Bucket Sort Variant
The solution for Sliding Subarray Beauty uses a variant of the Bucket Sort algorithm. The Bucket Sort is a sorting algorithm that works by distributing the elements of an array into a number of buckets. Each bucket is then sorted individually, either using a different sorting algorithm, or by recursively applying the bucket sort algorithm.
However, in this case, we don’t actually need to sort the buckets (or the subarrays). Instead, we are counting the number of negative integers and identifying the xth smallest negative number in each subarray.
Here, the “buckets” are the indices of the counter
array, which represent the values from 50 to 0. We use the counter
array to keep track of the counts of negative numbers in the current window. Each time the window moves, we update the counts for the incoming and outgoing numbers accordingly.
So while it’s not a direct application of Bucket Sort, this solution certainly leverages the same general principle of distributing elements into buckets based on their values.
The concept of this variant of Bucket Sort can be used to count the frequency of elements in a given array. In this case, we would count the number of occurrences of each element in the array instead of finding the smallest xth negative number. Here’s an example in Python3:


In this code, we first initialize a counter array of size 101, to account for all possible numbers between 50 and 50. We then iterate through the nums
list and increment the count of each number in the counter array. Finally, we iterate through the counter array and print the count of each number that appears in the nums
list.
This example illustrates the main principle of Bucket Sort – namely, distributing elements into different “buckets” based on their values – but without the sorting part. In the context of the original problem, a similar strategy is used, but instead of simply counting the number of occurrences, it’s used to find the xth smallest negative number in each subarray.
The Bucket Sort algorithm is used when the input data is uniformly distributed over a range and works by dividing this range into multiple smaller subranges or buckets and then individually sorting these buckets. However, this algorithm is often not used in its purest form in problem solving, but rather is applied in a variant manner to suit the problem at hand.
A common variant is counting frequencies of elements, which is often used to solve problems that involve counting specific elements in an array or string. The LeetCode problem “Top K Frequent Elements” (LeetCode 347) can be solved using this variant.
Here is a simple Python code for the frequency count variant (it doesn’t implement bucket sort fully but uses the concept):


In this code, Counter(nums)
is used to get the frequency of each number in nums
, and these frequencies are used as indices to put the numbers into corresponding ‘buckets’ (buckets[freq]
). After all numbers are put into their respective ‘buckets’, we create a flattened list (flat_list
) from these ‘buckets’ in reverse order, so the numbers with higher frequencies come first. The top k frequent numbers are returned by slicing the first k elements from the list.
Problem Categories
Bucket sort is a sorting algorithm that works by distributing the elements of an array into a number of buckets and then sorting these buckets individually. Each bucket is then sorted individually, either using a different sorting algorithm or recursively applying the bucket sort algorithm. Here are a few problem types where a variant of bucket sort might be used:
Problems involving frequency counts: If you have a problem where you need to sort items by their frequency of occurrence, bucket sort can be used. Each bucket can represent a frequency, and items can be placed into buckets based on their frequency.
Sorting floating point numbers: Bucket sort is particularly useful when the input is uniformly distributed over a range. For example, you can use bucket sort to sort an array of floating point numbers in the range from 0.0 to 1.0.
Order statistic problems: Problems that involve finding the kth smallest or largest element (also known as order statistics) can often be solved more efficiently with a bucket sort variant, especially when k is small.
Problems with a known, limited range: If you have a problem where the range of possible input values is known and not too large, bucket sort can be very efficient.
Data distribution problems: Bucket sort can also be used to solve problems related to data distribution, where the objective is to distribute a set of data evenly over a range.
External sorting: When the data to be sorted cannot fit into the main memory and resides in the slower external memory (like a hard drive), bucket sort can be used as it minimizes the number of disk transfers.
The effectiveness of bucket sort depends heavily on how the input data is distributed. If the data is distributed unevenly, or the range of potential inputs is very large, other sorting algorithms may be more appropriate.
The following are found in LeetCode:
Problems involving frequency counts: Problems such as “Top K Frequent Elements”, “Sort Characters By Frequency”, or “Find All Duplicates in an Array” require counting the frequency of elements. Bucket sort can be a helpful approach for these problems.
Sorting floating point numbers: Although not as common, problems may ask you to sort floating point numbers. You could potentially use a variant of bucket sort to address this, though most of the sorting problems in LeetCode deal with integer values.
Order statistic problems: There are many problems that involve finding the kth smallest or largest element. Problems like “Kth Largest Element in an Array”, “Kth Smallest Element in a Sorted Matrix”, or “Third Maximum Number” can often be solved more efficiently with a bucket sort variant, or other sorting approaches.
Problems with a known, limited range: LeetCode has many problems where the input range is known and limited. For instance, problems like “Sort Colors”, where elements in the input array can only be 0, 1, or 2.
Data distribution problems: While not as prevalent, there might be some problems which require the distribution of data to be considered.
In the case of external sorting, this concept is generally more applicable to systemslevel programming and is not as commonly seen in LeetCode problem sets, which tend to focus on inmemory algorithms.