Prompt
def merge_sort(arr):
if len(arr) <= 1:
return arr
mid = len(arr) // 2
left = merge_sort(arr[:mid])
right = merge_sort(arr[mid:])
return merge(left, right)
def merge(left, right):
result = []
i = j = 0
while i < len(left) and j < len(right):
if left[i] < right[j]:
result.append(left[i])
i += 1
else:
result.append(right[j])
j += 1
result.extend(left[i:])
result.extend(right[j:])
return result
arr = [38, 27, 43, 3, 9, 82, 10]
sorted_arr = merge_sort(arr)
print(sorted_arr)
Answer
Visual Representation of Merge Sort Algorithm
Pseudocode Representation
function merge_sort(arr)
if length of arr <= 1
return arr
mid = length of arr divided by 2
left = merge_sort(first half of arr)
right = merge_sort(second half of arr)
return merge(left, right)
function merge(left, right)
create an empty list called result
initialize variables i and j to 0
while i < length of left and j < length of right
if left[i] < right[j]
append left[i] to result
increment i by 1
else
append right[j] to result
increment j by 1
extend result with remaining elements in left and right
return result
arr = [38, 27, 43, 3, 9, 82, 10]
sorted_arr = merge_sort(arr)
print sorted_arr
Annotations
The algorithm starts with the
merge_sort
function that recursively divides the input array into halves until each sub-array has only one element.The
merge
function then merges the sorted sub-arrays back together in the correct order.The merging process compares elements from the left and right sub-arrays, appending the smaller element first to the
result
list.The final
result
list is created by extending it with the remaining elements from both sub-arrays.The sorted array is then returned and printed.
This visualization simplifies the sorting process, showcasing how the merge sort algorithm divides and conquers the input array to ultimately merge the sorted sub-arrays.
Description
Analyze and understand the merge sort algorithm through visual representation, pseudocode explanation, and step-by-step annotations.