不确定这是否可行。假设我有一个字典列表，如下所示：
stocks = [{'name': 'bob', 'avg_returns': '18.345', 'sd_returns': '2.14', 'var_returns': '34.2334'}, {another_dict}, {another_dict}]
然后我有另一个列表，如下：
weights_list = [(0.95, 0.025, 0.025),
(0.90, 0.05, 0.05),
(0.85, 0.075, 0.075),
(0.80, 0.1, 0.1),
(0.75, 0.125, 0.125),
(0.70, 0.15, 0.15)]
最终结果是将一个不同的（整个）字典附加到weights_list中的每组列表中，如下所示：
[({'name': 'bob', 'avg_returns': '18.345', 'sd_returns': '2.14', 'var_returns': '34.2334'}, 0.95),({another_dict}, 0.025), ({another_dict}, 0.025)]
我希望使用它的原因是我可以调用某些字典键值来乘以分配的相应权重。
我现在拥有的代码，全部由@zehnpaard编写，如下所示：
def portfolios(stocks, weights_list):
for x in stocks:
for stock_triplet in itertools.combinations(x, 3):
for weights in weights_list:
unique_weight_orders = set(itertools.permutations(weights))
for weight_order in unique_weight_orders:
yield zip(stock_triplet, weight_order)
for port in portfolios(stocks,weights_list):
print port
然而，这会为每个字典键打印出一个组合，而不是整个字典。我尝试了for x in len(stocks)，但它会返回错误'int' object is not iterable，因为很多人可能会假设。{/ p>
提前感谢您收到的任何帮助！
答案 0 :(得分：1)
我假设您的列表中有两个词典，如下所示：
stocks = [{'name': 'bob', 'avg_returns': '18.345', 'sd_returns': '2.14', 'var_returns': '34.2334'}, { 'abc': 456 }, { 'abc': 123, 98.6: 37 }]
以下代码段，使其打印每个组合：
for weights in weights_list:
unique_weight_orders = set(itertools.permutations(weights))
for weight_order in unique_weight_orders:
yield zip(stock_triplet, weight_order)
当前输出：
[('sd_returns', 0.95), ('var_returns', 0.025), ('name', 0.025)]
[('sd_returns', 0.025), ('var_returns', 0.025), ('name', 0.95)]
[('sd_returns', 0.025), ('var_returns', 0.95), ('name', 0.025)]
[('sd_returns', 0.9), ('var_returns', 0.05), ('name', 0.05)]
[('sd_returns', 0.05), ('var_returns', 0.9), ('name', 0.05)]
[('sd_returns', 0.05), ('var_returns', 0.05), ('name', 0.9)]
[('sd_returns', 0.075), ('var_returns', 0.075), ('name', 0.85)]
[('sd_returns', 0.075), ('var_returns', 0.85), ('name', 0.075)]
[('sd_returns', 0.85), ('var_returns', 0.075), ('name', 0.075)]
[('sd_returns', 0.1), ('var_returns', 0.1), ('name', 0.8)]
[('sd_returns', 0.8), ('var_returns', 0.1), ('name', 0.1)]
[('sd_returns', 0.1), ('var_returns', 0.8), ('name', 0.1)]
[('sd_returns', 0.75), ('var_returns', 0.125), ('name', 0.125)]
[('sd_returns', 0.125), ('var_returns', 0.125), ('name', 0.75)]
[('sd_returns', 0.125), ('var_returns', 0.75), ('name', 0.125)]
[('sd_returns', 0.7), ('var_returns', 0.15), ('name', 0.15)]
[('sd_returns', 0.15), ('var_returns', 0.7), ('name', 0.15)]
[('sd_returns', 0.15), ('var_returns', 0.15), ('name', 0.7)]
[('sd_returns', 0.95), ('var_returns', 0.025), ('avg_returns', 0.025)]
[('sd_returns', 0.025), ('var_returns', 0.025), ('avg_returns', 0.95)]
[('sd_returns', 0.025), ('var_returns', 0.95), ('avg_returns', 0.025)]
[('sd_returns', 0.9), ('var_returns', 0.05), ('avg_returns', 0.05)]
[('sd_returns', 0.05), ('var_returns', 0.9), ('avg_returns', 0.05)]
[('sd_returns', 0.05), ('var_returns', 0.05), ('avg_returns', 0.9)]
[('sd_returns', 0.075), ('var_returns', 0.075), ('avg_returns', 0.85)]
[('sd_returns', 0.075), ('var_returns', 0.85), ('avg_returns', 0.075)]
[('sd_returns', 0.85), ('var_returns', 0.075), ('avg_returns', 0.075)]
[('sd_returns', 0.1), ('var_returns', 0.1), ('avg_returns', 0.8)]
[('sd_returns', 0.8), ('var_returns', 0.1), ('avg_returns', 0.1)]
[('sd_returns', 0.1), ('var_returns', 0.8), ('avg_returns', 0.1)]
[('sd_returns', 0.75), ('var_returns', 0.125), ('avg_returns', 0.125)]
[('sd_returns', 0.125), ('var_returns', 0.125), ('avg_returns', 0.75)]
[('sd_returns', 0.125), ('var_returns', 0.75), ('avg_returns', 0.125)]
[('sd_returns', 0.7), ('var_returns', 0.15), ('avg_returns', 0.15)]
[('sd_returns', 0.15), ('var_returns', 0.7), ('avg_returns', 0.15)]
[('sd_returns', 0.15), ('var_returns', 0.15), ('avg_returns', 0.7)]
[('sd_returns', 0.95), ('name', 0.025), ('avg_returns', 0.025)]
[('sd_returns', 0.025), ('name', 0.025), ('avg_returns', 0.95)]
[('sd_returns', 0.025), ('name', 0.95), ('avg_returns', 0.025)]
[('sd_returns', 0.9), ('name', 0.05), ('avg_returns', 0.05)]
[('sd_returns', 0.05), ('name', 0.9), ('avg_returns', 0.05)]
[('sd_returns', 0.05), ('name', 0.05), ('avg_returns', 0.9)]
[('sd_returns', 0.075), ('name', 0.075), ('avg_returns', 0.85)]
[('sd_returns', 0.075), ('name', 0.85), ('avg_returns', 0.075)]
[('sd_returns', 0.85), ('name', 0.075), ('avg_returns', 0.075)]
[('sd_returns', 0.1), ('name', 0.1), ('avg_returns', 0.8)]
[('sd_returns', 0.8), ('name', 0.1), ('avg_returns', 0.1)]
[('sd_returns', 0.1), ('name', 0.8), ('avg_returns', 0.1)]
[('sd_returns', 0.75), ('name', 0.125), ('avg_returns', 0.125)]
[('sd_returns', 0.125), ('name', 0.125), ('avg_returns', 0.75)]
[('sd_returns', 0.125), ('name', 0.75), ('avg_returns', 0.125)]
[('sd_returns', 0.7), ('name', 0.15), ('avg_returns', 0.15)]
[('sd_returns', 0.15), ('name', 0.7), ('avg_returns', 0.15)]
[('sd_returns', 0.15), ('name', 0.15), ('avg_returns', 0.7)]
[('var_returns', 0.95), ('name', 0.025), ('avg_returns', 0.025)]
[('var_returns', 0.025), ('name', 0.025), ('avg_returns', 0.95)]
[('var_returns', 0.025), ('name', 0.95), ('avg_returns', 0.025)]
[('var_returns', 0.9), ('name', 0.05), ('avg_returns', 0.05)]
[('var_returns', 0.05), ('name', 0.9), ('avg_returns', 0.05)]
[('var_returns', 0.05), ('name', 0.05), ('avg_returns', 0.9)]
[('var_returns', 0.075), ('name', 0.075), ('avg_returns', 0.85)]
[('var_returns', 0.075), ('name', 0.85), ('avg_returns', 0.075)]
[('var_returns', 0.85), ('name', 0.075), ('avg_returns', 0.075)]
[('var_returns', 0.1), ('name', 0.1), ('avg_returns', 0.8)]
[('var_returns', 0.8), ('name', 0.1), ('avg_returns', 0.1)]
[('var_returns', 0.1), ('name', 0.8), ('avg_returns', 0.1)]
[('var_returns', 0.75), ('name', 0.125), ('avg_returns', 0.125)]
[('var_returns', 0.125), ('name', 0.125), ('avg_returns', 0.75)]
[('var_returns', 0.125), ('name', 0.75), ('avg_returns', 0.125)]
[('var_returns', 0.7), ('name', 0.15), ('avg_returns', 0.15)]
[('var_returns', 0.15), ('name', 0.7), ('avg_returns', 0.15)]
[('var_returns', 0.15), ('name', 0.15), ('avg_returns', 0.7)]
如果你改变它以跳过排列，就像这样：
for weights in weights_list:
yield zip(stock_triplet, weights)
它提供输出：
[('sd_returns', 0.95), ('var_returns', 0.025), ('name', 0.025)]
[('sd_returns', 0.9), ('var_returns', 0.05), ('name', 0.05)]
[('sd_returns', 0.85), ('var_returns', 0.075), ('name', 0.075)]
[('sd_returns', 0.8), ('var_returns', 0.1), ('name', 0.1)]
[('sd_returns', 0.75), ('var_returns', 0.125), ('name', 0.125)]
[('sd_returns', 0.7), ('var_returns', 0.15), ('name', 0.15)]
[('sd_returns', 0.95), ('var_returns', 0.025), ('avg_returns', 0.025)]
[('sd_returns', 0.9), ('var_returns', 0.05), ('avg_returns', 0.05)]
[('sd_returns', 0.85), ('var_returns', 0.075), ('avg_returns', 0.075)]
[('sd_returns', 0.8), ('var_returns', 0.1), ('avg_returns', 0.1)]
[('sd_returns', 0.75), ('var_returns', 0.125), ('avg_returns', 0.125)]
[('sd_returns', 0.7), ('var_returns', 0.15), ('avg_returns', 0.15)]
[('sd_returns', 0.95), ('name', 0.025), ('avg_returns', 0.025)]
[('sd_returns', 0.9), ('name', 0.05), ('avg_returns', 0.05)]
[('sd_returns', 0.85), ('name', 0.075), ('avg_returns', 0.075)]
[('sd_returns', 0.8), ('name', 0.1), ('avg_returns', 0.1)]
[('sd_returns', 0.75), ('name', 0.125), ('avg_returns', 0.125)]
[('sd_returns', 0.7), ('name', 0.15), ('avg_returns', 0.15)]
[('var_returns', 0.95), ('name', 0.025), ('avg_returns', 0.025)]
[('var_returns', 0.9), ('name', 0.05), ('avg_returns', 0.05)]
[('var_returns', 0.85), ('name', 0.075), ('avg_returns', 0.075)]
[('var_returns', 0.8), ('name', 0.1), ('avg_returns', 0.1)]
[('var_returns', 0.75), ('name', 0.125), ('avg_returns', 0.125)]
[('var_returns', 0.7), ('name', 0.15), ('avg_returns', 0.15)]
更改后的完整代码：
import itertools
stocks = [{'name': 'bob', 'avg_returns': '18.345', 'sd_returns': '2.14', 'var_returns': '34.2334'}, { 'abc': 456 }, { 'abc': 123, 98.6: 37 }]
weights_list = [(0.95, 0.025, 0.025),
(0.90, 0.05, 0.05),
(0.85, 0.075, 0.075),
(0.80, 0.1, 0.1),
(0.75, 0.125, 0.125),
(0.70, 0.15, 0.15)]
def portfolios(stocks, weights_list):
for x in stocks:
for stock_triplet in itertools.combinations(x, 3):
for weights in weights_list:
yield zip(stock_triplet, weights)
for port in portfolios(stocks,weights_list):
print port