Weighted average of a dictionary – Pandas

I have the following column in a data-frame (it is an example):

First row is: '{"100":10,"50":3,"-90":2}'.

Second row is: '{"100":70,"50":3,"-90":2,"-40":3}'.

I want to calculate a weighted average where the dictionary’s keys are the values and the dictionary’s values are the weights of the weighted average.

The final value of the first row should be: 64.666, which is (100*10+50*3-90*2)/(10+3+2); and the of the second row should be: 87.82.

For each dictionary there might be hundreds of keys/values and the column might have thousands of rows. How can I code it efficiently? Preferably vectorially.

>Solution :

You can use json.loads and pandas.Series.apply.

import json

def cal_avg(dct):
    dct = json.loads(dct)
    return sum(int(k)*v for k,v in dct.items()) / sum(dct[k] for k in dct)

df['dct'].apply(cal_avg)

Output:

0    64.666667
1    87.820513
Name: dct, dtype: float64

Input DataFrame:

import pandas as pd
df = pd.DataFrame({
    'dct': [
        '{"100":10,"50":3,"-90":2}',
        '{"100":70,"50":3,"-90":2,"-40":3}'
    ]
})