BUG: Resample upsampling return NaNs
Pandas resample bugs when upsampling a time serie with same size splits :

For instance, I have a time serie of **size 10**:

```
rng = pd.date_range('20130101',periods=10,freq='T')
ts=pd.Series(np.random.randn(len(rng)), index=rng)
```

`print(ts)`

```
2013-01-01 00:00:00   -1.811999
2013-01-01 00:01:00   -0.890837
2013-01-01 00:02:00   -0.363520
2013-01-01 00:03:00   -0.026245
2013-01-01 00:04:00    1.515072
2013-01-01 00:05:00    0.920129
2013-01-01 00:06:00   -0.125954
2013-01-01 00:07:00    0.588933
2013-01-01 00:08:00   -1.278408
2013-01-01 00:09:00   -0.172525
Freq: T, dtype: float64
```

When trying to resample in **N > 10** parts it doesn't work:

```
from datetime import timedelta
length = 11
timeSpan = (ts.index[-1]-ts.index[0]+timedelta(minutes=1))
rule = int(timeSpan.total_seconds()/length)
tsNew=ts.resample(str(rule)+"S").mean()
```

`print(tsNew)`

```
2013-01-01 00:00:00    1.845181
2013-01-01 00:00:54         NaN
2013-01-01 00:01:48         NaN
2013-01-01 00:02:42         NaN
2013-01-01 00:03:36         NaN
2013-01-01 00:04:30         NaN
2013-01-01 00:05:24         NaN
2013-01-01 00:06:18         NaN
2013-01-01 00:07:12         NaN
2013-01-01 00:08:06         NaN
2013-01-01 00:09:00   -0.997419
Freq: 54S, dtype: float64
```

Note: here is my versions:
`pd.show_versions()`

```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Darwin
OS-release: 14.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.15.2
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.5.0
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.1
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: 0.6.3.None
psycopg2: None
```

Thank you for your help
