Question

I can use array.resize(shape) to resize my array and have zeros added to those indices without any value. If my array is [1,2,3,4] and I use array.resize[5,0] I get [1,2,3,4,0]. How can I append / pad the zeros to the front, yielding [0,1,2,3,4]?

I am doing this dynamically – trying to use:

array.resize(arrayb.shape)

I would like to avoid (at all costs) making an in-memory copy of the array. That is to reverse the array, resize, and reverse again. Working with a view would be ideal.

Answer 1

I believe you can use slice assignment to do this. I see no reason why numpy would need to make a copy for an operation like this, as long as it does the necessary checks for overlaps (though of course as others have noted, resize may itself have to allocate a new block of memory). I tested this method with a very large array, and I saw no jump in memory usage.

>>> a = numpy.arange(10)
>>> a.resize(15)
>>> a[5:] = a[:10]
>>> a[0:5] = 0
>>> a
array([0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

The following showed no jump in memory usage for the assignment operation:

>>> a = numpy.arange(100000000)
>>> a.resize(150000000)
>>> a[50000000:] = a[:100000000]

I don’t know of a better way, and this is just a conjecture. Let me know if it doesn’t work.