DataFrame的级联and合并操作
级联操作（横向或纵向的拼接）
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pd.concat
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pd.append
import pandas as pd import numpy as np
pandas使用pd.concat函数，与np.concatenate函数类似，只是多了一些参数：
objs axis=0 keys join='outer' / 'inner':表示的是级联的方式，outer会将所有的项进行级联（忽略匹配和不匹配），而inner只会将匹配的项级联到一起，不匹配的不级联 ignore_index=False
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匹配级联
df1 = pd.DataFrame(data=np.random.randint(0,100,size=(5,3)),columns=['A','B','C']) df2 = pd.DataFrame(data=np.random.randint(0,100,size=(5,3)),columns=['A','D','C']) pd.concat((df1,df1),axis=1) #行列索引都一致的级联叫做匹配级联
A B C A B C
0 26 63 95 26 63 95
1 66 86 35 66 86 35
2 74 3 4 74 3 4
3 85 0 67 85 0 67
4 59 28 65 59 28 65
View Code
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不匹配级联
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不匹配指的是级联的维度的索引不一致。例如纵向级联时列索引不一致，横向级联时行索引不一致
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有2种连接方式：
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外连接：补NaN（默认模式）
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内连接：只连接匹配的项
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pd.concat((df1,df2),axis=0) # 默认 pd.concat((df1,df2),axis=0,join='inner') # 内 inner直把可以级联的级联，不能级联不处理（默认是outer保留所有值）
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如果想要保留数据的完整性必须使用outer（外连接）
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append函数的使用
df1.append(df1)
合并操作（合并对应的是数据，级联对应的是表格）
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merge与concat的区别在于，merge需要依据某一共同列来进行合并
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使用pd.merge()合并时，会自动根据两者相同column名称的那一列，作为key来进行合并。
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注意每一列元素的顺序不要求一致
一对一合并
from pandas import DataFrame
df1 = DataFrame({'employee':['Bob','Jake','Lisa'],
'group':['Accounting','Engineering','Engineering'],
})
df1
employee group
0 Bob Accounting
1 Jake Engineering
2 Lisa Engineering
View Code
df2 = DataFrame({'employee':['Lisa','Bob','Jake'],
'hire_date':[2004,2008,2012],
})
df2
employee hire_date 0 Lisa 2004 1 Bob 2008 2 Jake 2012View Code
pd.merge(df1,df2,on='employee') # 合并
employee group hire_date 0 Bob Accounting 2008 1 Jake Engineering 2012 2 Lisa Engineering 2004View Code
一对多合并
df3 = DataFrame({
'employee':['Lisa','Jake'],
'group':['Accounting','Engineering'],
'hire_date':[2004,2016]})
df3
employee group hire_date 0 Lisa Accounting 2004 1 Jake Engineering 2016View Code
df4 = DataFrame({'group':['Accounting','Engineering','Engineering'],
'supervisor':['Carly','Guido','Steve']
})
df4
group supervisor
0 Accounting Carly
1 Engineering Guido
2 Engineering Steve
View Code
pd.merge(df3,df4)#on如果不写，默认情况下使用两表中公有的列作为合并条件
employee group hire_date supervisor 0 Lisa Accounting 2004 Carly 1 Jake Engineering 2016 Guido 2 Jake Engineering 2016 SteveView Code
多对多合并
df1 = DataFrame({'employee':['Bob','Jake','Lisa'],
'group':['Accounting','Engineering','Engineering']})
df1
employee group 0 Bob Accounting 1 Jake Engineering 2 Lisa EngineeringView Code
df5 = DataFrame({'group':['Engineering','Engineering','HR'],
'supervisor':['Carly','Guido','Steve']
})
df5
group supervisor 0 Engineering Carly 1 Engineering Guido 2 HR SteveView Code
pd.merge(df1,df5,how='outer') # how='outer'合并方式 左右内外连接
employee group supervisor 0 Bob Accounting NaN 1 Jake Engineering Carly 2 Jake Engineering Guido 3 Lisa Engineering Carly 4 Lisa Engineering Guido 5 NaN HR SteveView Code
pd.merge(df1,df5,how='right')
employee group supervisor
0 Jake Engineering Carly
1 Lisa Engineering Carly
2 Jake Engineering Guido
3 Lisa Engineering Guido
4 NaN HR Steve
View Code
key的规范化
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当两张表没有可进行连接的列时，可使用left_on和right_on手动指定merge中左右两边的哪一列列作为连接的列
df1 = DataFrame({'employee':['Bobs','Linda','Bill'],
'group':['Accounting','Product','Marketing'],
'hire_date':[1998,2017,2018]})
df1
employee group hire_date
0 Bobs Accounting 1998
1 Linda Product 2017
2 Bill Marketing 2018
View Code
df5 = DataFrame({'name':['Lisa','Bobs','Bill'],
'hire_dates':[1998,2016,2007]})
df5
name hire_dates 0 Lisa 1998 1 Bobs 2016 2 Bill 2007View Code
pd.merge(df1,df5,left_on='employee',right_on='name')
employee group hire_date name hire_dates 0 Bobs Accounting 1998 Bobs 2016 1 Bill Marketing 2018 Bill 2007View Code
内合并与外合并:outer取并集 inner取交集（上面做过了）
df6 = DataFrame({'name':['Peter','Paul','Mary'],
'food':['fish','beans','bread']}
)
df7 = DataFrame({'name':['Mary','Joseph'],
'drink':['wine','beer']})
pd.concat((df6,df7),axis=0,join='inner')
name 0 Peter 1 Paul 2 Mary 0 Mary 1 JosephView Code
pd.concat((df6,df7),axis=0,join='outer')
name food drink 0 Peter fish NaN 1 Paul beans NaN 2 Mary bread NaN 0 Mary NaN wine 1 Joseph NaN beerView Code
标签：级联,group,df1,合并,DataFrame,Engineering,pd,employee From： https://www.cnblogs.com/erhuoyuan/p/16927977.html