熊猫测试一个列是否“ .isin”另一列？

Question

data = {'COL1':['United States of America', 'United Kingdom'], 'COL2':['States of America', 'United States']} 
df = pd.DataFrame(data)  
df['IS_COL1_IN_COL2'] = df['COL1'].isin(df['COL1'])
display(df)

上面的代码给出下面的结果。我期望值是False，但是我会变成True？您能否让我知道我的代码有什么问题？谢谢。

Answer 1

我认为您需要in和lambda函数：

df['IS_COL1_IN_COL2'] = df.apply(lambda x:x['COL1'] in x['COL2'], axis=1)
df['IS_COL2_IN_COL1'] = df.apply(lambda x:x['COL2'] in x['COL1'], axis=1)

print (df)
                       COL1               COL2  IS_COL1_IN_COL2  \
0  United States of America  States of America            False   
1            United Kingdom      United States            False   

   IS_COL2_IN_COL1  
0             True  
1            False  

---

功能Series.isin的工作原理不同-将COL1的每个值与COL2的所有值进行比较，检查更改的数据样本：

data = {'COL1':['United States of America', 'United Kingdom','USA','JAR'], 
        'COL2':['States of America', 'United States','UK', 'USA'],} 
df = pd.DataFrame(data)  

df['IS_COL1_IN_COL2_isin'] = df['COL1'].isin(df['COL2'])
df['IS_COL1_IN_COL2'] = df.apply(lambda x:x['COL1'] in x['COL2'], axis=1)
df['IS_COL2_IN_COL1'] = df.apply(lambda x:x['COL2'] in x['COL1'], axis=1)
print (df)
                       COL1               COL2  IS_COL1_IN_COL2_isin  \
0  United States of America  States of America                 False   
1            United Kingdom      United States                 False   
2                       USA                 UK                  True   
3                       JAR                USA                 False   

   IS_COL1_IN_COL2  IS_COL2_IN_COL1  
0            False             True  
1            False            False  
2            False            False  
3            False            False