Say I have a dataframe:

df = pl.DataFrame({'a':[1,1,1,1,2,2,2,2],'b':[1,2,1,2,1,2,1,2],'c':[10,11,12,13,14,15,18,17]})
print(df)
output:
shape: (8, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 1   ┆ 10  │
│ 1   ┆ 2   ┆ 11  │
│ 1   ┆ 1   ┆ 12  │
│ 1   ┆ 2   ┆ 13  │
│ 2   ┆ 1   ┆ 14  │
│ 2   ┆ 2   ┆ 15  │
│ 2   ┆ 1   ┆ 18  │
│ 2   ┆ 2   ┆ 17  │
└─────┴─────┴─────┘

Now, within each group according to a, I want to find the max of the last values according to the second level groups in b

something that gets achieved by the following code

df.with_columns(pl.col("c").last().over(["a", "b"])).group_by(pl.col("a")).agg(pl.col("c").max())
output:
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ c   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 2   ┆ 18  │
│ 1   ┆ 13  │
└─────┴─────┘

Is there a way to do this with one expression? Currently I would have to write a separate class for such cases that provides one expression to go to the with_columns and another expression for the group_by. I can do that but I am also trying to be better at polars and would like to know if it can be done with just one expression