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，则需要获取所有的主题名称，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)

所需的输出：

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

Answer 1

使用DataFrame.merge，将索引按DataFrame.reset_index转换为列，按DataFrame.set_index转换索引：

return fetch(baseUrl, {
        mode: "cors",
        method: "POST",
        headers: {
            Host: "localhost:44370",
            Allow: "GET, POST",
            Accept: "application/json, text/plain",
            "Content-Type": "application/json"
        },
        body: JSON.stringify({ Name: "tuesday", Department: "tuesday", visitorcount: 0 })
    })

涉及更改数据的解决方案：

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

对于合并DataFrame 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：

df3

仅用于filtr的最后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行使用boolean indexing：

Math

Answer 2

您可以使用合并：

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

合并具有不同索引的数据框

2 个答案: