convert a nested json response from API to a pandas dataframe using normalize-CodePudding

I've been trying to convert a json response from an api to a full panadas dataframe. I tried json normalize to achieve it unfortunately i was able to split it only to one level.

response = {
    "data": 
    {
        "result": [
            {
                "agent_info": {
                        "agent_id": "q321", 
                        "instances": [
                            {
                                "last_run_end": "2023-01-19T15:15:55.491Z", 
                                "mode": "Advanced", 
                                "is_enabled": "True", 
                                "run_duration": "00:00:00:031", 
                                "name": "john", 
                                "status": "Running", 
                                "node_id": "wq"
                            }, 
                            {
                                "last_run_end": "2023-01-19T15:15:55.491Z", 
                                "mode": "Advanced", 
                                "is_enabled": "True", 
                                "run_duration": "00:00:00:031", 
                                "name": "chris", 
                                "status": "Running", 
                                "node_id": "wq"
                            }
                        ]
                    }
                }, 
                {
                "agent_info": {
                        "agent_id": "q123", 
                        "instances": [
                            {
                                "last_run_end": "2023-01-19T15:15:55.491Z", 
                                "mode": "Advanced", 
                                "is_enabled": "True", 
                                "run_duration": "00:00:00:031", 
                                "name": "john", 
                                "status": "Running", 
                                "node_id": "wq"
                            }
                        ]
                    }
                }
            ]
        },
    "status": 200, 
    "servedBy": "ABC"
}
df=pd.json_normalize(response,["data",["result",]],["status","servedBy"])
df

Result

agent_info.agent_id                               agent_info.instances  \
0                q321  [{'last_run_end': '2023-01-19T15:15:55.491Z', ...   
1                q123  [{'last_run_end': '2023-01-19T15:15:55.491Z', ...   

  status servedBy  
0    200      ABC  
1    200      ABC

what i would like is that every key value to be a seperate column.. Any help or pointers ?

CodePudding user response：

You can first explode 'agent_info.instances' then create a dataframe from the exploded values that you will concat to the other columns:

df = pd.json_normalize(response,["data",["result",]],["status","servedBy"]).explode('agent_info.instances').reset_index(drop=True)
nested_val = pd.DataFrame(df['agent_info.instances'].values.tolist())
print(pd.concat([df.drop('agent_info.instances', axis=1), nested_val], axis=1))

output:

  agent_info.agent_id status servedBy              last_run_end      mode is_enabled  run_duration   name   status node_id
0                q321    200      ABC  2023-01-19T15:15:55.491Z  Advanced       True  00:00:00:031   john  Running      wq
1                q321    200      ABC  2023-01-19T15:15:55.491Z  Advanced       True  00:00:00:031  chris  Running      wq
2                q123    200      ABC  2023-01-19T15:15:55.491Z  Advanced       True  00:00:00:031   john  Running      wq

CodePudding user response：

Does this work for you?

df=pd.json_normalize(
    data = response,
    record_path = ["data","result","agent_info","instances"],
    meta = ["status","servedBy",["data","result","agent_info","agent_id"]],
    record_prefix = "agent.instance.",
)
print(df.T)

Output (transposed to fit better on the screen)