计算非零值的平均值 [英] Computing average of non-zero values

问题描述

我有一个列表，从中我可以得到非零值的平均值.

I have lists from whose I what the average of non-zero values.

E.G

 [2,2,0,0,0] -> 2    
 [1,1,0,1,0]  -> 1  
 [0,0,0,9,0] -> 9    
 [2,3,0,0,0] -> 2.5

当前我正在这样做:

list_ = [1,1,0,1,0]  
non_zero = [float(v) for v in list_ if v>0]
averge = sum(non_zero)/len(non_zero)

如何更有效地执行此操作?

How can I do this operation more efficiently?

推荐答案

如果以numpy数组开头，则可以使用

If you start with a numpy array, you can use np.nonzero to filter the array, then take the mean:

a = np.array([2,3,0,0,0])
average = a[np.nonzero(a)].mean()

您还可以通过布尔索引进行过滤，该索引似乎更快:

You could also filter by boolean indexing, which appears to be faster:

average = a[a!=0].mean()

您还可以使用a>0轻松更改上述方法以过滤正值.

You could also easily change the method above to filter for positive values by using a>0.

时间

使用以下设置:

a = np.random.randint(100, size=10**6)

我得到以下计时:

%timeit a[a!=0].mean()
100 loops, best of 3: 4.59 ms per loop

%timeit a[a.nonzero()].mean()
100 loops, best of 3: 9.82 ms per loop

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