boxplot (seaborn 출신)이 예상대로 플롯하지 않음

Question

boxplot이 예상대로 플롯되지 않습니다. 이 실제로 플롯 것입니다 :

이이 음모되어있는 것이다 :

이 코드 및 데이터입니다 :

from sklearn.ensemble import RandomForestClassifier 
    from sklearn.cross_validation import cross_val_score 
    scores = [] 
    for ne in range(1,41): ## ne is the number of trees 
     clf = RandomForestClassifier(n_estimators = ne) 
     score_list = cross_val_score(clf, X, Y, cv=10) 
     scores.append(score_list) 
     sns.boxplot(scores) # scores are list of arrays 
     plt.xlabel('Number of trees') 
     plt.ylabel('Classification score') 
     plt.title('Classification score as a function of the number of trees') 
     plt.show() 

scores = 

[array([ 0.8757764 , 0.86335404, 0.75625 , 0.85  , 0.86875 , 
     0.81875 , 0.79375 , 0.79245283, 0.8490566 , 0.85534591]), 
array([ 0.89440994, 0.8447205 , 0.79375 , 0.85  , 0.8625 , 
     0.85625 , 0.86875 , 0.88050314, 0.86792453, 0.8427673 ]), 
array([ 0.91304348, 0.9068323 , 0.83125 , 0.84375 , 0.8875 , 
     0.875  , 0.825  , 0.83647799, 0.83647799, 0.87421384]), 
array([ 0.86956522, 0.86956522, 0.85  , 0.875  , 0.88125 , 
     0.86875 , 0.8625 , 0.8490566 , 0.86792453, 0.89308176]), 

.. ..]

Answer 1

나는 첫번째 cre

import pandas as pd 

In [15]: scores 
Out[15]: 
[array([ 0.8757764 , 0.86335404, 0.75625 , 0.85  , 0.86875 , 0.81875 , 0.79375 , 0.79245283, 0.8490566 , 0.85534591]), 
array([ 0.89440994, 0.8447205 , 0.79375 , 0.85  , 0.8625 , 0.85625 , 0.86875 , 0.88050314, 0.86792453, 0.8427673 ]), 
array([ 0.91304348, 0.9068323 , 0.83125 , 0.84375 , 0.8875 , 0.875  , 0.825  , 0.83647799, 0.83647799, 0.87421384]), 
array([ 0.86956522, 0.86956522, 0.85  , 0.875  , 0.88125 , 0.86875 , 0.8625 , 0.8490566 , 0.86792453, 0.89308176])] 

In [16]: df = pd.DataFrame(scores) 

In [17]: df 
Out[17]: 
      0   1  2  3  4  5  6   7   8   9 
0 0.875776 0.863354 0.75625 0.85000 0.86875 0.81875 0.79375 0.792453 0.849057 0.855346 
1 0.894410 0.844720 0.79375 0.85000 0.86250 0.85625 0.86875 0.880503 0.867925 0.842767 
2 0.913043 0.906832 0.83125 0.84375 0.88750 0.87500 0.82500 0.836478 0.836478 0.874214 
3 0.869565 0.869565 0.85000 0.87500 0.88125 0.86875 0.86250 0.849057 0.867925 0.893082 

지금 우리가 쉽게 그릴 수 상자 그림 :

In [18]: sns.boxplot(data=df) 
Out[18]: <matplotlib.axes._subplots.AxesSubplot at 0xd121128> 

scores 중 팬더 DF 먹었다