More Pythonic/Pandaic approach to looping over a pandas Series

Question

This is most likely something very basic, but I can\'t figure it out. Suppose that I have a Series like this:

s1 = pd.Series([1, 1, 1, 2, 2, 2, 3, 3, 3, 4,         


        

Answer 1

You could also use np.add.reduceat by specifying the slices to be reduced at every 3rd element and compute their running sum:

>>> pd.Series(np.add.reduceat(s1.values, np.arange(0, s1.shape[0], 3)))
0     3
1     6
2     9
3    12
dtype: int64

Timing Constraints:

arr = np.repeat(np.arange(10**5), 3)
s = pd.Series(arr)
s.shape
(300000,)

# @IanS soln
%timeit s.rolling(3).sum()[2::3]        
100 loops, best of 3: 15.6 ms per loop

# @Divakar soln
%timeit pd.Series(np.bincount(np.arange(s.size)//3, s))  
100 loops, best of 3: 5.44 ms per loop

# @Nikolas Rieble soln
%timeit pd.Series(np.sum(np.array(s).reshape(len(s)/3,3), axis = 1))  
100 loops, best of 3: 2.17 ms per loop

# @Nikolas Rieble modified soln
%timeit pd.Series(np.sum(np.array(s).reshape(-1, 3), axis=1))  
100 loops, best of 3: 2.15 ms per loop

# @Divakar modified soln
%timeit pd.Series(s.values.reshape(-1,3).sum(1))
1000 loops, best of 3: 1.62 ms per loop

# Proposed solution in post
%timeit pd.Series(np.add.reduceat(s.values, np.arange(0, s.shape[0], 3)))
1000 loops, best of 3: 1.45 ms per loop