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小编典典

如何使用flatten_json递归地扁平化嵌套的JSON？

json

此问题特定于flatten_json从[GitHub

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

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。

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2020-07-27

共1个答案

小编典典

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

假设：

  • 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

如何data加载到flatten_json：

  • 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']]

实际示例：

  • 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
2020-07-27