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:

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Desired Output:

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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<->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!


1 Answer 1


Try using groupby with a custom function and apply.

df = pd.DataFrame()
df['RunNumber'] = ['Run1','Run1','Run2','Run2']

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)

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