outer-join

问题 In other words, for a nested/multiple JOIN SQL statement, is it safe to say that one should always use INNER JOIN first (either put it at the top line or by using parentheses to first INNER JOIN two tables) and make sure it precedes any OUTER JOIN ( LEFT , RIGHT , FULL )? My understanding is that matching columns (e.g., Primary Key column and Foreign Key column) usually don't have NULL values. And any non-matching rows including NULL from an OUTER JOIN result would be removed when being INNER

问题 I have the following table: create table #table ( time int, key char(1), val int ) with the following data: insert into #table (time, key, val) values (0,"a",1) insert into #table (time, key, val) values (0,"b",2) insert into #table (time, key, val) values (1,"a",10) insert into #table (time, key, val) values (2,"b",20) and I would like to come up with a join of that will yield the following rows/cols: 0 a 1 0 b 2 1 a 10 1 b 0 2 a 0 2 b 20 Which is basically the cartesian product of the

问题 I have the following table: create table #table ( time int, key char(1), val int ) with the following data: insert into #table (time, key, val) values (0,"a",1) insert into #table (time, key, val) values (0,"b",2) insert into #table (time, key, val) values (1,"a",10) insert into #table (time, key, val) values (2,"b",20) and I would like to come up with a join of that will yield the following rows/cols: 0 a 1 0 b 2 1 a 10 1 b 0 2 a 0 2 b 20 Which is basically the cartesian product of the

问题 My problem involves calculating differences in prices across products for each period. With the sample data below product = c('A','A','A','B','B','B','C','C','C') date = as.Date(c('2016-09-12','2016-09-19', '2016-09-26','2016-09-12','2016-09-19', '2016-09-26', '2016-09-12','2016-09-19', '2016-09-26')) price = as.numeric(c(17, 14.7, 15, 14.69, 14.64, 14.63, 13.15, 13.15, 13.15)) df <- data.frame(product, date, price) The challenge is in the grouping, without which a simple call to the outer

问题 I am doing full outer join on 4 tables on the same column. I want to generate only 1 row for each different value in the Join column. Inputs are: employee1 +---------------------+-----------------+--+ | employee1.personid | employee1.name | +---------------------+-----------------+--+ | 111 | aaa | | 222 | bbb | | 333 | ccc | +---------------------+-----------------+--+ employee2 +---------------------+----------------+--+ | employee2.personid | employee2.sal | +---------------------+--------

问题 I want implement left outer join in sqlalchemy. the sql query is like this: select * from skills left join user__skill on user__skill.skill_id=skills.id and user__skill.user_id=4 and what i wrote in sqlalchemy is : skills = db.session.query(Skill, User_Skill.skill_id).\ outerjoin(User_Skill, User_Skill.skill_id==Skill.id and User_Skill.user_id==4).\ order_by(Skill.name).all() but it doesn't filter for a user and show all users skills. how can i write this code? 回答1: EDIT: Use and_ from

问题 The following image can be found by searching for SQL join on the internet: Can we interpret this as a Venn diagram, in the following way? The circle labelled Table A is the set of records in Table A The circle labelled Table B is the set of records in Table B The intersection of the sets represents inner join (aka just join in SQL) Motivation The diagram shown, which appears in various forms widely on the net, looks awfully like a Venn diagram. So the first intuition is to think of it as a

问题 I have 2 datasets and want to do fuzzy join. Here is the two datasets. library(data.table) # data1 dt1 <- fread("NAME State type ABERCOMBIE TOWNSHIP ND TS ABERDEEN TOWNSHIP NJ TS ABERDEEN TOWNSHIP SD TS ABBOTSFORD CITY WI CI ABERDEEN CITY WA CI ADA TOWNSHIP MI TS ADAMS IL TS", header = T) # data2 dt2 <- fread("NAME State type ABERDEEN TWP N J NJ TS ABERDEEN WASH WA CI ABBOTSFORD WIS WI CI ADA TWP MICH MI TS ADA OHIO OH CI ADAMS MASS MA CI ADAMSVILLE ALA AL CI", header = T) Two datasets have

问题 I want to merge 2 dataframes with broadcast relationship: No common index, just want to find all pairs of the rows in the 2 dataframes. So want to make N row dataframe x M row dataframe = N*M row dataframe. Is there any rule to make this happen without using itertool? DF1= id quantity 0 1 20 1 2 23 DF2= name part 0 'A' 3 1 'B' 4 2 'C' 5 DF_merged= id quantity name part 0 1 20 'A' 3 1 1 20 'B' 4 2 1 20 'C' 5 3 2 23 'A' 3 4 2 23 'B' 4 5 2 23 'C' 5 回答1: You can use helper columns tmp filled 1 in

问题 Maybe a facepalm for you guys, but as a SQL query newbie, I'm having a syntax issue. Anyone know what's wrong? SELECT * FROM company C OUTER JOIN company_address A ON C.company_id = A.company_id WHERE A.company_id IS NULL Giving the error: #1064 - You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'OUTER JOIN company_address A ON C.company_id = A.company_id WHERE A.address_id ' at line 2 回答1: In MySQL you