extracting values from dataframe1 using conditions set in dataframe2 (pandas, python) _ Part 2

Question

I've asked a similar question in the past, however, its filtering with different conditions. hence, i'm posting this:

I have two dateframe (df1 & df2), i'm trying to figure out how to use conditions from df2 to extract values from df1 and use the extracted values in df2.

df1 = values to exact from

df2 = conditions for exaction and df where the extracted values are used

conditions: df2.ans = df2HJ & df2.P1 = df1 P2 colum

example if df2(df2.HJ = 99 & df2.P1 = 0); Ans = 76 (from df1) if df2(df2.HJ = 99 & df2.P1 = 0); Ans = 76 (from df1)

df1

╔════╦════╦══════╦
║ HJ ║ P1 ║  P2  ║
╠════╬════╬══════╬
║  5 ║ 51 ║  33  ║
║ 11 ║ 66 ║  45  ║
║ 21 ║  7 ║  55  ║
║ 99 ║  0 ║  76  ║
║ 15 ║ 11 ║  42  ║
╚════╩════╩══════╩

df2

╔════╦════╗
║ HJ ║ P1 ║
╠════╬════╣
║ 99 ║ 0  ║
║ 11 ║ 66 ║
║  5 ║ 51 ║
║ 21 ║ 7  ║
║ 11 ║ 66 ║
╚════╩════╝

expected result for df2 after exaction from df1

╔════╦════╦═══════╗
║ HJ ║ P1 ║  Ans  ║
╠════╬════╬═══════╣
║ 99 ║ 0  ║   76  ║
║ 11 ║ 66 ║   45  ║
║  5 ║ 51 ║   33  ║
║ 21 ║ 7  ║   55  ║
║ 11 ║ 66 ║   45  ║
╚════╩════╩═══════╝

code for df1

import pandas as pd
import numpy as np
data = {'HJ':[5,11,21,99,15],
'P1':[51,66,7,0,11]
,'P2':[ 33,45,55 ,76 ,42]}
df1 = pd.DataFrame(data)

code for df2

data = {'HJ':[99,11,5,21,11],
'P1':['0','66','51','7','66']}
df2 = pd.DataFrame(data)

Regards Thank you

Answer 1

I don't know why you want this example, cause your expected result its df1 with other order.

But if you want to connect different dataframes, it will be usefull use.join function: Join panda's function

Answer 2

Thanks to @Pol Renau Larrodé pointers i was able to solve the problem.

res = df2.merge(df1, how='inner', left_on=['HJ ', 'P1'], right_on=['HJ ', 'P1'])