将熊猫多索引系列转换为 Json python

Question

您好，我有两个类似下面的熊猫系列

盈亏

           Product Name      Price
Company A  Orange            3000
Company B  Apple             2000
           Grapes            1000

税

           Product Name      Price
Company A  Orange            100
Company B  Apple             100
           Grapes            10

我想把pandas系列转成以下JSON格式

{'PnL':{'Company A':{'productName':'Orange','price':3000},
        'Company B':[{'productName':'Apple','price':2000},
                     {'productName':'Grapes','price':1000}]
       },
 'Tax':{'Company A':{'productName':'Orange','price':100},
        'Company B':[{'productName':'Apple','price':100},
                     {'productName':'Grapes','price':10}]
       }
}

我尝试使用下面的代码

convertedJson = json.dumps([{'company': k[0], 'productName':k[1],'price': v} for k,v in df.items()])

但我无法形成我想要生成的 JSON。 谢谢你的帮助

Answer 1

您可以使用concat 将DaatFrames 连接在一起，然后将groupby 与to_dict 用于预期输出：

df = pd.concat([s1, s2], keys=('PnL','Tax')).reset_index()
df.columns = ['type','company','productName','price']
print (df)
  type    company productName  price
0  PnL  Company A      Orange   3000
1  PnL  Company B       Apple   2000
2  PnL  Company B      Grapes   1000
3  Tax  Company A      Orange   3000
4  Tax  Company B       Apple   2000
5  Tax  Company B      Grapes   1000

d = (df.groupby(['type','company'])['productName','price']
       .apply(lambda x: x.to_dict('r'))
       .reset_index(name='data')
       .groupby('type')['company','data']
       .apply(lambda x: x.set_index('company')['data'].to_dict())
       .to_json()
       )

---

print (d)

{
    "PnL": {
        "Company A": [{
            "productName": "Orange",
            "price": 3000
        }],
        "Company B": [{
            "productName": "Apple",
            "price": 2000
        }, {
            "productName": "Grapes",
            "price": 1000
        }]
    },
    "Tax": {
        "Company A": [{
            "productName": "Orange",
            "price": 3000
        }],
        "Company B": [{
            "productName": "Apple",
            "price": 2000
        }, {
            "productName": "Grapes",
            "price": 1000
        }]
    }
}