merge does not preserve order of data frame when left key has duplicate values 
```
import pandas as pd
dates = [  20140101, 20140102, 20140103]
states = [  "CA",  "NY", "CA"]
x = pd.DataFrame({ 'dates' : dates, 'states' : states })

#y = pandas.DataFrame({ 'state' : [ 'CA', 'OR' ], 'value' : [ 1, 2]})
y = pd.DataFrame({ 'states' : [ "CA", "NY" ], 'stateid' : [ 1, 2]})
z = pd.merge(x, y, how='left', on='states',)


x=
    dates   states
0   20140101    CA
1   20140102    NY
2   20140103    CA

y=
        stateid     states
0   1   CA
1   2   NY

z=   dates  states  stateid
0   20140101    CA  1
1   20140103    CA  1
2   20140102    NY  2
```

---

Note z is always sorted by "states" column whether argument sort=True or False.
This only happens when the x's states column is not unique. If x.states is unique(such as NY, CA, CT), sort=True and False behaves as expected.

This causes inconvenience when x is a time series and merge does not
preserve the time sequence.
