Mysql準備工作:

T1

T2

T3

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操作	mysql	mysql結果
inner join	select * from T1 inner join T2 on T1.user_id = T2.user_id
left outer join	select * from T1 left outer join T2 on T1.user_id=T2.user_id
right outer join	select * from T1 right outer join T2 on T1.user_id=T2.user_id
full outer join	select * from T1 left join T2 on T1.user_id = T2.user_id union select * from? right join T2 on T1.user_id = T2.user_id ;
cross join	select * from T1 cross join T2

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result= ? ?user_id user_name password ?score ?level
0 ? ? ? ?1 ? ? ?Jack ?Jackpwd ? ? 20 ? ? ?3
1 ? ? ? ?3 ? ? ? NaN ? ? ?NaN ? ? 50 ? ? ?6

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Python準備工作:

import pandas as pd T1= {'user_id': [1, 2],'user_name': ['Jack','owen'],'password': ['Jackpwd','owenpwd']}T2= {'user_id': [1, 3],'score': [20,50],'level': [3,6]}T1=pd.DataFrame(T1) print(T1) T2=pd.DataFrame(T2) print(T2)

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mysql操作	python代碼	python代碼運行結果
inner join	result=pd.merge(left=T1, right=T2, how='inner', left_on='user_id', right_on='user_id')	user_id user_name password ?score ?level ?1? ? ? ? ? ? ? ? ? ?Jack? ? ? ? ? ?Jackpwd ? ? 20 ? ? ?3
lefter outer join	result=pd.merge(left=T1, right=T2, how='left', left_on='user_id', right_on='user_id')	?user_id user_name password ?score ?level ?1? ? ? ? ? ? ? ? ? Jack? ? ? ? ? ? Jackpwd ? 20.0 ? ?3.0 ?2? ? ? ? ? ? ? ? ? owen? ? ? ? ? owenpwd NaN ? ?NaN
right outer join	result=pd.merge(left=T1, right=T2, how='right', left_on='user_id', right_on='user_id')	user_id user_name password ?score ?level 1? ? ? ? ? ? ? ? ? Jack? ? ? ? ? ? Jackpwd ? ? 20 ? ? ?3 3? ? ? ? ? ? ? ? ? ? NaN? ? ? ? ? ? ? ?NaN? ? ? ? ? ?50 ? ? ?6
full outer join	result=pd.merge(T1, T2, on='user_id', how='outer')	result= ? ?user_id user_name password ?score ?level 0 ? ? ? ?1 ? ? ?Jack ?Jackpwd ? 20.0 ? ?3.0 1 ? ? ? ?2 ? ? ?owen ?owenpwd ? ?NaN ? ?NaN 2 ? ? ? ?3 ? ? ? NaN ? ? ?NaN ? 50.0 ? ?6.0 ?
cross join	?	?

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Reference:

[1]JOIN IN PANDAS: MERGE DATA FRAMES (INNER, OUTER, RIGHT, LEFT JOIN) IN PANDAS PYTHON

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