python之根据现有的唯一值向数据框添加值

我有一个包含 2 列的数据框 DF:

CLASS   STUDENT 
'Sci'   'Francy' 
'Sci'   Vacant 
'math'  'Alex' 
'math'  'Arthur' 
'math'  'Katy' 
'eng'   'Jack' 
'eng'   Vacant 
'eng'   'Francy' 
'Hist'   'Francy' 
'Hist'   'Francy' 
 

我需要所有类(class)都有 1 个空缺 学生。其中一些已经有了。

结果

CLASS   STUDENT 
'Sci'   'Francy' 
'Sci'   Vacant 
'math'  'Alex' 
'math'  'Arthur' 
'math'  'Katy' 
'math'  Vacant 
'eng'   'Jack' 
'eng'   Vacant 
'eng'   'Francy' 
'Hist'   'Francy' 
'Hist'   'Francy' 
'Hist'   Vacant 
 

我试过了

unique_class = DF['unique_class'].drop_duplicates() 
vacant_column = pd.Series(['vacant'] * unique_class.shape[0]) 
temp_df = pd.concat([unique_class, vacant_column], axis=1, ignore_index=True) 
DF = DF.append(temp_df, ignore_index=True) 
DF.drop_duplicates(inplace=True) 

它有效，但似乎太多了。有更好的方法吗？

请您参考如下方法:

还有另一种方法:

# Copy of your data 
df = pd.DataFrame({ 
    "class": ["Sci", "Sci", "math", "math", "math", "eng", "eng", "eng", "Hist", "Hist"], 
    "student": ["Francy", "vacant", "Alex", "Arthur", "Katy", "Jack", "vacant", "Francy", "Francy", "Francy"] 
    }) 
 
# An identical DF with all students equal to "vacant" 
vacant_df = pd.DataFrame({"class": df["class"], "student": "vacant"}) 
 
# Remove existing 'vacant' from original DF and concatenate with de-duplicated vacant dataframe (to avoid duplicate 'vacant' entries) 
final_df = pd.concat([df.loc[df.student != "vacant", vacant_df.drop_duplicates("class")]) 

原始DF:

  class student 
8  Hist  Francy 
9  Hist  Francy 
0   Sci  Francy 
1   Sci  vacant 
5   eng    Jack 
6   eng  vacant 
7   eng  Francy 
2  math    Alex 
3  math  Arthur 
4  math    Katy 

最终DF:

  class student 
8  Hist  Francy 
9  Hist  Francy 
8  Hist  vacant 
0   Sci  Francy 
0   Sci  vacant 
5   eng    Jack 
7   eng  Francy 
5   eng  vacant 
2  math    Alex 
3  math  Arthur 
4  math    Katy 
2  math  vacant