EDIT: This question was asked in 2016 and similar questions have been posted on SO years later after the functionality was finally removed, e.g. module 'pandas' has
I suggest scipy.ndimage.filters.uniform_filter1d like in my answer to the linked question. It is also way faster for large arrays:
import numpy as np
from scipy.ndimage.filters import uniform_filter1d
N = 1000
x = np.random.random(100000)
%timeit pd.rolling_mean(x, window=N)
__main__:257: FutureWarning: pd.rolling_mean is deprecated for ndarrays and will be removed in a future version
The slowest run took 84.55 times longer than the fastest. This could mean that an intermediate result is being cached.
1 loop, best of 3: 7.37 ms per loop
%timeit uniform_filter1d(x, size=N)
10000 loops, best of 3: 190 µs per loop