Python 3.x 如何在dataframe的列（numpy对象）中找到满足条件的索引？

我有一个数据框，其中包含

numpy object

列。数据如下：

                          data
0           [1, 2, 2, 3, 4, 2]
1        [2, 4, 2, 5, 2, 3, 2]
2  [2, 2, 2, 8, 2, 3, 2, 9, 1]
...

                          data    index
0           [1, 2, 2, 3, 4, 2]    [0,4]
1        [2, 4, 2, 5, 2, 3, 2]    [1,3]
2  [2, 2, 2, 8, 2, 3, 2, 9, 1]    [3,7]
...

我想获取列中每个numpy的索引，以满足以下条件：

（>（mean+std））或（我认为您需要：

df['index']=df['data'].map（lambda x:np.where（np.logical_或
.减少（（（x>x.mean（）+x.std（）），
（x

对于验证解决方案：

df['index'] = df['data'].map(lambda x: ((x > x.mean() + x.std())))
df['index1'] = df['data'].map(lambda x: ((x < x.mean() - x.std())))
#https://stackoverflow.com/a/33375383/2901002
with pd.option_context('display.max_colwidth', 200):
    print (df)

                          data  \
0           [1, 2, 2, 3, 4, 2]   
1        [2, 4, 2, 5, 2, 3, 2]   
2  [2, 2, 2, 8, 2, 3, 2, 9, 1]   

                                                           index  \
0                      [False, False, False, False, True, False]   
1                [False, True, False, True, False, False, False]   
2  [False, False, False, True, False, False, False, True, False]   

                                                            index1  
0                        [True, False, False, False, False, False]  
1                [False, False, False, False, False, False, False]  
2  [False, False, False, False, False, False, False, False, False]  

df['index']=df['data'].map（λx:（（x>x.mean（）+x.std（）））
df['index1']=df['data'].map（λx:（（x

The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

df['index'] = df['data'].map(lambda x: np.where(np.logical_or
                                                  .reduce(((x > x.mean() + x.std()), 
                                                           (x < x.mean() - x.std()))))[0])
print (df)
                          data   index
0           [1, 2, 2, 3, 4, 2]  [0, 4]
1        [2, 4, 2, 5, 2, 3, 2]  [1, 3]
2  [2, 2, 2, 8, 2, 3, 2, 9, 1]  [3, 7]

df['index'] = df['data'].map(lambda x: ((x > x.mean() + x.std())))
df['index1'] = df['data'].map(lambda x: ((x < x.mean() - x.std())))
#https://stackoverflow.com/a/33375383/2901002
with pd.option_context('display.max_colwidth', 200):
    print (df)

                          data  \
0           [1, 2, 2, 3, 4, 2]   
1        [2, 4, 2, 5, 2, 3, 2]   
2  [2, 2, 2, 8, 2, 3, 2, 9, 1]   

                                                           index  \
0                      [False, False, False, False, True, False]   
1                [False, True, False, True, False, False, False]   
2  [False, False, False, True, False, False, False, True, False]   

                                                            index1  
0                        [True, False, False, False, False, False]  
1                [False, False, False, False, False, False, False]  
2  [False, False, False, False, False, False, False, False, False]