## What is the complexity of this python sort method?

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I have a list of lists and I am sorting them using the following

```data=sorted(data, key=itemgetter(0))
```

Was wondering what is the runtime complexity of this python method?

Provided `itemgetter(0)` is `O(1)` when used with `data`, the sort is `O(n log n)` both on average and in the worst case.

For more information on the sorting method used in Python, see Wikipedia.

algorithm - What is the complexity of this python sort method?, As others have mentioned, the built-in sorting algorithm of Python uses a special version of merge sort, called Timsort, which runs in time. That's pretty good; we  Python's sort is called timsort. Its average case performance is O(nlog(n)). Its average case performance is O(nlog(n)). It performs really well with pre-sorted lists because it's implementation is designed to seek out runs of sorted elements in the list.

sorted is like sort except that the first builds a new sorted list from an iterable while sort do sort in place. The main difference will be space complexity.

What is the time complexity of the Python built-in sorted function , The heap_sort function iterates over the array n times. Therefore the overall time complexity of the Heap Sort algorithm is O(nlog(n)). It is the Timsort, and Timsort is a kind of adaptive sorting algorithm based on merge sort and insertion sort, then I thought it belongs to the comparison sort, and it's said, no comparison sort can guarantee a time complexity smaller than lg(N!) ~ N log N.

It is the Timsort, and Timsort is a kind of adaptive sorting algorithm based on merge sort and insertion sort, then I thought it belongs to the comparison sort, and it's said, no comparison sort can guarantee a time complexity smaller than lg(N!) ~ N log N.

Sorting Algorithms in Python, This page documents the time-complexity (aka "Big O" or "Big Oh") of various operations Sort. O(n log n). O(n log n). Multiply. O(nk). O(nk). x in s. O(n). min(s)​, max(s) However you can do the method equivalents even if t is any iterable, for  For each word in the list, I store the frequency of letters in a Counter as values which is then sorted using the method sorted(). I know that sorted() has a worst time complexity of O(nlog(n)). Am I right to say that the worst time complexity of the whole code is O(n^2 log(n)) since sorted() is used inside a for loop?

TimeComplexity, The Bubble Sort Algorithm in Python. Implementing Bubble Sort in Python; Measuring Bubble Sort's Big O Runtime Complexity; Timing Your  Python List sort() The sort() method sorts the elements of a given list. The sort() method sorts the elements of a given list in a specific ascending or descending order.

Sorting Algorithms in Python – Real Python, Algorithm, Time Complexity. Best, Average, Worst. Selection Sort, Ω(n^2), θ(n^2), O(n^2). Bubble Sort, Ω(n), θ(n^2), O(n^2). Insertion Sort, Ω(n), θ(n^2), O(n^2). The sort() method sorts the list ascending by default. You can also make a function to decide the sorting criteria(s).

Time Complexities of all Sorting Algorithms, Timsort is a hybrid stable sorting algorithm, derived from merge sort and insertion sort, designed to perform well on many kinds of real-world data. It was implemented by Tim Peters in 2002 for use in the Python programming from Peter McIlroy's 1993 paper "Optimistic Sorting and Information Theoretic Complexity". The complexity involved in insertion sort can be reduced to O(log n) if the binary search method is adopted to search its sorted list each time to place an element from the unsorted number appropriately. This process can be referred to as Binary Insertion Sort.

• The same as of a simple `sorted`.
• `O(n log n)` like just about every other comparison sort used in a language library.
• And if you know the complexity of `itemgetter(0)` and it's not `O(1)` then you can still work out the overall complexity: `sorted` makes `n` calls to `itemgetter(0)` plus what you say.
• @MC: It does. However, that's hardly relevant for practical use. Besides, any sorting algorithm can be made to have `O(n)` best case.