How to sum two consequent columns in pandas and retrieve one as result?

I have a large dataset of 670 columns and 2856 rows. The idea is to sum two consequent rows and retrieve a single column and value as result. It’s important to not have replacement in the way the fist column + second, then the third + the fourth not the second + third.

the result should be

The example dataframe:

df = pd.DataFrame({"ID1" : [0,0,0,1,1,1] , "ID2" :[1,1,1,0,0,0], "ID3" : [0,1,1,1,0,1]},"ID4" : [0,0,0,0,0,0])
result = pd.DataFrame({"ID1/2" : [1,1,1,0,0,0] , "ID3/4" :[0,1,1,1,0,1]})

I have tried:

res = []
for i in range(len(df)):
              for j in range(1,len(df.columns),2):
                            res.append(data.iloc[i,j]+data.iloc[i,j-1])
result = pd.DataFrame(res)

In R the result is:

result <- matrix(nrow = nrow(df), ncol = ncol(df),)
for (i in seq(1,ncol(df),2)){
  result[,i] <- df[,i]+df[,i+1]
}
#Erasing the NAs columns
result <- result [,-seq(2,ncol(result ),2)]

>Solution :

N = len(df.columns)
new_df = df.groupby(np.arange(N) // 2, axis="columns").sum()
new_df.columns = [f"ID{j}{j+1}" for j in range(1, N, 2)]

• groupby every-2 columns and sum
  • floordivision of the range(len(columns)) by 2 gives the group numbers: 0, 0, 1, 1, 2, 2…
• form new columns with stepping by 2 over range(N) to get 1, 3…

to get

>>> new_df

        ID12  ID34
Index
First      1     1
Second     0     2