feat(0347_top-k-frequent-elements): add py3 soln

0347_top-k-frequent-elements/python3/smart.py

@@ -0,0 +1,7 @@
+# Time complexity: O(nlogn)
+# https://github.com/python/cpython/blob/f52d987abfda25e50469c9b6fe1d19f72453d2de/Lib/collections/__init__.py#L608
+from collections import Counter
+
+class Solution:
+    def topKFrequent(self, nums: List[int], k: int) -> List[int]:
+        return [val for val, _ in Counter(nums).most_common(k)]

0347_top-k-frequent-elements/python3/solution.py

@@ -0,0 +1,19 @@
+from heapq import heapify, heappop
+
+class Solution:
+    def topKFrequent(self, nums: List[int], k: int) -> List[int]:
+        c = Counter(nums)
+        
+        # Python heapq creates a min-heap but we want a max-heap built
+        # against counts. Tuples are comparable in Python by default so
+        # we can create a tuple of (count, element) to build the heap. To
+        # mimic a max-heap, we can just negate the counts.
+        heap = [(-count, val) for val, count in c.items()]
+        heapify(heap)
+        
+        result = [None] * k
+        
+        for i, _ in enumerate(result):
+            result[i] = heappop(heap)[1]
+        
+        return result