Arrays and Iteration

Two-Sum with Hash Lookup

Walk the array once, storing seen values in a lookup table. When the complement is already present, the result indices are known.

Algorithm

execution replay The checked-in replay follows the language-neutral state table for `array-two-sum-hash`.
cross-language comparison This Python DSA version keeps the same data and final output as every other DSA book in this wave.

Basic Implementation

basic.py
Replay: real traced execution (multi-file project)
arr = [2, 7, 11, 4, 5]
target = 9
seen = {}
result = None
for i, value in enumerate(arr):
    need = target - value
    if need in seen:
        result = [seen[need], i]
        break
    seen[value] = i
print(result)

  1. arr ← [2, 7, 11, 4, 5], target ← 9, seen ← {}

    1arr = [2, 7, 11, 4, 5]2target = 9
    values this step[2, 7, 11, 4, 5]arr9target{}seen

  2. seen ← {2: 0}, hit ← no

    5for i, value in enumerate(arr):6    need = target - value7    if need in seen:
    values this step{} → {2: 0}seennohit0i2arr[i]7need

  3. hit ← yes, result ← [0, 1]

    5for i, value in enumerate(arr):6    need = target - value7    if need in seen:
    values this stepyeshit[0, 1]result1i7arr[i]2need{2: 0}seen

Complexity

  • Time: O(n) average
  • Space: O(n)

Implementation notes

  • Python uses a plain dict for seen, mapping each integer value to its earlier index. need in seen hashes the integer complement and checks equality before the separate seen[need] lookup on a hit.
  • The checked-in loop is for i, value in enumerate(arr), so i and value are local name bindings from the list traversal. On a miss, seen[value] = i inserts or updates that dict entry; this fixture records only {2: 0} before finding the pair.
  • Hash collisions are not visible in the replay. The only new result container is the [seen[need], i] list allocated on the hit; it is normal Python GC-managed state once no references remain.