使用IF语句的Python用户定义函数不起作用

Question

我有以下数据框

Index   education   marital-status  occupation         gender    target
0       bachelors   never-married   adm-clerical       male      0
1       bachelors   spouse          exec-managerial    male      0
2       hs-grad     divorced        handlers-cleaners  male      0
3       11th        spouse          handlers-cleaners  male      0
4       bachelors   spouse          prof-specialty     female    0
5       masters     spouse          exec-managerial    female    0
6       other       other           other-service      female    0
7       hs-grad     spouse          exec-managerial    male      1
8       masters     never-married   prof-specialty     female    1
9       bachelors   spouse          exec-managerial    male      1

有人可以向我解释以下原因为何不起作用-我认为应该根据我已阅读的内容和所看到的内容进行应用。

def new_features(education, gender, target):

  if [((education == 'bachelors') & (gender == 'male') & (target == 1))]:
      result = 'educated_male_convert'
  elif [((education == 'bachelors') & (gender == 'female') & (target == 1))]:
      result = 'educated_female_convert'
  else:
      result = 'educated_not_determined'
  return result

df['new_col'] = df.apply(lambda row: new_features(row['education'], row['gender'], row['target']), axis=1)

它只是返回： educated_male_convert

我遵循了许多教程，并阅读了其他主题，并将相同的代码应用于自己的数据集-不知道我缺少什么。

任何帮助，将不胜感激

Answer 1

问题是您将if条件放在方括号中。 因此，代码不是在测试表达式if False: ... ，而是在测试if [False]: ... 并且由于任何非空列表的评估结果为True ，因此[False]评估结果为True ，并且代码转到错误的分支。

Answer 2

这也是这样做的另一种方式：

df['new_col'] = df.apply(lambda row: 'educated_male_convert' if row['education'] == 'bachelors' and row['gender'] == 'male' and row['target'] == 1
                      else ('educated_female_convert' if row['education'] == 'bachelors' and row['gender'] == 'female' and row['target'] == 1 
                      else ('educated_not_determined')), axis=1)
df

Answer 3

这是一个np.select解决方案：

c1=df.education=='bachelors' 
c2=df.gender=='male'
c3=df.target.astype(bool)
df['new_col']=np.select([c1&c2&c3,c1&~c2&c3],['educated_male_convert',
        'educated_female_convert'],'educated_not_determined')
print(df)

---

       education marital-status         occupation  gender  target  \
Index                                                                
0      bachelors  never-married       adm-clerical    male       0   
1      bachelors         spouse    exec-managerial    male       0   
2        hs-grad       divorced  handlers-cleaners    male       0   
3           11th         spouse  handlers-cleaners    male       0   
4      bachelors         spouse     prof-specialty  female       0   
5        masters         spouse    exec-managerial  female       0   
6          other          other      other-service  female       0   
7        hs-grad         spouse    exec-managerial    male       1   
8        masters  never-married     prof-specialty  female       1   
9      bachelors         spouse    exec-managerial    male       1   

                       new_col  
Index                           
0      educated_not_determined  
1      educated_not_determined  
2      educated_not_determined  
3      educated_not_determined  
4      educated_not_determined  
5      educated_not_determined  
6      educated_not_determined  
7      educated_not_determined  
8      educated_not_determined  
9        educated_male_convert