Flatten JSON with Dask DataFrames

I'm trying to flatten JSON arrays object (no files .json) in Dask dataframes, because I have a lot of data and my RAM is consumed by the processes are running constantly, so I need a solution in a parallel form.

That's the JSON I have:

[ {
        "id": "0001",
        "name": "Stiven",
        "location": [{
                "country": "Colombia",
                "department": "Choco",
                "city": "Quibdo"
            }, {
                "country": "Colombia",
                "department": "Antioquia",
                "city": "Medellin"
            }, {
                "country": "Colombia",
                "department": "Cundinamarca",
                "city": "Bogota"
            }
        ]
    }, {
        "id": "0002",
        "name": "Jhon Jaime",
        "location": [{
                "country": "Colombia",
                "department": "Valle del Cauca",
                "city": "Cali"
            }, {
                "country": "Colombia",
                "department": "Putumayo",
                "city": "Mocoa"
            }, {
                "country": "Colombia",
                "department": "Arauca",
                "city": "Arauca"
            }
        ]
    }, {
        "id": "0003",
        "name": "Francisco",
        "location": [{
                "country": "Colombia",
                "department": "Atlantico",
                "city": "Barranquilla"
            }, {
                "country": "Colombia",
                "department": "Bolivar",
                "city": "Cartagena"
            }, {
                "country": "Colombia",
                "department": "La Guajira",
                "city": "Riohacha"
            }
        ]
    }
]

That's the dataframe I have:

index   id    name         location
0       0001  Stiven       [{'country':'Colombia', 'department': 'Choco', 'city': 'Quibdo'}, {'country':'Colombia', 'department': 'Antioquia', 'city': 'Medellin'}, {'country':'Colombia', 'department': 'Cundinamarca', 'city': 'Bogota'}]
1       0002  Jhon Jaime   [{'country':'Colombia', 'department': 'Valle del Cauca', 'city': 'Cali'}, {'country':'Colombia', 'department': 'Putumayo', 'city': 'Mocoa'}, {'country':'Colombia', 'department': 'Arauca', 'city': 'Arauca'}]
2       0003  Francisco    [{'country':'Colombia', 'department': 'Atlantico', 'city': 'Barranquilla'}, {'country':'Colombia', 'department': 'Bolivar', 'city': 'Cartagena'}, {'country':'Colombia', 'department': 'La Guajira', 'city': 'Riohacha'}] 

I need to convert to dataframe per id something like this:

index   id    name         country   department       city
0       0001  Stiven       Colombia  Choco            Quibdo
1       0001  Stiven       Colombia  Antioquia        Medellin
2       0001  Stiven       Colombia  Cundinamarca     Bogota
3       0002  Jhon Jaime   Colombia  Valle del Cauca  Cali
4       0002  Jhon Jaime   Colombia  Putumayo         Mocoa
5       0002  Jhon Jaime   Colombia  Arauca           Arauca
6       0003  Francisco    Colombia  Atlantico        Barranquilla
7       0003  Francisco    Colombia  Bolivar          Cartagena 
8       0003  Francisco    Colombia  La Guajira       Riohacha   

All process must be in parallel with Dask. Any recommendation?

Thanks in advance.

I recommend solving this problem first with Pandas dataframes and then using the .map_partitions function to apply that function to all Pandas-partitions within the Dask dataframe.