Create a new dataframe from an old dataframe where the new dataframe contains row-wise avergae of columns at different locations in the old dataframe

Question

I have a dataframe called "frame" with 16 columns and 201 rows. A screenshot is attached that provides an example dataframe

enter image description here

Please note the screenshot is just an example, the original dataframe is much larger.

I would like to find an efficient way (maybe using for loop or writing a function) to row-wise average different columns in the dataframe. For instance, to find an average of column "rep" and "rep1" and column "repcycle" and "repcycle1" (similarly for set and setcycle) and save in a new dataframe with only averaged columns.

I have tried writing a code using iloc

newdf= frame[['sample']].copy()
newdf['rep_avg']=frame.iloc[:, [1,5]].mean(axis=1)  #average row-wise
newdf['repcycle_avg']=frame.iloc[:, [2,6]].mean(axis=1)
newdf['set_avg']=frame.iloc[:, [3,7]].mean(axis=1)  #average row-wise  
newdf['setcycle_avg']=frame.iloc[:, [4,8]].mean(axis=1)
newdf.columns = ['S', 'Re', 'Rec', 'Se', 'Sec']

The above code does the job, but it is tedious to note the locations for every column. I would rather like to automate this process since this is repeated for other data files too.

Answer 1

based on your desire "I would rather like to automate this process since this is repeated for other data files too" what I can think of is this below:

in [1]:  frame = pd.read_csv('your path')

result shown below, now as you can see what you want to average are columns 1,5 and 2,6 and so on.

out [1]:
    sample  rep   repcycle  set   setcycle  rep1    repcycle1   set1    setcycle1
0   66      40    4         5     3         40      4           5       3
1   78      20    5         6     3         20      5           6       3
2   90      50    6         9     4         50      6           9       4
3   45      70    7         3     2         70      7           7       2

so, we need to create 2 lists

in [2]: import numpy as np
        list_1 = np.arange(1,5,1).tolist()
in [3]: list_1
out[3]: [1,2,3,4]

this for the first half you want to average[rep,repcycle,set,setcycle]

in [4]: list_2 = [x+4 for x in list_1]
in [5]: list_2
out[5]: [5,6,7,8]

this for the second half you want to average[rep1,repcycle1,set1,setcycle1]

in [6]: result = pd.concat([frame.iloc[:, [x,y].mean(axis=1) for x, y in zip(list_1,list_2)],axis=1)
in [7]: result.columns = ['Re', 'Rec', 'Se', 'Sec']

and now you get what you want, and it's automate, all you need to do is change the two lists from above.

in [8]: result
out[8]:
    Re    Rec   Se   Sec
0   40.0  4.0   5.0  3.0
1   20.0  5.0   6.0  3.0
2   50.0  6.0   9.0  4.0
3   70.0  7.0   5.0  2.0