This question is based on my previous question.
I’ve got a Pandas dataframe like the one below. What I’m trying to do is calculating the mean of column r1 till r50, for every time that ‘5’ occurs in the respective s-column (r1-s2, r2-s2,… r50-s50).
s1 ... s50 r1 ... r50
5 5 0.5 1
1 5 0.43 0.5
5 1 1 0.43
5 5 1 1
In this case, in s1: 5 occures three times, so we take the average over 0.5+1+1=0.83, in s50: 5 occures three times, so we take the average over 1+0.5+1=0.83. I want to get the result in a new data frame. Can someone help me to calculate this? Thanks!
>Solution :
You can filter for columns starting with s, and for each column, select indexes where the item is 5, and select those rows from the column of the same name except with s replaced by r, and compute the mean:
s = df.filter(like='s').apply(lambda col: df.loc[col == 5, col.name.replace('s', 'r')].mean())
Output:
>>> s
s1 0.833333
s50 0.833333
dtype: float64
>>> s['s1']
0.8333333333333334