Adding new columns to dataframe from other dataframe according to list of indices in other dataframe

Question

I have two dataframes, each row in dataframe A has a list of indices corresponding to entries in dataframe B and a set of other values. I want to join the two dataframes in a way so that each of the entries in B has the other values in A where the index of the entry in B is in the list of indices in the entry in A.

So far, I have found a way of extracting the rows in B for the list of indices in each row in A but only row-by-row from this answer but then I am not sure where to go from here? Also not sure if there's a better way of doing it with Pandas dynamically as the size of the list of indices can change.

import pandas as pd
import numpy as np

# Inputs
A = pd.DataFrame.from_dict({
    "indices": [[0,1],[2,3],[4,5]],
    "a1": ["a","b","c"],
    "a2": [100,200,300]
})

print(A)
>>    indices a1   a2
>> 0  [0, 1]  a  100
>> 1  [2, 3]  b  200
>> 2  [4, 5]  c  300

B = pd.DataFrame.from_dict({
    "b": [10,20,30,40,50,60]
})

print(B)
>>     b
>> 0  10
>> 1  20
>> 2  30
>> 3  40
>> 4  50
>> 5  60

# This is the desired output
out = pd.DataFrame.from_dict({
    "b": [10,20,30,40,50,60],
    "a1": ["a","a", "b", "b", "c", "c"],
    "a2": [100,100,200,200,300,300]
})

print(out)
>>      b a1   a2
>> 0  10  a  100
>> 1  20  a  100
>> 2  30  b  200
>> 3  40  b  200
>> 4  50  c  300
>> 5  60  c  300

Answer 1

If you have pandas >=0.25, you can use explode:

C = A.explode('indices')

This gives:

  indices a1   a2
0       0  a  100
0       1  a  100
1       2  b  200
1       3  b  200
2       4  c  300
2       5  c  300

Then do:

output = pd.merge(B, C, left_index = True, right_on = 'indices')
output.index = output.indices.values    
output.drop('indices', axis = 1, inplace = True)

Final Output:

    b a1   a2
0  10  a  100
1  20  a  100
2  30  b  200
3  40  b  200
4  50  c  300
5  60  c  300