合并具有不同索引的数据框[重复]

Question

我有两个带有数据的数据框，现在我想将第二个数据框的字段合并到第一个。如果我调用第一个数据框的索引，则需要获取所有主题和主题名称，如下所示。谁能帮我这个？

import pandas as pd
sub_data = {'Subjectid':['10','11'],'Author':['Author1','Author2'],'SubjectName':['Maths', 'English']}
df1 = pd.DataFrame(sub_data)
print(df1)

topic_data = {'Topicid':['100','101','102'],'Subjectid':['10','10','11'],'TopicName':['Geometry','Trignometry', 'Tenses']}
df2 = pd.DataFrame(topic_data)
print(df2)

subtopic_data = {'Subtopicid':['1000','1001','1002'],'Topicid':['100','101','102'],'Subjectid':['10','10','11'],'SubtopicTopicName':['Lines','Angles', 'PresentTenses']}
df3 = pd.DataFrame(subtopic_data)
print(df3)

期望的输出：

    Author    SubjectName   topicid   TopicName       Subopicid SubtopicName
10  Author1       Maths     100       Geometry        1000       Lines
10  Author1       Maths     100       Trignometry     1001      Angles

Answer 1

你可以使用合并：

pd.merge(df1, df2, on ='Subjectid').set_index('Subjectid')

            Author  SubjectName Topicid TopicName
Subjectid               
10          Author1 Maths       100     Geometry
10          Author1 Maths       101     Trignometry
11          Author2 English     102     Tenses

Answer 2

使用DataFrame.merge 将索引转换为DataFrame.reset_index 的列和DataFrame.set_index 的列索引：

df = df1.merge(df2.reset_index().set_index('Subjectid'), left_index=True, right_index=True)
print (df)
     Author SubjectName index    TopicName
10  Author1       Maths   100     Geometry
10  Author1       Maths   101  Trignometry
11  Author2     English   102       Tenses

有问题的更改数据的解决方案：

df = df1.merge(df2, on='Subjectid').set_index('Subjectid').rename_axis(None)
print (df)
     Author SubjectName Topicid    TopicName
10  Author1       Maths     100     Geometry
10  Author1       Maths     101  Trignometry
11  Author2     English     102       Tenses

用于合并DataFrame df3:

df = (df1.merge(df2, on='Subjectid').set_index('Subjectid')
         .merge(df3, on=['Topicid','Subjectid']))
print (df)
    Author Subjectid SubjectName Topicid    TopicName Subtopicid  \
0  Author1        10       Maths     100     Geometry       1000   
1  Author1        10       Maths     101  Trignometry       1001   
2  Author2        11     English     102       Tenses       1002   

  SubtopicTopicName  
0             Lines  
1            Angles  
2     PresentTenses  

仅用于过滤的最后一个Math 行使用boolean indexing：

df4 = df[df['SubjectName'] == 'Maths']
print (df4)
    Author Subjectid SubjectName Topicid    TopicName Subtopicid  \
0  Author1        10       Maths     100     Geometry       1000   
1  Author1        10       Maths     101  Trignometry       1001   

  SubtopicTopicName  
0             Lines  
1            Angles