How to zip two 1d numpy array to 2d numpy array
PythonNumpyPython Problem Overview
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]]?
Python Solutions
Solution 1 - Python
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)
Solution 2 - Python
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]])
Solution 3 - Python
You can use zip
np.array(list(zip(a,b)))
array([[ 1, 6],
[ 2, 7],
[ 3, 8],
[ 4, 9],
[ 5, 10]])