## Bucket sort, counting sort and radix sort.

## GOAL

To understand the difference and pros and cons of “bucket sort”, “counting sort” and “radix sort” and implement them.

## Bucket sort

Bucket sort is one of the sorting algorithm that has “bucket” to put numbers in.

import random import itertools length = 50 #length of the list input_list = [random.randint(0, 99) for x in range(length)] #input: list that has 0~99 int elements #output: sorted list def bucket_sort(input_list): bucket = [[] for x in range(100)] for i in input_list: bucket[i].append(i) return list(itertools.chain.from_iterable(bucket)) print(input_list) print(bucket_sort(input_list))

#output [6, 27, 26, 38, 90, 80, 69, 14, 65, 53] [6, 14, 26, 27, 38, 53, 65, 69, 80, 90]

**Complexity**: time O(n), space O(r) as n is length of the list, r is range. In the worst case, space complexity is O(r+n)**Pros**: Fast algorithm, stable sort**Cons**: big memory, the range of input number should be limited.

## Counting sort

Counting sort is one of the derivation of bucket sort. Create Bucket and put the number of “occurrences” in it. Iterate the input list counting the number of occurrences.

import random length = 10 input_list = [random.randint(0, 99) for x in range(length)] def counting_sort(input_list): bucket = [0 for x in range(100)] for i in input_list: bucket[i] += 1 output = [] for idx, num in enumerate(bucket): for i in range(num): output.append(idx) return output print(input_list) print(counting_sort(input_list))

#output [84, 33, 72, 10, 31, 4, 4, 46, 89, 52] [4, 4, 10, 31, 33, 46, 52, 72, 84, 89]

**Complexity**: time O(n), space O(r) as n is length of the list, r is range.**Pros**: Fast algorithm, fixed size memory**Cons**: unstable, big memory, the range of input number should be limited.

## Radix sort

Radix sort is one of the derivation of bucket sort. Convert each elements into n-adic number and put each digit into the bucket.

import random import itertools length = 10 input_list = [random.randint(0, 99) for x in range(length)] def radix_sort(input_list): # n = 10 for i in (range(2)): #digit is 2 bucket= [[] for i in range(10)] for num in input_list: index = (num//(10**i)) % 10 bucket[index].append(num) input_list = list(itertools.chain.from_iterable(bucket)).copy() return input_list print(input_list) print(radix_sort(input_list))

#output [26, 4, 7, 48, 71, 31, 95, 20, 94, 55] [4, 7, 20, 26, 31, 48, 55, 71, 94, 95]

**Complexity**: time O(n), space O(d) as n is length of the list, d is number of digits. In the worst case, space complexity is O(d+n)**Pros**: Fast algorithm, small memory, stable sort**Cons**: the range of input number should be limited

Because it takes some time to calculate division and modulo, using shift operation of binary numbers is efficient.