Creating a table in Python using values of another table as new columns

I am working in Numpy and Pandas.

I have a table of loans with the features country and sector.

# LOAN	SECTOR	COUNTRY
Loan 1	food	germany
Loan 2	telecom	italy
Loan 3	auto	japan
Loan 4	food	japan
Loan 5	telecom	germany
Loan 6	auto	italy

I need to drop the duplicates by the sector and the country, ie. select the unique values of these 2 features, and use them as columns creating a table with boolean 1/0 if the loan is active in that country or sector, as follows:

# LOAN	food	telecom	auto	germany	italy	japan
Loan 1	1	0	0	1	0	0
Loan 2	0	1	0	0	1	0
Loan 3	0	0	1	0	0	1
Loan 4	1	0	0	0	0	1
Loan 5	0	1	0	1	0	0
Loan 6	0	0	1	0	1	0

So, Loan1 in the first table had food as sector and germany as country; then, in the second table it has 1 on columns food and germany and 0 on all the other columns.

It seems a pivot_table but I don't understand how I could put the 1/0 as values? Btw, what's the easiest way?

Thanks

source https://stackoverflow.com/questions/76409213/creating-a-table-in-python-using-values-of-another-table-as-new-columns

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