Merge Issues
I recently upgraded from pandas 0.90 to 0.10.1 and it seems a merge I was using is now broken.  For example, I have two dataframes that I am left merging on a Timestamp typed column.  

The first dataframe has three columns: Timestamp, float, Timestamp:

df1 = pd.DataFrame({'time':[pd.Timestamp('2013-01-01'),pd.Timestamp('2013-01-02')],'val':[15.6, 15.9], 'expiry':[pd.Timestamp('2013-01-16'), pd.Timestamp('2013-01-16')]})

The second DataFrame has three columns: Timestamp, Timestamp. float:

df2 = pd.DataFrame({'expiry':[pd.Timestamp('2013-01-16'), pd.Timestamp('2013-02-13')], 'badjfact':[.98, .78], 'volmat':[pd.Timestamp('2013-02-15'), pd.Timestamp('2013-03-15')]})

Merging these works using the following command:

> > > pd.merge(df1, df2, on='expiry', how='left')
> > >                expiry                time   val  badjfact              volmat
> > > 0 2013-01-16 00:00:00 2013-01-01 00:00:00  15.6      0.98 2013-02-15 00:00:00
> > > 1 2013-01-16 00:00:00 2013-01-02 00:00:00  15.9      0.98 2013-02-15 00:00:00

However, when I change the 'val' column in DataFrame 1 to a string, (or if I add a new string column to DataFrame 1), the merge will then have NaN values from the second dataframe:

df1 = pd.DataFrame({'time':[pd.Timestamp('2013-01-01'),pd.Timestamp('2013-01-02')],'val':['test1', 'test2'], 'expiry':[pd.Timestamp('2013-01-16'), pd.Timestamp('2013-01-16')]})

Broken output:

> > > pd.merge(df1, df2, on='expiry', how='left')
> > >                 expiry                 time    val  badjfact  volmat
> > > 0  2013-01-16 00:00:00  2013-01-01 00:00:00  test1       NaN     NaT
> > > 1  2013-01-16 00:00:00  2013-01-02 00:00:00  test2       NaN     NaT
