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 Answers
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 Comments
PM 2Ring
Alternatively,
np.stack((a, b), axis=-1), but your way is more compact.PM 2Ring
FWIW,
np.column_stack appears to be the fastest way to do this, see my comment on the question for details.Ryan M
Just a note -
np.dstack stacks arrays in the 3rd dimension. thus you'd be creating an unnecessary dimension. np.column_stack, or np.stack(...,axis=-1) are both more appropriate.
Guimoute
Why
axis=-1 and not +1?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]])
You can use zip
np.array(list(zip(a,b)))
array([[ 1, 6],
[ 2, 7],
[ 3, 8],
[ 4, 9],
[ 5, 10]])
4 Comments
EdChum
how is this different to @Ebe's answer?
akash karothiya
Well we both answered at the same time, i was almost 2-3 seconds late
PM 2Ring
Why use plain Python functions to do this when you're working with Numpy arrays?
akash karothiya
sure, i will go with @zipa's answer
zipcan be faster than calling Numpy functions, but for longer arrays the Numpy functions are much faster, withcolumn_stackbeing the fastest in my tests. Eg, for arrays of length 1000,column_stackis about 1000 times faster thanzip. Here's some relevant timeit code.