set row's value become column and fill it based on other column's row's value

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i have a dataframe like this:

id  category    year    freq
101 1           2020    1
101 1           2021    1
202 2           2020    2
202 2           2021    6
203 3           2021    2

I need to transform the dataframe based on id, category and year's value and fill the year's value with frequency for the year. The desired output is:

id  category    2020    2021
101 1           1       1
202 2           2       6
203 3           0       2

i have tried using one hot encoding, but the i can't fill each year's column with frequency.

one-hot-encoding transpose transform dataframe python
1 Answers

Seems like a job for df.pivot_table. Notice that we'll use fill_value=0 to replace missing values with 0 (to match your expected output):

>>> df.pivot_table(values="freq", index=["id", "category"], columns="year", fill_value=0)
year          2020  2021
id  category            
101 1            1     1
202 2            2     6
203 3            0     2
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