即使元素JSON数组零长度？(JSON array zero length even with elements?)

 不知道你为什么要这样做...但是使用itertuples 
 
for row in df.itertuples():
    if row.check:
        print('Yes')

Yes
Yes
Yes
Yes
Yes
Yes
Yes
 
 
 您可以使用map来映射您的值 
 
df.check.map({True: 'Yes', False: ''})

0        
1        
2        
3     Yes
4        
5        
6        
7     Yes
8     Yes
9     Yes
10    Yes
11    Yes
12    Yes
Name: check, dtype: object
 
 甚至assign它assign回专栏 
 
df.assign(mapped=df.check.map({True: 'Yes', False: ''}))

    Price  check mapped
0      10  False       
1      20  False       
2      30  False       
3      40   True    Yes
4      30  False       
5      20  False       
6      30  False       
7      40   True    Yes
8      50   True    Yes
9      60   True    Yes
10     70   True    Yes
11     80   True    Yes
12     90   True    Yes
 
 
 您可以使用pd.DataFrame.where ，它在第一个参数的计算结果为True时保留值，在False时用第二个参数填充。 如果未传递第二个参数，则放置np.nan 。 
 
df.Price.where(df.check, -99)

0    -99
1    -99
2    -99
3     40
4    -99
5    -99
6    -99
7     40
8     50
9     60
10    70
11    80
12    90
Name: Price, dtype: int64
 
 
 姐妹方法是mask ，反之亦然。 保持第一个参数为False 。 这是一个等同的陈述 
 
df.Price.mask(~df.check, -99)

0    -99
1    -99
2    -99
3     40
4    -99
5    -99
6    -99
7     40
8     50
9     60
10    70
11    80
12    90
Name: Price, dtype: int64

Not sure why you'd want to do this... but use itertuples
 
for row in df.itertuples():
    if row.check:
        print('Yes')

Yes
Yes
Yes
Yes
Yes
Yes
Yes
 
 
You can map your values with map
 
df.check.map({True: 'Yes', False: ''})

0        
1        
2        
3     Yes
4        
5        
6        
7     Yes
8     Yes
9     Yes
10    Yes
11    Yes
12    Yes
Name: check, dtype: object
 
And even assign it back to a column
 
df.assign(mapped=df.check.map({True: 'Yes', False: ''}))

    Price  check mapped
0      10  False       
1      20  False       
2      30  False       
3      40   True    Yes
4      30  False       
5      20  False       
6      30  False       
7      40   True    Yes
8      50   True    Yes
9      60   True    Yes
10     70   True    Yes
11     80   True    Yes
12     90   True    Yes
 
 
You can use pd.DataFrame.where which keeps the values when the first argument evaluates to True and fills in with the second argument when False. If the second argument isn't passed, np.nan is placed instead.
 
df.Price.where(df.check, -99)

0    -99
1    -99
2    -99
3     40
4    -99
5    -99
6    -99
7     40
8     50
9     60
10    70
11    80
12    90
Name: Price, dtype: int64
 
 
The sister method is mask which does the opposite. Keeps where the first argument is False. This is an equivalent statement
 
df.Price.mask(~df.check, -99)

0    -99
1    -99
2    -99
3     40
4    -99
5    -99
6    -99
7     40
8     50
9     60
10    70
11    80
12    90
Name: Price, dtype: int64