Resample bug with sparse indexes. Incorrectly assumes upsample
``` python
from pandas import *

dr = date_range(start='1/1/2012', freq='5min', periods=1000)
s = Series(np.array(100), index=dr)
# subset the data. 
s = s[s.index.hour < 7]
# len(s) is now 336


s.resample('10min', how=lambda x: len(x))
# Wrong, thinks we're upsampling
#2012-01-01 00:00:00    100
#2012-01-01 00:15:00    100
#2012-01-01 00:30:00    100
#2012-01-01 00:45:00    100
#2012-01-01 01:00:00    100

# 30min index has less labels.
s.resample('30min', how=lambda x: len(x))
# Right
#2012-01-01 00:00:00    1
#2012-01-01 00:30:00    6
#2012-01-01 01:00:00    6
#2012-01-01 01:30:00    6
#2012-01-01 02:00:00    6
```

What's going on here is that resample tests whether you are down/up sampling by the len of label arrays. If your source data is ordered but sparse, the resampling will sometimes assume you're up-sampling when you're not. 

I run into this with intraday finance data since mine is restricted to trading hours. 
