列表理解仅在一个索引中访问列表中的嵌套列表
List comprehension to access a nested list within a list only in one index
我需要的是访问嵌套列表的索引以从元组列表中的另一个索引中过滤元素...
例如，我有以下示例数据：
samples = [('waterfall03', 9)]
distances = [('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232, 0.0])]
所以在这种情况下，我有示例 waterfall03，其中我有数字 9，这是要从列表距离中的元组列表中的索引号 1 中删除的索引...即我想得到列表如下[12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232]
我写了下面的代码，但没有用
distances = [[n for j, n, m in enumerate(zip(subarray, samples)) if j != m] for i, subarray in enumerate(distances) if i == 1]
预期输出
distances = [('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232])]
更多测试用例
Samples = [('highway_bost321', 0), ('mountain_0871', 2), ('opencountry_043', 4), ('opencountry_053', 5), ('opencountry_123', 6), ('palace_019', 7), ('volcano_010', 8), ('waterfall03', 9)]
distances = [('highway_bost321', [0.0, 10.174343253183386, 10.947746706490813, 7.187637514988234, 7.660483562939873, 10.622402335214636, 10.737785768990813, 10.566917715980832, 10.819389772897063, 12.03400784892136]), ('mountain_0871', [10.947746706490813, 0.83758544921875, 0.0, 5.838234191502578, 5.363256367154217, 1.3175048828125, 3.0810546875, 6.634500456993904, 0.78460693359375, 13.91981431878607]), ('opencountry_043', [7.660483562939873, 4.668136048210366, 5.363256367154217, 2.8142531243329643, 0.0, 5.347678752303554, 3.236628762987552, 5.377074226549635, 5.634096535101808, 13.405988675235129]), ('opencountry_053', [10.622402335214636, 0.73712158203125, 1.3175048828125, 5.876377140065916, 5.347678752303554, 0.0, 3.1134033203125, 6.11476575647307, 1.97711181640625, 13.144871284931902]), ('opencountry_123', [10.737785768990813, 3.01690673828125, 3.0810546875, 5.560564920669245, 3.236628762987552, 3.1134033203125, 0.0, 5.71620618616057, 3.09417724609375, 13.597874214619402]), ('palace_019', [10.566917715980832, 5.890665007775154, 6.634500456993904, 3.7392389463104037, 5.377074226549635, 6.11476575647307, 5.71620618616057, 0.0, 6.493675414476205, 12.731745772059295]), ('volcano_010', [10.819389772897063, 1.294677734375, 0.78460693359375, 5.709877257908828, 5.634096535101808, 1.97711181640625, 3.09417724609375, 6.493675414476205, 0.0, 13.90083238519232]), ('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232, 0.0])]
预期产出
distances = [('highway_bost321', [10.174343253183386, 10.947746706490813, 7.187637514988234, 7.660483562939873, 10.622402335214636, 10.737785768990813, 10.566917715980832, 10.819389772897063, 12.03400784892136]),
('mountain_0871', [10.947746706490813, 0.83758544921875, 5.838234191502578, 5.363256367154217, 1.3175048828125, 3.0810546875, 6.634500456993904, 0.78460693359375, 13.91981431878607]),
('opencountry_043', [7.660483562939873, 4.668136048210366, 5.363256367154217, 2.8142531243329643, 5.347678752303554, 3.236628762987552, 5.377074226549635, 5.634096535101808, 13.405988675235129]),
('opencountry_053', [10.622402335214636, 0.73712158203125, 1.3175048828125, 5.876377140065916, 5.347678752303554, 3.1134033203125, 6.11476575647307, 1.97711181640625, 13.144871284931902]),
('opencountry_123', [10.737785768990813, 3.01690673828125, 3.0810546875, 5.560564920669245, 3.236628762987552, 3.1134033203125, 5.71620618616057, 3.09417724609375, 13.597874214619402]),
('palace_019', [10.566917715980832, 5.890665007775154, 6.634500456993904, 3.7392389463104037, 5.377074226549635, 6.11476575647307, 5.71620618616057, 6.493675414476205, 12.731745772059295]),
('volcano_010', [10.819389772897063, 1.294677734375, 0.78460693359375, 5.709877257908828, 5.634096535101808, 1.97711181640625, 3.09417724609375, 6.493675414476205, 13.90083238519232]),
('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232])]
因此，如果您遇到以下情况：
distances = [
('highway_bost321', [0.0, 10.174343253183386, 10.947746706490813, 7.187637514988234, 7.660483562939873, 10.622402335214636, 10.737785768990813, 10.566917715980832, 10.819389772897063, 12.03400784892136]),
('mountain_0871', [10.947746706490813, 0.83758544921875, 0.0, 5.838234191502578, 5.363256367154217, 1.3175048828125, 3.0810546875, 6.634500456993904, 0.78460693359375, 13.91981431878607]),
('opencountry_043', [7.660483562939873, 4.668136048210366, 5.363256367154217, 2.8142531243329643, 0.0, 5.347678752303554, 3.236628762987552, 5.377074226549635, 5.634096535101808, 13.405988675235129]),
('opencountry_053', [10.622402335214636, 0.73712158203125, 1.3175048828125, 5.876377140065916, 5.347678752303554, 0.0, 3.1134033203125, 6.11476575647307, 1.97711181640625, 13.144871284931902]),
('opencountry_123', [10.737785768990813, 3.01690673828125, 3.0810546875, 5.560564920669245, 3.236628762987552, 3.1134033203125, 0.0, 5.71620618616057, 3.09417724609375, 13.597874214619402]),
('palace_019', [10.566917715980832, 5.890665007775154, 6.634500456993904, 3.7392389463104037, 5.377074226549635, 6.11476575647307, 5.71620618616057, 0.0, 6.493675414476205, 12.731745772059295]),
('volcano_010', [10.819389772897063, 1.294677734375, 0.78460693359375, 5.709877257908828, 5.634096535101808, 1.97711181640625, 3.09417724609375, 6.493675414476205, 0.0, 13.90083238519232]),
('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232, 0.0])
]
sample = [
('highway_bost321', 0),
('mountain_0871', 2),
('opencountry_043', 4),
('opencountry_053', 5),
('opencountry_123', 6),
('palace_019', 7),
('volcano_010', 8),
('waterfall03', 9)
]
然后你可以使用类似的东西：
result = [
(k, vs[:index] + vs[index+1:]) for (key, index) in samples
for k, vs in distances if k == key
]
哪个让你：
[('highway_bost321', [10.174343253183386, 10.947746706490813, 7.187637514988234, 7.660483562939873, 10.622402335214636, 10.737785768990813, 10.566917715980832, 10.819389772897063, 12.03400784892136]),
('mountain_0871', [10.947746706490813, 0.83758544921875, 5.838234191502578, 5.363256367154217, 1.3175048828125, 3.0810546875, 6.634500456993904, 0.78460693359375, 13.91981431878607]),
('opencountry_043', [7.660483562939873, 4.668136048210366, 5.363256367154217, 2.8142531243329643, 5.347678752303554, 3.236628762987552, 5.377074226549635, 5.634096535101808, 13.405988675235129]),
('opencountry_053', [10.622402335214636, 0.73712158203125, 1.3175048828125, 5.876377140065916, 5.347678752303554, 3.1134033203125, 6.11476575647307, 1.97711181640625, 13.144871284931902]),
('opencountry_123', [10.737785768990813, 3.01690673828125, 3.0810546875, 5.560564920669245, 3.236628762987552, 3.1134033203125, 5.71620618616057, 3.09417724609375, 13.597874214619402]),
('palace_019', [10.566917715980832, 5.890665007775154, 6.634500456993904, 3.7392389463104037, 5.377074226549635, 6.11476575647307, 5.71620618616057, 6.493675414476205, 12.731745772059295]),
('volcano_010', [10.819389772897063, 1.294677734375, 0.78460693359375, 5.709877257908828, 5.634096535101808, 1.97711181640625, 3.09417724609375, 6.493675414476205, 13.90083238519232]),
('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232])]
但这是非常低效的。这将是多项式时间。注意内部循环，k, vs in distances if k == key 这是非常浪费的。
相反，距离应该是一个字典，
distances = dict(distances)
那么你可以这样做：
{key: distances[key][:index] + distances[key][index+1:] for key, index in samples}
因为在你的输出中你被重新分配了 distances 变量，我认为你可以从内部列表中弹出元素：
def remove_samples_in_distances(samples, distances):
for sample, index in samples:
for name, list_of_distances in distances:
if sample == name:
list_of_distances.pop(index)
运行 它与你的测试用例：
samples = [('waterfall03', 9)]
distances = [('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232, 0.0])]
remove_samples_in_distances(samples, distances)
print(distances == [('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232])])
# output: True
我需要的是访问嵌套列表的索引以从元组列表中的另一个索引中过滤元素... 例如，我有以下示例数据：
samples = [('waterfall03', 9)]
distances = [('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232, 0.0])]
所以在这种情况下，我有示例 waterfall03，其中我有数字 9，这是要从列表距离中的元组列表中的索引号 1 中删除的索引...即我想得到列表如下[12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232]
我写了下面的代码，但没有用
distances = [[n for j, n, m in enumerate(zip(subarray, samples)) if j != m] for i, subarray in enumerate(distances) if i == 1]
预期输出
distances = [('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232])]
更多测试用例
Samples = [('highway_bost321', 0), ('mountain_0871', 2), ('opencountry_043', 4), ('opencountry_053', 5), ('opencountry_123', 6), ('palace_019', 7), ('volcano_010', 8), ('waterfall03', 9)]
distances = [('highway_bost321', [0.0, 10.174343253183386, 10.947746706490813, 7.187637514988234, 7.660483562939873, 10.622402335214636, 10.737785768990813, 10.566917715980832, 10.819389772897063, 12.03400784892136]), ('mountain_0871', [10.947746706490813, 0.83758544921875, 0.0, 5.838234191502578, 5.363256367154217, 1.3175048828125, 3.0810546875, 6.634500456993904, 0.78460693359375, 13.91981431878607]), ('opencountry_043', [7.660483562939873, 4.668136048210366, 5.363256367154217, 2.8142531243329643, 0.0, 5.347678752303554, 3.236628762987552, 5.377074226549635, 5.634096535101808, 13.405988675235129]), ('opencountry_053', [10.622402335214636, 0.73712158203125, 1.3175048828125, 5.876377140065916, 5.347678752303554, 0.0, 3.1134033203125, 6.11476575647307, 1.97711181640625, 13.144871284931902]), ('opencountry_123', [10.737785768990813, 3.01690673828125, 3.0810546875, 5.560564920669245, 3.236628762987552, 3.1134033203125, 0.0, 5.71620618616057, 3.09417724609375, 13.597874214619402]), ('palace_019', [10.566917715980832, 5.890665007775154, 6.634500456993904, 3.7392389463104037, 5.377074226549635, 6.11476575647307, 5.71620618616057, 0.0, 6.493675414476205, 12.731745772059295]), ('volcano_010', [10.819389772897063, 1.294677734375, 0.78460693359375, 5.709877257908828, 5.634096535101808, 1.97711181640625, 3.09417724609375, 6.493675414476205, 0.0, 13.90083238519232]), ('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232, 0.0])]
预期产出
distances = [('highway_bost321', [10.174343253183386, 10.947746706490813, 7.187637514988234, 7.660483562939873, 10.622402335214636, 10.737785768990813, 10.566917715980832, 10.819389772897063, 12.03400784892136]),
('mountain_0871', [10.947746706490813, 0.83758544921875, 5.838234191502578, 5.363256367154217, 1.3175048828125, 3.0810546875, 6.634500456993904, 0.78460693359375, 13.91981431878607]),
('opencountry_043', [7.660483562939873, 4.668136048210366, 5.363256367154217, 2.8142531243329643, 5.347678752303554, 3.236628762987552, 5.377074226549635, 5.634096535101808, 13.405988675235129]),
('opencountry_053', [10.622402335214636, 0.73712158203125, 1.3175048828125, 5.876377140065916, 5.347678752303554, 3.1134033203125, 6.11476575647307, 1.97711181640625, 13.144871284931902]),
('opencountry_123', [10.737785768990813, 3.01690673828125, 3.0810546875, 5.560564920669245, 3.236628762987552, 3.1134033203125, 5.71620618616057, 3.09417724609375, 13.597874214619402]),
('palace_019', [10.566917715980832, 5.890665007775154, 6.634500456993904, 3.7392389463104037, 5.377074226549635, 6.11476575647307, 5.71620618616057, 6.493675414476205, 12.731745772059295]),
('volcano_010', [10.819389772897063, 1.294677734375, 0.78460693359375, 5.709877257908828, 5.634096535101808, 1.97711181640625, 3.09417724609375, 6.493675414476205, 13.90083238519232]),
('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232])]
因此，如果您遇到以下情况：
distances = [
('highway_bost321', [0.0, 10.174343253183386, 10.947746706490813, 7.187637514988234, 7.660483562939873, 10.622402335214636, 10.737785768990813, 10.566917715980832, 10.819389772897063, 12.03400784892136]),
('mountain_0871', [10.947746706490813, 0.83758544921875, 0.0, 5.838234191502578, 5.363256367154217, 1.3175048828125, 3.0810546875, 6.634500456993904, 0.78460693359375, 13.91981431878607]),
('opencountry_043', [7.660483562939873, 4.668136048210366, 5.363256367154217, 2.8142531243329643, 0.0, 5.347678752303554, 3.236628762987552, 5.377074226549635, 5.634096535101808, 13.405988675235129]),
('opencountry_053', [10.622402335214636, 0.73712158203125, 1.3175048828125, 5.876377140065916, 5.347678752303554, 0.0, 3.1134033203125, 6.11476575647307, 1.97711181640625, 13.144871284931902]),
('opencountry_123', [10.737785768990813, 3.01690673828125, 3.0810546875, 5.560564920669245, 3.236628762987552, 3.1134033203125, 0.0, 5.71620618616057, 3.09417724609375, 13.597874214619402]),
('palace_019', [10.566917715980832, 5.890665007775154, 6.634500456993904, 3.7392389463104037, 5.377074226549635, 6.11476575647307, 5.71620618616057, 0.0, 6.493675414476205, 12.731745772059295]),
('volcano_010', [10.819389772897063, 1.294677734375, 0.78460693359375, 5.709877257908828, 5.634096535101808, 1.97711181640625, 3.09417724609375, 6.493675414476205, 0.0, 13.90083238519232]),
('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232, 0.0])
]
sample = [
('highway_bost321', 0),
('mountain_0871', 2),
('opencountry_043', 4),
('opencountry_053', 5),
('opencountry_123', 6),
('palace_019', 7),
('volcano_010', 8),
('waterfall03', 9)
]
然后你可以使用类似的东西：
result = [
(k, vs[:index] + vs[index+1:]) for (key, index) in samples
for k, vs in distances if k == key
]
哪个让你：
[('highway_bost321', [10.174343253183386, 10.947746706490813, 7.187637514988234, 7.660483562939873, 10.622402335214636, 10.737785768990813, 10.566917715980832, 10.819389772897063, 12.03400784892136]),
('mountain_0871', [10.947746706490813, 0.83758544921875, 5.838234191502578, 5.363256367154217, 1.3175048828125, 3.0810546875, 6.634500456993904, 0.78460693359375, 13.91981431878607]),
('opencountry_043', [7.660483562939873, 4.668136048210366, 5.363256367154217, 2.8142531243329643, 5.347678752303554, 3.236628762987552, 5.377074226549635, 5.634096535101808, 13.405988675235129]),
('opencountry_053', [10.622402335214636, 0.73712158203125, 1.3175048828125, 5.876377140065916, 5.347678752303554, 3.1134033203125, 6.11476575647307, 1.97711181640625, 13.144871284931902]),
('opencountry_123', [10.737785768990813, 3.01690673828125, 3.0810546875, 5.560564920669245, 3.236628762987552, 3.1134033203125, 5.71620618616057, 3.09417724609375, 13.597874214619402]),
('palace_019', [10.566917715980832, 5.890665007775154, 6.634500456993904, 3.7392389463104037, 5.377074226549635, 6.11476575647307, 5.71620618616057, 6.493675414476205, 12.731745772059295]),
('volcano_010', [10.819389772897063, 1.294677734375, 0.78460693359375, 5.709877257908828, 5.634096535101808, 1.97711181640625, 3.09417724609375, 6.493675414476205, 13.90083238519232]),
('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232])]
但这是非常低效的。这将是多项式时间。注意内部循环，k, vs in distances if k == key 这是非常浪费的。
相反，距离应该是一个字典，
distances = dict(distances)
那么你可以这样做：
{key: distances[key][:index] + distances[key][index+1:] for key, index in samples}
因为在你的输出中你被重新分配了 distances 变量，我认为你可以从内部列表中弹出元素：
def remove_samples_in_distances(samples, distances):
for sample, index in samples:
for name, list_of_distances in distances:
if sample == name:
list_of_distances.pop(index)
运行 它与你的测试用例：
samples = [('waterfall03', 9)]
distances = [('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232, 0.0])]
remove_samples_in_distances(samples, distances)
print(distances == [('waterfall03', [12.03400784892136, 13.17207261956732, 13.91981431878607, 13.095577523116825, 13.405988675235129, 13.144871284931902, 13.597874214619402, 12.731745772059295, 13.90083238519232])])
# output: True