I have a dataframe with 4 columns an ID and three categories that results fell into
<80% 80-90 >90
id
1 2 4 4
2 3 6 1
3 7 0 3
I would like to convert it to percentages ie:
<80% 80-90 >90
id
1 20% 40% 40%
2 30% 60% 10%
3 70% 0% 30%
this seems like it should be within pandas capabilities but I just can't figure it out.
Thanks in advance!
You can do this using basic pandas operators .div and .sum, using the axis argument to make sure the calculations happen the way you want:
cols = ['<80%', '80-90', '>90']
df[cols] = df[cols].div(df[cols].sum(axis=1), axis=0).multiply(100)
df[cols].sum(axis=1). axis=1 makes the summation occur across the rows, rather than down the columns.df[cols].div(df[cols].sum(axis=1), axis=0). axis=0 makes the division happen across the columns.100 so they are percentages between 0 and 100 instead of proportions between 0 and 1 (or you can skip this step and store them as proportions).df/df.sum()
If you want to divide the sum of rows, transpose it first.
Tim Tian's answer pretty much worked for me, but maybe this helps if you have a df with several columns and want to do a % column wise.
df_pct = df/df[df.columns].sum()*100I was having trouble because I wanted to have the result of a pd.pivot_table expressed as a %, but couldn't get it to work. So I just used that code on the resulting table itself and it worked.