how to find max of a columns with same name

Question

im having some trouble with this data frame where columns having the same name have to be reduced to values with at least one "1" as "1".

+---+---+---+---+---+---+---+---+---+
| a | a | a | b | c | c | c | d | d |
+---+---+---+---+---+---+---+---+---+
| 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 |
| 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 |
| 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
+---+---+---+---+---+---+---+---+---+

to something like this using "or" condition for every column for a huge dataset could be a time-consuming task so I am having trouble figuring it out. I used max(axis=1, level=0) still couldn't make it.

my desired output :

+---+---+---+---+
| a | b | c | d |
+---+---+---+---+
| 1 | 1 | 1 | 1 |
| 0 | 1 | 1 | 1 |
| 1 | 0 | 1 | 0 |
+---+---+---+---+

Answer 1

Check with max

df = df.max(level=0, axis=1)