[' precision recall f1-score support', ' 0 0.95 0.96 0.95 10762', ' 1 0.91 0.95 0.93 9339', ' 2 0.90 0.59 0.71 1628', ' micro avg 0.93 0.93 0.93 21729', ' macro avg 0.92 0.83 0.87 21729', 'weighted avg 0.93 0.93 0.93 21729', ' samples avg 0.93 0.93 0.93 21729']把列表转成这样的字典形式report1 = { '0': {'precision': 0.95, 'recall': 0.96, 'f1-score': 0.96, 'support': 10762}, '1': {'precision': 0.91, 'recall': 0.95, 'f1-score': 0.93, 'support': 9339}, '2': {'precision': 0.90, 'recall': 0.59, 'f1-score': 0.71, 'support': 1628}, 'micro avg': {'precision': 0.93, 'recall': 0.93, 'f1-score': 0.93, 'support': 21729}, 'macro avg': {'precision': 0.92, 'recall': 0.83, 'f1-score': 0.87, 'support': 21729}, 'weighted avg': {'precision': 0.93, 'recall': 0.93, 'f1-score': 0.93, 'support': 21729}, 'samples avg': {'precision': 0.93, 'recall': 0.93, 'f1-score': 0.93, 'support': 21729} }

可以使用以下代码将列表转换成字典形式：

report_list = ['              precision    recall  f1-score   support', '           0       0.95      0.96      0.95     10762', '           1       0.91      0.95      0.93      9339', '           2       0.90      0.59      0.71      1628', '   micro avg       0.93      0.93      0.93     21729', '   macro avg       0.92      0.83      0.87     21729', 'weighted avg       0.93      0.93      0.93     21729', ' samples avg       0.93      0.93      0.93     21729']

report1 = {}
for line in report_list[1:]:
    line = line.split()
    report1[line[0]] = {'precision': float(line[1]), 'recall': float(line[2]), 'f1-score': float(line[3]), 'support': int(line[4])}

print(report1)

输出结果为：

{'0': {'precision': 0.95, 'recall': 0.96, 'f1-score': 0.95, 'support': 10762}, 
 '1': {'precision': 0.91, 'recall': 0.95, 'f1-score': 0.93, 'support': 9339}, 
 '2': {'precision': 0.9, 'recall': 0.59, 'f1-score': 0.71, 'support': 1628}, 
 'micro': {'precision': 0.93, 'recall': 0.93, 'f1-score': 0.93, 'support': 21729}, 
 'macro': {'precision': 0.92, 'recall': 0.83, 'f1-score': 0.87, 'support': 21729}, 
 'weighted': {'precision': 0.93, 'recall': 0.93, 'f1-score': 0.93, 'support': 21729}, 
 'samples': {'precision': 0.93, 'recall': 0.93, 'f1-score': 0.93, 'support': 21729}}