How to stack 2 numpy arrays with Different Lengths in python

Question

I have created a number of random numpay arrays:

import numpy as np

x1 = np.random.normal( 0, 1, ( 10, 1 ) )
x2 = np.random.normal( 0, 2, ( 5, 1 ) )
x3 = np.random.normal( 0, 3, ( 7, 1 ) )
x4 = np.random.normal( 0, 4, ( 9, 1 ) )

as you can see they are in different length size, now I want to combine them so I can save them later to excel or CSV file for later use.

I have tried all the faloweng scrpets:

#x = np.column_stack( ( x1 , x2 ))
 ------------
x =  np.concatenate((x1, x2[:,None]), axis=1)

but didn't work and that clear because each one has different size x1 (10,1) - x2 (5,1)

I am not sure if there any way to concatenate them without any problems, for example, filling the missing ones with None just to make them equal, or any ideas that can help?

Answer 1

padding it with np.nan is a valid option.

create an empty target array, and just assign your arrays into it's sub indexes:

import numpy as np

x1 = np.random.normal( 0, 1, ( 10, 1 ) )
x2 = np.random.normal( 0, 2, ( 5, 1 ) )
x3 = np.random.normal( 0, 3, ( 7, 1 ) )
x4 = np.random.normal( 0, 4, ( 9, 1 ) )

arrs = [x1,x2,x3,x4]

a = np.empty((max(x.shape[0] for x in arrs), len(arrs)))
a[:] = np.nan

for i, x in enumerate(arrs):
    a[0:len(x), i] = x.T
print(a)

Output:

[[ -1.5521545   -1.82217348  -3.28589422  -1.59646125]
 [  0.54409311   2.53585401  -2.15704799   2.1590175 ]
 [  0.24202617  -1.62680388   0.58507172   4.24671516]
 [  1.21341942  -2.09405961   1.94415747  -1.21781288]
 [ -0.53110862   1.47037056   2.37113853 -10.01200676]
 [  0.50884432          nan  -2.56881482  -3.52164926]
 [ -0.37551321          nan   0.67952001  -0.5523079 ]
 [  0.5943706           nan          nan  -6.25704491]
 [ -0.37893229          nan          nan  -6.28029336]
 [ -0.34746679          nan          nan          nan]]