I have got a sequence of strings - 0000001, 0000002, 0000003.... upto 2 million. They are not contiguous. Meaning there are gaps. Say after 0000003 the next string might be 0000006. I need to find out all these gaps. In the above case (0000004, 0000005).
This is what I have done so far -
gaps  = list()
total = len(curr_ids)
for i in range(total):
    tmp_id = '%s' %(str(i).zfill(7))
    if tmp_id in curr_ids:
        continue
    else:
        gaps.append(tmp_id)
return gaps
But as you would have guessed, this is slow since I am using list. If I use a dict, to pre-populate curr_ids it'll be faster. But what's the complexity to populating a hash-table? What's the fastest way to do this. 
You could sort the list of ids and then step through it once only:
def find_gaps(ids):
    """Generate the gaps in the list of ids."""
    j = 1
    for id_i in sorted(ids):
        while True:
            id_j = '%07d' % j
            j += 1
            if id_j >= id_i:
                break
            yield id_j
>>> list(find_gaps(["0000001", "0000003", "0000006"]))
['0000002', '0000004', '0000005']
If the input list is already in order, then you can avoid the sorted (though it does little harm: Python's adaptive mergesort is O(n) if the list is already sorted).
For storing sequence of 2 millions ints you can use bitarray. Here each bit means one integer (the integer of that index in bitarray). Example code:
gaps = []
# bitarray is 0 based
a = bitarray.bitarray(total + 1)
a.setall(False)
for sid in curr_ids:
    a[int(sid)] = True
for i in range(1, total):
    if not a[i]:
        gaps.append('%07d' %(i))
return gaps
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