pandas data manipulation in python -

i have data frame df columns id , n1, calculate column n2, logic first value should equal n1 each id , next value 0.888/0.999 , on. , next id. can without using loop in pandas

id  n1  n2 1111    0.999   0.999 1111    0.888   0.888888889 1111    0.777   0.875 1111    0.666   0.857142857 1111    0.555   0.833333333 1111    0.444   0.8 1111    0.333   0.75 2222    0.998   0.998 2222    0.887   0.888777555 2222    0.776   0.874859076 2222    0.665   0.856958763 2222    0.554   0.833082707 2222    0.443   0.799638989 2222    0.332   0.749435666 2222    0.221   0.665662651 

yes, can use groupby(), transform() , shift() fillna(1) allow first value.

df['n2'] = df.groupby("id")['n1'].transform(lambda x: x/x.shift(1).fillna(1)) df        id     n1        n2 0   1111  0.999  0.999000 1   1111  0.888  0.888889 2   1111  0.777  0.875000 3   1111  0.666  0.857143 4   1111  0.555  0.833333 5   1111  0.444  0.800000 6   1111  0.333  0.750000 7   2222  0.998  0.998000 8   2222  0.887  0.888778 9   2222  0.776  0.874859 10  2222  0.665  0.856959 11  2222  0.554  0.833083 12  2222  0.443  0.799639 13  2222  0.332  0.749436 14  2222  0.221  0.665663