Aug 17, 2022 · array.array is also a reasonable way to represent a mutable string in Python 2.x (array('B', bytes)). However, Python 2.6+ and 3.x offer a mutable byte string as bytearray . However, if you want to do math on a homogeneous array of numeric data, then you're much better off using NumPy, which can automatically vectorize operations on complex ...

The above examples trivialized the problem of calculating differences. Assuming sorting or de-duplication definitely make it easier to compute the difference, but if your comparison cannot afford those assumptions then you'll need a non-trivial implementation of a diff algorithm.

Accessing the single elements by index is very slow because every index access is a method call in python; numpy.diff is slow because it has to first convert the list to a ndarray. Obviously if you start with an ndarray it will be much faster: In [22]: arr = np.array(L) In [23]: %timeit np.diff(arr) 100 loops, best of 3: 3.02 ms per loop

.tolist() appears to convert all of the values recursively to python primitives (list), whereas list creates a python list from an iterable. Since the numpy array is an array of arrays, list(...) creates a list of arrays. You can think of list as a function that looks like this:

Feb 23, 2012 · Python List vs. Array - when to use? I'm working on a few projects in Python, and I have a few questions: What's the difference between Arrays and Lists? If it's not obvious from question 1, which should I use? How do you use the preferred one? (create array/list, add item, remove item, pick random item)

And the results were that list executes fastest here (Python 3.3, NumPy 1.8): np 1: 2.14277 s np 2: 0.77008 s np 2 vectorized: 0.44117 s list 1: 0.29795 s list 2: 0.66529 s array 1: 0.66134 s array 2: 0.88299 s Which seems to be counterintuitive. There doesn't seem to be any advantage in using numpy or array over list for these simple examples.

Numpy arrays is a typed array, the array in memory stores a homogenous, densely packed numbers. Python list is a heterogeneous list, the list in memory stores references to objects rather than the number themselves. This means that Python list requires dereferencing a pointer every time the code needs to access the number.

Oct 8, 2022 · Python stores each element of a list as a separate data object, and the references to those objects are stored in Python's list data type. My past reading indicates that the list is stored as an array, but I've measured the list's time complexity and I suspect that it may technically be a hash table. Python's lists may be ordered, and you can ...

May 27, 2010 · list are more than plain arrays. You can initialize them without giving the number of items. You can append/push to them, you can remove/pop/del items from them, you can have lists of different types of objects (e.g., [1,'e', [3]]), you can have recursive lists... and you can slice lists, which means getting a new list with only a few of the items.

Dec 1, 2016 · A list has dynamic size and can hold any type of object, whereas an array has a fixed size and entries of uniform type. In a list of lists, each sublist can have different sizes. An array has fixed dimensions along each axis. An array is stored in a contiguous block of memory, whereas the objects in a list can be stored anywhere on the heap.