merge

问题 Im having a slight issue merging the following statements declare @From DATE SET @From = '01/01/2014' declare @To DATE SET @To = '31/01/2014' --ISSUED SB SELECT COUNT(pm.DateAppIssued) AS Issued, pm.Lender, pm.AmountRequested, p.CaseTypeID FROM BPS.dbo.tbl_Profile_Mortgage AS pm INNER JOIN BPS.dbo.tbl_Profile AS p ON pm.FK_ProfileId = p.Id WHERE CaseTypeID = 2 AND (CONVERT(DATE,DateAppIssued, 103) Between CONVERT(DATE,@From,103) and CONVERT(DATE,@To,103)) And Lender > '' GROUP BY pm.Lender,p

问题 I need to roll back to a previous version of my project, but I haven't really been creating baselines. Whats the best way to do this. I think I have a large clearfsimport operation that marks the current version that my desired view would pre-date. What is the best way to create a view like this. I was hoping I could do something based on date that would just create a view that includes all versions from Date X and before, or perhaps Activity X and before. I do have a large number of files

问题 I am downloading file parts using socket, saving them as .part1, .part2 etc.( However I am not sure if I should). I am trying to merge them in a .bin file. Can any of you recommend me a way to do that? Should I read them all as binary and then append it? I don't even know if that makes sense actually. 回答1: Why don't you open the first part for input, and open an output streamof the form out = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(outFile))); then read in your

问题 I have a panel dataset: panel and a dataset with a list of events: Events . For the panel dataset, an equal panelID shows that two observations belong together. panelID = c(1:50) year= c(2001:2010) country = c("NLD", "GRC", "GBR") n <- 2 library(data.table) set.seed(123) Panel <- data.table(panelID = rep(sample(panelID), each = n), country = rep(sample(country, length(panelID), replace = T), each = n), year = c(replicate(length(panelID), sample(year, n))), some_NA = sample(0:5, 6), some_NA

问题 I have a panel dataset: panel and a dataset with a list of events: Events . For the panel dataset, an equal panelID shows that two observations belong together. panelID = c(1:50) year= c(2001:2010) country = c("NLD", "GRC", "GBR") n <- 2 library(data.table) set.seed(123) Panel <- data.table(panelID = rep(sample(panelID), each = n), country = rep(sample(country, length(panelID), replace = T), each = n), year = c(replicate(length(panelID), sample(year, n))), some_NA = sample(0:5, 6), some_NA

问题 My goal is to collapse the below table into one single column and this question deals specifically with the blue row below. The table has three categorical variables and 6 analysis/quantitative variables. Columns C1 and C2 are the only variables that need to match for a successful join. All blank cells are NaNs and python code for copying is below. These rows are exported independently because they have information found in other related tables, not included in the export. Question . (Blue)

问题 The scenario A long existing bug was spotted out and the problematic commit was found by bisec. So the bug fixer created a branch (say branch A ), just for all the tags and releases after this point can merge this branch to fix the bug. A was then merged to master . Everything is fine. Later on, Another developer that works on branch B merged the branch to master . After this point, we found that the bug was back. The merge commit for B to master shows that it uses B 's version and so it

问题 This question and answer covers object literals but the answer does not work when using index signature object types. e.g: type UniqueObject<T, U> = { [K in keyof U]: K extends keyof T ? never : U[K] } export function mergeUnique <T, U, V> ( a: T, b?: UniqueObject<T, U>, c?: UniqueObject<T & U, V>, ) { return { ...a, ...b, ...c, } } type Obj = { [index: string]: number | undefined } const a: Obj = { a: undefined } const b: Obj = { b: 3 } // should all pass const res01 = mergeUnique({ a:

问题 I'm trying to merge two csv files into a json using Apache nifi. Two csv's are persons.csv containing information about people: Id|Name|Surname ABC-123|John|Smith ABC-111|Allan|Wood ABC-001|Grace|Kelly And the second csv contains list of events these people have attended: EId|PId|Date|Desc 1|ABC-123|2017-05-01|"Groove party" 2|ABC-111|2017-06-01|"Snack No. One" 3|ABC-123|2017-06-01|"The night out" I'm using a flow of (Nifi flow on git hub): GetFile UpdateAttribute (schema.name) Split Records