Average numeric columns and return single string for string columns using pandas groupby

I have a dataframe that looks like the following:

    ID  Type    Size
0   123 Red     5
1   456 Blue    7
2   789 Yellow  12
3   789 Yellow  4

I now want to aggregate by ID and take the mean of the size for duplicates. However, I wish to only return the same string for Type, not concatenate it. I have attempted to capture this using agg:

df = pd.DataFrame({'ID' : [123, 456, 789, 789], 'Type' : ['Red', 'Blue', 'Yellow', 'Yellow'], 'Size' : [5, 7, 12, 4]})

def identity(x):
    return x

special_columns = ['Type']
aggfuncs = {col: statistics.mean for col in df.columns}
aggfuncs.update({col:identity for col in special_columns})
df.groupby(['ID'], as_index=False).agg(aggfuncs)

However, this still turns into an array of the repeated string:

    ID  Type              Size
0   123 Red                 5
1   456 Blue                7
2   789 [Yellow, Yellow]    8

The end result I wanted was:

    ID  Type              Size
0   123 Red                 5
1   456 Blue                7
2   789 Yellow              8

How can this be achieved?

If each ID has one corresponding type, this should work

# use both ID and Type as grouper
res = df.groupby(["ID", "Type"], as_index=False)["Size"].mean()
res