Map values from one dataframe column properly to other column dataframe

Question

I have two data frames in which one column is common. I want to compare both the data frames and map the value of the 2nd column in data frame 2 to the first if matched properly. For example, I have one data frame say treasury_shares with column entity_Id another data frame say soiValues with columns entityId and attribute_value. I want to compare the entity_Id and create a new column in data frame A with attribute_value values properly assigned to entity_Id in the data frame treasury_shares as they were in data frame soiValues.

Currently what I am doing is treasury_shares.insert(6, "SOI priority",soiValues['attribute_value'], True) I am only inserting the attribute_value values as it is what I get from the database in the form of data frame soiValues. But the SOI priority values are assigned in an improper manner in the data frame treasury_shared to the respective entity IDs with my approach. Wrong attribute_value values are been assigned.

Can anyone help me in building the logic for this? Will the below code work?

for index, row in treasury_shares.iterrows():
    for index,row in soiValues.iterrows():
        if (treasury_shares['entity_id']==soiValues['entity_id']):
            treasury_shares['SOI priority']=soiValues['attribute_value']

Answer 1

You should check out merge function in Pandas library

A quick demo for the same -

import pandas as pd  
      
lst = [['A', 1], ['B', 2], 
       ['C', 3], ['D', 4]] 
   
df1 = pd.DataFrame(lst, columns =['x1', 'x2']) 

lst2 = [['A', 'Hello'], ['B', 'Hola'], 
       ['C', 'Hi'], ['D', 'Hey']] 
     
df2 = pd.DataFrame(lst, columns =['x1', 'x3'])

df1.merge(df2,on='x1',how='left')

If there are values of x1 in df1 with no corresponding values in df2 you will get a row with NaN in x3 column

If there are values of x1 in df2 with no corresponding values in df1 it will not show up in the output currently but can be added by tinkering with the 'how' parameter