left-join

问题 I've an old table like this: user> id | name | address | comments And now I've to create an "alias" table to allow some users to have an alias name for some reasons. I've created a new table 'user_alias' like this: user_alias> name | user But now I have a problem due my poor SQL level... How to join both tables to generate something like this: 1 | my_name | my_address | my_comments 1 | my_alias | my_address | my_comments 2 | other_name | other_address | other_comments I mean, I want to make a

问题 I am getting different results based on a filter condition in a query based on where I place the filter condition. My questions are: Is there a technical difference between these queries? Is there anything in the SQL standard that explains the different recordsets from the queries? Given the simplified scenario: --Table: Parent Columns: ID, Name, Description --Table: Child Columns: ID, ParentID, Name, Description --Query 1 SELECT p.ID, p.Name, p.Description, c.ID, c.Name, c.Description FROM

问题 I am getting different results based on a filter condition in a query based on where I place the filter condition. My questions are: Is there a technical difference between these queries? Is there anything in the SQL standard that explains the different recordsets from the queries? Given the simplified scenario: --Table: Parent Columns: ID, Name, Description --Table: Child Columns: ID, ParentID, Name, Description --Query 1 SELECT p.ID, p.Name, p.Description, c.ID, c.Name, c.Description FROM

问题 This question already has answers here : Pandas Merging 101 (2 answers) Closed 2 years ago . How to merge (left join) using column value from dataframe A and index of dataframe B? For example: >>> A >>> B lkey value rkey value 0 foo 1 0 foo 5 1 bar 2 1 bar 6 2 baz 3 2 qux 7 3 foo 4 3 bar 8 to get: lkey value_x rkey value_y 0 foo 1 bar 6 1 bar 2 qux 7 2 baz 3 bar 8 3 foo 4 NaN NaN 回答1: try using left_on and right_index to do the merging, like: m = pd.merge(dfA, dfB, right_index = True, left_on

问题 This question already has answers here : Pandas Merging 101 (2 answers) Closed 2 years ago . How to merge (left join) using column value from dataframe A and index of dataframe B? For example: >>> A >>> B lkey value rkey value 0 foo 1 0 foo 5 1 bar 2 1 bar 6 2 baz 3 2 qux 7 3 foo 4 3 bar 8 to get: lkey value_x rkey value_y 0 foo 1 bar 6 1 bar 2 qux 7 2 baz 3 bar 8 3 foo 4 NaN NaN 回答1: try using left_on and right_index to do the merging, like: m = pd.merge(dfA, dfB, right_index = True, left_on

问题 these are my tables: `room`(roomID,roomNum) `customer`(customerID,Surname,etc) `contract`(contractID,roomID,weekNum) `paymen`t(paymentID,customerID,contractID,YearKoino) and when i use the following query: `select` room.roomnum `from` payment,contract,room,customer `where` payment.contractID = contract.contractID `and` contract.roomID=room.roomID `and` customer.customerID=payment.customerID `and` contract.weeknum='40' `and` payment.YearKoino='2007' ; the results i am getting are: +---------+

问题 these are my tables: `room`(roomID,roomNum) `customer`(customerID,Surname,etc) `contract`(contractID,roomID,weekNum) `paymen`t(paymentID,customerID,contractID,YearKoino) and when i use the following query: `select` room.roomnum `from` payment,contract,room,customer `where` payment.contractID = contract.contractID `and` contract.roomID=room.roomID `and` customer.customerID=payment.customerID `and` contract.weeknum='40' `and` payment.YearKoino='2007' ; the results i am getting are: +---------+

问题 create table #t1 (id int) create table #t2 (id int) insert into #t1 values (1) insert into #t1 values (2) insert into #t1 values (3) insert into #t2 values (1) insert into #t2 values (2) I ran the below queries, and I get 2 different outputs. Second is the desired output. I cannot understand the reason for output given by query1. Please assist me to understand the result. -- Query1 select * from #t1 a left join #t2 b on a.id = b.id and b.id is null -- Query2 select * from #t1 a left join #t2

问题 I'm running into an issue with which I can't figure out how to correctly configure a join. I'm using reporting software that utilizes the (+) indicators in the WHERE clause for our Oracle database. I have two tables: CHECK and TRANSACTION. A check can have multiple transactions, but a transaction doesn't necessarily have a corresponding check. Both tables have indicators identifying current records called CURRENT that is either a 'Y' or 'N'. Join option 1: Select * FROM TXN,CHK WHERE TXN.CHK

问题 I've got one master table, which has items stored in multiple levels, parents and childs, and there is a second table which may or may not have additional data. I need to query two levels from my master table and have a left join on my second table, but because of the ordering within my query this will not work. SELECT something FROM master as parent, master as child LEFT JOIN second as parentdata ON parent.secondary_id = parentdata.id LEFT JOIN second as childdata ON child.secondary_id =