import pandas as pd
import numpy as np
data=pd.DataFrame(np.random.randint(10,size=(5,3)),columns=['a','b','c'])
>
 a b c
0 3 8 2
1 9 9 5
2 4 5 1
3 2 7 5
4 1 2 8

print(data['c'][data['c'].isin([1])])
>
2 1
Name: c, dtype: int64

print(data['c'][-data['c'].isin([1])])
>
0 2
1 5
3 5
4 8
Name: c, dtype: int64

print(data['c'][-data['c'].isin([1,2])])
>
1 5
3 5
4 8
Name: c, dtype: int64

print(data[-data.isin([1,2])])#按Series逻辑操作df发现会出现NAN并没有删除掉
>
 a b c
0 3.0 8.0 NaN
1 9.0 9.0 5.0
2 4.0 5.0 NaN
3 NaN 7.0 5.0
4 NaN NaN 8.0
print(data[-data.isin([1,2])].dropna())#我们只需要再加一个dropna删除空值就好了
>
a b c
1 9.0 9.0 5.0

print(data['c'][data['c']!=1])
>
0 2
1 5
3 5
4 8
Name: c, dtype: int64

print(data['c'][(data['c']!=1)&((data['c']!=2))])
>
1 5
3 5
4 8
Name: c, dtype: int64

print(data[(data['a']!=1)&(data['c']!=2)]
>
 a b c
1 9 9 5
2 4 5 1
3 2 7 5

print(data[(data!=1)&(data!=2)].dropna()) #与isin原理相同
 a b c
1 9.0 9.0 5.0