Row and Column search to map values to another df with python - loops

Question

I need to be able to find the values from one df in another by looking at each value per column per row.
Basically I have this df which is my main_df (note this is a mock up of the data in reality I have hundreds of columns and thousands of rows):
user_id
fav_fruit
fav_veg
basket
1
apple
potato
apple
2
pear
potato
fruit
3
banana
carrot
fruit
4
apple
broccoli
carrot
And I have another df whihch is my auxiliary_df:
category
answer
value
fav_fruit
apple
0.5
fav_fruit
pear
0.5
fav_fruit
banana
0.8
fav_veg
potato
0.7
fav_veg
carrot
1
fav_veg
broccoli
1
basket
apple
3
basket
fruit
5
basket
carrot
3
I need to sear for each user id (row loop) find the column name in the auxiliary_df - category column, and within the answers in that category map what i have in my main_df as value with the auxiliary_df to get the value of that table so at the end I get the following:
user_id
fav_fruit
fav_veg
basket
1
0.5
0.7
3
2
0.5
0.7
5
3
0.8
1
5
4
0.5
1
3
So at this point I repalced all the values from my main_df with the numerical values from the auxiliary_df. I cannot ma just by answer since many answers are repeated across categories so unsure how t map it properly. I dont know how to do this, I tried to do a dictionary to map but couldnt make it work to get the correct dection of category with answer and value. Also, I cannot hardcode anything since is hundreds of columns and values that change regularly.

Answer 1

You can .pivot() the auxiliary_df and .map():
tmp = auxiliary_df.pivot(index='answer', columns='category', values='value')
main_df = main_df.set_index('user_id')
for c in main_df:
main_df[c] = main_df[c].map(tmp[c])
print(main_df.reset_index())
Prints:
user_id fav_fruit fav_veg basket
0 1 0.5 0.7 3.0
1 2 0.5 0.7 5.0
2 3 0.8 1.0 5.0
3 4 0.5 1.0 3.0