Date: 2026-06-06
Time: 17:57
minimum-distance-to-the-target-element/solution.pyThis file solves LeetCode 1848 — Minimum Distance to the Target Element. It's one of ~500 problem solutions in the leetcode-implementations repo, each following the same directory convention: {problem-slug}/solution.py.
The file owns exactly one responsibility: given an array, a target value, and a starting index, find the closest index containing the target.
getmindistance(nums, target, start) -> int — The sole public function. It takes a list of integers, a target value, and a starting index, and returns the minimum absolute distance |i - start| across all indices i where nums[i] == target.
The contract is simple: the caller guarantees that target exists in nums at least once (per the problem constraints). The function returns a non-negative integer.
Generator expression inside min() — The entire solution is a single expression. It uses a generator (not a list comprehension), so it streams candidates into min() without materializing a full list. This is idiomatic Python for "find the best match across a filtered set."
enumerate for index+value iteration — Standard Python idiom to avoid manual index tracking.
Single-pass linear scan — No sorting, no binary search, no preprocessing. It walks the array once, evaluating every element. For this problem's constraints (array length up to 10^4), this is the right call — anything fancier would be over-engineering.
Imports: None. The function uses only builtins (min, abs, enumerate).
Imported by: The "Imported By" list in the prompt is misleading — those are test files across many other problems, likely an artifact of a shared test harness or import scanning tool picking up the entire solution directory. The real consumer is minimum-distance-to-the-target-element/test_solution.py.
1. enumerate(nums) yields (index, value) pairs.
2. The generator filter if v == target keeps only indices where the value matches.
3. abs(i - start) computes the distance for each matching index.
4. min() selects the smallest distance.
There's no intermediate state, no mutation, no branching — it's a pure functional pipeline.
nums: If no element matches target, the generator is empty and min() raises ValueError. The function relies on the caller (or LeetCode's constraints) to guarantee at least one match.start is a valid index: The function doesn't bounds-check start. It doesn't need to — start is only used in the distance calculation, not as an array index.None. If the precondition (target exists in nums) is violated, min() raises ValueError with "min() arg is an empty sequence". This is the right behavior for a LeetCode solution — fail loud rather than return a nonsensical value.