Python Pandas-拼合嵌套JSON

本文介绍了Python Pandas-拼合嵌套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

使用我试图转换为Pandas数据框的嵌套JSON数据. json_normalize 函数提供了一种方法要做到这一点.

Working with Nested JSON data that I am trying to transform to a Pandas dataframe. The json_normalize function offers a way to accomplish this.

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

我利用该功能对位置进行了归一化，但是，嵌套部分活动"不是平坦的.

I utilized the function to normalize locations, however, the nested part 'activity' is not flat.

这是我的尝试:

activity_data = json_normalize(d, 'locations', ['activity','type', 'confidence'],
                               meta_prefix='Prefix.',
                               errors='ignore')

DataFrame:

[{u'activity': [{u'confidence': 100, u'type': ...   -10.0   NaN 377777377   -1224229340 1542652023196

活动"列仍具有嵌套元素，我需要在其自己的列中对其进行拆包.

The Activity column still has nested elements which I need unpacked in its own column.

任何建议/提示将不胜感激.

Any suggestions/tips would be much appreciated.

使用递归展平嵌套的`dicts`

在Python中进行递归思考
在Python中平整JSON对象
展平
以下函数将用于展平_source_list

Use recursion to flatten the nested `dicts`

Thinking Recursively in Python
Flattening JSON objects in Python
flatten
The following function, will be used to flatten _source_list

def flatten_json(nested_json: dict, exclude: list=['']) -> 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}_')
        elif type(x) is list:
            i = 0
            for a in x:
                flatten(a, f'{name}{i}_')
                i += 1
        else:
            out[name[:-1]] = x

    flatten(nested_json)
    return out

数据:

要创建数据集，我使用了给定的数据.
data是json

Data:

To create the dataset, I used the given data.
data is a json

data = {'locations': [{'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}]}

使用`flatten_json`:

df = pd.DataFrame([flatten_json(x) for x in data['locations']])

输出:

 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

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dicts