Python如何合并嵌套JSON？求替代R中tbl_json的方案

你有如下嵌套JSON结构：

{
  "index": "exp-000005",
  "type": "_doc",
  "score": 9.502488,
  "source": {
    "verb": "REPLIED",
    "timestamp": "2022-01-20T08:14:00+00:00",
    "in_context": {
      "screen_width": "3440",
      "screen_height": "1440",
      "build_version": "7235",
      "question": "Hallo",
      "request_time": "403",
      "status": "success"
    }
  }
}

想要将其转为单层JSON：

{
  "index": "exp-000005",
  "type": "_doc",
  "score": 9.502488,
  "verb": "REPLIED",
  "timestamp": "2022-01-20T08:14:00+00:00",
  "screen_width": "3440",
  "screen_height": "1440",
  "build_version": "7235",
  "question": "Hallo",
  "request_time": "403",
  "status": "success"
}

之前在R中用tbl_json实现，以下是Python里的几种替代方案：

方法1：手动递归实现（无需第三方库） #

自己写递归函数遍历嵌套字典，直接将所有键值对展平到单层：

def flatten_json(nested_dict, parent_key='', sep=''):
    items = []
    for k, v in nested_dict.items():
        new_key = f"{parent_key}{sep}{k}" if parent_key else k
        if isinstance(v, dict):
            items.extend(flatten_json(v, new_key, sep=sep).items())
        else:
            items.append((new_key, v))
    return dict(items)

# 测试使用
nested_data = {
  "index": "exp-000005",
  "type": "_doc",
  "score": 9.502488,
  "source": {
    "verb": "REPLIED",
    "timestamp": "2022-01-20T08:14:00+00:00",
    "in_context": {
      "screen_width": "3440",
      "screen_height": "1440",
      "build_version": "7235",
      "question": "Hallo",
      "request_time": "403",
      "status": "success"
    }
  }
}

flat_data = flatten_json(nested_data)
print(flat_data)

这个函数会自动处理所有嵌套层级，输出结果和目标格式完全匹配。

方法2：使用flatten_json第三方库 #

专门用于JSON扁平化的工具库，用法简洁：

1. 先安装库：

pip install flatten_json

2. 代码实现：

from flatten_json import flatten

nested_data = {
  "index": "exp-000005",
  "type": "_doc",
  "score": 9.502488,
  "source": {
    "verb": "REPLIED",
    "timestamp": "2022-01-20T08:14:00+00:00",
    "in_context": {
      "screen_width": "3440",
      "screen_height": "1440",
      "build_version": "7235",
      "question": "Hallo",
      "request_time": "403",
      "status": "success"
    }
  }
}

# 设置sep=''避免键名添加分隔符，匹配目标格式
flat_data = flatten(nested_data, sep='')
print(flat_data)

方法3：使用pandas的json_normalize #

适合处理批量JSON数据，还能直接转换为DataFrame格式：

import pandas as pd

nested_data = {
  "index": "exp-000005",
  "type": "_doc",
  "score": 9.502488,
  "source": {
    "verb": "REPLIED",
    "timestamp": "2022-01-20T08:14:00+00:00",
    "in_context": {
      "screen_width": "3440",
      "screen_height": "1440",
      "build_version": "7235",
      "question": "Hallo",
      "request_time": "403",
      "status": "success"
    }
  }
}

# 展平数据并转为字典
df = pd.json_normalize(nested_data, sep='')
flat_data = df.to_dict(orient='records')[0]
print(flat_data)

内容的提问来源于stack exchange，提问作者threxx