Pandas中两个dataframe的交集和差集的示例代码

创建测试数据：

import pandas as pd
import numpy as np
 
#create a dataframe
df1 = {
  'subject':['semester1','semester2','semester3','semester4','semester1',
        'semester2','semester3'],
  'score':[62,47,55,74,31,77,85]}
 
df2 = {
  'subject':['semester1','semester2','semester3','semester4'],
  'score':[90,47,85,74]}
 
 
df1 = pd.dataframe(df1,columns=['subject','score'])
df2 = pd.dataframe(df2,columns=['subject','score'])
 
print(df1)
print(df2)

运行结果：

求两个dataframe的交集

intersected_df = pd.merge(df1, df2, how='inner')
print(intersected_df)

也可以指定求交集的列：

intersected_df = pd.merge(df1, df2, on=['subject'], how='inner')
print(intersected_df)

求差集

df2-df1：

set_diff_df = pd.concat([df2, df1, df1]).drop_duplicates(keep=false)
print(set_diff_df)

df1-df2：

set_diff_df = pd.concat([df1, df2, df2]).drop_duplicates(keep=false)
print(set_diff_df)

另一种求差集的方法是：

以df1-df2为例：

df1 = df1.append(df2)
df1 = df1.append(df2)
set_diff_df = df1.drop_duplicates(subset=['subject', 'score'],keep=false)
print(set_diff_df)

得到的df1-df2结果是一样的：

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