如何在 Python numpy 中将行和列变成变量？

Question

我需要计算其索引(row + column) % 5的平均值。 我了解如何调用特定索引，但我无法将行和列定义为变量 i 和 j，以便我可以创建需要满足的条件。

data = np.random.normal(1, 0.5, size=(8, 9)) #generate an array
a() # empty list where the values whose indices meet the condition are going to be saved
i, j = np.ix_(data) <- does not work
for x in data
    if i + j % 5:
        a.add(x)

Answer 1

您的问题不清楚(row + column) % 5要求。 但是假设您想要(row + column) % 5 == 0的值，您可以构建索引器并利用高级索引。

np.random.seed(1)
data = np.random.normal(1, 0.5, size=(8, 9)) #generate an array

idx = [(i, j)
    for i in range(data.shape[0])
    for j in range(data.shape[1])
    if (i + j) % 5 == 0
]
x = [e[0] for e in idx]
y = [e[1] for e in idx]

filtered = data[(x, y)] # advanced indexing
result = mean(filtered)

print(data)
print(idx)
print(filtered)
print(result)

输出

[[ 1.81217268  0.69412179  0.73591412  0.46351569  1.43270381 -0.15076935
   1.87240588  0.61939655  1.15951955]
 [ 0.87531481  1.73105397 -0.03007035  0.8387914   0.80797282  1.56688472
   0.45005437  0.9137859   0.56107079]
 [ 1.02110687  1.29140761  0.44969041  1.57236185  1.45079536  1.25124717
   1.45042797  0.65813607  0.93855489]
 [ 0.53211528  0.86605596  1.26517773  0.65416962  0.80162324  0.65641365
   0.57739718  0.66437693  0.9936677 ]
 [ 0.44134483  1.11720785  1.82990109  1.37102208  0.90408222  0.55618552
   0.62642085  1.8462273   1.02540388]
 [ 0.68150218  1.09545774  2.05012757  1.06007948  1.30860155  1.15008516
   0.82387508  0.4287409   0.82532864]
 [ 0.89555288  1.2933116   1.41949171  1.46555104  1.14279366  1.44257058
   0.62280103  1.62643408  1.25646491]
 [ 0.85095358  1.24425907  0.96221414  1.56581469  1.75990841  2.0927877
   0.30175183  0.2779431   0.74776707]]

[(0, 0), (0, 5), (1, 4), (2, 3), (2, 8), (3, 2), (3, 7), (4, 1), (4, 6), (5, 0), (5, 5), (6, 4), (7, 3), (7, 8)]

[ 1.81217268 -0.15076935  0.80797282  1.57236185  0.93855489  1.26517773
  0.66437693  1.11720785  0.62642085  0.68150218  1.15008516  1.14279366
  1.56581469  0.74776707]

0.9958170735553157

Answer 2

另一个解决方案，更容易遵循：

import numpy as np 
np.random.seed(1)

data = np.random.normal(1, 0.5, size=(8, 9)) 
a = []
for ix,iy in np.ndindex(data.shape):
    if (ix+iy) % 5 == 0:
        a.append(data[ix, iy])
mean = np.array(a).mean()

print(a)
print(mean)