有条件地将复杂的JSON转换为三元组的python元组答案

【问题标题】：Convert complex JSON to python tuple of three conditionally有条件地将复杂的JSON转换为三元组的python元组
【发布时间】：2023-01-24 21:09:19
【问题描述】：

我有这个 JSON

{
    "journal.pbio.0050304.xml": {
        "sentence": [
            [
                {"entity_group": "literal", "score": 0.9961686, "word": "The anterior\u2013posterior (A\u2013P) axis ", "start": 0, "end": 299}
            ],
            [
                {"entity_group": "literal", "score": 0.9932352, "word": "RA, Fgfs, and Wnts are all produced at the posterior of the embryo, and might therefore be expected to form posterior-", "start": 0, "end": 118},
                {"entity_group": "metaphoric", "score": 0.874372, "word": "to", "start": 118, "end": 120},
                {"entity_group": "literal", "score": 0.99049604, "word": "-anterior gradients (for Fgf8", "start": 120, "end": 149},
                {"entity_group": "metaphoric", "score": 0.9993481, "word": "this", "start": 150, "end": 154}
            ]
        ]
    },
    "journal.pbio.0050093.xml": {
        "sentence": [
            [
                {"entity_group": "literal", "score": 0.9961686, "word": "The anterior\u2013posterior (A\u2013P) axis ", "start": 0, "end": 299}
            ]
        ]
    }
}

我只想接受实体组，开始和结束并将它们转换为元组，如下所示： [(0, 299, 'literal'),(186, 194, 'literal'), ('metaphoric', 196, 199)]，等等。我该怎么做？

【问题讨论】：

你的元组结构似乎不一致。最初是 (start, end, entity_group)，然后是 (entity_group, start, end)。这背后的逻辑是什么？

标签： python json python-3.x data-structures

【解决方案1】：

是这样的吗？

from pprint import pprint

data = {
    "journal.pbio.0050304.xml": {
        "sentence": [
            [
                {"entity_group": "literal", "score": 0.9961686, "word": "The anterioru2013posterior (Au2013P) axis ", "start": 0, "end": 299}
            ],
            [
                {"entity_group": "literal", "score": 0.9932352, "word": "RA, Fgfs, and Wnts are all produced at the posterior of the embryo, and might therefore be expected to form posterior-", "start": 0, "end": 118},
                {"entity_group": "metaphoric", "score": 0.874372, "word": "to", "start": 118, "end": 120},
                {"entity_group": "literal", "score": 0.99049604, "word": "-anterior gradients (for Fgf8", "start": 120, "end": 149},
                {"entity_group": "metaphoric", "score": 0.9993481, "word": "this", "start": 150, "end": 154}
            ]
        ]
    },
    "journal.pbio.0050093.xml": {
        "sentence": [
            [
                {"entity_group": "literal", "score": 0.9961686, "word": "The anterioru2013posterior (Au2013P) axis ", "start": 0, "end": 299}
            ]
        ]
    }
}


for file in data.values():
    for idx1, sentence in enumerate(file["sentence"]):
        new_sentence = [word for word in sentence]
        for idx2, word in enumerate(sentence):
            new_sentence[idx2] = (word["start"], word["end"], word["entity_group"])
        file["sentence"][idx1] = new_sentence

pprint(data)

结果：

{'journal.pbio.0050093.xml': {'sentence': [[(0, 299, 'literal')]]},
 'journal.pbio.0050304.xml': {'sentence': [[(0, 299, 'literal')],
                                           [(0, 118, 'literal'),
                                            (118, 120, 'metaphoric'),
                                            (120, 149, 'literal'),
                                            (150, 154, 'metaphoric')]]}}

【讨论】：

【解决方案2】：

您只需要先遍历字典值，然后遍历列表和子列表，如下所示：

data = {
    "journal.pbio.0050304.xml": {
        "sentence": [
            [
                {"entity_group": "literal", "score": 0.9961686, "word": "The anterioru2013posterior (Au2013P) axis ", "start": 0, "end": 299}
            ],
            [
                {"entity_group": "literal", "score": 0.9932352, "word": "RA, Fgfs, and Wnts are all produced at the posterior of the embryo, and might therefore be expected to form posterior-", "start": 0, "end": 118},
                {"entity_group": "metaphoric", "score": 0.874372, "word": "to", "start": 118, "end": 120},
                {"entity_group": "literal", "score": 0.99049604, "word": "-anterior gradients (for Fgf8", "start": 120, "end": 149},
                {"entity_group": "metaphoric", "score": 0.9993481, "word": "this", "start": 150, "end": 154}
            ]
        ]
    },
    "journal.pbio.0050093.xml": {
        "sentence": [
            [
                {"entity_group": "literal", "score": 0.9961686, "word": "The anterioru2013posterior (Au2013P) axis ", "start": 0, "end": 299}
            ]
        ]
    }
}

output = []

for v in data.values():
    for s in v.get('sentence', []):
        for d in s:
            output.append((d.get('start'), d.get('end'), d.get('entity_group')))

print(output)

输出：

[(0, 299, 'literal'), (0, 118, 'literal'), (118, 120, 'metaphoric'), (120, 149, 'literal'), (150, 154, 'metaphoric'), (0, 299, 'literal')]

【讨论】：