Функция для поиска последовательных значений в списке ⇐ Python

[Anonymous]

У меня есть следующий список:

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list = [
[2, 'A', '1'],
[6, 'A', '2'],
[6, 'S', '3'],
[9, 'A', '4'],
[6, 'A', '5'],
[6, 'A', '6'],
[6, 'S', '7'],
[9, 'A', '8'],
[9, 'A', '9'],
[6, 'A', '10'],
[10, 'S', '11'],
[13, 'S', '12'],
[13, 'S', '13'],
[16, 'A', '14']
]

The first position of list is the level, the second the type of node and the third is the value.
I need to concatenate all values that have a higher level than before (i-1). I can have multiple A and S in each level, but every time I find a S type I need to still search until I find a higher level of S or A type. My desired output:

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['1.2', '1.3.4', '1.5', '1.6', '1.7.8', '1.7.9', '1.10.11.13.14']

I tried to achieve this output with the following code:

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def concat_levels(levels):
result = []
stack = []

for level in levels:
num, typ, name = level

while stack and stack [-1][0] >= num:
stack.pop()

if typ == 'A':
stack.append((num,name))
elif typ == 'S':
if stack:
current_path = '.'.join([x[1] for x in stack])
result.append(current_path + '.' + name)
return result

However, when I run my function I got the following output:

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['1.3', '1.7', '1.10.11', '1.10.12', '1.10.13']

Apparently my function is appending only on

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type == 'S'

, but why?
CONTEXT: This list is an abstracted version of a pyspark dataframe schema where A represents array type and S represents Structure type. Why 12 is not in the output? Because 12 is a struct (S type). It’s 1.10.11.13.14 Because the level 16 represents that value 14 is inside the value and level 13 structure. Since the schema is ordered, every time there is two S types in the same level, if an A type appears after a S type, then the last S type should be considered (always respecting the level rule)


Источник: https://stackoverflow.com/questions/781 ... -on-a-list