在 Python 中展平嵌套的 JSON

Question

我是 Python 新手，我很困惑（我已经浏览了多个其他 stackoverflow 和其他网站，但仍然无法使其正常工作）。

我有以下来自 API 连接的 json

    {
   "results":[
      {
         "group":{
            "mediaType":"chat",
            "queueId":"67d9fb5e-26b2-4db5-b062-bbcfa8d2ca0d"
         },
         "data":[
            {
               "interval":"2021-01-14T13:12:19.000Z/2022-01-14T13:12:19.000Z",
               "metrics":[
                  {
                     "metric":"nOffered",
                     "qualifier":null,
                     "stats":{
                        "max":null,
                        "min":null,
                        "count":14,
                        "count_negative":null,
                        "count_positive":null,
                        "sum":null,
                        "current":null,
                        "ratio":null,
                        "numerator":null,
                        "denominator":null,
                        "target":null
                     }
                  }
               ],
               "views":null
            }
         ]
      }
   ]
}

而我主要想摆脱的是（或至少是类似的东西）

MediaType	QueueId	NOffered
Chat	67d9fb5e-26b2-4db5-b062-bbcfa8d2ca0d	14

这样的事情可能吗？我已经尝试了多种方法，我要么在一行中将所有内容全部搞定，要么只是得到不同的错误。

Answer 1

您得到的错误表明您错过了某些值实际上是数组中的字典。

假设您想要展平您的 json 文件以检索以下键：mediaType、queueId、count。

这些可以通过以下示例代码检索：

import json
with open(path_to_json_file, 'r') as f:
    json_dict = json.load(f)

for result in json_dict.get("results"):
    media_type = result.get("group").get("mediaType")
    queue_id = result.get("group").get("queueId")
    n_offered = result.get("data")[0].get("metrics")[0].get("count") 

如果您的 data 和 metrics 键有多个索引，您将不得不使用 for 循环相应地检索每个 count 值。

Answer 2

假设 API 响应的格式始终相同，您是否考虑过硬编码提取所需的数据？

这应该可以工作：将response 定义为 API 输出：

response =     {
   "results":[
      {
          "group":{
            "mediaType":"chat",
            "queueId":"67d9fb5e-26b2-4db5-b062-bbcfa8d2ca0d"
          },
          "data":[
            {
               "interval":"2021-01-14T13:12:19.000Z/2022-01-14T13:12:19.000Z",
               "metrics":[
                  {
                     "metric":"nOffered",
                     "qualifier":'null',
                     "stats":{
                        "max":'null',
                        "min":'null',
                        "count":14,
                        "count_negative":'null',
                        "count_positive":'null',
                        "sum":'null',
                        "current":'null',
                        "ratio":'null',
                        "numerator":'null',
                        "denominator":'null',
                        "target":'null'
                     }
                  }
               ],
               "views":'null'
            }
         ]
      }
   ]
}

您可以按如下方式提取结果：

results = response["results"][0]

{
    "mediaType": results["group"]["mediaType"],
    "queueId": results["group"]["queueId"],
    "nOffered": results["data"][0]["metrics"][0]["stats"]["count"]
}

给了

{
    'mediaType': 'chat',
    'queueId': '67d9fb5e-26b2-4db5-b062-bbcfa8d2ca0d',
    'nOffered': 14
}

Answer 3

import pandas as pd
tree=     {
   "results":[
      {
         "group":{
            "mediaType":"chat",
            "queueId":"67d9fb5e-26b2-4db5-b062-bbcfa8d2ca0d"
         },
         "data":[
            {
               "interval":"2021-01-14T13:12:19.000Z/2022-01-14T13:12:19.000Z",
               "metrics":[
                  {
                     "metric":"nOffered",
                     "qualifier":"null",
                     "stats":{
                        "max":"null",
                        "min":"null",
                        "count":14,
                        "count_negative":"null",
                        "count_positive":"null",
                        "sum":"null",
                        "current":"null",
                        "ratio":"null",
                        "numerator":"null",
                        "denominator":"null",
                        "target":"null"
                     }
                  }
               ],
               "views":"null"
            }
         ]
      }
   ]
}


def traverse_parser_dfs(master_tree):
  flatten_tree_node = []
  def _process_leaves(tree:dict,prefix:str = "node", tree_node:dict = dict(), update:bool = True):
      is_nested = False
      if isinstance(tree,dict):
        for k in tree.keys():
            if type(tree[k]) == str:
                colName = prefix + "_" + k
                tree_node[colName] = tree[k]
            elif type(tree[k]) == dict:
                prefix += "_" + k
                leave = tree[k]
                _process_leaves(leave,prefix = prefix, tree_node = tree_node, update = False)
        for k in tree.keys():
            if type(tree[k]) == list:
                is_nested = True
                prefix += "_" + k
                for leave in tree[k]:
                    _process_leaves(leave,prefix = prefix, tree_node = tree_node.copy())
        if not is_nested and update:
            flatten_tree_node.append(tree_node)
        
  _process_leaves(master_tree)
  df = pd.DataFrame(flatten_tree_node)
  df.columns = df.columns.str.replace("@", "_")
  df.columns = df.columns.str.replace("#", "_")
  return df


print(traverse_parser_dfs(tree))


  node_results_group_mediaType  ... node_results_group_data_metrics_stats_target
0                         chat  ...                                         null

[1 rows x 16 columns]