pythonjsonpandasdataframe

Convert Pandas Dataframe to nested JSON


I am trying to convert a Pandas Dataframe to a nested JSON. The function .to_json() doesn't give me enough flexibility for my aim.

Here are some data points of the dataframe (in csv, comma separated):

,ID,Location,Country,Latitude,Longitude,timestamp,tide
0,1,BREST,FRA,48.383,-4.495,1807-01-01,6905.0  
1,1,BREST,FRA,48.383,-4.495,1807-02-01,6931.0  
2,1,BREST,FRA,48.383,-4.495,1807-03-01,6896.0  
3,1,BREST,FRA,48.383,-4.495,1807-04-01,6953.0  
4,1,BREST,FRA,48.383,-4.495,1807-05-01,7043.0  
2508,7,CUXHAVEN 2,DEU,53.867,8.717,1843-01-01,7093.0  
2509,7,CUXHAVEN 2,DEU,53.867,8.717,1843-02-01,6688.0  
2510,7,CUXHAVEN 2,DEU,53.867,8.717,1843-03-01,6493.0  
2511,7,CUXHAVEN 2,DEU,53.867,8.717,1843-04-01,6723.0  
2512,7,CUXHAVEN 2,DEU,53.867,8.717,1843-05-01,6533.0  
4525,9,MAASSLUIS,NLD,51.918,4.25,1848-02-01,6880.0  
4526,9,MAASSLUIS,NLD,51.918,4.25,1848-03-01,6700.0  
4527,9,MAASSLUIS,NLD,51.918,4.25,1848-04-01,6775.0  
4528,9,MAASSLUIS,NLD,51.918,4.25,1848-05-01,6580.0  
4529,9,MAASSLUIS,NLD,51.918,4.25,1848-06-01,6685.0  
6540,8,WISMAR 2,DEU,53.898999999999994,11.458,1848-07-01,6957.0  
6541,8,WISMAR 2,DEU,53.898999999999994,11.458,1848-08-01,6944.0  
6542,8,WISMAR 2,DEU,53.898999999999994,11.458,1848-09-01,7084.0  
6543,8,WISMAR 2,DEU,53.898999999999994,11.458,1848-10-01,6898.0  
6544,8,WISMAR 2,DEU,53.898999999999994,11.458,1848-11-01,6859.0  
8538,10,SAN FRANCISCO,USA,37.806999999999995,-122.465,1854-07-01,6909.0  
8539,10,SAN FRANCISCO,USA,37.806999999999995,-122.465,1854-08-01,6940.0  
8540,10,SAN FRANCISCO,USA,37.806999999999995,-122.465,1854-09-01,6961.0  
8541,10,SAN FRANCISCO,USA,37.806999999999995,-122.465,1854-10-01,6952.0  
8542,10,SAN FRANCISCO,USA,37.806999999999995,-122.465,1854-11-01,6952.0  

There is a lot of repetitive information and I would like to have a JSON like this:

[
{
    "ID": 1,
    "Location": "BREST",
    "Latitude": 48.383,
    "Longitude": -4.495,
    "Country": "FRA",
    "Tide-Data": {
        "1807-02-01": 6931,
        "1807-03-01": 6896,
        "1807-04-01": 6953,
        "1807-05-01": 7043
    }
},
{
    "ID": 5,
    "Location": "HOLYHEAD",
    "Latitude": 53.31399999999999,
    "Longitude": -4.62,
    "Country": "GBR",
    "Tide-Data": {
        "1807-02-01": 6931,
        "1807-03-01": 6896,
        "1807-04-01": 6953,
        "1807-05-01": 7043
    }
}
]

How could I achieve this?

Code to reproduce the dataframe:

# input json
json_str = '[{"ID":1,"Location":"BREST","Country":"FRA","Latitude":48.383,"Longitude":-4.495,"timestamp":"1807-01-01","tide":6905},{"ID":1,"Location":"BREST","Country":"FRA","Latitude":48.383,"Longitude":-4.495,"timestamp":"1807-02-01","tide":6931},{"ID":1,"Location":"BREST","Country":"DEU","Latitude":48.383,"Longitude":-4.495,"timestamp":"1807-03-01","tide":6896},{"ID":7,"Location":"CUXHAVEN 2","Country":"DEU","Latitude":53.867,"Longitude":-8.717,"timestamp":"1843-01-01","tide":7093},{"ID":7,"Location":"CUXHAVEN 2","Country":"DEU","Latitude":53.867,"Longitude":-8.717,"timestamp":"1843-02-01","tide":6688},{"ID":7,"Location":"CUXHAVEN 2","Country":"DEU","Latitude":53.867,"Longitude":-8.717,"timestamp":"1843-03-01","tide":6493}]'

# load json object
data_list = json.loads(json_str)

# create dataframe
df = pd.json_normalize(data_list, None, None)

Solution

  • 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'))