pandas DataFrame:标准化一个JSON列并与其他列合并 [英] pandas DataFrame: normalize one JSON column and merge with other columns

问题描述

我有一个pandas DataFrame，其中包含一列，其中包含多个JSON数据项作为字典列表.我想规范化JSON列并复制非JSON列:

I have a pandas DataFrame containing one column with multiple JSON data items as list of dicts. I want to normalize the JSON column and duplicate the non-JSON columns:

# creating dataframe
df_actions = pd.DataFrame(columns=['id', 'actions'])
rows = [[12,json.loads('[{"type": "a","value": "17"},{"type": "b","value": "19"}]')],
   [15, json.loads('[{"type": "a","value": "1"},{"type": "b","value": "3"},{"type": "c","value": "5"}]')]]
df_actions.loc[0] = rows[0]
df_actions.loc[1] = rows[1]

>>>df_actions
   id                                            actions
0  12  [{'type': 'a', 'value': '17'}, {'type': 'b', '...
1  15  [{'type': 'a', 'value': '1'}, {'type': 'b', 'v...

我想要

>>>df_actions_parsed
   id      type    value
   12      a        17
   12      b        19
   15      a        1
   15      b        3
   15      c        5

我可以使用以下方式标准化JSON数据:

I can normalize JSON data using:

pd.concat([pd.DataFrame(json_normalize(x)) for x in df_actions['actions']],ignore_index=True)

但是我不知道如何将其重新连接到原始DataFrame的id列.

but I don't know how to join that back to the id column of the original DataFrame.

推荐答案

您可以使用 concat 与dict comprehension和 join 更改为原始内容:

You can use concat with dict comprehension with pop for extract column, remove second level and join to original:

df1 = (pd.concat({i: pd.DataFrame(x) for i, x in df_actions.pop('actions').items()})
         .reset_index(level=1, drop=True)
         .join(df_actions)
         .reset_index(drop=True))

与什么相同:

df1 = (pd.concat({i: json_normalize(x) for i, x in df_actions.pop('actions').items()})
         .reset_index(level=1, drop=True)
         .join(df_actions)
         .reset_index(drop=True))

---

print (df1)
  type value  id
0    a    17  12
1    b    19  12
2    a     1  15
3    b     3  15
4    c     5  15

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