If we wanted to apply a function across rows, were there currently is no built in method, like rank_horizontal, what is the fastest way?

data = {0: [0, 1, 0, 1, 1, 0, 1, 0, 1, 1],
 1: [0, 0, 0, 0, 0, 0, 1, 0, 1, 1],
 2: [0, 0, 1, 0, 1, 0, 1, 0, 0, 1],
 3: [0, 1, 0, 0, 1, 0, 0, 0, 1, 0],
 4: [0, 1, 1, 0, 1, 0, 0, 0, 1, 1]}

Input df (pandas):

   0  1  2  3  4
0  0  0  0  0  0
1  1  0  0  1  1
2  0  0  1  0  1
3  1  0  0  0  0
4  1  0  1  1  1
5  0  0  0  0  0
6  1  1  1  0  0
7  0  0  0  0  0
8  1  1  0  1  1
9  1  1  1  0  1

In pandas we can do this:

df.rank(axis=1)

In polars, this is one way to do it. Is there a faster way?

(df2.select(
    pl.concat_arr(
        pl.all()).arr.eval(pl.element().rank())
        )
    .with_row_index()
    .explode('0')
    .with_columns(
        pl.col('index').cum_count().sub(1).over('index').alias('cc')
        ).pivot(index = 'index',on = 'cc'))

Output:

     0    1    2    3    4
0  3.0  3.0  3.0  3.0  3.0
1  4.0  1.5  1.5  4.0  4.0
2  2.0  2.0  4.5  2.0  4.5
3  5.0  2.5  2.5  2.5  2.5
4  3.5  1.0  3.5  3.5  3.5
5  3.0  3.0  3.0  3.0  3.0
6  4.0  4.0  4.0  1.5  1.5
7  3.0  3.0  3.0  3.0  3.0
8  3.5  3.5  1.0  3.5  3.5
9  3.5  3.5  3.5  1.0  3.5

Also, why does cum_count() start at 1 and not 0?