JSON to multiple DataFrames with Pandas

Question

I have the following JSON data:

{
  "categories": [
    {
      "category_id": "11decadd",
      "name": "Com",
      "category_type": "Type",
      "position": 5,
      "vela_defined": True,
      "created_at": "2017-02-15 01:49:23 -0700",
      "updated_at": "2017-02-15 01:49:23 -0700"
    },
    {
      "category_id": "c7010763",
      "name": "none",
      "category_type": "EquipmentStatus",
      "position": 1,
      "vela_defined": True,
      "created_at": "2017-02-15 01:49:23 -0700",
      "updated_at": "2018-03-01 04:20:38 -0700"
    }
  ],
  "customizable_categories": [
    {
      "customizable_category_id": "435ae18b",
      "name": "NA",
      "category_id": "11decadd",
      "position": 1,
      "created_at": "2017-02-15 01:49:23 -0700",
      "updated_at": "2017-02-15 01:49:23 -0700"
    },
    {
      "customizable_category_id": "51e607d8",
      "name": "Third Party",
      "category_id": "fafab667",
      "position": 2,
      "created_at": "2017-02-15 01:49:23 -0700",
      "updated_at": "2017-02-15 01:49:23 -0700"
    }
  ],
  "equipment_category_status_sets": [

  ]
}

and Im attempting to turn it into 3x Pandas data frames (as named by the JSON top level entry)

But cant seem to get it to load at all. Any advice?

jezrael · Accepted Answer · 2018-05-17 13:00:57Z

2

I think need dictionary comprehension with DataFrame contructor for dictionary of DataFrames:

dfs = {k:pd.DataFrame(v) for k, v in d.items()}

print (dfs['categories'])

  category_id     ...      vela_defined
0    11decadd     ...              True
1    c7010763     ...              True

[2 rows x 7 columns]

print (dfs['customizable_categories'])

  category_id            ...                             updated_at
0    11decadd            ...              2017-02-15 01:49:23 -0700
1    fafab667            ...              2017-02-15 01:49:23 -0700

[2 rows x 6 columns]

print (dfs['equipment_category_status_sets'])

Empty DataFrame
Columns: []
Index: []

answered May 17, 2018 at 13:00

jezrael

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