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 Weighted average with multiple weights and groups
#1
I am a beginner in Python and I am trying to improve my code - so I would appreciate some advice on how to improve the efficiency of the following.

I have the following dataset:


petdata = {
    'animal' : ['dog', 'cat', 'fish'],
    'male_1' : [0.57, 0.72, 0.62],
    'female_1' : [0.43, 0.28, 0.38],
    'age_01_1': [0.10,0.16,0.15],
    'age_15_1':[0.17,0.29,0.26],
    'age_510_1':[0.15,0.19,0.19],
    'age_1015_1':[0.18,0.16,0.17],
    'age_1520_1':[0.20,0.11,0.12],
    'age_20+_1':[0.20,0.09,0.10],
    'male_2' : [0.57, 0.72, 0.62],
    'female_2' : [0.43, 0.28, 0.38],
    'age_01_2': [0.10,0.16,0.15],
    'age_15_2':[0.17,0.29,0.26],
    'age_510_2':[0.15,0.19,0.19],
    'age_1015_2':[0.18,0.16,0.17],
    'age_1520_2':[0.20,0.11,0.12],
    'age_20+_2':[0.20,0.09,0.10],
    'weight_1': [10,20,30],
    'weight_2':[40,50,60]
}

df = pd.DataFrame(petdata) 

I want to calculate a weighted average for the animals in my dataset using weight_1 for all the variable that end with "_1" and weight_2 for all the variables that end with "_2".

I am doing it in this way at the moment:


df['male_wav_1']=np.nansum((df['male_1']*df['weight_1'])/df['weight_1'].sum())
df['female_wav_1']=np.nansum((df['female_1']*df['weight_1'])/df['weight_1'].sum())


df['male_wav_2']=np.nansum((df['male_2']*df['weight_2'])/df['weight_2'].sum())
df['female_wav_2']=np.nansum((df['female_2']*df['weight_2'])/df['weight_2'].sum())

And this is for every single column in my dataframe. I realise this is not very neat, so can anyone give me some advice on how to improve the process?

I have tried to:
  • reshape the data from wide to long
    define a function for the weighted average

But I was unsuccessful with both. The issue not in the reshaping, I can do that, but they I am not clear on how to apply the different weights to the different groups I have in my data.

Many thanks for any help.
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