apply loop adds an imaginary row
The following code illustrates the issue:

``` python
import pandas
from pandas import Timestamp

test_data = {'A': {Timestamp('2015-05-05 19:59:09'): 90.659, Timestamp('2015-05-22 19:59:02'): 90.810},
             'B': {Timestamp('2015-05-05 19:59:09'): 100.0, Timestamp('2015-05-22 19:59:02'): 100.0}}

def disp(row):
    print('processing row:\n%s' % row)
    return pandas.Series({'test': 1})

test_data_df = pandas.DataFrame(test_data)
print(test_data_df.apply(disp, axis=1))
```

Since there are only 2 rows, "processing row" should only appear twice, but instead it appears 3 times. In fact it is called twice for the first row: the first time with the index timestamp localized, the second time with no localization.
## Execution result

```
processing row:
A     90.659
B    100.000
Name: 2015-05-05T21:59:09.000000000+0200, dtype: float64
processing row:
A     90.659
B    100.000
Name: 2015-05-05 19:59:09, dtype: float64
processing row:
A     90.81
B    100.00
Name: 2015-05-22 19:59:02, dtype: float64
                     test
2015-05-05 19:59:09     1
2015-05-22 19:59:02     1
```
## INSTALLED VERSIONS

```
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.17.0
nose: None
pip: 7.1.2
setuptools: 18.4
Cython: None
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: None
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.7
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
pandas version: None
```
