slicing dataframe by column value and saving as separate dataframe

Question

I'd like to ask if there are any smart ways of slicing pandas DataFrame in to smaller ones by column value. What I want to achieve is:

Having dataframe like this:

df = pd.DataFrame(data = {'ID' : [1, 1, 1, 2, 2, 2, 3, 3, 3], 'revenue': [10, 20, 30, 40, 50, 60, 70, 80, 90]})

I want to iterate trough unique ID values, that I have in list by simply doing: id_list = df.ID.unique() and save it in separate dataframes with assigned name like df_ID (it would be nice, to create dataframe object name with specific ID in it)

Of course there is possibility of just filtering df by column values like: df_1 = df[['ID' == 1]] but the problem is if new IDs will be added to my df, I don't want to create another df manually

I was thinking if there is a way of doing something like:

def df_by_ID(df):
    df_IDs = df.ID.unique()
    for ID in df_IDs:
        new_df = df[['ID' = ID]]
#(i know what's below is not correct, altough I have no clue how to describe that part differently)
        f'df_{ID}' = new_df 
        return df_ID

Thanks for your help!