Are There Dynamic Arrays In Numpy?

Let's say I create 2 numpy arrays, one of which is an empty array and one which is of size 1000x1000 made up of zeros: import numpy as np; A1 = np.array([]) A2 = np.zeros([1000,100

Solution 1:

This can't be done in numpy, and it technically can't be done in MATLAB either. What MATLAB is doing behind-the-scenes is creating an entire new matrix, then copying all the data to the new matrix, then deleting the old matrix. It is not dynamically resizing, that isn't actually possible because of how arrays/matrices work. This is extremely slow, especially for large arrays, which is why MATLAB nowadays warns you not to do it.

Numpy, like MATLAB, cannot resize arrays (actually, unlike MATLAB it technically can, but only if you are lucky so I would advise against trying). But in order to avoid the sort of confusion and slow code this causes in MATLAB, numpy requires that you explicitly make the new array (using np.zeros) then copy the data over.

Python, unlike MATLAB, actually does have a truly resizable data structure: the list. Lists still require there to be enough elements, since this avoids silent indexing errors that are hard to catch in MATLAB, but you can resize an array with very good performance. You can make an effectively n-dimensional list by using nested lists of lists. Then, once the list is done, you can convert it to a numpy array.