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Time limit exceeded

When a solution is too slow, it's typically because the algorithm is one or more orders of magnitude worse than intended for the challenges. So take a hard look at the time complexity of the implementation.

In your implementation, it all comes down to this expression:

sorted(set(x+"%d"%(k.count(x)) for x in k))

• k.count(x) : the centerpiece of the expression, to find the count of some value in a list. The count function does this by iterating over all elements k, and checking each for equality with x. Time complexity: \$O(N)\$, where N is the number of items in k

• ... for x in k : do something for each item in k. Again, \$O(N)\$, where N is the number of items in k

• set(...) : probably \$O(N)\$

• sorted(...) : probably \$O(N\log(N))\$

In total that gives: \$O(N^2) + O(N) + O(N\log(N))\$

Can this be better?

• You need to sort, that's for sure. That's fast enough.

• Do you really need a set? You used a set because the previous operations generate a list with duplicates. Using a set to remove duplicates is fine, but I would question why are duplicates at all in the first place.

• The previous two items actually don't even matter. It's the slowest operation in the \$O(N^2) + O(N) + O(N\log(N))\$ that will drag everything down, the \$O(N^2)\$, so that's what you really need to optimize.

Instead of iterating over every account number for each account number, you could iterate only once, building a dictionary with counts along the way:

def print_sorted_with_count(accounts):
    counts = {}
    for account in accounts:
        counts[account] = counts.get(account, 0) + 1
    for account, count in sorted(counts.items()):
        print('{} {}'.format(account, count))

This implementation reduces the time complexity from \$O(N^2)\$ to \$O(N)\$, and also gets rid of an \$O(N)\$ operation (no more need for the set.)

Naming

The variable names in the posted code are horrible, and it makes it very hard to read. Avoid single-letter variable names, try to use descriptive names.

Coding style

I suggest to follow the recommendations of PEP8.