Leetcode #2658: Maximum Number of Fish in a Grid
In this guide, we solve Leetcode #2658 Maximum Number of Fish in a Grid in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
You are given a 0-indexed 2D matrix grid of size m x n, where (r, c) represents: A land cell if grid[r][c] = 0, or A water cell containing grid[r][c] fish, if grid[r][c] > 0. A fisher can start at any water cell (r, c) and can do the following operations any number of times: Catch all the fish at cell (r, c), or Move to any adjacent water cell.
Quick Facts
- Difficulty: Medium
- Premium: No
- Tags: Depth-First Search, Breadth-First Search, Union Find, Array, Matrix
Intuition
We need to explore a structure deeply before backing up, which suits DFS.
DFS keeps local context on the call stack and is easy to implement recursively.
Approach
Define a recursive DFS that carries the necessary state.
Combine child results as the recursion unwinds.
Steps:
- Define a recursive DFS with state.
- Visit children and combine results.
- Return the final aggregation.
Example
Input: grid = [[0,2,1,0],[4,0,0,3],[1,0,0,4],[0,3,2,0]]
Output: 7
Explanation: The fisher can start at cell (1,3) and collect 3 fish, then move to cell (2,3) and collect 4 fish.
Python Solution
class Solution:
def findMaxFish(self, grid: List[List[int]]) -> int:
def dfs(i: int, j: int) -> int:
cnt = grid[i][j]
grid[i][j] = 0
for a, b in pairwise((-1, 0, 1, 0, -1)):
x, y = i + a, j + b
if 0 <= x < m and 0 <= y < n and grid[x][y]:
cnt += dfs(x, y)
return cnt
m, n = len(grid), len(grid[0])
ans = 0
for i in range(m):
for j in range(n):
if grid[i][j]:
ans = max(ans, dfs(i, j))
return ans
Complexity
The time complexity is , and the space complexity is . The space complexity is .
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.