[英]dictionary-comprehension with conditional calling nested list-comprehension
I have a dictionary of keys and lists. 我有一个键和列表字典。 I'd like to iterate through the dictionary, get each list, iterate through each list and apply a condition, then append that filtered list to a new dictionary. 我想迭代字典,获取每个列表,遍历每个列表并应用条件,然后将过滤后的列表附加到新字典。
The function already works imperatively. 该功能已经势在必行。 Can I do the same functionally with list and dict comprehensions? 我可以使用list和dict理解在功能上做同样的事情吗? The main blocker is that the wrapping dict-comp has a conditional which needs length of the list-comp. 主要的阻塞是包装dict-comp有一个条件,需要list-comp的长度。
Here it is working imperatively: 这里的工作势在必行:
filtered_prediction_dict = {}
for prediction, confidence_intervals in prediction_dict.items():
filtered_confidence_intervals = []
for i in confidence_intervals:
if i > threshold:
filtered_confidence_intervals.append(i)
if len(filtered_confidence_intervals) >= 1:
filtered_prediction_dict[prediction] = filtered_confidence_intervals
I was wondering if I could do the same thing functionally with comprehensions, something like this: 我想知道我是否可以在功能上用理解做同样的事情,像这样:
filtered_prediction_dict = {prediction: [i for i in confidence_intervals if i > threshold] for prediction, confidence_intervals in prediction_dict.items() if len(filtered_confidence_intervals) >= 1}
Of course, python's linter points out that filtered_confidence_intervals hasn't yet been defined in len(filtered_confidence_intervals) in the conditional. 当然,python的linter指出尚未在条件中的len(filtered_confidence_intervals)中定义filtered_confidence_intervals。
Any way around this? 有什么方法吗?
You can put the two conditions you apply on each of the confidence intervals in a single statement. 您可以将您在每个置信区间上应用的两个条件放在一个语句中。 Also, I recommend putting the filtering for confidence intervals in a list comprehension statement in any case. 此外,我建议在任何情况下都将置信区间的过滤放在列表推导语句中。
The two conditions: 这两个条件:
if i > threshold
) 置信区间>阈值( if i > threshold
) len(filtered_confidence_intervals) >= 1
) 一个或多个置信区间大于阈值( len(filtered_confidence_intervals) >= 1
) Expressed in a single statement: 用一个声明表达:
any(ci > threshold for ci in confidence_intervals)
The resulting list-comprehension version (split up for readability): 生成的列表理解版本 (为便于阅读而分开):
{
p: [ci for ci in cis if ci > threshold] # only keep ci > threshold
for p, cis in prediction_dict.items() # iterate through the items
if any(ci > threshold for ci in cis) # only consider items with at least one ci > threshold
}
IMHO this is not less readable than for
-loops, but I guess this is a matter of taste and use. 恕我直言,这不小于可读for
-loops,但我想这是品味和使用的问题。
If you want to keep for
-looping: 如果你想保持for
-looping:
filtered_prediction_dict = {}
for prediction, confidence_intervals in prediction_dict.items():
if any(ci > threshold for ci in confidence_intervals):
filtered_prediction_dict[prediction] = [ci for ci in confidence_intervals if ci > threshold]
A note to your comment about the python's linter pointing out that filtered_confidence_intervals
hasn't yet been defined: 关于python的linter的评论的注释,指出尚未定义filtered_confidence_intervals
:
Very often linters are quite accurate and this case is no exception. 很多时候,直径非常准确,这种情况也不例外。 filtered_confidence_intervals
is defined per item in prediction_dict
so there is no way you can iterate through prediction_dict
and have a test about the length of filtered_confidence_intervals
. filtered_confidence_intervals
每项定义在prediction_dict
所以没有办法,你可以通过迭代prediction_dict
并有一个关于长度测试filtered_confidence_intervals
。
You would need to replace the statement: 你需要替换声明:
len(filtered_confidence_intervals) >= 1
in the list comprehension by 在列表中理解
len([ci for ci in confidence_intervals if ci > threshold]) >= 1
you can use: 您可以使用:
filtered_prediction_dict = {prediction: [i for i in confidence_intervals if i > threshold] for prediction, confidence_intervals in prediction_dict.items() if any(e >= threshold for e in confidence_intervals)}
in this way you check that your filtered_prediction_dict doesn't have any empty list 通过这种方式,您可以检查您的filtered_prediction_dict没有任何空列表
or you can use: 或者您可以使用:
filtered_prediction_dict = {prediction: [i for i in confidence_intervals if i > threshold] for prediction, confidence_intervals in prediction_dict.items() if max(confidence_intervals) >= threshold}
the second version iterate twice over each element from your lists, the first has some redundant iterations, but even so both solutions may be faster than using for statements 第二个版本对列表中的每个元素进行两次迭代,第一个版本有一些冗余迭代,但即便如此,两个解决方案都可能比使用for语句更快
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