issue_comments: 253408063

We do try to stay consistent with pandas except for the last position. Here's the unit test where we verify that behavior.

Using x=0 from your example in Pandas:

``` Python In [1]: import pandas as pd s In [2]: data = pd.Series([0, 3, 6])

In [3]: data.rolling(3, center=True, min_periods=1).mean() Out[3]: 0 1.5 1 3.0 2 4.5 ```

If I remember correctly, and my brain is a bit like mush right now so I could be wrong, bottleneck and pandas handle this case differently so we had to make a decision. We choose to use bottleneck (for speed) but to do our best to stay consistent with pandas. Back to your example, this time just with bottleneck:

Python In [4]: bn.move_mean(data, 3, min_count=1) Out[4]: array([ 0. , 1.5, 3. ])

So, as you can see, bottleneck does something totally different that wouldn't otherwise work with center=True unless we did our little shift trick. I'm not really sure the best way to correct for this difference in the last position except to either a) try to push a center=True option into bottleneck (may not be possible), or b) write a bunch of logic on our end bridge the gap between these two (may be laborious). Of course, I'm open to ideas.