如何使用flatten_json递归地扁平化嵌套的JSON？

Z时代
2024-01-10
分类：问答

回答：

Repo使用：flatten](https://github.com/amirziai/flatten)

该软件包位于pypi flatten-json 0.1.7上，可以与pip install flatten-json

此问题特定于软件包的以下组件：

    def flatten_json(nested_json: dict, exclude: list=[''], sep: str='_') -> dict:
        """
        Flatten a list of nested dicts.
        """
        out = dict()
        def flatten(x: (list, dict, str), name: str='', exclude=exclude):
            if type(x) is dict:
                for a in x:
                    if a not in exclude:
                        flatten(x[a], f'{name}{a}{sep}')
            elif type(x) is list:
                i = 0
                for a in x:
                    flatten(a, f'{name}{i}{sep}')
                    i += 1
            else:
                out[name[:-1]] = x
        flatten(nested_json)
        return out

使用递归展平嵌套 `dicts`

用Python递归思考

在Python中展平JSON对象

嵌套如何`data`？：

flatten_json 已用于解压缩最终超过100000列的文件

展平的JSON是否可以展平？

是的，这个问题不能解决这个问题。但是，如果您安装flatten软件包，则有一种unflatten方法，但我尚未对其进行测试。

回答：

如何展平一个`JSON`或`dict`一个常见问题，对此有很多答案。

此答案集中于使用flatten_json递归展平嵌套dict或JSON

回答：

该答案假定您已经将JSON或dict加载到了某个变量（例如，文件，api等）中
- 在这种情况下，我们将使用 data

回答：

它接受一个dict，如功能类型提示所示。

最常见的形式`data`：

只是一句话： {}
- flatten_json(data)

字典列表： [{}, {}, {}]
- [flatten_json(x) for x in data]

带有顶级密钥的JSON，其中值重复： {1: {}, 2: {}, 3: {}}
- [flatten_json(data[key]) for key in data.keys()]

其他
- {'key': [{}, {}, {}]}： [flatten_json(x) for x in data['key']]

回答：

我通常会扁平data化成pandas.DataFrame
- 载pandas有import pandas as pd

资料1：

{
    "id": 1,
    "class": "c1",
    "owner": "myself",
    "metadata": {
        "m1": {
            "value": "m1_1",
            "timestamp": "d1"
        },
        "m2": {
            "value": "m1_2",
            "timestamp": "d2"
        },
        "m3": {
            "value": "m1_3",
            "timestamp": "d3"
        },
        "m4": {
            "value": "m1_4",
            "timestamp": "d4"
        }
    },
    "a1": {
        "a11": [
        ]
    },
    "m1": {},
    "comm1": "COMM1",
    "comm2": "COMM21529089656387",
    "share": "xxx",
    "share1": "yyy",
    "hub1": "h1",
    "hub2": "h2",
    "context": [
    ]
}

展平1：

    df = pd.DataFrame([flatten_json(data)])
     id class   owner metadata_m1_value metadata_m1_timestamp metadata_m2_value metadata_m2_timestamp metadata_m3_value metadata_m3_timestamp metadata_m4_value metadata_m4_timestamp  comm1               comm2 share share1 hub1 hub2
      1    c1  myself              m1_1                    d1              m1_2                    d2              m1_3                    d3              m1_4                    d4  COMM1  COMM21529089656387   xxx    yyy   h1   h2

资料2：

[{ 'accuracy': 17, 'activity': [{ 'activity': [{ 'confidence': 100, 'type': 'STILL' } ], 'timestampMs': '1542652' } ], 'altitude': -10, 'latitudeE7': 3777321, 'longitudeE7': -122423125, 'timestampMs': '1542654', 'verticalAccuracy': 2 }, { 'accuracy': 17, 'activity': [{ 'activity': [{ 'confidence': 100, 'type': 'STILL' } ], 'timestampMs': '1542652' } ], 'altitude': -10, 'latitudeE7': 3777321, 'longitudeE7': -122423125, 'timestampMs': '1542654', 'verticalAccuracy': 2 }, { 'accuracy': 17, 'activity': [{ 'activity': [{ 'confidence': 100, 'type': 'STILL' } ], 'timestampMs': '1542652' } ], 'altitude': -10, 'latitudeE7': 3777321, 'longitudeE7': -122423125, 'timestampMs': '1542654', 'verticalAccuracy': 2 } ]

展平2：

df = pd.DataFrame([flatten_json(x) for x in data]) accuracy activity_0_activity_0_confidence activity_0_activity_0_type activity_0_timestampMs altitude latitudeE7 longitudeE7 timestampMs verticalAccuracy 17 100 STILL 1542652 -10 3777321 -122423125 1542654 2 17 100 STILL 1542652 -10 3777321 -122423125 1542654 2 17 100 STILL 1542652 -10 3777321 -122423125 1542654 2

资料3：

{ "1": { "VENUE": "JOEBURG", "COUNTRY": "HAE", "ITW": "XAD", "RACES": { "1": { "NO": 1, "TIME": "12:35" }, "2": { "NO": 2, "TIME": "13:10" }, "3": { "NO": 3, "TIME": "13:40" }, "4": { "NO": 4, "TIME": "14:10" }, "5": { "NO": 5, "TIME": "14:55" }, "6": { "NO": 6, "TIME": "15:30" }, "7": { "NO": 7, "TIME": "16:05" }, "8": { "NO": 8, "TIME": "16:40" } } }, "2": { "VENUE": "FOOBURG", "COUNTRY": "ABA", "ITW": "XAD", "RACES": { "1": { "NO": 1, "TIME": "12:35" }, "2": { "NO": 2, "TIME": "13:10" }, "3": { "NO": 3, "TIME": "13:40" }, "4": { "NO": 4, "TIME": "14:10" }, "5": { "NO": 5, "TIME": "14:55" }, "6": { "NO": 6, "TIME": "15:30" }, "7": { "NO": 7, "TIME": "16:05" }, "8": { "NO": 8, "TIME": "16:40" } } } }

展平3：

df = pd.DataFrame([flatten_json(data[key]) for key in data.keys()]) VENUE COUNTRY ITW RACES_1_NO RACES_1_TIME RACES_2_NO RACES_2_TIME RACES_3_NO RACES_3_TIME RACES_4_NO RACES_4_TIME RACES_5_NO RACES_5_TIME RACES_6_NO RACES_6_TIME RACES_7_NO RACES_7_TIME RACES_8_NO RACES_8_TIME JOEBURG HAE XAD 1 12:35 2 13:10 3 13:40 4 14:10 5 14:55 6 15:30 7 16:05 8 16:40 FOOBURG ABA XAD 1 12:35 2 13:10 3 13:40 4 14:10 5 14:55 6 15:30 7 16:05 8 16:40

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