merge

问题 I have cells containing duplicate values that i want to merge quickly. The table looks like this: Sub MergeCells() Application.DisplayAlerts = False Dim n As Name Dim fc As FormatCondition Dim Rng As Range, R As Range Dim lRow As Long Dim I&, J& Dim arr As Variant ReDim arr(1 To 1) As Variant With ThisWorkbook.Sheets("tst") Set Rng = .Range("A2:D11") lRow = Rng.End(xlDown).Row For J = 1 To 4 For I = lRow To 2 Step -1 'last row to 2nd row If Trim(UCase(.Cells(I, J))) = Trim(UCase(.Cells(I - 1,

问题 I want to match two similar dataframes in R. Both dfs have partially the same variables and a key (id), which contain missing values: library(tidyverse) df1 <- as_tibble(list(id = seq(1:6), v1 = c(1, 0, NA, 1, 0, NA), v2 = c(NA, NA, 0, 0, 1, NA), v3 = c(1, 0 , 1, 1, 1, NA))) df1 # A tibble: 6 x 4 id v1 v2 v3 <int> <dbl> <dbl> <dbl> 1 1 1 NA 1 2 2 0 NA 0 3 3 NA 0 1 4 4 1 0 1 5 5 0 1 1 6 6 NA NA NA df2 <- as_tibble(list(id = seq(1:6), v1 = c(1, NA, 0, 1, 0, 1), v2 = c(1, 0, 0, NA, 1, 1), v4 = c

问题 MAJOR EDIT to clarify as answers are wrong I have a data.table with group columns (split_by), key columns (key_by) and trait ids columns (intersect_by) I want in each group of split_by, keep only the rows where the trait ids are shared by all the present keys in the group. For example: dt <- data.table(id = 1:6, key1 = 1, key2 = c(1:2, 2), group_id1= 1, group_id2= c(1:2, 2:1, 1:2), trait_id1 = 1, trait_id2 = 2:1) setkey(dt, group_id1, group_id2, trait_id1, trait_id2) dt id key1 key2 group_id1

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 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 I am currently merging 2 dataframes with an outer join, but after merging, I see all the rows are duplicated even when the columns I did the merge upon contain the same values. In detail: list_1 = pd.read_csv('list_1.csv') list_2 = pd.read_csv('list_2.csv') merged_list = pd.merge(list_1 , list_2 , on=['email_address'], how='inner') with the following input and results: list_1: email_address, name, surname john.smith@email.com, john, smith john.smith@email.com, john, smith elvis@email.com,

问题 I would like to know how if there exists any python function to merge two dictionary and combine all values that have a common key. I have found function to append two dict, to merge two dict but not to combine its values. Example: D1 = [{k1: v01}, {k3: v03}, {k4: v04},}], D2 = [{k1: v11}, {k2: v12}, {k4: v14},}], this should be the expected result: D3 = [ {k1: [v01, v11]}, {k2: [ v12]}, {K3: [v03 ]}, {k4: [v04, v14]}, ] 回答1: A solution, without importing anything: # First initialize data,

问题 I would like to know how if there exists any python function to merge two dictionary and combine all values that have a common key. I have found function to append two dict, to merge two dict but not to combine its values. Example: D1 = [{k1: v01}, {k3: v03}, {k4: v04},}], D2 = [{k1: v11}, {k2: v12}, {k4: v14},}], this should be the expected result: D3 = [ {k1: [v01, v11]}, {k2: [ v12]}, {K3: [v03 ]}, {k4: [v04, v14]}, ] 回答1: A solution, without importing anything: # First initialize data,