Minimum Swaps to Group All 1's together - sliding window technique
Intuition\n Describe your first thoughts on how to solve this problem. \nTo solve the problem of finding the minimum number of swaps required to group all 1s in
# Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\nTo solve the problem of finding the minimum number of swaps required to group all 1s in a binary array, we need to find the smallest window size containing the maximum number of 1s and then calculate how many 0s are in that window. This number will be the minimum number of swaps needed.\n\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\n-  **Count the Number of 1s:** Calculate the total number of 1s in the array, denoted as k.\n- **Special Cases:** If there are no 1s or only one 1, no swaps are needed.\n- **Extend the Array:** To handle the circular nature of the problem, create a doubled array which is the concatenation of the array with itself.\n- **Sliding Window Technique:** Use a sliding window of size k to find the maximum number of 1s in any such window within the doubled array.\n- **Calculate Swaps:** The minimum swaps required will be k (total number of 1s) minus the maximum number of 1s found in any window of size k.\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n$$O(n)$$, where n is the length of the array. This is because the sliding window operation traverses the array linearly.\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n$$O(n)$$ due to the space needed for the doubled array.\n# Code\n```\nclass Solution:\n    def minSwaps(self, nums: List[int]) -> int:\n        k = sum(nums)  # Total number of 1\'s in the array\n\n        if k == 0 or k == 1:\n            return 0   # No swaps needed if there are no 1\'s or just one 1\n\n        doubled_arr = nums + nums # Extend the array to handle circular nature\n\n        current_ones = sum(doubled_arr[:k]) # Number of 1\'s in first window\n        max_ones_in_window = current_ones # Initialize max ones in any windows\n\n        for i in range(1, len(nums)): # slide the windows \n            current_ones = current_ones - doubled_arr[i - 1] + doubled_arr[i + k - 1]\n            max_ones_in_window = max(max_ones_in_window, current_ones)\n\n        return k - max_ones_in_window # Calculate minimum swaps required\n\n```\n\n## I\u2019m glad you found this useful! If it made a difference, an upvote would be greatly appreciated. Wishing you lots of success on your coding journey!
['Array', 'Sliding Window', 'Python3']