python - 使用 Pandas 将 JSON 转换为多个 DataFrame

Question

我有以下 JSON 数据:

{
  "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": [

  ]
}

我试图将它变成 3x Pandas 数据帧(由 JSON 顶级条目命名)

但似乎根本无法加载它。有什么建议吗？

Answer 1

我认为需要使用 DataFrame 构造函数来理解 DataFrame 字典:

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: []