Question

我有如下所示的数据框对象，

   index                Date     Poly_1     Poly_2  Poly_2_WLS     Poly_3  
0      0 2017-01-04 08:45:00  70.195597  83.613845   83.613845  99.041125   
1      1 2017-01-04 08:53:00  70.195597  83.613845   83.613845  99.041125   
2      2 2017-01-04 09:00:00  70.195597  83.613845   83.613845  99.041125   
3      3 2017-12-13 08:45:00  70.195597  83.613845   83.613845  99.041125   
4      4 2017-12-13 08:53:00  70.195597  83.613845   83.613845  99.041125

我正在使用以下代码按月对以上数据进行分组，

dfgrp=df.groupby(pd.Grouper(key='Date',freq="M"),as_index=False)

稍后我想使用以下代码将分组数据转换为json格式；

dfgrp.to_json(date_format='iso',orient='records')

但是由于某种原因，我遇到了错误

AttributeError：无法访问“ DataFrameGroupBy”对象的可调用属性“ to_json”，请尝试使用“ apply”方法

请告诉我，如何将以上数据帧转换为json？

编辑：

我从下面的答案中使用了下面的代码行；并能够生成JSON。

df.groupby([df.Date.dt.month)])['Poly_1','Poly_2','Poly_2_WLS','Poly_3'].sum().reset_index().to_json()

但是JSON以这种格式生成

`{"Date":{"0":1,"1":2,"2":3,"3":4,"4":5,"5":9,"6":10,"7":11,"8":12},"Poly_1":{"0":46187.2636499188,"1":56636.9594359758,"2":53218.6089763865,"3":41100.9574106447,"4":49317.907305443,"5":2670.6255284702,"6":34887.4415455112,"7":45857.8601621408,"8":21635.3343188418},"Poly_2":{"0":46193.719351124,"1":56193.0159455145,"2":52890.1916931438,"3":41119.1740551722,"4":49648.1531559606,"5":2767.3530477022,"6":34704.8815525262,"7":45918.9353954344,"8":22077.5341367508},"Poly_2_WLS":{"0":46193.719351124,"1":56193.0159455145,"2":52890.1916931438,"3":41119.1740551722,"4":49648.1531559606,"5":2767.3530477022,"6":34704.8815525262,"7":45918.9353954344,"8":22077.5341367508},"Poly_3":{"0":46037.6280724075,"1":56111.2211081627,"2":53059.8469394733,"3":41282.9093221716,"4":49670.016727901,"5":2660.8721082338,"6":34724.1756869611,"7":45721.7694774285,"8":22244.5188905397}`}

但是，我想要JSON，就像下面的格式一样；

预期的示例Json格式：

{
    ,
    "Poly_1": {
        "Jan": 46187.2636499188,
        "Feb": 56636.9594359758,
        "Mar": 53218.6089763865,
        "Apr": 41100.9574106447,
        "May": 49317.907305443,
        "Jun": 2670.6255284702,
        "July": 34887.4415455112,
        "Aug": 45857.8601621408,
        "Sept": 21635.3343188418
    }


}

请建议我如何获得高于预期格式的格式？

谢谢

Answer 1

我在回答中假设您希望对所有多边形进行求和（否则，只需更改下面的代码）。

我将以这种方式对其进行分组，以将7月设为（7）：

df.groupby([df.Date.dt.month)])['Poly_1','Poly_2','Poly_2_WLS','Poly_3'].sum().reset_index().to_json()

注意：我添加了sum，reset_index，然后才添加到to_json。

您分组的df如下所示：

    Date    Poly_1  Poly_2      Poly_2_WLS  Poly_3
0   7   350.977985  418.069225  418.069225  495.205625

您的json将是（如果您不重置索引）。

'{"Poly_1":{"7":350.977985},"Poly_2":{"7":418.069225},"Poly_2_WLS":{"7":418.069225},"Poly_3":{"7":495.205625}}'

另一个指针：如果您和石斑鱼一起去，您将拥有2019-07-31，并且您的json看起来像这样（1564531200000）

df.groupby([pd.Grouper(key='Date',freq="M")])['Poly_1','Poly_2','Poly_2_WLS','Poly_3'].sum().to_json()

'{"Poly_1":{"1564531200000":350.977985},"Poly_2":{"1564531200000":418.069225},"Poly_2_WLS":{"1564531200000":418.069225},"Poly_3":{"1564531200000":495.205625}}'

因此请调整您的需求。

熊猫肮脏的月份并将其转换为json

1 个答案: