pandas DataFrame 交集并集补集

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1.场景，对于colums都相同的dataframe做过滤的时候

例如：

df1 = DataFrame([[‘a‘, 10, ‘男‘], 
                 [‘b‘, 11, ‘男‘], 
                 [‘c‘, 11, ‘女‘], 
                 [‘a‘, 10, ‘女‘],
                 [‘c‘, 11, ‘男‘]], 
                columns=[‘name‘, ‘age‘, ‘sex‘])

df2 = DataFrame([[‘a‘, 10, ‘男‘], 
                 [‘b‘, 11, ‘女‘]],
                columns=[‘name‘, ‘age‘, ‘sex‘])

取交集：print(pd.merge(df1,df2,on=[‘name‘, ‘age‘, ‘sex‘]))
取并集：print(pd.merge(df1,df2,on=[‘name‘, ‘age‘, ‘sex‘], how=‘outer‘))
取差集(从df1中过滤df1在df2中存在的行)：

df1 = df1.append(df2)
df1 = df1.append(df2)
df1 = df1.drop_duplicates(subset=[‘name‘, ‘age‘, ‘sex‘],keep=False)
print(df1)

代码：

# -*- coding:utf-8 -*-
__version__ = ‘1.0.0.0‘
"""
@brief  :   简介
@details:   详细信息
@author :   zhphuang
@date   :   2018-10-29
"""

import pandas as pd
from pandas import *

df1 = DataFrame([[‘a‘, 10, ‘男‘],
                 [‘b‘, 11, ‘男‘],
                 [‘c‘, 11, ‘女‘],
                 [‘a‘, 10, ‘女‘],
                 [‘c‘, 11, ‘男‘]],
                columns=[‘name‘, ‘age‘, ‘sex‘])
print("df1:\n%s\n\n" % df1)
df2 = DataFrame([[‘a‘, 10, ‘男‘],
                 [‘b‘, 11, ‘女‘]],
                columns=[‘name‘, ‘age‘, ‘sex‘])
print("df2:\n%s\n\n" % df2)
# 取交集
print("交集:\n%s\n\n" % pd.merge(df1,df2,on=[‘name‘, ‘age‘, ‘sex‘]))

# 取并集
print("并集:\n%s\n\n" % pd.merge(df1,df2,on=[‘name‘, ‘age‘, ‘sex‘], how=‘outer‘))

# 从df1中过滤df1在df2中存在的行，也就是取补集
df1 = df1.append(df2)
df1 = df1.append(df2)
print("补集(从df1中过滤df1在df2中存在的行):\n%s\n\n" % df1.drop_duplicates(subset=[‘name‘, ‘age‘, ‘sex‘],keep=False))

截图 

pandas DataFrame 交集并集补集

标签：brief   技术   port   场景   auth   inf   bsp   set   rom   

原文地址：https://www.cnblogs.com/niuniuc/p/9873134.html