对比集合Set | 详解Pandas的DataFrame如何做交集并集差集与对称差集

Posted 2022-02-10 叶庭云

一、简介

"""
@Author   ：叶庭云
@公众号    ：AI庭云君
@CSDN     ：https://yetingyun.blog.csdn.net/
"""

Python的数据类型集合：由不同元素组成的集合，集合中是一组无序排列的可 Hash 的值（不可变类型），可以作为字典的Key

Pandas中的DataFrame：DataFrame是一个表格型的数据结构，可以理解为带有标签的二维数组。

常用的集合操作如下图所示：

二、交集

• pandas的 merge 功能默认为 inner 连接，可以实现取交集
• 集合 set 可以直接用 & 取交集

import pandas as pd

print("CSDN叶庭云：https://yetingyun.blog.csdn.net/")
set1 = "Python", "Go", "C++", "Java"
set2 = "Go", "C++", "javascript", "C"
set1 & set2

df1 = pd.DataFrame([
        ['1', 'Python'],
        ['2', 'Go'],
        ['3', 'C++'],
        ['4', 'Java'],
    ], columns=['id','name'])


df2 = pd.DataFrame([
        ['2','Go'],
        ['3','C++'],
        ['5','JavaScript'],
        ['6','C'],
    ], columns=['id','name'])

pd.merge(df1, df2, on=['id','name'])

操作如下所示：

三、并集

• Pandas的 merge 方法里参数 how 的取值有 “left”, “right”, “inner”, “outer”，默认是inner。outer外连接可以实现取并集。另一种方法也可以df1.append(df2)后去重，保留第一次出现的也可以实现取并集。
• 集合 set 可以直接用 | 取并集

set1 = "Python", "Go", "C++", "Java"
set2 = "Go", "C++", "JavaScript", "C"
set1 | set2

print("CSDN叶庭云：https://yetingyun.blog.csdn.net/")

df1 = pd.DataFrame([
        ['1', 'Python'],
        ['2', 'Go'],
        ['3', 'C++'],
        ['4', 'Java'],
    ], columns=['id','name'])


df2 = pd.DataFrame([
        ['2','Go'],
        ['3','C++'],
        ['5','JavaScript'],
        ['6','C'],
    ], columns=['id','name'])

pd.merge(df1, df2,
         on=['id','name'],
         how='outer')
         
df3 = df1.append(df2)
df3.drop_duplicates(subset=['id'], keep="first")

四、差集

set1 = "Python", "Go", "C++", "Java"
set2 = "Go", "C++", "JavaScript", "C"
set1 - set2

print("CSDN叶庭云：https://yetingyun.blog.csdn.net/")
set1 = "Python", "Go", "C++", "Java"
set2 = "Go", "C++", "JavaScript", "C"
set2 - set1

# df1-df2
df1 = pd.DataFrame([
        ['1', 'Python'],
        ['2', 'Go'],
        ['3', 'C++'],
        ['4', 'Java'],
    ], columns=['id','name'])


df2 = pd.DataFrame([
        ['2','Go'],
        ['3','C++'],
        ['5','JavaScript'],
        ['6','C'],
    ], columns=['id','name'])

df1 = df1.append(df2)
df1 = df1.append(df2)
set_diff_df = df1.drop_duplicates(subset=df1.columns,
                                  keep=False)
set_diff_df

# df2-df1
df1 = pd.DataFrame([
        ['1', 'Python'],
        ['2', 'Go'],
        ['3', 'C++'],
        ['4', 'Java'],
    ], columns=['id','name'])

df2 = pd.DataFrame([
        ['2','Go'],
        ['3','C++'],
        ['5','JavaScript'],
        ['6','C'],
    ], columns=['id','name'])

print("CSDN叶庭云：https://yetingyun.blog.csdn.net/")
df2 = df2.append(df1)
df2 = df2.append(df1)
set_diff_df = df2.drop_duplicates(subset=df2.columns,
                                  keep=False)
set_diff_df

# df1-df2
df1 = pd.DataFrame([
        ['1', 'Python'],
        ['2', 'Go'],
        ['3', 'C++'],
        ['4', 'Java'],
    ], columns=['id','name'])


df2 = pd.DataFrame([
        ['2','Go'],
        ['3','C++'],
        ['5','JavaScript'],
        ['6','C'],
    ], columns=['id','name'])

pd.concat([df1, df2, df2]).drop_duplicates(keep=False)

# df2-df1
df1 = pd.DataFrame([
        ['1', 'Python'],
        ['2', 'Go'],
        ['3', 'C++'],
        ['4', 'Java'],
    ], columns=['id','name'])


df2 = pd.DataFrame([
        ['2','Go'],
        ['3','C++'],
        ['5','JavaScript'],
        ['6','C'],
    ], columns=['id','name'])

pd.concat([df2, df1, df1]).drop_duplicates(keep=False)

五、对称差集

print("CSDN叶庭云：https://yetingyun.blog.csdn.net/")
set1 = "Python", "Go", "C++", "Java"
set2 = "Go", "C++", "JavaScript", "C"
set1 ^ set2    # 对称差集

# 去重   不保留重复的：即可实现取对称差集
df3 = df1.append(df2)

df3.drop_duplicates(subset=['id'], keep=False)

推荐学习：

• https://www.jianshu.com/p/877e2bc11d93
• https://www.runoob.com/python3/python3-set.html