python - 将嵌套字典转换为数据框

Question

我的字典看起来像这样

mydict = 
{240594.0: {1322.0: 1.6899999999999999, 1323.0: 1.6900000000000002, 1324.0: 1.6899999999999999, 1325.0: 1.6899999999999999, 1326.0: 1.6899999999999999, 1327.0: 1.6900000000000002, 1328.0: 1.6899999999999999, 1329.0: 1.6899999999999999, 1356.0: 1.6900000000000002, 1357.0: 1.6900000000000002, 1358.0: 1.6899999999999999, 1359.0: 1.6900000000000002, 1360.0: 1.6900000000000002, ...},

226918.0: {1322.0: 1.6900000000000002, 1323.0: 1.6899999999999999, 1324.0: 1.6900000000000002, 1325.0: 1.6899999999999999, 1326.0: 1.6900000000000002, 1327.0: 1.6899999999999999, 1328.0: 1.6900000000000002, 1329.0: 1.6899999999999999, 1352.0: 1.6900000000000002, 1353.0: 1.6900000000000002, 1354.0: 1.6899999999999999 ...}}

这是 {iri_key: {week:price, week:price ...}, iri_key: {...}} 的实际值我想将此字典转换为数据框，如下所示

         week week  week ...
irikey: price price price ...
irikey: ...    ...   ...

在上述情况

           1322.0                  ...
240594.0   1.6899999999999999      ...
226918.0   1.6900000000000002      ...

我怎样才能做到这一点？

Answer 1

正如您可能已经发现的那样，DataFrame(mydict) 是有效的代码。您只需进行转置 (.T) 即可获得所需的结果。

就代码可读性和直接性而言，有一种更好的方法:使用特定的 DataFrame 构造函数 DataFrame.from_dict，它具有关键字参数 orient。

In [2]: DataFrame.from_dict(mydict, orient='index')
Out[2]: 
        1356  1357  1358  1359  1360  1322  1323  1324  1325  1326  1327  \
226918   NaN   NaN   NaN   NaN   NaN  1.69  1.69  1.69  1.69  1.69  1.69   
240594  1.69  1.69  1.69  1.69  1.69  1.69  1.69  1.69  1.69  1.69  1.69   

        1328  1329  1352  1353  1354  
226918  1.69  1.69  1.69  1.69  1.69  
240594  1.69  1.69   NaN   NaN   NaN  

[2 rows x 16 columns]

从您提供的示例数据中可以看出，缺失值和可变长度得到了正确处理。