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Remove columns with same value from a dataframe

I've got a data frame like this one

1    1    1    K    1    K    K
2    1    2    K    1    K    K
3    8    3    K    1    K    K
4    8    2    K    1    K    K
1    1    1    K    1    K    K
2    1    2    K    1    K    K

I want to remove all the columns with the same value, i.e K, so my result will be like this

1    1    1    1    
2    1    2    1   
3    8    3    1  
4    8    2    1  
1    1    1    1 
2    1    2    1  

I try to iterate in a for by columns but I didn't get anything. Any ideas?

like image 375
user976991 Avatar asked Dec 05 '11 16:12

user976991


People also ask

How do you remove a specific value from a data frame?

Use drop() method to delete rows based on column value in pandas DataFrame, as part of the data cleansing, you would be required to drop rows from the DataFrame when a column value matches with a static value or on another column value.


2 Answers

To select columns with more than one value regardless of type:

uniquelength <- sapply(d,function(x) length(unique(x)))
d <- subset(d, select=uniquelength>1)

?

(Oops, Roman's question is right -- this could knock out your column 5 as well)

Maybe (edit: thanks to comments!)

isfac <- sapply(d,inherits,"factor")
d <- subset(d,select=!isfac | uniquelength>1)

or

d <- d[,!isfac | uniquelength>1]
like image 66
Ben Bolker Avatar answered Oct 10 '22 11:10

Ben Bolker


Here's a solution that'll work to remove any replicated columns (including, e.g., pairs of replicated character, numeric, or factor columns). That's how I read the OP's question, and even if it's a misreading, it seems like an interesting question as well.

df <- read.table(text=" 
1    1    1    K    1    K    K
2    1    2    K    1    K    K
3    8    3    K    1    K    K
4    8    2    K    1    K    K
1    1    1    K    1    K    K
2    1    2    K    1    K    K")

# Need to run duplicated() in 'both directions', since  it considers
# the first example to be **not** a duplicate.
repdCols <- as.logical(duplicated(as.list(df), fromLast=FALSE) + 
                       duplicated(as.list(df), fromLast=TRUE))
# [1] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE

df[!repdCols]
#   V1 V2 V3 V5
# 1  1  1  1  1
# 2  2  1  2  1
# 3  3  8  3  1
# 4  4  8  2  1
# 5  1  1  1  1
# 6  2  1  2  1
like image 31
Josh O'Brien Avatar answered Oct 10 '22 10:10

Josh O'Brien