如何简化 data.table 逻辑并使其在 pandas 中可行？
How to simplify data.table logic and make it doable in pandas?
我有一个包含多列数值的数据框。我想要新的列来比较其他列的值并将其列名指定为标签。我已经在 r 中理解了它的逻辑，但想知道我应该如何在 python 中轻松地做到这一点。当我们尝试在需要比较多列值的地方添加新列并分配具有最大值的列名时，任何人都可以指出如何在 python 中完成此操作吗？有什么想法吗？
可重现的例子
这是 R 中 100% 可重现的示例：
library(data.table)
df <- data.frame(a = sample(seq(1:10), size=10), b = sample(LETTERS[1:10], size=10), cnt=sample(seq(1:100), size=5),
RECENT_MOV= sample(seq(1:1000), size = 10),
RETIRED= sample(seq(1:200), size = 10),
SERV_EMPL= sample(seq(1:500), size = 10),
SUB_BUS=sample(seq(1:2000), size = 10),
WORK_HOME=sample(seq(1:1200), size = 10)
)
dt <- as.data.table(df)
write.csv(dt, "sample.csv")
label = c("RECENT_MOV", "RETIRED", "SERV_EMPL", "SUB_BUS","WORK_HOME")
df$category <- NA_character_
df[, row_ind:= 1:nrow(df)]
df[cnt > 2, category := names(which.max(.SD[, label, with = FALSE])), by = row_ind]
当前输出为：
> dput(dt)
structure(list(a = c(5L, 10L, 1L, 6L, 7L, 3L, 2L, 8L, 4L, 9L),
b = c("E", "A", "D", "H", "J", "F", "G", "I", "C", "B"),
cnt = c(13L, 88L, 45L, 92L, 70L, 13L, 88L, 45L, 92L, 70L),
RECENT_MOV = c(70L, 195L, 620L, 572L, 354L, 648L, 798L, 657L,
233L, 672L), RETIRED = c(189L, 195L, 191L, 88L, 148L, 186L,
39L, 78L, 158L, 55L), SERV_EMPL = c(65L, 151L, 415L, 383L,
255L, 207L, 210L, 470L, 181L, 188L), SUB_BUS = c(894L, 829L,
1798L, 502L, 897L, 1461L, 744L, 1991L, 260L, 1697L), WORK_HOME = c(553L,
739L, 454L, 137L, 435L, 1042L, 316L, 697L, 517L, 1158L),
category = c("SUB_BUS", "SUB_BUS", "SUB_BUS", "RECENT_MOV",
"SUB_BUS", "SUB_BUS", "RECENT_MOV", "SUB_BUS", "WORK_HOME",
"SUB_BUS"), row_ind = 1:10), row.names = c(NA, -10L), class = c("data.table",
"data.frame"), .internal.selfref = <pointer: 0x0000015a64b61ef0>)
我目前的 python 尝试
import pandas as pd
df=pd.read_csv("sample.csv", index_col=None, header=0)
label = ["RECENT_MOV", "RETIRED", "SERV_EMPL", "SUB_BUS","WORK_HOME"]
df['category'] = pd.NA
df['row_ind'] = range(1,len(df))
然而，我无法以 pythonic 方式制作此行：
df[cnt > 2, category := names(which.max(.SD[, label, with = FALSE])), by = row_ind]
基本上，这行说创建名为类别的新列变量，其中比较标签中具有最大值的列，其列名将被分配为类别列中的值。在 python 中我应该如何轻松做到这一点？
逻辑翻译:
df[cnt > 2, category := names(which.max(.SD[, label, with = FALSE])), by = row_ind]
这一行告诉我们首先按 cnt > 2 所在的 cnt 列进行过滤，然后比较 df[["RECENT_MOV", "RETIRED", "SERV_EMPL", "SUB_BUS","WORK_HOME"]] 的列值并按行选择具有最高值的列并分配该列的名称列作为 df['category']=col_name_with_highest_value_in_each_row.
的值
理想产出
这是我想要在 python 中产生的理想输出：
a b cnt RECENT_MOV RETIRED SERV_EMPL SUB_BUS WORK_HOME category row_ind
1 5 E 13 70 189 65 894 553 SUB_BUS 1
2 10 A 88 195 195 151 829 739 SUB_BUS 2
3 1 D 45 620 191 415 1798 454 SUB_BUS 3
4 6 H 92 572 88 383 502 137 RECENT_MOV 4
5 7 J 70 354 148 255 897 435 SUB_BUS 5
6 3 F 13 648 186 207 1461 1042 SUB_BUS 6
7 2 G 88 798 39 210 744 316 RECENT_MOV 7
8 8 I 45 657 78 470 1991 697 SUB_BUS 8
9 4 C 92 233 158 181 260 517 WORK_HOME 9
10 9 B 70 672 55 188 1697 1158 SUB_BUS 10
pandas 这其实很简单。列出要搜索的列，然后使用 idxmax 和 axis=1:
# Filter out rows where `cnt` is less than or equal to 2
df = df[df['cnt'] > 2]
# Determine category for each row
search_cols = ['RECENT_MOV', 'RETIRED', 'SERV_EMPL', 'SUB_BUS', 'WORK_HOME']
df['category'] = df[search_cols].idxmax(axis=1)
# Assign row indexes
df['row_ind'] = df.index
输出：
>>> df
a b cnt RECENT_MOV RETIRED SERV_EMPL SUB_BUS WORK_HOME category row_ind
1 1 C 76 452 62 55 115 247 RECENT_MOV 1
2 7 E 14 50 165 337 1165 810 SUB_BUS 2
3 2 A 46 523 167 423 784 707 SUB_BUS 3
4 3 H 3 38 144 473 745 437 SUB_BUS 4
5 5 I 59 743 127 261 351 190 RECENT_MOV 5
6 8 J 76 143 49 470 1612 935 SUB_BUS 6
7 4 D 14 818 101 418 1919 314 SUB_BUS 7
8 6 F 46 714 9 446 1432 938 SUB_BUS 8
9 10 B 3 585 160 14 107 489 RECENT_MOV 9
10 9 G 59 814 73 449 937 287 SUB_BUS 10
我有一个包含多列数值的数据框。我想要新的列来比较其他列的值并将其列名指定为标签。我已经在 r 中理解了它的逻辑，但想知道我应该如何在 python 中轻松地做到这一点。当我们尝试在需要比较多列值的地方添加新列并分配具有最大值的列名时，任何人都可以指出如何在 python 中完成此操作吗？有什么想法吗？
可重现的例子
这是 R 中 100% 可重现的示例：
library(data.table)
df <- data.frame(a = sample(seq(1:10), size=10), b = sample(LETTERS[1:10], size=10), cnt=sample(seq(1:100), size=5),
RECENT_MOV= sample(seq(1:1000), size = 10),
RETIRED= sample(seq(1:200), size = 10),
SERV_EMPL= sample(seq(1:500), size = 10),
SUB_BUS=sample(seq(1:2000), size = 10),
WORK_HOME=sample(seq(1:1200), size = 10)
)
dt <- as.data.table(df)
write.csv(dt, "sample.csv")
label = c("RECENT_MOV", "RETIRED", "SERV_EMPL", "SUB_BUS","WORK_HOME")
df$category <- NA_character_
df[, row_ind:= 1:nrow(df)]
df[cnt > 2, category := names(which.max(.SD[, label, with = FALSE])), by = row_ind]
当前输出为：
> dput(dt)
structure(list(a = c(5L, 10L, 1L, 6L, 7L, 3L, 2L, 8L, 4L, 9L),
b = c("E", "A", "D", "H", "J", "F", "G", "I", "C", "B"),
cnt = c(13L, 88L, 45L, 92L, 70L, 13L, 88L, 45L, 92L, 70L),
RECENT_MOV = c(70L, 195L, 620L, 572L, 354L, 648L, 798L, 657L,
233L, 672L), RETIRED = c(189L, 195L, 191L, 88L, 148L, 186L,
39L, 78L, 158L, 55L), SERV_EMPL = c(65L, 151L, 415L, 383L,
255L, 207L, 210L, 470L, 181L, 188L), SUB_BUS = c(894L, 829L,
1798L, 502L, 897L, 1461L, 744L, 1991L, 260L, 1697L), WORK_HOME = c(553L,
739L, 454L, 137L, 435L, 1042L, 316L, 697L, 517L, 1158L),
category = c("SUB_BUS", "SUB_BUS", "SUB_BUS", "RECENT_MOV",
"SUB_BUS", "SUB_BUS", "RECENT_MOV", "SUB_BUS", "WORK_HOME",
"SUB_BUS"), row_ind = 1:10), row.names = c(NA, -10L), class = c("data.table",
"data.frame"), .internal.selfref = <pointer: 0x0000015a64b61ef0>)
我目前的 python 尝试
import pandas as pd
df=pd.read_csv("sample.csv", index_col=None, header=0)
label = ["RECENT_MOV", "RETIRED", "SERV_EMPL", "SUB_BUS","WORK_HOME"]
df['category'] = pd.NA
df['row_ind'] = range(1,len(df))
然而，我无法以 pythonic 方式制作此行：
df[cnt > 2, category := names(which.max(.SD[, label, with = FALSE])), by = row_ind]
基本上，这行说创建名为类别的新列变量，其中比较标签中具有最大值的列，其列名将被分配为类别列中的值。在 python 中我应该如何轻松做到这一点？
逻辑翻译:
df[cnt > 2, category := names(which.max(.SD[, label, with = FALSE])), by = row_ind]
这一行告诉我们首先按 cnt > 2 所在的 cnt 列进行过滤，然后比较 df[["RECENT_MOV", "RETIRED", "SERV_EMPL", "SUB_BUS","WORK_HOME"]] 的列值并按行选择具有最高值的列并分配该列的名称列作为 df['category']=col_name_with_highest_value_in_each_row.
理想产出
这是我想要在 python 中产生的理想输出：
a b cnt RECENT_MOV RETIRED SERV_EMPL SUB_BUS WORK_HOME category row_ind
1 5 E 13 70 189 65 894 553 SUB_BUS 1
2 10 A 88 195 195 151 829 739 SUB_BUS 2
3 1 D 45 620 191 415 1798 454 SUB_BUS 3
4 6 H 92 572 88 383 502 137 RECENT_MOV 4
5 7 J 70 354 148 255 897 435 SUB_BUS 5
6 3 F 13 648 186 207 1461 1042 SUB_BUS 6
7 2 G 88 798 39 210 744 316 RECENT_MOV 7
8 8 I 45 657 78 470 1991 697 SUB_BUS 8
9 4 C 92 233 158 181 260 517 WORK_HOME 9
10 9 B 70 672 55 188 1697 1158 SUB_BUS 10
pandas 这其实很简单。列出要搜索的列，然后使用 idxmax 和 axis=1:
# Filter out rows where `cnt` is less than or equal to 2
df = df[df['cnt'] > 2]
# Determine category for each row
search_cols = ['RECENT_MOV', 'RETIRED', 'SERV_EMPL', 'SUB_BUS', 'WORK_HOME']
df['category'] = df[search_cols].idxmax(axis=1)
# Assign row indexes
df['row_ind'] = df.index
输出：
>>> df
a b cnt RECENT_MOV RETIRED SERV_EMPL SUB_BUS WORK_HOME category row_ind
1 1 C 76 452 62 55 115 247 RECENT_MOV 1
2 7 E 14 50 165 337 1165 810 SUB_BUS 2
3 2 A 46 523 167 423 784 707 SUB_BUS 3
4 3 H 3 38 144 473 745 437 SUB_BUS 4
5 5 I 59 743 127 261 351 190 RECENT_MOV 5
6 8 J 76 143 49 470 1612 935 SUB_BUS 6
7 4 D 14 818 101 418 1919 314 SUB_BUS 7
8 6 F 46 714 9 446 1432 938 SUB_BUS 8
9 10 B 3 585 160 14 107 489 RECENT_MOV 9
10 9 G 59 814 73 449 937 287 SUB_BUS 10