Neighborhood Maxima Finder
Intuition\n Describe your first thoughts on how to solve this problem. \nBy scanning through the grid and zooming into small regions, it pinpoints the peak valu
# Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\nBy scanning through the grid and zooming into small regions, it pinpoints the peak value within each local area. This approach is handy for tasks like identifying prominent features or hotspots within a dataset, enabling efficient analysis of spatial patterns in applications such as image processing or geographical mapping.\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\n\nTo solve the problem, we\'ll traverse the grid, examining each 3x3 neighborhood. At each position, we\'ll find the maximum value within the local area and store it in a result matrix. This way, we ensure that we capture the highest value within each neighborhood, providing insights into localized peaks within the grid. Handling edge cases where the neighborhood extends beyond the grid boundaries is essential for completeness. Overall, this approach efficiently identifies local maxima, making it suitable for tasks requiring neighborhood analysis.\n\n# Complexity\n- Time complexity:\n$$O(n^2)$$\n\n- Space complexity:\n$$O(n^2)$$\n\n# Code\n```\nclass Solution:\n    def largestLocal(self, grid: List[List[int]]) -> List[List[int]]:\n        def solve(temp):\n            maxi = temp[0][0]\n            for row in temp:\n                for el in row:\n                    if el > maxi:\n                        maxi = el\n            return maxi\n        res=[]\n        for i in range(len(grid)-2):\n            te=[]\n            for j in range(len(grid[0])-2):\n                temp = [row[j:j+3] for row in grid[i:i+3]]\n                x=solve(temp)\n                te.append(x)\n            res.append(te)\n        return res\n```
['Array', 'Math', 'Divide and Conquer', 'Recursion', 'Python', 'Python3', 'Pandas']