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

Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about How to zip two 1d numpy array to 2d numpy array in Python. So Here I am Explain to you all the possible Methods here.

How to zip two 1d numpy array to 2d numpy array?

1. How to zip two 1d numpy array to 2d numpy array?

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).

2. zip two 1d numpy array to 2d numpy array

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).

Method 1

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)```

Method 2

```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]])```

Summery

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.