按列名和多索引向多索引数据框添加值

Question

我仍然对 Pandas 中多索引的工作方式感到很困惑。我创建了一个多索引如下：

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'])

然后我用它们制作了一个空的 DataFrame 并添加了一个列名“pair”：

results_df = pd.DataFrame(index=index)
results_df.columns.names = ['pair']

填充了一些玩具数据（results_df['attr1-attr2'] = [1,2,3,4,5,6,7,8,9,10,11,12]），它看起来像这样：

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

但是，我想从字典中添加值而不是假人。对于每个 attr-attr 对，字典中的条目如下所示：

'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
    }
  }
}

以下实际示例数据供您使用：

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}}}}

所以对于每个'attr-attr'（最上面的键）作为列名，我想将其值添加到多索引中的相应行。但是，我似乎无法找到一种有效的方法来做到这一点。缺失值应为np.nan。我尝试循环字典并使用query()[]，但这没有用。

for attr, attr_d in correlations.items():
    for correl, correl_d in attr_d.items():
        for split, split_d in correl_d.items():
            results_df.query(f"correlator == {correl} and expertise == {split} and coeff_p == 'p'")[attr] = split_d['p']
            results_df.query(f"correlator == {correl} and expertise == {split} and coeff_p != 'p'")[attr] = split_d['r'] if 'r' in split_d else split_d['rho'] if 'rho' in split_d else split['tau']

> pandas.core.computation.ops.UndefinedVariableError: name 'pearson' is not defined

我知道数据比较复杂，所以如果有不清楚的地方请告诉我。

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），您可以取消堆叠结果：

df = df.unstack(level=0)

注意：我认为您的示例数据中有错误，其中'PROFESSIONAL': {'STUDENT': ...。如果这不是一个错误，我只是误解了一些东西，请告诉我。