I have 2 dicts which are as follows:
{
"states": 
         [
           {
             "status": "BV",  
             "median": 240.0 
            }, 
            {
             "status": "CORR", 
             "median": 720.0
            }, 
         ]
}   
and
{
"diseases": [
    {
        "status": "BV",  
        "median": 100.0,
        "disease_name": "Lupus"
    }, 
    {
        "status": "BV", 
        "median": 128.0,
        "disease_name": "Pulmonary Arterial Hypertension"
    }, 
    {
        "status": "CORR", 
        "median": 321.0,
        "disease_name": "Pulmonary Arterial Hypertension"
    }, 
    {
        "status": "CORR",
        "median": 670.0,
        "disease_name": "Rheumatology"
    }
]
}
How would one go about merging the 2 dicts into one using pandas so that the output is as follows:
{
"states": 
        [
            {
                "status": "BV",
                "median": 240.0, 
                "drilldown": "BV"
            }, 
            {
                "status": "CORR",
                "median": 720.0,
                "drilldown": "CORR"
            }
        ],
"drilldown": 
        [
          {
            "name":"BV",
            "data":
                    [
                      {
                        "median": 100.0,
                        "disease_name": "Lupus"
                      },
                      {
                        "median": 128.0, 
                        "disease_name": "Pulmonary Arterial Hypertension"
                      }
                    ]        
          },               
          {
            "name":"CORR",
            "data":
                    [
                      {
                        "median": 321.0,
                        "disease_name": "Lupus"
                      },
                      {
                        "median": 670.0, 
                        "disease_name": "Rheumatology"
                      }
                  ]        
            }
          ]
}
i did try giving it a shot by iterating over the lists but it looks very hacky and is computationally expensive.Was wondering if there is a better way to do it with Pandas ?
 
     
     
    