0

我有以下 JSON 结构：

{
  "comments_v2": [
    {
      "timestamp": 1196272984,
      "data": [
        {
          "comment": {
            "timestamp": 1196272984,
            "comment": "OSI Beach Party Weekend, CA",
            "author": "xxxx"
          }
        }
      ],
      "title": "xxxx commented on his own photo."
    },
    {
      "timestamp": 1232918783,
      "data": [
        {
          "comment": {
            "timestamp": 1232918783,
            "comment": "We'll see about that.",
            "author": "xxxx"
          }
        }
      ]
    }
  ]
}

我正在尝试将此 JSON 扁平化为 pandas 数据框，这是我的解决方案：

# Read file
df = pd.read_json(codecs.open(infile, "r", "utf-8-sig"))

# Normalize
df = pd.json_normalize(df["comments_v2"])
child_column = pd.json_normalize(df["data"])
child_column = pd.concat([child_column.drop([0], axis=1), child_column[0].apply(pd.Series)], axis=1)
df_merge = df.join(child_column)
df_merge.drop(["data"], axis=1, inplace=True)

生成的数据框如下：

时间戳 标题 评论.时间戳 评论.comment 评论作者 评论组
1196272984 xxxx 评论了他自己的照片 1196272984 OSI 海滩派对周末，加利福尼亚 XXXXXX 钠

有没有更简单的方法来扁平化 JSON 以获得上面显示的结果？

谢谢！

4

2 回答 2

1

用作record_path='data'的参数pd.json_normalize：

import json
import codecs

with codecs.open(infile, 'r', 'utf-8-sig') as jsonfile:
    data = json.load(jsonfile)
    df = pd.json_normalize(data['comments_v2'], 'data')

输出：

>>> df
   comment.timestamp              comment.comment comment.author
0         1196272984  OSI Beach Party Weekend, CA           xxxx
1         1232918783        We'll see about that.           xxxx
于 2021-11-07T19:06:34.777 回答
1

尝试 flatten_json （在本例中将 json 设置为 js）

from flatten_json import flatten^M
dic_flattened = (flatten(d, '.') for d in list(js['comments_v2']))^M
df = pd.DataFrame(dic_flattened)^M
df


    timestamp  data.0.comment.timestamp       data.0.comment.comment data.0.comment.author                             title
0  1196272984                1196272984  OSI Beach Party Weekend, CA                  xxxx  xxxx commented on his own photo.
1  1232918783                1232918783        We'll see about that.                  xxxx                               NaN
于 2021-11-07T19:30:47.300 回答