Question

我仍然对Pandas中多指数的运作感到困惑。我创建了一个多索引如下：

  class C1{

     public:

     //void *getData() {return data;} //Legacy implementation*
     char *getData() {return data;}  //My new implementation

     private:
       char data[100];
   };

   int main()
   {
       C1 myobj;   
       unsigned char* begin;

       begin=static_cast<unsigned char*>(myobj.getData());  *//<== This gives compile error.use reinterpret_cast ?*
       return 0;
   }

然后我从中创建了一个空的DataFrame并添加了一个列名＆＃39; pair＆＃39;：

import pandas as pd
import numpy as np

arrays = [np.array(['pearson', 'pearson', 'pearson', 'pearson', 'spearman', 'spearman',
                    'spearman', 'spearman', 'kendall', 'kendall', 'kendall', 'kendall']),
          np.array(['PROFESSIONAL', 'PROFESSIONAL', 'STUDENT', 'STUDENT',
                    'PROFESSIONAL', 'PROFESSIONAL', 'STUDENT', 'STUDENT',
                    'PROFESSIONAL', 'PROFESSIONAL', 'STUDENT', 'STUDENT']),
          np.array(['r', 'p', 'r', 'p', 'rho', 'p', 'rho', 'p', 'tau', 'p', 'tau', 'p'])]

tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['correlator', 'expertise', 'coeff-p'])

填写了一些玩具数据（results_df = pd.DataFrame(index=index) results_df.columns.names = ['pair']），它看起来像这样：

results_df['attr1-attr2'] = [1,2,3,4,5,6,7,8,9,10,11,12]

然而，我想要从字典中添加值，而不是虚拟。对于每个attr-attr对，字典的条目看起来像这样：

pair                             attr1-attr2
correlator expertise    coeff-p             
pearson    PROFESSIONAL r                  1
                        p                  2
           STUDENT      r                  3
                        p                  4
spearman   PROFESSIONAL rho                5
                        p                  6
           STUDENT      rho                7
                        p                  8
kendall    PROFESSIONAL tau                9
                        p                 10
           STUDENT      tau               11
                        p                 12

以下实际示例数据供您使用：

'attr-attr': {
  'pearson': {
    'STUDENT': {
      'r': VALUE,
      'p': VALUE
    },
    'PROFESSIONAL': {
      'r': VALUE,
      'p': VALUE
    }
  },
  'spearman': {
    'STUDENT': {
      'r': VALUE,
      'p': VALUE
    },
    'PROFESSIONAL': {
      'r': VALUE,
      'p': VALUE
    }
  }
  'kendall': {
    'STUDENT': {
      'r': VALUE,
      'p': VALUE
    },
    'PROFESSIONAL': {
      'r': VALUE,
      'p': VALUE
    }
  }
}

因此对于每个人来说都是如此。（最顶层的键）作为列名，我想将其值添加到多索引中的相应行。但是，我似乎无法以有效的方式找到一种方法。遗漏的值应为correlations = {'NormNedit-NormEC_TOT': {'pearson': {'PROFESSIONAL': {'r': 0.13615071018351657, 'p': 0.0002409555504769095}}, 'spearman': {'STUDENT': {'rho': 0.10867061294616957, 'p': 0.003437711066527592}, 'PROFESSIONAL': {'tau': 0.08185775947238913, 'p': 0.003435247172206748}}, 'kendall': {'STUDENT': {'tau': 0.08185775947238913, 'p': 0.003435247172206748}}}, 'NormLiteral-NormEC_TOT': {'pearson': {'PROFESSIONAL': {'r': 0.13615071018351657, 'p': 0.0002409555504769095}, 'STUDENT': {'tau': 0.08185775947238913, 'p': 0.003435247172206748}}, 'spearman': {'STUDENT': {'rho': 0.10867061294616957, 'p': 0.003437711066527592}, 'PROFESSIONAL': {'r': 0.13615071018351657, 'p': 0.0002409555504769095}}, 'kendall': {'STUDENT': {'tau': 0.08185775947238913, 'p': 0.003435247172206748}}}, 'NormHTra-NormEC_TOT': {'pearson': {'STUDENT': {'r': 0.13615071018351657, 'p': 0.0002409555504769095}}, 'spearman': {'STUDENT': {'rho': 0.10867061294616957, 'p': 0.003437711066527592}, 'PROFESSIONAL': {'r': 0.13615071018351657, 'p': 0.0002409555504769095}}, 'kendall': {'STUDENT': {'tau': 0.08185775947238913, 'p': 0.003435247172206748}}}, 'NormScatter-NormEC_TOT': {'pearson': {'STUDENT': {'r': 0.13615071018351657, 'p': 0.0002409555504769095}}, 'spearman': {'STUDENT': {'rho': 0.10867061294616957, 'p': 0.003437711066527592}, 'PROFESSIONAL': {'tau': 0.08185775947238913, 'p': 0.003435247172206748}}, 'kendall': {'PROFESSIONAL': {'tau': 0.08185775947238913, 'p': 0.003435247172206748}}}, 'NormCrossS-NormEC_TOT': {'pearson': {'STUDENT': {'r': 0.13615071018351657, 'p': 0.0002409555504769095}, 'PROFESSIONAL': {'rho': 0.10867061294616957, 'p': 0.003437711066527592}}, 'spearman': {'STUDENT': {'rho': 0.10867061294616957, 'p': 0.003437711066527592}, 'PROFESSIONAL': {'rho': 0.10867061294616957, 'p': 0.003437711066527592}}, 'kendall': {'PROFESSIONAL': {'tau': 0.08185775947238913, 'p': 0.003435247172206748}}}, 'NormPdur-NormEC_TOT': {'pearson': {'STUDENT': {'r': 0.13615071018351657, 'p': 0.0002409555504769095}, 'PROFESSIONAL': {'rho': 0.10867061294616957, 'p': 0.003437711066527592}}, 'spearman': {'STUDENT': {'rho': 0.10867061294616957, 'p': 0.003437711066527592}}, 'kendall': {'PROFESSIONAL': {'tau': 0.08185775947238913, 'p': 0.003435247172206748}}}}。我尝试循环字典并使用np.nan，但这没有用。

query()[]

我知道数据相对复杂，所以如果不清楚，请告诉我。

Answer 1

您可以调整Wouter Overmeire's answer to this question以从嵌套字典中创建多索引数据框：

d = correlations
df = pd.DataFrame.from_dict({(i,j,k): d[i][j][k]
   for i in d.keys() 
   for j in d[i].keys()
   for k in d[i][j].keys()
   }, orient='index').stack()

然后，如果您希望列来自嵌套字典的最高级别（attr-attr级别），则可以将结果取消堆叠：

df = df.unstack(level=0)

~~注意：我认为样本数据存在错误，'PROFESSIONAL': {'STUDENT': ...。如果这不是一个错误，我只是误解了一些事情，请告诉我。~~

按列名称和多索引向多索引数据框添加值

1 个答案: