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The transpose() function is used to transpose index and columns. Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. If True, the underlying data is copied. Otherwise (default), no copy is made if possible.
Method #2: Using pivot() method. In order to convert a column to row name/index in dataframe, Pandas has a built-in function Pivot. Now, let's say we want Result to be the rows/index, and columns be name in our dataframe, to achieve this pandas has provided a method called Pivot.
To convert a column values to column names, we can use dcast function of reshape2 package. For example, if we have a data frame called df that contains two columns say x and y, where x is categorical and y is numerical. Now if we want to convert the categories in x as column names then it can be done as dcast(df,y~x).
Also included is rowid_to_column() , which adds a column at the start of the dataframe of ascending sequential row ids starting at 1. Note that this will remove any existing row names.
This should do:
samp2 <- samp[,-1]
rownames(samp2) <- samp[,1]
So in short, no there is no alternative to reassigning.
Edit: Correcting myself, one can also do it in place: assign rowname attributes, then remove column:
R> df<-data.frame(a=letters[1:10], b=1:10, c=LETTERS[1:10])
R> rownames(df) <- df[,1]
R> df[,1] <- NULL
R> df
b c
a 1 A
b 2 B
c 3 C
d 4 D
e 5 E
f 6 F
g 7 G
h 8 H
i 9 I
j 10 J
R>
As of 2016 you can also use the tidyverse.
library(tidyverse)
samp %>% remove_rownames %>% column_to_rownames(var="names")
in one line
> samp.with.rownames <- data.frame(samp[,-1], row.names=samp[,1])
You can execute this in 2 simple statements:
row.names(samp) <- samp$names
samp[1] <- NULL
It looks like the one-liner got even simpler along the line (currently using R 3.5.3):
# generate original data.frame
df <- data.frame(a = letters[1:10], b = 1:10, c = LETTERS[1:10])
# use first column for row names
df <- data.frame(df, row.names = 1)
The column used for row names is removed automatically.
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