Question 109538 in Json Question posted in
Our archive of expertly curated questions and answers provides insights and solutions to common problems related to this popular data interchange format. From parsing and manipulating JSON data to integrating it with various programming languages and web services, our archive has got you covered. Start exploring today and take your JSON skills to the next level

How to convert dataframe into multiple json objects?

I would like to ask help on how to creating nested json from dataframe.
I have a dataframe

 name     value  xAxis1    yAxis1  xAxis2    yAxis2           lineStyle
0  LCL  0.205512       0  0.205512     200  0.205512  {'type': 'dashed'}
1  UCL  0.327907       0  0.327907     200  0.327907  {'type': 'dashed'}
2   CL  0.269737       0  0.269737     200  0.269737  {'type': 'dashed'}

The problem is I want to separate into two json objects.
Here is my attempt:

[
    {
        "name":x['name'],
        "xAxis":x['xAxis1'],
        "yAxis":x['yAxis1'],
        "lineStyle":x['lineStyle']} for _, x in global_line.iterrows()
]

It gives the result:

[{'name': 'LCL',
  'xAxis': 0,
  'yAxis': 0.20551206141285452,
  'lineStyle': {'type': 'dashed'}},
 {'name': 'UCL',
  'xAxis': 0,
  'yAxis': 0.327906798236399,
  'lineStyle': {'type': 'dashed'}},
 {'name': 'CL',
  'xAxis': 0,
  'yAxis': 0.2697369344707218,
  'lineStyle': {'type': 'dashed'}}]

The response that I want :

{
    "data": [
        [
            {
                "name": "LCL",
                "xAxis1": 0,
                "yAxis1": 0.205512,
                "lineStyle": {
                    "type": "dashed"
                }
            },
            {
                "xAxis2": 200,
                "yAxis2": 0.205512
            }
        ],
        [
            {
                "name": "UCL",
                "xAxis1": 0,
                "yAxis1": 0.327907,
                "lineStyle": {
                    "type": "dashed"
                }
            },
            {
                "xAxis2": 200,
                "yAxis2": 0.327907
            }
        ]
    ], 
...
}

Any help and suggestion would be much appreciated.
Thank you.

Tags:

3

Answers


  1. 0

    Use DataFrame.to_dict with join in list comprehension:

    a = df[['name','xAxis1','yAxis1','lineStyle']].to_dict(orient='records')
b = df[['xAxis2','yAxis2']].to_dict(orient='records')

d = {"data": [[x,  y] for x, y in zip(a, b)]}

    {
    'data': [
        [{
            'name': 'LCL',
            'xAxis1': 0,
            'yAxis1': 0.205512,
            'lineStyle': {
                'type': 'dashed'
            }
        }, {
            'xAxis2': 200,
            'yAxis2': 0.205512
        }],
        [{
            'name': 'UCL',
            'xAxis1': 0,
            'yAxis1': 0.327907,
            'lineStyle': {
                'type': 'dashed'
            }
        }, {
            'xAxis2': 200,
            'yAxis2': 0.327907
        }],
        [{
            'name': 'CL',
            'xAxis1': 0,
            'yAxis1': 0.269737,
            'lineStyle': {
                'type': 'dashed'
            }
        }, {
            'xAxis2': 200,
            'yAxis2': 0.269737
        }]
    ]
}
    Login or Signup to reply.
  2. 0

    Based on of your code (but using itertuples that is more efficient than iterrows):

    {'data': [[{k: getattr(t, k) for k in ['name', 'xAxis1', 'yAxis1', 'lineStyle']},
           {k: getattr(t, k) for k in ['xAxis2', 'yAxis2']},
          ] for t in global_line.itertuples()]}

    Output:

    {'data': [[{'name': 'LCL',
    'xAxis1': 0,
    'yAxis1': 0.205512,
    'lineStyle': "{'type': 'dashed'}"},
   {'xAxis2': 200, 'yAxis2': 0.205512}],
  [{'name': 'UCL',
    'xAxis1': 0,
    'yAxis1': 0.327907,
    'lineStyle': "{'type': 'dashed'}"},
   {'xAxis2': 200, 'yAxis2': 0.327907}],
  [{'name': 'CL',
    'xAxis1': 0,
    'yAxis1': 0.269737,
    'lineStyle': "{'type': 'dashed'}"},
   {'xAxis2': 200, 'yAxis2': 0.269737}]]}
    Login or Signup to reply.
  3. 0

    Another option with keys separation:

    ax2 = ['xAxis2', 'yAxis2']  # secondary keys
main_keys = df.columns[~df.columns.isin(ax2)]
[[{k:d[k] for k in main_keys}, {k:d[k] for k in ax2}] for d in df.to_dict('records')]

    [[{'name': 'LCL',
   'value': 0.205512,
   'xAxis1': 0,
   'yAxis1': 0.205512,
   'lineStyle': {'type': 'dashed'}},
  {'xAxis2': 200, 'yAxis2': 0.205512}],
 [{'name': 'UCL',
   'value': 0.327907,
   'xAxis1': 0,
   'yAxis1': 0.327907,
   'lineStyle': {'type': 'dashed'}},
  {'xAxis2': 200, 'yAxis2': 0.327907}],
 [{'name': 'CL',
   'value': 0.269737,
   'xAxis1': 0,
   'yAxis1': 0.269737,
   'lineStyle': {'type': 'dashed'}},
  {'xAxis2': 200, 'yAxis2': 0.269737}]]
    Login or Signup to reply.
Please signup or login to give your own answer.

Back To Top
Search