Python - Конвертируйте dict во вложенный dict

Question

У меня есть dict :

{'Logistic Regression': u'                                precision    recall  f1-score   support\n\n              APAR Information       0.74      1.00      0.85       844\nAffected Products and Versions       0.00      0.00      0.00        18\n                        Answer       0.00      0.00      0.00        30\n   Applicable component levels       0.96      0.85      0.90       241\n             Error description       0.48      0.56      0.52       754\n                     Local fix       0.89      0.03      0.06       266\n                Modules/Macros       0.96      0.87      0.91       326\n                       Problem       0.00      0.00      0.00        63\n               Problem summary       0.51      0.73      0.60       721\n           Related information       0.00      0.00      0.00        22\n         Resolving The Problem       0.00      0.00      0.00        60\n                 Temporary fix       0.00      0.00      0.00        32\n                  circumvenion       0.00      0.00      0.00       124\n                     component       0.00      0.00      0.00        49\n                 temporary_fix       0.00      0.00      0.00         2\n\n                     micro avg       0.64      0.64      0.64      3552\n                     macro avg       0.30      0.27      0.26      3552\n                  weighted avg       0.60      0.64      0.58      3552\n'}

или

                                precision    recall  f1-score   support

              APAR Information       0.74      1.00      0.85       844
Affected Products and Versions       0.00      0.00      0.00        18
                        Answer       0.00      0.00      0.00        30
   Applicable component levels       0.96      0.85      0.90       241
             Error description       0.48      0.56      0.52       754
                     Local fix       0.89      0.03      0.06       266
                Modules/Macros       0.96      0.87      0.91       326
                       Problem       0.00      0.00      0.00        63
               Problem summary       0.51      0.73      0.60       721
           Related information       0.00      0.00      0.00        22
         Resolving The Problem       0.00      0.00      0.00        60
                 Temporary fix       0.00      0.00      0.00        32
                  circumvenion       0.00      0.00      0.00       124
                     component       0.00      0.00      0.00        49
                 temporary_fix       0.00      0.00      0.00         2

                     micro avg       0.64      0.64      0.64      3552
                     macro avg       0.30      0.27      0.26      3552
                  weighted avg       0.60      0.64      0.58      3552

, и я хочу преобразовать этот dict во вложенный, что-то вроде

{'Logistic Regression':
{'APAR Information':'0.74','1.00','0.85','844'},
{'Affected Products and Versions':'0.00','0.00','0.00','18'}
.
.
.}

Как этого добиться?Это можно сделать с помощью встроенных функций dict?

Answer 1

Вы можете использовать Pandas сторонних производителей для преобразования в фрейм данных с помощью pd.read_fwf («с фиксированной шириной»).Ваши данные грязные, вам может потребоваться написать логику для расчета ширины столбцов или добавить их вручную.С учетом входного словаря d:

from io import StringIO
import pandas as pd

df = pd.read_fwf(StringIO(d['Logistic Regression']), widths=[30, 11, 10, 10, 10])\
       .dropna().rename(columns={'Unnamed: 0': 'index'}).set_index('index')

print(df)

                                precision  recall  f1-score  support
index                                                               
APAR Information                     0.74    1.00      0.85    844.0
Affected Products and Versions       0.00    0.00      0.00     18.0
Answer                               0.00    0.00      0.00     30.0
Applicable component levels          0.96    0.85      0.90    241.0
Error description                    0.48    0.56      0.52    754.0
Local fix                            0.89    0.03      0.06    266.0
Modules/Macros                       0.96    0.87      0.91    326.0
Problem                              0.00    0.00      0.00     63.0
Problem summary                      0.51    0.73      0.60    721.0
Related information                  0.00    0.00      0.00     22.0
Resolving The Problem                0.00    0.00      0.00     60.0
Temporary fix                        0.00    0.00      0.00     32.0
circumvenion                         0.00    0.00      0.00    124.0
component                            0.00    0.00      0.00     49.0
temporary_fix                        0.00    0.00      0.00      2.0
micro avg                            0.64    0.64      0.64   3552.0
macro avg                            0.30    0.27      0.26   3552.0
weighted avg                         0.60    0.64      0.58   3552.0

Затем используйте словарь для понимания:

res = {'Logistic Regression': {idx: df.loc[idx].tolist() for idx in df.index}}

print(res)

{'Logistic Regression':
 {'APAR Information': [0.74, 1.0, 0.85, 844.0],
  'Affected Products and Versions': [0.0, 0.0, 0.0, 18.0],
  'Answer': [0.0, 0.0, 0.0, 30.0],
  'Applicable component levels': [0.96, 0.85, 0.9, 241.0],
  'Error description': [0.48, 0.56, 0.52, 754.0],
  'Local fix': [0.89, 0.03, 0.06, 266.0],
  'Modules/Macros': [0.96, 0.87, 0.91, 326.0],
  'Problem': [0.0, 0.0, 0.0, 63.0],
  'Problem summary': [0.51, 0.73, 0.6, 721.0],
  'Related information': [0.0, 0.0, 0.0, 22.0],
  'Resolving The Problem': [0.0, 0.0, 0.0, 60.0],
  'Temporary fix': [0.0, 0.0, 0.0, 32.0],
  'circumvenion': [0.0, 0.0, 0.0, 124.0],
  'component': [0.0, 0.0, 0.0, 49.0],
  'macro avg': [0.3, 0.27, 0.26, 3552.0],
  'micro avg': [0.64, 0.64, 0.64, 3552.0],
  'temporary_fix': [0.0, 0.0, 0.0, 2.0],
  'weighted avg': [0.6, 0.64, 0.58, 3552.0]}}

Answer 2

Это один подход.

Демонстрация:

d = {'Logistic Regression': u'                                precision    recall  f1-score   support\n\n              APAR Information       0.74      1.00      0.85       844\nAffected Products and Versions       0.00      0.00      0.00        18\n                        Answer       0.00      0.00      0.00        30\n   Applicable component levels       0.96      0.85      0.90       241\n             Error description       0.48      0.56      0.52       754\n                     Local fix       0.89      0.03      0.06       266\n                Modules/Macros       0.96      0.87      0.91       326\n                       Problem       0.00      0.00      0.00        63\n               Problem summary       0.51      0.73      0.60       721\n           Related information       0.00      0.00      0.00        22\n         Resolving The Problem       0.00      0.00      0.00        60\n                 Temporary fix       0.00      0.00      0.00        32\n                  circumvenion       0.00      0.00      0.00       124\n                     component       0.00      0.00      0.00        49\n                 temporary_fix       0.00      0.00      0.00         2\n\n                     micro avg       0.64      0.64      0.64      3552\n                     macro avg       0.30      0.27      0.26      3552\n                  weighted avg       0.60      0.64      0.58      3552\n'}
result = {}
for i, v in enumerate(d["Logistic Regression"].splitlines()):
    if i == 0:
        continue
    val = v.strip().split("       ")
    if val[0]:
        result[val[0]] = " ".join(val[1:]).split()

for k, v in result.items():
    print(k)
    print(v)

Вывод:

weighted avg
[u'0.60', u'0.64', u'0.58', u'3552']
Local fix
[u'0.89', u'0.03', u'0.06', u'266']
Affected Products and Versions
[u'0.00', u'0.00', u'0.00', u'18']
component
[u'0.00', u'0.00', u'0.00', u'49']
Resolving The Problem
[u'0.00', u'0.00', u'0.00', u'60']
Error description
[u'0.48', u'0.56', u'0.52', u'754']
Problem summary
[u'0.51', u'0.73', u'0.60', u'721']
macro avg
[u'0.30', u'0.27', u'0.26', u'3552']
Related information
[u'0.00', u'0.00', u'0.00', u'22']
Applicable component levels
[u'0.96', u'0.85', u'0.90', u'241']
micro avg
[u'0.64', u'0.64', u'0.64', u'3552']
Answer
[u'0.00', u'0.00', u'0.00', u'30']
APAR Information
[u'0.74', u'1.00', u'0.85', u'844']
Problem
[u'0.00', u'0.00', u'0.00', u'63']
Modules/Macros
[u'0.96', u'0.87', u'0.91', u'326']
temporary_fix
[u'0.00', u'0.00', u'0.00', u'2']
circumvenion
[u'0.00', u'0.00', u'0.00', u'124']
Temporary fix
[u'0.00', u'0.00', u'0.00', u'32']