在 Python 中展开嵌套的 JSON

Question

I'm new to Python and I'm quite stuck (I've gone through multiple other stackoverflows and other sites and still can't get this to work).

I've the below json coming out of an API connection

    {
   "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
            }
         ]
      }
   ]
}

and what I'm mainly looking to get out of it is (or at least something as close as)

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

Is something like that possible? I've tried multiple things and I either get the whole of this out in one line or just get different errors.

Answer 1

The error you got indicates you missed that some of your values are actually a dictionary within an array.

Assuming you want to flatten your json file to retrieve the following keys: mediaType , queueId , count .

These can be retrieved by the following sample code:

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") 

If your data and metrics keys will have multiple indices you will have to use a for loop to retrieve every count value accordingly.

Answer 2

Assuming that the format of the API response is always the same, have you considered hardcoding the extraction of the data you want?

This should work: With response defined as the API output:

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'
            }
         ]
      }
   ]
}

You can extract the results as follows:

results = response["results"][0]

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

which gives

{
    '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]