如何将熊猫系列转换为所需的 JSON 格式？

Question

我有以下数据，我需要对其应用聚合函数，然后是 groupby。

我的数据如下：data.csv

id,category,sub_category,count
0,x,sub1,10
1,x,sub2,20
2,x,sub2,10
3,y,sub3,30
4,y,sub3,5
5,y,sub4,15
6,z,sub5,20

在这里，我试图按子类别进行计数。之后，我需要以 JSON 格式存储结果。以下代码可以帮助我实现这一目标。 test.py

import pandas as pd
df = pd.read_csv('data.csv')
sub_category_total = df['count'].groupby([df['category'], df['sub_category']]).sum()
print sub_category_total.reset_index().to_json(orient = "records")

上面的代码给了我以下格式。

[{"category":"x","sub_category":"sub1","count":10},{"category":"x","sub_category":"sub2","count":30},{"category":"y","sub_category":"sub3","count":35},{"category":"y","sub_category":"sub4","count":15},{"category":"z","sub_category":"sub5","count":20}]

但是，我想要的格式如下：

{
"x":[{
     "sub_category":"sub1",
     "count":10
     },
     {
     "sub_category":"sub2",
      "count":30}],
"y":[{
     "sub_category":"sub3",
     "count":35
     },
     {
     "sub_category":"sub4",
     "count":15}],
"z":[{
     "sub_category":"sub5",
      "count":20}]
}

通过关注@How to convert pandas DataFrame result to user defined json format 的讨论，我将test.py 的最后两行替换为，

g = df.groupby('category')[["sub_category","count"]].apply(lambda x: x.to_dict(orient='records'))
print g.to_json()

它给了我以下输出。

{"x":[{"count":10,"sub_category":"sub1"},{"count":20,"sub_category":"sub2"},{"count":10,"sub_category":"sub2"}],"y":[{"count":30,"sub_category":"sub3"},{"count":5,"sub_category":"sub3"},{"count":15,"sub_category":"sub4"}],"z":[{"count":20,"sub_category":"sub5"}]}

虽然上述结果与我想要的格式有些相似，但我无法在此处执行任何聚合函数，因为它会抛出错误说'numpy.int64' object has no attribute 'to_dict'。因此，我最终得到了数据文件中的所有行。

有人可以帮我实现上述 JSON 格式吗？

Answer 1

我觉得你可以先用sum聚合，参数as_index=False加到groupby，所以输出是Dataframedf1再用other solution：

df1 = (df.groupby(['category','sub_category'], as_index=False)['count'].sum())
print (df1)
  category sub_category  count
0        x         sub1     10
1        x         sub2     30
2        y         sub3     35
3        y         sub4     15
4        z         sub5     20

g = df1.groupby('category')[["sub_category","count"]]
       .apply(lambda x: x.to_dict(orient='records'))

print (g.to_json())

{
    "x": [{
        "sub_category": "sub1",
        "count": 10
    }, {
        "sub_category": "sub2",
        "count": 30
    }],
    "y": [{
        "sub_category": "sub3",
        "count": 35
    }, {
        "sub_category": "sub4",
        "count": 15
    }],
    "z": [{
        "sub_category": "sub5",
        "count": 20
    }]
}