join

问题 I have to some query and creat columns operations in HiveQL. For example, app col1 app1 anybody love me? app2 I hate u app3 this hat is good app4 I don't like this one app5 oh my god app6 damn you. app7 such nice girl app8 xxxxx app9 pretty prefect app10 don't love me. app11 xxx anybody? I want to match a keyword list like ['anybody', 'love', 'you', 'xxx', 'don't'] and select the matched keyword result as a new column, named keyword as follows: app keyword app1 anybody, love app4 I don't like

问题 I'm importing data from multiple excel files using the readxl package and I made a function in my script so that I only import specific sheets that I need read_excel_sheets <- function(excelDoc) { sheets <- readxl::excel_sheets(excelDoc) sheets <- sheets[4:6] x <- lapply(sheets, function(X) readxl::read_excel(excelDoc, sheet = X)) return(x) } #load files in folder rawfiles <- list.files() IMPORT <- lapply(rawfiles, FUN = read_excel_sheets) After loading the files in my folder into my script,

问题 Both Uwe's and GKi's answer are correct. Gki received the bounty because Uwe was late for that, but Uwe's solution runs about 15x as fast I have two datasets that contain scores for different patients on multiple measuring moments like so: df1 <- data.frame("ID" = c("patient1","patient1","patient1","patient1","patient2","patient3"), "Days" = c(0,25,235,353,100,538), "Score" = c(NA,2,3,4,5,6), stringsAsFactors = FALSE) df2 <- data.frame("ID" = c("patient1","patient1","patient1","patient1",

问题 I've been practicing and learning wrangling R data frames with columns that contain lubridate data types, such as an example problem in my other question. Now, I am trying to do the equivalent of joining two data frames, but joining them by whether one timestamp in one data frame falls within an interval in the other data frame. For example: This is df1 : > glimpse(df1) Observations: 6,160 Variables: 4 $ upload_id <int> 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, ... $ site_id

问题 Note: this question is a copy of this one but with different wording, and a suggestion for data.table instead of dplyr I have two datasets that contain scores for different patients on multiple measuring moments like so: dt1 <- data.frame("ID" = c("patient1","patient1","patient1","patient1","patient2","patient3"), "Days" = c(0,10,25,340,100,538), "Score" = c(NA,2,3,99,5,6), stringsAsFactors = FALSE) dt2 <- data.frame("ID" = c("patient1","patient1","patient1","patient1","patient2","patient2",

问题 Note: this question is a copy of this one but with different wording, and a suggestion for data.table instead of dplyr I have two datasets that contain scores for different patients on multiple measuring moments like so: dt1 <- data.frame("ID" = c("patient1","patient1","patient1","patient1","patient2","patient3"), "Days" = c(0,10,25,340,100,538), "Score" = c(NA,2,3,99,5,6), stringsAsFactors = FALSE) dt2 <- data.frame("ID" = c("patient1","patient1","patient1","patient1","patient2","patient2",

问题 I am trying to convert a sql query to python. The sql statement is as follows: select * from table 1 union select * from table 2 union select * from table 3 union select * from table 4 Now I have those tables in 4 dataframe df1, df2, df3, df4 and I would like to union 4 pandas dataframe which would match the result as the same as sql query. I am confused of what operation to be used which is equivalent to sql union? Thanks in advance!! Note: The column name for all the dataframes are the same

问题 I am trying to convert a sql query to python. The sql statement is as follows: select * from table 1 union select * from table 2 union select * from table 3 union select * from table 4 Now I have those tables in 4 dataframe df1, df2, df3, df4 and I would like to union 4 pandas dataframe which would match the result as the same as sql query. I am confused of what operation to be used which is equivalent to sql union? Thanks in advance!! Note: The column name for all the dataframes are the same

问题 I am trying to convert a sql query to python. The sql statement is as follows: select * from table 1 union select * from table 2 union select * from table 3 union select * from table 4 Now I have those tables in 4 dataframe df1, df2, df3, df4 and I would like to union 4 pandas dataframe which would match the result as the same as sql query. I am confused of what operation to be used which is equivalent to sql union? Thanks in advance!! Note: The column name for all the dataframes are the same

问题 I have a USERSEARCH table that should be used for fast substring searches for users. This feature is for an autocomplete search that occurs while someone is typing in a username or name. However, the query I am interested in will only show matches from users the subset of users the searcher follows. This is found in the USERRELATIONSHIP table. USERSEARCH ----------------------------------------------- user_id(FK) username_ngram name_ngram 1 "AleBoy leBoy eBoy..." "Ale le e" 2 "craze123