Python pandas equivalent to R groupby mutate

Question

So in R when I have a data frame consisting of say 4 columns, call it df and I want to compute the ratio by sum product of a group, I can it in such a way:

// generate data df = data.frame(a=c(1,1,0,1,0),b=c(1,0,0,1,0),c=c(10,5,1,5,10),d=c(3,1,2,1,2)); | a   b   c    d | | 1   1   10   3 | | 1   0   5    1 | | 0   0   1    2 | | 1   1   5    1 | | 0   0   10   2 | // compute sum product ratio df = df%>% group_by(a,b) %>%       mutate(           ratio=c/sum(c*d)       ); | a   b   c    d  ratio | | 1   1   10   3  0.286 | | 1   1   5    1  0.143 | | 1   0   5    1  1     | | 0   0   1    2  0.045 | | 0   0   10   2  0.454 | 

But in python I need to resort to loops. I know there should be a more elegant way than raw loops in python, anyone got any ideas?

Answer 1

It can be done with similar syntax with groupby() and apply():

df['ratio'] = df.groupby(['a','b'], group_keys=False).apply(lambda g: g.c/(g.c * g.d).sum())