从数据框中查找/搜索值以创建新列

Question

我正在尝试根据从其他列和行中搜索数据在数据框中创建新列。计算此类列值的最佳/禁食方法是什么。

我尝试过使用 lambda 和外部函数，但没有结果。

1. 谁能详细说明获得最终结果的方法以及从计算时间来看哪种方法是最佳的。

2. 我们可以分配函数/lambda 来计算这些值吗？

3. 我们能否实现数据框，使其保持对列中计算值的函数的引用，而不是计算值本身？基于其他列/行数据的动态结果。

data = { 
            'ID':[1, 2, 3, 4 ,5],                  
            'Name':['Andy', 'Rob', 'Tony', 'John', 'Lui'],
            'M_Name':['Lui', 'Lui', 'Lui','NoData', 'John']
             } 

df = pd.DataFrame(data) 

Original DataFrame:
    ID  M_Name  Name
0   1     Lui  Andy
1   2     Lui   Rob
2   3     Lui  Tony
3   4  NoData  John
4   5    John   Lui

data_after = { 
            'ID':[1, 2, 3, 4 ,5],                  
            'Name':['Andy', 'Rob', 'Tony', 'John', 'Lui'],
            'M_Name':['Lui', 'Lui', 'Lui','NoData', 'John'],    
            'ID_by_M_Name':[5, 5, 5, 'NoData', '4']
             } 

df1 = pd.DataFrame(data_after)

Processed DataFrame:
    ID ID_by_M_Name  M_Name  Name
0   1          5     Lui  Andy
1   2          5     Lui   Rob
2   3          5     Lui  Tony
3   4     NoData  NoData  John
4   5          4    John   Lui

I have tried two ways to get ID but not sure how to use them in assign

getID = lambda name: df.loc[df['Name'] == name]['ID'].iloc[0]

def mID(name):
    return df.loc[df['Name'] == name]['ID'].iloc[0]

For each row we want to find ID of M_Name for specifc Name. 
e.g. for Name='Andy' we have M_Name = 'Lui' and Lui's ID(5)
For Lui M_name is John and John's ID is 4

print(getID('Lui'))
print(mID('Lui'))

df['ID'] = df.assign(mID(df['M_Name']), axis=1 )

IndexError: 单个位置索引器超出范围

Answer 1

将Series.replace 或Series.map 与Series.fillna 一起使用：

df['ID_by_M_Name'] = df['M_Name'].replace(df.set_index('Name')['ID'])
#assign alternative
#df = df.assign(ID_by_M_Name = df['M_Name'].replace(df.set_index('Name')['ID']))

df['ID_by_M_Name'] = df['M_Name'].map(df.set_index('Name')['ID']).fillna(df['M_Name'])
#assign alternative
#df=df.assign(ID_by_M_Name=df['M_Name'].map(df.set_index('Name')['ID']).fillna(df['M_Name']))

---

print (df)

   ID  Name  M_Name ID_by_M_Name
0   1  Andy     Lui            5
1   2   Rob     Lui            5
2   3  Tony     Lui            5
3   4  John  NoData       NoData
4   5   Lui    John            4

如果新列的重要位置使用DataFrame.insert:

df.insert(1, 'ID_by_M_Name', df['M_Name'].replace(df.set_index('Name')['ID']))
print (df)

   ID ID_by_M_Name  Name  M_Name
0   1            5  Andy     Lui
1   2            5   Rob     Lui
2   3            5  Tony     Lui
3   4       NoData  John  NoData
4   5            4   Lui    John