如何使用 flatten_json 递归地展平嵌套的 JSON [英] How to flatten a nested JSON recursively, with flatten_json

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本文介绍了如何使用 flatten_json 递归地展平嵌套的 JSON的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

该软件包位于 pypi flatten-json 0.1.7 上，可以使用 flatten-json 0.1.7代码>pip install flatten-json
此问题特定于软件包的以下组件:

The package is on pypi flatten-json 0.1.7 and can be installed with pip install flatten-json
This question is specific to the following component of the package:

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

使用递归扁平化嵌套的`dicts`

在 Python 中递归思考
在 Python 中展平 JSON 对象

flatten_json 被用来解压一个超过 100000 列的文件

flatten_json has been used to unpack a file that ended up being over 100000 columns

是的，这个问题不包括那个.但是，如果你安装了flatten 包，有一个unflatten 方法，但我没有测试过.

Yes, this question doesn't cover that. However, if you install the flatten package, there is an unflatten method, but I haven't tested it.

How to flatten a `JSON` or `dict` 是一个常见的问题，有很多答案.

此答案侧重于使用 flatten_json 递归地展平嵌套的 dict 或 JSON.

How to flatten a `JSON` or `dict` is a common question, to which there are many answers.

This answer focuses on using flatten_json to recursively flatten a nested dict or JSON.

这个答案假设您已经将 JSON 或 dict 加载到某个变量(例如文件、api 等)中

在这种情况下，我们将使用 data

This answer assumes you already have the JSON or dict loaded into some variable (e.g. file, api, etc.)

In this case we will use data

它接受一个 dict，如函数类型提示所示.

It accepts a dict, as shown by the function type hint.

只是一个字典:{}

flatten_json(data)

[flatten_json(x) for x in data]

[flatten_json(data[key]) for key in data.keys()]

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

我通常将 data 扁平化为 pandas.DataFrame 以供进一步分析.

加载 pandas 和 import pandas as pd

I typically flatten data into a pandas.DataFrame for further analysis.

Load pandas with import pandas as pd

{
    "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

数据 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

数据 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

其他示例:

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如何使用 flatten_json 递归地展平嵌套的 JSON [英] How to flatten a nested JSON recursively, with flatten_json

问题描述

使用递归扁平化嵌套的`dicts`

推荐答案

How to flatten a `JSON` or `dict` 是一个常见的问题，有很多答案.

How to flatten a `JSON` or `dict` is a common question, to which there are many answers.

展平 1:

数据 2:

展平 2:

数据 3:

展平 3:

其他示例:

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如何使用 flatten_json 递归地展平嵌套的 JSON [英] How to flatten a nested JSON recursively, with flatten_json

问题描述

使用递归扁平化嵌套的dicts

推荐答案

How to flatten a JSON or dict 是一个常见的问题，有很多答案.

How to flatten a JSON or dict is a common question, to which there are many answers.

展平 1:

数据 2:

展平 2:

数据 3:

展平 3:

其他示例:

相关文章

Python最新文章

热门教程

热门工具

登录 关闭

使用递归扁平化嵌套的`dicts`

How to flatten a `JSON` or `dict` 是一个常见的问题，有很多答案.

How to flatten a `JSON` or `dict` is a common question, to which there are many answers.

登录关闭