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POPython group by array a, and summarize array b - Performance
 

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  1. POPython group by array a, and summarize array b - Performance
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    <p>Given two unordered arrays of same lengths a and b:</p> <pre><code>a = [7,3,5,7,5,7] b = [0.2,0.1,0.3,0.1,0.1,0.2] </code></pre> <p>I'd like to group by the elements in a:</p> <pre><code>aResult = [7,3,5] </code></pre> <p>summing over the elements in b (Example used to summarize a probability density function):</p> <pre><code>bResult = [0.2 + 0.1 + 0.2, 0.1, 0.3 + 0.1] = [0.5, 0.1, 0.4] </code></pre> <p>Alternatively, random a and b in python:</p> <pre><code>import numpy as np a = np.random.randint(1,10,10000) b = np.array([1./len(a)]*len(a)) </code></pre> <p>I have two approaches, which for sure are far from the lower performance boundary. Approach 1 (at least nice and short): Time: 0.769315958023</p> <pre><code>def approach_2(a,b): bResult = [sum(b[i == a]) for i in np.unique(a)] aResult = np.unique(a) </code></pre> <p>Approach 2 (numpy.groupby, horribly slow) Time: 4.65299129486</p> <pre><code>def approach_2(a,b): tmp = [(a[i],b[i]) for i in range(len(a))] tmp2 = np.array(tmp, dtype = [('a', float),('b', float)]) tmp2 = np.sort(tmp2, order='a') bResult = [] aResult = [] for key, group in groupby(tmp2, lambda x: x[0]): aResult.append(key) bResult.append(sum([i[1] for i in group])) </code></pre> <p>Update: Approach3, by Pablo. Time: 1.0265750885</p> <pre><code>def approach_Pablo(a,b): pdf = defaultdict(int); for x,y in zip(a,b): pdf[x] += y </code></pre> <p>Update: Approach 4, by Unutbu. Time: 0.184849023819 [WINNER SO FAR, but a as integer only]</p> <pre><code>def unique_Unutbu(a,b): x=np.bincount(a,weights=b) aResult = np.unique(a) bResult = x[aResult] </code></pre> <p>Maybe someone finds a smarter solution to this problem than me :)</p>
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