Chained assignment yields unexpected result with timedelta64 values (np v1.8, pd v0.13.1)
It seems to be possible to create a broken object by assigning to a slice of a column of dtype `timedelta64`.  In this object, there seem to be two separate copies of a column, one of which is obtained when that column is selected _by name_ and the other of which is visible to all of the `DataFrame`'s other methods.

``` python

import numpy as np
import pandas as pd

temp = []
temp.append(pd.DataFrame({'A': np.arange(4) + np.timedelta64('NaT')},
                         index=pd.date_range('2014-1-1', periods=4, freq='D')))
temp.append(pd.DataFrame({'A': np.arange(4) + np.timedelta64('NaT')},
                         index=pd.date_range('2014-1-1', periods=4, freq='D')))
temp.append(pd.DataFrame({'A': np.arange(4) + np.timedelta64('NaT')},
                         index=pd.date_range('2014-1-1', periods=4, freq='D')))
temp.append(pd.DataFrame({'A': np.arange(4) + np.timedelta64(1000000000)},
                         index=pd.date_range('2014-1-1', periods=4, freq='D')))
temp.append(pd.DataFrame({'A': np.arange(4) + np.nan},
                         index=np.arange(4)))

  # broken, see below
temp[0]['A'][:-1] = np.diff(temp[0].index.values)
  # using a series instead of an ndarray does not change the behavior
temp[1]['A'][:-1] = pd.Series(np.diff(temp[1].index.values), index=temp[1].index[:-1])
  # avoiding chained assignment *does* change the behavior
temp[2]['A'] = pd.Series(np.diff(temp[2].index.values), index=temp[2].index[:-1])
  # avoiding NaT does not change the behavior
temp[3]['A'][:-1] = np.diff(temp[3].index.values)
  # avoiding timedeltas *does* change the behavior
temp[4]['A'][:-1] = np.diff(temp[4].index.values)

for idx, x in enumerate(temp):
    print "temp[%d]:" %idx
    print x
    print x['A']
    print x.A
    print x.loc[:,'A']
    print x.iloc[:,0]
    print x.iloc[0,:].loc['A']
    print x.loc[:,'A'].iloc[0]

# temp[0]:
#              A
#2014-01-01 NaT
#2014-01-02 NaT
#2014-01-03 NaT
#2014-01-04 NaT
# 
# [4 rows x 1 columns]
#2014-01-01   1 days
#2014-01-02   1 days
#2014-01-03   1 days
#2014-01-04      NaT
# Freq: D, Name: A, dtype: timedelta64[ns]
#2014-01-01   1 days
#2014-01-02   1 days
#2014-01-03   1 days
#2014-01-04      NaT
# Freq: D, Name: A, dtype: timedelta64[ns]
#2014-01-01   1 days
#2014-01-02   1 days
#2014-01-03   1 days
#2014-01-04      NaT
# Freq: D, Name: A, dtype: timedelta64[ns]
#2014-01-01   NaT
#2014-01-02   NaT
#2014-01-03   NaT
#2014-01-04   NaT
# Freq: D, Name: A, dtype: timedelta64[ns]
# NaT
#86400000000000 nanoseconds
# ...

pd.show_versions()
# 
# INSTALLED VERSIONS
# ------------------
# commit: None
# python: 2.7.5.final.0
# python-bits: 64
# OS: Darwin
# OS-release: 13.2.0
# machine: x86_64
# processor: i386
# byteorder: little
# LC_ALL: None
# LANG: en_US.UTF-8
# 
# pandas: 0.13.1
# Cython: None
# numpy: 1.8.0
# scipy: 0.12.1
# statsmodels: None
# IPython: 1.1.0
# sphinx: None
# patsy: None
# scikits.timeseries: None
# dateutil: 2.2
# pytz: 2013.9
# bottleneck: None
# tables: None
# numexpr: None
# matplotlib: 1.3.0
# openpyxl: None
# xlrd: None
# xlwt: None
# xlsxwriter: None
# sqlalchemy: None
# lxml: None
# bs4: None
# html5lib: None
# bq: None
# apiclient: None
```
