top-k elements bucket sort

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

@@ -0,0 +1,46 @@
+class Solution:
+    def topKFrequent(self, nums: List[int], k: int) -> List[int]:
+        '''
+        Approach using bucket sort
+        '''
+        
+        c = Counter(nums)
+        
+        # Idea here is that the list indices will represent
+        # the actual counts and we store elements matching thise
+        # counts under the index. If size n list is provided, max
+        # count possible is n but lists are 0-indexed so we set
+        # num_bucket to have n+1 size.
+        num_bucket = [[] for _ in range(len(nums) + 1)]
+        
+        # Later down, we will iterate in reverse through `num_bucket` and
+        # instead of iterating through the entire list, we can take note
+        # of the max count we observe and start from there instead.
+        max_count_seen = -1
+        
+        for num, count in c.items():
+            num_bucket[count].append(num)
+                
+            max_count_seen = max(max_count_seen, count)
+        
+        result = []
+        j = max_count_seen
+        
+        # Iterate reverse through num_bucket (note the max_count_seen
+        # optimization) and fill the result array until k elements are
+        # added
+        while j >= 0:
+            for num in num_bucket[j]:
+                result.append(num)
+                
+                # Possible that we filled k elements already in this inner
+                # loop, so get out
+                if len(result) == k:
+                    break
+            
+            if len(result) == k:
+                break
+            
+            j -= 1
+        
+        return result

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

@@ -1,3 +1,4 @@
+from collections import Counter
 from heapq import heapify, heappop
 
 class Solution: