组和平均NumPy矩阵 [英] Group and average NumPy matrix

问题描述

假设我有一个任意的numpy矩阵，如下所示:

Say I have an arbitrary numpy matrix that looks like this:

arr = [[  6.0   12.0   1.0]
       [  7.0   9.0   1.0]
       [  8.0   7.0   1.0]
       [  4.0   3.0   2.0]
       [  6.0   1.0   2.0]
       [  2.0   5.0   2.0]
       [  9.0   4.0   3.0]
       [  2.0   1.0   4.0]
       [  8.0   4.0   4.0]
       [  3.0   5.0   4.0]]

对按第三列号分组的行进行平均的有效方法是什么?

What would be an efficient way of averaging rows that are grouped by their third column number?

预期输出为:

result = [[  7.0  9.33  1.0]
          [  4.0  3.0  2.0]
          [  9.0  4.0  3.0]
          [  4.33  3.33  4.0]]

推荐答案

您可以这样做:

for x in sorted(np.unique(arr[...,2])):
    results.append([np.average(arr[np.where(arr[...,2]==x)][...,0]), 
                    np.average(arr[np.where(arr[...,2]==x)][...,1]),
                    x])

测试:

>>> arr
array([[  6.,  12.,   1.],
       [  7.,   9.,   1.],
       [  8.,   7.,   1.],
       [  4.,   3.,   2.],
       [  6.,   1.,   2.],
       [  2.,   5.,   2.],
       [  9.,   4.,   3.],
       [  2.,   1.,   4.],
       [  8.,   4.,   4.],
       [  3.,   5.,   4.]])
>>> results=[]
>>> for x in sorted(np.unique(arr[...,2])):
...     results.append([np.average(arr[np.where(arr[...,2]==x)][...,0]), 
...                     np.average(arr[np.where(arr[...,2]==x)][...,1]),
...                     x])
... 
>>> results
[[7.0, 9.3333333333333339, 1.0], [4.0, 3.0, 2.0], [9.0, 4.0, 3.0], [4.333333333333333, 3.3333333333333335, 4.0]]

不需要对数组arr进行排序，并且所有中间数组都是视图(即，不是新的数据数组).可以直接从这些视图中有效地计算出平均值.

The array arr does not need to be sorted, and all the intermediate arrays are views (ie, not new arrays of data). The average is calculated efficiently directly from those views.

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