Think You Know Big-O? Python Performance Explained Clearly

Key Takeaways

Oh no, you didn't by Mr. Shiny 608. Big O notation is a way of thinking about the computational complexity of an algorithm. 01 that means the computation runs in constant time. It could take a million years, but if it's always a million years, regardless of how big the chunk of data is, then we just simplify that to order one. Really, what this comes down to is how does it change depending on how much data you give it. So N means proportional to the data. So the more you give it data, n items of data, the longer it takes scaling with the amount of data. I could have a constant time algorithm that runs slower than an order n algorithm for some size of data. Right? So I might be able to run through a 100 things faster than my constant time. But as we approach infinity, order n should take longer. But there's a difference between theory and practice. And big O often gets mistaken for measuring speed when what it does is it expresses how performance degrades as the size of the data increases and constants are more or less ignored. So unless you're dealing with really large amounts of data, sometimes this really doesn't matter.