Populate predefined numpy array with arrays as columns

Question

Something I can't figure out by reading the Python documentation and stackoverflow. Probably I'm thinking in the wrong direction..

Let's say I've a predefined 2D Numpy array as follow:

a = np.zeros(shape=(3,2)) 
print a
array([[ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.]])

Now I would like to populate each column of this 2D array with a 1D data array (one by one), as in:

b = np.array([1,2,3])

# Some code, that I just can't figure out. I've studied insert, column_stack, 
# h_stack, append. Nothing seems to do what I need

print a
array([[ 1.,  0.],
       [ 2.,  0.],
       [ 3.,  0.]])

c = np.array([4,5,6])

# Some code, that I just can't figure out. I've studied insert, column_stack, 
# h_stack, append. Nothing seems to do what I need

print a
array([[ 1.,  4.],
       [ 2.,  5.],
       [ 3.,  6.]])

Any suggestions would be appreciated!

lvc · Accepted Answer · 2013-06-06 10:51:06Z

1

You can assign to columns with slicing:

>>> a[:,0] = b
>>> a
array([[ 1.,  0.],
       [ 2.,  0.],
       [ 3.,  0.]])

To assign them all at once instead of one at a time, use np.column_stack:

>>> np.column_stack((b, c))
array([[1, 4],
       [2, 5],
       [3, 6]])

If you need it back in the same array, rather than just having the same name, you can assign to a slice containing the whole matrix (as is common with lists):

>>> a[:] = np.column_stack((b, c))
>>> a
array([[ 1.,  4.],
       [ 2.,  5.],
       [ 3.,  6.]])

answered Jun 6, 2013 at 10:51

lvc

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