Time complexity is all about 'how your code slows as your data grows'. It's usually discussed in terms of 'Big O' notation, which focuses on the order of magnitude for a given operation regardless of its exact number of computations.
In 'Big O' land, we care about whether an operation is n times slower based on the length of our data. The time complexity curves help us understand the impact of operations on data as it scales.
Lists in Python have varying time complexities for common operations like append, pop, and insert. Understanding these complexities is essential for efficient code implementation.
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