Python 计算存储在列表中的2D numpy数组的平均值
a: 我可以做Python 计算存储在列表中的2D numpy数组的平均值,python,numpy,for-loop,Python,Numpy,For Loop,a: 我可以做a[0]。意思是，我会得到想要的结果。我想用for循环对'a'的整个长度执行此操作 我试过： [array([[0.10865657, 0.10638294, 0.10471012, 0.09508586, 0.09283491], [0.10892282, 0.10664408, 0.10496752, 0.09531553, 0.09305617], [0.11664 , 0.1143077 , 0.11259081, 0.1026154 ,
a[0]。意思是，我会得到想要的结果。我想用for循环对'a'
的整个长度执行此操作
我试过：
[array([[0.10865657, 0.10638294, 0.10471012, 0.09508586, 0.09283491],
[0.10892282, 0.10664408, 0.10496752, 0.09531553, 0.09305617],
[0.11664 , 0.1143077 , 0.11259081, 0.1026154 , 0.10025029],
[0.11626453, 0.11392252, 0.11219875, 0.10217754, 0.09980005]]),
array([[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
[0.04267657, 0.04255925, 0.04253528, 0.04520177, 0.04655534],
...
TypeError:“int”对象不可编辑
首先，dist[0]。mean
返回一个函数，而不返回平均值。通常，您需要dist[0].mean（）
使用列表理解可以轻松避免for循环：
mean_all = []
for i in len(dist):
mean = dist[i].mean
mean_all.append(mean)
from numpy import array
dist = [array([[0.10865657, 0.10638294, 0.10471012, 0.09508586, 0.09283491],
[0.10892282, 0.10664408, 0.10496752, 0.09531553, 0.09305617],
[0.11664 , 0.1143077 , 0.11259081, 0.1026154 , 0.10025029],
[0.11626453, 0.11392252, 0.11219875, 0.10217754, 0.09980005]]),
array([[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
[0.04267657, 0.04255925, 0.04253528, 0.04520177, 0.04655534]])]
mean_all = [dist[i].mean() for i in range(len(dist))]
print(mean_all)
[0.10536720549999998, 0.04307523133333334]
如果您真的想将用于
循环，请使用以下命令：
mean_all = []
for i in len(dist):
mean = dist[i].mean
mean_all.append(mean)
from numpy import array
dist = [array([[0.10865657, 0.10638294, 0.10471012, 0.09508586, 0.09283491],
[0.10892282, 0.10664408, 0.10496752, 0.09531553, 0.09305617],
[0.11664 , 0.1143077 , 0.11259081, 0.1026154 , 0.10025029],
[0.11626453, 0.11392252, 0.11219875, 0.10217754, 0.09980005]]),
array([[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
[0.04267657, 0.04255925, 0.04253528, 0.04520177, 0.04655534]])]
mean_all = [dist[i].mean() for i in range(len(dist))]
print(mean_all)
[0.10536720549999998, 0.04307523133333334]
首先，dist[0]。mean
返回一个函数，而不返回平均值。通常，您需要dist[0].mean（）mean_all = []
for i in len(dist):
mean = dist[i].mean
mean_all.append(mean)
from numpy import array
dist = [array([[0.10865657, 0.10638294, 0.10471012, 0.09508586, 0.09283491],
[0.10892282, 0.10664408, 0.10496752, 0.09531553, 0.09305617],
[0.11664 , 0.1143077 , 0.11259081, 0.1026154 , 0.10025029],
[0.11626453, 0.11392252, 0.11219875, 0.10217754, 0.09980005]]),
array([[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
[0.04267657, 0.04255925, 0.04253528, 0.04520177, 0.04655534]])]
mean_all = [dist[i].mean() for i in range(len(dist))]
print(mean_all)
[0.10536720549999998, 0.04307523133333334]
如果您真的想将
用于mean_all = []
for i in len(dist):
mean = dist[i].mean
mean_all.append(mean)
from numpy import array
dist = [array([[0.10865657, 0.10638294, 0.10471012, 0.09508586, 0.09283491],
[0.10892282, 0.10664408, 0.10496752, 0.09531553, 0.09305617],
[0.11664 , 0.1143077 , 0.11259081, 0.1026154 , 0.10025029],
[0.11626453, 0.11392252, 0.11219875, 0.10217754, 0.09980005]]),
array([[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
[0.04267657, 0.04255925, 0.04253528, 0.04520177, 0.04655534]])]
mean_all = [dist[i].mean() for i in range(len(dist))]
print(mean_all)
[0.10536720549999998, 0.04307523133333334]
使用正确的使用范围（）格式
使用正确的使用范围（）格式
用于范围内的i（len（dist））mean\u all=[arr.mean（）用于范围内的arr]dist[i]。mean用于范围内的i（len（dist））或更多pythonic:mean_all=[arr.mean（）表示范围内的arr（dist）]
tip1:dist[i]。mean
返回一个函数，tip2：需要表示范围内i（len（dist））
。请参阅我的答案以获得完整的解释