列表字典的笛卡尔积 [英] Cartesian product of a dictionary of lists

问题描述

我正在尝试编写一些代码来测试一堆输入参数的笛卡尔积。

I'm trying to write some code to test out the Cartesian product of a bunch of input parameters.

我查看了 itertools ，但是它的 product 函数并不是我想要的。是否有一种简单明了的简单方法来制作一个字典，在每个值中包含任意数量的键和任意数量的键，然后生成具有下一个排列的字典？

I've looked at itertools, but its product function is not exactly what I want. Is there a simple obvious way to take a dictionary with an arbitrary number of keys and an arbitrary number of elements in each value, and then yield a dictionary with the next permutation?

输入：

options = {"number": [1,2,3], "color": ["orange","blue"] }
print list( my_product(options) )

示例输出：

[ {"number": 1, "color": "orange"},
  {"number": 1, "color": "blue"},
  {"number": 2, "color": "orange"},
  {"number": 2, "color": "blue"},
  {"number": 3, "color": "orange"},
  {"number": 3, "color": "blue"}
]

推荐答案

好，谢谢@dfan告诉我找错地方了。我现在知道了：

Ok, thanks @dfan for telling me I was looking in the wrong place. I've got it now:

from itertools import product
def my_product(inp):
    return (dict(zip(inp.keys(), values)) for values in product(*inp.values())

编辑：经过多年的Python经验，我认为更好的解决方案是接受 kwargs 而不是输入字典；调用样式与原始 itertools.product 的样式更相似。我还认为编写生成器函数（而不是返回生成器表达式的函数）可以使代码更清晰。因此：

EDIT: after years more Python experience, I think a better solution is to accept kwargs rather than a dictionary of inputs; the call style is more analogous to that of the original itertools.product. Also I think writing a generator function, rather than a function that returns a generator expression, makes the code clearer. So:

def product_dict(**kwargs):
    keys = kwargs.keys()
    vals = kwargs.values()
    for instance in itertools.product(*vals):
        yield dict(zip(keys, instance))

，如果您需要传递字典，请输入 list（product_dict（** mydict））。使用 kwargs 进行更改，而不是使用任意输入cla ss是它可以防止对键/值进行排序，至少直到Python 3.6为止。

and if you need to pass in a dict, list(product_dict(**mydict)). The one notable change using kwargs rather than an arbitrary input class is that it prevents the keys/values from being ordered, at least until Python 3.6.

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