Numpy `resize` gives an unexpected array size when using -1 syntax

Question

I am trying to understand the difference between numpy.resize and numpy.reshape. I know that resize will return a new array and that reshape will maintain the same underlying data and only adjust the size (I'm assuming it does this by changing the stride). However, for both functions I would expect to be able to use the -1 syntax to specify an axis size. However, this appears to only work for reshape. For example, trying to reshape/resize this 1-D array of shape (444,) to an array of shape (4, 111) gives two different results depending on whether you use resize or reshape:

import numpy as np

test = np.arange(0, 444)
print(test.shape)
print(np.resize(test, (4, -1)).shape)
print(np.reshape(test, (4, -1)).shape)

prints

(444,)
(4, 110)
(4, 111)

I'm assuming that I'm missing something about the resize function, but I would expect it to either output a shape that is compatible with (444,) or throw an error.

The resize method on the array itself explicitly forbids negative dimensions, I wonder if this method should be doing the same thing. — user3483203
– user3483203, Commented Dec 2, 2019 at 21:58
@user3483203 Aha, you are right. Maybe this should actually be a bug in the numpy repo... — Max Taggart
– Max Taggart, Commented Dec 2, 2019 at 22:01
resize isn't used often, and is best if you want to change the total number of elements. That is, it can clip or pad. Beware the function and method behave quite differently. reshape can't change the total number of elements, and is widely used to change the total number of dimensions - to 2d, or 1d etc. I wouldn't treat them as alternatives. — hpaulj
– hpaulj, Commented Dec 2, 2019 at 22:24
Where in the documentation does np.resize explain its use of -1? — hpaulj
– hpaulj, Commented Dec 2, 2019 at 22:57

Daniel F · Accepted Answer · 2019-12-03 09:46:50Z

Going through the source code of np.resize, you can see what is going on, and that -1 was never intended as an input:

def resize(a, new_shape):
    if isinstance(new_shape, (int, nt.integer)):
        new_shape = (new_shape,)
    a = ravel(a)
    Na = len(a)   # Na = 444
    total_size = um.multiply.reduce(new_shape)   # total_size = 4 * -1 = -4 (?!?)
    if Na == 0 or total_size == 0:
        return mu.zeros(new_shape, a.dtype)

    n_copies = int(total_size / Na)    # n_copies = 0, luckily
    extra = total_size % Na            # -4 % 444 = 440 (this is where the 110 comes from)

    if extra != 0:  # True
        n_copies = n_copies + 1        # n_copies = 1 
        extra = Na - extra             # extra = 444 - 440 = 4

    a = concatenate((a,) * n_copies)   # a stays same
    if extra > 0:                      # True
        a = a[:-extra]                 # a = a[:-4]
    return reshape(a, new_shape)       # this is .reshape(4, -1) which now gives (4, 110)

hpaulj · Accepted Answer · 2019-12-02 22:56:13Z

np.resize behavior with -1 is not documented. But it can be deduced from the Python code, or the following examples:

In [312]: np.resize(np.arange(12),(1,-1))                                       
Out[312]: array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]])
In [313]: np.resize(np.arange(12),(2,-1))                                       
Out[313]: 
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])
In [314]: np.resize(np.arange(12),(3,-1))                                       
Out[314]: 
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
In [315]: np.resize(np.arange(12),(4,-1))                                       
Out[315]: 
array([[0, 1],
       [2, 3],
       [4, 5],
       [6, 7]])
# (5,-1) error
In [317]: np.resize(np.arange(12),(6,-1))                                       
Out[317]: 
array([[0],
       [1],
       [2],
       [3],
       [4],
       [5]])

So in general

a = np.arange(n)
np.resize(a, (m,-1))
np.reshape(a[:(n-m)], (m,-1))

that is it clips the input by m elements, and attempts the reshape. Not exactly useful, is it?

In your case test[:-4].reshape(4,-1).

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