[FIXED] How can I append a multidimensional array to a new dimension with Numpy?

Issue

I have an empty list: x = [].

I have a numpy array, y, of shape: (180, 161). I can't necessarily define x to be an np.empty of a particular shape, because I won't know the shape of y ahead of time.

I want to append y to x so that x will have a .shape of (1, 180, 161).

Then if I append more, I want it to be (n, 180, 161)

I tried .append and .stack, but I've had a variety of errors:

TypeError: only size-1 arrays can be converted to Python scalars

ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 3 dimension(s) and the array at index 1 has 2 dimension(s)

And so on. It seems that this should be simple, but it's strangely difficult.

Solution

Assuming all items in x have the same shape, you can first construct a list and then construct the NumPy array from the list.

There, you have two options:

• np.array() which is faster but not flexible
• np.stack() which is slower but allows you to choose over which axis should the stack happen (it is roughly equivalent to np.array().transpose(...).copy()

The code would look like:

import numpy as np


n = 100
x = [np.random.randint(0, 10, (10, 20)) for _ in range(n)]

# same as: y = np.stack(x, 0)
y = np.array(x)

print(y.shape)
# (100, 10, 20)

---

Of course this line:

x = [np.random.randint(0, 10, (10, 20)) for _ in range(n)]

can be replaced with:

x = []
for _ in range(n):
    x.append(np.random.randint(0, 10, (10, 20)))

---

You could also use np.append(), e.g.:

def stacker(arrs):
    result = arrs[0][None, ...]
    for arr in arrs[1:]:
        result = np.append(result, arr[None, ...], 0)
    return result

but with horrific performances:

n = 1000
shape = (100, 100)
x = [np.random.randint(0, n, shape) for _ in range(n)]

%timeit np.array(x)
# 10 loops, best of 3: 21.1 ms per loop
%timeit np.stack(x)
# 10 loops, best of 3: 21.6 ms per loop
%timeit stacker(x)
# 1 loop, best of 3: 11 s per loop

and, as you can see, performance-wise, the list-based method is way faster.

Answered By - norok2