Since you mention arrays and arange, I presume you're using Numpy. The way to create one array by applying a function to each element from another array is by using vectorize . More on reddit.com

It's ordered that way because that's the nature of a linked list. Usually, each element of a linked list consists of more than just the pointer to the next element in the list. You'll usually see linked lists used when new elements might be picked up or dropped from a list at any time, there is a need to be able to traverse the list, insertion or deletion is "expensive" (e.g., time-consuming), and the items being stored don't need to be stored in order in one big chunk of memory. It's just faster to include a pointer to the next element as part of the structure than to shuffle everything around whenever an insertion or deletion is needed. I suspect that this linked list consists of nothing but that pointer so that you can learn the nature of linked lists without being distracted by any additional overhead in each element in the list that you don't need to consider. Did that answer your question, or do you still have questions? More on reddit.com

Most implementations store the length of an array in the object itself. Whenever you insert/remove it gets updated. Thus you're not iterating to get the length you're just getting a value. More on reddit.com

Python is a high level programming language. While it’s possible to use libraries to get something resembling a C-style array, the Python programming language provides the list data-type which is an abstraction over an array that allows you to put arbitrary objects in it (it’s not restricted to a single type), and expand and reduce it dynamically (it handles memory allocation and de-allocation under the hood). The techniques you’re learning about at the moment are useful for understanding how programming works at a low level, but the whole point of a language like Python is that most of those techniques have been implemented for you (generally in quite optimised forms) in data structures that are easier and more pleasant to use while still being reasonably performant. Array doubling is useful because it provides amortised-constant runtime and linear memory usage when you’re wanting to expand a fixed-size array in individual steps, but that’s also what the append method does in Python’s list type. Of course for learning purposes you can just pretend that method doesn’t exist and implement its equivalent with your own code, it’s just good to be aware that when you’re programming in Python that kind of Han handling will generally be handled for you, and you’ll be operating at a more abstract level where you instead focus more on the actual ideas you’re trying to implement. Note that the understanding you’re gaining is valuable though. If you end up working with numerics (e.g. for data analysis or signal processing) then Python’s lists can be inefficient, in which case you’ll likely end up using arrays from the numpy library or DataFrames from the pandas or polars libraries. In those cases the expectation is to know how much data you have in advance, so appending is a linear-time operation that copies all the data to a slightly larger array every time, and is not recommended. More on reddit.com