如何使用通配符在 Pandas 中展平 json？

如何解决如何使用通配符在 Pandas 中展平 json？

鉴于此 json：

"World_Regions": {
    "Americas": {
        "0": {
            "Name": "north America","Category_Average": "54.53","Stocks_%": "55.44","Benchmark": "59.02"
        },"1": {
            "Name": "Latin America","Category_Average": "0.87","Stocks_%": "1.14","Benchmark": "0.93"
        }
    },"Greater Asia": {
        "0": {
            "Name": "Japan","Category_Average": "6.58","Stocks_%": "3.74","Benchmark": "7.76"
        },"1": {
            "Name": "Australasia","Category_Average": "1.79","Stocks_%": "7.45","Benchmark": "2.17"
        },"2": {
            "Name": "Asia Developed","Category_Average": "5.56","Stocks_%": "7.27","Benchmark": "4.57"
        },"3": {
            "Name": "Asia Emerging","Category_Average": "6.63","Stocks_%": "2.96","Benchmark": "6.58"
        }
    },

我想得到这个结果：

Name    Category_Average    Stocks_%    Benchmark
0   north America   54.53   55.44   59.02
1   Latin America   0.87    1.14    0.93
2   Japan   6.58    3.74    7.76
3   Australasia 1.79    7.45    2.17
4   Asia Developed  5.56    7.27    4.57
6   Asia Emerging   6.63    2.96    6.58

但不幸的是，区域名称（美洲/大亚洲）的不同名称导致了问题。 我现在正在一个命令中尝试干净地执行此操作，我可以通过执行此操作来获得结果：

pd.DataFrame.from_dict(jsonFile['World_Regions']['Greater Asia']).transpose()
        Name    Category_Average    Stocks_%    Benchmark
    0   Japan   6.58    3.74    7.76
    1   Australasia 1.79    7.45    2.17
    2   Asia Developed  5.56    7.27    4.57
    3   Asia Emerging   6.63    2.96    6.58

然后对美洲也一样，然后合并数据帧。有没有更直接的方法（即一个命令？）

解决方法

您可以尝试连接各个值：

pd.concat([pd.DataFrame(x).T for x in a['World_Regions'].values()],ignore_index=True)

或者在传递给 DataFrame 之前展平数据：

pd.DataFrame([y for x in a['World_Regions'].values()
                for y in x.values() ])

输出：

             Name Category_Average Stocks_% Benchmark
0   North America            54.53    55.44     59.02
1   Latin America             0.87     1.14      0.93
2           Japan             6.58     3.74      7.76
3     Australasia             1.79     7.45      2.17
4  Asia Developed             5.56     7.27      4.57
5   Asia Emerging             6.63     2.96      6.58