Python 将多索引数据帧转换为JSON
考虑具有多索引的数据帧: 上述数据帧需要转换为json,如下所示:Python 将多索引数据帧转换为JSON,python,pandas,pandas-groupby,multi-index,Python,Pandas,Pandas Groupby,Multi Index,考虑具有多索引的数据帧: 上述数据帧需要转换为json,如下所示: bodyContent": [ { "time": "31/03/2020 02:17:01", "tag_5764_virtual_device_135": -0.97 }, { "time": "31/03/2020 02:17:12", "tag_5764_virtual_device_135":
bodyContent": [
{
"time": "31/03/2020 02:17:01",
"tag_5764_virtual_device_135": -0.97
},
{
"time": "31/03/2020 02:17:12",
"tag_5764_virtual_device_135": -0.97
},
{
"time": "31/03/2020 02:17:22",
"tag_5764_virtual_device_135": -0.97
},
{
"time": "31/03/2020 02:18:37",
"tag_5764_virtual_device_136": -0.98
},
{
"time": "31/03/2020 02:18:47",
"tag_5764_virtual_device_136": -0.98
},
{
"time": "31/03/2020 02:18:57",
"tag_5764_virtual_device_136": -0.98
}
]
目前,我正在拆分DF,然后重命名列,然后合并它,然后转换为json
有没有更好的方法让我使用熊猫
感谢您的帮助 我发现可以按如下方式进行: 如果数据帧是df: df.columns=[''.'.joincol表示df.columns中的列] df.reset_indexinplace=True df_list=json.loadsdf.to_jsonorient='records' 对于df_列表中的每个: 正文内容列表。每个附件 希望这对某人有用 df.columns=['.'.joincol[::-1]表示df.columns中的列] df=df.reset_index.renamecolumns={'timestamp':'time'} jsonbody=list{k:{k1:v1表示k1,v1表示v.items,如果pd.notnullv1}\ 对于k,df.to_dictorient='index'.items}.value中的v
bodyContent": [
{
"time": "31/03/2020 02:17:01",
"tag_5764_virtual_device_135": -0.97
},
{
"time": "31/03/2020 02:17:12",
"tag_5764_virtual_device_135": -0.97
},
{
"time": "31/03/2020 02:17:22",
"tag_5764_virtual_device_135": -0.97
},
{
"time": "31/03/2020 02:18:37",
"tag_5764_virtual_device_136": -0.98
},
{
"time": "31/03/2020 02:18:47",
"tag_5764_virtual_device_136": -0.98
},
{
"time": "31/03/2020 02:18:57",
"tag_5764_virtual_device_136": -0.98
}
]