Rename Multiple Columns by Names

Rename multiple columns by names

setnames from the data.tablepackage will work on data.frames or data.tables

library(data.table)
d <- data.frame(a=1:2,b=2:3,d=4:5)
setnames(d, old = c('a','d'), new = c('anew','dnew'))
d


 #   anew b dnew
 # 1    1 2    4
 # 2    2 3    5

Note that changes are made by reference, so no copying (even for data.frames!)

A quick way to rename multiple columns with unique names using dplyr

We can use !!! with rename by passing a named vector

library(dplyr)
library(stringr)
df1 <- df %>% 
     rename(!!! setNames(names(df)[-1], str_c(month.abb[1:4], 17)))

-output

df1
# A tibble: 2 x 5
#  Product Jan17 Feb17 Mar17 Apr17
#  <chr>   <dbl> <dbl> <dbl> <dbl>
#1 Eggs     35.8  39.2  40.1  41.1
#2 Chicken  36.8  39.8  43.4  41.3

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Or use rename_with

df %>% 
     rename_with(~str_c(month.abb[1:4], 17), -1)

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If the column names should be converted to Date formatted

nm1 <- format(as.Date(as.numeric(names(df)[-1]), origin = '1896-01-01'), '%b%y')

df %>%
    rename_with(~ nm1, -1)
# A tibble: 2 x 5
#  Product Jan17 Feb17 Mar17 Apr17
#  <chr>   <dbl> <dbl> <dbl> <dbl>
#1 Eggs     35.8  39.2  40.1  41.1
#2 Chicken  36.8  39.8  43.4  41.3

pandas rename multiple columns using regex pattern

You could use a regex that matches the "US-" at the beginning like this:

df.columns = df.columns.str.replace("^US-", "", regex=True)

It replaces the matching "US-" with an empty string.

Also, if you know the columns that you want to transform you could apply slicing on their names to remove the first 3 characters:

df.columns = df.columns.str.slice(3)

Of course, this will affect columns that do not match your condition (i.e. do not begin with "US-")

Renaming column names in Pandas

Just assign it to the .columns attribute:

>>> df = pd.DataFrame({'$a':[1,2], '$b': [10,20]})
>>> df
   $a  $b
0   1  10
1   2  20

>>> df.columns = ['a', 'b']
>>> df
   a   b
0  1  10
1  2  20

Rename multiple columns by extracting contents in parentheses for a list of dataframes

Using lapply to manipulate the elements of the list dfs, we can use some regex to do the job.

dfs <- lapply(dfs, function(x) {
  col_num <- grep('pred|error', colnames(x))
  colnames(x)[col_num] <- gsub('.*\\(|\\)', '', colnames(x)[col_num]); x
})

Output

> dfs
[[1]]
        id 2021-10(actual) 2021-11(actual) 2021-12(actual)      pred     error
1 M0000607             8.9             7.3             6.1  6.113632 0.7198461
2 M0000609            15.7            14.8            14.2 14.162432 0.1544640
3 M0000612             5.3             3.1             3.5  3.288373 1.2259926

[[2]]
        id 2021-09(actual) 2021-10(actual) 2021-11(actual)      pred     error
1 M0000607            10.3             8.9             7.3  8.352098 1.9981091
2 M0000609            17.3            15.7            14.8 13.973182 0.4143733
3 M0000612             6.4             5.3             3.1  3.164683 0.3420726

[[3]]
        id 2021-08(actual) 2021-09(actual) 2021-10(actual)      pred     error
1 M0000607            12.6            10.3             8.9  9.619846 0.9455678
2 M0000609            19.2            17.3            15.7 15.545536 4.8832500
3 M0000612             8.3             6.4             5.3  6.525993 1.2158196

R rename multiple columns with wildcard with rename_with()

We can use rename_all with str_replace

library(dplyr)
library(stringr)
tbl2 <- tbl1 %>%
     rename_all(~ str_replace_all(str_replace(., '^[^_]+_(.*)_(.)[^.]+$', "\\1\\2"), '_', "-"))

-output

tbl2
# A tibble: 0 x 3
# … with 3 variables: `BBB1-P1E` <dbl>, `BBB2-P2E` <dbl>, `BBB2-P3E` <dbl>

data

tbl1 <- structure(list(AAA_BBB1_P1_Elev = numeric(0), AAA_BBB2_P2_Elev = numeric(0), 
    AAA_BBB2_P3_Elev = numeric(0)), row.names = integer(0), class = c("tbl_df", 
"tbl", "data.frame"))

Rename multiple columns by index using an iterative name in pandas

You can iterate over it to change the names. Change the range to get your desired range like ...numerate(df.columns[1:3])..

names = ['Dog','Cat']

for index, column_name in enumerate(df.columns[1:3]):
    df.rename(columns={column_name: f'Q{names[index]}{index+1}'}, inplace=True)

output

    A   QDog1   QCat2   D   E
0   a   4   7   1   a
1   b   5   8   3   a
2   c   4   9   5   a
3   d   5   4   7   b
4   e   5   2   1   b
5   f   4   3   0   b

How to change multiple column names at one time?

A simple == check, checks for eqaulity. So 1 == 1 is true. However if you want to check multiple values, you have to use a different operator (I know, weird right?!) essentially in the line
names(dmodel112)= c("name1, "name2", "name3") you try to compare an entire vector (the colnames of your dataframe) against a vector.

Beter is names(dmodel112) %in% c("name1, "name2", "name3")

that way you check against every option in the vector c("name1", "name2", etc)

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