stacking numpy arrays?

Question

I am trying to stack arrays horizontally, using numpy hstack, but can't get it to work. Instead, it all comes out in one list, instead of a 'matrix-looking' 2D array.

import numpy as np
y = np.array([0,2,-6,4,1])
y_bool = y > 0
y_bool = [1 if l == True else 0 for l in y_bool] #convert to decimals for classification
y_range = range(0,len(y))
print y
print y_bool
print y_range
print np.hstack((y,y_bool,y_range))

Prints this:

[ 0  2 -6  4  1]
[0, 1, 0, 1, 1]
[0, 1, 2, 3, 4]
[ 0  2 -6  4  1  0  1  0  1  1  0  1  2  3  4]

How do I instead get the last line to look like this:

[0 0 0
 2 1 1
-6 0 2
 4 1 3]

Answer 1

If you want to create a 2D array, do:

print np.transpose(np.array((y, y_bool, y_range)))
# [[ 0  0  0]
#  [ 2  1  1]
#  [-6  0  2]
#  [ 4  1  3]
#  [ 1  1  4]]

Answer 2

Well, close enough h is for horizontal/column wise, if you check its help, you will see under See Also

vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third axis).
concatenate : Join a sequence of arrays together.

Edit: First thought vstack does it, but it would be if np.vstack(...).T or np.dstack(...).squeeze(). Other then that the "problem" is that the arrays are 1D and you want them to act like 2D, so you could do:

print np.hstack([np.asarray(a)[:,np.newaxis] for a in (y,y_bool,y_range)])

the np.asarray is there just in case one of the variables is a list. The np.newaxis makes them 2D to make it clearer what happens when concatenating.