for Loop for multiplying a value of each row with a specific column from another table based on condition in Python

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I have table A

NAME STAGE Quan Products A B
XYD1234 PROD 4643 A, C, T, Y 1 0
FGY4567 TEST 4739 A, U, p, Y 0 1
YUD4568 QUE 45632 I, Y, O, P 0 1
OKE4556 STACK 97474 A, C, P, Y 1 0

Table B:

STAGE A B
PROD 10 5
TEST 20 10
QUE 30 15
STACK 49 25

This is the output I want.
If the stage of each row matches the stage in table B, then multiply Column A of Table A with column A of table B.

NAME STAGE Quan Products A B A_STAGE B_STAGE
XYD1234 PROD 4643 A, C, T, Y 1 0 10 0
FGY4567 TEST 4739 A, U, p, Y 0 1 0 10
YUD4568 QUE 45632 I, Y, O, P 0 1 0 15
OKE4556 STACK 97474 A, C, P, Y 1 0 49 0

If this the code I have but it is not working as it should be.

for i in range(0, len(A)):
  A.loc['Stage'] == B['Stage']
  A['A_Stage'] = A['A'] * B['A']
pandas numpy python
1 Answers
import pandas as pd
df1 = pd.DataFrame({'NAME':['XYD1234','FGY4567','YUD4568','OKE4556'],
                    'STAGE':['PROD','TEST','QUE','STACK'], 
                    'Quan':[4643,4739,45632,97474], 
                    'Products':['A, C, T, Y','A, U, p, Y','I, Y, O, P','A, C, P, Y'], 
                    'A':[1, 0, 0, 1], 
                    'B':[0, 1, 1, 0]})
df2 = pd.DataFrame({'STAGE':['PROD','TEST','QUE','STACK'],
                    'A':[10, 20, 30, 49], 
                    'B':[5, 10, 15, 25]})
df2.columns = ['STAGE', 'A_STAGE', 'B_STAGE']
df1 = df1.merge(df2, on='STAGE')
df1.loc[:,'A_STAGE'] *= df1.loc[:,'A']
df1.loc[:,'B_STAGE'] *= df1.loc[:,'B']
userYE4D0
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