feat: top-k-frequent-elements challenge

Author: IbatoLionDev

2-NORMAL/Python/10-top-k-frequent-elements/proposed-solution/main.py

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+# Challenge: Given an array of numbers, create a function that returns the k elements that appear most frequently.
+# Consider using data structures like hash maps and heaps to efficiently manage frequency.
+# Provide two variants using OOP: one with hash map and sorting, and another with hash map and heap.
+
+from top_k_hashmap import TopKFrequentHashMap
+from top_k_heap import TopKFrequentHeap
+
+def main():
+    nums = [1,1,1,2,2,3,3,3,3,4,4,5]
+    k = 3
+
+    print("Using HashMap and Sorting:")
+    top_k_hashmap = TopKFrequentHashMap(nums)
+    print(top_k_hashmap.top_k_frequent(k))
+
+    print("Using HashMap and Heap:")
+    top_k_heap = TopKFrequentHeap(nums)
+    print(top_k_heap.top_k_frequent(k))
+
+if __name__ == "__main__":
+    main()

2-NORMAL/Python/10-top-k-frequent-elements/proposed-solution/top_k_hashmap.py

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+from typing import List
+from collections import Counter
+
+class TopKFrequentHashMap:
+    def __init__(self, nums: List[int]):
+        self.nums = nums
+
+    def top_k_frequent(self, k: int) -> List[int]:
+        count = Counter(self.nums)
+        # Sort elements by frequency in descending order
+        sorted_items = sorted(count.items(), key=lambda x: x[1], reverse=True)
+        return [item[0] for item in sorted_items[:k]]

2-NORMAL/Python/10-top-k-frequent-elements/proposed-solution/top_k_heap.py

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+from typing import List
+from collections import Counter
+import heapq
+
+class TopKFrequentHeap:
+    def __init__(self, nums: List[int]):
+        self.nums = nums
+
+    def top_k_frequent(self, k: int) -> List[int]:
+        count = Counter(self.nums)
+        # Use a heap to get the k elements with highest frequency
+        return [item for item, freq in heapq.nlargest(k, count.items(), key=lambda x: x[1])]