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Is there a list of the different data structures and their big-O access times for Python?
I was rummaging through the standard library docs and didn't see any, hence the question. :-)
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Big-O notation is a metrics used to find algorithm complexity. Basically, Big-O notation signifies the relationship between the input to the algorithm and the steps required to execute the algorithm. It is denoted by a big "O" followed by opening and closing parenthesis.
Big O Notation is a way to measure an algorithm's efficiency. It measures the time it takes to run your function as the input grows. Or in other words, how well does the function scale. There are two parts to measuring efficiency — time complexity and space complexity.
Big O notation ranks an algorithms' efficiency Same goes for the “6” in 6n^4, actually. Therefore, this function would have an order growth rate, or a “big O” rating, of O(n^4) . When looking at many of the most commonly used sorting algorithms, the rating of O(n log n) in general is the best that can be achieved.
Here is the summary for in : list - Average: O(n) set/dict - Average: O(1), Worst: O(n)
It's not in the manual, it's here on the python wiki.
The table also includes complexities for methods for the data structures as well. Thanks for asking, I'd never seen this before until I looked for it.
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