pd.rolling_mean for window = 1 does not exactly match input time series
In order to have a more generic notation in my code, I want to express my original time series as a moving average over 1 period.
Quite unexpectedly, using pandas pd.rolling_mean function, the two are not exactly the same:

```
import pandas as pd
import numpy as np

np.random.seed(1)

ts = pd.Series(np.random.rand(1000))

mavg = pd.rolling_mean(ts, 1)

(ts - mavg).describe()
Out[120]: 
count    1.000000e+03
mean     6.284973e-16
std      3.877250e-16
min     -3.330669e-16
25%      3.330669e-16
50%      5.551115e-16
75%      8.881784e-16
max      1.554312e-15
dtype: float64

any((ts - mavg).dropna()>0)
Out[121]: True
```

Clearly the difference is very small and due to float rounding. Still I wonder if the function should not simply default to return the input when window = 1
