create and save dataframes from other dataframe

Question

An extract from my df (35k rows in total):

stop_id                      time
7909    2022-04-06T03:47:00+03:00
7909    2022-04-06T04:07:00+03:00
1009413 2022-04-06T04:10:00+03:00
1002246 2022-04-06T04:19:00+03:00
1009896 2022-04-06T04:20:00+03:00

I want to create separate dataframes for every unique stop_id column, in each dataframe I need to have stop_id (which is constant for every row), time field with only unique values and number column which aggregates the rows with the same time value. So, assuming there are 50 unique stop_id values, I want to get 50 separate csv files containing all the data above. How can I do this?

hope the explanation doesn't seem messy

I have this line of code

df.groupby(['time']).agg({'time':'size','stop_id': ", ".join})

but it doesn't keep the value of stop_id

expected output: csv1

stop_id   time                      number   
7909      2022-04-06T03:47:00+03:00 1
7909      2022-04-06T04:07:00+03:00 1
7909      2022-04-06T05:00:00+03:00 2
...

csv2

stop_id      time                      number   
1009413      2022-04-06T04:10:00+03:00 1
1009413      2022-04-06T04:19:00+03:00 3
1009413      2022-04-06T04:30:00+03:00 5
...