how to calculate percentage with nested dictionary

Question

I'm stuck with how to calculate percentages with nested dictionary. I have a dictionay defined by old_dict = {'X': {'a': 0.69, 'b': 0.31}, 'Y': {'a': 0.96, 'c': 0.04}} , and I know the percentage of X and Y are in the table:

input= {"name":['X','Y'],"percentage":[0.9,0.1]}
table = pd.DataFrame(input)

OUTPUT:
   name percentage
0   X   0.9
1   Y   0.1

But I hope to use the percentage of X and Y to multiply by a,b, c separately. That is, X*a = 0.9*0.69 , X*b = 0.9*0.31 , Y*a = 0.1*0.96 , Y*c = 0.1*0.04 ... so that I can find the mixed percentage of a, b, and c, and finally got a new dictionary new_dict = {'a': 0.717, 'b': 0.279,'c': 0.004} .

I'm struggling with how to break through the nested dictionary and how to link X and Y with the corresponding value in the table. Can anyone help me? Thank you!

Answer 1

You could use a DataFrame for the first dictionary and a Series for the second and perform an aligned multiplication, then sum :

old_dict = {'X': {'a': 0.69, 'b': 0.31}, 'Y': {'a': 0.96, 'c': 0.04}}
df = pd.DataFrame(old_dict)

inpt = {"name":['X','Y'],"percentage":[0.9,0.1]}
table = pd.DataFrame(inpt)

# convert table to series:
ser = table.set_index('name')['percentage']
# alternative build directly a Series:
# ser = pd.Series(dict(zip(*inpt.values())))

# compute expected values:
out = (df*ser).sum(axis=1).to_dict()

output: {'a': 0.717, 'b': 0.279, 'c': 0.004}