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Editor to share with you a sample analysis of the performance of python insertion sorting, I believe most people do not know much about it, so share this article for your reference, I hope you can learn a lot after reading this article, let's go to know it!

1. The space complexity is O (1), which is an in-situ sorting algorithm.

No additional storage space is required except that temporary variables are required to store exchanged data and subscripts at run time.

2. Stability. For elements with the same value, choose to insert the elements that appear later behind the elements that appear before.

This can ensure that the original order remains the same, so it is a stable sorting algorithm.

3. Time complexity, the best time complexity is O (n).

If the data is orderly, there is no need to move any data. When searching for the insertion position, we can search the insertion position in an ordered interval from tail to tail, and we only need to compare it once at a time to determine the insertion position. If the data is in reverse order, each time is equivalent to inserting new data in the first place of the data, so you need to move a lot of data, and the worst time complexity is O (n ^ 2). Normal time complexity, because the average time complexity of inserting elements in data is O (n), for insert sorting, each insert operation is equivalent to inserting a data into the array and performing n inserts in a loop, so the average time complexity is O (n ^ 2).

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