Sum columns values with common values in another column

I have this dataframe:

I want to sum all the distances that have been done each day, giving the next dataframe:

distances = [
    (32.2,1),
    (40.2,1),
    (22.5,2),
    (37.6,2),
    (5.6,2),
    (5.8,3),
    (9.7,3),
    (10.2,3),
    (12.3,4),
    (15.2,4),
]
             
expected_result = [
    (72.5,1),
    (65.5,2),
    (25.7,3),
    (27.5,4),
]
             
distances = pd.DataFrame(distances, columns = ['distance','day'])
expected_result = pd.DataFrame(expected_result, columns = ['distance','day'])

I’m new to pandas so I don’t know exactly how to do it.

>Solution :

You can group the data by "day" then sum it

distances = distances.groupby('day').sum()

If you want to sort the data according to distance you can use this

distances = distances.sort_values(by=['distance'], ascending=False)