Cannot create new percent columns from CSV file I am fairly new to coding and cannot figure out how to create the AB_perc and CD_perc columns in the Desired Output table below.

The original csv file I am using has all sorts ot DNA sequencing data, so below is a simplified example of what I am trying to do.  

CSV file:



Desired Output:



I was able to generate the AB_sum and CD_sum columns by using:

df['AB_sum'] = df['A'] + df['B']
df['CD_sum'] = df['C'] + df['D']
df.groupby('RunNumber', as_index=False).agg({'AB_sum': 'sum', 'Confusion C&lt;-&gt;G': 'sum'})


But I cannot figure out how to create the AB_perc and CD_perc columns. 


For example:


To calculate the percentage of AB for Run 1 (AB_perc: 79.94), the AB_sum value, 570, is divided by 713, the sum of all orange numbers in the csv file, and then multiplied by 100

I tried several things all day and nothing worked for me. I'm sure there's a simple solution, but I feel like if I try any harder my nose will start to bleed. Haha.

Any help would be much appreciated!
Try using groupby with a custom function and apply.

df = pd.DataFrame()
df['RunNumber'] = ['Run1','Run1','Run2','Run2']
df['A']=[500,60,20,30]
df['B']=[5,5,2,2]
df['C']=[5,10,2,6]
df['D']=[3,4,5,4]
df['E']=[65,56,56,44]

def calculate_sum_percent(input_df):
    output_df = pd.Series()
    output_df['AB_sum']=input_df['A'].sum() + input_df['B'].sum()
    output_df['CD_sum']=input_df['C'].sum() + input_df['D'].sum()
    output_df['AB_perc']=100*(input_df['A'].sum() + input_df['B'].sum())/input_df.sum().sum()
    output_df['CD_perc']=100*(input_df['C'].sum() + input_df['D'].sum())/input_df.sum().sum()
    return output_df

stats_df = df.groupby('RunNumber').apply(calculate_sum_percent)
display(stats_df)