Convert Pandas Dataframe to nested JSON

UPDATE:

j = (df.groupby(['ID','Location','Country','Latitude','Longitude'])
       .apply(lambda x: x[['timestamp','tide']].to_dict('records'))
       .reset_index()
       .rename(columns={0:'Tide-Data'})
       .to_json(orient="records"))
     

Result (formatted):

In [103]: print(json.dumps(json.loads(j), indent=2, sort_keys=True))
[
  {
    "Country": "FRA",
    "ID": 1,
    "Latitude": 48.383,
    "Location": "BREST",
    "Longitude": -4.495,
    "Tide-Data": [
      {
        "tide": 6905.0,
        "timestamp": "1807-01-01"
      },
      {
        "tide": 6931.0,
        "timestamp": "1807-02-01"
      },
      {
        "tide": 6896.0,
        "timestamp": "1807-03-01"
      },
      {
        "tide": 6953.0,
        "timestamp": "1807-04-01"
      },
      {
        "tide": 7043.0,
        "timestamp": "1807-05-01"
      }
    ]
  },
  {
    "Country": "DEU",
    "ID": 7,
    "Latitude": 53.867,
    "Location": "CUXHAVEN 2",
    "Longitude": 8.717,
    "Tide-Data": [
      {
        "tide": 7093.0,
        "timestamp": "1843-01-01"
      },
      {
        "tide": 6688.0,
        "timestamp": "1843-02-01"
      },
      {
        "tide": 6493.0,
        "timestamp": "1843-03-01"
      },
      {
        "tide": 6723.0,
        "timestamp": "1843-04-01"
      },
      {
        "tide": 6533.0,
        "timestamp": "1843-05-01"
      }
    ]
  },
  {
    "Country": "DEU",
    "ID": 8,
    "Latitude": 53.899,
    "Location": "WISMAR 2",
    "Longitude": 11.458,
    "Tide-Data": [
      {
        "tide": 6957.0,
        "timestamp": "1848-07-01"
      },
      {
        "tide": 6944.0,
        "timestamp": "1848-08-01"
      },
      {
        "tide": 7084.0,
        "timestamp": "1848-09-01"
      },
      {
        "tide": 6898.0,
        "timestamp": "1848-10-01"
      },
      {
        "tide": 6859.0,
        "timestamp": "1848-11-01"
      }
    ]
  },
  {
    "Country": "NLD",
    "ID": 9,
    "Latitude": 51.918,
    "Location": "MAASSLUIS",
    "Longitude": 4.25,
    "Tide-Data": [
      {
        "tide": 6880.0,
        "timestamp": "1848-02-01"
      },
      {
        "tide": 6700.0,
        "timestamp": "1848-03-01"
      },
      {
        "tide": 6775.0,
        "timestamp": "1848-04-01"
      },
      {
        "tide": 6580.0,
        "timestamp": "1848-05-01"
      },
      {
        "tide": 6685.0,
        "timestamp": "1848-06-01"
      }
    ]
  },
  {
    "Country": "USA",
    "ID": 10,
    "Latitude": 37.807,
    "Location": "SAN FRANCISCO",
    "Longitude": -122.465,
    "Tide-Data": [
      {
        "tide": 6909.0,
        "timestamp": "1854-07-01"
      },
      {
        "tide": 6940.0,
        "timestamp": "1854-08-01"
      },
      {
        "tide": 6961.0,
        "timestamp": "1854-09-01"
      },
      {
        "tide": 6952.0,
        "timestamp": "1854-10-01"
      },
      {
        "tide": 6952.0,
        "timestamp": "1854-11-01"
      }
    ]
  }
]

OLD answer:

You can do it using groupby(), apply() and to_json() methods:

j = (df.groupby(['ID','Location','Country','Latitude','Longitude'], as_index=False)
       .apply(lambda x: dict(zip(x.timestamp,x.tide)))
       .reset_index()
       .rename(columns={0:'Tide-Data'})
       .to_json(orient="records"))

Output:

In [112]: print(json.dumps(json.loads(j), indent=2, sort_keys=True))
[
  {
    "Country": "FRA",
    "ID": 1,
    "Latitude": 48.383,
    "Location": "BREST",
    "Longitude": -4.495,
    "Tide-Data": {
      "1807-01-01": 6905.0,
      "1807-02-01": 6931.0,
      "1807-03-01": 6896.0,
      "1807-04-01": 6953.0,
      "1807-05-01": 7043.0
    }
  },
  {
    "Country": "DEU",
    "ID": 7,
    "Latitude": 53.867,
    "Location": "CUXHAVEN 2",
    "Longitude": 8.717,
    "Tide-Data": {
      "1843-01-01": 7093.0,
      "1843-02-01": 6688.0,
      "1843-03-01": 6493.0,
      "1843-04-01": 6723.0,
      "1843-05-01": 6533.0
    }
  },
  {
    "Country": "DEU",
    "ID": 8,
    "Latitude": 53.899,
    "Location": "WISMAR 2",
    "Longitude": 11.458,
    "Tide-Data": {
      "1848-07-01": 6957.0,
      "1848-08-01": 6944.0,
      "1848-09-01": 7084.0,
      "1848-10-01": 6898.0,
      "1848-11-01": 6859.0
    }
  },
  {
    "Country": "NLD",
    "ID": 9,
    "Latitude": 51.918,
    "Location": "MAASSLUIS",
    "Longitude": 4.25,
    "Tide-Data": {
      "1848-02-01": 6880.0,
      "1848-03-01": 6700.0,
      "1848-04-01": 6775.0,
      "1848-05-01": 6580.0,
      "1848-06-01": 6685.0
    }
  },
  {
    "Country": "USA",
    "ID": 10,
    "Latitude": 37.807,
    "Location": "SAN FRANCISCO",
    "Longitude": -122.465,
    "Tide-Data": {
      "1854-07-01": 6909.0,
      "1854-08-01": 6940.0,
      "1854-09-01": 6961.0,
      "1854-10-01": 6952.0,
      "1854-11-01": 6952.0
    }
  }
]

PS if you don’t care of idents you can write directly to JSON file:

(df.groupby(['ID','Location','Country','Latitude','Longitude'], as_index=False)
   .apply(lambda x: dict(zip(x.timestamp,x.tide)))
   .reset_index()
   .rename(columns={0:'Tide-Data'})
   .to_json('/path/to/file_name.json', orient="records"))