DataFrame division results inaccurate
From a list of Dates and Values in csv format:

```
2011, 1, 10, 1000000
2011, 1, 11, 998785
2011, 1, 12, 1002940
2011, 1, 13, 1004815
2011, 1, 14, 1009415
...
2011, 12, 14, 1115020
2011, 12, 15, 1113532
2011, 12, 16, 1116016
2011, 12, 19, 1117444
2011, 12, 20, 1133860
```

read in via `numpy.loadtxt(...)` and converted to a DataFrame as follows:

``` python
import numpy as np
from pandas.core.frame import DataFrame as df
import datetime as dt

resultsFile = 'values.csv'

dtResults = np.dtype([('year',np.int),('month',np.int),('day',np.int),('value',np.float)])
results   = np.loadtxt(resultsFile, dtype=dtResults, delimiter=",")
dates     = [dt.datetime(r[0], r[1], r[2], 16) for r in results]
results   = df([{'Date':d,'Portfolio':r[3]} for d,r in zip(dates,results)]).set_index('Date')
```

when performing the following simple daily returns calculation:

``` python
returns = results.diff()[1:]/results
returns.ix[0] = np.zeros(returns.shape[1]) 
```

I end up with the following results:

``` python
                     Portfolio
Date                                    
2011-01-10 16:00:00         0.
2011-01-11 16:00:00  -0.001216
2011-01-12 16:00:00   0.004143
2011-01-13 16:00:00   0.001866
2011-01-14 16:00:00   0.004557
...
2011-12-14 16:00:00  -0.009245
2011-12-15 16:00:00  -0.001336
2011-12-16 16:00:00   0.002226
2011-12-19 16:00:00   0.001278
2011-12-20 16:00:00   0.014478
```

which are inaccurate.

Results should be:

``` python
array([[ 0.        ],
       [-0.001215  ],
       [ 0.00416005],
       [ 0.0018695 ],
       [ 0.00457796],
       ...
       [-0.00916   ],
       [-0.00133451],
       [ 0.00223074],
       [ 0.00127955],
       [ 0.01469067]])
```

as calculated using np array methods via the following:

``` python
from copy import deepcopy
vals = np.asarray(deepcopy(results))
vals[1:,] = (vals[1:,]-vals[:-1,])/vals[:-1,]
vals[0,] = np.zeros(vals.shape[1])
```

My environment is:
- PC: Mac OS X 10.8.2 on 2.6 GHz Intel Core i7 MacBook Pro
- Python: 2.7.3
- Pandas: 0.9.1
- Numpy: 1.6.2

Update:

I have been playing around with the code, and realised that:

``` python
returns = results.diff()[1:]/results
returns.ix[0] = np.zeros(returns.shape[1]) 
```

and

``` python
returns = results.diff()/results
returns.ix[0] = np.zeros(returns.shape[1]) 
```

oddly give the exact same results...so it is clear I bad assumptions about pandas DataFrame indexing, or there is an issue in indexing during division operation.

Additionally, why do I get the following results when evaluating?

``` python
>>>returns[1:]-returns[:-1]
                     Portfolio
Date                                    
2011-01-10 16:00:00        NaN
2011-01-11 16:00:00          0
2011-01-12 16:00:00          0
2011-01-13 16:00:00          0
2011-01-14 16:00:00          0
...
2011-12-14 16:00:00          0
2011-12-15 16:00:00          0
2011-12-16 16:00:00          0
2011-12-19 16:00:00          0
2011-12-20 16:00:00        NaN
```
