用rstudio中的数据帧中的每个条目制作一个列表 数据数据

如果我们有更多的列，那么一个选项是使用 across 来循环感兴趣的列，然后使用 glue 用当前列的值插入列 'Animal' 以及该列的列名 (cur_column())

library(dplyr)
df %>% 
  mutate(across(-Animal,~ glue::glue("{Animal}-{Cur_column()}: {.}"),.names = "{.col}_attributes"))
#  Animal Color      Weight  Color_attributes        Weight_attributes
#1   Deer Brown Less than 3 Deer-Color: Brown Deer-Weight: Less than 3
#2    Pig Green More than 3  Pig-Color: Green  Pig-Weight: More than 3

数据

df <- structure(list(Animal = c("Deer","Pig"),Color = c("Brown","Green"),Weight = c("Less than 3","More than 3")),class = "data.frame",row.names = c(NA,-2L))

在基础 R 中使用 apply :

cols <- names(df)[-1]
apply(df,1,function(X) sprintf('%s-%s',x[1],paste(cols,x[-1],sep = ':')))

#     [,1]                      [,2]                    
#[1,] "Deer-Color:Brown"        "Pig-Color:Green"       
#[2,] "Deer-Weight:Less than 3" "Pig-Weight:More than 3"

你想要一个像下面这样的列表吗？

> Map(as.list,split(df[-1],df[1]))
$Deer
$Deer$Color
[1] "Brown"

$Deer$Weight
[1] "Less than 3"


$Pig
$Pig$Color
[1] "Green"

$Pig$Weight
[1] "More than 3"

如果我理解正确，OP 希望输出以长格式重新整形（称为“列表”或“向量”）。

这是一种使用 $ time mawk2 'BEGIN { FS = "^$"; FILename = ARGV[ARGC-1]; cmd = "tail -c 2 \""FILename"\""; cmd | getline XRS; close(cmd); RS = ( length(XRS) == 1 ) ? ORS : XRS ; } { bytes += length } END { print FILename " :: " bytes + NR * length(RS) }' genieMV_204583_1.mp4 genieMV_204583_1.mp4 :: 259105690 real 0m0.092s m23lyricsRTM_Dict_15.txt :: 1961512986 real 0m0.950s $ ls -AlnFT "${m3t}" genieMV_204583_1.mp4 -rw-r--r-- 1 501 20 1961512986 Mar 12 07:24:11 2021 m23lyricsRTM_Dict_15.txt -r--r--r--@ 1 501 20 259105690 Jan 25 09:31:43 2021 genieMV_204583_1.mp4 和 @H_205_54@melt() 的可能方法。它适用于任意数量的属性列。

glue_data()

glue::glue_data(
  data.table::melt(ds,id.var = "Animal"),"{Animal}-{variablE}: {value}")

或者，您可以使用 Deer-Color: Brown Pig-Color: Green Deer-Weight: Less than 3 Pig-Weight: More than 3 进行整形。

数据

tidyr::pivot_longer()

我会把它分解成更小的问题。

1. 创建动物属性对
2. 创建“列表”。我假设你的意思是矢量。

import pandas as pd 
from itertools import combinations

df = pd.read_csv('namen.csv',sep=";")

arr = ['name1','name2','name3']
r = 2 
combs = [x for x in combinations(arr,2) ] 

df_new = pd.DataFrame(columns=['A','B']) 

for i in range(0,len(combs)-1):
    df_temp = df[[combs[i][0],combs[i][1]]].copy()
    df_temp.columns = df_new.columns
    df_new = pd.concat([df_kanten,df_new],ignore_index=TruE)