Json with correct Structure in Python

Question

I have a csv file which i am reading in python and I am producing this JSON object using

data_df_json = data_df.to_json(orient='records',date_format = 'iso');

{  
      {  
         "A":"aaa",
         "B":"nnn",
         "C":"ccc",
         "D":100000036789562,
         "E":"sdsds",
         "F":130346,
         "G":"2017-09-05T16:36:30.000Z"
      }

}

I am trying to achieve this

{  
"Items":[
      {  
         "A":"aaa",
         "B":"nnn",
         "C":"ccc",
         "D":100000036789562,
         "E":"sdsds",
         "F":130346,
         "G":"2017-09-05T16:36:30.000Z"
      }
  ]
}

So what i did was to insert in a default column and set in the default values to Items. I then did a group by

data_df_json = engagement_data_df.groupby('Items').apply(lambda df: data_df.to_dict(orient='records')).to_json(date_format='iso')

Its giving me the right format but now with an additional field called Items

{  
"Items":[
      {  
         "A":"aaa",
         "B":"nnn",
         "C":"ccc",
         "D":100000036789562,
         "E":"sdsds",
         "F":130346,
         "G":"2017-09-05T16:36:30.000Z",
         "Items": "Items"

      }
  ]
}

I dont want the items contained in my object. Is there a better way?

Tiny.D · Accepted Answer · 2017-10-26 02:39:27Z

1

The issue is that you insert in a default column and set in the default values to Items, the data_df will contain the new column Items, that's why there is an additional field called Items, you can drop the column before you convert to dict like this:

data_df_json = engagement_data_df.groupby('Items').apply(lambda df: data_df.drop('Items', axis=1).to_dict(orient='records')).to_json(date_format='iso')

answered Oct 26, 2017 at 2:39

Tiny.D

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