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Pandas的DataFrame如何做交集,并集,差集与对称差集

叶庭云   人气:6

一、简介

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DataFrameDataFrame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

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

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)

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