如何在 Python 中规范化包含列表应保留为列表的 json 文件 |熊猫？

编程问答 2022-06-02

问题描述

我正在尝试使用 json_normalize 函数将 json 文件转换为数据帧。

源 JSON

  • json 是一个字典列表，看起来像这样：

    {
      "sport_key": "basketball_ncaab","sport_nice": "NCAAB","teams": [
          "Bryant Bulldogs","Wagner Seahawks"
      ],"commence_time": 1608152400,"home_team": "Bryant Bulldogs","sites": [
          {
              "site_key": "marathonbet","site_nice": "Marathon Bet","last_update": 1608156452,"odds": {
                  "h2h": [
                      1.28,3.54
                  ]
              }
          },{
              "site_key": "sport888","site_nice": "888sport","odds": {
                  "h2h": [
                      1.13,5.8
                  ]
              }
          },{
              "site_key": "unibet","site_nice": "Unibet","last_update": 1608156434,5.8
                  ]
              }
          }
      ],"sites_count": 3
  }

问题是未来的一列包含一个列表（应该是这种情况），但是在 json_normalize 函数的元部分中包含此列会引发以下错误：

ValueError: operands Could not be broadcast together with shape (22,) (11,)

当我尝试在以下代码的列表中添加“团队”时出现错误：

pd.json_normalize(data,'sites',['sport_key','sport_nice','home_team','teams'])

解决方法

假设 data 是字典列表，您仍然可以使用 json_normalize，但您必须为 teams 中的每个对应字典单独分配 data 列：> 

def normalize(d):
    return pd.json_normalize(d,'sites',['sport_key','sport_nice','home_team'])\
           .assign(teams=[d['teams']]*len(d['sites']))


df = pd.concat([normalize(d) for d in data],ignore_index=True)

或者您可以尝试：

data = [{**d,'teams': ','.join(d['teams'])} for d in data]
df = pd.json_normalize(data,'home_team','teams'])
df['teams'] = df['teams'].str.split(',')

结果：

      site_key     site_nice  last_update      odds.h2h         sport_key sport_nice        home_team                               teams
0  marathonbet  Marathon Bet   1608156452  [1.28,3.54]  basketball_ncaab      NCAAB  Bryant Bulldogs  [Bryant Bulldogs,Wagner Seahawks]
1     sport888      888sport   1608156452   [1.13,5.8]  basketball_ncaab      NCAAB  Bryant Bulldogs  [Bryant Bulldogs,Wagner Seahawks]
2       unibet        Unibet   1608156434   [1.13,Wagner Seahawks]
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