python - How to zip two 1d numpy array to 2d numpy array

59
votes

I have two numpy 1d arrays, e.g:

a = np.array([1,2,3,4,5])
b = np.array([6,7,8,9,10])

Then how can I get one 2d array [[1,6], [2,7], [3,8], [4,9], [5, 10]]?

3
pythonnumpy
For very small arrays, zip can be faster than calling Numpy functions, but for longer arrays the Numpy functions are much faster, with column_stack being the fastest in my tests. Eg, for arrays of length 1000, column_stack is about 1000 times faster than zip. Here's some relevant timeit code. - PM 2Ring

3 Answers

49
votes

The answer lies in your question:

np.array(list(zip(a,b)))

Edit:

Although my post gives the answer as requested by the OP, the conversion to list and back to NumPy array takes some overhead (noticeable for large arrays).

Hence, dstack would be a computationally efficient alternative (ref. @zipa's answer). I was unaware of dstack at the time of posting this answer so credits to @zipa for introducing it to this post.

Edit 2:

As can be seen in the duplicate question, np.c_ is even shorter than np.dstack.

>>> import numpy as np
>>> a = np.arange(1, 6)
>>> b = np.arange(6, 11)
>>> 
>>> a
array([1, 2, 3, 4, 5])
>>> b
array([ 6,  7,  8,  9, 10])
>>> np.c_[a, b]
array([[ 1,  6],
       [ 2,  7],
       [ 3,  8],
       [ 4,  9],
       [ 5, 10]])
131
votes

If you have numpy arrays you can use dstack():

import numpy as np

a = np.array([1,2,3,4,5])
b = np.array([6,7,8,9,10])

c = np.dstack((a,b))
#or
d = np.column_stack((a,b))

>>> c
array([[[ 1,  6],
        [ 2,  7],
        [ 3,  8],
        [ 4,  9],
        [ 5, 10]]])
>>> d
array([[ 1,  6],
       [ 2,  7],
       [ 3,  8],
       [ 4,  9],
       [ 5, 10]])

>>> c.shape
(1, 5, 2)
>>> d.shape
(5, 2)
4
votes

You can use zip

np.array(list(zip(a,b)))
array([[ 1,  6],
   [ 2,  7],
   [ 3,  8],
   [ 4,  9],
   [ 5, 10]])