Python weighted random choices from lists with different probability, comparing two Pandas DataFrame from CSVs

Python beginner, here. I am attempting to take a pandas DataFrame (created from a CSV) and use weighted random choices to choose from another DataFrame (created from a CSV). What I have is two pandas DataFrames that read something like this:

Weighted Percentages of codes:

SECTION	CODE	Final_Per
B1	800	5%
B1	801	65%
B1	802	30%
B2	900	30%
B2	901	70%
B3	600	50%
B3	601	50%

Input pandas DataFrame to run weighted percentages on:

SECTION	NUMBER
B1	14
B2	25
B3	12

These are just examples of my tables rather than the entirety of the tables themselves. What I need to do is store these weighted probabilities whether in a dictionary, lists, or pandas dataframes (not sure what's best) - and take my second table above and apply the 'Final_Per' %'s to the 'NUMBER' column and output the result. So B1's result would be 14 values, 5% being code 800, 65% being code 801, and 30% being code 802. Currently, the tables are CSV's and I am turning them into pandas dataframes and attempting to take some lessons learned from this article https://pynative.com/python-weighted-random-choices-with-probability/ to no success. Does anybody have suggestions on how to handle this correctly? Thank you.