You can do this easily manually for each column like this:
df['A_perc'] = df['A']/df['sum']
If you want to do this in one step for all columns, you can use the div method (http://pandas.pydata.org/pandas-docs/stable/basics.html#matching-broadcasting-behavior):
ds.div(ds['sum'], axis=0)
And if you want this in one step added to the same dataframe:
>>> ds.join(ds.div(ds['sum'], axis=0), rsuffix='_perc')
A B C D sum A_perc B_perc \
1 0.151722 0.935917 1.033526 0.941962 3.063127 0.049532 0.305543
2 0.033761 1.087302 1.110695 1.401260 3.633017 0.009293 0.299283
3 0.761368 0.484268 0.026837 1.276130 2.548603 0.298739 0.190013
C_perc D_perc sum_perc
1 0.337409 0.307517 1
2 0.305722 0.385701 1
3 0.010530 0.500718 1