddenly do what's expected.
2) The np.may_share_memory() call must be there.  If you omit it, it will suddenly do what's expected.
3) A friend of mine attempted to reproduce this bug on Pandas 0.11.0, but he couldn't get it to work.  Then again, he may have messed up conditions 1 and/or 2 -- he tried it before I realized the previous two conditions.

I am using Pandas '0.12.0' installed from pip on Linux Mint 14.  Here is the (relevant) output from print_versions.py:

```
INSTALLED VERSIONS
------------------
Python: 2.7.3.final.0
OS: Linux 3.5.0-17-generic #28-Ubuntu SMP Tue Oct 9 19:31:23 UTC 2012 x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8

Cython: Not installed
Numpy: 1.6.2
Scipy: 0.10.1
matplotlib: 1.1.1
lxml: 2.3.5
```

I have no idea where the root cause of this bug is -- it may not necessarily be pandas's fault.  I just hope someone else can reproduce it...
