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In Python 2.4 and above, both the sorted() function and the in-place list.sort() method take a key= parameter that allows the user to provide a "key function" (like foo in the examples above). In Python 3 and above, use of the key function is the only way to specify a custom sort order (the previously supported cmp= parameter that allowed the ...
One implementation can be described as arranging the data sequence in a two-dimensional array and then sorting the columns of the array using insertion sort. The worst-case time complexity of Shellsort is an open problem and depends on the gap sequence used, with known complexities ranging from O ( n 2 ) to O ( n 4/3 ) and Θ( n log 2 n ).
As another example, many sorting algorithms rearrange arrays into sorted order in-place, including: bubble sort, comb sort, selection sort, insertion sort, heapsort, and Shell sort. These algorithms require only a few pointers, so their space complexity is O(log n). [1] Quicksort operates in-place on the data to be sorted.
This is done by merging runs until certain criteria are fulfilled. Timsort has been Python's standard sorting algorithm since version 2.3 (since version 3.11 using the Powersort merge policy [5]), and is used to sort arrays of non-primitive type in Java SE 7, [6] on the Android platform, [7] in GNU Octave, [8] on V8, [9] and Swift. [10]
For example, bubble sort is () on a list that is already sorted, while quicksort would still perform its entire () sorting process. While any sorting algorithm can be made O ( n ) {\displaystyle O(n)} on a presorted list simply by checking the list before the algorithm runs, improved performance on almost-sorted lists is harder to replicate.
Gnome sort works by building a sorted list one element at a time, getting each item to the proper place in a series of swaps. The average running time is O(n 2) but tends towards O(n) if the list is initially almost sorted. [4] [note 1] Dick Grune described the sorting method with the following story: [3]
A sorting algorithm that checks if the array is sorted until a miracle occurs. It continually checks the array until it is sorted, never changing the order of the array. [ 10 ] Because the order is never altered, the algorithm has a hypothetical time complexity of O ( ∞ ) , but it can still sort through events such as miracles or single-event ...
The following Python implementation [1] [circular reference] performs cycle sort on an array, counting the number of writes to that array that were needed to sort it. Python def cycle_sort ( array ) -> int : """Sort an array in place and return the number of writes.""" writes = 0 # Loop through the array to find cycles to rotate.