代码

import pandas as pd
import numpy as np left=pd.DataFrame({'key':['K0','K1','K2','K3'],
'A':['A0','A1','A3','A3'],
'B':['B0','B1','B2','B3'],}) right=pd.DataFrame({'key':['K0','K1','K2','K3'],
'C':['C0','C1','C3','C3'],
'D':['D0','D1','D2','D3'],}) print('-1-')
print(left)
print(right) res = pd.merge(left,right,on='key')
print(res) left=pd.DataFrame({'key1':['K0','K0','K1','K2'],
'key2':['K0','K1','K0','K1'],
'A':['A0','A1','A3','A3'],
'B':['B0','B1','B2','B3'],}) right=pd.DataFrame({'key1':['K0','K1','K1','K2'],
'key2':['K0','K0','K0','K0'],
'C':['C0','C1','C3','C3'],
'D':['D0','D1','D2','D3'],}) print('-2-')
res = pd.merge(left,right,on=['key1','key2'])
print(left)
print(right)
print(res) # default
print('-3-')
res = pd.merge(left,right,on=['key1','key2'],how='inner')
print(left)
print(right)
print(res) print('-4-')
res = pd.merge(left,right,on=['key1','key2'],how='outer')
print(left)
print(right)
print(res) print('-5-')
res = pd.merge(left,right,on=['key1','key2'],how='right')
print(left)
print(right)
print(res) print('-6-')
res = pd.merge(left,right,on=['key1','key2'],how='left')
print(left)
print(right)
print(res) print('-7-')
df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']})
df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
print(df1)
print(df2)
res = pd.merge(df1,df2,on='col1',how='outer',indicator=True)
print(res) res = pd.merge(df1,df2,on='col1',how='outer',indicator=True)
print(res) res = pd.merge(df1,df2,on='col1',how='outer',indicator='indicator_column')
print(res) df1 = pd.DataFrame({'A':['A0','A1','A2'],
'B':['B0','B1','B2']},
index=['K0','K1','K2']) df2 = pd.DataFrame({'C':['C0','C1','C2'],
'D':['D0','D1','D2']},
index=['K0','K1','K2']) print(df1)
print(df2) print('-8-')
res=pd.merge(left,right,left_index=True,right_index=True,how='outer')
print(res) print('-9-')
res=pd.merge(left,right,left_index=True,right_index=True,how='inner')
print(res) boys = pd.DataFrame({'k':['K0','K1','K2'],'age':[1,2,3]})
girls = pd.DataFrame({'k':['K0','K0','K3'],'age':[4,5,6]}) print('-10-')
print(boys)
print(girls) res = pd.merge(boys, girls, on='k', suffixes=['_boy','_girl'],how='inner')
print(res) res = pd.merge(boys, girls, on='k', suffixes=['_boy','_girl'],how='outer')
print(res)

  

输出

-1-
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K2 A3 B2
3 K3 A3 B3
key C D
0 K0 C0 D0
1 K1 C1 D1
2 K2 C3 D2
3 K3 C3 D3
key A B C D
0 K0 A0 B0 C0 D0
1 K1 A1 B1 C1 D1
2 K2 A3 B2 C3 D2
3 K3 A3 B3 C3 D3
-2-
key1 key2 A B
0 K0 K0 A0 B0
1 K0 K1 A1 B1
2 K1 K0 A3 B2
3 K2 K1 A3 B3
key1 key2 C D
0 K0 K0 C0 D0
1 K1 K0 C1 D1
2 K1 K0 C3 D2
3 K2 K0 C3 D3
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K1 K0 A3 B2 C1 D1
2 K1 K0 A3 B2 C3 D2
-3-
key1 key2 A B
0 K0 K0 A0 B0
1 K0 K1 A1 B1
2 K1 K0 A3 B2
3 K2 K1 A3 B3
key1 key2 C D
0 K0 K0 C0 D0
1 K1 K0 C1 D1
2 K1 K0 C3 D2
3 K2 K0 C3 D3
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K1 K0 A3 B2 C1 D1
2 K1 K0 A3 B2 C3 D2
-4-
key1 key2 A B
0 K0 K0 A0 B0
1 K0 K1 A1 B1
2 K1 K0 A3 B2
3 K2 K1 A3 B3
key1 key2 C D
0 K0 K0 C0 D0
1 K1 K0 C1 D1
2 K1 K0 C3 D2
3 K2 K0 C3 D3
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K0 K1 A1 B1 NaN NaN
2 K1 K0 A3 B2 C1 D1
3 K1 K0 A3 B2 C3 D2
4 K2 K1 A3 B3 NaN NaN
5 K2 K0 NaN NaN C3 D3
-5-
key1 key2 A B
0 K0 K0 A0 B0
1 K0 K1 A1 B1
2 K1 K0 A3 B2
3 K2 K1 A3 B3
key1 key2 C D
0 K0 K0 C0 D0
1 K1 K0 C1 D1
2 K1 K0 C3 D2
3 K2 K0 C3 D3
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K1 K0 A3 B2 C1 D1
2 K1 K0 A3 B2 C3 D2
3 K2 K0 NaN NaN C3 D3
-6-
key1 key2 A B
0 K0 K0 A0 B0
1 K0 K1 A1 B1
2 K1 K0 A3 B2
3 K2 K1 A3 B3
key1 key2 C D
0 K0 K0 C0 D0
1 K1 K0 C1 D1
2 K1 K0 C3 D2
3 K2 K0 C3 D3
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K0 K1 A1 B1 NaN NaN
2 K1 K0 A3 B2 C1 D1
3 K1 K0 A3 B2 C3 D2
4 K2 K1 A3 B3 NaN NaN
-7-
col1 col_left
0 0 a
1 1 b
col1 col_right
0 1 2
1 2 2
2 2 2
col1 col_left col_right _merge
0 0 a NaN left_only
1 1 b 2.0 both
2 2 NaN 2.0 right_only
3 2 NaN 2.0 right_only
col1 col_left col_right _merge
0 0 a NaN left_only
1 1 b 2.0 both
2 2 NaN 2.0 right_only
3 2 NaN 2.0 right_only
col1 col_left col_right indicator_column
0 0 a NaN left_only
1 1 b 2.0 both
2 2 NaN 2.0 right_only
3 2 NaN 2.0 right_only
A B
K0 A0 B0
K1 A1 B1
K2 A2 B2
C D
K0 C0 D0
K1 C1 D1
K2 C2 D2
-8-
key1_x key2_x A B key1_y key2_y C D
0 K0 K0 A0 B0 K0 K0 C0 D0
1 K0 K1 A1 B1 K1 K0 C1 D1
2 K1 K0 A3 B2 K1 K0 C3 D2
3 K2 K1 A3 B3 K2 K0 C3 D3
-9-
key1_x key2_x A B key1_y key2_y C D
0 K0 K0 A0 B0 K0 K0 C0 D0
1 K0 K1 A1 B1 K1 K0 C1 D1
2 K1 K0 A3 B2 K1 K0 C3 D2
3 K2 K1 A3 B3 K2 K0 C3 D3
-10-
k age
0 K0 1
1 K1 2
2 K2 3
k age
0 K0 4
1 K0 5
2 K3 6
k age_boy age_girl
0 K0 1 4
1 K0 1 5
k age_boy age_girl
0 K0 1.0 4.0
1 K0 1.0 5.0
2 K1 2.0 NaN
3 K2 3.0 NaN
4 K3 NaN 6.0

  

17-numpy笔记-莫烦pandas-5的更多相关文章

  1. 16-numpy笔记-莫烦pandas-4

    代码 import pandas as pd import numpy as np dates = pd.date_range('20130101', periods=6) df=pd.DataFra ...

  2. 15-numpy笔记-莫烦pandas-3

    代码 import pandas as pd import numpy as np dates = pd.date_range('20130101', periods=6) df=pd.DataFra ...

  3. 14-numpy笔记-莫烦pandas-2

    代码 import pandas as pd import numpy as np dates = pd.date_range('20130101', periods=6) df=pd.DataFra ...

  4. 18-numpy笔记-莫烦pandas-6-plot显示

    代码 import pandas as pd import numpy as np import matplotlib.pyplot as plt data = pd.Series(np.random ...

  5. 13-numpy笔记-莫烦pandas-1

    代码 import pandas as pd import numpy as np s = pd.Series([1,3,6,np.nan, 44,1]) print('-1-') print(s) ...

  6. 11-numpy笔记-莫烦基础操作1

    代码 import numpy as np array = np.array([[1,2,5],[3,4,6]]) print('-1-') print('数组维度', array.ndim) pri ...

  7. 12-numpy笔记-莫烦基本操作2

    代码 import numpy as np A = np.arange(3,15) print('-1-') print(A) print('-2-') print(A[3]) A = np.aran ...

  8. Python pandas & numpy 笔记

    记性不好,多记录些常用的东西,真·持续更新中::先列出一些常用的网址: 参考了的 莫烦python pandas DOC numpy DOC matplotlib 常用 习惯上我们如此导入: impo ...

  9. tensorflow学习笔记-bili莫烦

    bilibili莫烦tensorflow视频教程学习笔记 1.初次使用Tensorflow实现一元线性回归 # 屏蔽警告 import os os.environ[' import numpy as ...

随机推荐

  1. 剑指Offer-5.用两个栈实现队列(C++/Java)

    题目: 用两个栈来实现一个队列,完成队列的Push和Pop操作. 队列中的元素为int类型. 分析: 栈的特点是先进后出,队列的特点则是先进先出. 题目要求我们用两个栈来实现一个队列,栈和队列都有入栈 ...

  2. Git修改和配置用户名和邮箱

    git在push/push to时需要使用到user.name和user.email,切记一定要现配置好查看user.name/user.email git config user.name git ...

  3. 使用VisualVM 进行性能分析及调优

    概述 开发大型 Java 应用程序的过程中难免遇到内存泄露.性能瓶颈等问题,比如文件.网络.数据库的连接未释放,未优化的算法等.随着应用程序的持续运行,可能会造成整个系统运行效率下降,严重的则会造成系 ...

  4. spring cloud 与spring boot 版本不匹配引发的问题总结

    为了将前期项目慢慢转移到微服务上,今天开始搭建eureka服务时,出现以下错误: org.springframework.context.ApplicationContextException: Un ...

  5. win10每次开机都会自检系统盘(非硬件故障)——解决方案2019.07.12

    1.最近反复遇到了这个问题,之前遇到这个问题就把系统重装了,没想到今天又遇到了,目前系统东西太多了,重装太麻烦了,就下决心解决一下. 2.不要使用网络上流传的修改注册表的方案,把注册表的那个键值删除那 ...

  6. Python复杂对象转JSON

    Python复杂对象转JSON在Python对于简单的对象转json还是比较简单的,如下: import json d = {'a': 'aaa', 'b': ['b1', 'b2', 'b3'], ...

  7. 如何在 C# 中自定义 Comparer,以实现按中文拼音(a-z)来排序

    1. 为何要自定义 Comparer a. 先看如下代码 class Program { public static void Main(string[] args) { List<string ...

  8. .Net MVC 提示未能加载文件或程序集

    最近在开发.Net MVC程序时,突然出现未能加载文件或程序集的错误, 错误1 错误2 猜测时由于引用了Swagger,导致Swagger依赖的组件版本和现有版本冲突(现在仍未确定是这个原因),浪费了 ...

  9. Java生鲜电商平台-电商促销业务分析设计与系统架构

    Java生鲜电商平台-电商促销业务分析设计与系统架构 说明:Java开源生鲜电商平台-电商促销业务分析设计与系统架构,列举的是常见的促销场景与源代码下载 左侧为享受促销的资格,常见为这三种: 首单 大 ...

  10. 2019 北森java面试笔试题 (含面试题解析)

      本人5年开发经验.18年年底开始跑路找工作,在互联网寒冬下成功拿到阿里巴巴.今日头条.北森等公司offer,岗位是Java后端开发,因为发展原因最终选择去了北森,入职一年时间了,也成为了面试官,之 ...