Python zip numpy arrays of different dimension

Question

I'd like to zip numpy arrays befor saving it via np.save . For zipping one dimensional arrays I use

import numpy as np

a = np.ones(4) * 5.
b = np.ones(4) * 4.
data = np.array(zip(a, b))

which does exactly what I want. Now I have more data, say like

c = numpy.ones((2, 4))

but

data = np.array(zip(a, b, c))

does not work. I could do

data = np.array(zip(a, b, c[0], c[1]))

instead, but the "depth" of c changes. My solution is

data = np.insert(c, 0, b, axis=0)
data = np.insert(data, 0, a, axis=0)
data = zip(*data)

but that reads kind of suboptimal. Thanks for an advice.

Answer 1

I would use numpy.hstack/vstack:

a = np.ones(4) * 5
b = np.ones(4) * 4
c = np.ones((2, 4))
data = np.vstack([a,b,c]).T

Edit: I actually mostly use np.row_stack/column_stack nowadays, as I find it more natural than hstack or vstack:

    data = np.column_stack([a,b,c.T])

Answer 2

Use the * opertor to "unpack" c when calling zip :

data = np.array(zip(a, b, *c))
data.shape
=> (4, 4)

(You can avoid zip and use a direct numpy approach (eg using vstack , as @metaperture suggested), which is arguably a better approach. However, this answer demostrates the correct way to do exactly what you were trying to do originally)

Answer 3

I would not recommend using zip if your only objective is to save multiple arrays of different dimensions, as the title of this question suggests. Use np.savez or np.savez_compressed instead. These functions were meant to save multiple arrays (of arbitrary dimensions).

Using zip is particularly bad as it won't port to Python3 , where zip returns an iterator and not a list of tuples. Calling np.array on the iterator creates an array with a single element of dtype object , which is probably not what you want. I was not able to recover the zipped data after a save and a load.