从嵌套的 json 列表中展平 Pandas DataFrame

Flatten Pandas DataFrame from nested json list(从嵌套的 json 列表中展平 Pandas DataFrame)

问题描述

也许有人可以帮助我.我试图将以下 ist 整合到一个熊猫数据帧中:

perhaps somebody could help me. I tried to flat the following ist into a pandas dataframe:

[{u'_id': u'2',
  u'_index': u'list',
  u'_score': 1.4142135,
  u'_source': {u'name': u'name3'},
  u'_type': u'doc'},
 {u'_id': u'5',
  u'_index': u'list',
  u'_score': 1.4142135,
  u'_source': {u'dat': u'2016-12-12', u'name': u'name2'},
  u'_type': u'doc'},
 {u'_id': u'1',
  u'_index': u'list',
  u'_score': 1.4142135,
  u'_source': {u'name': u'name1'},
  u'_type': u'doc'}]

结果应该是这样的:

|_id   | _index | _score | name | dat        | _type |
------------------------------------------------------
|1     |list    |1.4142..| name1| nan        | doc   |
|2     |list    |1.4142..| name3| nan        | doc   |
|3     |list    |1.4142..| name1| 2016-12-12 | doc   |

但我所做的一切都无法得到想要的结果.我用过这样的东西:

But all I tried to do is not possible to get the desired result. I used something like this:

df = pd.concat(map(pd.DataFrame.from_dict, res['hits']['hits']), axis=1)['_source'].T

但是后来我丢失了 _source 字段之外的类型.我也尝试使用

But then I loose the types wich is outside the _source field. I also tried to work with

test = pd.DataFrame(list)
for index, row in test.iterrows():
  test.loc[index,'d'] = 

但我不知道如何使用字段 _source 并将其附加到原始数据框.

But I have no idea how to come to the point to use the field _source and append it to the original data frame.

有人知道如何做到这一点并成为想要的结果吗?

Did somebody has an idea how to to that and become the desired outcome?

推荐答案

使用 json_normalize:

from pandas.io.json import json_normalize  

df = json_normalize(data)
print (df)
  _id _index    _score _source.dat _source.name _type
0   2   list  1.414214         NaN        name3   doc
1   5   list  1.414214  2016-12-12        name2   doc
2   1   list  1.414214         NaN        name1   doc

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