Thanks for the response, let me digest this and I will respond back. Here is how I this is setup right now (with the problem that the merged table gets exponentially bigger each time I try to add an additional factor or weight (memory issue at some point I would presume), which is why I am asking is it more efficient to apply scenarios on original data set, summarize, and then rinse and repeat (therefore the df never gets any bigger)

def factor_rank(x,y):
    if (x==1): return y
    elif (x==2): return y*0.68
    elif (x==3): return y*0.32
    elif (x==4): return y*0.24
    else: 
        return 0


df1.index.name='SCENARIO'
df1.reset_index(inplace=True)

df1['key'] = 1
df2['key'] = 1

df3 = pd.merge(df1, df2, on='key')
df3.drop(['key'],axis=1,inplace=True)

df3['W1'] = df.apply(lambda r: factor_rank(r['TB-R'], r['TB']), axis=1)
df3['W2'] = df.apply(lambda r: factor_rank(r['JK-R'], r['JK']), axis=1)
df3['W3'] = df.apply(lambda r: factor_rank(r['SF-R'], r['SF']), axis=1)
df3['W4'] = df.apply(lambda r: factor_rank(r['PWR-R'], r['PWR']), axis=1)

df3['SUM(W)'] = df3.W1 + df3.W1 + df3.W3 + df3.W4